sxmb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'date': '17-5-2006', 'result_spe': '42520'},\n",
      " {'date': '15-5-2006', 'result_spe': '63784'}]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from pprint import pprint\n",
    "with open('C:/Users/ADMIN/Desktop/sxmb.json',encoding='utf-8') as f:\n",
    "    data = json.load(f)\n",
    "pprint(data)\n",
    "#data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>result_spe</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42520</td>\n",
       "      <td>2006-05-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23982</td>\n",
       "      <td>2006-05-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>63784</td>\n",
       "      <td>2006-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51235</td>\n",
       "      <td>2006-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4877</td>\n",
       "      <td>2006-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>98628</td>\n",
       "      <td>2020-10-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>40128</td>\n",
       "      <td>2020-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>36852</td>\n",
       "      <td>2020-12-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>42857</td>\n",
       "      <td>2020-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>57980</td>\n",
       "      <td>2020-08-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      result_spe       date\n",
       "0          42520 2006-05-17\n",
       "1          23982 2006-05-16\n",
       "2          63784 2006-05-15\n",
       "3          51235 2006-05-18\n",
       "4           4877 2006-05-20\n",
       "...          ...        ...\n",
       "4995       98628 2020-10-08\n",
       "4996       40128 2020-11-08\n",
       "4997       36852 2020-12-08\n",
       "4998       42857 2020-08-13\n",
       "4999       57980 2020-08-15\n",
       "\n",
       "[5000 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_json('C:/Users/ADMIN/PycharmProjects/Hamy/tutorial/tutorial/Output/nsxmb.json')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ADMIN\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0       20\n",
       "1       82\n",
       "2       84\n",
       "3       35\n",
       "4       77\n",
       "        ..\n",
       "4995    28\n",
       "4996    28\n",
       "4997    52\n",
       "4998    57\n",
       "4999    80\n",
       "Name: result_spe, Length: 5000, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,5000):\n",
    "    data['result_spe'][i] = int(data['result_spe'][i])%100\n",
    "data['result_spe']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>result_spe</th>\n",
       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>2006-05-17</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>2006-05-16</td>\n",
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       "      <th>2</th>\n",
       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>2006-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>28</td>\n",
       "      <td>2020-10-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>28</td>\n",
       "      <td>2020-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>52</td>\n",
       "      <td>2020-12-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>57</td>\n",
       "      <td>2020-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>80</td>\n",
       "      <td>2020-08-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      result_spe       date\n",
       "0             20 2006-05-17\n",
       "1             82 2006-05-16\n",
       "2             84 2006-05-15\n",
       "3             35 2006-05-18\n",
       "4             77 2006-05-20\n",
       "...          ...        ...\n",
       "4995          28 2020-10-08\n",
       "4996          28 2020-11-08\n",
       "4997          52 2020-12-08\n",
       "4998          57 2020-08-13\n",
       "4999          80 2020-08-15\n",
       "\n",
       "[5000 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>result_spe</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-01-06</td>\n",
       "      <td>2020-05-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>2006-10-09</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-19</td>\n",
       "      <td>2020-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>2006-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-01-08</td>\n",
       "      <td>2020-07-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>2015-07-19</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-10-30</td>\n",
       "      <td>2020-04-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>2017-12-26</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-04-07</td>\n",
       "      <td>2020-08-01</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-29</td>\n",
       "      <td>2020-05-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>2016-03-24</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-07-07</td>\n",
       "      <td>2020-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>2009-05-12</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-06-10</td>\n",
       "      <td>2020-07-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>2016-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-11-17</td>\n",
       "      <td>2020-05-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>2009-10-27</td>\n",
       "      <td>1</td>\n",
       "      <td>2006-08-28</td>\n",
       "      <td>2019-08-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date                                             \n",
       "           count unique        top freq      first       last\n",
       "result_spe                                                   \n",
       "0             49     49 2020-05-19    1 2006-01-06 2020-05-19\n",
       "1             60     60 2006-10-09    1 2006-06-19 2020-03-15\n",
       "2             51     51 2006-01-08    1 2006-01-08 2020-07-15\n",
       "3             48     48 2015-07-19    1 2006-10-30 2020-04-23\n",
       "4             40     40 2017-12-26    1 2006-04-07 2020-08-01\n",
       "...          ...    ...        ...  ...        ...        ...\n",
       "95            41     41 2010-07-16    1 2006-06-29 2020-05-15\n",
       "96            47     47 2016-03-24    1 2006-07-07 2020-11-02\n",
       "97            50     50 2009-05-12    1 2006-06-10 2020-07-24\n",
       "98            60     60 2016-12-29    1 2006-11-17 2020-05-22\n",
       "99            48     48 2009-10-27    1 2006-08-28 2019-08-02\n",
       "\n",
       "[100 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.groupby('result_spe').describe()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "result_spe\n",
       "0      0\n",
       "1      1\n",
       "2      2\n",
       "3      3\n",
       "4      4\n",
       "      ..\n",
       "95    95\n",
       "96    96\n",
       "97    97\n",
       "98    98\n",
       "99    99\n",
       "Name: result_spe, Length: 100, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.groupby('result_spe').result_spe.max()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "df = data.groupby('result_spe').result_spe.count()\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.title(\"Count\")\n",
    "tmp = dict(df)\n",
    "#tmp.values()\n",
    "plt.ylabel(\"Frequence\")\n",
    "plt.bar(tmp.keys(),tmp.values(),color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f334c0e908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "sns.distplot(df)\n",
    "#sns.jointplot(x=\"x\",y=\"y\",data = tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1f335568888>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr=[]\n",
    "for x in tmp:\n",
    "    arr.append({x,tmp.get(x)})\n",
    "ntmp = pd.DataFrame(arr,columns=[\"x\",\"y\"])\n",
    "sns.jointplot(x=\"x\",y=\"y\",data = ntmp)\n",
    "#sns.jointplot(x=tmp.keys(),y=tmp.values(),data=tmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>41</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>48</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     x   y\n",
       "0    0  49\n",
       "1    1  60\n",
       "2    2  51\n",
       "3   48   3\n",
       "4   40   4\n",
       "..  ..  ..\n",
       "95  41  95\n",
       "96  96  47\n",
       "97  97  50\n",
       "98  98  60\n",
       "99  48  99\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ntmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f334b971c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"x\", y=\"y\", data=ntmp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 26,\n",
       " 1: 216,\n",
       " 2: 75,\n",
       " 3: 143,\n",
       " 4: 212,\n",
       " 5: 170,\n",
       " 6: 9,\n",
       " 7: 147,\n",
       " 8: 39,\n",
       " 9: 138,\n",
       " 10: 80,\n",
       " 11: 72,\n",
       " 12: 9,\n",
       " 13: 36,\n",
       " 14: 16,\n",
       " 15: 217,\n",
       " 16: 21,\n",
       " 17: 150,\n",
       " 18: 8,\n",
       " 19: 384,\n",
       " 20: 499,\n",
       " 21: 241,\n",
       " 22: 85,\n",
       " 23: 352,\n",
       " 24: 387,\n",
       " 25: 56,\n",
       " 26: 204,\n",
       " 27: 11,\n",
       " 28: 31,\n",
       " 29: 179,\n",
       " 30: 51,\n",
       " 31: 25,\n",
       " 32: 130,\n",
       " 33: 4,\n",
       " 34: 84,\n",
       " 35: 168,\n",
       " 36: 23,\n",
       " 37: 181,\n",
       " 38: 169,\n",
       " 39: 109}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data.loc[data.result_spe==30].date\n",
    "tmp = dict(df)\n",
    "arr = list(tmp.values())\n",
    "arr.sort()\n",
    "parse = dict()\n",
    "for i in range(len(arr)-1):\n",
    "    parse[i] = (arr[i+1]-arr[i]).days\n",
    "    #print(arr[i+1]-arr[i])\n",
    "parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(parse.keys(),parse.values(),color = 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}