Humidity Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "from sklearn import linear_model\n",
    "pd.set_option(\"display.max_rows\",140)\n",
    "from sklearn.metrics import r2_score\n",
    "import itertools\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import wikipedia\n",
    "import random\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.width', 2000) \n",
    "pd.set_option ('display.max_columns' , 2000)\n",
    "pd.set_option(\"display.max_rows\", 2000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def comply_good_df (Model, x_data , y_data, cv, show_elim_features = False):\n",
    "    Inner_skf = StratifiedKFold(n_splits= cv, random_state= 17)\n",
    "    features = x_data.columns.tolist()\n",
    "    complied_df = pd.DataFrame()\n",
    "    best_temp_score = 0\n",
    "    best_gen_score = 0\n",
    "    for iter in range(x_data.shape[1]):\n",
    "        for feature in features:\n",
    "            temp_df = complied_df.copy()\n",
    "            temp_df[feature] = x_data[feature]\n",
    "            feature_scores = cross_val_score(Model, temp_df, y_data, cv = Inner_skf,\n",
    "                                             scoring= 'r2', n_jobs= -1)\n",
    "            mean_score_with_feature =  feature_scores.mean()\n",
    "            if mean_score_with_feature > best_temp_score:\n",
    "                best_temp_score = mean_score_with_feature\n",
    "                best_feature = feature\n",
    "        if best_temp_score > best_gen_score:\n",
    "            complied_df[best_feature] = x_data[best_feature]\n",
    "            features.remove(best_feature)\n",
    "            best_gen_score = best_temp_score\n",
    "        else:\n",
    "            break\n",
    "    elim_features = list(set(x_data.columns) - set(complied_df.columns))\n",
    "    if show_elim_features:\n",
    "        return complied_df, elim_features\n",
    "    else:\n",
    "        return complied_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_model_output (Model, x_data, y_data, num_cross_val = 5, add_feature = True):\n",
    "    x_data = x_data.copy()\n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "    Inner_SKF = StratifiedKFold(n_splits=  num_cross_val, shuffle= True, random_state= 17)\n",
    "    Model_outputs = pd.Series(np.zeros(x_data.shape[0]))\n",
    "    for train_df_id , apply_df_id in Inner_SKF.split(x_data, y_data):\n",
    "        train_df_x = x_data.iloc[train_df_id, :]\n",
    "        train_df_Y = y_data[train_df_id]\n",
    "        apply_df = x_data.iloc[apply_df_id, :]\n",
    "        Model.fit(train_df_x, train_df_Y)\n",
    "        out_put = pd.Series(Model.predict(apply_df))\n",
    "        Model_outputs[apply_df_id] = out_put\n",
    "    if add_feature:\n",
    "        x_data[str(Model).split('(')[0]] = Model_outputs\n",
    "        return x_data\n",
    "    else:\n",
    "        return Model_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_features (x_data, y_data, deep = 3,add_features = False, use_each_column = False, deep_each_col = 1):\n",
    "    x_data = x_data.copy()\n",
    "    List_features = x_data.columns.tolist()\n",
    "    Tree_features = pd.DataFrame()\n",
    "    if use_each_column:\n",
    "        Tree = DecisionTreeRegressor(max_depth= deep_each_col, random_state= 17)\n",
    "        for feature in ['Avg ° C', 'Millimetres','SUN Total Hours', 'SUN Clear Days', 'Humidity State']:\n",
    "            Tree.fit(np.array(x_data[feature]).reshape(-1, 1), y_data)\n",
    "            Threshold = Tree.tree_.threshold.tolist()\n",
    "            for Thresh in Threshold:\n",
    "                if Thresh == -2:\n",
    "                    continue\n",
    "                else:\n",
    "                    if add_features:\n",
    "                        x_data[feature+ ' '+ '<=' + ' '+str(Thresh)] = x_data[feature].apply(lambda x:int(x <= Thresh))\n",
    "                    else:\n",
    "                        Tree_features[feature+ ' '+ '<=' + ' '+str(Thresh)] = x_data[feature].apply(lambda x:int(x <= Thresh))\n",
    "        if add_features:\n",
    "            return x_data\n",
    "        else:\n",
    "            return  Tree_features\n",
    "    else:\n",
    "        Tree = DecisionTreeRegressor(max_depth= deep, random_state= 17)\n",
    "        Tree.fit(x_data, y_data)\n",
    "        Features = Tree.tree_.feature.tolist()\n",
    "        Threshold = Tree.tree_.threshold.tolist()\n",
    "        for Thresh, Feature in [*zip(Threshold, Features)]:\n",
    "            if Thresh == -2 and Feature ==  -2:\n",
    "                continue\n",
    "            else:\n",
    "                if add_features:\n",
    "                    Col = List_features[Feature]\n",
    "                    x_data[Col+ ' '+ '<=' + ' '+str(Thresh)] = x_data[Col].apply(lambda x:int(x <= Thresh))\n",
    "                else:\n",
    "                    Col = List_features[Feature]\n",
    "                    Tree_features[Col+ ' '+ '<=' + ' '+str(Thresh)] = x_data[Col].apply(lambda x:int(x <= Thresh))\n",
    "        if add_features:\n",
    "            return x_data\n",
    "        else:\n",
    "            return  Tree_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_tree_features_to_test (features, test):\n",
    "    test = test.copy()\n",
    "    features_ = [*map(lambda x: (x.split('<=')[0].rstrip(),float(x.split('<=')[1])),features)]\n",
    "    pos = 0\n",
    "    for col, thresh in features_:\n",
    "        test[features[pos]] = test[col].apply(lambda x: int(x <= thresh))\n",
    "        pos += 1\n",
    "    return test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree_features_on_cv (Model, x_data, y_data, scoring = 'roc_auc', num_cv = 5, \n",
    "                         shuffle = True, deep_tree = 1, use_each_column = False, deep_each_col = 1):\n",
    "    x_data = x_data.copy()\n",
    "    Inner_SKF = StratifiedKFold(random_state= 17, n_splits=num_cv, shuffle = shuffle)\n",
    "    scores = []\n",
    "    for train_df_id, apply_df_id in Inner_SKF.split(x_data, y_data):\n",
    "        train_df_x = x_data.iloc[train_df_id, :]\n",
    "        train_df_y = y_data[train_df_id]\n",
    "        apply_df_x = x_data.iloc[apply_df_id, :]\n",
    "        apply_df_y = y_data[apply_df_id]\n",
    "        train_df_x_tree = tree_features(train_df_x, \n",
    "                                        train_df_y,\n",
    "                                        use_each_column= use_each_column, \n",
    "                                        deep_each_col= deep_each_col,\n",
    "                                        deep= deep_tree,\n",
    "                                        add_features=True)\n",
    "        Model.fit(train_df_x_tree, train_df_y)\n",
    "        Features = (tree_features(train_df_x, \n",
    "                                        train_df_y,\n",
    "                                        use_each_column= use_each_column, \n",
    "                                        deep = deep_tree,\n",
    "                                        deep_each_col= deep_each_col,\n",
    "                                        add_features = False)).columns\n",
    "        apply_df_x_treee = apply_tree_features_to_test(Features, apply_df_x)\n",
    "        if scoring == 'r2':\n",
    "            Y_pred = Model.predict(apply_df_x_treee)\n",
    "            r_2 = r2_score(Y_pred, apply_df_y)\n",
    "            scores.append(r_2)\n",
    "        elif scoring == 'roc_auc':\n",
    "            Y_pred = Model.predict_proba(apply_df_x_treee)\n",
    "            roc_auc = roc_auc_score(Y_pred, apply_df_y)\n",
    "            scores.append(roc_auc)\n",
    "    return np.array(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_out_put_on_cv (Model, x_data, y_data, num_cv = 5, num_cv_model = 5,  shuffle = True, scoring = 'roc_auc'):\n",
    "    x_data = x_data.copy()\n",
    "    Inner_SKF = StratifiedKFold(random_state= 17, n_splits=num_cv, shuffle = shuffle)\n",
    "    warnings.filterwarnings('ignore')\n",
    "    scores = []\n",
    "    mean_inner_scores = []\n",
    "    for train_id, test_id in Inner_SKF.split(x_data, y_data):\n",
    "        x_train = x_data.iloc[train_id,:]\n",
    "        x_train.index = np.arange(x_train.shape[0])\n",
    "        y_train = y_data[train_id]\n",
    "        y_train.index = np.arange(x_train.shape[0])\n",
    "        x_test = x_data.iloc[test_id,:]\n",
    "        x_test.index = np.arange(x_test.shape[0])\n",
    "        y_test = y_data[test_id]\n",
    "        y_test.index = np.arange(x_test.shape[0])\n",
    "        x_train_model_out_put = add_model_output(Model, x_train,  y_train, num_cross_val= num_cv_model, add_feature= True)\n",
    "        if scoring == 'roc_auc':\n",
    "            Inner_scores = cross_val_score(Model,\n",
    "                                           x_train_model_out_put,\n",
    "                                           y_train,\n",
    "                                           n_jobs= -1, \n",
    "                                           cv= Inner_SKF, \n",
    "                                           scoring='roc_auc')\n",
    "            Mean_cv = Inner_scores.mean()\n",
    "            mean_inner_scores.append(Mean_cv)\n",
    "            Model.fit(x_train ,y_train)\n",
    "            x_test['Model_output'] = Model.predict_proba(x_test)\n",
    "            Model.fit(x_train_model_out_put, y_train)\n",
    "            Predicted_val = Model.predict_proba(x_test)\n",
    "            roc_auc = roc_auc_score(Predicted_val, y_test)\n",
    "            scores.append(roc_auc)\n",
    "        elif scoring == 'r2':\n",
    "            Inner_scores = cross_val_score(Model,\n",
    "                                           x_train_model_out_put,\n",
    "                                           y_train,\n",
    "                                           n_jobs= -1, \n",
    "                                           cv= Inner_SKF, \n",
    "                                           scoring='r2')\n",
    "            Mean_cv = Inner_scores.mean()\n",
    "            mean_inner_scores.append(Mean_cv)\n",
    "            Model.fit(x_train ,y_train)\n",
    "            x_test['Model_output'] = Model.predict(x_test)\n",
    "            Model.fit(x_train_model_out_put, y_train)\n",
    "            Predicted_val = Model.predict(x_test)\n",
    "            r_2 = r2_score(Predicted_val, y_test)\n",
    "            scores.append(r_2)\n",
    "    return np.array(scores) , np.array(mean_inner_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def power_fetures (Model, x_data, y_data, thresh, power_ , num_cv = 5, add_features = False):\n",
    "    Inner_SKF = StratifiedKFold(n_splits=num_cv, random_state= 17)\n",
    "    x_data = x_data.copy()\n",
    "    init_x_data = x_data.copy()\n",
    "    int_float_col = []\n",
    "    base_score = (cross_val_score(Model, init_x_data, y_data, n_jobs= -1, cv = Inner_SKF, scoring = 'r2')).mean()\n",
    "    for col in x_data.columns:\n",
    "        unique_values = len(set(x_data[col]))\n",
    "        if x_data[col].dtype == 'float64' or x_data[col].dtype == 'int64' and unique_values > thresh:\n",
    "            int_float_col.append(col)\n",
    "    for power in np.arange(2, power_ + 1):\n",
    "        x_data_pow = init_x_data[int_float_col] ** power\n",
    "        x_data_pow.columns = [*map(lambda x: x + ' ' +'in'+' '+str(power)+' '+'power' , x_data_pow.columns)]\n",
    "        power_features = x_data_pow.columns.tolist()\n",
    "        for i in np.arange(len(int_float_col)):\n",
    "            max_score = 0\n",
    "            for col in power_features:\n",
    "                Inner_x_data = x_data.copy()\n",
    "                Inner_x_data[col] = x_data_pow[col]\n",
    "                score = (cross_val_score(Model, Inner_x_data,\n",
    "                                         y_data, n_jobs= -1, cv = Inner_SKF, scoring = 'r2')).mean()\n",
    "                if score > max_score:\n",
    "                    max_score = score\n",
    "                    best_feature = col\n",
    "            if max_score > base_score:\n",
    "                base_score = max_score\n",
    "                power_features.remove(best_feature)\n",
    "                x_data[best_feature] = x_data_pow[best_feature]\n",
    "            else:\n",
    "                break\n",
    "    if add_features:\n",
    "        return x_data\n",
    "    else:\n",
    "        add_features = list(set(x_data.columns) - set(init_x_data.columns))\n",
    "        return x_data.loc[:,add_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mult_fetures (Model, x_data, y_data, thresh, num_cv = 5, max_features = False, mult_comb = 5, add_features = False):\n",
    "    def accum_prodict_df (df):\n",
    "        prodict_df = pd.Series(np.ones(df.shape[0]))\n",
    "        for col in df.columns:\n",
    "            prodict_df *= df[col]\n",
    "        return prodict_df\n",
    "    Inner_SKF = StratifiedKFold(n_splits=num_cv, random_state= 17)\n",
    "    x_data = x_data.copy()\n",
    "    init_x_data = x_data.copy()\n",
    "    int_float_col = []\n",
    "    base_score = (cross_val_score(Model, init_x_data, y_data, n_jobs= -1, cv = Inner_SKF, scoring = 'r2')).mean()\n",
    "    for col in x_data.columns:\n",
    "        unique_values = len(set(x_data[col]))\n",
    "        if x_data[col].dtype == 'float64' or x_data[col].dtype == 'int64' and unique_values > thresh:\n",
    "            int_float_col.append(col)\n",
    "    if max_features:\n",
    "        mult_comb = len(int_float_col)\n",
    "    for num_comb in np.arange(2, mult_comb + 1):\n",
    "        feature_combinations = [*itertools.combinations(int_float_col, num_comb)]\n",
    "        x_data_comb = pd.DataFrame()\n",
    "        for comb in feature_combinations:\n",
    "            df_feature_mult = init_x_data.loc[:, list(comb)]\n",
    "            df__multiplied = accum_prodict_df(df_feature_mult)\n",
    "            x_data_comb['PRODUCT OF'+ ' ('+', '.join(df_feature_mult.columns)+')'] = df__multiplied\n",
    "        comb_features = x_data_comb.columns.tolist()\n",
    "        for i in np.arange(len(int_float_col)):\n",
    "            max_score = 0\n",
    "            for col in comb_features:\n",
    "                Inner_x_data = x_data.copy()\n",
    "                Inner_x_data[col] = x_data_comb[col]\n",
    "                score = (cross_val_score(Model, Inner_x_data,\n",
    "                                         y_data, n_jobs= -1, cv = Inner_SKF, scoring = 'r2')).mean()\n",
    "                if score > max_score:\n",
    "                    max_score = score\n",
    "                    best_feature = col\n",
    "            if max_score > base_score:\n",
    "                base_score = max_score\n",
    "                comb_features.remove(best_feature)\n",
    "                x_data[best_feature] = x_data_comb[best_feature]\n",
    "            else:\n",
    "                break\n",
    "    if add_features:\n",
    "        return x_data\n",
    "    else:\n",
    "        add_features = list(set(x_data.columns) - set(init_x_data.columns))\n",
    "        return x_data.loc[:,add_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_power_features_to_test (x_test, power_features):\n",
    "    x_test = x_test.copy()\n",
    "    for feature in power_features:\n",
    "        col = feature.split(' in ')[0]\n",
    "        power = int(feature.split(' in ')[1].split(' ')[0])\n",
    "        x_test[feature] = x_test[col] ** power  \n",
    "    return x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_mult_features_to_test (x_test, mult_features): \n",
    "    def accum_prodict_df (df):\n",
    "        prodict_df = pd.Series(np.ones(df.shape[0]))\n",
    "        for col in df.columns:\n",
    "            prodict_df *= df[col]\n",
    "        return prodict_df\n",
    "    x_test = x_test.copy()\n",
    "    for feature in mult_features:\n",
    "        list_features = (feature.split(' OF ')[1].lstrip('(').rstrip(')')).split(', ')\n",
    "        x_test[feature] = accum_prodict_df(x_test[list_features])\n",
    "    return x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_range (num):\n",
    "    if num > 30:\n",
    "        return 30\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.width', 1000) \n",
    "pd.set_option ('display.max_columns' , 1000)\n",
    "pd.set_option(\"display.max_rows\", 1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "New_air_ports_data = pd.read_csv('Data for comp/Extended Air Port Data.csv')\n",
    "New_air_ports_data = New_air_ports_data[['IATA','Latitude', 'Longitude']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.read_csv('Data for comp/Exended all data climate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.merge(New_air_ports_data, left_on='Origin', right_on='IATA')\n",
    "all_data = all_data.merge(New_air_ports_data, left_on='Dest', right_on='IATA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop(columns=['IATA_x','IATA_y','Unnamed: 0.1'], inplace= True)\n",
    "all_data.sort_values(by = 'Unnamed: 0', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.index = np.arange(all_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('Data for comp/Humidity1.xlsx')\n",
    "Cities = data['City']\n",
    "climate_features_state = pd.read_excel('Data for comp/Climate features .xlsx')\n",
    "humidity_state = climate_features_state.iloc[:, 17:]\n",
    "data = data.sample(frac=1)\n",
    "data = data.sample(frac = 1)\n",
    "data.set_index(np.arange(data.shape[0]),inplace= True)\n",
    "states = climate_features_state['State']\n",
    "humidity_state['State'] = states\n",
    "data['State'] = data['City'].apply(lambda x: x.split(',')[1].lstrip())\n",
    "data['City'] = data['City'].apply(lambda x: x.split(',')[0].rstrip())\n",
    "y_data = data['Humidity']\n",
    "data.drop(columns=['Humidity'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cities_corr = pd.DataFrame({'City':pd.Series(list(set(Cities)))})\n",
    "Cities_corr['Latitude'] = np.NaN\n",
    "Cities_corr['Longitude'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(Cities_corr.shape[0]):\n",
    "    try:\n",
    "        page = wikipedia.page(Cities_corr.iloc[i, 0])\n",
    "        cordinates = [*map(float, page.coordinates)]\n",
    "    except:\n",
    "        cordinates = [np.NaN, np.NaN]\n",
    "    Cities_corr.iloc[i, 1] = cordinates[0]\n",
    "    Cities_corr.iloc[i, 2] = cordinates[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cities_corr.iloc[24, 1] = 38.9101\n",
    "Cities_corr.iloc[24, 2] = -77.0147"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cities_corr['City'] = Cities_corr['City'].apply(lambda x: x.split(',')[0].rstrip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.merge(Cities_corr, on= 'City', how= 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['City'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "climate_features_state = climate_features_state.loc[:,'Spring SUN Total Hours':'Winter SUN Clear Days']\n",
    "climate_features_state.drop([50, 51], inplace= True)\n",
    "climate_sun_hours = climate_features_state.iloc[:, [0, 2, 4, 6]]\n",
    "climate_sun_days =  climate_features_state.iloc[:, [1, 3, 5, 7]]\n",
    "climate_sun_hours['State'] = states\n",
    "climate_sun_days['State'] = states\n",
    "climate_sun_hours.set_index('State', inplace= True)\n",
    "climate_sun_days.set_index('State', inplace= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_climate_sun_days = dict()\n",
    "dict_climate_sun_hours = dict()\n",
    "for state in climate_sun_hours.index:\n",
    "    internal_dict1 = dict()\n",
    "    for col in climate_sun_hours.columns[:4]:\n",
    "        col_ = col.split(' ')[0]\n",
    "        internal_dict1[col_] = climate_sun_hours.loc[state, col]\n",
    "    dict_climate_sun_hours[state] = internal_dict1\n",
    "    internal_dict2 = dict()\n",
    "    for col__ in climate_sun_days.columns[:4]:\n",
    "        col___ = col__.split(' ')[0]\n",
    "        internal_dict2[col___] = climate_sun_days.loc[state, col__]\n",
    "    dict_climate_sun_days[state] = internal_dict2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SUN Total Hours'] = np.zeros(data.shape[0])\n",
    "data['SUN Clear Days'] = np.zeros(data.shape[0])\n",
    "for i in np.arange(data.shape[0]):\n",
    "    State = data.loc[i, 'State']\n",
    "    Season = data.loc[i, 'Season']\n",
    "    data.loc[i ,'SUN Total Hours'] = dict_climate_sun_hours[State][Season]\n",
    "    data.loc[i ,'SUN Clear Days'] = dict_climate_sun_days[State][Season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "humidity_state.index = humidity_state['State']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_state_humidity = dict()\n",
    "for state in humidity_state['State'].tolist():\n",
    "    columns = humidity_state.columns[:-1]\n",
    "    morning_col = columns[[*map(lambda x: 'Morn­ing' in x, columns)]]\n",
    "    afternoon_col = columns[[*map(lambda x: 'After­noon' in x, columns)]]\n",
    "    dict_part_day = dict()\n",
    "    dict_season = dict()\n",
    "    for col in morning_col:\n",
    "        season = col.split(' ')[0]\n",
    "        dict_season[season] = humidity_state.loc[state, col]\n",
    "    dict_part_day['Morning'] = dict_season\n",
    "    dict_season_ = dict()\n",
    "    for col in afternoon_col:\n",
    "        season = col.split(' ')[0]\n",
    "        dict_season_[season] = humidity_state.loc[state, col]\n",
    "    dict_part_day['Afternoon'] = dict_season_\n",
    "    dict_state_humidity[state] = dict_part_day "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Afternoon'], inplace= True)\n",
    "data.rename(columns={'Morning':'Part of day'}, inplace= True)\n",
    "dict_part_day = {1: 'Morning', 0: 'Afternoon'}\n",
    "data['Part of day'] = data['Part of day'].map(dict_part_day)\n",
    "data['Humidity State'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(data.shape[0]):\n",
    "    State = data.loc[i, 'State']\n",
    "    Part_of_day = data.loc[i, 'Part of day']\n",
    "    Season = data.loc[i, 'Season']\n",
    "    data.loc[i, 'Humidity State'] = dict_state_humidity[State][Part_of_day][Season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['Season', 'State'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.get_dummies(data)\n",
    "data.drop(columns=['Part of day_Morning', 'Spring','Summer','Fall','Winter'], inplace= True)\n",
    "data['Month'] = data['Month'].astype(str)\n",
    "data = pd.get_dummies(data)\n",
    "data = data[['Avg ° C',\n",
    " 'Millimetres',\n",
    " 'Latitude',\n",
    " 'Longitude',\n",
    " 'SUN Total Hours',\n",
    " 'SUN Clear Days',\n",
    " 'Humidity State',\n",
    " 'Part of day_Afternoon',\n",
    " 'Month_1',\n",
    " 'Month_2',\n",
    " 'Month_3',\n",
    " 'Month_4',\n",
    " 'Month_5',\n",
    " 'Month_6',\n",
    " 'Month_7',\n",
    " 'Month_8',\n",
    " 'Month_9',\n",
    "'Month_10',\n",
    " 'Month_11',\n",
    " 'Month_12']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lin_reg = LinearRegression(n_jobs= -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Skf = StratifiedKFold(n_splits= 5, random_state= 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_features_df = power_fetures(Lin_reg, data, y_data, thresh = 5, power_= 8, num_cv= 10)\n",
    "mulf_features_df = mult_fetures(Lin_reg, data, y_data, thresh = 5, max_features=True, num_cv= 10)\n",
    "data1 = pd.concat([data,power_features_df,mulf_features_df], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_power = pd.concat([data, power_features_df], axis= 1)\n",
    "combined_features_df = mult_fetures(Lin_reg, data_power , y_data, thresh = 5, mult_comb= 2, num_cv= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection = set(combined_features_df).intersection(set(mulf_features_df))\n",
    "combined_features_df.drop(columns = intersection ,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "power_features = list(power_features_df.columns)\n",
    "mult_features = list(mulf_features_df.columns)\n",
    "combined_features = list(combined_features_df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.concat([data,power_features_df,mulf_features_df,combined_features_df ], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = cross_val_score(Lin_reg, data2, y_data, cv = Skf, scoring = 'r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9368945897262211,\n",
       " 0.015630231105243714,\n",
       " array([0.94515193, 0.91683328, 0.93222503, 0.96235615, 0.92790655]))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scoring.mean(), scoring.std(), scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x = all_data[['Month Origin Temp',\n",
    "                             'Month Origin Prec',\n",
    "                             'Month',\n",
    "                             'Latitude_x',\n",
    "                             'Longitude_x',\n",
    "                             'Sun hours State_x',\n",
    "                             'Clear days State_x',\n",
    "                             'Humidity',\n",
    "                             'DepTime']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_it_afternoon (num):\n",
    "    if num > 1200:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x['DepTime'] = all_data_state_x['DepTime'].apply(is_it_afternoon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x['Month'] = all_data_state_x['Month'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x = pd.get_dummies(all_data_state_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x = all_data_state_x[['Month Origin Temp',\n",
    " 'Month Origin Prec',\n",
    " 'Latitude_x',\n",
    " 'Longitude_x',\n",
    " 'Sun hours State_x',\n",
    " 'Clear days State_x',\n",
    " 'Humidity',\n",
    " 'DepTime',\n",
    " 'Month_1',\n",
    " 'Month_2',\n",
    " 'Month_3',\n",
    " 'Month_4',\n",
    " 'Month_5',\n",
    " 'Month_6',\n",
    " 'Month_7',\n",
    " 'Month_8',\n",
    " 'Month_9',\n",
    " 'Month_10',\n",
    " 'Month_11',\n",
    " 'Month_12',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x.columns = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x_pow = add_power_features_to_test(all_data_state_x, power_features)\n",
    "all_data_state_x_pow_mult = add_mult_features_to_test(all_data_state_x_pow, mult_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_x_pow_mult_comb = add_mult_features_to_test(all_data_state_x_pow_mult, combined_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lin_reg.fit(data2, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_humidity = pd.DataFrame({'Predicted':Lin_reg.predict(all_data_state_x_pow_mult_comb)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f758d5df160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 394
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Predicted_humidity.hist(bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hundred (num):\n",
    "    if num > 100:\n",
    "        return 100\n",
    "    elif num < 0:\n",
    "        return 10\n",
    "    else:\n",
    "        return num\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_humidity = Predicted_humidity['Predicted'].apply(hundred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f758d555e20>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Predicted_humidity.hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_y = all_data['Target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Predicted':Predicted_humidity, 'Target': all_y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parameters_diff_classes (df, box_plots = True):\n",
    "    if box_plots:\n",
    "       sns.boxplot(x= df.columns[1], y = df.columns[0], data= df) \n",
    "    mean_std = pd.concat([df.groupby('Target').mean(),\n",
    "                          df.groupby('Target').std(),\n",
    "                         df.groupby('Target').median()], axis= 1)\n",
    "    mean_std.columns = ['MEAN', 'STD', 'MEDIAN']\n",
    "    diff_mean = np.abs(mean_std.loc[0, 'MEAN'] - mean_std.loc[1, 'MEAN']) ,\n",
    "    diff_std = np.abs(mean_std.loc[0, 'STD'] - mean_std.loc[1, 'STD'])\n",
    "    diff_median = np.abs(mean_std.loc[0, 'MEDIAN'] - mean_std.loc[1, 'MEDIAN'])\n",
    "    diff_df = pd.DataFrame({'MEAN':   diff_mean, 'STD': diff_std, 'MEDIAN':diff_median})\n",
    "    df = pd.concat([mean_std,  diff_df])\n",
    "    df.index = [0, 1, 'difference between classes']\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66.241133</td>\n",
       "      <td>17.483883</td>\n",
       "      <td>64.238769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59.767591</td>\n",
       "      <td>15.496168</td>\n",
       "      <td>56.898032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>6.473542</td>\n",
       "      <td>1.987715</td>\n",
       "      <td>7.340737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 MEAN        STD     MEDIAN\n",
       "0                           66.241133  17.483883  64.238769\n",
       "1                           59.767591  15.496168  56.898032\n",
       "difference between classes   6.473542   1.987715   7.340737"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_y = all_data[['Month Dest Temp',\n",
    "                             'Month Dest Prec',\n",
    "                             'Month',\n",
    "                             'Latitude_y',\n",
    "                             'Longitude_y',\n",
    "                             'Sun hours State_y',\n",
    "                             'Clear days State_y',\n",
    "                             'Humidity State_y',\n",
    "                             'Land time']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_y['Land time'] = all_data_state_y['Land time'].apply(is_it_afternoon)\n",
    "all_data_state_y['Month'] = all_data_state_y['Month'].astype(str)\n",
    "all_data_state_y = pd.get_dummies(all_data_state_y)\n",
    "all_data_state_y = all_data_state_y[['Month Dest Temp',\n",
    " 'Month Dest Prec',\n",
    " 'Latitude_y',\n",
    " 'Longitude_y',\n",
    " 'Sun hours State_y',\n",
    " 'Clear days State_y',\n",
    " 'Humidity State_y',\n",
    " 'Land time',\n",
    " 'Month_1',\n",
    " 'Month_2',\n",
    " 'Month_3',\n",
    " 'Month_4',\n",
    " 'Month_5',\n",
    " 'Month_6',\n",
    " 'Month_7',\n",
    " 'Month_8',\n",
    " 'Month_9',\n",
    " 'Month_10',\n",
    " 'Month_11',\n",
    " 'Month_12',]]\n",
    "all_data_state_y.columns = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_y_pow = add_power_features_to_test(all_data_state_y, power_features)\n",
    "all_data_state_y_pow_mult = add_mult_features_to_test(all_data_state_y_pow, mult_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data_state_y_pow_mult_comb = add_mult_features_to_test(all_data_state_y_pow_mult, combined_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_humidity1 = pd.DataFrame({'Predicted':Lin_reg.predict(all_data_state_y_pow_mult_comb)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f758045d790>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 394
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Predicted_humidity1.hist(bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_humidity1 = Predicted_humidity1['Predicted'].apply(hundred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f758f10f700>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 611
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Predicted_humidity1.hist(bins = 50, figsize = (10, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'all_y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-6feab41e3bf8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Predicted'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mPredicted_humidity1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Target'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mall_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'all_y' is not defined"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'Predicted':Predicted_humidity1, 'Target': all_y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.865460</td>\n",
       "      <td>17.640745</td>\n",
       "      <td>60.798130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57.930445</td>\n",
       "      <td>16.673192</td>\n",
       "      <td>56.527095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>5.935016</td>\n",
       "      <td>0.967553</td>\n",
       "      <td>4.271035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 MEAN        STD     MEDIAN\n",
       "0                           63.865460  17.640745  60.798130\n",
       "1                           57.930445  16.673192  56.527095\n",
       "difference between classes   5.935016   0.967553   4.271035"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "New_air_ports_data1 = pd.read_csv('Data for comp/Extended Air Port Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "Airport_acre = New_air_ports_data1[['IATA', 'Acre']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = pd.read_excel('Data for comp/Humidity1.xlsx')\n",
    "data1 = data1[data1['Morning'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_city_avg = dict()\n",
    "dict_city_mill = dict()\n",
    "Cities = set(data1['City'])\n",
    "for city in Cities:\n",
    "    df_city = data1[data1['City'] == city]\n",
    "    inner_dict_avg = dict()\n",
    "    inner_dict_mill = dict()\n",
    "    for index in df_city.index:\n",
    "        Month = df_city.loc[index,'Month']\n",
    "        Avg = df_city.loc[index,'Avg ° C']\n",
    "        Mill = df_city.loc[index,'Millimetres']\n",
    "        inner_dict_avg[Month] = Avg\n",
    "        inner_dict_mill[Month] = Mill\n",
    "    dict_city_avg[city] = inner_dict_avg\n",
    "    dict_city_mill[city] = inner_dict_mill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df = pd.read_excel('Data for comp/Cloud.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_cl = Cloud_df[['Season','Month','City','Cloud']]\n",
    "Cloud_df_fog = Cloud_df[['Season','Month','City','Fog']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_cl['State'] = Cloud_df_cl['City'].apply(lambda x: x.split(', ')[1])\n",
    "Cloud_df_cl['city'] = Cloud_df_cl['City'].apply(lambda x: x.split(', ')[0])\n",
    "Cloud_df_cl['State Humidity Morning'] = np.NaN\n",
    "Cloud_df_cl['State Humidity Afternoon'] = np.NaN\n",
    "Cloud_df_cl['State Sun Days'] = np.NaN\n",
    "Cloud_df_cl['State Sun Hours'] = np.NaN\n",
    "Cloud_df_cl['Avg Temperature'] = np.NaN\n",
    "Cloud_df_cl['Avg Millimetres'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in Cloud_df_cl.index:\n",
    "    Season = Cloud_df_cl.loc[i,'Season']\n",
    "    Month = Cloud_df_cl.loc[i,'Month']\n",
    "    State = Cloud_df_cl.loc[i,'State']\n",
    "    City = Cloud_df_cl.loc[i,'City']\n",
    "    Cloud_df_cl.loc[i,'State Humidity Morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    Cloud_df_cl.loc[i,'State Humidity Afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "    Cloud_df_cl.loc[i,'State Sun Days'] = dict_climate_sun_days[State][Season]\n",
    "    Cloud_df_cl.loc[i,'State Sun Hours'] = dict_climate_sun_hours[State][Season]\n",
    "    Cloud_df_cl.loc[i,'Avg Temperature'] = dict_city_avg[City][Month]\n",
    "    Cloud_df_cl.loc[i,'Avg Millimetres'] = dict_city_mill[City][Month]\n",
    "Cloud_df_cl = Cloud_df_cl.merge(Cities_corr, left_on= 'city', right_on= 'City', how= 'left')\n",
    "Cloud_df_cl = Cloud_df_cl.sample(frac=1)\n",
    "Cloud_df_cl = Cloud_df_cl.sample(frac = 1)\n",
    "y_cloud = Cloud_df_cl['Cloud']\n",
    "Cloud_df_cl.drop(columns = ['City_x', 'State', 'city','City_y', 'Season', 'Cloud'], inplace = True)\n",
    "Cloud_df_cl['Month'] = Cloud_df_cl['Month'].astype(str)\n",
    "Cloud_df_cl = pd.get_dummies(Cloud_df_cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_fog['State'] = Cloud_df_fog['City'].apply(lambda x: x.split(', ')[1])\n",
    "Cloud_df_fog['city'] = Cloud_df_fog['City'].apply(lambda x: x.split(', ')[0])\n",
    "Cloud_df_fog['State Humidity Morning'] = np.NaN\n",
    "Cloud_df_fog['State Humidity Afternoon'] = np.NaN\n",
    "Cloud_df_fog['State Sun Days'] = np.NaN\n",
    "Cloud_df_fog['State Sun Hours'] = np.NaN\n",
    "Cloud_df_fog['Avg Temperature'] = np.NaN\n",
    "Cloud_df_fog['Avg Millimetres'] = np.NaN\n",
    "for i in Cloud_df_fog.index:\n",
    "    Season = Cloud_df_fog.loc[i,'Season']\n",
    "    Month = Cloud_df_fog.loc[i,'Month']\n",
    "    State = Cloud_df_fog.loc[i,'State']\n",
    "    City = Cloud_df_fog.loc[i,'City']\n",
    "    Cloud_df_fog.loc[i,'State Humidity Morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    Cloud_df_fog.loc[i,'State Humidity Afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "    Cloud_df_fog.loc[i,'State Sun Days'] = dict_climate_sun_days[State][Season]\n",
    "    Cloud_df_fog.loc[i,'State Sun Hours'] = dict_climate_sun_hours[State][Season]\n",
    "    Cloud_df_fog.loc[i,'Avg Temperature'] = dict_city_avg[City][Month]\n",
    "    Cloud_df_fog.loc[i,'Avg Millimetres'] = dict_city_mill[City][Month]\n",
    "Cloud_df_fog = Cloud_df_fog.merge(Cities_corr, left_on= 'city', right_on= 'City', how= 'left')\n",
    "Cloud_df_fog = Cloud_df_fog.sample(frac=1)\n",
    "Cloud_df_fog = Cloud_df_fog.sample(frac = 1)\n",
    "y_fog = Cloud_df_fog['Fog']\n",
    "Cloud_df_fog.drop(columns = ['City_x', 'State', 'city','City_y', 'Season', 'Fog'], inplace = True)\n",
    "Cloud_df_fog['Month'] = Cloud_df_fog['Month'].astype(str)\n",
    "Cloud_df_fog = pd.get_dummies(Cloud_df_fog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin = all_data[['Season', 'Month','State_x', 'Clear days State_x','Sun hours State_x',\n",
    "                                  'Month Origin Temp','Month Origin Prec','Latitude_x','Longitude_x']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin['Humidity state morning'] = np.NaN\n",
    "cloud_all_data_origin['Humidity state afternoon'] = np.NaN\n",
    "for i in cloud_all_data_origin.index:\n",
    "    State = cloud_all_data_origin.loc[i,'State_x']\n",
    "    Season = cloud_all_data_origin.loc[i,'Season']\n",
    "    cloud_all_data_origin.loc[i,'Humidity state morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    cloud_all_data_origin.loc[i,'Humidity state afternoon'] = dict_state_humidity[State]['Afternoon'][Season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin.drop(columns=['Season', 'State_x'], inplace= True)\n",
    "cloud_all_data_origin['Month'] = cloud_all_data_origin['Month'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin = cloud_all_data_origin[['Humidity state morning',\n",
    "                                               'Humidity state afternoon',\n",
    "                                               'Clear days State_x',\n",
    "                                               'Sun hours State_x',\n",
    "                                               'Month Origin Temp',\n",
    "                                               'Month Origin Prec',\n",
    "                                               'Latitude_x',\n",
    "                                               'Longitude_x',\n",
    "                                               'Month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin = pd.get_dummies(cloud_all_data_origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin.columns = Cloud_df_cl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_cl_power = power_fetures(Lin_reg, Cloud_df_cl, y_cloud, thresh = 5, power_= 8 , num_cv = 5, add_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_cl_mult = mult_fetures (Lin_reg, Cloud_df_cl, y_cloud, thresh = 5, num_cv = 5, max_features = True, add_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "Cloud_df_cl_power_mult = pd.concat([Cloud_df_cl,Cloud_df_cl_power,Cloud_df_cl_mult], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "Power_features_cl = Cloud_df_cl_power.columns\n",
    "Mult_features_cl = Cloud_df_cl_mult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean1 = cloud_all_data_origin['State Humidity Morning'].mean()\n",
    "mean2 = cloud_all_data_origin['State Humidity Afternoon'].mean()\n",
    "cloud_all_data_origin['State Humidity Morning'] = cloud_all_data_origin['State Humidity Morning'].fillna(mean1)\n",
    "cloud_all_data_origin['State Humidity Afternoon'] = cloud_all_data_origin['State Humidity Afternoon'].fillna(mean2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_origin_power = add_power_features_to_test(cloud_all_data_origin, Power_features_cl)\n",
    "cloud_all_data_origin_mult = add_mult_features_to_test(cloud_all_data_origin_power, Mult_features_cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_dest = all_data[['Season', 'Month','State_y', 'Clear days State_y','Sun hours State_y',\n",
    "                                  'Month Dest Temp','Month Dest Prec','Latitude_y','Longitude_y']]\n",
    "cloud_all_data_dest['Humidity state morning'] = np.NaN\n",
    "cloud_all_data_dest['Humidity state afternoon'] = np.NaN\n",
    "for i in cloud_all_data_dest.index:\n",
    "    State = cloud_all_data_dest.loc[i,'State_y']\n",
    "    Season = cloud_all_data_dest.loc[i,'Season']\n",
    "    cloud_all_data_dest.loc[i,'Humidity state morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    cloud_all_data_dest.loc[i,'Humidity state afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "cloud_all_data_dest.drop(columns=['Season', 'State_y'], inplace= True)\n",
    "cloud_all_data_dest['Month'] = cloud_all_data_dest['Month'].astype(str)\n",
    "cloud_all_data_dest = cloud_all_data_dest[['Humidity state morning',\n",
    "                                               'Humidity state afternoon',\n",
    "                                               'Clear days State_y',\n",
    "                                               'Sun hours State_y',\n",
    "                                               'Month Dest Temp',\n",
    "                                               'Month Dest Prec',\n",
    "                                               'Latitude_y',\n",
    "                                               'Longitude_y',\n",
    "                                               'Month']]\n",
    "cloud_all_data_dest = pd.get_dummies(cloud_all_data_dest)\n",
    "cloud_all_data_dest.columns = Cloud_df_cl.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean11 = cloud_all_data_dest['State Humidity Morning'].mean()\n",
    "mean22 = cloud_all_data_dest['State Humidity Afternoon'].mean()\n",
    "cloud_all_data_dest['State Humidity Morning'] = cloud_all_data_dest['State Humidity Morning'].fillna(mean11)\n",
    "cloud_all_data_dest['State Humidity Afternoon'] = cloud_all_data_dest['State Humidity Afternoon'].fillna(mean22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "cloud_all_data_dest_power = add_power_features_to_test(cloud_all_data_dest, Power_features_cl)\n",
    "cloud_all_data_dest_mult = add_mult_features_to_test(cloud_all_data_dest_power, Mult_features_cl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lin_reg.fit(Cloud_df_cl_power_mult, y_cloud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_cloud_origin = Lin_reg.predict(cloud_all_data_origin_mult)\n",
    "Predicted_cloud_dest = Lin_reg.predict(cloud_all_data_dest_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "Skf = StratifiedKFold(n_splits= 5, random_state= 17\n",
    "                     )\n",
    "scores_fog = cross_val_score(Lin_reg,fog_power_mult, y_fog, cv = Skf, scoring = 'r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.76372979, 0.72525627, 0.80837017, 0.81518804, 0.76489355]),\n",
       " 0.7754875629147283,\n",
       " 0.03295771536650862)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_fog, scores_fog.mean(), scores_fog.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_power = power_fetures(Lin_reg,\n",
    "                                 Cloud_df_fog,\n",
    "                                 y_fog,\n",
    "                                 thresh = 5,\n",
    "                                 power_= 8 ,\n",
    "                                 num_cv = 10,\n",
    "                                 add_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_mult = mult_fetures (Lin_reg,\n",
    "                                 Cloud_df_fog, \n",
    "                                 y_fog, \n",
    "                                 thresh = 5,\n",
    "                                 num_cv = 10, \n",
    "                                 max_features = True,\n",
    "                                 add_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fog_power = pd.concat([Cloud_df_fog, fog_power], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_features = mult_fetures (Lin_reg,\n",
    "                                 data_fog_power, \n",
    "                                 y_fog, \n",
    "                                 thresh = 5,\n",
    "                                 num_cv = 10, \n",
    "                                 mult_comb= 2,\n",
    "                                 add_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "intersection = set(combined_features).intersection(set(fog_mult))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_features.drop(columns = intersection, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_power_mult = pd.concat([Cloud_df_fog,fog_power,fog_mult], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_power_fetures = fog_power.columns\n",
    "fog_mult_features = fog_mult.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_data_origin_power = add_power_features_to_test(cloud_all_data_origin , fog_power_fetures)\n",
    "fog_data_origin_power_mult = add_mult_features_to_test(fog_data_origin_power,fog_mult_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lin_reg.fit(fog_power_mult, y_fog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_origin_fog  = Lin_reg.predict(fog_data_origin_power_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "def origin_fog_mean (num):\n",
    "    if num > 30:\n",
    "        return 21.508794232883446\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dest_fog_mean (num):\n",
    "    if num > 30:\n",
    "        return 21.683486841225378\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'Predicted':Predicted_origin_fog})\n",
    "df1['Predicted'] = df1['Predicted'].apply( origin_fog_mean )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f75794dc160>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1['Predicted'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "fog_data_dest_power = add_power_features_to_test(cloud_all_data_dest , fog_power_fetures)\n",
    "fog_data_dest_power_mult = add_mult_features_to_test(fog_data_dest_power,fog_mult_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_dest_fog  = Lin_reg.predict(fog_data_dest_power_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'Predicted':Predicted_dest_fog})\n",
    "df2['Predicted'] = df2['Predicted'].apply(dest_fog_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f75797cdac0>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Predicted'].hist(bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.754494</td>\n",
       "      <td>5.120969</td>\n",
       "      <td>12.028358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.123707</td>\n",
       "      <td>4.968653</td>\n",
       "      <td>12.302490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>0.369213</td>\n",
       "      <td>0.152316</td>\n",
       "      <td>0.274132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 MEAN       STD     MEDIAN\n",
       "0                           10.754494  5.120969  12.028358\n",
       "1                           11.123707  4.968653  12.302490\n",
       "difference between classes   0.369213  0.152316   0.274132"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7579784ca0>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Predicted'].hist(bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data = pd.read_excel('Data for comp/Sun Data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "State_sun_percent = pd.read_excel('Data for comp/Sun Percent State.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data['State'] = Sun_data['City'].apply(lambda x: x.split(', ')[1])\n",
    "Sun_data['city'] = Sun_data['City'].apply(lambda x: x.split(', ')[0])\n",
    "Sun_data['State Humidity Morning'] = np.NaN\n",
    "Sun_data['State Humidity Afternoon'] = np.NaN\n",
    "Sun_data['State Sun Days'] = np.NaN\n",
    "Sun_data['State Sun Hours'] = np.NaN\n",
    "Sun_data['Avg Temperature'] = np.NaN\n",
    "Sun_data['Avg Millimetres'] = np.NaN\n",
    "for i in Sun_data.index:\n",
    "    Season = Sun_data.loc[i,'Season']\n",
    "    Month = Sun_data.loc[i,'Month']\n",
    "    State = Sun_data.loc[i,'State']\n",
    "    City = Sun_data.loc[i,'City']\n",
    "    Sun_data.loc[i,'State Humidity Morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    Sun_data.loc[i,'State Humidity Afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "    Sun_data.loc[i,'State Sun Days'] = dict_climate_sun_days[State][Season]\n",
    "    Sun_data.loc[i,'State Sun Hours'] = dict_climate_sun_hours[State][Season]\n",
    "    Sun_data.loc[i,'Avg Temperature'] = dict_city_avg[City][Month]\n",
    "    Sun_data.loc[i,'Avg Millimetres'] = dict_city_mill[City][Month]\n",
    "Sun_data = Sun_data.merge(Cities_corr, left_on= 'city', right_on= 'City', how= 'left')\n",
    "Sun_data = Sun_data.sample(frac=1)\n",
    "Sun_data = Sun_data.sample(frac = 1)\n",
    "y__clear_days = Sun_data['Clear Days']\n",
    "Y_per_sun = Sun_data['% Sun']\n",
    "Sun_data.drop(columns = ['City_x', 'city','City_y', 'Clear Days', '% Sun'], inplace = True)\n",
    "Sun_data['Month'] = Sun_data['Month'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dict_State_sun_percent = dict()\n",
    "for state in set(State_sun_percent['State']):\n",
    "    State_df = State_sun_percent[State_sun_percent['State'] == state]\n",
    "    Inner_dict = dict()\n",
    "    for i in State_df.index:\n",
    "        Season = State_df.loc[i,'Season']\n",
    "        Sun_percent = State_df.loc[i,'% Sun']\n",
    "        Inner_dict[Season] = Sun_percent\n",
    "    Dict_State_sun_percent[state] = Inner_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data['State Sun Percent'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in Sun_data.index:\n",
    "    try:\n",
    "        Season = Sun_data.loc[i, 'Season']\n",
    "        State = Sun_data.loc[i, 'State']\n",
    "        Sun_data.loc[i,'State Sun Percent'] =  Dict_State_sun_percent[State][Season]\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_mean_season = dict()\n",
    "for season in  set(Sun_data['Season']):\n",
    "    df = Sun_data[Sun_data['Season'] == season]\n",
    "    mean = df['State Sun Percent'].mean()\n",
    "    dict_mean_season[season] = mean\n",
    "for i in Sun_data[Sun_data['State Sun Percent'].isna()].index:\n",
    "    Season = Sun_data.loc[i, 'Season']\n",
    "    Sun_data.loc[i,'State Sun Percent'] = dict_mean_season[Season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data.drop(columns=['Season','State'], inplace= True)\n",
    "Sun_data = pd.get_dummies(Sun_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Lin_reg = LinearRegression(n_jobs= -1)\n",
    "Skf = StratifiedKFold(n_splits= 5, random_state= 17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = cross_val_score(Lin_reg, Sun_data_power_mult, Y_per_sun, cv = Skf, scoring = 'r2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8992218071220103,\n",
       " 0.011310449730830096,\n",
       " array([0.88422838, 0.89178038, 0.90746401, 0.91608553, 0.89655075]))"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean(), scores.std(), scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data_power = power_fetures(Lin_reg, Sun_data, Y_per_sun, thresh = 5, power_= 8, num_cv= 10)\n",
    "Sun_data_mult = mult_fetures(Lin_reg, Sun_data, Y_per_sun, thresh = 5, max_features= True, num_cv= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data_power_features = Sun_data_power.columns\n",
    "Sun_data_mult_fetures = Sun_data_mult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sun_data_power_mult = pd.concat([Sun_data, Sun_data_power,Sun_data_mult], axis= 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest = all_data[['Season', 'Month','State_y', 'Clear days State_y','Sun hours State_y',\n",
    "                                  'Month Dest Temp','Month Dest Prec','Latitude_y','Longitude_y']]\n",
    "sun_data_dest['Humidity state morning'] = np.NaN\n",
    "sun_data_dest['Humidity state afternoon'] = np.NaN\n",
    "for i in sun_data_dest.index:\n",
    "    State = sun_data_dest.loc[i,'State_y']\n",
    "    Season = sun_data_dest.loc[i,'Season']\n",
    "    sun_data_dest.loc[i,'Humidity state morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    sun_data_dest.loc[i,'Humidity state afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "sun_data_dest['Month'] = sun_data_dest['Month'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest['State Sun Percent'] = np.NaN\n",
    "for i in sun_data_dest.index:\n",
    "    try:\n",
    "        Season = sun_data_dest.loc[i, 'Season']\n",
    "        State = sun_data_dest.loc[i, 'State_y']\n",
    "        sun_data_dest.loc[i,'State Sun Percent'] =  Dict_State_sun_percent[State][Season]\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_mean_season_1 = dict()\n",
    "for season in  set(sun_data_dest['Season']):\n",
    "    df = Sun_data[sun_data_dest['Season'] == season]\n",
    "    mean = df['State Sun Percent'].mean()\n",
    "    dict_mean_season_1[season] = mean\n",
    "for i in sun_data_dest[sun_data_dest['State Sun Percent'].isna()].index:\n",
    "    Season = sun_data_dest.loc[i, 'Season']\n",
    "    sun_data_dest.loc[i,'State Sun Percent'] = dict_mean_season_1[Season]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_sun_1 = sun_data_dest['Humidity state morning'].mean()\n",
    "mean_sun_2 = sun_data_dest['Humidity state afternoon'].mean()\n",
    "sun_data_dest['Humidity state morning'] = sun_data_dest['Humidity state morning'].fillna(mean_sun_1)\n",
    "sun_data_dest['Humidity state afternoon'] = sun_data_dest['Humidity state afternoon'].fillna(mean_sun_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest.drop(columns=['Season','State_y'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest = sun_data_dest[['Humidity state morning',\n",
    "                                               'Humidity state afternoon',\n",
    "                                               'Clear days State_y',\n",
    "                                               'Sun hours State_y',\n",
    "                                               'Month Dest Temp',\n",
    "                                               'Month Dest Prec',\n",
    "                                               'Latitude_y',\n",
    "                                               'Longitude_y',\n",
    "                                            'State Sun Percent',\n",
    "                                               'Month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest = pd.get_dummies(sun_data_dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest.columns = Sun_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_dest_power = add_power_features_to_test(sun_data_dest, Sun_data_power_features)\n",
    "sun_data_dest_power_mult = add_mult_features_to_test(sun_data_dest_power, Sun_data_mult_fetures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Lin_reg.fit(Sun_data_power_mult, Y_per_sun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_sun_recent_dest = Lin_reg.predict(sun_data_dest_power_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_range_sun (num):\n",
    "    if num > 100:\n",
    "        return 100\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sun = pd.DataFrame({'Predicted':Predicted_sun_recent_dest})\n",
    "df_sun['Predicted'] = df_sun['Predicted'].apply(make_range_sun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f757900eb20>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EhL9I8swkD89wqs/PJfmnJB9N8u7W2hfnO0Br7XVVdXGSlyd5cZLbknwjydtaa+fOU357VT0pyUlJnpthdOL6JOckObm19u156lxRVQ9L8uYMpzM9NcnVSc5Icmpr7dqd/ygAAGDtWpGA0Fo7Jckpiyz7Z0n+bJnH+XCSDy+h/M1JTh4fi63zD0lesPTWAQDA2renXaQMAADMkIAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0G2bdAABgfdh00nm7VH/racesUEuAXWEEAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6MykDAAL2NWZgQHWIiMIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQbZt0AAIAk2XTSecuuu/W0Y1awJbB3M4IAAAB0AgIAANAJCAAAQCcgAAAA3YoEhKo6rqreVVVfrKrrq6pV1Zk7qXNUVX2yqq6tqpuq6uKqenVV3XEHdU6oqq9V1Q1VdV1VbamqY3dQfr+qOrWqLq2qW6rqmqr6aFUdsYM6962qD1TVP1bV9qraWlXvqKqDFvfTAACAtWulRhDemOTlSX45yVU7K1xVT0/yhSSPTfKxJO9Jsk+SP0py1gJ1Tk/yoST3TvL+JGcmOTLJJ6rq5fOU3zfJZ5L8XpLrk7wzyd8keWaSr1fVI+epc2iSi5K8IMnXxvZcmeRVSf62qu62s9cGAABr2Urd5vQ1SX6Q5LtJHpfkgoUKVtUBGT7g/zTJ5tba18f1b0ryuSTHVdVzWmtnTdU5KsnrklyR5OGttZ+M69+W4QP96VV1bmtt69ShXpvk0UnOTvLs1tptY52PJDknyQeq6sjJ+tEfJ7lHkle21t41dfy3j6/xLUlessSfDQAArBkrMoLQWrugtXZ5a60tovhxSe6e5KxJOBj3cUuGkYgk+e05dSYfyt8yCQdjna0ZRh/2zfCtf5KkqmqqzuunQ0Br7eNJvpjkQRnCzKTOIUmOTjLZ57STk9yY5HlVtf8iXiMAAKxJs7hI+Qnj8tPzbPtCkpuSHDWeIrSYOp+aUyZJDk1yvySXtda+t8g6k3+fP2dUIa21bUm+nOTOSR41z/4AAGBdmMVMyg8Yl5fN3dBau7WqvpfkwUkOSXLJ+I39fZLc0Fq7ep79XT4uD1/MMXaxztFjnc8uUCZJUlUXLbDpgdu2bcuWLVt2VH232LZtW5LM5NisDn28d9DPq+vEI2+dyXHvud9sj78WrcX3hPfz+jfLPp4cezlmMYJw4Li8boHtk/V3XWb51awDAADryixGEHamxuVirmeYtpTyyznGouu01h467w6qLtq4ceOvbt68eQmHXRmT5DqLY7M69PHeQT+vruefdN5MjjsZOTj9m3vif9N7pq2/sXnWTVgy7+f1b5Z9vHHjxmXXncUIwuSb+AMX2H7AnHI7Kz/fN/9LPcZy6wAAwLoyi4Bw6bg8fO6GqtqQ5P5Jbs0w/0BaazdmmFvhLlV173n2d9i4nL52YMFjrHAdAABYV2YRED43Lp88z7bHZrhT0IWtte2LrPOUOWWSYb6E7yc5vKruv8g6k7kbjq6q2/1cqmpjhjkVbk7ylXn2BwAA68IsAsLZSX6U5DlV9bDJyqq6U5LfH5++d06d943LN1TVQVN1NiV5WZLtST44WT/OxzCp89bpD/zjLM6PSfLtJJ+fqnNFkvOTTPY57dQk+yf583FEAwAA1qUVufqpqp6R5Bnj03uNy1+rqg+N//5Ra+3EJGmtXV9VL8oQFLZU1VlJrk3ytAy3Gj07yUem999au3Cczfi1SS6uqrOT7JPk2UkOTvKKObMoJ8nbkxybYWK2r1bVZzPMjXB8hrkWfmvufAdJXprkwiRnVNUTk1yS5JFJHp/h1KI3LPFHAwAAa8pK3R7hl5OcMGfdIeMjSf4+yYmTDa21c6rqcRk+cD8ryZ2SfDdDADhjvhmZW2uvq6qLk7w8yYuT3JbkG0ne1lo7d57y26vqSUlOSvLcJK9Jcn2Sc5Kc3Fr79jx1rhhHNd6c4XSmpya5OskZSU5trV27uB8HAACsTSsSEFprpyQ5ZYl1vpzhA/hS6nw4yYeXUP7mJCePj8XW+YckL1hKuwAAYL2YxTUIAADAHkpAAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAug2zbgAA7Mymk85bdt2tpx2zgi0BWP+MIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdG5zCsC6tiu3SAXYGxlBAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKDbMOsGAADM0qaTzlt23a2nHbOCLYE9g4AAsBfZlQ9CiQ9DAHsDpxgBAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0JkHAQBY83Z1jg/g3xhBAAAAOgEBAADonGIEwKpwCgjA2mAEAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoTpe1FvnXVdXn+Micq2nraMSvcGgAA9kRGEAAAgE5AAAAAupkFhKraWlVtgccPF6hzVFV9sqquraqbquriqnp1Vd1xB8c5oaq+VlU3VNV1VbWlqo7dQfn9qurUqrq0qm6pqmuq6qNVdcRKvG4AANiTzfoahOuSvGOe9TfMXVFVT0/yV0luSfKRJNcm+fUkf5Tk0UmOn6fO6Ulel+QHSd6fZJ8kz0nyiap6RWvt3XPK75vkM+P+vp7knUl+Ydz3MVX1hNbaV5f1SgEAYA2YdUD4l9baKTsrVFUHZPiA/9Mkm1trXx/XvynJ55IcV1XPaa2dNVXnqAzh4IokD2+t/WRc/7YkFyU5varOba1tnTrUazOEg7OTPLu1dttY5yNJzknygao6crIeAADWm7VyDcJxSe6e5KxJOEiS1totSd44Pv3tOXVeMi7fMgkHY52tSd6TZN8kL5isr6qaqvP66RDQWvt4ki8meVCSx63A6wEAgD3SrAPCvlX1n6vqd6vqVVX1+AWuJ3jCuPz0PNu+kOSmJEeNpwgtps6n5pRJkkOT3C/JZa217y2yDgAArCvVWpvNgau2JvnFeTZ9L8kLWmufnyr7P5M8LMnDWmsXzbOvbyV5cJIHtdYuqar9M1zHcENrbeM85X8+yT8nuaa1ds9x3TFJzk1ybmvt1+epc1ySv0zy0dbas3fy2n6mjaMHHnbYYXf+kz/5kx1V3y22bduWW/71tvzTzcur/5D7HLiyDWLFbdu2LUmycePP/MqzjuxqP3/rqut26fi78rdgV4+9N7nnfsNyuX+zWT278p7wd3v9m2Ufv/jFL87ll1/+jdbaQ5dad5YjCB9M8sQk90qyf5Ijk/zfSTYl+VRV/dJU2cm7b6H/XSbr77rM8sutAwAA68rMLlJurZ06Z9W3krykqm7IcHHxKUmeucjd1WS3S23GEsou+hgLJbWqumjjxo2/unnz5iUcdmVs2bIlf3/VdTn9m8vr8q2/sXllG8SK27JlS5JkFr9frJ5d7eflzqY+sSt/C3b12HuTE4+8NUmW/Teb1bMr7wl/t9e/WfbxroxazPoahPm8b1w+dmrd5Nv7hcbxDphTbmfl5xstWOoxAABg3dkTA8I143L/qXWXjsvD5xauqg1J7p/k1iRXJklr7cYkVyW5S1Xde55jHDYuL1vMMXZQBwAA1pU9MSD82ri8cmrd58blk+cp/9gkd05yYWtt+yLrPGVOmWSYL+H7SQ6vqvsvsg4AAKwrMwkIVfXgqjp4nvW/mGQyu/GZU5vOTvKjJM+pqodNlb9Tkt8fn753zu4mpyq9oaoOmqqzKcnLkmzPcKF0kqQNt3Oa1HlrVd1hqs7TkzwmybeT9LsrAQDAejOrq5+OT3JSVV2Q4bam2zLMQ3BMkjsl+WSS0yeFW2vXV9WLMgSFLVV1VpJrkzwtyQPG9R+ZPkBr7cKqenuG2ZEvrqqzk+yT5NlJDk7yijmzKCfJ25Mcm2Fitq9W1WczzI1wfIa5Fn7LLMoAAKxnswoIF2T4YP8rGU4p2j/JvyT5UpK/SPIXbc4EDa21c6rqcUnekORZGYLEdzMEgDPmlh/rvK6qLk7y8iQvTnJbkm8keVtr7dx5ym+vqiclOSnJc5O8Jsn1Sc5JcnJr7dsr8NoB1qxN7kQEsO7NJCCMk6At+VSd1tqXkzx1iXU+nOTDSyh/c5KTxwcAAOxV3GAZYI351lXXmVMAgN1mT7yLEQAAMCMCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQbZt0AAABW16aTzlt23a2nHbOCLWFPZAQBAADoBAQAAKATEAAAgE5AAAAAOhcpA8zAci8QPPHIW1e4JcCu2JWLfSfv5+cvcx8uFmZ3MYIAAAB0RhAAlmlXvjkEgD2VEQQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoNsy6AQAALN2mk86bdRNYp4wgAAAAnYAAAAB0AgIAANAJCAAAQOciZWBN25WL9LaedswKtgQA1gcjCAAAQCcgAAAAnYAAAAB0rkEA9lomGQKAn2UEAQAA6IwgAACwKnZ15Nbd51aHgAAAwKI5PXP9c4oRAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEC3YdYNANaHTSedt+y6W087ZgVbAsB65f+a1SEgAN2u/OEFANYHpxgBAACdgAAAAHQCAgAA0AkIAABAJyAAAACdgAAAAHQCAgAA0AkIAABAZ6I0WEe+ddV1eb7JzgCAXSAgwG5gKvilMYMzAOw5BIR5VNV9k7w5yZOT3C3J1UnOSXJqa+0ns2wbAACra7lfZJ145K15yH0OXOHW7H4CwhxVdWiSC5PcI8nHk3wnySOSvCrJk6vq0a21H8+wiQAAsNsICD/rjzOEg1e21t41WVlVb0/ymiRvSfKSGbUNAIBlcDrr4rmL0ZSqOiTJ0Um2JnnPnM0nJ7kxyfOqav9VbhoAAKwKIwi394RxeX5r7bbpDa21bVX15QwB4lFJPrvajdsbudgXAGB1VWtt1m3YY1TV25KcmOTE1tofzrP93UleluSlrbX37mA/Fy2w6Zf23XffO97vfvdbkfYuxW233ZbbWsutt+287Hzu9HN3XNkGLdIt//rTmRx3Ldowjgcut49ZG/Tz3kE/7x308/q34Q7JHapyhzus/kk73//+97N9+/ZrW2t3W2pdIwi3N7nM/LoFtk/W33WZ+//p9u3br7v88su3LrP+rnjguPzODI7N6tDHewf9vHfQz3sH/bz+zbKPNyW5fjkVBYSlqXG5w2GX1tpDV6EtSzIZ1dgT28bK0Md7B/28d9DPewf9vP6t1T52kfLtTUYIFrph7QFzygEAwJxR4QoAAAkSSURBVLoiINzepePy8AW2HzYuL1uFtgAAwKoTEG7vgnF5dFXd7mdTVRuTPDrJzUm+stoNAwCA1SAgTGmtXZHk/AwXdbxszuZTk+yf5M9bazeuctMAAGBVuEj5Z700yYVJzqiqJya5JMkjkzw+w6lFb5hh2wAAYLcyD8I8quoXkrw5yZOT3C3J1UnOSXJqa+3aWbYNAAB2JwEBAADoXIMAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCOtcVd23qj5QVf9YVduramtVvaOqDpp121icqrpbVb2wqj5WVd+tqpur6rqq+lJV/Zeqmvd9XFVHVdUnq+raqrqpqi6uqldX1R1X+zWwPFX1vKpq4+OFC5TRz2tUVT2mqv6qqq4e/z5fXVXnV9VT5ymrn9eYqjpm7M8fjH+3r6yqv6yqX1ugvD7eA1XVcVX1rqr6YlVdP/49PnMndZbcl1V1QlV9rapuGP+P31JVx678K1oc8yCsY1V1aIZZoe+R5ONJvpPkERlmhb40yaNbaz+eXQtZjKp6SZL3Zpiw74Ik309yzyT/McmBSf4qyfFt6s1cVU8f19+S5CNJrk3y60kekOTs1trxq/kaWLpxwsZvJrljkrskeVFr7U/nlNHPa1RVvTHJf0vyoyTnZnh//3ySX0lyQWvt9VNl9fMaU1V/kOT1SX6cYaLVHyX5d0melmRDkt9srZ05VV4f76Gq6u+S/FKSG5L8IMkDk/yP1tp/XqD8kvuyqk5P8rpx/2cn2SfJc5IcnOQVrbV3r/DL2rnWmsc6fST56yRt/OWaXv/2cf37Zt1Gj0X14xMy/HG5w5z198oQFlqSZ02tPyDJNUm2J3nY1Po7ZQiMLclzZv26PHbY55Xkb5JckeRtY5+9cE4Z/bxGH0mOH/vnM0k2zrP95/Tz2n2Mf5t/muSHSe4xZ9vjxz67Uh+vjcfYZ4eNf5c3j/1x5gJll9yXSY4a1383yUFT6zdlCJi3JNm02q/bKUbrVFUdkuToJFuTvGfO5pOT3JjkeVW1/yo3jSVqrX2utfaJ1tptc9b/MMn7xqebpzYdl+TuSc5qrX19qvwtSd44Pv3t3ddiVsArMwTDF2R4r85HP69B4ymBf5DkpiTPba1tm1umtfavU0/189rzixlO4f5qa+2a6Q2ttQuSbMvQpxP6eA/WWrugtXZ5Gz+178Ry+vIl4/ItrbWfTNXZmuHz274Z/i9YVQLC+vWEcXn+PB8styX5cpI7J3nUajeMFTX5IHHr1LpJ3396nvJfyPDB5Kiq2nd3NozlqaojkpyW5J2ttS/soKh+XpuOSnL/JJ9M8pPxPPXfqapXLXBuun5eey5P8r+TPKKqfn56Q1U9NsnGDCOEE/p4/VhOX+6ozqfmlFk1AsL69YBxedkC2y8fl4evQlvYDapqQ5LfHJ9O/2FZsO9ba7cm+V6Gc2AP2a0NZMnGPv2LDKeO/e5Oiuvntenh4/Kfknwjw/UHpyV5R5ILq+rzVTX97bJ+XmNaa9cm+Z0M14p9u6r+pKr+e1V9NMn5GU4t+69TVfTx+rGkvhzP4rhPkhtaa1fPs7+ZfVbbsNoHZNUcOC6vW2D7ZP1dV6Et7B6nJXlIkk+21v56ar2+X7t+L8NFqv9na+3mnZTVz2vTPcblSzJ8WHhSkq9mOC3lD5P8X0n+Mv922qB+XoNaa++oqq1JPpDkRVObvpvkQ3NOPdLH68dS+3KP7XsjCHuvGpduY7UGVdUrM9zx4DtJnrfU6uNS3+9BquoRGUYN/rC19rcrsctxqZ/3LJPbHFaS41prn22t3dBa+/+SPDPDXUwet9CtMOehn/dAVfX6DHej+VCSQ5Psn+ShSa5M8j+q6q1L2d241Mdr33L7ctX7XkBYvyap88AFth8wpxxrRFW9LMk7k3w7yePH4exp+n6NmTq16LIkb1pkNf28Nk0uQryytfa/pjeMo0aT0cBHjEv9vMZU1eYMF6L/P62117bWrmyt3dRa+0aGEHhVkteNNxNJ9PF6stS+3Fn5nY0w7DYCwvp16bhc6Ly1w8blQtcosAeqqlcneXeSb2UIBz+cp9iCfT9+EL1/houar9xd7WTJ7pKhv45IcsvU5Ggtw13HkuT947p3jM/189o06bd/WWD7JEDsN6e8fl47JpNbXTB3Q2vtpiRfy/D561fG1fp4/VhSX7bWbswQGO9SVfeeZ38z+6wmIKxfkz9MR8+dabeqNiZ5dJKbk3xltRvG8lTV7yT5oyR/lyEcXLNA0c+NyyfPs+2xGe5edWFrbfvKt5Jl2p7kzxZ4/L9jmS+NzyenH+nntekLGT4gHFZV+8yz/SHjcuu41M9rz+QONXdfYPtk/f8el/p4/VhOX+6ozlPmlFk9s5h0wmN1HjFR2rp5ZDjtpCX5epKDd1L2gCT/HJPurItHklOy8ERp+nkNPpKcOfbP789Z/x+S3JZhdOGu+nltPpL8p7FffpjkPnO2PWXs45uT3E0fr61HFjdR2pL6MnvoRGk1NoJ1qKoOzfALeY8kH09ySZJHZpgV8LIkR7XWfjy7FrIYVXVChgvdfprkXZn/XMStrbUPTdV5RoYL5G5JclaGqd6flnGq9yT/qXnzrwlVdUqG04xe1Fr70znb9PMaVFX3yDAXzb9L8sUMp5z8Yobz01uGCdT+cqq8fl5DxlH7v85wh6ptST6WISwckeH0o0ry6tbaO6fq6OM91Ng3zxif3ivDncauzPDeTZIftdZOnFN+SX1ZVX+Y5LUZblJwdpJ9kjw7yd0yfMn77t3y4nZk1mnMY/c+kvxCkg8muTrDcObfZ7jAdYffQnvsOY/82zfIO3psmafeozNOxpTh26pvJnlNkjvO+jV5LKv/X7jAdv28Bh9JDs4wmvu98W/zjzN8kfMo/bz2H0l+LsmrM5zGe32G08quyTDvxdH6eO08FvF/8NaV6MskJyT5n0luzBAsP5/k2Fm9biMIAABA5yJlAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACg+/8BitpBlgukWOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sun['Predicted'].hist(bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.265805</td>\n",
       "      <td>14.614153</td>\n",
       "      <td>61.993676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.374331</td>\n",
       "      <td>14.574586</td>\n",
       "      <td>61.792220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>0.891474</td>\n",
       "      <td>0.039567</td>\n",
       "      <td>0.201456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 MEAN        STD     MEDIAN\n",
       "0                           61.265805  14.614153  61.993676\n",
       "1                           60.374331  14.574586  61.792220\n",
       "difference between classes   0.891474   0.039567   0.201456"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df_sun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_origin = all_data[['Season', 'Month','State_x', 'Clear days State_x','Sun hours State_x',\n",
    "                                  'Month Origin Temp','Month Origin Prec','Latitude_x','Longitude_x']]\n",
    "sun_data_origin['Humidity state morning'] = np.NaN\n",
    "sun_data_origin['Humidity state afternoon'] = np.NaN\n",
    "for i in sun_data_origin.index:\n",
    "    State = sun_data_origin.loc[i,'State_x']\n",
    "    Season = sun_data_origin.loc[i,'Season']\n",
    "    sun_data_origin.loc[i,'Humidity state morning'] = dict_state_humidity[State]['Morning'][Season]\n",
    "    sun_data_origin.loc[i,'Humidity state afternoon'] = dict_state_humidity[State]['Afternoon'][Season]\n",
    "sun_data_origin['Month'] = sun_data_origin['Month'].astype(str)\n",
    "sun_data_origin['State Sun Percent'] = np.NaN\n",
    "for i in sun_data_dest.index:\n",
    "    try:\n",
    "        Season = sun_data_origin.loc[i, 'Season']\n",
    "        State = sun_data_origin.loc[i, 'State_x']\n",
    "        sun_data_origin.loc[i,'State Sun Percent'] =  Dict_State_sun_percent[State][Season]\n",
    "    except:\n",
    "        continue\n",
    "dict_mean_season_2 = dict()\n",
    "for season in  set(sun_data_origin['Season']):\n",
    "    df = Sun_data[sun_data_origin['Season'] == season]\n",
    "    mean = df['State Sun Percent'].mean()\n",
    "    dict_mean_season_2[season] = mean\n",
    "for i in sun_data_origin[sun_data_origin['State Sun Percent'].isna()].index:\n",
    "    Season = sun_data_origin.loc[i, 'Season']\n",
    "    sun_data_origin.loc[i,'State Sun Percent'] = dict_mean_season_2[Season]\n",
    "mean_sun_11 = sun_data_origin['Humidity state morning'].mean()\n",
    "mean_sun_22 = sun_data_origin['Humidity state afternoon'].mean()\n",
    "sun_data_origin['Humidity state morning'] = sun_data_origin['Humidity state morning'].fillna(mean_sun_11)\n",
    "sun_data_origin['Humidity state afternoon'] = sun_data_origin['Humidity state afternoon'].fillna(mean_sun_22)\n",
    "sun_data_origin.drop(columns=['Season','State_x'], inplace= True)\n",
    "sun_data_origin = sun_data_origin[['Humidity state morning',\n",
    "                                               'Humidity state afternoon',\n",
    "                                               'Clear days State_x',\n",
    "                                               'Sun hours State_x',\n",
    "                                               'Month Origin Temp',\n",
    "                                               'Month Origin Prec',\n",
    "                                               'Latitude_x',\n",
    "                                               'Longitude_x',\n",
    "                                            'State Sun Percent',\n",
    "                                               'Month']]\n",
    "sun_data_origin = pd.get_dummies(sun_data_origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_origin.columns = Sun_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "sun_data_origin_power = add_power_features_to_test(sun_data_origin, Sun_data_power_features)\n",
    "sun_data_origin_power_mult = add_mult_features_to_test(sun_data_origin_power, Sun_data_mult_fetures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_sun_recent_origin = Lin_reg.predict(sun_data_origin_power_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sun1 = pd.DataFrame({'Predicted':Predicted_sun_recent_origin})\n",
    "df_sun1['Predicted'] = df_sun1['Predicted'].apply(make_range_sun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7578fa5b50>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sun1['Predicted'].hist(bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61.310523</td>\n",
       "      <td>14.640998</td>\n",
       "      <td>61.993676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60.425832</td>\n",
       "      <td>14.383888</td>\n",
       "      <td>61.853663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>0.884691</td>\n",
       "      <td>0.257110</td>\n",
       "      <td>0.140013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 MEAN        STD     MEDIAN\n",
       "0                           61.310523  14.640998  61.993676\n",
       "1                           60.425832  14.383888  61.853663\n",
       "difference between classes   0.884691   0.257110   0.140013"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df_sun1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clear_days_power = power_fetures(Lin_reg, Sun_data, y__clear_days, thresh = 5, power_= 8, num_cv= 10)\n",
    "Clear_days_mult = mult_fetures(Lin_reg, Sun_data, y__clear_days, thresh = 5, max_features= True, num_cv= 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(n_jobs=-1)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Clear_days_power_mult = pd.concat([Sun_data,Clear_days_power,Clear_days_mult], axis = 1)\n",
    "Lin_reg.fit(Clear_days_power_mult, y__clear_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clear_days_power_features = Clear_days_power.columns \n",
    "Clear_days_mult_features = Clear_days_mult.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clear_days_origin_power = add_power_features_to_test(sun_data_origin, Clear_days_power_features)\n",
    "Clear_days_origin_power_mult = add_mult_features_to_test(Clear_days_origin_power, Clear_days_mult_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "Clear_days_dest_power = add_power_features_to_test(sun_data_dest, Clear_days_power_features)\n",
    "Clear_days_dest_power_mult = add_mult_features_to_test(Clear_days_dest_power, Clear_days_mult_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "Predicted_clear_days_origin = Lin_reg.predict(Clear_days_origin_power_mult)\n",
    "Predicted_clear_days_dest =  Lin_reg.predict(Clear_days_dest_power_mult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_range (num):\n",
    "    if num > 30:\n",
    "        return 30\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clear_days_origin = pd.DataFrame({'Predicted':Predicted_clear_days_origin})\n",
    "df_clear_days_dest = pd.DataFrame({'Predicted':Predicted_clear_days_dest})\n",
    "df_clear_days_origin['Predicted'] = df_clear_days_origin['Predicted'].apply(make_range)\n",
    "df_clear_days_dest['Predicted'] = df_clear_days_dest['Predicted'].apply(make_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7579146280>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MnqT0e3PnO0jyiiRXJTm/qp6d5PqMbpp+VkaXFp2+oDcEAADWqOV6itGvJDl5zrqDhleSfDPJOCB8OMkLkjw1o0t9firJPyX5WJJ3ttaunHaA1topVXVtklcleVmS+5Nck+Tc1trFU8pvq6pfT3Jakt/JaHTiziQXJTmjtXbdlDo3V9VTkrw5o8uZfiPJd5Ocn+Ss1trtO34rAABg7VqWgNBaOzPJmQss+z+S/I8lHueDST64iPL3JDljeC20zreSvHTxrQMAgLVvtd2kDAAAzJCAAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAN0es24AwFq18bRLllx3yznHL2NLAGD5GEEAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOiWJSBU1QlV9Y6qurKq7qyqVlUf2UGdo6rqU1V1e1XdXVXXVtXrqurB26lzclV9uaruqqo7qmpzVT13O+X3qaqzquqGqrq3qm6rqo9V1WHbqfOYqnpfVf1jVW2rqi1V9baqOmBh7wYAAKxdyzWC8MYkr0ryK0m+s6PCVfW8JJ9P8owkH0/yriR7JnlrkgvmqXNekg8keXSSv0zykSSHJ/lkVb1qSvm9knw2yZ8muTPJ25P8zyQvSPKVqjpySp2Dk1yd5KVJvjy05xtJXpvk76rqwB39bgAAsJbtsUz7eX2Sbyf5epJnJrlivoJVtV9GH/B/nGRTa+0rw/o3Jbk8yQlVdVJr7YKJOkclOSXJzUme2lr74bD+3Iw+0J9XVRe31rZMHOoNSY5OcmGSF7XW7h/qfDTJRUneV1WHj9cP3p3kEUle01p7x8Tx3zL8jmcnefki3xsAAFgzliUgtNZ6IKiqHRU/IcnPJPnQOBwM+7i3qt6Y5LIkf5QHjiSMP5SfPQ4HQ50tVfWuJG/K6Fv/M4Y21ESdP54MAa21T1TVlUmenokwU1UHJTk2yZaMRjQmnZHkZUleXFWntNZ+tKNfEpZq42mXzLoJAMBubBY3KR8zLD8zZdvnk9yd5KjhEqGF1Pn0nDJJcnCSxya5sbV2ywLrjP996ZxRhbTWtib5QpKHJHnalP0BAMC6sFyXGC3G44fljXM3tNbuq6pbkjwxyUFJrq+qfZP8XJK7WmvfnbK/m4bloQs5xk7WOXaoc9k8ZZIkVXX1PJuesHXr1mzevHl71XeJrVu3JslMjs108/XJqYffN4PWkCSP3Ge0XIk+cC4unL9fq48+WZ30y+ozyz4ZH3spZjGCsP+wvGOe7eP1D1ti+ZWsAwAA68osRhB2ZHwTQ1tkvcWUX8oxFlyntXbE1B1UXb1hw4Ynb9q0aRGHXR7j5DqLYzPdfH3yEvcgzMx45OC8f9j1fxq3/O6mXX6M9cLfr9VHn6xO+mX1mWWfbNiwYcl1ZzGCMP4mfv95tu83p9yOyk/75n+xx1hqHQAAWFdmERBuGJaHzt1QVXskeVyS+zKafyDDE4O+k+ShVfXoKfs7ZFhO3jsw7zGWuQ4AAKwrswgIlw/L46Zse0ZGTwq6qrW2bYF1njOnTDKaL+HWJIdW1eMWWGf8qNZjq+oB70tVbchoToV7knxxyv4AAGBdmEVAuDDJ95OcVFVPGa+sqr2T/Nnw43vm1HnvsDy9qg6YqLMxySuTbEvy/vH61lqbqPPnkx/4h1mcn57kuiSfm6hzc5JLk4z3OemsJPtmNHeDORAAAFi3luVOvKp6fpLnDz8+alj+WlV9YPj391trpyZJa+3OqvrDjILC5qq6IMntSX4ro0eNXpjko5P7b61dNcxm/IYk11bVhUn2TPKiJA9P8uo5sygnyVuSPDejidm+VFWXZTQ3wokZzbXwe3PnO0jyiiRXJTm/qp6d5PokRyZ5VkaXFp2+yLcGAADWlOV6VMevJDl5zrqDhleSfDPJqeMNrbWLquqZGX3gfmGSvZN8PaMAcP4wAvAArbVTquraJK/KaFbj+5Nck+Tc1trFU8pvq6pfT3Jakt9J8vokdya5KMkZrbXrptS5eRjVeHNGlzP9RpLvJjk/yVmttdsX9nYAAMDatCwBobV2ZpIzF1nnCxl9AF9MnQ8m+eAiyt+T5IzhtdA630ry0sW0CwAA1otZ3IMAAACsUgICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdHvMugEAu6ONp12y5Lpbzjl+GVsCAA9kBAEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoNtj1g2A9WjjaZfssMyph9+XJHnJAsoCAKwUIwgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQ7THrBgCwdmw87ZIl191yzvHL2BIAdhUjCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB05kEAWGPMRQDArmQEAQAA6AQEAACgExAAAIBOQAAAADo3Ke9GvvqdO/KSJd7c6MZGAIDdgxEEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6GYWEKpqS1W1eV7fm6fOUVX1qaq6varurqprq+p1VfXg7Rzn5Kr6clXdVVV3VNXmqnrudsrvU1VnVdUNVXVvVd1WVR+rqsOW4/cGAIDVbI8ZH/+OJG+bsv6uuSuq6nlJ/jrJvUk+muT2JL+Z5K1Jjk5y4pQ65yU5Jcm3k/xlkj2TnJTkk1X16tbaO+eU3yvJZ4f9fSXJ25P8/LDv46vqmNbal5b0mwIAwBow64DwL621M3dUqKr2y+gD/o+TbGqtfWVY/6Yklyc5oapOaq1dMFHnqIzCwc1Jntpa++Gw/twkVyc5r6oubq1tmTjUGzIKBxcmeVFr7f6hzkeTXJTkfVV1+Hg9AACsN2vlHoQTkvxMkgvG4SBJWmv3Jnnj8OMfzanz8mF59jgcDHW2JHlXkr2SvHS8vqpqos4fT4aA1tonklyZ5BeTPHMZfh8AAFiVZh0Q9qqq/1BVf1JVr62qZ81zP8Exw/IzU7Z9PsndSY4aLhFaSJ1PzymTJAcneWySG1trtyywDgAArCvVWpvNgau2JPmFKZtuSfLS1trnJsr+v0mekuQprbWrp+zrq0memOQXW2vXV9W+Gd3HcFdrbcOU8j+d5J+T3NZae+Sw7vgkFye5uLX2m1PqnJDk/0nysdbai3bwu/1EGwdPOOSQQx7yF3/xF9urvkts3bo19/7r/fmne5ZW/5d+bv/lbdA699Xv3LHDMo/cZ7Rcap+w/HaHPtnZc3kh/20v97G3bt2aJNmw4Sf+nDMj+mR10i+rzyz75GUve1luuumma1prRyy27ixHEN6f5NlJHpVk3ySHJ/nvSTYm+XRVPWmi7Pj/KvP9n2m8/mFLLL/UOgAAsK7M7Cbl1tpZc1Z9NcnLq+qujG4uPjPJCxa4uxrvdrHNWETZBR9jvqRWVVdv2LDhyZs2bVrEYZfH5s2b883v3JHz/mFpXb7ldzctb4PWuZecdskOy5x6+H1JsuQ+YfntDn2ys+fyQv7bXu5jb968OUkyi7+dTKdPVif9svrMsk92ZtRi1vcgTPPeYfmMiXXjb+/nG5/eb065HZWfNlqw2GMAAMC6sxoDwm3Dct+JdTcMy0PnFq6qPZI8Lsl9Sb6RJK21HyX5TpKHVtWjpxzjkGF540KOsZ06AACwrqzGgPBrw/IbE+suH5bHTSn/jCQPSXJVa23bAus8Z06ZZDRfwq1JDq2qxy2wDgAArCszCQhV9cSqeviU9b+QZDy78UcmNl2Y5PtJTqqqp0yU3zvJnw0/vmfO7saXKp1eVQdM1NmY5JVJtmV0o3SSpI0e5zSu8+dV9aCJOs9L8vQk1yXpT1cCAID1ZlZ34p2Y5LSquiKjx5puzWgeguOT7J3kU0nOGxdurd1ZVX+YUVDYXFUXJLk9yW8lefyw/qOTB2itXVVVb8loduRrq+rCJHsmeVGShyd59ZxZlJPkLUmem9HEbF+qqssymhvhxIzmWvg9syjvPjbuxM2YAABr1awCwhUZfbD/dxldUrRvkn9J8rdJPpzkw23OBA2ttYuq6plJTk/ywoyCxNczCgDnzy0/1Dmlqq5N8qokL0tyf5JrkpzbWrt4SvltVfXrSU5L8jtJXp/kziQXJTmjtXbdMvzuAACwas0kIAyToC36Up3W2heS/MYi63wwyQcXUf6eJGcMLwAA2K2sxpuUAQCAGREQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoBMQAACATkAAAAC6PWbdANiejaddsuS6W845fhlbAgCwezCCAAAAdEYQAHYjOzMqB8DuwQgCAADQGUFg3fJNKQDA4hlBAAAAOgEBAADoBAQAAKATEAAAgE5AAAAAOgEBAADoBAQAAKATEAAAgM5EaQDAstjZCSq3nHP8MrUE2BkCAgCr3le/c0dessQPnz50AiyOS4wAAIDOCAIAK2Kpl5+cevh9y9wSALbHCAIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQCcgAAAAnYAAAAB0AgIAANCZSRkA5rHU2Z/Htpxz/DK1BGDlGEEAAAA6AQEAAOgEBAAAoBMQAACATkAAAAA6AQEAAOg85hSAdW1nH1UKsLsRENjl/M8ZAGDtcIkRAADQCQgAAEAnIAAAAJ2AAAAAdG5SBoB1ZmceDrHlnOOXsSXAWiQgAMAq5AlwwKy4xAgAAOgEBAAAoBMQAACATkAAAAA6AQEAAOgEBAAAoPOYUwDYRTyqFFiLjCAAAACdgAAAAHQCAgAA0LkHAQBY83bmfo8t5xy/jC2BtU9AYEHcaAcAsHtwiREAANAJCAAAQCcgAAAAnYAAAAB0AgIAANAJCAAAQOcxpwBAt5DHWp96+H1Jkpd4BDasS0YQAACAzgjCFFX1mCRvTnJckgOTfDfJRUnOaq39cJZtAwDYWWaeZnsEhDmq6uAkVyV5RJJPJPlakl9N8tokx1XV0a21H8ywiQDAMvJhGR7IJUY/6d0ZhYPXtNae31o7rbV2TJK3Jnl8krNn2joAANiFjCBMqKqDkhybZEuSd83ZfEaSlyV5cVWd0lr70Qo3DwDWtZ35Jh9YPgLCAx0zLC9trd0/uaG1trWqvpBRgHhakstWunEAwOqys6HGJUqsRtVam3UbVo2qOjfJqUlOba39tynb35nklUle0Vp7z3b2c/U8m5601157Pfixj33ssrR3Me6///7c31ruu3/HZVkZewwX+OmT1UOfrE76ZfXRJ8tn75968LLt6/77Rx3yoAft+Arye//1x0s+znK2ebHWWrsX0yfL7dZbb822bdtub60duNi6RhAeaP9hecc828frH7bE/f9427Ztd9x0001bllh/ZzxhWH5tBsdmOn2y+uiT1Um/rD76ZHXSL6vPLPtkY5I7l1JRQFicGpbbHXZprR2xAm1ZlPGoxrc3zpcAAAhgSURBVGps2+5Kn6w++mR10i+rjz5ZnfTL6rNW+8RTjB5oPEKw/zzb95tTDgAA1hUB4YFuGJaHzrP9kGF54wq0BQAAVpyA8EBXDMtjq+oB701VbUhydJJ7knxxpRsGAAArQUCY0Fq7OcmlGd3U8co5m89Ksm+SD5kDAQCA9cpNyj/pFUmuSnJ+VT07yfVJjkzyrIwuLTp9hm0DAIBdyjwIU1TVzyd5c5LjkhyY5LtJLkpyVmvt9lm2DQAAdiUBAQAA6NyDAAAAdAICAADQCQgAAEAnIAAAAJ2AAAAAdAICAADQCQjrXFU9pqreV1X/WFXbqmpLVb2tqg6Yddt2R8P73+Z5fW/W7VvPquqEqnpHVV1ZVXcO7/lHdlDnqKr6VFXdXlV3V9W1VfW6qnrwSrV7PVtMn1TVxu2cO62qLljp9q9HVXVgVf1BVX28qr5eVfdU1R1V9bdV9ftVNfVzg3Nl11psvzhfVkZV/dequqyqvjX0ye1V9b+q6oyqOnCeOmviXDGT8jpWVQdnNCv0I5J8IsnXkvxqktcmOa6qjm6t/WCGTdxd3ZHkbVPW37XSDdnNvDHJkzJ6n7+d5AnbK1xVz0vy10nuTfLRJLcn+c0kb01ydJITd2VjdxOL6pPB/85o4sq5vrqM7dqdnZjkPRlNEHpFkluTPDLJbyf5qyTPqaoT28QkSs6VFbHofhk4X3at1ye5Jslnk9yWZN8kT0tyZpKXVdXTWmvfGhdeU+dKa81rnb6S/E2SluTVc9a/ZVj/3lm3cXd7JdmSZMus27E7vpI8K8khSSrJpuEc+Mg8ZffL6I/9tiRPmVi/d0ahuyU5ada/01p/LbJPNg7bPzDrdq/nV5JjMvrA8qA56x+V0YfSluSFE+udK6uzX5wvK9Mve8+z/uzh/X/3xLo1da64xGidqqqDkhyb0QfSd83ZfEaSHyV5cVXtu8JNg5lorV3RWrupDX+Rd+CEJD+T5ILW2lcm9nFvRt96J8kf7YJm7lYW2SesgNba5a21T7bW7p+z/ntJ3jv8uGlik3NlBSyhX1gBw3/n03xsWB4ysW5NnSsuMVq/jhmWl075g7K1qr6QUYB4WpLLVrpxu7m9quo/JHlsRkHt2iSfb639eLbNYsL4/PnMlG2fT3J3kqOqaq/W2raVaxZJfraq/lOSA5P8IMnftdaunXGbdhf/Oizvm1jnXJm9af0y5nyZjd8clpPv9Zo6VwSE9evxw/LGebbflFFAODQCwkp7VJIPz1l3S1W9tLX2uVk0iJ8w7/nTWruvqm5J8sQkByW5fiUbRv798OqqanOSk1trt86kRbuBqtojyX8cfpz8gONcmaHt9MuY82UFVNWpSR6aZP8kT0nyf2YUDs6ZKLamzhWXGK1f+w/LO+bZPl7/sBVoC//m/UmenVFI2DfJ4Un+e0bXi366qp40u6Yxwfmz+tyd5L8kOSLJAcPrmRndsLkpyWUumdylzknyS0k+1Vr7m4n1zpXZmq9fnC8r69SMLt9+XUbh4DNJjm2t/fNEmTV1rggIu68alq79XUGttbOGa0n/qbV2d2vtq621l2d04/g+GT35gNXP+bPCWmu3tdb+tLV2TWvtX4bX5zMaCf1Skv8jyR/MtpXrU1W9JskpGT0J78WLrT4snSvLbHv94nxZWa21R7XWKqMv/347o1GA/1VVT17EblbVuSIgrF/jJLr/PNv3m1OO2RrfZPaMmbaCMefPGtFauy+jxzwmzp9lV1WvTPL2JNcleVZr7fY5RZwrM7CAfpnK+bJrDV/+fTyjIHZgkg9NbF5T54qAsH7dMCwPnWf7+M76+e5RYGXdNiwN+a4O854/wzW/j8vohsBvrGSjmNd4GN/5s4yq6nVJ3pnRM/OfNTwxZy7nygpbYL9sj/NlF2utfTOj8PbEqvrpYfWaOlcEhPXrimF57JQZFjdkNCHHPUm+uNINY6pfG5ar4g8DuXxYHjdl2zOSPCTJVavhSRMkGT2NLXH+LJuq+s8ZTd709xl9CL1tnqLOlRW0iH7ZHufLyvjZYTl+QuGaOlcEhHWqtXZzkkszuvn1lXM2n5XRNwcfaq39aIWbttuqqidW1cOnrP+FjL4NSpKPrGyrmMeFSb6f5KSqesp4ZVXtneTPhh/fM4uG7a6q6siq2nPK+mMyms00cf4si6p6U0Y3v16d5Nmtte9vp7hzZYUspl+cL7teVT2hqh41Zf2DqursJI/I6AP/D4dNa+pcKfPTrF9VdXBGs/M9IsknMnps1pEZzV56Y5KjWms/mF0Ldy9VdWaS0zIa3bklydYkByc5PqOZFD+V5AWttf9vVm1cz6rq+UmeP/z4qCT/V0bfoF05rPt+a+3UOeUvTHJvkguS3J7ktzJ6VN2FSf5vE3ztnMX0yfBoxicm2Zzk28P2X86/PVv8Ta218f9kWaKqOjnJBzL61vMdmX499JbW2gcm6jhXdrHF9ovzZdcbLvU6N6M5DG7OaJ6JR2b0tKiDknwvoyB33USdNXOuCAjrXFX9fJI3ZzSkdWCS7ya5KMlZC72pieVRVc9M8vIk/y7/9pjTf8loqPjDST68Wv4wrEdDQDtjO0W+2VrbOKfO0UlOz+gSsL2TfD3J+5Kcb2K7nbeYPqmq30/ygowe6fjTSX4qyT8l+bsk72ytXTnfTli4BfRJknyutbZpTj3nyi602H5xvux6VfVLGc18fHSSx2T0eNIfZfQF7CUZ/bf/E5+z1sq5IiAAAACdexAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIBOQAAAADoBAQAA6AQEAACgExAAAIDu/weVp29hjeq0OQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_clear_days_dest['Predicted'].hist(bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MEAN</th>\n",
       "      <th>STD</th>\n",
       "      <th>MEDIAN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.967025</td>\n",
       "      <td>4.609416</td>\n",
       "      <td>8.562843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.820939</td>\n",
       "      <td>4.463515</td>\n",
       "      <td>8.353760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>difference between classes</th>\n",
       "      <td>0.146085</td>\n",
       "      <td>0.145901</td>\n",
       "      <td>0.209083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                MEAN       STD    MEDIAN\n",
       "0                           8.967025  4.609416  8.562843\n",
       "1                           8.820939  4.463515  8.353760\n",
       "difference between classes  0.146085  0.145901  0.209083"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters_diff_classes(df_clear_days_dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Predicted Origin humidity'] = Predicted_humidity\n",
    "all_data['Predicted Dest humidity'] = Predicted_humidity1\n",
    "all_data['Predicted Sun percent Origin'] = Predicted_sun_recent_origin\n",
    "all_data['Predicted Sun percent Dest'] = Predicted_sun_recent_dest\n",
    "all_data['Predicted Cloud days Origin'] = Predicted_cloud_origin\n",
    "all_data['Predicted Cloud days Dest'] = Predicted_cloud_dest\n",
    "all_data['Predicted Fog Days Origin'] = Predicted_origin_fog\n",
    "all_data['Predicted Fog Days Dest'] = Predicted_dest_fog\n",
    "all_data['Predicted Clear days Origin'] = Predicted_clear_days_origin\n",
    "all_data['Predicted Clear days Dest'] = Predicted_clear_days_dest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f757912df10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f75792350d0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f757990b640>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7579ccc760>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7579edb100>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f75795237f0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7579523910>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7578ca8580>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7578c3e730>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f7578d0f130>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7578c08be0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7578625f70>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 589,
       "width": 615
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data.loc[:, 'Predicted Origin humidity':'Predicted Clear days Dest'].hist(figsize = (10, 10), bins = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_100 = ['Predicted Sun percent Origin','Predicted Sun percent Dest']\n",
    "range_30 = ['Predicted Cloud days Origin',\n",
    "             'Predicted Cloud days Dest',\n",
    "             'Predicted Fog Days Origin',\n",
    "             'Predicted Fog Days Dest',\n",
    "             'Predicted Clear days Origin',\n",
    "             'Predicted Clear days Dest' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_range_30 (num):\n",
    "    if num > 30:\n",
    "        return 30\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num\n",
    "def make_range_100 (num):\n",
    "    if num > 100:\n",
    "        return 100\n",
    "    elif num < 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return num\n",
    "for col in range_100:\n",
    "    mean_col = all_data[col].mean()\n",
    "    all_data[col] = all_data[col].apply(make_range_100)\n",
    "for col in range_30:\n",
    "    mean_col = all_data[col].mean()\n",
    "    all_data[col] = all_data[col].apply(make_range_30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "New_air_ports_data_ = pd.read_csv('Data for comp/Extended Air Port Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "Airport_acre = New_air_ports_data_[['IATA', 'Acre']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.merge(Airport_acre, left_on='Origin', right_on='IATA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = all_data.merge(Airport_acre, left_on='Dest', right_on='IATA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.drop(columns=['IATA_x', 'IATA_y'], inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.sort_values(by = 'Unnamed: 0', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.index = np.arange(all_data.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.to_csv('Extended all data acre climate.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>Dep part of day</th>\n",
       "      <th>Distance</th>\n",
       "      <th>Season</th>\n",
       "      <th>Is it holyday</th>\n",
       "      <th>Is it weekend</th>\n",
       "      <th>Part of month</th>\n",
       "      <th>Land time</th>\n",
       "      <th>Land part of day</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Origin_City</th>\n",
       "      <th>State_x</th>\n",
       "      <th>Dest_City</th>\n",
       "      <th>State_y</th>\n",
       "      <th>Origin_Population</th>\n",
       "      <th>Dest_Population</th>\n",
       "      <th>AL_in_Top_fleet_size</th>\n",
       "      <th>AL_in_Top_passengers_carried</th>\n",
       "      <th>Origin airport in top</th>\n",
       "      <th>Dest airport in top</th>\n",
       "      <th>Avg ° C Temp State_x</th>\n",
       "      <th>Prec Mill State_x</th>\n",
       "      <th>Sun hours State_x</th>\n",
       "      <th>Clear days State_x</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Avg ° C Temp State_y</th>\n",
       "      <th>Prec Mill State_y</th>\n",
       "      <th>Sun hours State_y</th>\n",
       "      <th>Clear days State_y</th>\n",
       "      <th>Humidity State_y</th>\n",
       "      <th>Target</th>\n",
       "      <th>Month Origin Temp</th>\n",
       "      <th>Month Origin Prec</th>\n",
       "      <th>Month Dest Temp</th>\n",
       "      <th>Month Dest Prec</th>\n",
       "      <th>Latitude_x</th>\n",
       "      <th>Longitude_x</th>\n",
       "      <th>Latitude_y</th>\n",
       "      <th>Longitude_y</th>\n",
       "      <th>Predicted Origin humidity</th>\n",
       "      <th>Predicted Dest humidity</th>\n",
       "      <th>Predicted Sun percent Origin</th>\n",
       "      <th>Predicted Sun percent Dest</th>\n",
       "      <th>Predicted Cloud days Origin</th>\n",
       "      <th>Predicted Cloud days Dest</th>\n",
       "      <th>Predicted Fog Days Origin</th>\n",
       "      <th>Predicted Fog Days Dest</th>\n",
       "      <th>Predicted Clear days Origin</th>\n",
       "      <th>Predicted Clear days Dest</th>\n",
       "      <th>Acre_x</th>\n",
       "      <th>Acre_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>21</td>\n",
       "      <td>7</td>\n",
       "      <td>1934</td>\n",
       "      <td>Evening</td>\n",
       "      <td>732</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>weekend</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1952</td>\n",
       "      <td>Evening</td>\n",
       "      <td>AA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>DFW</td>\n",
       "      <td>Atlanta Georgia</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>Dallas Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>491670.0</td>\n",
       "      <td>1342479.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>25.9</td>\n",
       "      <td>120.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.480387</td>\n",
       "      <td>102.531364</td>\n",
       "      <td>30.091760</td>\n",
       "      <td>53.665215</td>\n",
       "      <td>33.6367</td>\n",
       "      <td>-84.4281</td>\n",
       "      <td>32.8969</td>\n",
       "      <td>-97.0381</td>\n",
       "      <td>56.995682</td>\n",
       "      <td>44.308767</td>\n",
       "      <td>66.276447</td>\n",
       "      <td>75.011196</td>\n",
       "      <td>10.587615</td>\n",
       "      <td>5.726324</td>\n",
       "      <td>17.179699</td>\n",
       "      <td>3.939748</td>\n",
       "      <td>7.234382</td>\n",
       "      <td>14.130266</td>\n",
       "      <td>4700.0</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>1548</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>834</td>\n",
       "      <td>Spring</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1717</td>\n",
       "      <td>Evening</td>\n",
       "      <td>US</td>\n",
       "      <td>PIT</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Pittsburgh Pennsylvania</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Orlando Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>301494.0</td>\n",
       "      <td>281804.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>93.0</td>\n",
       "      <td>716.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>21.1</td>\n",
       "      <td>92.0</td>\n",
       "      <td>881.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.575985</td>\n",
       "      <td>79.604050</td>\n",
       "      <td>22.068436</td>\n",
       "      <td>56.979034</td>\n",
       "      <td>40.4960</td>\n",
       "      <td>-80.2460</td>\n",
       "      <td>28.4294</td>\n",
       "      <td>-81.3089</td>\n",
       "      <td>49.721209</td>\n",
       "      <td>52.715776</td>\n",
       "      <td>51.515451</td>\n",
       "      <td>72.864013</td>\n",
       "      <td>16.063327</td>\n",
       "      <td>9.181363</td>\n",
       "      <td>12.009822</td>\n",
       "      <td>12.479321</td>\n",
       "      <td>5.411895</td>\n",
       "      <td>10.629498</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1422</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>416</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1506</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>XE</td>\n",
       "      <td>RDU</td>\n",
       "      <td>CLE</td>\n",
       "      <td>Raleigh North Carolina</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Cleveland Ohio</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>465776.0</td>\n",
       "      <td>385252.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>617.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>11.6</td>\n",
       "      <td>75.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.431257</td>\n",
       "      <td>104.310570</td>\n",
       "      <td>17.938000</td>\n",
       "      <td>84.351920</td>\n",
       "      <td>35.8800</td>\n",
       "      <td>-78.7900</td>\n",
       "      <td>41.4117</td>\n",
       "      <td>-81.8497</td>\n",
       "      <td>56.863165</td>\n",
       "      <td>57.454128</td>\n",
       "      <td>62.399421</td>\n",
       "      <td>56.131459</td>\n",
       "      <td>11.047117</td>\n",
       "      <td>12.686203</td>\n",
       "      <td>15.508097</td>\n",
       "      <td>14.544045</td>\n",
       "      <td>10.699696</td>\n",
       "      <td>8.501155</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>1717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>1015</td>\n",
       "      <td>Morning</td>\n",
       "      <td>872</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Holyday</td>\n",
       "      <td>weekend</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1248</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>OO</td>\n",
       "      <td>DEN</td>\n",
       "      <td>MEM</td>\n",
       "      <td>Denver Colorado</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>Memphis Tennessee</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>704961.0</td>\n",
       "      <td>650878.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7</td>\n",
       "      <td>30.0</td>\n",
       "      <td>734.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>14.8</td>\n",
       "      <td>101.0</td>\n",
       "      <td>591.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.644444</td>\n",
       "      <td>13.777777</td>\n",
       "      <td>11.416416</td>\n",
       "      <td>113.696740</td>\n",
       "      <td>39.8617</td>\n",
       "      <td>-104.6731</td>\n",
       "      <td>35.0425</td>\n",
       "      <td>-89.9767</td>\n",
       "      <td>64.270550</td>\n",
       "      <td>53.766165</td>\n",
       "      <td>66.164895</td>\n",
       "      <td>49.561778</td>\n",
       "      <td>7.804933</td>\n",
       "      <td>13.979788</td>\n",
       "      <td>5.306081</td>\n",
       "      <td>12.938037</td>\n",
       "      <td>10.770813</td>\n",
       "      <td>9.180970</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>3900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1828</td>\n",
       "      <td>Evening</td>\n",
       "      <td>423</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>weekend</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1913</td>\n",
       "      <td>Evening</td>\n",
       "      <td>WN</td>\n",
       "      <td>MDW</td>\n",
       "      <td>OMA</td>\n",
       "      <td>Chicago Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Omaha Nebraska</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>2711069.0</td>\n",
       "      <td>476271.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12.1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>41.0</td>\n",
       "      <td>610.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.373750</td>\n",
       "      <td>77.966670</td>\n",
       "      <td>11.762500</td>\n",
       "      <td>63.483334</td>\n",
       "      <td>41.7861</td>\n",
       "      <td>-87.7525</td>\n",
       "      <td>41.3012</td>\n",
       "      <td>-95.8954</td>\n",
       "      <td>53.951794</td>\n",
       "      <td>51.295852</td>\n",
       "      <td>56.403333</td>\n",
       "      <td>61.442845</td>\n",
       "      <td>12.472606</td>\n",
       "      <td>11.198502</td>\n",
       "      <td>12.415616</td>\n",
       "      <td>9.607548</td>\n",
       "      <td>10.712294</td>\n",
       "      <td>12.333502</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199995</th>\n",
       "      <td>199995</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>852</td>\n",
       "      <td>Morning</td>\n",
       "      <td>187</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>1.0</td>\n",
       "      <td>911</td>\n",
       "      <td>Morning</td>\n",
       "      <td>WN</td>\n",
       "      <td>CRP</td>\n",
       "      <td>HOU</td>\n",
       "      <td>Corpus Christi Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston Texas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>325568.0</td>\n",
       "      <td>2316750.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>69.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.577505</td>\n",
       "      <td>66.477090</td>\n",
       "      <td>27.422642</td>\n",
       "      <td>154.366030</td>\n",
       "      <td>27.7428</td>\n",
       "      <td>-97.4019</td>\n",
       "      <td>29.6456</td>\n",
       "      <td>-95.2789</td>\n",
       "      <td>87.114865</td>\n",
       "      <td>93.405822</td>\n",
       "      <td>70.783575</td>\n",
       "      <td>63.955349</td>\n",
       "      <td>7.541064</td>\n",
       "      <td>8.696163</td>\n",
       "      <td>8.518621</td>\n",
       "      <td>12.118894</td>\n",
       "      <td>8.267620</td>\n",
       "      <td>6.055168</td>\n",
       "      <td>2457.0</td>\n",
       "      <td>1304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199996</th>\n",
       "      <td>199996</td>\n",
       "      <td>11</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "      <td>1446</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>1515</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>weekend</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1528</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>UA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>LAS</td>\n",
       "      <td>Chicago Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Las Vegas Nevada</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2711069.0</td>\n",
       "      <td>635262.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>19.0</td>\n",
       "      <td>851.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.123750</td>\n",
       "      <td>47.211105</td>\n",
       "      <td>13.610368</td>\n",
       "      <td>9.213924</td>\n",
       "      <td>41.9786</td>\n",
       "      <td>-87.9047</td>\n",
       "      <td>36.0800</td>\n",
       "      <td>-115.1522</td>\n",
       "      <td>62.054104</td>\n",
       "      <td>26.000133</td>\n",
       "      <td>46.776313</td>\n",
       "      <td>80.091318</td>\n",
       "      <td>15.101896</td>\n",
       "      <td>6.852302</td>\n",
       "      <td>11.192172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.790553</td>\n",
       "      <td>14.838620</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199997</th>\n",
       "      <td>199997</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>1509</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>438</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1555</td>\n",
       "      <td>Afternoon</td>\n",
       "      <td>OO</td>\n",
       "      <td>ORD</td>\n",
       "      <td>SGF</td>\n",
       "      <td>Chicago Illinois</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Springfield Missouri</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>2711069.0</td>\n",
       "      <td>167116.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>58.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-3.572500</td>\n",
       "      <td>54.788890</td>\n",
       "      <td>0.415194</td>\n",
       "      <td>54.761284</td>\n",
       "      <td>41.9786</td>\n",
       "      <td>-87.9047</td>\n",
       "      <td>37.2456</td>\n",
       "      <td>-93.3886</td>\n",
       "      <td>75.394448</td>\n",
       "      <td>67.794322</td>\n",
       "      <td>31.447262</td>\n",
       "      <td>34.512838</td>\n",
       "      <td>22.167850</td>\n",
       "      <td>21.074797</td>\n",
       "      <td>17.388019</td>\n",
       "      <td>16.609883</td>\n",
       "      <td>2.818379</td>\n",
       "      <td>5.103112</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199998</th>\n",
       "      <td>199998</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>804</td>\n",
       "      <td>Morning</td>\n",
       "      <td>761</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>1.0</td>\n",
       "      <td>925</td>\n",
       "      <td>Morning</td>\n",
       "      <td>DL</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>New York New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>Atlanta Georgia</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>8437478.0</td>\n",
       "      <td>491670.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.8</td>\n",
       "      <td>73.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>554.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>82.658970</td>\n",
       "      <td>7.070852</td>\n",
       "      <td>117.077280</td>\n",
       "      <td>40.7750</td>\n",
       "      <td>-73.8750</td>\n",
       "      <td>33.6367</td>\n",
       "      <td>-84.4281</td>\n",
       "      <td>84.429685</td>\n",
       "      <td>85.574901</td>\n",
       "      <td>30.759416</td>\n",
       "      <td>42.866995</td>\n",
       "      <td>22.271851</td>\n",
       "      <td>19.400098</td>\n",
       "      <td>14.242078</td>\n",
       "      <td>18.166407</td>\n",
       "      <td>2.043567</td>\n",
       "      <td>5.458865</td>\n",
       "      <td>2384.0</td>\n",
       "      <td>4700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199999</th>\n",
       "      <td>199999</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>834</td>\n",
       "      <td>Morning</td>\n",
       "      <td>297</td>\n",
       "      <td>Fall</td>\n",
       "      <td>Not Holyday</td>\n",
       "      <td>not weekend</td>\n",
       "      <td>4.0</td>\n",
       "      <td>905</td>\n",
       "      <td>Morning</td>\n",
       "      <td>OO</td>\n",
       "      <td>MKE</td>\n",
       "      <td>MSP</td>\n",
       "      <td>Milwaukee Wisconsin</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Minneapolis Minnesota</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>593725.0</td>\n",
       "      <td>420925.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>72.0</td>\n",
       "      <td>498.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>6.4</td>\n",
       "      <td>57.0</td>\n",
       "      <td>527.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.844751</td>\n",
       "      <td>58.744698</td>\n",
       "      <td>7.527565</td>\n",
       "      <td>50.050460</td>\n",
       "      <td>42.9273</td>\n",
       "      <td>-87.9017</td>\n",
       "      <td>44.8819</td>\n",
       "      <td>-93.2217</td>\n",
       "      <td>83.764679</td>\n",
       "      <td>83.783169</td>\n",
       "      <td>55.449720</td>\n",
       "      <td>53.292399</td>\n",
       "      <td>13.692603</td>\n",
       "      <td>14.980478</td>\n",
       "      <td>12.683815</td>\n",
       "      <td>10.719914</td>\n",
       "      <td>9.283534</td>\n",
       "      <td>8.926884</td>\n",
       "      <td>2180.0</td>\n",
       "      <td>2930.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200000 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0  Month  DayofMonth  DayOfWeek  DepTime Dep part of day  Distance  Season Is it holyday Is it weekend  Part of month  Land time Land part of day UniqueCarrier Origin Dest              Origin_City         State_x              Dest_City    State_y  Origin_Population  Dest_Population  AL_in_Top_fleet_size  AL_in_Top_passengers_carried  Origin airport in top  Dest airport in top  Avg ° C Temp State_x  Prec Mill State_x  Sun hours State_x  Clear days State_x  Humidity  Avg ° C Temp State_y  Prec Mill State_y  Sun hours State_y  Clear days State_y  Humidity State_y  Target  Month Origin Temp  Month Origin Prec  Month Dest Temp  Month Dest Prec  Latitude_x  Longitude_x  Latitude_y  Longitude_y  Predicted Origin humidity  Predicted Dest humidity  Predicted Sun percent Origin  Predicted Sun percent Dest  Predicted Cloud days Origin  Predicted Cloud days Dest  Predicted Fog Days Origin  Predicted Fog Days Dest  Predicted Clear days Origin  Predicted Clear days Dest   Acre_x  \\\n",
       "0                0      8          21          7     1934        Evening        732  Summer   Not Holyday       weekend            3.0       1952         Evening             AA    ATL  DFW          Atlanta Georgia         Georgia           Dallas Texas      Texas           491670.0        1342479.0                     0                             0                      1                    1                  25.9              120.0              882.0                22.0      54.0                  27.3               69.0              935.0                41.0              44.0     0.0          26.480387         102.531364        30.091760        53.665215     33.6367     -84.4281     32.8969     -97.0381                  56.995682                44.308767                     66.276447                   75.011196                    10.587615                   5.726324                  17.179699                 3.939748                     7.234382                  14.130266   4700.0   \n",
       "1                1      4          20          3     1548       Afternoon       834  Spring   Not Holyday   not weekend            3.0       1717         Evening             US    PIT  MCO  Pittsburgh Pennsylvania    Pennsylvania        Orlando Florida    Florida           301494.0         281804.0                     0                             0                      1                    1                   8.6               93.0              716.0                19.0      49.0                  21.1               92.0              881.0                33.0              52.0     0.0           9.575985          79.604050        22.068436        56.979034     40.4960     -80.2460     28.4294     -81.3089                  49.721209                52.715776                     51.515451                   72.864013                    16.063327                   9.181363                  12.009822                12.479321                     5.411895                  10.629498  10000.0   \n",
       "2                2      9           2          5     1422       Afternoon       416    Fall   Not Holyday   not weekend            1.0       1506        Afternoon            XE    RDU  CLE   Raleigh North Carolina  North Carolina         Cleveland Ohio       Ohio           465776.0         385252.0                     0                             0                      1                    1                  15.7              100.0              617.0                34.0      53.0                  11.6               75.0              499.0                24.0              56.0     0.0          22.431257         104.310570        17.938000        84.351920     35.8800     -78.7900     41.4117     -81.8497                  56.863165                57.454128                     62.399421                   56.131459                    11.047117                  12.686203                  15.508097                14.544045                    10.699696                   8.501155   5000.0   \n",
       "3                3     11          25          6     1015         Morning       872    Fall       Holyday       weekend            4.0       1248        Afternoon            OO    DEN  MEM          Denver Colorado        Colorado      Memphis Tennessee  Tennessee           704961.0         650878.0                     0                             0                      1                    0                   7.7               30.0              734.0                42.0      60.0                  14.8              101.0              591.0                33.0              52.0     0.0           4.644444          13.777777        11.416416       113.696740     39.8617    -104.6731     35.0425     -89.9767                  64.270550                53.766165                     66.164895                   49.561778                     7.804933                  13.979788                   5.306081                12.938037                    10.770813                   9.180970   2384.0   \n",
       "4                4     10           7          6     1828        Evening        423    Fall   Not Holyday       weekend            1.0       1913         Evening             WN    MDW  OMA         Chicago Illinois        Illinois         Omaha Nebraska   Nebraska          2711069.0         476271.0                     1                             1                      1                    0                  12.1               80.0              565.0                29.0      56.0                   9.7               41.0              610.0                34.0              52.0     1.0          12.373750          77.966670        11.762500        63.483334     41.7861     -87.7525     41.3012     -95.8954                  53.951794                51.295852                     56.403333                   61.442845                    12.472606                  11.198502                  12.415616                 9.607548                    10.712294                  12.333502   2384.0   \n",
       "...            ...    ...         ...        ...      ...             ...       ...     ...           ...           ...            ...        ...              ...           ...    ...  ...                      ...             ...                    ...        ...                ...              ...                   ...                           ...                    ...                  ...                   ...                ...                ...                 ...       ...                   ...                ...                ...                 ...               ...     ...                ...                ...              ...              ...         ...          ...         ...          ...                        ...                      ...                           ...                         ...                          ...                        ...                        ...                      ...                          ...                        ...      ...   \n",
       "199995      199995      6           5          2      852         Morning       187  Summer   Not Holyday   not weekend            1.0        911          Morning            WN    CRP  HOU     Corpus Christi Texas           Texas          Houston Texas      Texas           325568.0        2316750.0                     1                             1                      0                    1                  27.3               69.0              935.0                41.0      82.0                  27.3               69.0              935.0                41.0              82.0     NaN          27.577505          66.477090        27.422642       154.366030     27.7428     -97.4019     29.6456     -95.2789                  87.114865                93.405822                     70.783575                   63.955349                     7.541064                   8.696163                   8.518621                12.118894                     8.267620                   6.055168   2457.0   \n",
       "199996      199996     11          24          6     1446       Afternoon      1515    Fall   Not Holyday       weekend            4.0       1528        Afternoon            UA    ORD  LAS         Chicago Illinois        Illinois       Las Vegas Nevada     Nevada          2711069.0         635262.0                     0                             0                      1                    1                  12.1               80.0              565.0                29.0      56.0                  10.1               19.0              851.0                46.0              30.0     NaN           3.123750          47.211105        13.610368         9.213924     41.9786     -87.9047     36.0800    -115.1522                  62.054104                26.000133                     46.776313                   80.091318                    15.101896                   6.852302                  11.192172                 0.000000                     7.790553                  14.838620   2384.0   \n",
       "199997      199997      1          30          2     1509       Afternoon       438  Winter   Not Holyday   not weekend            4.0       1555        Afternoon            OO    ORD  SGF         Chicago Illinois        Illinois   Springfield Missouri   Missouri          2711069.0         167116.0                     0                             0                      1                    0                  -2.1               56.0              422.0                21.0      66.0                   0.2               58.0              471.0                24.0              56.0     NaN          -3.572500          54.788890         0.415194        54.761284     41.9786     -87.9047     37.2456     -93.3886                  75.394448                67.794322                     31.447262                   34.512838                    22.167850                  21.074797                  17.388019                16.609883                     2.818379                   5.103112   2384.0   \n",
       "199998      199998      1           5          5      804         Morning       761  Winter   Not Holyday   not weekend            1.0        925          Morning            DL    LGA  ATL        New York New York        New York        Atlanta Georgia    Georgia          8437478.0         491670.0                     1                             1                      1                    1                  -4.8               73.0              289.0                 8.0      77.0                   8.8              116.0              554.0                25.0              81.0     NaN           0.048788          82.658970         7.070852       117.077280     40.7750     -73.8750     33.6367     -84.4281                  84.429685                85.574901                     30.759416                   42.866995                    22.271851                  19.400098                  14.242078                18.166407                     2.043567                   5.458865   2384.0   \n",
       "199999      199999     10          29          1      834         Morning       297    Fall   Not Holyday   not weekend            4.0        905          Morning            OO    MKE  MSP      Milwaukee Wisconsin       Wisconsin  Minneapolis Minnesota  Minnesota           593725.0         420925.0                     0                             0                      0                    1                   7.6               72.0              498.0                23.0      88.0                   6.4               57.0              527.0                25.0              82.0     NaN          10.844751          58.744698         7.527565        50.050460     42.9273     -87.9017     44.8819     -93.2217                  83.764679                83.783169                     55.449720                   53.292399                    13.692603                  14.980478                  12.683815                10.719914                     9.283534                   8.926884   2180.0   \n",
       "\n",
       "        Acre_y  \n",
       "0       2384.0  \n",
       "1       2384.0  \n",
       "2       1717.0  \n",
       "3       3900.0  \n",
       "4       2384.0  \n",
       "...        ...  \n",
       "199995  1304.0  \n",
       "199996  2800.0  \n",
       "199997  2384.0  \n",
       "199998  4700.0  \n",
       "199999  2930.0  \n",
       "\n",
       "[200000 rows x 57 columns]"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Airport</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>IATA</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Acre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Birmingham–Shuttlesworth International Airport</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>BHM</td>\n",
       "      <td>33.563900</td>\n",
       "      <td>-86.752200</td>\n",
       "      <td>2170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dothan Regional Airport</td>\n",
       "      <td>Dothan</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>DHN</td>\n",
       "      <td>31.321100</td>\n",
       "      <td>-85.449400</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Huntsville International Airport</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>HSV</td>\n",
       "      <td>34.637200</td>\n",
       "      <td>-86.775000</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Mobile Regional Airport</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>MOB</td>\n",
       "      <td>30.691400</td>\n",
       "      <td>-88.242800</td>\n",
       "      <td>1717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Montgomery Regional Airport</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>MGM</td>\n",
       "      <td>32.300600</td>\n",
       "      <td>-86.393900</td>\n",
       "      <td>1907.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Adak Airport</td>\n",
       "      <td>Adak</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>ADK</td>\n",
       "      <td>51.878100</td>\n",
       "      <td>-176.646100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Lake Hood Seaplane Base</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.216700</td>\n",
       "      <td>-149.900000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Merrill Field</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>MRI</td>\n",
       "      <td>61.213600</td>\n",
       "      <td>-149.844400</td>\n",
       "      <td>436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Ted Stevens Anchorage International Airport</td>\n",
       "      <td>Anchorage</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>ANC</td>\n",
       "      <td>61.174200</td>\n",
       "      <td>-149.998300</td>\n",
       "      <td>4608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Aniak Airport</td>\n",
       "      <td>Aniak</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>ANI</td>\n",
       "      <td>61.581700</td>\n",
       "      <td>-159.543100</td>\n",
       "      <td>1722.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Wiley Post–Will Rogers Memorial Airport</td>\n",
       "      <td>Barrow</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>BRW</td>\n",
       "      <td>71.285600</td>\n",
       "      <td>-156.766100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Bethel Airport</td>\n",
       "      <td>Bethel</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>BET</td>\n",
       "      <td>60.778600</td>\n",
       "      <td>-161.837200</td>\n",
       "      <td>1056.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Merle K. Smith Airport</td>\n",
       "      <td>Cordova</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>CDV</td>\n",
       "      <td>60.491700</td>\n",
       "      <td>-145.477500</td>\n",
       "      <td>2959.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Deadhorse Airport</td>\n",
       "      <td>Deadhorse</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>SCC</td>\n",
       "      <td>70.205600</td>\n",
       "      <td>-148.511700</td>\n",
       "      <td>6506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Dillingham Airport</td>\n",
       "      <td>Dillingham</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>DLG</td>\n",
       "      <td>59.044700</td>\n",
       "      <td>-158.505600</td>\n",
       "      <td>620.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Fairbanks International Airport</td>\n",
       "      <td>Fairbanks</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>FAI</td>\n",
       "      <td>64.815000</td>\n",
       "      <td>-147.856400</td>\n",
       "      <td>3470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Edward G. Pitka Sr. Airport</td>\n",
       "      <td>Galena</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>GAL</td>\n",
       "      <td>64.736100</td>\n",
       "      <td>-156.937500</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Gustavus Airport</td>\n",
       "      <td>Gustavus</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>GST</td>\n",
       "      <td>58.425300</td>\n",
       "      <td>-135.707500</td>\n",
       "      <td>1821.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>Homer Airport</td>\n",
       "      <td>Homer</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>HOM</td>\n",
       "      <td>59.643100</td>\n",
       "      <td>-151.525800</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Juneau International Airport</td>\n",
       "      <td>Juneau</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>JNU</td>\n",
       "      <td>58.355000</td>\n",
       "      <td>-134.576400</td>\n",
       "      <td>662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Kenai Municipal Airport</td>\n",
       "      <td>Kenai</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>ENA</td>\n",
       "      <td>60.573300</td>\n",
       "      <td>-151.244700</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Ketchikan International Airport</td>\n",
       "      <td>Ketchikan</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>KTN</td>\n",
       "      <td>55.354200</td>\n",
       "      <td>-131.711100</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>King Salmon Airport</td>\n",
       "      <td>King Salmon</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AKN</td>\n",
       "      <td>58.676400</td>\n",
       "      <td>-156.648600</td>\n",
       "      <td>5277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Kodiak Airport</td>\n",
       "      <td>Kodiak</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>ADQ</td>\n",
       "      <td>57.749700</td>\n",
       "      <td>-152.493900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Ralph Wien Memorial Airport</td>\n",
       "      <td>Kotzebue</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>OTZ</td>\n",
       "      <td>66.884722</td>\n",
       "      <td>-162.598611</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Nome Airport</td>\n",
       "      <td>Nome</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>OME</td>\n",
       "      <td>64.512200</td>\n",
       "      <td>-165.445300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Petersburg James A. Johnson Airport</td>\n",
       "      <td>Petersburg</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>PSG</td>\n",
       "      <td>56.801400</td>\n",
       "      <td>-132.946100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Sitka Rocky Gutierrez Airport</td>\n",
       "      <td>Sitka</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>SIT</td>\n",
       "      <td>57.047200</td>\n",
       "      <td>-135.361700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>St. Mary's Airport</td>\n",
       "      <td>St. Mary's</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>KSM</td>\n",
       "      <td>49.913300</td>\n",
       "      <td>-6.291700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>Unalakleet Airport</td>\n",
       "      <td>Unalakleet</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>UNK</td>\n",
       "      <td>63.888333</td>\n",
       "      <td>-160.798889</td>\n",
       "      <td>5837.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Unalaska Airport</td>\n",
       "      <td>Unalaska</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>DUT</td>\n",
       "      <td>53.898900</td>\n",
       "      <td>-166.545000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Valdez Airport</td>\n",
       "      <td>Valdez</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>VDZ</td>\n",
       "      <td>61.130800</td>\n",
       "      <td>-146.348300</td>\n",
       "      <td>836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>Wrangell Airport</td>\n",
       "      <td>Wrangell</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>WRG</td>\n",
       "      <td>56.484400</td>\n",
       "      <td>-132.369700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>Yakutat Airport</td>\n",
       "      <td>Yakutat</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>YAK</td>\n",
       "      <td>59.503300</td>\n",
       "      <td>-139.660300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Laughlin/Bullhead International Airport</td>\n",
       "      <td>Bullhead City</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>IFP</td>\n",
       "      <td>35.156100</td>\n",
       "      <td>-114.559400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>Flagstaff Pulliam Airport</td>\n",
       "      <td>Flagstaff</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>FLG</td>\n",
       "      <td>35.140300</td>\n",
       "      <td>-111.669200</td>\n",
       "      <td>763.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>36</td>\n",
       "      <td>Grand Canyon National Park Airport</td>\n",
       "      <td>Grand Canyon</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>GCN</td>\n",
       "      <td>36.055300</td>\n",
       "      <td>-112.121800</td>\n",
       "      <td>859.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>37</td>\n",
       "      <td>Phoenix–Mesa Gateway Airport</td>\n",
       "      <td>Mesa</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZA</td>\n",
       "      <td>33.422200</td>\n",
       "      <td>-111.822800</td>\n",
       "      <td>3020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Page Municipal Airport</td>\n",
       "      <td>Page</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>PGA</td>\n",
       "      <td>36.926100</td>\n",
       "      <td>-111.448300</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Grand Canyon West Airport</td>\n",
       "      <td>Peach Springs</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>GCW</td>\n",
       "      <td>36.012200</td>\n",
       "      <td>-113.811100</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Phoenix Sky Harbor International Airport</td>\n",
       "      <td>Phoenix</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>PHX</td>\n",
       "      <td>33.434200</td>\n",
       "      <td>-112.011700</td>\n",
       "      <td>3400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>Tucson International Airport</td>\n",
       "      <td>Tucson</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>TUS</td>\n",
       "      <td>32.116100</td>\n",
       "      <td>-110.941100</td>\n",
       "      <td>7938.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>Yuma International Airport</td>\n",
       "      <td>Yuma</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>YUM</td>\n",
       "      <td>32.656700</td>\n",
       "      <td>-114.606100</td>\n",
       "      <td>3100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>Northwest Arkansas Regional Airport</td>\n",
       "      <td>Fayetteville</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>XNA</td>\n",
       "      <td>36.281700</td>\n",
       "      <td>-94.307800</td>\n",
       "      <td>2184.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>Fort Smith Regional Airport</td>\n",
       "      <td>Fort Smith</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>FSM</td>\n",
       "      <td>35.336700</td>\n",
       "      <td>-94.367500</td>\n",
       "      <td>1359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>Bill and Hillary Clinton National Airport</td>\n",
       "      <td>Little Rock</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>LIT</td>\n",
       "      <td>34.730000</td>\n",
       "      <td>-92.220000</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>Texarkana Regional Airport</td>\n",
       "      <td>Texarkana</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>TXK</td>\n",
       "      <td>33.453600</td>\n",
       "      <td>-93.991100</td>\n",
       "      <td>964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>Arcata Airport</td>\n",
       "      <td>Arcata</td>\n",
       "      <td>California</td>\n",
       "      <td>ACV</td>\n",
       "      <td>40.977800</td>\n",
       "      <td>-124.108300</td>\n",
       "      <td>745.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>Meadows Field</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>California</td>\n",
       "      <td>BFL</td>\n",
       "      <td>35.433900</td>\n",
       "      <td>-119.057800</td>\n",
       "      <td>1357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Bob Hope Airport</td>\n",
       "      <td>Burbank</td>\n",
       "      <td>California</td>\n",
       "      <td>BUR</td>\n",
       "      <td>34.200600</td>\n",
       "      <td>-118.358600</td>\n",
       "      <td>555.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>McClellan–Palomar Airport</td>\n",
       "      <td>Carlsbad</td>\n",
       "      <td>California</td>\n",
       "      <td>CRQ</td>\n",
       "      <td>33.128300</td>\n",
       "      <td>-117.280000</td>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>Mc Clellan Palomar Airport-Crq</td>\n",
       "      <td>Carlsbad</td>\n",
       "      <td>California</td>\n",
       "      <td>CLD</td>\n",
       "      <td>33.128300</td>\n",
       "      <td>-117.280000</td>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>52</td>\n",
       "      <td>Chico Municipal Airport</td>\n",
       "      <td>Chico</td>\n",
       "      <td>California</td>\n",
       "      <td>CIC</td>\n",
       "      <td>39.795300</td>\n",
       "      <td>-121.858300</td>\n",
       "      <td>1475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>Del Norte County Airport</td>\n",
       "      <td>Crescent City</td>\n",
       "      <td>California</td>\n",
       "      <td>CEC</td>\n",
       "      <td>41.780300</td>\n",
       "      <td>-124.236700</td>\n",
       "      <td>544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>Imperial County Airport</td>\n",
       "      <td>El Centro</td>\n",
       "      <td>California</td>\n",
       "      <td>IPL</td>\n",
       "      <td>32.834200</td>\n",
       "      <td>-115.578600</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>Fresno Yosemite International Airport</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>California</td>\n",
       "      <td>FAT</td>\n",
       "      <td>36.776700</td>\n",
       "      <td>-119.718900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>Inyokern Airport</td>\n",
       "      <td>Inyokern</td>\n",
       "      <td>California</td>\n",
       "      <td>IYK</td>\n",
       "      <td>35.658600</td>\n",
       "      <td>-117.829400</td>\n",
       "      <td>1640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>Long Beach Airport</td>\n",
       "      <td>Long Beach</td>\n",
       "      <td>California</td>\n",
       "      <td>LGB</td>\n",
       "      <td>33.817800</td>\n",
       "      <td>-118.151700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>Los Angeles International Airport</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>LAX</td>\n",
       "      <td>33.942500</td>\n",
       "      <td>-118.408100</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>Mammoth Yosemite Airport</td>\n",
       "      <td>Mammoth Lakes</td>\n",
       "      <td>California</td>\n",
       "      <td>MMH</td>\n",
       "      <td>37.624200</td>\n",
       "      <td>-118.838900</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>Modesto City Co-Harry Sham Field Airport</td>\n",
       "      <td>Modesto</td>\n",
       "      <td>California</td>\n",
       "      <td>MOD</td>\n",
       "      <td>37.625833</td>\n",
       "      <td>-120.954444</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>Monterey Regional Airport</td>\n",
       "      <td>Monterey</td>\n",
       "      <td>California</td>\n",
       "      <td>MRY</td>\n",
       "      <td>36.586900</td>\n",
       "      <td>-121.843100</td>\n",
       "      <td>496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>Oakland International Airport</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>California</td>\n",
       "      <td>OAK</td>\n",
       "      <td>37.721400</td>\n",
       "      <td>-122.220800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>Ontario International Airport</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>California</td>\n",
       "      <td>ONT</td>\n",
       "      <td>34.056100</td>\n",
       "      <td>-117.601100</td>\n",
       "      <td>1741.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>Oxnard Airport</td>\n",
       "      <td>Oxnard</td>\n",
       "      <td>California</td>\n",
       "      <td>OXR</td>\n",
       "      <td>34.200800</td>\n",
       "      <td>-119.207200</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>Palm Springs International Airport</td>\n",
       "      <td>Palm Springs</td>\n",
       "      <td>California</td>\n",
       "      <td>PSP</td>\n",
       "      <td>33.829700</td>\n",
       "      <td>-116.506700</td>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>Palmdale Regional/USAF Plant 42 Airport</td>\n",
       "      <td>Palmdale</td>\n",
       "      <td>California</td>\n",
       "      <td>PMD</td>\n",
       "      <td>34.629400</td>\n",
       "      <td>-118.084400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>Redding Municipal Airport</td>\n",
       "      <td>Redding</td>\n",
       "      <td>California</td>\n",
       "      <td>RDD</td>\n",
       "      <td>40.508900</td>\n",
       "      <td>-122.293300</td>\n",
       "      <td>1584.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>Sacramento International Airport</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>California</td>\n",
       "      <td>SMF</td>\n",
       "      <td>38.695600</td>\n",
       "      <td>-121.590800</td>\n",
       "      <td>6000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>69</td>\n",
       "      <td>San Diego International Airport</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>California</td>\n",
       "      <td>SAN</td>\n",
       "      <td>32.733600</td>\n",
       "      <td>-117.189700</td>\n",
       "      <td>663.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>San Francisco International Airport</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>SFO</td>\n",
       "      <td>37.618900</td>\n",
       "      <td>-122.375000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>Norman Y. Mineta San José International Airport</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>California</td>\n",
       "      <td>SJC</td>\n",
       "      <td>37.362800</td>\n",
       "      <td>-121.929200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>San Luis Obispo County Regional Airport</td>\n",
       "      <td>San Luis Obispo</td>\n",
       "      <td>California</td>\n",
       "      <td>SBP</td>\n",
       "      <td>35.236900</td>\n",
       "      <td>-120.641900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>73</td>\n",
       "      <td>John Wayne Airport</td>\n",
       "      <td>Santa Ana</td>\n",
       "      <td>California</td>\n",
       "      <td>SNA</td>\n",
       "      <td>33.675600</td>\n",
       "      <td>-117.868300</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>Santa Barbara Municipal Airport</td>\n",
       "      <td>Santa Barbara</td>\n",
       "      <td>California</td>\n",
       "      <td>SBA</td>\n",
       "      <td>34.426100</td>\n",
       "      <td>-119.840300</td>\n",
       "      <td>948.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>Santa Maria Public Airport</td>\n",
       "      <td>Santa Maria</td>\n",
       "      <td>California</td>\n",
       "      <td>SMX</td>\n",
       "      <td>34.898900</td>\n",
       "      <td>-120.457500</td>\n",
       "      <td>2516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>76</td>\n",
       "      <td>Charles M. Schulz–Sonoma County Airport</td>\n",
       "      <td>Santa Rosa</td>\n",
       "      <td>California</td>\n",
       "      <td>STS</td>\n",
       "      <td>38.508900</td>\n",
       "      <td>-122.812800</td>\n",
       "      <td>1125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>Stockton Metropolitan Airport</td>\n",
       "      <td>Stockton</td>\n",
       "      <td>California</td>\n",
       "      <td>SCK</td>\n",
       "      <td>37.894200</td>\n",
       "      <td>-121.238300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Visalia Municipal Airport</td>\n",
       "      <td>Visalia</td>\n",
       "      <td>California</td>\n",
       "      <td>VIS</td>\n",
       "      <td>36.318600</td>\n",
       "      <td>-119.392800</td>\n",
       "      <td>821.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>Aspen-Pitkin County Airport</td>\n",
       "      <td>Aspen</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>ASE</td>\n",
       "      <td>39.223100</td>\n",
       "      <td>-106.868900</td>\n",
       "      <td>573.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>City of Colorado Springs Municipal Airport</td>\n",
       "      <td>Colorado Springs</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>COS</td>\n",
       "      <td>38.805800</td>\n",
       "      <td>-104.700800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>Denver International Airport</td>\n",
       "      <td>Denver</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>DEN</td>\n",
       "      <td>39.861700</td>\n",
       "      <td>-104.673100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>Durango-La Plata County Airport</td>\n",
       "      <td>Durango</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>DRO</td>\n",
       "      <td>37.151400</td>\n",
       "      <td>-107.753900</td>\n",
       "      <td>1281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>83</td>\n",
       "      <td>Eagle County Regional Airport</td>\n",
       "      <td>Eagle</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>EGE</td>\n",
       "      <td>39.642500</td>\n",
       "      <td>-106.917800</td>\n",
       "      <td>632.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>Grand Junction Regional Airport</td>\n",
       "      <td>Grand Junction</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>GJT</td>\n",
       "      <td>39.122500</td>\n",
       "      <td>-108.526700</td>\n",
       "      <td>2357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>85</td>\n",
       "      <td>Gunnison-Crested Butte Regional Airport</td>\n",
       "      <td>Gunnison</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>GUC</td>\n",
       "      <td>38.867700</td>\n",
       "      <td>-106.977300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>Yampa Valley Airport</td>\n",
       "      <td>Hayden</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>HDN</td>\n",
       "      <td>40.481100</td>\n",
       "      <td>-107.217800</td>\n",
       "      <td>671.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87</td>\n",
       "      <td>Montrose Regional Airport</td>\n",
       "      <td>Montrose</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>MTJ</td>\n",
       "      <td>37.953900</td>\n",
       "      <td>-107.908600</td>\n",
       "      <td>966.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>88</td>\n",
       "      <td>Telluride Regional Airport</td>\n",
       "      <td>Telluride</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>TEX</td>\n",
       "      <td>37.953900</td>\n",
       "      <td>-107.908600</td>\n",
       "      <td>542.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>Bradley International Airport</td>\n",
       "      <td>Hartford</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>BDL</td>\n",
       "      <td>41.939200</td>\n",
       "      <td>-72.683300</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>90</td>\n",
       "      <td>Tweed New Haven Regional Airport</td>\n",
       "      <td>New Haven</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>HVN</td>\n",
       "      <td>41.263900</td>\n",
       "      <td>-72.886700</td>\n",
       "      <td>394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>91</td>\n",
       "      <td>Wilmington Airport</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>ILG</td>\n",
       "      <td>39.678600</td>\n",
       "      <td>-75.606700</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>92</td>\n",
       "      <td>Daytona Beach International Airport</td>\n",
       "      <td>Daytona Beach</td>\n",
       "      <td>Florida</td>\n",
       "      <td>DAB</td>\n",
       "      <td>29.184700</td>\n",
       "      <td>-81.060600</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>Fort Lauderdale–Hollywood International Airport</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>FLL</td>\n",
       "      <td>26.072500</td>\n",
       "      <td>-80.152800</td>\n",
       "      <td>1380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>Southwest Florida International Airport</td>\n",
       "      <td>Fort Myers</td>\n",
       "      <td>Florida</td>\n",
       "      <td>RSW</td>\n",
       "      <td>26.536100</td>\n",
       "      <td>-81.755300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>Gainesville Regional Airport</td>\n",
       "      <td>Gainesville</td>\n",
       "      <td>Florida</td>\n",
       "      <td>GNV</td>\n",
       "      <td>29.690000</td>\n",
       "      <td>-82.271700</td>\n",
       "      <td>1650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>Jacksonville International Airport</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>Florida</td>\n",
       "      <td>JAX</td>\n",
       "      <td>30.494200</td>\n",
       "      <td>-81.687800</td>\n",
       "      <td>7911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>Key West International Airport</td>\n",
       "      <td>Key West</td>\n",
       "      <td>Florida</td>\n",
       "      <td>EYW</td>\n",
       "      <td>24.556100</td>\n",
       "      <td>-81.759400</td>\n",
       "      <td>334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>The Florida Keys Marathon Airport</td>\n",
       "      <td>Marathon</td>\n",
       "      <td>Florida</td>\n",
       "      <td>MTH</td>\n",
       "      <td>24.726400</td>\n",
       "      <td>-81.040300</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99</td>\n",
       "      <td>Melbourne International Airport</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Florida</td>\n",
       "      <td>MLB</td>\n",
       "      <td>28.102800</td>\n",
       "      <td>-80.645300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100</td>\n",
       "      <td>Miami International Airport</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>MIA</td>\n",
       "      <td>25.793300</td>\n",
       "      <td>-80.290600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>101</td>\n",
       "      <td>Naples Airport</td>\n",
       "      <td>Naples</td>\n",
       "      <td>Florida</td>\n",
       "      <td>APF</td>\n",
       "      <td>40.884400</td>\n",
       "      <td>14.290800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>102</td>\n",
       "      <td>Orlando International Airport</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>Florida</td>\n",
       "      <td>MCO</td>\n",
       "      <td>28.429400</td>\n",
       "      <td>-81.308900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>103</td>\n",
       "      <td>Orlando Sanford International Airport</td>\n",
       "      <td>Orlando</td>\n",
       "      <td>Florida</td>\n",
       "      <td>SFB</td>\n",
       "      <td>28.777800</td>\n",
       "      <td>-81.237500</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>104</td>\n",
       "      <td>Panama City-Bay Co International Airport</td>\n",
       "      <td>Panama City</td>\n",
       "      <td>Florida</td>\n",
       "      <td>PFN</td>\n",
       "      <td>8.983300</td>\n",
       "      <td>-79.516700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>105</td>\n",
       "      <td>Northwest Florida Beaches International Airport</td>\n",
       "      <td>Panama City Beach</td>\n",
       "      <td>Florida</td>\n",
       "      <td>ECP</td>\n",
       "      <td>30.358300</td>\n",
       "      <td>-85.795600</td>\n",
       "      <td>4000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>106</td>\n",
       "      <td>Pensacola International Airport</td>\n",
       "      <td>Pensacola</td>\n",
       "      <td>Florida</td>\n",
       "      <td>PNS</td>\n",
       "      <td>30.473300</td>\n",
       "      <td>-87.186700</td>\n",
       "      <td>1211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>107</td>\n",
       "      <td>Punta Gorda Airport</td>\n",
       "      <td>Punta Gorda</td>\n",
       "      <td>Florida</td>\n",
       "      <td>PGD</td>\n",
       "      <td>26.918900</td>\n",
       "      <td>-81.990800</td>\n",
       "      <td>1934.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>108</td>\n",
       "      <td>Sarasota–Bradenton International Airport</td>\n",
       "      <td>Sarasota</td>\n",
       "      <td>Florida</td>\n",
       "      <td>SRQ</td>\n",
       "      <td>27.395600</td>\n",
       "      <td>-82.554400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>109</td>\n",
       "      <td>Northeast Florida Regional Airport</td>\n",
       "      <td>St. Augustine</td>\n",
       "      <td>Florida</td>\n",
       "      <td>UST</td>\n",
       "      <td>29.959300</td>\n",
       "      <td>-81.339700</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>110</td>\n",
       "      <td>St. Pete–Clearwater International Airport</td>\n",
       "      <td>St. Petersburg</td>\n",
       "      <td>Florida</td>\n",
       "      <td>PIE</td>\n",
       "      <td>27.910000</td>\n",
       "      <td>-82.687500</td>\n",
       "      <td>1900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>111</td>\n",
       "      <td>Tallahassee International Airport</td>\n",
       "      <td>Tallahassee</td>\n",
       "      <td>Florida</td>\n",
       "      <td>TLH</td>\n",
       "      <td>30.396700</td>\n",
       "      <td>-84.350300</td>\n",
       "      <td>2485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>Tampa International Airport</td>\n",
       "      <td>Tampa</td>\n",
       "      <td>Florida</td>\n",
       "      <td>TPA</td>\n",
       "      <td>27.975600</td>\n",
       "      <td>-82.533300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>113</td>\n",
       "      <td>Destin–Fort Walton Beach Airport / Eglin Air F...</td>\n",
       "      <td>Valparaiso</td>\n",
       "      <td>Florida</td>\n",
       "      <td>VPS</td>\n",
       "      <td>30.483300</td>\n",
       "      <td>-86.525300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>114</td>\n",
       "      <td>Palm Beach International Airport</td>\n",
       "      <td>West Palm Beach</td>\n",
       "      <td>Florida</td>\n",
       "      <td>PBI</td>\n",
       "      <td>26.683200</td>\n",
       "      <td>-80.095600</td>\n",
       "      <td>2120.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>115</td>\n",
       "      <td>Southwest Georgia Regional Airport</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>ABY</td>\n",
       "      <td>31.535600</td>\n",
       "      <td>-84.194400</td>\n",
       "      <td>980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>116</td>\n",
       "      <td>Hartsfield–Jackson Atlanta International Airport</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>ATL</td>\n",
       "      <td>33.636700</td>\n",
       "      <td>-84.428100</td>\n",
       "      <td>4700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>Augusta Regional Airport</td>\n",
       "      <td>Augusta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>AGS</td>\n",
       "      <td>33.370000</td>\n",
       "      <td>-81.964400</td>\n",
       "      <td>1411.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>118</td>\n",
       "      <td>Brunswick Golden Isles Airport</td>\n",
       "      <td>Brunswick</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>BQK</td>\n",
       "      <td>31.158900</td>\n",
       "      <td>-81.489200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>119</td>\n",
       "      <td>Columbus Metropolitan Airport</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>CSG</td>\n",
       "      <td>32.516400</td>\n",
       "      <td>-84.938900</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>Middle Georgia Regional Airport</td>\n",
       "      <td>Macon</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>MCN</td>\n",
       "      <td>32.692800</td>\n",
       "      <td>-83.649200</td>\n",
       "      <td>1149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>121</td>\n",
       "      <td>Savannah/Hilton Head International Airport</td>\n",
       "      <td>Savannah</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>SAV</td>\n",
       "      <td>32.127500</td>\n",
       "      <td>-81.202200</td>\n",
       "      <td>3650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>122</td>\n",
       "      <td>Valdosta Regional Airport</td>\n",
       "      <td>Valdosta</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>VLD</td>\n",
       "      <td>30.781389</td>\n",
       "      <td>-83.276111</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>123</td>\n",
       "      <td>Antonio B. Won Pat International Airport</td>\n",
       "      <td>Agana</td>\n",
       "      <td>Guam</td>\n",
       "      <td>GUM</td>\n",
       "      <td>13.483889</td>\n",
       "      <td>144.797222</td>\n",
       "      <td>1657.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>124</td>\n",
       "      <td>Hilo International Airport</td>\n",
       "      <td>Hilo</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>ITO</td>\n",
       "      <td>19.720300</td>\n",
       "      <td>-155.048300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>125</td>\n",
       "      <td>Daniel K. Inouye International Airport</td>\n",
       "      <td>Honolulu</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>HNL</td>\n",
       "      <td>21.318600</td>\n",
       "      <td>-157.922500</td>\n",
       "      <td>4220.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>126</td>\n",
       "      <td>Kahului Airport</td>\n",
       "      <td>Kahului</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>OGG</td>\n",
       "      <td>20.898600</td>\n",
       "      <td>-156.430600</td>\n",
       "      <td>1391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>127</td>\n",
       "      <td>Kona International Airport at Keahole</td>\n",
       "      <td>Kailua-Kona</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>KOA</td>\n",
       "      <td>19.738900</td>\n",
       "      <td>-156.045600</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>Molokai Airport</td>\n",
       "      <td>Kaunakakai</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>MKK</td>\n",
       "      <td>21.152800</td>\n",
       "      <td>-157.096400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>129</td>\n",
       "      <td>Lanai Airport</td>\n",
       "      <td>Lanai City</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>LNY</td>\n",
       "      <td>20.785600</td>\n",
       "      <td>-156.951400</td>\n",
       "      <td>505.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>130</td>\n",
       "      <td>Lihue Airport</td>\n",
       "      <td>Lihue</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>LIH</td>\n",
       "      <td>21.976100</td>\n",
       "      <td>-159.338900</td>\n",
       "      <td>915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>131</td>\n",
       "      <td>Boise Airport</td>\n",
       "      <td>Boise</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>BOI</td>\n",
       "      <td>43.564400</td>\n",
       "      <td>-116.222800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>132</td>\n",
       "      <td>Friedman Memorial Airport</td>\n",
       "      <td>Hailey</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>SUN</td>\n",
       "      <td>43.503900</td>\n",
       "      <td>-114.295600</td>\n",
       "      <td>211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>133</td>\n",
       "      <td>Idaho Falls Regional Airport</td>\n",
       "      <td>Idaho Falls</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>IDA</td>\n",
       "      <td>43.513600</td>\n",
       "      <td>-112.070800</td>\n",
       "      <td>866.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>Lewiston-Nez Perce County Airport</td>\n",
       "      <td>Lewiston</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>LWS</td>\n",
       "      <td>46.374400</td>\n",
       "      <td>-117.015300</td>\n",
       "      <td>865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>135</td>\n",
       "      <td>Pocatello Regional Airport</td>\n",
       "      <td>Pocatello</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>PIH</td>\n",
       "      <td>42.909700</td>\n",
       "      <td>-112.595800</td>\n",
       "      <td>3374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>Magic Valley Regional Airport</td>\n",
       "      <td>Twin Falls</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>TWF</td>\n",
       "      <td>42.481700</td>\n",
       "      <td>-114.487800</td>\n",
       "      <td>1468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>137</td>\n",
       "      <td>MidAmerica St. Louis Airport / Scott Air Force...</td>\n",
       "      <td>Belleville</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>BLV</td>\n",
       "      <td>38.545300</td>\n",
       "      <td>-89.835300</td>\n",
       "      <td>7003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>138</td>\n",
       "      <td>Central Illinois Regional Airport at Bloomingt...</td>\n",
       "      <td>Bloomington</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>BMI</td>\n",
       "      <td>40.477200</td>\n",
       "      <td>-88.915800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>139</td>\n",
       "      <td>University of Illinois - Willard Airport</td>\n",
       "      <td>Champaign</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>CMI</td>\n",
       "      <td>40.039200</td>\n",
       "      <td>-88.278100</td>\n",
       "      <td>1799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>140</td>\n",
       "      <td>Chicago Midway International Airport</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>MDW</td>\n",
       "      <td>41.786100</td>\n",
       "      <td>-87.752500</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>141</td>\n",
       "      <td>Chicago O'Hare International Airport</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>ORD</td>\n",
       "      <td>41.978600</td>\n",
       "      <td>-87.904700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>142</td>\n",
       "      <td>Williamson County Regional Airport</td>\n",
       "      <td>Marion</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>MWA</td>\n",
       "      <td>37.755000</td>\n",
       "      <td>-89.011100</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>Quad City International Airport</td>\n",
       "      <td>Moline</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>MLI</td>\n",
       "      <td>41.448600</td>\n",
       "      <td>-90.507500</td>\n",
       "      <td>2021.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>144</td>\n",
       "      <td>General Wayne A. Downing Peoria International ...</td>\n",
       "      <td>Peoria</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>PIA</td>\n",
       "      <td>40.664200</td>\n",
       "      <td>-89.693300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>145</td>\n",
       "      <td>Quincy Regional Airport</td>\n",
       "      <td>Quincy</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>UIN</td>\n",
       "      <td>39.942800</td>\n",
       "      <td>-91.194700</td>\n",
       "      <td>1101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>146</td>\n",
       "      <td>Chicago Rockford International Airport</td>\n",
       "      <td>Rockford</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>RFD</td>\n",
       "      <td>42.195300</td>\n",
       "      <td>-89.097200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>147</td>\n",
       "      <td>Abraham Lincoln Capital Airport</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>SPI</td>\n",
       "      <td>39.847000</td>\n",
       "      <td>-89.668000</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>148</td>\n",
       "      <td>Evansville Regional Airport</td>\n",
       "      <td>Evansville</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>EVV</td>\n",
       "      <td>38.038300</td>\n",
       "      <td>-87.530800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>Fort Wayne International Airport</td>\n",
       "      <td>Fort Wayne</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>FWA</td>\n",
       "      <td>40.978300</td>\n",
       "      <td>-85.195300</td>\n",
       "      <td>3351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>150</td>\n",
       "      <td>Indianapolis International Airport</td>\n",
       "      <td>Indianapolis</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>IND</td>\n",
       "      <td>39.717200</td>\n",
       "      <td>-86.294400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>151</td>\n",
       "      <td>South Bend International Airport</td>\n",
       "      <td>South Bend</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>SBN</td>\n",
       "      <td>41.708300</td>\n",
       "      <td>-86.317200</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>The Eastern Iowa Airport</td>\n",
       "      <td>Cedar Rapids</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>CID</td>\n",
       "      <td>41.889100</td>\n",
       "      <td>-91.700300</td>\n",
       "      <td>3288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>153</td>\n",
       "      <td>Des Moines International Airport</td>\n",
       "      <td>Des Moines</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>DSM</td>\n",
       "      <td>41.533900</td>\n",
       "      <td>-93.663100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>154</td>\n",
       "      <td>Dubuque Regional Airport</td>\n",
       "      <td>Dubuque</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>DBQ</td>\n",
       "      <td>42.401900</td>\n",
       "      <td>-90.709400</td>\n",
       "      <td>1240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>Sioux Gateway Airport</td>\n",
       "      <td>Sioux City</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>SUX</td>\n",
       "      <td>42.402500</td>\n",
       "      <td>-96.384400</td>\n",
       "      <td>2460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>156</td>\n",
       "      <td>Waterloo Regional Airport</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>ALO</td>\n",
       "      <td>43.460800</td>\n",
       "      <td>-80.378600</td>\n",
       "      <td>2583.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>157</td>\n",
       "      <td>Garden City Regional Airport</td>\n",
       "      <td>Garden City</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>GCK</td>\n",
       "      <td>37.927500</td>\n",
       "      <td>-100.724400</td>\n",
       "      <td>1848.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>158</td>\n",
       "      <td>Manhattan Regional Airport</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>MHK</td>\n",
       "      <td>39.141100</td>\n",
       "      <td>-96.671700</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>159</td>\n",
       "      <td>Topeka Regional Airport</td>\n",
       "      <td>Topeka</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>FOE</td>\n",
       "      <td>38.950800</td>\n",
       "      <td>-95.663600</td>\n",
       "      <td>2854.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>160</td>\n",
       "      <td>Wichita Dwight D. Eisenhower National Airport</td>\n",
       "      <td>Wichita</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>ICT</td>\n",
       "      <td>37.650000</td>\n",
       "      <td>-97.433100</td>\n",
       "      <td>3248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>161</td>\n",
       "      <td>Cincinnati/Northern Kentucky International Air...</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>CVG</td>\n",
       "      <td>39.048900</td>\n",
       "      <td>-84.667800</td>\n",
       "      <td>7700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>162</td>\n",
       "      <td>Blue Grass Airport</td>\n",
       "      <td>Lexington</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>LEX</td>\n",
       "      <td>38.036400</td>\n",
       "      <td>-84.605800</td>\n",
       "      <td>911.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>163</td>\n",
       "      <td>Louisville International Airport</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>SDF</td>\n",
       "      <td>38.174200</td>\n",
       "      <td>-85.736400</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>164</td>\n",
       "      <td>Owensboro-Daviess County Regional Airport</td>\n",
       "      <td>Owensboro</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>OWB</td>\n",
       "      <td>37.738900</td>\n",
       "      <td>-87.166900</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>165</td>\n",
       "      <td>Barkley Regional Airport</td>\n",
       "      <td>Paducah</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>PAH</td>\n",
       "      <td>37.060300</td>\n",
       "      <td>-88.773100</td>\n",
       "      <td>1018.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>166</td>\n",
       "      <td>Alexandria International Airport</td>\n",
       "      <td>Alexandria</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>AEX</td>\n",
       "      <td>31.327500</td>\n",
       "      <td>-92.548600</td>\n",
       "      <td>3212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>167</td>\n",
       "      <td>Baton Rouge Metropolitan Airport</td>\n",
       "      <td>Baton Rouge</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>BTR</td>\n",
       "      <td>30.532800</td>\n",
       "      <td>-91.150000</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>168</td>\n",
       "      <td>Lafayette Regional Airport</td>\n",
       "      <td>Lafayette</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>LFT</td>\n",
       "      <td>30.216700</td>\n",
       "      <td>-92.033300</td>\n",
       "      <td>746.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>169</td>\n",
       "      <td>Lake Charles Regional Airport</td>\n",
       "      <td>Lake Charles</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>LCH</td>\n",
       "      <td>30.126100</td>\n",
       "      <td>-93.223300</td>\n",
       "      <td>1878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>170</td>\n",
       "      <td>Monroe Regional Airport</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>MLU</td>\n",
       "      <td>32.510800</td>\n",
       "      <td>-92.037800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>171</td>\n",
       "      <td>Louis Armstrong New Orleans International Airport</td>\n",
       "      <td>New Orleans</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>MSY</td>\n",
       "      <td>29.993300</td>\n",
       "      <td>-90.258100</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>172</td>\n",
       "      <td>Shreveport Regional Airport</td>\n",
       "      <td>Shreveport</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>SHV</td>\n",
       "      <td>32.514700</td>\n",
       "      <td>-93.747200</td>\n",
       "      <td>1625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>173</td>\n",
       "      <td>Bangor International Airport</td>\n",
       "      <td>Bangor</td>\n",
       "      <td>Maine</td>\n",
       "      <td>BGR</td>\n",
       "      <td>44.807200</td>\n",
       "      <td>-68.828100</td>\n",
       "      <td>2079.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>174</td>\n",
       "      <td>Portland International Jetport</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Maine</td>\n",
       "      <td>PWM</td>\n",
       "      <td>43.646100</td>\n",
       "      <td>-70.309200</td>\n",
       "      <td>726.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>175</td>\n",
       "      <td>Northern Maine Regional Airport at Presque Isle</td>\n",
       "      <td>Presque Isle</td>\n",
       "      <td>Maine</td>\n",
       "      <td>PQI</td>\n",
       "      <td>46.688900</td>\n",
       "      <td>-68.044700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>176</td>\n",
       "      <td>Knox County Regional Airport</td>\n",
       "      <td>Rockland</td>\n",
       "      <td>Maine</td>\n",
       "      <td>RKD</td>\n",
       "      <td>44.060000</td>\n",
       "      <td>-69.099200</td>\n",
       "      <td>538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>177</td>\n",
       "      <td>Baltimore/Washington International Thurgood Ma...</td>\n",
       "      <td>Baltimore</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>BWI</td>\n",
       "      <td>39.175300</td>\n",
       "      <td>-76.668300</td>\n",
       "      <td>3160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>178</td>\n",
       "      <td>Hagerstown Regional Airport</td>\n",
       "      <td>Hagerstown</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>HGR</td>\n",
       "      <td>39.708600</td>\n",
       "      <td>-77.726400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>179</td>\n",
       "      <td>Salisbury-Ocean City Wicomico Regional Airport</td>\n",
       "      <td>Salisbury</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>SBY</td>\n",
       "      <td>38.340000</td>\n",
       "      <td>-75.509400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>180</td>\n",
       "      <td>Gen. Edward Lawrence Logan International Airport</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>BOS</td>\n",
       "      <td>42.363100</td>\n",
       "      <td>-71.006400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>181</td>\n",
       "      <td>Barnstable Municipal Airport</td>\n",
       "      <td>Hyannis</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>HYA</td>\n",
       "      <td>41.700000</td>\n",
       "      <td>-70.300000</td>\n",
       "      <td>639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>182</td>\n",
       "      <td>Nantucket Memorial Airport</td>\n",
       "      <td>Nantucket</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>ACK</td>\n",
       "      <td>41.252900</td>\n",
       "      <td>-70.059000</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>183</td>\n",
       "      <td>Provincetown Municipal Airport</td>\n",
       "      <td>Provincetown</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>PVC</td>\n",
       "      <td>43.607200</td>\n",
       "      <td>-110.737800</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>184</td>\n",
       "      <td>Martha's Vineyard Airport</td>\n",
       "      <td>Vineyard Haven</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>MVY</td>\n",
       "      <td>41.393100</td>\n",
       "      <td>-70.614400</td>\n",
       "      <td>688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>185</td>\n",
       "      <td>Worcester Regional Airport</td>\n",
       "      <td>Worcester</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>ORH</td>\n",
       "      <td>42.267222</td>\n",
       "      <td>-71.875833</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>186</td>\n",
       "      <td>Alpena County Regional Airport</td>\n",
       "      <td>Alpena</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>APN</td>\n",
       "      <td>45.078100</td>\n",
       "      <td>-83.560300</td>\n",
       "      <td>3084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>187</td>\n",
       "      <td>Houghton County Memorial Airport</td>\n",
       "      <td>Calumet</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>CMX</td>\n",
       "      <td>47.168300</td>\n",
       "      <td>-88.489200</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>188</td>\n",
       "      <td>Detroit Metropolitan Wayne County Airport</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>DTW</td>\n",
       "      <td>42.212500</td>\n",
       "      <td>-83.353300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>189</td>\n",
       "      <td>Delta County Airport</td>\n",
       "      <td>Escanaba</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>ESC</td>\n",
       "      <td>45.722800</td>\n",
       "      <td>-87.093600</td>\n",
       "      <td>944.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>190</td>\n",
       "      <td>Bishop International Airport</td>\n",
       "      <td>Flint</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>FNT</td>\n",
       "      <td>42.965600</td>\n",
       "      <td>-83.743600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>191</td>\n",
       "      <td>Gerald R. Ford International Airport</td>\n",
       "      <td>Grand Rapids</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>GRR</td>\n",
       "      <td>42.880800</td>\n",
       "      <td>-85.522800</td>\n",
       "      <td>3127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>192</td>\n",
       "      <td>Ford Airport</td>\n",
       "      <td>Iron Mountain</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>IMT</td>\n",
       "      <td>42.880800</td>\n",
       "      <td>-85.522800</td>\n",
       "      <td>3127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>193</td>\n",
       "      <td>Kalamazoo/Battle Creek International Airport</td>\n",
       "      <td>Kalamazoo</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>AZO</td>\n",
       "      <td>42.235000</td>\n",
       "      <td>-85.551900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>194</td>\n",
       "      <td>Capital Region International Airport</td>\n",
       "      <td>Lansing</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>LAN</td>\n",
       "      <td>42.778600</td>\n",
       "      <td>-84.586200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>195</td>\n",
       "      <td>Sawyer International Airport</td>\n",
       "      <td>Marquette</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>MQT</td>\n",
       "      <td>46.353600</td>\n",
       "      <td>-87.395300</td>\n",
       "      <td>2100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>Muskegon County Airport</td>\n",
       "      <td>Muskegon</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>MKG</td>\n",
       "      <td>43.167800</td>\n",
       "      <td>-86.235600</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>197</td>\n",
       "      <td>Pellston Regional Airport of Emmet County</td>\n",
       "      <td>Pellston</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>PLN</td>\n",
       "      <td>45.570800</td>\n",
       "      <td>-84.796700</td>\n",
       "      <td>1675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>198</td>\n",
       "      <td>MBS International Airport</td>\n",
       "      <td>Saginaw</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>MBS</td>\n",
       "      <td>43.532800</td>\n",
       "      <td>-84.079700</td>\n",
       "      <td>3200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>Chippewa County International Airport</td>\n",
       "      <td>Sault Ste. Marie</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>CIU</td>\n",
       "      <td>46.250800</td>\n",
       "      <td>-84.472500</td>\n",
       "      <td>1850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>200</td>\n",
       "      <td>Cherry Capital Airport</td>\n",
       "      <td>Traverse City</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>TVC</td>\n",
       "      <td>44.741700</td>\n",
       "      <td>-85.582200</td>\n",
       "      <td>1026.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>201</td>\n",
       "      <td>Bemidji Regional Airport</td>\n",
       "      <td>Bemidji</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>BJI</td>\n",
       "      <td>47.510833</td>\n",
       "      <td>-94.934722</td>\n",
       "      <td>1740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>202</td>\n",
       "      <td>Brainerd Lakes Regional Airport</td>\n",
       "      <td>Brainerd</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>BRD</td>\n",
       "      <td>46.402200</td>\n",
       "      <td>-94.135600</td>\n",
       "      <td>2597.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>203</td>\n",
       "      <td>Duluth International Airport</td>\n",
       "      <td>Duluth</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>DLH</td>\n",
       "      <td>46.842200</td>\n",
       "      <td>-92.193600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>204</td>\n",
       "      <td>Range Regional Airport</td>\n",
       "      <td>Hibbing</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>HIB</td>\n",
       "      <td>47.386700</td>\n",
       "      <td>-92.838900</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>205</td>\n",
       "      <td>Falls International Airport</td>\n",
       "      <td>International Falls</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>INL</td>\n",
       "      <td>48.565600</td>\n",
       "      <td>-93.402200</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>206</td>\n",
       "      <td>Minneapolis–St. Paul International Airport</td>\n",
       "      <td>Minneapolis</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>MSP</td>\n",
       "      <td>44.881900</td>\n",
       "      <td>-93.221700</td>\n",
       "      <td>2930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>207</td>\n",
       "      <td>Rochester International Airport</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>RST</td>\n",
       "      <td>43.118900</td>\n",
       "      <td>-77.672500</td>\n",
       "      <td>2400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>208</td>\n",
       "      <td>St. Cloud Regional Airport</td>\n",
       "      <td>St. Cloud</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>STC</td>\n",
       "      <td>45.546700</td>\n",
       "      <td>-94.060000</td>\n",
       "      <td>1414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>209</td>\n",
       "      <td>Golden Triangle Regional Airport</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>GTR</td>\n",
       "      <td>33.448300</td>\n",
       "      <td>-88.591400</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>210</td>\n",
       "      <td>Gulfport–Biloxi International Airport</td>\n",
       "      <td>Gulfport</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>GPT</td>\n",
       "      <td>30.407200</td>\n",
       "      <td>-89.070000</td>\n",
       "      <td>1400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>211</td>\n",
       "      <td>Jackson–Evers International Airport</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>JAN</td>\n",
       "      <td>33.636700</td>\n",
       "      <td>-84.428100</td>\n",
       "      <td>3381.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>212</td>\n",
       "      <td>Key Field Airport</td>\n",
       "      <td>Meridian</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MEI</td>\n",
       "      <td>32.332500</td>\n",
       "      <td>-88.751900</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>213</td>\n",
       "      <td>Tupelo Regional Airport</td>\n",
       "      <td>Tupelo</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>TUP</td>\n",
       "      <td>34.268100</td>\n",
       "      <td>-88.770000</td>\n",
       "      <td>1061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>214</td>\n",
       "      <td>Columbia Regional Airport</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>COU</td>\n",
       "      <td>38.818100</td>\n",
       "      <td>-92.219700</td>\n",
       "      <td>1538.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>215</td>\n",
       "      <td>Joplin Regional Airport</td>\n",
       "      <td>Joplin</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>JLN</td>\n",
       "      <td>37.151700</td>\n",
       "      <td>-94.498300</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>216</td>\n",
       "      <td>Kansas City International Airport</td>\n",
       "      <td>Kansas City</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>MCI</td>\n",
       "      <td>39.297500</td>\n",
       "      <td>-94.713900</td>\n",
       "      <td>10680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>217</td>\n",
       "      <td>Springfield-Branson National Airport</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>SGF</td>\n",
       "      <td>37.245600</td>\n",
       "      <td>-93.388600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>218</td>\n",
       "      <td>St. Louis Lambert International Airport</td>\n",
       "      <td>St. Louis</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>STL</td>\n",
       "      <td>38.747200</td>\n",
       "      <td>-90.361400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>219</td>\n",
       "      <td>Billings Logan International Airport</td>\n",
       "      <td>Billings</td>\n",
       "      <td>Montana</td>\n",
       "      <td>BIL</td>\n",
       "      <td>45.807800</td>\n",
       "      <td>-108.542800</td>\n",
       "      <td>2300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>220</td>\n",
       "      <td>Bozeman Yellowstone International Airport</td>\n",
       "      <td>Bozeman</td>\n",
       "      <td>Montana</td>\n",
       "      <td>BZN</td>\n",
       "      <td>45.777000</td>\n",
       "      <td>-111.152000</td>\n",
       "      <td>2481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>221</td>\n",
       "      <td>Bert Mooney Airport</td>\n",
       "      <td>Butte</td>\n",
       "      <td>Montana</td>\n",
       "      <td>BTM</td>\n",
       "      <td>45.954700</td>\n",
       "      <td>-112.497500</td>\n",
       "      <td>890.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>222</td>\n",
       "      <td>Great Falls International Airport</td>\n",
       "      <td>Great Falls</td>\n",
       "      <td>Montana</td>\n",
       "      <td>GTF</td>\n",
       "      <td>47.482800</td>\n",
       "      <td>-111.370600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>223</td>\n",
       "      <td>Helena Regional Airport</td>\n",
       "      <td>Helena</td>\n",
       "      <td>Montana</td>\n",
       "      <td>HLN</td>\n",
       "      <td>46.606700</td>\n",
       "      <td>-111.982800</td>\n",
       "      <td>1224.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>224</td>\n",
       "      <td>Glacier Park International Airport</td>\n",
       "      <td>Kalispell</td>\n",
       "      <td>Montana</td>\n",
       "      <td>FCA</td>\n",
       "      <td>48.197800</td>\n",
       "      <td>-114.316100</td>\n",
       "      <td>1525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>225</td>\n",
       "      <td>Missoula International Airport</td>\n",
       "      <td>Missoula</td>\n",
       "      <td>Montana</td>\n",
       "      <td>MSO</td>\n",
       "      <td>46.916400</td>\n",
       "      <td>-114.090600</td>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>226</td>\n",
       "      <td>Sidney–Richland Municipal Airport</td>\n",
       "      <td>Sidney</td>\n",
       "      <td>Montana</td>\n",
       "      <td>SDY</td>\n",
       "      <td>47.706900</td>\n",
       "      <td>-104.192500</td>\n",
       "      <td>335.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>227</td>\n",
       "      <td>Yellowstone Airport</td>\n",
       "      <td>West Yellowstone</td>\n",
       "      <td>Montana</td>\n",
       "      <td>WYS</td>\n",
       "      <td>44.688300</td>\n",
       "      <td>-111.117800</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>228</td>\n",
       "      <td>Central Nebraska Regional Airport</td>\n",
       "      <td>Grand Island</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>GRI</td>\n",
       "      <td>40.967500</td>\n",
       "      <td>-98.309700</td>\n",
       "      <td>1847.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>229</td>\n",
       "      <td>Lincoln Airport</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>LNK</td>\n",
       "      <td>40.851100</td>\n",
       "      <td>-96.759200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>230</td>\n",
       "      <td>Eppley Airfield</td>\n",
       "      <td>Omaha</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>OMA</td>\n",
       "      <td>41.301200</td>\n",
       "      <td>-95.895400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>231</td>\n",
       "      <td>Boulder City Municipal Airport</td>\n",
       "      <td>Boulder City</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>BLD</td>\n",
       "      <td>35.947200</td>\n",
       "      <td>-114.860300</td>\n",
       "      <td>530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>232</td>\n",
       "      <td>Elko Regional Airport</td>\n",
       "      <td>Elko</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>EKO</td>\n",
       "      <td>40.825000</td>\n",
       "      <td>-115.791700</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>233</td>\n",
       "      <td>McCarran International Airport</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>LAS</td>\n",
       "      <td>36.080000</td>\n",
       "      <td>-115.152200</td>\n",
       "      <td>2800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>234</td>\n",
       "      <td>North Las Vegas Airport</td>\n",
       "      <td>Las Vegas</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>VGT</td>\n",
       "      <td>36.210600</td>\n",
       "      <td>-115.194400</td>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>235</td>\n",
       "      <td>Reno/Tahoe International Airport</td>\n",
       "      <td>Reno</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>RNO</td>\n",
       "      <td>39.499200</td>\n",
       "      <td>-119.768100</td>\n",
       "      <td>1450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>236</td>\n",
       "      <td>Lebanon Municipal Airport</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>LEB</td>\n",
       "      <td>43.626100</td>\n",
       "      <td>-72.304200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>237</td>\n",
       "      <td>Manchester–Boston Regional Airport</td>\n",
       "      <td>Manchester</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>MHT</td>\n",
       "      <td>42.932500</td>\n",
       "      <td>-71.435600</td>\n",
       "      <td>1500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>238</td>\n",
       "      <td>Portsmouth International Airport at Pease</td>\n",
       "      <td>Portsmouth</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>PSM</td>\n",
       "      <td>43.078100</td>\n",
       "      <td>-70.823300</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>239</td>\n",
       "      <td>Atlantic City International Airport</td>\n",
       "      <td>Atlantic City</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>ACY</td>\n",
       "      <td>39.457500</td>\n",
       "      <td>-74.577200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>240</td>\n",
       "      <td>Newark Liberty International Airport</td>\n",
       "      <td>Newark</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>EWR</td>\n",
       "      <td>40.692500</td>\n",
       "      <td>-74.168600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>241</td>\n",
       "      <td>Trenton Mercer Airport</td>\n",
       "      <td>Trenton</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>TTN</td>\n",
       "      <td>40.276700</td>\n",
       "      <td>-74.813300</td>\n",
       "      <td>1345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>242</td>\n",
       "      <td>Albuquerque International Sunport</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>ABQ</td>\n",
       "      <td>35.110800</td>\n",
       "      <td>-106.610000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>243</td>\n",
       "      <td>Lea County Regional Airport</td>\n",
       "      <td>Hobbs</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>HOB</td>\n",
       "      <td>32.687500</td>\n",
       "      <td>-103.216900</td>\n",
       "      <td>898.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>244</td>\n",
       "      <td>Roswell International Air Center</td>\n",
       "      <td>Roswell</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>ROW</td>\n",
       "      <td>33.301600</td>\n",
       "      <td>-104.530600</td>\n",
       "      <td>5029.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>245</td>\n",
       "      <td>Santa Fe Regional Airport</td>\n",
       "      <td>Santa Fe</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>SAF</td>\n",
       "      <td>35.617200</td>\n",
       "      <td>-106.089400</td>\n",
       "      <td>2128.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>246</td>\n",
       "      <td>Albany International Airport</td>\n",
       "      <td>Albany</td>\n",
       "      <td>New York</td>\n",
       "      <td>ALB</td>\n",
       "      <td>42.749167</td>\n",
       "      <td>-73.801944</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>247</td>\n",
       "      <td>Greater Binghamton Airport</td>\n",
       "      <td>Binghamton</td>\n",
       "      <td>New York</td>\n",
       "      <td>BGM</td>\n",
       "      <td>42.208600</td>\n",
       "      <td>-75.979700</td>\n",
       "      <td>1199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>248</td>\n",
       "      <td>Buffalo Niagara International Airport</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>New York</td>\n",
       "      <td>BUF</td>\n",
       "      <td>43.107200</td>\n",
       "      <td>-78.946100</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>249</td>\n",
       "      <td>Elmira/Corning Regional Airport</td>\n",
       "      <td>Elmira</td>\n",
       "      <td>New York</td>\n",
       "      <td>ELM</td>\n",
       "      <td>42.093900</td>\n",
       "      <td>-76.809700</td>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>250</td>\n",
       "      <td>Republic Airport</td>\n",
       "      <td>Farmingdale</td>\n",
       "      <td>New York</td>\n",
       "      <td>FRG</td>\n",
       "      <td>40.728900</td>\n",
       "      <td>-73.413300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>Long Island MacArthur Airport</td>\n",
       "      <td>Islip</td>\n",
       "      <td>New York</td>\n",
       "      <td>ISP</td>\n",
       "      <td>40.795278</td>\n",
       "      <td>-73.100278</td>\n",
       "      <td>1311.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>252</td>\n",
       "      <td>Ithaca Tompkins Regional Airport</td>\n",
       "      <td>Ithaca</td>\n",
       "      <td>New York</td>\n",
       "      <td>ITH</td>\n",
       "      <td>42.491400</td>\n",
       "      <td>-76.458600</td>\n",
       "      <td>531.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>253</td>\n",
       "      <td>John F. Kennedy International Airport</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>JFK</td>\n",
       "      <td>40.639700</td>\n",
       "      <td>-73.778900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>254</td>\n",
       "      <td>LaGuardia Airport</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>LGA</td>\n",
       "      <td>40.775000</td>\n",
       "      <td>-73.875000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>255</td>\n",
       "      <td>Stewart International Airport</td>\n",
       "      <td>Newburgh</td>\n",
       "      <td>New York</td>\n",
       "      <td>SWF</td>\n",
       "      <td>41.504200</td>\n",
       "      <td>-74.104700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>256</td>\n",
       "      <td>Niagara Falls International Airport</td>\n",
       "      <td>Niagara Falls</td>\n",
       "      <td>New York</td>\n",
       "      <td>IAG</td>\n",
       "      <td>43.107200</td>\n",
       "      <td>-78.946100</td>\n",
       "      <td>1067.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>257</td>\n",
       "      <td>Plattsburgh International Airport</td>\n",
       "      <td>Plattsburgh</td>\n",
       "      <td>New York</td>\n",
       "      <td>PBG</td>\n",
       "      <td>44.650800</td>\n",
       "      <td>-73.468100</td>\n",
       "      <td>1912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>258</td>\n",
       "      <td>Greater Rochester International Airport</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>New York</td>\n",
       "      <td>ROC</td>\n",
       "      <td>43.118900</td>\n",
       "      <td>-77.672500</td>\n",
       "      <td>1136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>259</td>\n",
       "      <td>Syracuse Hancock International Airport</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>New York</td>\n",
       "      <td>SYR</td>\n",
       "      <td>43.111100</td>\n",
       "      <td>-76.106400</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>260</td>\n",
       "      <td>Watertown International Airport</td>\n",
       "      <td>Watertown</td>\n",
       "      <td>New York</td>\n",
       "      <td>ART</td>\n",
       "      <td>43.991900</td>\n",
       "      <td>-76.020600</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>261</td>\n",
       "      <td>Westchester County Airport</td>\n",
       "      <td>White Plains</td>\n",
       "      <td>New York</td>\n",
       "      <td>HPN</td>\n",
       "      <td>41.066900</td>\n",
       "      <td>-73.707500</td>\n",
       "      <td>702.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>262</td>\n",
       "      <td>Asheville Regional Airport</td>\n",
       "      <td>Asheville</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>AVL</td>\n",
       "      <td>35.436100</td>\n",
       "      <td>-82.541700</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>263</td>\n",
       "      <td>Charlotte/Douglas International Airport</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>CLT</td>\n",
       "      <td>35.213900</td>\n",
       "      <td>-80.943100</td>\n",
       "      <td>5558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>264</td>\n",
       "      <td>Concord Regional Airport</td>\n",
       "      <td>Concord</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>USA</td>\n",
       "      <td>35.387778</td>\n",
       "      <td>-80.709167</td>\n",
       "      <td>750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>265</td>\n",
       "      <td>Fayetteville Regional Airport</td>\n",
       "      <td>Fayetteville</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>FAY</td>\n",
       "      <td>34.991100</td>\n",
       "      <td>-78.880300</td>\n",
       "      <td>1343.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>266</td>\n",
       "      <td>Piedmont Triad International Airport</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>GSO</td>\n",
       "      <td>35.955700</td>\n",
       "      <td>-80.005300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>267</td>\n",
       "      <td>Pitt-Greenville Airport</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>PGV</td>\n",
       "      <td>35.590000</td>\n",
       "      <td>-77.380000</td>\n",
       "      <td>872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>268</td>\n",
       "      <td>Hickory Regional Airport</td>\n",
       "      <td>Hickory</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>HKY</td>\n",
       "      <td>35.741100</td>\n",
       "      <td>-81.389400</td>\n",
       "      <td>739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>269</td>\n",
       "      <td>Albert J. Ellis Airport</td>\n",
       "      <td>Jacksonville</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>OAJ</td>\n",
       "      <td>34.829200</td>\n",
       "      <td>-77.612200</td>\n",
       "      <td>675.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>270</td>\n",
       "      <td>Kinston Regional Jetport At Stallings Field Ai...</td>\n",
       "      <td>Kinston</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>ISO</td>\n",
       "      <td>35.331400</td>\n",
       "      <td>-77.608900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>271</td>\n",
       "      <td>Coastal Carolina Regional Airport</td>\n",
       "      <td>New Bern</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>EWN</td>\n",
       "      <td>35.073100</td>\n",
       "      <td>-77.043100</td>\n",
       "      <td>785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>272</td>\n",
       "      <td>Moore County Airport</td>\n",
       "      <td>Pinehurst</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>SOP</td>\n",
       "      <td>35.237200</td>\n",
       "      <td>-79.391100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>273</td>\n",
       "      <td>Raleigh-Durham International Airport</td>\n",
       "      <td>Raleigh</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>RDU</td>\n",
       "      <td>35.880000</td>\n",
       "      <td>-78.790000</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>274</td>\n",
       "      <td>Wilmington International Airport</td>\n",
       "      <td>Wilmington</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>ILM</td>\n",
       "      <td>34.270600</td>\n",
       "      <td>-77.902500</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>275</td>\n",
       "      <td>Bismarck Municipal Airport</td>\n",
       "      <td>Bismarck</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>BIS</td>\n",
       "      <td>46.772800</td>\n",
       "      <td>-100.745800</td>\n",
       "      <td>2425.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>276</td>\n",
       "      <td>Dickinson Theodore Roosevelt Regional Airport</td>\n",
       "      <td>Dickinson</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>DIK</td>\n",
       "      <td>46.797200</td>\n",
       "      <td>-102.801900</td>\n",
       "      <td>626.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>277</td>\n",
       "      <td>Hector International Airport</td>\n",
       "      <td>Fargo</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>FAR</td>\n",
       "      <td>46.877200</td>\n",
       "      <td>-96.789400</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>278</td>\n",
       "      <td>Grand Forks International Airport</td>\n",
       "      <td>Grand Forks</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>GFK</td>\n",
       "      <td>47.949200</td>\n",
       "      <td>-97.176100</td>\n",
       "      <td>1618.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>279</td>\n",
       "      <td>Minot International Airport</td>\n",
       "      <td>Minot</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>MOT</td>\n",
       "      <td>48.257800</td>\n",
       "      <td>-101.278100</td>\n",
       "      <td>1563.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>280</td>\n",
       "      <td>Sloulin Field International Airport</td>\n",
       "      <td>Williston</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>ISN</td>\n",
       "      <td>48.178100</td>\n",
       "      <td>-103.642200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>281</td>\n",
       "      <td>Akron-Canton Regional Airport</td>\n",
       "      <td>Akron</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>CAK</td>\n",
       "      <td>40.915000</td>\n",
       "      <td>-81.443700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>282</td>\n",
       "      <td>Cincinnati Municipal Lunken Airport</td>\n",
       "      <td>Cincinnati</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>LUK</td>\n",
       "      <td>39.103300</td>\n",
       "      <td>-84.418600</td>\n",
       "      <td>1140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>283</td>\n",
       "      <td>Cleveland-Hopkins International Airport</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>CLE</td>\n",
       "      <td>41.411700</td>\n",
       "      <td>-81.849700</td>\n",
       "      <td>1717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>284</td>\n",
       "      <td>John Glenn Columbus International Airport</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>CMH</td>\n",
       "      <td>39.998100</td>\n",
       "      <td>-82.891900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>285</td>\n",
       "      <td>Rickenbacker International Airport</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>LCK</td>\n",
       "      <td>39.998100</td>\n",
       "      <td>-82.891900</td>\n",
       "      <td>4342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>286</td>\n",
       "      <td>James M. Cox Dayton International Airport</td>\n",
       "      <td>Dayton</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>DAY</td>\n",
       "      <td>39.902200</td>\n",
       "      <td>-84.219400</td>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>287</td>\n",
       "      <td>Toledo Express Airport</td>\n",
       "      <td>Toledo</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>TOL</td>\n",
       "      <td>41.586800</td>\n",
       "      <td>-83.807800</td>\n",
       "      <td>2345.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>288</td>\n",
       "      <td>Youngstown-Warren Regional Airport</td>\n",
       "      <td>Youngstown</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>YNG</td>\n",
       "      <td>41.260833</td>\n",
       "      <td>-80.679167</td>\n",
       "      <td>1468.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>289</td>\n",
       "      <td>Lawton–Fort Sill Regional Airport</td>\n",
       "      <td>Lawton</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>LAW</td>\n",
       "      <td>34.567800</td>\n",
       "      <td>-98.416400</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>290</td>\n",
       "      <td>Will Rogers World Airport</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OKC</td>\n",
       "      <td>35.393056</td>\n",
       "      <td>-97.600833</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>291</td>\n",
       "      <td>Tulsa International Airport</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>TUL</td>\n",
       "      <td>36.198300</td>\n",
       "      <td>-95.888100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>292</td>\n",
       "      <td>Eugene Airport</td>\n",
       "      <td>Eugene</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>EUG</td>\n",
       "      <td>44.051900</td>\n",
       "      <td>-123.086700</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>293</td>\n",
       "      <td>Rogue Valley International-Medford Airport</td>\n",
       "      <td>Medford</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>MFR</td>\n",
       "      <td>42.374200</td>\n",
       "      <td>-122.873600</td>\n",
       "      <td>905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>294</td>\n",
       "      <td>Southwest Oregon Regional Airport</td>\n",
       "      <td>North Bend</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>OTH</td>\n",
       "      <td>43.417200</td>\n",
       "      <td>-124.262800</td>\n",
       "      <td>619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>295</td>\n",
       "      <td>Portland International Airport</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>PDX</td>\n",
       "      <td>45.588600</td>\n",
       "      <td>-122.597500</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>296</td>\n",
       "      <td>Redmond Municipal Airport</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>RDM</td>\n",
       "      <td>44.254200</td>\n",
       "      <td>-121.149700</td>\n",
       "      <td>2518.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>297</td>\n",
       "      <td>McNary Field Airport</td>\n",
       "      <td>Salem</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>SLE</td>\n",
       "      <td>44.909400</td>\n",
       "      <td>-123.002500</td>\n",
       "      <td>751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>298</td>\n",
       "      <td>Lehigh Valley International Airport</td>\n",
       "      <td>Allentown</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>ABE</td>\n",
       "      <td>40.652300</td>\n",
       "      <td>-75.440500</td>\n",
       "      <td>2278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>Erie International Airport</td>\n",
       "      <td>Erie</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>ERI</td>\n",
       "      <td>42.083100</td>\n",
       "      <td>-80.181900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>300</td>\n",
       "      <td>Arnold Palmer Regional Airport</td>\n",
       "      <td>Latrobe</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>LBE</td>\n",
       "      <td>40.274700</td>\n",
       "      <td>-79.406700</td>\n",
       "      <td>945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>301</td>\n",
       "      <td>Harrisburg International Airport</td>\n",
       "      <td>Middletown</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>MDT</td>\n",
       "      <td>40.193100</td>\n",
       "      <td>-76.763300</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>302</td>\n",
       "      <td>Philadelphia International Airport</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>PHL</td>\n",
       "      <td>39.871900</td>\n",
       "      <td>-75.241100</td>\n",
       "      <td>2302.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>303</td>\n",
       "      <td>Pittsburgh International Airport</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>PIT</td>\n",
       "      <td>40.496000</td>\n",
       "      <td>-80.246000</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>304</td>\n",
       "      <td>University Park Airport</td>\n",
       "      <td>State College</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>SCE</td>\n",
       "      <td>40.849167</td>\n",
       "      <td>-77.848611</td>\n",
       "      <td>1091.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>305</td>\n",
       "      <td>Wilkes-Barre/Scranton International Airport</td>\n",
       "      <td>Wilkes-Barre</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>AVP</td>\n",
       "      <td>41.251000</td>\n",
       "      <td>-75.906000</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>306</td>\n",
       "      <td>Williamsport Regional Airport</td>\n",
       "      <td>Williamsport</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>IPT</td>\n",
       "      <td>41.241700</td>\n",
       "      <td>-76.921700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>307</td>\n",
       "      <td>Rafael Hernández International Airport</td>\n",
       "      <td>Aguadilla</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>BQN</td>\n",
       "      <td>18.495000</td>\n",
       "      <td>-67.129400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>308</td>\n",
       "      <td>José Aponte de la Torre Airport</td>\n",
       "      <td>Ceiba</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>NRR</td>\n",
       "      <td>18.245300</td>\n",
       "      <td>-65.643300</td>\n",
       "      <td>1646.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>309</td>\n",
       "      <td>Benjamín Rivera Noriega Airport</td>\n",
       "      <td>Culebra</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>CPX</td>\n",
       "      <td>18.313300</td>\n",
       "      <td>-65.304400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>310</th>\n",
       "      <td>310</td>\n",
       "      <td>Mercedita International Airport</td>\n",
       "      <td>Ponce</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>PSE</td>\n",
       "      <td>18.008300</td>\n",
       "      <td>-66.563100</td>\n",
       "      <td>274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>311</td>\n",
       "      <td>Fernando Luis Ribas Dominicci Airport</td>\n",
       "      <td>San Juan</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>SIG</td>\n",
       "      <td>18.456700</td>\n",
       "      <td>-66.098300</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>312</td>\n",
       "      <td>Luis Muñoz Marín International Airport</td>\n",
       "      <td>San Juan</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>SJU</td>\n",
       "      <td>18.439200</td>\n",
       "      <td>-66.001900</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>313</td>\n",
       "      <td>Antonio Rivera Rodríguez Airport</td>\n",
       "      <td>Vieques</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>VQS</td>\n",
       "      <td>18.134700</td>\n",
       "      <td>-65.493900</td>\n",
       "      <td>124.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>314</td>\n",
       "      <td>Block Island State Airport</td>\n",
       "      <td>Block Island</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>BID</td>\n",
       "      <td>41.168100</td>\n",
       "      <td>-71.577800</td>\n",
       "      <td>136.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>315</td>\n",
       "      <td>Theodore Francis Green State Airport</td>\n",
       "      <td>Providence</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>PVD</td>\n",
       "      <td>41.723900</td>\n",
       "      <td>-71.428300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>316</td>\n",
       "      <td>Westerly State Airport</td>\n",
       "      <td>Westerly</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>WST</td>\n",
       "      <td>41.349700</td>\n",
       "      <td>-71.803300</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>317</td>\n",
       "      <td>Charleston International Airport / Charleston AFB</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>CHS</td>\n",
       "      <td>32.898600</td>\n",
       "      <td>-80.040600</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>318</td>\n",
       "      <td>Columbia Metropolitan Airport</td>\n",
       "      <td>Columbia</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>CAE</td>\n",
       "      <td>33.937000</td>\n",
       "      <td>-81.105300</td>\n",
       "      <td>2600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>319</td>\n",
       "      <td>Florence Regional Airport</td>\n",
       "      <td>Florence</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>FLO</td>\n",
       "      <td>34.185300</td>\n",
       "      <td>-79.723900</td>\n",
       "      <td>1436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>320</td>\n",
       "      <td>Greenville-Spartanburg International Airport</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>GSP</td>\n",
       "      <td>34.716700</td>\n",
       "      <td>-82.183300</td>\n",
       "      <td>3500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>321</td>\n",
       "      <td>Hilton Head Airport</td>\n",
       "      <td>Hilton Head Island</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>HHH</td>\n",
       "      <td>32.224400</td>\n",
       "      <td>-80.697500</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>322</td>\n",
       "      <td>Myrtle Beach International Airport</td>\n",
       "      <td>Myrtle Beach</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>MYR</td>\n",
       "      <td>33.679700</td>\n",
       "      <td>-78.928300</td>\n",
       "      <td>3795.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>323</td>\n",
       "      <td>Aberdeen Regional Airport</td>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>ABR</td>\n",
       "      <td>57.202500</td>\n",
       "      <td>-2.198100</td>\n",
       "      <td>1284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>324</td>\n",
       "      <td>Rapid City Regional Airport</td>\n",
       "      <td>Rapid City</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>RAP</td>\n",
       "      <td>44.076000</td>\n",
       "      <td>-103.228000</td>\n",
       "      <td>1655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>325</td>\n",
       "      <td>Sioux Falls Regional Airport</td>\n",
       "      <td>Sioux Falls</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>FSD</td>\n",
       "      <td>43.581900</td>\n",
       "      <td>-96.741900</td>\n",
       "      <td>1570.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>326</td>\n",
       "      <td>Tri-Cities Regional Airport</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TRI</td>\n",
       "      <td>36.475300</td>\n",
       "      <td>-82.407500</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>327</td>\n",
       "      <td>Chattanooga Metropolitan Airport</td>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>CHA</td>\n",
       "      <td>35.035300</td>\n",
       "      <td>-85.203900</td>\n",
       "      <td>950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>328</td>\n",
       "      <td>McGhee Tyson Airport</td>\n",
       "      <td>Knoxville</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TYS</td>\n",
       "      <td>35.811100</td>\n",
       "      <td>-83.993900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>329</td>\n",
       "      <td>Memphis International Airport</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>MEM</td>\n",
       "      <td>35.042500</td>\n",
       "      <td>-89.976700</td>\n",
       "      <td>3900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>330</td>\n",
       "      <td>Nashville International Airport</td>\n",
       "      <td>Nashville</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>BNA</td>\n",
       "      <td>36.126700</td>\n",
       "      <td>-86.681900</td>\n",
       "      <td>3900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>331</td>\n",
       "      <td>Abilene Regional Airport</td>\n",
       "      <td>Abilene</td>\n",
       "      <td>Texas</td>\n",
       "      <td>ABI</td>\n",
       "      <td>32.413200</td>\n",
       "      <td>-99.683100</td>\n",
       "      <td>1644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>332</td>\n",
       "      <td>Rick Husband Amarillo International Airport</td>\n",
       "      <td>Amarillo</td>\n",
       "      <td>Texas</td>\n",
       "      <td>AMA</td>\n",
       "      <td>35.219400</td>\n",
       "      <td>-101.705800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>333</td>\n",
       "      <td>Austin-Bergstrom International Airport</td>\n",
       "      <td>Austin</td>\n",
       "      <td>Texas</td>\n",
       "      <td>AUS</td>\n",
       "      <td>30.194400</td>\n",
       "      <td>-97.670000</td>\n",
       "      <td>4242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>334</td>\n",
       "      <td>Jack Brooks Regional Airport</td>\n",
       "      <td>Beaumont</td>\n",
       "      <td>Texas</td>\n",
       "      <td>BPT</td>\n",
       "      <td>29.950833</td>\n",
       "      <td>-94.020556</td>\n",
       "      <td>1799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>335</td>\n",
       "      <td>Brownsville/South Padre Island International A...</td>\n",
       "      <td>Brownsville</td>\n",
       "      <td>Texas</td>\n",
       "      <td>BRO</td>\n",
       "      <td>25.906900</td>\n",
       "      <td>-97.425800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>336</td>\n",
       "      <td>Easterwood Airport</td>\n",
       "      <td>College Station</td>\n",
       "      <td>Texas</td>\n",
       "      <td>CLL</td>\n",
       "      <td>30.674400</td>\n",
       "      <td>-96.370000</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>337</td>\n",
       "      <td>Corpus Christi International Airport</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>Texas</td>\n",
       "      <td>CRP</td>\n",
       "      <td>27.742800</td>\n",
       "      <td>-97.401900</td>\n",
       "      <td>2457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>338</td>\n",
       "      <td>Dallas Love Field</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>DAL</td>\n",
       "      <td>32.847200</td>\n",
       "      <td>-96.851700</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>339</td>\n",
       "      <td>Dallas/Fort Worth International Airport</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>DFW</td>\n",
       "      <td>32.896900</td>\n",
       "      <td>-97.038100</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>340</td>\n",
       "      <td>El Paso International Airport</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>Texas</td>\n",
       "      <td>ELP</td>\n",
       "      <td>31.807200</td>\n",
       "      <td>-106.377500</td>\n",
       "      <td>6670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>341</td>\n",
       "      <td>Killeen-Fort Hood Regional Airport / Robert Gr...</td>\n",
       "      <td>Fort Hood</td>\n",
       "      <td>Texas</td>\n",
       "      <td>GRK</td>\n",
       "      <td>31.067200</td>\n",
       "      <td>-97.828900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>342</td>\n",
       "      <td>Valley International Airport</td>\n",
       "      <td>Harlingen</td>\n",
       "      <td>Texas</td>\n",
       "      <td>HRL</td>\n",
       "      <td>26.227200</td>\n",
       "      <td>-97.655000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>343</td>\n",
       "      <td>George Bush Intercontinental Airport</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>IAH</td>\n",
       "      <td>29.984400</td>\n",
       "      <td>-95.341400</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>344</td>\n",
       "      <td>William P. Hobby Airport</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>HOU</td>\n",
       "      <td>29.645600</td>\n",
       "      <td>-95.278900</td>\n",
       "      <td>1304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>345</td>\n",
       "      <td>Laredo International Airport</td>\n",
       "      <td>Laredo</td>\n",
       "      <td>Texas</td>\n",
       "      <td>LRD</td>\n",
       "      <td>27.543900</td>\n",
       "      <td>-99.461700</td>\n",
       "      <td>1796.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>346</td>\n",
       "      <td>East Texas Regional Airport</td>\n",
       "      <td>Longview</td>\n",
       "      <td>Texas</td>\n",
       "      <td>GGG</td>\n",
       "      <td>32.383900</td>\n",
       "      <td>-94.711400</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>347</td>\n",
       "      <td>Lubbock Preston Smith International Airport</td>\n",
       "      <td>Lubbock</td>\n",
       "      <td>Texas</td>\n",
       "      <td>LBB</td>\n",
       "      <td>33.663600</td>\n",
       "      <td>-101.820600</td>\n",
       "      <td>3000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>348</td>\n",
       "      <td>McAllen Miller International Airport</td>\n",
       "      <td>McAllen</td>\n",
       "      <td>Texas</td>\n",
       "      <td>MFE</td>\n",
       "      <td>26.175800</td>\n",
       "      <td>-98.238600</td>\n",
       "      <td>370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>349</td>\n",
       "      <td>Midland International Airport</td>\n",
       "      <td>Midland</td>\n",
       "      <td>Texas</td>\n",
       "      <td>MAF</td>\n",
       "      <td>31.942500</td>\n",
       "      <td>-102.201900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>350</td>\n",
       "      <td>San Angelo Regional Airport</td>\n",
       "      <td>San Angelo</td>\n",
       "      <td>Texas</td>\n",
       "      <td>SJT</td>\n",
       "      <td>31.355000</td>\n",
       "      <td>-100.496400</td>\n",
       "      <td>1503.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>351</td>\n",
       "      <td>San Antonio International Airport</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>Texas</td>\n",
       "      <td>SAT</td>\n",
       "      <td>29.526700</td>\n",
       "      <td>-98.471900</td>\n",
       "      <td>2305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>352</td>\n",
       "      <td>Tyler Pounds Regional Airport</td>\n",
       "      <td>Tyler</td>\n",
       "      <td>Texas</td>\n",
       "      <td>TYR</td>\n",
       "      <td>32.353900</td>\n",
       "      <td>-95.402800</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>353</td>\n",
       "      <td>Victoria Regional Airport</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Texas</td>\n",
       "      <td>VCT</td>\n",
       "      <td>28.852500</td>\n",
       "      <td>-96.918600</td>\n",
       "      <td>1766.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>354</td>\n",
       "      <td>Waco Regional Airport</td>\n",
       "      <td>Waco</td>\n",
       "      <td>Texas</td>\n",
       "      <td>ACT</td>\n",
       "      <td>31.611400</td>\n",
       "      <td>-97.230600</td>\n",
       "      <td>1369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>355</td>\n",
       "      <td>Wichita Falls Municipal Airport / Sheppard Air...</td>\n",
       "      <td>Wichita Falls</td>\n",
       "      <td>Texas</td>\n",
       "      <td>SPS</td>\n",
       "      <td>33.988900</td>\n",
       "      <td>-98.491900</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>356</td>\n",
       "      <td>Cyril E. King Airport</td>\n",
       "      <td>Charlotte Amalie</td>\n",
       "      <td>U.S. Virgin Islands</td>\n",
       "      <td>STT</td>\n",
       "      <td>18.337200</td>\n",
       "      <td>-64.973300</td>\n",
       "      <td>280.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>357</td>\n",
       "      <td>Henry E Rohlsen Airport</td>\n",
       "      <td>Christiansted</td>\n",
       "      <td>U.S. Virgin Islands</td>\n",
       "      <td>STX</td>\n",
       "      <td>17.704400</td>\n",
       "      <td>-64.801700</td>\n",
       "      <td>1455.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>358</td>\n",
       "      <td>Cedar City Regional Airport</td>\n",
       "      <td>Cedar City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>CDC</td>\n",
       "      <td>37.700800</td>\n",
       "      <td>-113.098900</td>\n",
       "      <td>1040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>359</td>\n",
       "      <td>Ogden-Hinckley Airport</td>\n",
       "      <td>Ogden</td>\n",
       "      <td>Utah</td>\n",
       "      <td>OGD</td>\n",
       "      <td>41.195600</td>\n",
       "      <td>-112.013100</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>360</td>\n",
       "      <td>Provo Municipal Airport</td>\n",
       "      <td>Provo</td>\n",
       "      <td>Utah</td>\n",
       "      <td>PVU</td>\n",
       "      <td>40.219200</td>\n",
       "      <td>-111.723400</td>\n",
       "      <td>869.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>361</td>\n",
       "      <td>Salt Lake City International Airport</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Utah</td>\n",
       "      <td>SLC</td>\n",
       "      <td>40.788300</td>\n",
       "      <td>-111.977800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>362</td>\n",
       "      <td>St. George Regional Airport</td>\n",
       "      <td>St. George</td>\n",
       "      <td>Utah</td>\n",
       "      <td>SGU</td>\n",
       "      <td>37.036400</td>\n",
       "      <td>-113.510300</td>\n",
       "      <td>1204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>363</td>\n",
       "      <td>Burlington International Airport</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>BTV</td>\n",
       "      <td>44.471900</td>\n",
       "      <td>-73.153300</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>364</td>\n",
       "      <td>Charlottesville–Albemarle Airport</td>\n",
       "      <td>Charlottesville</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>CHO</td>\n",
       "      <td>38.138600</td>\n",
       "      <td>-78.452800</td>\n",
       "      <td>754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>365</td>\n",
       "      <td>Lynchburg Regional Airport</td>\n",
       "      <td>Lynchburg</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>LYH</td>\n",
       "      <td>37.325300</td>\n",
       "      <td>-79.201100</td>\n",
       "      <td>872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>366</td>\n",
       "      <td>Newport News/Williamsburg International Airport</td>\n",
       "      <td>Newport News</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>PHF</td>\n",
       "      <td>37.131900</td>\n",
       "      <td>-76.493100</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>367</td>\n",
       "      <td>Norfolk International Airport</td>\n",
       "      <td>Norfolk</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>ORF</td>\n",
       "      <td>36.894700</td>\n",
       "      <td>-76.201100</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>368</td>\n",
       "      <td>Richmond International Airport</td>\n",
       "      <td>Richmond</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>RIC</td>\n",
       "      <td>49.194700</td>\n",
       "      <td>-123.183900</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>369</td>\n",
       "      <td>Roanoke Regional Airport</td>\n",
       "      <td>Roanoke</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>ROA</td>\n",
       "      <td>37.325600</td>\n",
       "      <td>-79.975600</td>\n",
       "      <td>912.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>370</td>\n",
       "      <td>Ronald Reagan Washington National Airport</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>DCA</td>\n",
       "      <td>38.852200</td>\n",
       "      <td>-77.037800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371</th>\n",
       "      <td>371</td>\n",
       "      <td>Washington Dulles International Airport</td>\n",
       "      <td>Washington</td>\n",
       "      <td>DC</td>\n",
       "      <td>IAD</td>\n",
       "      <td>38.944400</td>\n",
       "      <td>-77.455800</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>372</td>\n",
       "      <td>Bellingham International Airport</td>\n",
       "      <td>Bellingham</td>\n",
       "      <td>Washington</td>\n",
       "      <td>BLI</td>\n",
       "      <td>48.792500</td>\n",
       "      <td>-122.537500</td>\n",
       "      <td>2190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>373</td>\n",
       "      <td>Friday Harbor Airport</td>\n",
       "      <td>Friday Harbor</td>\n",
       "      <td>Washington</td>\n",
       "      <td>FRD</td>\n",
       "      <td>48.521900</td>\n",
       "      <td>-123.024400</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>374</td>\n",
       "      <td>Tri-Cities Airport</td>\n",
       "      <td>Pasco</td>\n",
       "      <td>Washington</td>\n",
       "      <td>PSC</td>\n",
       "      <td>36.475300</td>\n",
       "      <td>-82.407500</td>\n",
       "      <td>2235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>375</td>\n",
       "      <td>William R. Fairchild International Airport</td>\n",
       "      <td>Port Angeles</td>\n",
       "      <td>Washington</td>\n",
       "      <td>CLM</td>\n",
       "      <td>48.120278</td>\n",
       "      <td>-123.499722</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>376</td>\n",
       "      <td>Pullman/Moscow Regional Airport</td>\n",
       "      <td>Pullman</td>\n",
       "      <td>Washington</td>\n",
       "      <td>PUW</td>\n",
       "      <td>46.744000</td>\n",
       "      <td>-117.108000</td>\n",
       "      <td>467.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>377</td>\n",
       "      <td>King County International Airport</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>BFI</td>\n",
       "      <td>47.530000</td>\n",
       "      <td>-122.301900</td>\n",
       "      <td>634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>378</td>\n",
       "      <td>Seattle–Tacoma International Airport</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>SEA</td>\n",
       "      <td>47.448900</td>\n",
       "      <td>-122.309400</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>379</td>\n",
       "      <td>Spokane International Airport</td>\n",
       "      <td>Spokane</td>\n",
       "      <td>Washington</td>\n",
       "      <td>GEG</td>\n",
       "      <td>47.620000</td>\n",
       "      <td>-117.533889</td>\n",
       "      <td>6140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>380</td>\n",
       "      <td>Walla Walla Regional Airport</td>\n",
       "      <td>Walla Walla</td>\n",
       "      <td>Washington</td>\n",
       "      <td>ALW</td>\n",
       "      <td>46.090000</td>\n",
       "      <td>-118.290000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>381</td>\n",
       "      <td>Pangborn Memorial Airport</td>\n",
       "      <td>Wenatchee</td>\n",
       "      <td>Washington</td>\n",
       "      <td>EAT</td>\n",
       "      <td>47.398056</td>\n",
       "      <td>-120.205833</td>\n",
       "      <td>665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>382</td>\n",
       "      <td>Yakima Air Terminal</td>\n",
       "      <td>Yakima</td>\n",
       "      <td>Washington</td>\n",
       "      <td>YKM</td>\n",
       "      <td>46.568100</td>\n",
       "      <td>-120.544200</td>\n",
       "      <td>825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>383</td>\n",
       "      <td>Yeager Airport</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>CRW</td>\n",
       "      <td>38.375800</td>\n",
       "      <td>-81.593100</td>\n",
       "      <td>767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>384</td>\n",
       "      <td>North Central West Virginia Airport</td>\n",
       "      <td>Clarksburg</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>CKB</td>\n",
       "      <td>39.296700</td>\n",
       "      <td>-80.228100</td>\n",
       "      <td>434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>385</td>\n",
       "      <td>Tri-State Airport</td>\n",
       "      <td>Huntington</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>HTS</td>\n",
       "      <td>38.366900</td>\n",
       "      <td>-82.558600</td>\n",
       "      <td>1300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>386</td>\n",
       "      <td>Greenbrier Valley Airport</td>\n",
       "      <td>Lewisburg</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>LWB</td>\n",
       "      <td>37.858300</td>\n",
       "      <td>-80.399400</td>\n",
       "      <td>472.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>387</td>\n",
       "      <td>Morgantown Municipal Airport</td>\n",
       "      <td>Morgantown</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>MGW</td>\n",
       "      <td>39.642800</td>\n",
       "      <td>-79.916400</td>\n",
       "      <td>494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>388</td>\n",
       "      <td>Appleton International Airport</td>\n",
       "      <td>Appleton</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>ATW</td>\n",
       "      <td>44.258100</td>\n",
       "      <td>-88.519200</td>\n",
       "      <td>1638.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>389</td>\n",
       "      <td>Chippewa Valley Regional Airport</td>\n",
       "      <td>Eau Claire</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>EAU</td>\n",
       "      <td>44.865800</td>\n",
       "      <td>-91.484200</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>390</td>\n",
       "      <td>Green Bay–Austin Straubel International Airport</td>\n",
       "      <td>Green Bay</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>GRB</td>\n",
       "      <td>44.484700</td>\n",
       "      <td>-88.129700</td>\n",
       "      <td>2441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>391</td>\n",
       "      <td>La Crosse Regional Airport</td>\n",
       "      <td>La Crosse</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>LSE</td>\n",
       "      <td>43.879200</td>\n",
       "      <td>-91.256700</td>\n",
       "      <td>1380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>392</td>\n",
       "      <td>Dane County Regional Airport</td>\n",
       "      <td>Madison</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>MSN</td>\n",
       "      <td>43.070000</td>\n",
       "      <td>-89.420000</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>393</td>\n",
       "      <td>General Mitchell International Airport</td>\n",
       "      <td>Milwaukee</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>MKE</td>\n",
       "      <td>42.927300</td>\n",
       "      <td>-87.901700</td>\n",
       "      <td>2180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>394</td>\n",
       "      <td>Central Wisconsin Airport</td>\n",
       "      <td>Mosinee</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>CWA</td>\n",
       "      <td>44.777500</td>\n",
       "      <td>-89.666667</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>395</td>\n",
       "      <td>Rhinelander-Oneida County Airport</td>\n",
       "      <td>Rhinelander</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>RHI</td>\n",
       "      <td>45.630800</td>\n",
       "      <td>-89.466400</td>\n",
       "      <td>1259.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>396</td>\n",
       "      <td>Casper/Natrona County International Airport</td>\n",
       "      <td>Casper</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>CPR</td>\n",
       "      <td>42.908100</td>\n",
       "      <td>-106.464400</td>\n",
       "      <td>5150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>397</td>\n",
       "      <td>Cheyenne Regional Airport</td>\n",
       "      <td>Cheyenne</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>CYS</td>\n",
       "      <td>41.155600</td>\n",
       "      <td>-104.810600</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>398</td>\n",
       "      <td>Yellowstone Regional Airport</td>\n",
       "      <td>Cody</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>COD</td>\n",
       "      <td>44.520300</td>\n",
       "      <td>-109.023900</td>\n",
       "      <td>694.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>399</td>\n",
       "      <td>Gillette-Campbell County Airport</td>\n",
       "      <td>Gillette</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>GCC</td>\n",
       "      <td>44.348900</td>\n",
       "      <td>-105.539400</td>\n",
       "      <td>894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>400</td>\n",
       "      <td>Jackson Hole Airport</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>JAC</td>\n",
       "      <td>43.607200</td>\n",
       "      <td>-110.737800</td>\n",
       "      <td>533.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>401</td>\n",
       "      <td>Riverton Regional Airport</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>RIW</td>\n",
       "      <td>43.064200</td>\n",
       "      <td>-108.459700</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>402</td>\n",
       "      <td>Southwest Wyoming Regional Airport</td>\n",
       "      <td>Rock Springs</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>RKS</td>\n",
       "      <td>41.594200</td>\n",
       "      <td>-109.065300</td>\n",
       "      <td>1242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>403</td>\n",
       "      <td>Sheridan County Airport</td>\n",
       "      <td>Sheridan</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>SHR</td>\n",
       "      <td>44.769200</td>\n",
       "      <td>-106.980300</td>\n",
       "      <td>1550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>404</td>\n",
       "      <td>Boston Logan International Airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.363100</td>\n",
       "      <td>-71.006400</td>\n",
       "      <td>2384.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                            Airport                 City                State IATA   Latitude   Longitude     Acre\n",
       "0             0     Birmingham–Shuttlesworth International Airport           Birmingham              Alabama  BHM  33.563900  -86.752200   2170.0\n",
       "1             1                            Dothan Regional Airport               Dothan              Alabama  DHN  31.321100  -85.449400   1150.0\n",
       "2             2                   Huntsville International Airport           Huntsville              Alabama  HSV  34.637200  -86.775000   6000.0\n",
       "3             3                            Mobile Regional Airport               Mobile              Alabama  MOB  30.691400  -88.242800   1717.0\n",
       "4             4                        Montgomery Regional Airport           Montgomery              Alabama  MGM  32.300600  -86.393900   1907.0\n",
       "5             5                                       Adak Airport                 Adak               Alaska  ADK  51.878100 -176.646100   2384.0\n",
       "6             6                            Lake Hood Seaplane Base            Anchorage               Alaska  NaN  61.216700 -149.900000   2384.0\n",
       "7             7                                      Merrill Field            Anchorage               Alaska  MRI  61.213600 -149.844400    436.0\n",
       "8             8        Ted Stevens Anchorage International Airport            Anchorage               Alaska  ANC  61.174200 -149.998300   4608.0\n",
       "9             9                                      Aniak Airport                Aniak               Alaska  ANI  61.581700 -159.543100   1722.0\n",
       "10           10            Wiley Post–Will Rogers Memorial Airport               Barrow               Alaska  BRW  71.285600 -156.766100   2384.0\n",
       "11           11                                     Bethel Airport               Bethel               Alaska  BET  60.778600 -161.837200   1056.0\n",
       "12           12                             Merle K. Smith Airport              Cordova               Alaska  CDV  60.491700 -145.477500   2959.0\n",
       "13           13                                  Deadhorse Airport            Deadhorse               Alaska  SCC  70.205600 -148.511700   6506.0\n",
       "14           14                                 Dillingham Airport           Dillingham               Alaska  DLG  59.044700 -158.505600    620.0\n",
       "15           15                    Fairbanks International Airport            Fairbanks               Alaska  FAI  64.815000 -147.856400   3470.0\n",
       "16           16                        Edward G. Pitka Sr. Airport               Galena               Alaska  GAL  64.736100 -156.937500   1250.0\n",
       "17           17                                   Gustavus Airport             Gustavus               Alaska  GST  58.425300 -135.707500   1821.0\n",
       "18           18                                      Homer Airport                Homer               Alaska  HOM  59.643100 -151.525800   1040.0\n",
       "19           19                       Juneau International Airport               Juneau               Alaska  JNU  58.355000 -134.576400    662.0\n",
       "20           20                            Kenai Municipal Airport                Kenai               Alaska  ENA  60.573300 -151.244700   1200.0\n",
       "21           21                    Ketchikan International Airport            Ketchikan               Alaska  KTN  55.354200 -131.711100   2600.0\n",
       "22           22                                King Salmon Airport          King Salmon               Alaska  AKN  58.676400 -156.648600   5277.0\n",
       "23           23                                     Kodiak Airport               Kodiak               Alaska  ADQ  57.749700 -152.493900   2384.0\n",
       "24           24                        Ralph Wien Memorial Airport             Kotzebue               Alaska  OTZ  66.884722 -162.598611   1480.0\n",
       "25           25                                       Nome Airport                 Nome               Alaska  OME  64.512200 -165.445300   2384.0\n",
       "26           26                Petersburg James A. Johnson Airport           Petersburg               Alaska  PSG  56.801400 -132.946100   2384.0\n",
       "27           27                      Sitka Rocky Gutierrez Airport                Sitka               Alaska  SIT  57.047200 -135.361700   2384.0\n",
       "28           28                                 St. Mary's Airport           St. Mary's               Alaska  KSM  49.913300   -6.291700   2384.0\n",
       "29           29                                 Unalakleet Airport           Unalakleet               Alaska  UNK  63.888333 -160.798889   5837.0\n",
       "30           30                                   Unalaska Airport             Unalaska               Alaska  DUT  53.898900 -166.545000   2384.0\n",
       "31           31                                     Valdez Airport               Valdez               Alaska  VDZ  61.130800 -146.348300    836.0\n",
       "32           32                                   Wrangell Airport             Wrangell               Alaska  WRG  56.484400 -132.369700   2384.0\n",
       "33           33                                    Yakutat Airport              Yakutat               Alaska  YAK  59.503300 -139.660300   2384.0\n",
       "34           34            Laughlin/Bullhead International Airport        Bullhead City              Arizona  IFP  35.156100 -114.559400   2384.0\n",
       "35           35                          Flagstaff Pulliam Airport            Flagstaff              Arizona  FLG  35.140300 -111.669200    763.0\n",
       "36           36                 Grand Canyon National Park Airport         Grand Canyon              Arizona  GCN  36.055300 -112.121800    859.0\n",
       "37           37                       Phoenix–Mesa Gateway Airport                 Mesa              Arizona  AZA  33.422200 -111.822800   3020.0\n",
       "38           38                             Page Municipal Airport                 Page              Arizona  PGA  36.926100 -111.448300    555.0\n",
       "39           39                          Grand Canyon West Airport        Peach Springs              Arizona  GCW  36.012200 -113.811100    350.0\n",
       "40           40           Phoenix Sky Harbor International Airport              Phoenix              Arizona  PHX  33.434200 -112.011700   3400.0\n",
       "41           41                       Tucson International Airport               Tucson              Arizona  TUS  32.116100 -110.941100   7938.0\n",
       "42           42                         Yuma International Airport                 Yuma              Arizona  YUM  32.656700 -114.606100   3100.0\n",
       "43           43                Northwest Arkansas Regional Airport         Fayetteville             Arkansas  XNA  36.281700  -94.307800   2184.0\n",
       "44           44                        Fort Smith Regional Airport           Fort Smith             Arkansas  FSM  35.336700  -94.367500   1359.0\n",
       "45           45          Bill and Hillary Clinton National Airport          Little Rock             Arkansas  LIT  34.730000  -92.220000   2000.0\n",
       "46           46                         Texarkana Regional Airport            Texarkana             Arkansas  TXK  33.453600  -93.991100    964.0\n",
       "47           47                                     Arcata Airport               Arcata           California  ACV  40.977800 -124.108300    745.0\n",
       "48           48                                      Meadows Field          Bakersfield           California  BFL  35.433900 -119.057800   1357.0\n",
       "49           49                                   Bob Hope Airport              Burbank           California  BUR  34.200600 -118.358600    555.0\n",
       "50           50                          McClellan–Palomar Airport             Carlsbad           California  CRQ  33.128300 -117.280000    466.0\n",
       "51           51                     Mc Clellan Palomar Airport-Crq             Carlsbad           California  CLD  33.128300 -117.280000    466.0\n",
       "52           52                            Chico Municipal Airport                Chico           California  CIC  39.795300 -121.858300   1475.0\n",
       "53           53                           Del Norte County Airport        Crescent City           California  CEC  41.780300 -124.236700    544.0\n",
       "54           54                            Imperial County Airport            El Centro           California  IPL  32.834200 -115.578600    370.0\n",
       "55           55              Fresno Yosemite International Airport               Fresno           California  FAT  36.776700 -119.718900   2384.0\n",
       "56           56                                   Inyokern Airport             Inyokern           California  IYK  35.658600 -117.829400   1640.0\n",
       "57           57                                 Long Beach Airport           Long Beach           California  LGB  33.817800 -118.151700   2384.0\n",
       "58           58                  Los Angeles International Airport          Los Angeles           California  LAX  33.942500 -118.408100   3500.0\n",
       "59           59                           Mammoth Yosemite Airport        Mammoth Lakes           California  MMH  37.624200 -118.838900    230.0\n",
       "60           60           Modesto City Co-Harry Sham Field Airport              Modesto           California  MOD  37.625833 -120.954444   2384.0\n",
       "61           61                          Monterey Regional Airport             Monterey           California  MRY  36.586900 -121.843100    496.0\n",
       "62           62                      Oakland International Airport              Oakland           California  OAK  37.721400 -122.220800   2384.0\n",
       "63           63                      Ontario International Airport              Ontario           California  ONT  34.056100 -117.601100   1741.0\n",
       "64           64                                     Oxnard Airport               Oxnard           California  OXR  34.200800 -119.207200    230.0\n",
       "65           65                 Palm Springs International Airport         Palm Springs           California  PSP  33.829700 -116.506700    940.0\n",
       "66           66            Palmdale Regional/USAF Plant 42 Airport             Palmdale           California  PMD  34.629400 -118.084400   2384.0\n",
       "67           67                          Redding Municipal Airport              Redding           California  RDD  40.508900 -122.293300   1584.0\n",
       "68           68                   Sacramento International Airport           Sacramento           California  SMF  38.695600 -121.590800   6000.0\n",
       "69           69                    San Diego International Airport            San Diego           California  SAN  32.733600 -117.189700    663.0\n",
       "70           70                San Francisco International Airport        San Francisco           California  SFO  37.618900 -122.375000   2384.0\n",
       "71           71    Norman Y. Mineta San José International Airport             San Jose           California  SJC  37.362800 -121.929200   2384.0\n",
       "72           72            San Luis Obispo County Regional Airport      San Luis Obispo           California  SBP  35.236900 -120.641900   2384.0\n",
       "73           73                                 John Wayne Airport            Santa Ana           California  SNA  33.675600 -117.868300    504.0\n",
       "74           74                    Santa Barbara Municipal Airport        Santa Barbara           California  SBA  34.426100 -119.840300    948.0\n",
       "75           75                         Santa Maria Public Airport          Santa Maria           California  SMX  34.898900 -120.457500   2516.0\n",
       "76           76            Charles M. Schulz–Sonoma County Airport           Santa Rosa           California  STS  38.508900 -122.812800   1125.0\n",
       "77           77                      Stockton Metropolitan Airport             Stockton           California  SCK  37.894200 -121.238300   2384.0\n",
       "78           78                          Visalia Municipal Airport              Visalia           California  VIS  36.318600 -119.392800    821.0\n",
       "79           79                        Aspen-Pitkin County Airport                Aspen             Colorado  ASE  39.223100 -106.868900    573.0\n",
       "80           80         City of Colorado Springs Municipal Airport     Colorado Springs             Colorado  COS  38.805800 -104.700800   2384.0\n",
       "81           81                       Denver International Airport               Denver             Colorado  DEN  39.861700 -104.673100   2384.0\n",
       "82           82                    Durango-La Plata County Airport              Durango             Colorado  DRO  37.151400 -107.753900   1281.0\n",
       "83           83                      Eagle County Regional Airport                Eagle             Colorado  EGE  39.642500 -106.917800    632.0\n",
       "84           84                    Grand Junction Regional Airport       Grand Junction             Colorado  GJT  39.122500 -108.526700   2357.0\n",
       "85           85            Gunnison-Crested Butte Regional Airport             Gunnison             Colorado  GUC  38.867700 -106.977300   2384.0\n",
       "86           86                               Yampa Valley Airport               Hayden             Colorado  HDN  40.481100 -107.217800    671.0\n",
       "87           87                          Montrose Regional Airport             Montrose             Colorado  MTJ  37.953900 -107.908600    966.0\n",
       "88           88                         Telluride Regional Airport            Telluride             Colorado  TEX  37.953900 -107.908600    542.0\n",
       "89           89                      Bradley International Airport             Hartford          Connecticut  BDL  41.939200  -72.683300   2021.0\n",
       "90           90                   Tweed New Haven Regional Airport            New Haven          Connecticut  HVN  41.263900  -72.886700    394.0\n",
       "91           91                                 Wilmington Airport           Wilmington             Delaware  ILG  39.678600  -75.606700   1250.0\n",
       "92           92                Daytona Beach International Airport        Daytona Beach              Florida  DAB  29.184700  -81.060600   1800.0\n",
       "93           93    Fort Lauderdale–Hollywood International Airport      Fort Lauderdale              Florida  FLL  26.072500  -80.152800   1380.0\n",
       "94           94            Southwest Florida International Airport           Fort Myers              Florida  RSW  26.536100  -81.755300   2384.0\n",
       "95           95                       Gainesville Regional Airport          Gainesville              Florida  GNV  29.690000  -82.271700   1650.0\n",
       "96           96                 Jacksonville International Airport         Jacksonville              Florida  JAX  30.494200  -81.687800   7911.0\n",
       "97           97                     Key West International Airport             Key West              Florida  EYW  24.556100  -81.759400    334.0\n",
       "98           98                 The Florida Keys Marathon Airport              Marathon              Florida  MTH  24.726400  -81.040300    197.0\n",
       "99           99                    Melbourne International Airport            Melbourne              Florida  MLB  28.102800  -80.645300   2384.0\n",
       "100         100                        Miami International Airport                Miami              Florida  MIA  25.793300  -80.290600   2384.0\n",
       "101         101                                     Naples Airport               Naples              Florida  APF  40.884400   14.290800   2384.0\n",
       "102         102                      Orlando International Airport              Orlando              Florida  MCO  28.429400  -81.308900   2384.0\n",
       "103         103              Orlando Sanford International Airport              Orlando              Florida  SFB  28.777800  -81.237500   3000.0\n",
       "104         104         Panama City-Bay Co International Airport            Panama City              Florida  PFN   8.983300  -79.516700   2384.0\n",
       "105         105    Northwest Florida Beaches International Airport    Panama City Beach              Florida  ECP  30.358300  -85.795600   4000.0\n",
       "106         106                    Pensacola International Airport            Pensacola              Florida  PNS  30.473300  -87.186700   1211.0\n",
       "107         107                                Punta Gorda Airport          Punta Gorda              Florida  PGD  26.918900  -81.990800   1934.0\n",
       "108         108           Sarasota–Bradenton International Airport             Sarasota              Florida  SRQ  27.395600  -82.554400   2384.0\n",
       "109         109                 Northeast Florida Regional Airport        St. Augustine              Florida  UST  29.959300  -81.339700      1.0\n",
       "110         110          St. Pete–Clearwater International Airport       St. Petersburg              Florida  PIE  27.910000  -82.687500   1900.0\n",
       "111         111                  Tallahassee International Airport          Tallahassee              Florida  TLH  30.396700  -84.350300   2485.0\n",
       "112         112                        Tampa International Airport                Tampa              Florida  TPA  27.975600  -82.533300   2384.0\n",
       "113         113  Destin–Fort Walton Beach Airport / Eglin Air F...           Valparaiso              Florida  VPS  30.483300  -86.525300   2384.0\n",
       "114         114                   Palm Beach International Airport      West Palm Beach              Florida  PBI  26.683200  -80.095600   2120.0\n",
       "115         115                 Southwest Georgia Regional Airport               Albany              Georgia  ABY  31.535600  -84.194400    980.0\n",
       "116         116   Hartsfield–Jackson Atlanta International Airport              Atlanta              Georgia  ATL  33.636700  -84.428100   4700.0\n",
       "117         117                           Augusta Regional Airport              Augusta              Georgia  AGS  33.370000  -81.964400   1411.0\n",
       "118         118                     Brunswick Golden Isles Airport            Brunswick              Georgia  BQK  31.158900  -81.489200   2384.0\n",
       "119         119                      Columbus Metropolitan Airport             Columbus              Georgia  CSG  32.516400  -84.938900    680.0\n",
       "120         120                    Middle Georgia Regional Airport                Macon              Georgia  MCN  32.692800  -83.649200   1149.0\n",
       "121         121         Savannah/Hilton Head International Airport             Savannah              Georgia  SAV  32.127500  -81.202200   3650.0\n",
       "122         122                          Valdosta Regional Airport             Valdosta              Georgia  VLD  30.781389  -83.276111    760.0\n",
       "123         123           Antonio B. Won Pat International Airport                Agana                 Guam  GUM  13.483889  144.797222   1657.0\n",
       "124         124                         Hilo International Airport                 Hilo               Hawaii  ITO  19.720300 -155.048300   2384.0\n",
       "125         125             Daniel K. Inouye International Airport             Honolulu               Hawaii  HNL  21.318600 -157.922500   4220.0\n",
       "126         126                                    Kahului Airport              Kahului               Hawaii  OGG  20.898600 -156.430600   1391.0\n",
       "127         127              Kona International Airport at Keahole          Kailua-Kona               Hawaii  KOA  19.738900 -156.045600   2700.0\n",
       "128         128                                    Molokai Airport           Kaunakakai               Hawaii  MKK  21.152800 -157.096400   2384.0\n",
       "129         129                                      Lanai Airport           Lanai City               Hawaii  LNY  20.785600 -156.951400    505.0\n",
       "130         130                                      Lihue Airport                Lihue               Hawaii  LIH  21.976100 -159.338900    915.0\n",
       "131         131                                      Boise Airport                Boise                Idaho  BOI  43.564400 -116.222800   2384.0\n",
       "132         132                          Friedman Memorial Airport               Hailey                Idaho  SUN  43.503900 -114.295600    211.0\n",
       "133         133                       Idaho Falls Regional Airport          Idaho Falls                Idaho  IDA  43.513600 -112.070800    866.0\n",
       "134         134                  Lewiston-Nez Perce County Airport             Lewiston                Idaho  LWS  46.374400 -117.015300    865.0\n",
       "135         135                         Pocatello Regional Airport            Pocatello                Idaho  PIH  42.909700 -112.595800   3374.0\n",
       "136         136                      Magic Valley Regional Airport           Twin Falls                Idaho  TWF  42.481700 -114.487800   1468.0\n",
       "137         137  MidAmerica St. Louis Airport / Scott Air Force...           Belleville             Illinois  BLV  38.545300  -89.835300   7003.0\n",
       "138         138  Central Illinois Regional Airport at Bloomingt...          Bloomington             Illinois  BMI  40.477200  -88.915800   2384.0\n",
       "139         139           University of Illinois - Willard Airport            Champaign             Illinois  CMI  40.039200  -88.278100   1799.0\n",
       "140         140               Chicago Midway International Airport              Chicago             Illinois  MDW  41.786100  -87.752500   2384.0\n",
       "141         141               Chicago O'Hare International Airport              Chicago             Illinois  ORD  41.978600  -87.904700   2384.0\n",
       "142         142                 Williamson County Regional Airport               Marion             Illinois  MWA  37.755000  -89.011100   1300.0\n",
       "143         143                    Quad City International Airport               Moline             Illinois  MLI  41.448600  -90.507500   2021.0\n",
       "144         144  General Wayne A. Downing Peoria International ...               Peoria             Illinois  PIA  40.664200  -89.693300   2384.0\n",
       "145         145                            Quincy Regional Airport               Quincy             Illinois  UIN  39.942800  -91.194700   1101.0\n",
       "146         146             Chicago Rockford International Airport             Rockford             Illinois  RFD  42.195300  -89.097200   2384.0\n",
       "147         147                    Abraham Lincoln Capital Airport          Springfield             Illinois  SPI  39.847000  -89.668000   2300.0\n",
       "148         148                        Evansville Regional Airport           Evansville              Indiana  EVV  38.038300  -87.530800   2384.0\n",
       "149         149                   Fort Wayne International Airport           Fort Wayne              Indiana  FWA  40.978300  -85.195300   3351.0\n",
       "150         150                 Indianapolis International Airport         Indianapolis              Indiana  IND  39.717200  -86.294400   2384.0\n",
       "151         151                   South Bend International Airport           South Bend              Indiana  SBN  41.708300  -86.317200   2200.0\n",
       "152         152                           The Eastern Iowa Airport         Cedar Rapids                 Iowa  CID  41.889100  -91.700300   3288.0\n",
       "153         153                   Des Moines International Airport           Des Moines                 Iowa  DSM  41.533900  -93.663100   2384.0\n",
       "154         154                           Dubuque Regional Airport              Dubuque                 Iowa  DBQ  42.401900  -90.709400   1240.0\n",
       "155         155                              Sioux Gateway Airport           Sioux City                 Iowa  SUX  42.402500  -96.384400   2460.0\n",
       "156         156                          Waterloo Regional Airport             Waterloo                 Iowa  ALO  43.460800  -80.378600   2583.0\n",
       "157         157                       Garden City Regional Airport          Garden City               Kansas  GCK  37.927500 -100.724400   1848.0\n",
       "158         158                         Manhattan Regional Airport            Manhattan               Kansas  MHK  39.141100  -96.671700    680.0\n",
       "159         159                            Topeka Regional Airport               Topeka               Kansas  FOE  38.950800  -95.663600   2854.0\n",
       "160         160      Wichita Dwight D. Eisenhower National Airport              Wichita               Kansas  ICT  37.650000  -97.433100   3248.0\n",
       "161         161  Cincinnati/Northern Kentucky International Air...           Cincinnati             Kentucky  CVG  39.048900  -84.667800   7700.0\n",
       "162         162                                 Blue Grass Airport            Lexington             Kentucky  LEX  38.036400  -84.605800    911.0\n",
       "163         163                   Louisville International Airport           Louisville             Kentucky  SDF  38.174200  -85.736400   1500.0\n",
       "164         164          Owensboro-Daviess County Regional Airport            Owensboro             Kentucky  OWB  37.738900  -87.166900    880.0\n",
       "165         165                           Barkley Regional Airport              Paducah             Kentucky  PAH  37.060300  -88.773100   1018.0\n",
       "166         166                   Alexandria International Airport           Alexandria            Louisiana  AEX  31.327500  -92.548600   3212.0\n",
       "167         167                   Baton Rouge Metropolitan Airport          Baton Rouge            Louisiana  BTR  30.532800  -91.150000   1250.0\n",
       "168         168                         Lafayette Regional Airport            Lafayette            Louisiana  LFT  30.216700  -92.033300    746.0\n",
       "169         169                      Lake Charles Regional Airport         Lake Charles            Louisiana  LCH  30.126100  -93.223300   1878.0\n",
       "170         170                            Monroe Regional Airport               Monroe            Louisiana  MLU  32.510800  -92.037800   2384.0\n",
       "171         171  Louis Armstrong New Orleans International Airport          New Orleans            Louisiana  MSY  29.993300  -90.258100   1500.0\n",
       "172         172                        Shreveport Regional Airport           Shreveport            Louisiana  SHV  32.514700  -93.747200   1625.0\n",
       "173         173                       Bangor International Airport               Bangor                Maine  BGR  44.807200  -68.828100   2079.0\n",
       "174         174                     Portland International Jetport             Portland                Maine  PWM  43.646100  -70.309200    726.0\n",
       "175         175    Northern Maine Regional Airport at Presque Isle         Presque Isle                Maine  PQI  46.688900  -68.044700   2384.0\n",
       "176         176                       Knox County Regional Airport             Rockland                Maine  RKD  44.060000  -69.099200    538.0\n",
       "177         177  Baltimore/Washington International Thurgood Ma...            Baltimore             Maryland  BWI  39.175300  -76.668300   3160.0\n",
       "178         178                        Hagerstown Regional Airport           Hagerstown             Maryland  HGR  39.708600  -77.726400   2384.0\n",
       "179         179     Salisbury-Ocean City Wicomico Regional Airport            Salisbury             Maryland  SBY  38.340000  -75.509400   2384.0\n",
       "180         180   Gen. Edward Lawrence Logan International Airport               Boston        Massachusetts  BOS  42.363100  -71.006400   2384.0\n",
       "181         181                       Barnstable Municipal Airport              Hyannis        Massachusetts  HYA  41.700000  -70.300000    639.0\n",
       "182         182                         Nantucket Memorial Airport            Nantucket        Massachusetts  ACK  41.252900  -70.059000   1200.0\n",
       "183         183                     Provincetown Municipal Airport         Provincetown        Massachusetts  PVC  43.607200 -110.737800    310.0\n",
       "184         184                          Martha's Vineyard Airport       Vineyard Haven        Massachusetts  MVY  41.393100  -70.614400    688.0\n",
       "185         185                         Worcester Regional Airport            Worcester        Massachusetts  ORH  42.267222  -71.875833   1000.0\n",
       "186         186                     Alpena County Regional Airport               Alpena             Michigan  APN  45.078100  -83.560300   3084.0\n",
       "187         187                   Houghton County Memorial Airport              Calumet             Michigan  CMX  47.168300  -88.489200   1996.0\n",
       "188         188          Detroit Metropolitan Wayne County Airport              Detroit             Michigan  DTW  42.212500  -83.353300   2384.0\n",
       "189         189                               Delta County Airport             Escanaba             Michigan  ESC  45.722800  -87.093600    944.0\n",
       "190         190                       Bishop International Airport                Flint             Michigan  FNT  42.965600  -83.743600   2384.0\n",
       "191         191               Gerald R. Ford International Airport         Grand Rapids             Michigan  GRR  42.880800  -85.522800   3127.0\n",
       "192         192                                       Ford Airport        Iron Mountain             Michigan  IMT  42.880800  -85.522800   3127.0\n",
       "193         193       Kalamazoo/Battle Creek International Airport            Kalamazoo             Michigan  AZO  42.235000  -85.551900   2384.0\n",
       "194         194               Capital Region International Airport              Lansing             Michigan  LAN  42.778600  -84.586200   2384.0\n",
       "195         195                       Sawyer International Airport            Marquette             Michigan  MQT  46.353600  -87.395300   2100.0\n",
       "196         196                            Muskegon County Airport             Muskegon             Michigan  MKG  43.167800  -86.235600   1200.0\n",
       "197         197          Pellston Regional Airport of Emmet County             Pellston             Michigan  PLN  45.570800  -84.796700   1675.0\n",
       "198         198                          MBS International Airport              Saginaw             Michigan  MBS  43.532800  -84.079700   3200.0\n",
       "199         199              Chippewa County International Airport     Sault Ste. Marie             Michigan  CIU  46.250800  -84.472500   1850.0\n",
       "200         200                             Cherry Capital Airport        Traverse City             Michigan  TVC  44.741700  -85.582200   1026.0\n",
       "201         201                           Bemidji Regional Airport              Bemidji            Minnesota  BJI  47.510833  -94.934722   1740.0\n",
       "202         202                    Brainerd Lakes Regional Airport             Brainerd            Minnesota  BRD  46.402200  -94.135600   2597.0\n",
       "203         203                       Duluth International Airport               Duluth            Minnesota  DLH  46.842200  -92.193600   2384.0\n",
       "204         204                             Range Regional Airport              Hibbing            Minnesota  HIB  47.386700  -92.838900   1600.0\n",
       "205         205                        Falls International Airport  International Falls            Minnesota  INL  48.565600  -93.402200    681.0\n",
       "206         206         Minneapolis–St. Paul International Airport          Minneapolis            Minnesota  MSP  44.881900  -93.221700   2930.0\n",
       "207         207                    Rochester International Airport            Rochester            Minnesota  RST  43.118900  -77.672500   2400.0\n",
       "208         208                         St. Cloud Regional Airport            St. Cloud            Minnesota  STC  45.546700  -94.060000   1414.0\n",
       "209         209                   Golden Triangle Regional Airport             Columbus          Mississippi  GTR  33.448300  -88.591400   1000.0\n",
       "210         210              Gulfport–Biloxi International Airport             Gulfport          Mississippi  GPT  30.407200  -89.070000   1400.0\n",
       "211         211                Jackson–Evers International Airport              Jackson          Mississippi  JAN  33.636700  -84.428100   3381.0\n",
       "212         212                                  Key Field Airport             Meridian          Mississippi  MEI  32.332500  -88.751900   1000.0\n",
       "213         213                            Tupelo Regional Airport               Tupelo          Mississippi  TUP  34.268100  -88.770000   1061.0\n",
       "214         214                          Columbia Regional Airport             Columbia             Missouri  COU  38.818100  -92.219700   1538.0\n",
       "215         215                            Joplin Regional Airport               Joplin             Missouri  JLN  37.151700  -94.498300    970.0\n",
       "216         216                  Kansas City International Airport          Kansas City             Missouri  MCI  39.297500  -94.713900  10680.0\n",
       "217         217               Springfield-Branson National Airport          Springfield             Missouri  SGF  37.245600  -93.388600   2384.0\n",
       "218         218            St. Louis Lambert International Airport            St. Louis             Missouri  STL  38.747200  -90.361400   2384.0\n",
       "219         219               Billings Logan International Airport             Billings              Montana  BIL  45.807800 -108.542800   2300.0\n",
       "220         220          Bozeman Yellowstone International Airport              Bozeman              Montana  BZN  45.777000 -111.152000   2481.0\n",
       "221         221                                Bert Mooney Airport                Butte              Montana  BTM  45.954700 -112.497500    890.0\n",
       "222         222                  Great Falls International Airport          Great Falls              Montana  GTF  47.482800 -111.370600   2384.0\n",
       "223         223                            Helena Regional Airport               Helena              Montana  HLN  46.606700 -111.982800   1224.0\n",
       "224         224                 Glacier Park International Airport            Kalispell              Montana  FCA  48.197800 -114.316100   1525.0\n",
       "225         225                     Missoula International Airport             Missoula              Montana  MSO  46.916400 -114.090600   2700.0\n",
       "226         226                  Sidney–Richland Municipal Airport               Sidney              Montana  SDY  47.706900 -104.192500    335.0\n",
       "227         227                                Yellowstone Airport     West Yellowstone              Montana  WYS  44.688300 -111.117800    735.0\n",
       "228         228                  Central Nebraska Regional Airport         Grand Island             Nebraska  GRI  40.967500  -98.309700   1847.0\n",
       "229         229                                    Lincoln Airport              Lincoln             Nebraska  LNK  40.851100  -96.759200   2384.0\n",
       "230         230                                    Eppley Airfield                Omaha             Nebraska  OMA  41.301200  -95.895400   2384.0\n",
       "231         231                     Boulder City Municipal Airport         Boulder City               Nevada  BLD  35.947200 -114.860300    530.0\n",
       "232         232                              Elko Regional Airport                 Elko               Nevada  EKO  40.825000 -115.791700    700.0\n",
       "233         233                     McCarran International Airport            Las Vegas               Nevada  LAS  36.080000 -115.152200   2800.0\n",
       "234         234                            North Las Vegas Airport            Las Vegas               Nevada  VGT  36.210600 -115.194400    920.0\n",
       "235         235                   Reno/Tahoe International Airport                 Reno               Nevada  RNO  39.499200 -119.768100   1450.0\n",
       "236         236                          Lebanon Municipal Airport              Lebanon        New Hampshire  LEB  43.626100  -72.304200   2384.0\n",
       "237         237                 Manchester–Boston Regional Airport           Manchester        New Hampshire  MHT  42.932500  -71.435600   1500.0\n",
       "238         238          Portsmouth International Airport at Pease           Portsmouth        New Hampshire  PSM  43.078100  -70.823300   3000.0\n",
       "239         239                Atlantic City International Airport        Atlantic City           New Jersey  ACY  39.457500  -74.577200   2384.0\n",
       "240         240               Newark Liberty International Airport               Newark           New Jersey  EWR  40.692500  -74.168600   2384.0\n",
       "241         241                             Trenton Mercer Airport              Trenton           New Jersey  TTN  40.276700  -74.813300   1345.0\n",
       "242         242                  Albuquerque International Sunport          Albuquerque           New Mexico  ABQ  35.110800 -106.610000   2384.0\n",
       "243         243                        Lea County Regional Airport                Hobbs           New Mexico  HOB  32.687500 -103.216900    898.0\n",
       "244         244                   Roswell International Air Center              Roswell           New Mexico  ROW  33.301600 -104.530600   5029.0\n",
       "245         245                          Santa Fe Regional Airport             Santa Fe           New Mexico  SAF  35.617200 -106.089400   2128.0\n",
       "246         246                       Albany International Airport               Albany             New York  ALB  42.749167  -73.801944   1000.0\n",
       "247         247                         Greater Binghamton Airport           Binghamton             New York  BGM  42.208600  -75.979700   1199.0\n",
       "248         248              Buffalo Niagara International Airport              Buffalo             New York  BUF  43.107200  -78.946100   1000.0\n",
       "249         249                    Elmira/Corning Regional Airport               Elmira             New York  ELM  42.093900  -76.809700   1000.0\n",
       "250         250                                   Republic Airport          Farmingdale             New York  FRG  40.728900  -73.413300   2384.0\n",
       "251         251                      Long Island MacArthur Airport                Islip             New York  ISP  40.795278  -73.100278   1311.0\n",
       "252         252                   Ithaca Tompkins Regional Airport               Ithaca             New York  ITH  42.491400  -76.458600    531.0\n",
       "253         253              John F. Kennedy International Airport             New York             New York  JFK  40.639700  -73.778900   2384.0\n",
       "254         254                                  LaGuardia Airport             New York             New York  LGA  40.775000  -73.875000   2384.0\n",
       "255         255                      Stewart International Airport             Newburgh             New York  SWF  41.504200  -74.104700   2384.0\n",
       "256         256                Niagara Falls International Airport        Niagara Falls             New York  IAG  43.107200  -78.946100   1067.0\n",
       "257         257                  Plattsburgh International Airport          Plattsburgh             New York  PBG  44.650800  -73.468100   1912.0\n",
       "258         258            Greater Rochester International Airport            Rochester             New York  ROC  43.118900  -77.672500   1136.0\n",
       "259         259             Syracuse Hancock International Airport             Syracuse             New York  SYR  43.111100  -76.106400   2000.0\n",
       "260         260                    Watertown International Airport            Watertown             New York  ART  43.991900  -76.020600   1060.0\n",
       "261         261                         Westchester County Airport         White Plains             New York  HPN  41.066900  -73.707500    702.0\n",
       "262         262                         Asheville Regional Airport            Asheville       North Carolina  AVL  35.436100  -82.541700    900.0\n",
       "263         263            Charlotte/Douglas International Airport            Charlotte       North Carolina  CLT  35.213900  -80.943100   5558.0\n",
       "264         264                           Concord Regional Airport              Concord       North Carolina  USA  35.387778  -80.709167    750.0\n",
       "265         265                      Fayetteville Regional Airport         Fayetteville       North Carolina  FAY  34.991100  -78.880300   1343.0\n",
       "266         266               Piedmont Triad International Airport           Greensboro       North Carolina  GSO  35.955700  -80.005300   2384.0\n",
       "267         267                            Pitt-Greenville Airport           Greenville       North Carolina  PGV  35.590000  -77.380000    872.0\n",
       "268         268                           Hickory Regional Airport              Hickory       North Carolina  HKY  35.741100  -81.389400    739.0\n",
       "269         269                            Albert J. Ellis Airport         Jacksonville       North Carolina  OAJ  34.829200  -77.612200    675.0\n",
       "270         270  Kinston Regional Jetport At Stallings Field Ai...              Kinston       North Carolina  ISO  35.331400  -77.608900   2384.0\n",
       "271         271                  Coastal Carolina Regional Airport             New Bern       North Carolina  EWN  35.073100  -77.043100    785.0\n",
       "272         272                               Moore County Airport            Pinehurst       North Carolina  SOP  35.237200  -79.391100   2384.0\n",
       "273         273               Raleigh-Durham International Airport              Raleigh       North Carolina  RDU  35.880000  -78.790000   5000.0\n",
       "274         274                   Wilmington International Airport           Wilmington       North Carolina  ILM  34.270600  -77.902500   1800.0\n",
       "275         275                         Bismarck Municipal Airport             Bismarck         North Dakota  BIS  46.772800 -100.745800   2425.0\n",
       "276         276      Dickinson Theodore Roosevelt Regional Airport            Dickinson         North Dakota  DIK  46.797200 -102.801900    626.0\n",
       "277         277                       Hector International Airport                Fargo         North Dakota  FAR  46.877200  -96.789400   2500.0\n",
       "278         278                  Grand Forks International Airport          Grand Forks         North Dakota  GFK  47.949200  -97.176100   1618.0\n",
       "279         279                        Minot International Airport                Minot         North Dakota  MOT  48.257800 -101.278100   1563.0\n",
       "280         280                Sloulin Field International Airport            Williston         North Dakota  ISN  48.178100 -103.642200   2384.0\n",
       "281         281                      Akron-Canton Regional Airport                Akron                 Ohio  CAK  40.915000  -81.443700   2384.0\n",
       "282         282                Cincinnati Municipal Lunken Airport           Cincinnati                 Ohio  LUK  39.103300  -84.418600   1140.0\n",
       "283         283            Cleveland-Hopkins International Airport            Cleveland                 Ohio  CLE  41.411700  -81.849700   1717.0\n",
       "284         284          John Glenn Columbus International Airport             Columbus                 Ohio  CMH  39.998100  -82.891900   2384.0\n",
       "285         285                 Rickenbacker International Airport             Columbus                 Ohio  LCK  39.998100  -82.891900   4342.0\n",
       "286         286          James M. Cox Dayton International Airport               Dayton                 Ohio  DAY  39.902200  -84.219400   4200.0\n",
       "287         287                             Toledo Express Airport               Toledo                 Ohio  TOL  41.586800  -83.807800   2345.0\n",
       "288         288                 Youngstown-Warren Regional Airport           Youngstown                 Ohio  YNG  41.260833  -80.679167   1468.0\n",
       "289         289                  Lawton–Fort Sill Regional Airport               Lawton             Oklahoma  LAW  34.567800  -98.416400   1300.0\n",
       "290         290                          Will Rogers World Airport        Oklahoma City             Oklahoma  OKC  35.393056  -97.600833   2384.0\n",
       "291         291                        Tulsa International Airport                Tulsa             Oklahoma  TUL  36.198300  -95.888100   2384.0\n",
       "292         292                                     Eugene Airport               Eugene               Oregon  EUG  44.051900 -123.086700   2600.0\n",
       "293         293         Rogue Valley International-Medford Airport              Medford               Oregon  MFR  42.374200 -122.873600    905.0\n",
       "294         294                  Southwest Oregon Regional Airport           North Bend               Oregon  OTH  43.417200 -124.262800    619.0\n",
       "295         295                     Portland International Airport             Portland               Oregon  PDX  45.588600 -122.597500   3000.0\n",
       "296         296                          Redmond Municipal Airport              Redmond               Oregon  RDM  44.254200 -121.149700   2518.0\n",
       "297         297                              McNary Field Airport                 Salem               Oregon  SLE  44.909400 -123.002500    751.0\n",
       "298         298                Lehigh Valley International Airport            Allentown         Pennsylvania  ABE  40.652300  -75.440500   2278.0\n",
       "299         299                         Erie International Airport                 Erie         Pennsylvania  ERI  42.083100  -80.181900   2384.0\n",
       "300         300                     Arnold Palmer Regional Airport              Latrobe         Pennsylvania  LBE  40.274700  -79.406700    945.0\n",
       "301         301                   Harrisburg International Airport           Middletown         Pennsylvania  MDT  40.193100  -76.763300    680.0\n",
       "302         302                 Philadelphia International Airport         Philadelphia         Pennsylvania  PHL  39.871900  -75.241100   2302.0\n",
       "303         303                   Pittsburgh International Airport           Pittsburgh         Pennsylvania  PIT  40.496000  -80.246000  10000.0\n",
       "304         304                            University Park Airport        State College         Pennsylvania  SCE  40.849167  -77.848611   1091.0\n",
       "305         305        Wilkes-Barre/Scranton International Airport         Wilkes-Barre         Pennsylvania  AVP  41.251000  -75.906000     23.0\n",
       "306         306                      Williamsport Regional Airport         Williamsport         Pennsylvania  IPT  41.241700  -76.921700   2384.0\n",
       "307         307             Rafael Hernández International Airport            Aguadilla          Puerto Rico  BQN  18.495000  -67.129400   2384.0\n",
       "308         308                    José Aponte de la Torre Airport                Ceiba          Puerto Rico  NRR  18.245300  -65.643300   1646.0\n",
       "309         309                    Benjamín Rivera Noriega Airport              Culebra          Puerto Rico  CPX  18.313300  -65.304400   2384.0\n",
       "310         310                    Mercedita International Airport                Ponce          Puerto Rico  PSE  18.008300  -66.563100    274.0\n",
       "311         311              Fernando Luis Ribas Dominicci Airport             San Juan          Puerto Rico  SIG  18.456700  -66.098300    102.0\n",
       "312         312             Luis Muñoz Marín International Airport             San Juan          Puerto Rico  SJU  18.439200  -66.001900   1600.0\n",
       "313         313                   Antonio Rivera Rodríguez Airport              Vieques          Puerto Rico  VQS  18.134700  -65.493900    124.0\n",
       "314         314                         Block Island State Airport         Block Island         Rhode Island  BID  41.168100  -71.577800    136.0\n",
       "315         315               Theodore Francis Green State Airport           Providence         Rhode Island  PVD  41.723900  -71.428300   2384.0\n",
       "316         316                             Westerly State Airport             Westerly         Rhode Island  WST  41.349700  -71.803300    321.0\n",
       "317         317  Charleston International Airport / Charleston AFB           Charleston       South Carolina  CHS  32.898600  -80.040600   2384.0\n",
       "318         318                      Columbia Metropolitan Airport             Columbia       South Carolina  CAE  33.937000  -81.105300   2600.0\n",
       "319         319                          Florence Regional Airport             Florence       South Carolina  FLO  34.185300  -79.723900   1436.0\n",
       "320         320       Greenville-Spartanburg International Airport           Greenville       South Carolina  GSP  34.716700  -82.183300   3500.0\n",
       "321         321                                Hilton Head Airport   Hilton Head Island       South Carolina  HHH  32.224400  -80.697500    131.0\n",
       "322         322                 Myrtle Beach International Airport         Myrtle Beach       South Carolina  MYR  33.679700  -78.928300   3795.0\n",
       "323         323                          Aberdeen Regional Airport             Aberdeen         South Dakota  ABR  57.202500   -2.198100   1284.0\n",
       "324         324                        Rapid City Regional Airport           Rapid City         South Dakota  RAP  44.076000 -103.228000   1655.0\n",
       "325         325                       Sioux Falls Regional Airport          Sioux Falls         South Dakota  FSD  43.581900  -96.741900   1570.0\n",
       "326         326                        Tri-Cities Regional Airport              Bristol            Tennessee  TRI  36.475300  -82.407500   2384.0\n",
       "327         327                   Chattanooga Metropolitan Airport          Chattanooga            Tennessee  CHA  35.035300  -85.203900    950.0\n",
       "328         328                               McGhee Tyson Airport            Knoxville            Tennessee  TYS  35.811100  -83.993900   2384.0\n",
       "329         329                      Memphis International Airport              Memphis            Tennessee  MEM  35.042500  -89.976700   3900.0\n",
       "330         330                    Nashville International Airport            Nashville            Tennessee  BNA  36.126700  -86.681900   3900.0\n",
       "331         331                           Abilene Regional Airport              Abilene                Texas  ABI  32.413200  -99.683100   1644.0\n",
       "332         332        Rick Husband Amarillo International Airport             Amarillo                Texas  AMA  35.219400 -101.705800   2384.0\n",
       "333         333             Austin-Bergstrom International Airport               Austin                Texas  AUS  30.194400  -97.670000   4242.0\n",
       "334         334                       Jack Brooks Regional Airport             Beaumont                Texas  BPT  29.950833  -94.020556   1799.0\n",
       "335         335  Brownsville/South Padre Island International A...          Brownsville                Texas  BRO  25.906900  -97.425800   2384.0\n",
       "336         336                                 Easterwood Airport      College Station                Texas  CLL  30.674400  -96.370000    700.0\n",
       "337         337               Corpus Christi International Airport       Corpus Christi                Texas  CRP  27.742800  -97.401900   2457.0\n",
       "338         338                                  Dallas Love Field               Dallas                Texas  DAL  32.847200  -96.851700   2384.0\n",
       "339         339            Dallas/Fort Worth International Airport               Dallas                Texas  DFW  32.896900  -97.038100   2384.0\n",
       "340         340                      El Paso International Airport              El Paso                Texas  ELP  31.807200 -106.377500   6670.0\n",
       "341         341  Killeen-Fort Hood Regional Airport / Robert Gr...            Fort Hood                Texas  GRK  31.067200  -97.828900   2384.0\n",
       "342         342                       Valley International Airport            Harlingen                Texas  HRL  26.227200  -97.655000   2384.0\n",
       "343         343               George Bush Intercontinental Airport              Houston                Texas  IAH  29.984400  -95.341400  10000.0\n",
       "344         344                           William P. Hobby Airport              Houston                Texas  HOU  29.645600  -95.278900   1304.0\n",
       "345         345                       Laredo International Airport               Laredo                Texas  LRD  27.543900  -99.461700   1796.0\n",
       "346         346                        East Texas Regional Airport             Longview                Texas  GGG  32.383900  -94.711400   1300.0\n",
       "347         347        Lubbock Preston Smith International Airport              Lubbock                Texas  LBB  33.663600 -101.820600   3000.0\n",
       "348         348               McAllen Miller International Airport              McAllen                Texas  MFE  26.175800  -98.238600    370.0\n",
       "349         349                      Midland International Airport              Midland                Texas  MAF  31.942500 -102.201900   2384.0\n",
       "350         350                        San Angelo Regional Airport           San Angelo                Texas  SJT  31.355000 -100.496400   1503.0\n",
       "351         351                  San Antonio International Airport          San Antonio                Texas  SAT  29.526700  -98.471900   2305.0\n",
       "352         352                      Tyler Pounds Regional Airport                Tyler                Texas  TYR  32.353900  -95.402800   1200.0\n",
       "353         353                          Victoria Regional Airport             Victoria                Texas  VCT  28.852500  -96.918600   1766.0\n",
       "354         354                              Waco Regional Airport                 Waco                Texas  ACT  31.611400  -97.230600   1369.0\n",
       "355         355  Wichita Falls Municipal Airport / Sheppard Air...        Wichita Falls                Texas  SPS  33.988900  -98.491900   2384.0\n",
       "356         356                              Cyril E. King Airport     Charlotte Amalie  U.S. Virgin Islands  STT  18.337200  -64.973300    280.0\n",
       "357         357                            Henry E Rohlsen Airport        Christiansted  U.S. Virgin Islands  STX  17.704400  -64.801700   1455.0\n",
       "358         358                        Cedar City Regional Airport           Cedar City                 Utah  CDC  37.700800 -113.098900   1040.0\n",
       "359         359                             Ogden-Hinckley Airport                Ogden                 Utah  OGD  41.195600 -112.013100    720.0\n",
       "360         360                            Provo Municipal Airport                Provo                 Utah  PVU  40.219200 -111.723400    869.0\n",
       "361         361               Salt Lake City International Airport       Salt Lake City                 Utah  SLC  40.788300 -111.977800   2384.0\n",
       "362         362                        St. George Regional Airport           St. George                 Utah  SGU  37.036400 -113.510300   1204.0\n",
       "363         363                   Burlington International Airport           Burlington              Vermont  BTV  44.471900  -73.153300   2384.0\n",
       "364         364                  Charlottesville–Albemarle Airport      Charlottesville             Virginia  CHO  38.138600  -78.452800    754.0\n",
       "365         365                         Lynchburg Regional Airport            Lynchburg             Virginia  LYH  37.325300  -79.201100    872.0\n",
       "366         366    Newport News/Williamsburg International Airport         Newport News             Virginia  PHF  37.131900  -76.493100   1800.0\n",
       "367         367                      Norfolk International Airport              Norfolk             Virginia  ORF  36.894700  -76.201100   1300.0\n",
       "368         368                     Richmond International Airport             Richmond             Virginia  RIC  49.194700 -123.183900   2500.0\n",
       "369         369                           Roanoke Regional Airport              Roanoke             Virginia  ROA  37.325600  -79.975600    912.0\n",
       "370         370          Ronald Reagan Washington National Airport           Washington                   DC  DCA  38.852200  -77.037800   2384.0\n",
       "371         371            Washington Dulles International Airport           Washington                   DC  IAD  38.944400  -77.455800   2384.0\n",
       "372         372                   Bellingham International Airport           Bellingham           Washington  BLI  48.792500 -122.537500   2190.0\n",
       "373         373                              Friday Harbor Airport        Friday Harbor           Washington  FRD  48.521900 -123.024400    145.0\n",
       "374         374                                 Tri-Cities Airport                Pasco           Washington  PSC  36.475300  -82.407500   2235.0\n",
       "375         375         William R. Fairchild International Airport         Port Angeles           Washington  CLM  48.120278 -123.499722    797.0\n",
       "376         376                    Pullman/Moscow Regional Airport              Pullman           Washington  PUW  46.744000 -117.108000    467.0\n",
       "377         377                  King County International Airport              Seattle           Washington  BFI  47.530000 -122.301900    634.0\n",
       "378         378               Seattle–Tacoma International Airport              Seattle           Washington  SEA  47.448900 -122.309400   2500.0\n",
       "379         379                      Spokane International Airport              Spokane           Washington  GEG  47.620000 -117.533889   6140.0\n",
       "380         380                       Walla Walla Regional Airport          Walla Walla           Washington  ALW  46.090000 -118.290000   2384.0\n",
       "381         381                          Pangborn Memorial Airport            Wenatchee           Washington  EAT  47.398056 -120.205833    665.0\n",
       "382         382                                Yakima Air Terminal               Yakima           Washington  YKM  46.568100 -120.544200    825.0\n",
       "383         383                                     Yeager Airport           Charleston        West Virginia  CRW  38.375800  -81.593100    767.0\n",
       "384         384                North Central West Virginia Airport           Clarksburg        West Virginia  CKB  39.296700  -80.228100    434.0\n",
       "385         385                                  Tri-State Airport           Huntington        West Virginia  HTS  38.366900  -82.558600   1300.0\n",
       "386         386                          Greenbrier Valley Airport            Lewisburg        West Virginia  LWB  37.858300  -80.399400    472.0\n",
       "387         387                       Morgantown Municipal Airport           Morgantown        West Virginia  MGW  39.642800  -79.916400    494.0\n",
       "388         388                     Appleton International Airport             Appleton            Wisconsin  ATW  44.258100  -88.519200   1638.0\n",
       "389         389                   Chippewa Valley Regional Airport           Eau Claire            Wisconsin  EAU  44.865800  -91.484200   2384.0\n",
       "390         390    Green Bay–Austin Straubel International Airport            Green Bay            Wisconsin  GRB  44.484700  -88.129700   2441.0\n",
       "391         391                         La Crosse Regional Airport            La Crosse            Wisconsin  LSE  43.879200  -91.256700   1380.0\n",
       "392         392                       Dane County Regional Airport              Madison            Wisconsin  MSN  43.070000  -89.420000   2384.0\n",
       "393         393             General Mitchell International Airport            Milwaukee            Wisconsin  MKE  42.927300  -87.901700   2180.0\n",
       "394         394                          Central Wisconsin Airport              Mosinee            Wisconsin  CWA  44.777500  -89.666667   1800.0\n",
       "395         395                  Rhinelander-Oneida County Airport          Rhinelander            Wisconsin  RHI  45.630800  -89.466400   1259.0\n",
       "396         396        Casper/Natrona County International Airport               Casper              Wyoming  CPR  42.908100 -106.464400   5150.0\n",
       "397         397                          Cheyenne Regional Airport             Cheyenne              Wyoming  CYS  41.155600 -104.810600   1060.0\n",
       "398         398                       Yellowstone Regional Airport                 Cody              Wyoming  COD  44.520300 -109.023900    694.0\n",
       "399         399                   Gillette-Campbell County Airport             Gillette              Wyoming  GCC  44.348900 -105.539400    894.0\n",
       "400         400                               Jackson Hole Airport              Jackson              Wyoming  JAC  43.607200 -110.737800    533.0\n",
       "401         401                          Riverton Regional Airport             Riverton              Wyoming  RIW  43.064200 -108.459700   1250.0\n",
       "402         402                 Southwest Wyoming Regional Airport         Rock Springs              Wyoming  RKS  41.594200 -109.065300   1242.0\n",
       "403         403                            Sheridan County Airport             Sheridan              Wyoming  SHR  44.769200 -106.980300   1550.0\n",
       "404         404                 Boston Logan International Airport                  NaN                  NaN  NaN  42.363100  -71.006400   2384.0"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "New_air_ports_data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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