cities-and-towns-of-germany.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/rrb8695/cities-and-towns-of-germany?scriptVersionId=168270523\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"29787279","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.execute_input":"2024-03-22T09:48:01.050349Z","iopub.status.busy":"2024-03-22T09:48:01.049505Z","iopub.status.idle":"2024-03-22T09:48:02.004623Z","shell.execute_reply":"2024-03-22T09:48:02.003456Z"},"papermill":{"duration":0.979815,"end_time":"2024-03-22T09:48:02.007553","exception":false,"start_time":"2024-03-22T09:48:01.027738","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["/kaggle/input/deutschland-cities/Deutschland_Cities.csv\n"]}],"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"]},{"cell_type":"code","execution_count":2,"id":"e87baf9b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.052334Z","iopub.status.busy":"2024-03-22T09:48:02.050829Z","iopub.status.idle":"2024-03-22T09:48:02.122723Z","shell.execute_reply":"2024-03-22T09:48:02.121463Z"},"papermill":{"duration":0.098881,"end_time":"2024-03-22T09:48:02.126232","exception":false,"start_time":"2024-03-22T09:48:02.027351","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</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","    </tr>\n","    <tr>\n","      <th>607</th>\n","      <td>Rotenburg</td>\n","      <td>53.0851</td>\n","      <td>9.3879</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Lower Saxony</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>608</th>\n","      <td>Karlstadt</td>\n","      <td>49.9603</td>\n","      <td>9.7724</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>609</th>\n","      <td>Neustadt</td>\n","      <td>49.5656</td>\n","      <td>8.8437</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>610</th>\n","      <td>Homberg</td>\n","      <td>51.0299</td>\n","      <td>9.4026</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>611</th>\n","      <td>Garmisch-Partenkirchen</td>\n","      <td>47.4985</td>\n","      <td>11.1044</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>612 rows × 9 columns</p>\n","</div>"],"text/plain":["                       city      lat      lng  country iso2  \\\n","0                    Berlin  52.5167  13.3833  Germany   DE   \n","1                   Hamburg  53.5500  10.0000  Germany   DE   \n","2                    Munich  48.1372  11.5755  Germany   DE   \n","3                   Cologne  50.9422   6.9578  Germany   DE   \n","4                 Frankfurt  50.1136   8.6797  Germany   DE   \n","..                      ...      ...      ...      ...  ...   \n","607               Rotenburg  53.0851   9.3879  Germany   DE   \n","608               Karlstadt  49.9603   9.7724  Germany   DE   \n","609                Neustadt  49.5656   8.8437  Germany   DE   \n","610                 Homberg  51.0299   9.4026  Germany   DE   \n","611  Garmisch-Partenkirchen  47.4985  11.1044  Germany   DE   \n","\n","                 admin_name  capital  population  population_proper  \n","0                    Berlin  primary   3644826.0          3644826.0  \n","1                   Hamburg    admin   1841179.0          1841179.0  \n","2                   Bavaria    admin   1471508.0          1471508.0  \n","3    North Rhine-Westphalia      NaN   1085664.0          1085664.0  \n","4                     Hesse    minor    753056.0           753056.0  \n","..                      ...      ...         ...                ...  \n","607            Lower Saxony    minor         NaN                NaN  \n","608                 Bavaria    minor         NaN                NaN  \n","609                   Hesse    minor         NaN                NaN  \n","610                   Hesse    minor         NaN                NaN  \n","611                 Bavaria    minor         NaN                NaN  \n","\n","[612 rows x 9 columns]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["df = pd.read_csv(\"/kaggle/input/deutschland-cities/Deutschland_Cities.csv\")\n","df"]},{"cell_type":"code","execution_count":3,"id":"fa478e9b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.169095Z","iopub.status.busy":"2024-03-22T09:48:02.168654Z","iopub.status.idle":"2024-03-22T09:48:02.176836Z","shell.execute_reply":"2024-03-22T09:48:02.175538Z"},"papermill":{"duration":0.032992,"end_time":"2024-03-22T09:48:02.17942","exception":false,"start_time":"2024-03-22T09:48:02.146428","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["pandas.core.frame.DataFrame"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["type(df)"]},{"cell_type":"code","execution_count":4,"id":"25ebf907","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.232025Z","iopub.status.busy":"2024-03-22T09:48:02.230806Z","iopub.status.idle":"2024-03-22T09:48:02.25268Z","shell.execute_reply":"2024-03-22T09:48:02.250755Z"},"papermill":{"duration":0.052946,"end_time":"2024-03-22T09:48:02.257049","exception":false,"start_time":"2024-03-22T09:48:02.204103","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['T',\n"," 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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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        city      lat      lng  country iso2              admin_name  capital  \\\n","0     Berlin  52.5167  13.3833  Germany   DE                  Berlin  primary   \n","1    Hamburg  53.5500  10.0000  Germany   DE                 Hamburg    admin   \n","2     Munich  48.1372  11.5755  Germany   DE                 Bavaria    admin   \n","3    Cologne  50.9422   6.9578  Germany   DE  North Rhine-Westphalia      NaN   \n","4  Frankfurt  50.1136   8.6797  Germany   DE                   Hesse    minor   \n","\n","   population  population_proper  \n","0   3644826.0          3644826.0  \n","1   1841179.0          1841179.0  \n","2   1471508.0          1471508.0  \n","3   1085664.0          1085664.0  \n","4    753056.0           753056.0  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":6,"id":"0ee7d983","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.385286Z","iopub.status.busy":"2024-03-22T09:48:02.384508Z","iopub.status.idle":"2024-03-22T09:48:02.40094Z","shell.execute_reply":"2024-03-22T09:48:02.400094Z"},"papermill":{"duration":0.041948,"end_time":"2024-03-22T09:48:02.404042","exception":false,"start_time":"2024-03-22T09:48:02.362094","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>607</th>\n","      <td>Rotenburg</td>\n","      <td>53.0851</td>\n","      <td>9.3879</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Lower Saxony</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>608</th>\n","      <td>Karlstadt</td>\n","      <td>49.9603</td>\n","      <td>9.7724</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>609</th>\n","      <td>Neustadt</td>\n","      <td>49.5656</td>\n","      <td>8.8437</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>610</th>\n","      <td>Homberg</td>\n","      <td>51.0299</td>\n","      <td>9.4026</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>611</th>\n","      <td>Garmisch-Partenkirchen</td>\n","      <td>47.4985</td>\n","      <td>11.1044</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                       city      lat      lng  country iso2    admin_name  \\\n","607               Rotenburg  53.0851   9.3879  Germany   DE  Lower Saxony   \n","608               Karlstadt  49.9603   9.7724  Germany   DE       Bavaria   \n","609                Neustadt  49.5656   8.8437  Germany   DE         Hesse   \n","610                 Homberg  51.0299   9.4026  Germany   DE         Hesse   \n","611  Garmisch-Partenkirchen  47.4985  11.1044  Germany   DE       Bavaria   \n","\n","    capital  population  population_proper  \n","607   minor         NaN                NaN  \n","608   minor         NaN                NaN  \n","609   minor         NaN                NaN  \n","610   minor         NaN                NaN  \n","611   minor         NaN                NaN  "]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["df.tail()"]},{"cell_type":"code","execution_count":7,"id":"4dd8239f","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.454542Z","iopub.status.busy":"2024-03-22T09:48:02.453448Z","iopub.status.idle":"2024-03-22T09:48:02.461941Z","shell.execute_reply":"2024-03-22T09:48:02.460756Z"},"papermill":{"duration":0.038256,"end_time":"2024-03-22T09:48:02.464831","exception":false,"start_time":"2024-03-22T09:48:02.426575","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["(612, 9)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["df.shape"]},{"cell_type":"code","execution_count":8,"id":"f77374a0","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.515374Z","iopub.status.busy":"2024-03-22T09:48:02.513734Z","iopub.status.idle":"2024-03-22T09:48:02.522506Z","shell.execute_reply":"2024-03-22T09:48:02.521296Z"},"papermill":{"duration":0.036939,"end_time":"2024-03-22T09:48:02.525023","exception":false,"start_time":"2024-03-22T09:48:02.488084","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["Index(['city', 'lat', 'lng', 'country', 'iso2', 'admin_name', 'capital',\n","       'population', 'population_proper'],\n","      dtype='object')"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.columns"]},{"cell_type":"code","execution_count":9,"id":"1ebb0f16","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.569987Z","iopub.status.busy":"2024-03-22T09:48:02.569522Z","iopub.status.idle":"2024-03-22T09:48:02.577242Z","shell.execute_reply":"2024-03-22T09:48:02.576106Z"},"papermill":{"duration":0.03304,"end_time":"2024-03-22T09:48:02.579731","exception":false,"start_time":"2024-03-22T09:48:02.546691","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["9"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["len(df.columns)"]},{"cell_type":"code","execution_count":10,"id":"946c577f","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.624902Z","iopub.status.busy":"2024-03-22T09:48:02.624456Z","iopub.status.idle":"2024-03-22T09:48:02.651928Z","shell.execute_reply":"2024-03-22T09:48:02.650659Z"},"papermill":{"duration":0.052932,"end_time":"2024-03-22T09:48:02.654274","exception":false,"start_time":"2024-03-22T09:48:02.601342","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 612 entries, 0 to 611\n","Data columns (total 9 columns):\n"," #   Column             Non-Null Count  Dtype  \n","---  ------             --------------  -----  \n"," 0   city               612 non-null    object \n"," 1   lat                612 non-null    float64\n"," 2   lng                612 non-null    float64\n"," 3   country            612 non-null    object \n"," 4   iso2               612 non-null    object \n"," 5   admin_name         612 non-null    object \n"," 6   capital            358 non-null    object \n"," 7   population         602 non-null    float64\n"," 8   population_proper  602 non-null    float64\n","dtypes: float64(4), object(5)\n","memory usage: 43.2+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":11,"id":"978f85b8","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.70026Z","iopub.status.busy":"2024-03-22T09:48:02.699887Z","iopub.status.idle":"2024-03-22T09:48:02.727867Z","shell.execute_reply":"2024-03-22T09:48:02.726709Z"},"papermill":{"duration":0.054335,"end_time":"2024-03-22T09:48:02.730492","exception":false,"start_time":"2024-03-22T09:48:02.676157","status":"completed"},"tags":[]},"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>lat</th>\n","      <th>lng</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>612.000000</td>\n","      <td>612.000000</td>\n","      <td>6.020000e+02</td>\n","      <td>6.020000e+02</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>50.788434</td>\n","      <td>9.350428</td>\n","      <td>7.681314e+04</td>\n","      <td>7.637644e+04</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.623459</td>\n","      <td>2.063536</td>\n","      <td>1.994928e+05</td>\n","      <td>1.984961e+05</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>47.498500</td>\n","      <td>6.083800</td>\n","      <td>5.312000e+03</td>\n","      <td>5.312000e+03</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>49.480400</td>\n","      <td>7.642525</td>\n","      <td>2.608500e+04</td>\n","      <td>2.608500e+04</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>50.976700</td>\n","      <td>8.910500</td>\n","      <td>3.620850e+04</td>\n","      <td>3.620850e+04</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>51.849600</td>\n","      <td>10.874375</td>\n","      <td>5.887200e+04</td>\n","      <td>5.868850e+04</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>54.781900</td>\n","      <td>14.987200</td>\n","      <td>3.644826e+06</td>\n","      <td>3.644826e+06</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              lat         lng    population  population_proper\n","count  612.000000  612.000000  6.020000e+02       6.020000e+02\n","mean    50.788434    9.350428  7.681314e+04       7.637644e+04\n","std      1.623459    2.063536  1.994928e+05       1.984961e+05\n","min     47.498500    6.083800  5.312000e+03       5.312000e+03\n","25%     49.480400    7.642525  2.608500e+04       2.608500e+04\n","50%     50.976700    8.910500  3.620850e+04       3.620850e+04\n","75%     51.849600   10.874375  5.887200e+04       5.868850e+04\n","max     54.781900   14.987200  3.644826e+06       3.644826e+06"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"markdown","id":"1f7a24bf","metadata":{"papermill":{"duration":0.021893,"end_time":"2024-03-22T09:48:02.839046","exception":false,"start_time":"2024-03-22T09:48:02.817153","status":"completed"},"tags":[]},"source":["Listing down all columns"]},{"cell_type":"code","execution_count":12,"id":"a30e46fa","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:02.885375Z","iopub.status.busy":"2024-03-22T09:48:02.884974Z","iopub.status.idle":"2024-03-22T09:48:02.892496Z","shell.execute_reply":"2024-03-22T09:48:02.891147Z"},"papermill":{"duration":0.033749,"end_time":"2024-03-22T09:48:02.895062","exception":false,"start_time":"2024-03-22T09:48:02.861313","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['city',\n"," 'lat',\n"," 'lng',\n"," 'country',\n"," 'iso2',\n"," 'admin_name',\n"," 'capital',\n"," 'population',\n"," 'population_proper']"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["all_columns = list(df.columns)\n","all_columns \n"]},{"cell_type":"code","execution_count":null,"id":"e9aeb360","metadata":{"execution":{"iopub.execute_input":"2024-02-04T13:39:21.620228Z","iopub.status.busy":"2024-02-04T13:39:21.619874Z","iopub.status.idle":"2024-02-04T13:39:21.627218Z","shell.execute_reply":"2024-02-04T13:39:21.625895Z","shell.execute_reply.started":"2024-02-04T13:39:21.620201Z"},"papermill":{"duration":0.022022,"end_time":"2024-03-22T09:48:02.939556","exception":false,"start_time":"2024-03-22T09:48:02.917534","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"markdown","id":"60981498","metadata":{"papermill":{"duration":0.021826,"end_time":"2024-03-22T09:48:02.983741","exception":false,"start_time":"2024-03-22T09:48:02.961915","status":"completed"},"tags":[]},"source":["What is the total population in the dataset?"]},{"cell_type":"code","execution_count":13,"id":"13f0a448","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.030175Z","iopub.status.busy":"2024-03-22T09:48:03.029759Z","iopub.status.idle":"2024-03-22T09:48:03.048516Z","shell.execute_reply":"2024-03-22T09:48:03.047255Z"},"papermill":{"duration":0.045156,"end_time":"2024-03-22T09:48:03.051233","exception":false,"start_time":"2024-03-22T09:48:03.006077","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        city      lat      lng  country iso2              admin_name  capital  \\\n","0     Berlin  52.5167  13.3833  Germany   DE                  Berlin  primary   \n","1    Hamburg  53.5500  10.0000  Germany   DE                 Hamburg    admin   \n","2     Munich  48.1372  11.5755  Germany   DE                 Bavaria    admin   \n","3    Cologne  50.9422   6.9578  Germany   DE  North Rhine-Westphalia      NaN   \n","4  Frankfurt  50.1136   8.6797  Germany   DE                   Hesse    minor   \n","\n","   population  population_proper  \n","0   3644826.0          3644826.0  \n","1   1841179.0          1841179.0  \n","2   1471508.0          1471508.0  \n","3   1085664.0          1085664.0  \n","4    753056.0           753056.0  "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":14,"id":"dc5ab8fa","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.09876Z","iopub.status.busy":"2024-03-22T09:48:03.098387Z","iopub.status.idle":"2024-03-22T09:48:03.108555Z","shell.execute_reply":"2024-03-22T09:48:03.107353Z"},"papermill":{"duration":0.037102,"end_time":"2024-03-22T09:48:03.11108","exception":false,"start_time":"2024-03-22T09:48:03.073978","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0      3644826.0\n","1      1841179.0\n","2      1471508.0\n","3      1085664.0\n","4       753056.0\n","         ...    \n","607          NaN\n","608          NaN\n","609          NaN\n","610          NaN\n","611          NaN\n","Name: population, Length: 612, dtype: float64"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["population_column = df[\"population\"]\n","population_column"]},{"cell_type":"code","execution_count":15,"id":"3443d6e3","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.160181Z","iopub.status.busy":"2024-03-22T09:48:03.159734Z","iopub.status.idle":"2024-03-22T09:48:03.166222Z","shell.execute_reply":"2024-03-22T09:48:03.165441Z"},"papermill":{"duration":0.033316,"end_time":"2024-03-22T09:48:03.168445","exception":false,"start_time":"2024-03-22T09:48:03.135129","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["pandas.core.series.Series"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["type(population_column)"]},{"cell_type":"code","execution_count":16,"id":"534b47ce","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.216748Z","iopub.status.busy":"2024-03-22T09:48:03.216346Z","iopub.status.idle":"2024-03-22T09:48:03.233157Z","shell.execute_reply":"2024-03-22T09:48:03.231817Z"},"papermill":{"duration":0.044226,"end_time":"2024-03-22T09:48:03.236044","exception":false,"start_time":"2024-03-22T09:48:03.191818","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['T',\n"," '_AXIS_LEN',\n"," '_AXIS_ORDERS',\n"," '_AXIS_TO_AXIS_NUMBER',\n"," '_HANDLED_TYPES',\n"," '__abs__',\n"," '__add__',\n"," '__and__',\n"," '__annotations__',\n"," '__array__',\n"," '__array_priority__',\n"," '__array_ufunc__',\n"," '__bool__',\n"," '__class__',\n"," '__contains__',\n"," '__copy__',\n"," '__deepcopy__',\n"," '__delattr__',\n"," '__delitem__',\n"," '__dict__',\n"," '__dir__',\n"," '__divmod__',\n"," '__doc__',\n"," '__eq__',\n"," '__finalize__',\n"," '__float__',\n"," '__floordiv__',\n"," '__format__',\n"," '__ge__',\n"," '__getattr__',\n"," '__getattribute__',\n"," '__getitem__',\n"," '__getstate__',\n"," '__gt__',\n"," '__hash__',\n"," '__iadd__',\n"," '__iand__',\n"," '__ifloordiv__',\n"," '__imod__',\n"," '__imul__',\n"," '__init__',\n"," '__init_subclass__',\n"," '__int__',\n"," '__invert__',\n"," '__ior__',\n"," '__ipow__',\n"," '__isub__',\n"," '__iter__',\n"," '__itruediv__',\n"," '__ixor__',\n"," '__le__',\n"," '__len__',\n"," '__lt__',\n"," '__matmul__',\n"," 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'lt',\n"," 'map',\n"," 'mask',\n"," 'max',\n"," 'mean',\n"," 'median',\n"," 'memory_usage',\n"," 'min',\n"," 'mod',\n"," 'mode',\n"," 'mul',\n"," 'multiply',\n"," 'name',\n"," 'nbytes',\n"," 'ndim',\n"," 'ne',\n"," 'nlargest',\n"," 'notna',\n"," 'notnull',\n"," 'nsmallest',\n"," 'nunique',\n"," 'pad',\n"," 'pct_change',\n"," 'pipe',\n"," 'plot',\n"," 'pop',\n"," 'pow',\n"," 'prod',\n"," 'product',\n"," 'quantile',\n"," 'radd',\n"," 'rank',\n"," 'ravel',\n"," 'rdiv',\n"," 'rdivmod',\n"," 'reindex',\n"," 'reindex_like',\n"," 'rename',\n"," 'rename_axis',\n"," 'reorder_levels',\n"," 'repeat',\n"," 'replace',\n"," 'resample',\n"," 'reset_index',\n"," 'rfloordiv',\n"," 'rmod',\n"," 'rmul',\n"," 'rolling',\n"," 'round',\n"," 'rpow',\n"," 'rsub',\n"," 'rtruediv',\n"," 'sample',\n"," 'searchsorted',\n"," 'sem',\n"," 'set_axis',\n"," 'set_flags',\n"," 'shape',\n"," 'shift',\n"," 'size',\n"," 'skew',\n"," 'sort_index',\n"," 'sort_values',\n"," 'squeeze',\n"," 'std',\n"," 'sub',\n"," 'subtract',\n"," 'sum',\n"," 'swapaxes',\n"," 'swaplevel',\n"," 'tail',\n"," 'take',\n"," 'to_clipboard',\n"," 'to_csv',\n"," 'to_dict',\n"," 'to_excel',\n"," 'to_frame',\n"," 'to_hdf',\n"," 'to_json',\n"," 'to_latex',\n"," 'to_list',\n"," 'to_markdown',\n"," 'to_numpy',\n"," 'to_period',\n"," 'to_pickle',\n"," 'to_sql',\n"," 'to_string',\n"," 'to_timestamp',\n"," 'to_xarray',\n"," 'transform',\n"," 'transpose',\n"," 'truediv',\n"," 'truncate',\n"," 'tz_convert',\n"," 'tz_localize',\n"," 'unique',\n"," 'unstack',\n"," 'update',\n"," 'value_counts',\n"," 'values',\n"," 'var',\n"," 'view',\n"," 'where',\n"," 'xs']"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["dir(population_column)"]},{"cell_type":"code","execution_count":17,"id":"1f82bc64","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.285003Z","iopub.status.busy":"2024-03-22T09:48:03.284594Z","iopub.status.idle":"2024-03-22T09:48:03.29204Z","shell.execute_reply":"2024-03-22T09:48:03.290955Z"},"papermill":{"duration":0.034984,"end_time":"2024-03-22T09:48:03.294529","exception":false,"start_time":"2024-03-22T09:48:03.259545","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["46241511.0"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["population_column.sum()"]},{"cell_type":"code","execution_count":18,"id":"81c17896","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.344509Z","iopub.status.busy":"2024-03-22T09:48:03.344131Z","iopub.status.idle":"2024-03-22T09:48:03.352081Z","shell.execute_reply":"2024-03-22T09:48:03.350919Z"},"papermill":{"duration":0.035521,"end_time":"2024-03-22T09:48:03.354301","exception":false,"start_time":"2024-03-22T09:48:03.31878","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["46241511.0"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["# What is the total population in the dataset?\n","df[\"population\"].sum()"]},{"cell_type":"code","execution_count":null,"id":"b05e29cc","metadata":{"papermill":{"duration":0.024966,"end_time":"2024-03-22T09:48:03.403209","exception":false,"start_time":"2024-03-22T09:48:03.378243","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":19,"id":"a283a854","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.452963Z","iopub.status.busy":"2024-03-22T09:48:03.452509Z","iopub.status.idle":"2024-03-22T09:48:03.46978Z","shell.execute_reply":"2024-03-22T09:48:03.468604Z"},"papermill":{"duration":0.045169,"end_time":"2024-03-22T09:48:03.472295","exception":false,"start_time":"2024-03-22T09:48:03.427126","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     city      lat      lng  country iso2 admin_name  capital  population  \\\n","0  Berlin  52.5167  13.3833  Germany   DE     Berlin  primary   3644826.0   \n","\n","   population_proper  \n","0          3644826.0  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["\n","# Which city has the highest population?\n","df[df[\"population\"] == df[\"population\"].max()]"]},{"cell_type":"code","execution_count":20,"id":"6eed0187","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.522598Z","iopub.status.busy":"2024-03-22T09:48:03.521823Z","iopub.status.idle":"2024-03-22T09:48:03.52962Z","shell.execute_reply":"2024-03-22T09:48:03.528496Z"},"papermill":{"duration":0.035664,"end_time":"2024-03-22T09:48:03.532124","exception":false,"start_time":"2024-03-22T09:48:03.49646","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["3644826.0"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["max_value = df[\"population\"].max()\n","max_value"]},{"cell_type":"code","execution_count":21,"id":"b811185c","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.584083Z","iopub.status.busy":"2024-03-22T09:48:03.583671Z","iopub.status.idle":"2024-03-22T09:48:03.599339Z","shell.execute_reply":"2024-03-22T09:48:03.597982Z"},"papermill":{"duration":0.045081,"end_time":"2024-03-22T09:48:03.601862","exception":false,"start_time":"2024-03-22T09:48:03.556781","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     city      lat      lng  country iso2 admin_name  capital  population  \\\n","0  Berlin  52.5167  13.3833  Germany   DE     Berlin  primary   3644826.0   \n","\n","   population_proper  \n","0          3644826.0  "]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["df[df[\"population\"] == max_value]"]},{"cell_type":"code","execution_count":22,"id":"57eca773","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.653273Z","iopub.status.busy":"2024-03-22T09:48:03.652822Z","iopub.status.idle":"2024-03-22T09:48:03.669764Z","shell.execute_reply":"2024-03-22T09:48:03.668892Z"},"papermill":{"duration":0.045662,"end_time":"2024-03-22T09:48:03.672258","exception":false,"start_time":"2024-03-22T09:48:03.626596","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>601</th>\n","      <td>Cochem</td>\n","      <td>50.1469</td>\n","      <td>7.1667</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Rhineland-Palatinate</td>\n","      <td>minor</td>\n","      <td>5312.0</td>\n","      <td>5312.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       city      lat     lng  country iso2            admin_name capital  \\\n","601  Cochem  50.1469  7.1667  Germany   DE  Rhineland-Palatinate   minor   \n","\n","     population  population_proper  \n","601      5312.0             5312.0  "]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["\n","# Which city has the lowest population?\n","df[df[\"population\"] == df[\"population\"].min()]\n"]},{"cell_type":"code","execution_count":null,"id":"f3a11588","metadata":{"papermill":{"duration":0.025623,"end_time":"2024-03-22T09:48:03.723307","exception":false,"start_time":"2024-03-22T09:48:03.697684","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":23,"id":"41139f5b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.774867Z","iopub.status.busy":"2024-03-22T09:48:03.774484Z","iopub.status.idle":"2024-03-22T09:48:03.781976Z","shell.execute_reply":"2024-03-22T09:48:03.780915Z"},"papermill":{"duration":0.036432,"end_time":"2024-03-22T09:48:03.784288","exception":false,"start_time":"2024-03-22T09:48:03.747856","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["76813.14119601328"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["\n","# What is the average population of all the cities?\n","df[\"population\"].mean()"]},{"cell_type":"code","execution_count":24,"id":"6909ffd7","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.836932Z","iopub.status.busy":"2024-03-22T09:48:03.836491Z","iopub.status.idle":"2024-03-22T09:48:03.845137Z","shell.execute_reply":"2024-03-22T09:48:03.843976Z"},"papermill":{"duration":0.038191,"end_time":"2024-03-22T09:48:03.847889","exception":false,"start_time":"2024-03-22T09:48:03.809698","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["76813.14119601328"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["df[\"population\"].mean()"]},{"cell_type":"code","execution_count":25,"id":"5b7bed93","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.899938Z","iopub.status.busy":"2024-03-22T09:48:03.899516Z","iopub.status.idle":"2024-03-22T09:48:03.906563Z","shell.execute_reply":"2024-03-22T09:48:03.905738Z"},"papermill":{"duration":0.035965,"end_time":"2024-03-22T09:48:03.908955","exception":false,"start_time":"2024-03-22T09:48:03.87299","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['primary', 'admin', nan, 'minor']"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["# How many capital cities are in the dataset?\n","list(df[\"capital\"].unique())"]},{"cell_type":"code","execution_count":26,"id":"5642ce72","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:03.962742Z","iopub.status.busy":"2024-03-22T09:48:03.962341Z","iopub.status.idle":"2024-03-22T09:48:03.972137Z","shell.execute_reply":"2024-03-22T09:48:03.971029Z"},"papermill":{"duration":0.038927,"end_time":"2024-03-22T09:48:03.974432","exception":false,"start_time":"2024-03-22T09:48:03.935505","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["capital\n","minor      342\n","admin       15\n","primary      1\n","Name: count, dtype: int64"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["df[\"capital\"].value_counts()"]},{"cell_type":"code","execution_count":27,"id":"63b72bf0","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.027946Z","iopub.status.busy":"2024-03-22T09:48:04.027504Z","iopub.status.idle":"2024-03-22T09:48:04.052614Z","shell.execute_reply":"2024-03-22T09:48:04.051435Z"},"papermill":{"duration":0.055394,"end_time":"2024-03-22T09:48:04.055428","exception":false,"start_time":"2024-03-22T09:48:04.000034","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</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","    </tr>\n","    <tr>\n","      <th>607</th>\n","      <td>Rotenburg</td>\n","      <td>53.0851</td>\n","      <td>9.3879</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Lower Saxony</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>608</th>\n","      <td>Karlstadt</td>\n","      <td>49.9603</td>\n","      <td>9.7724</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>609</th>\n","      <td>Neustadt</td>\n","      <td>49.5656</td>\n","      <td>8.8437</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>610</th>\n","      <td>Homberg</td>\n","      <td>51.0299</td>\n","      <td>9.4026</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>611</th>\n","      <td>Garmisch-Partenkirchen</td>\n","      <td>47.4985</td>\n","      <td>11.1044</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>612 rows × 9 columns</p>\n","</div>"],"text/plain":["                       city      lat      lng  country iso2  \\\n","0                    Berlin  52.5167  13.3833  Germany   DE   \n","1                   Hamburg  53.5500  10.0000  Germany   DE   \n","2                    Munich  48.1372  11.5755  Germany   DE   \n","3                   Cologne  50.9422   6.9578  Germany   DE   \n","4                 Frankfurt  50.1136   8.6797  Germany   DE   \n","..                      ...      ...      ...      ...  ...   \n","607               Rotenburg  53.0851   9.3879  Germany   DE   \n","608               Karlstadt  49.9603   9.7724  Germany   DE   \n","609                Neustadt  49.5656   8.8437  Germany   DE   \n","610                 Homberg  51.0299   9.4026  Germany   DE   \n","611  Garmisch-Partenkirchen  47.4985  11.1044  Germany   DE   \n","\n","                 admin_name  capital  population  population_proper  \n","0                    Berlin  primary   3644826.0          3644826.0  \n","1                   Hamburg    admin   1841179.0          1841179.0  \n","2                   Bavaria    admin   1471508.0          1471508.0  \n","3    North Rhine-Westphalia      NaN   1085664.0          1085664.0  \n","4                     Hesse    minor    753056.0           753056.0  \n","..                      ...      ...         ...                ...  \n","607            Lower Saxony    minor         NaN                NaN  \n","608                 Bavaria    minor         NaN                NaN  \n","609                   Hesse    minor         NaN                NaN  \n","610                   Hesse    minor         NaN                NaN  \n","611                 Bavaria    minor         NaN                NaN  \n","\n","[612 rows x 9 columns]"]},"execution_count":27,"metadata":{},"output_type":"execute_result"}],"source":["df"]},{"cell_type":"markdown","id":"37987792","metadata":{"papermill":{"duration":0.0273,"end_time":"2024-03-22T09:48:04.109125","exception":false,"start_time":"2024-03-22T09:48:04.081825","status":"completed"},"tags":[]},"source":["Give me the administrative states with its capitals."]},{"cell_type":"code","execution_count":28,"id":"22b243ee","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.162191Z","iopub.status.busy":"2024-03-22T09:48:04.161795Z","iopub.status.idle":"2024-03-22T09:48:04.178471Z","shell.execute_reply":"2024-03-22T09:48:04.177296Z"},"papermill":{"duration":0.045965,"end_time":"2024-03-22T09:48:04.180992","exception":false,"start_time":"2024-03-22T09:48:04.135027","status":"completed"},"tags":[]},"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>admin_name</th>\n","      <th>city</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>Berlin</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>Hamburg</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Bavaria</td>\n","      <td>Munich</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Bremen</td>\n","      <td>Bremen</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Baden-Württemberg</td>\n","      <td>Stuttgart</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>North Rhine-Westphalia</td>\n","      <td>Düsseldorf</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Saxony</td>\n","      <td>Dresden</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Lower Saxony</td>\n","      <td>Hannover</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>Hesse</td>\n","      <td>Wiesbaden</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>Schleswig-Holstein</td>\n","      <td>Kiel</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>Saxony-Anhalt</td>\n","      <td>Magdeburg</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>Thuringia</td>\n","      <td>Erfurt</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>Rhineland-Palatinate</td>\n","      <td>Mainz</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>Saarland</td>\n","      <td>Saarbrücken</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>Brandenburg</td>\n","      <td>Potsdam</td>\n","    </tr>\n","    <tr>\n","      <th>83</th>\n","      <td>Mecklenburg-Western Pomerania</td>\n","      <td>Schwerin</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                       admin_name         city\n","0                          Berlin       Berlin\n","1                         Hamburg      Hamburg\n","2                         Bavaria       Munich\n","5                          Bremen       Bremen\n","6               Baden-Württemberg    Stuttgart\n","7          North Rhine-Westphalia   Düsseldorf\n","10                         Saxony      Dresden\n","12                   Lower Saxony     Hannover\n","23                          Hesse    Wiesbaden\n","27             Schleswig-Holstein         Kiel\n","31                  Saxony-Anhalt    Magdeburg\n","37                      Thuringia       Erfurt\n","40           Rhineland-Palatinate        Mainz\n","41                       Saarland  Saarbrücken\n","43                    Brandenburg      Potsdam\n","83  Mecklenburg-Western Pomerania     Schwerin"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["admin_capital = df[(df[\"capital\"] == \"admin\") | (df[\"capital\"] == \"primary\")]\n","admin_capital[[\"admin_name\", \"city\"]]\n"]},{"cell_type":"code","execution_count":29,"id":"a8f37993","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.236292Z","iopub.status.busy":"2024-03-22T09:48:04.235178Z","iopub.status.idle":"2024-03-22T09:48:04.241694Z","shell.execute_reply":"2024-03-22T09:48:04.240766Z"},"papermill":{"duration":0.036536,"end_time":"2024-03-22T09:48:04.244129","exception":false,"start_time":"2024-03-22T09:48:04.207593","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["The number of total cities in dataframe : 607\n"]}],"source":["print(\"The number of total cities in dataframe :\",len(list(df[\"city\"].unique())))"]},{"cell_type":"code","execution_count":30,"id":"228ce71e","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.298997Z","iopub.status.busy":"2024-03-22T09:48:04.297961Z","iopub.status.idle":"2024-03-22T09:48:04.306327Z","shell.execute_reply":"2024-03-22T09:48:04.305046Z"},"papermill":{"duration":0.038651,"end_time":"2024-03-22T09:48:04.308963","exception":false,"start_time":"2024-03-22T09:48:04.270312","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["532871.9333333333"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["\n","# What is the average population of capital cities?\n","df[df[\"capital\"] == \"admin\"][\"population\"].mean()"]},{"cell_type":"markdown","id":"c870c4c5","metadata":{"papermill":{"duration":0.025937,"end_time":"2024-03-22T09:48:04.361287","exception":false,"start_time":"2024-03-22T09:48:04.33535","status":"completed"},"tags":[]},"source":["Give me the list of those cities whose population is greater than mean population in the dataset."]},{"cell_type":"code","execution_count":31,"id":"3ad88a06","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.416985Z","iopub.status.busy":"2024-03-22T09:48:04.41582Z","iopub.status.idle":"2024-03-22T09:48:04.426699Z","shell.execute_reply":"2024-03-22T09:48:04.425535Z"},"papermill":{"duration":0.041569,"end_time":"2024-03-22T09:48:04.42916","exception":false,"start_time":"2024-03-22T09:48:04.387591","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0      3644826.0\n","1      1841179.0\n","2      1471508.0\n","3      1085664.0\n","4       753056.0\n","         ...    \n","607          NaN\n","608          NaN\n","609          NaN\n","610          NaN\n","611          NaN\n","Name: population, Length: 612, dtype: float64"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["df[\"population\"]"]},{"cell_type":"code","execution_count":32,"id":"8129c54b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.485328Z","iopub.status.busy":"2024-03-22T09:48:04.484002Z","iopub.status.idle":"2024-03-22T09:48:04.492318Z","shell.execute_reply":"2024-03-22T09:48:04.490717Z"},"papermill":{"duration":0.039219,"end_time":"2024-03-22T09:48:04.494928","exception":false,"start_time":"2024-03-22T09:48:04.455709","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Mean population of dataset is : 76813.14119601328\n"]}],"source":["mean_population_of_dataset = df[\"population\"].mean()\n","print(\"Mean population of dataset is :\",mean_population_of_dataset)"]},{"cell_type":"code","execution_count":33,"id":"da4c5eb0","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.550595Z","iopub.status.busy":"2024-03-22T09:48:04.55016Z","iopub.status.idle":"2024-03-22T09:48:04.577123Z","shell.execute_reply":"2024-03-22T09:48:04.576199Z"},"papermill":{"duration":0.057942,"end_time":"2024-03-22T09:48:04.579605","exception":false,"start_time":"2024-03-22T09:48:04.521663","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</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","    </tr>\n","    <tr>\n","      <th>103</th>\n","      <td>Norderstedt</td>\n","      <td>53.7064</td>\n","      <td>10.0103</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Schleswig-Holstein</td>\n","      <td>NaN</td>\n","      <td>79159.0</td>\n","      <td>79159.0</td>\n","    </tr>\n","    <tr>\n","      <th>104</th>\n","      <td>Delmenhorst</td>\n","      <td>53.0506</td>\n","      <td>8.6317</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Lower Saxony</td>\n","      <td>minor</td>\n","      <td>77607.0</td>\n","      <td>77607.0</td>\n","    </tr>\n","    <tr>\n","      <th>105</th>\n","      <td>Bamberg</td>\n","      <td>49.8917</td>\n","      <td>10.8917</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>minor</td>\n","      <td>77592.0</td>\n","      <td>77592.0</td>\n","    </tr>\n","    <tr>\n","      <th>106</th>\n","      <td>Viersen</td>\n","      <td>51.2556</td>\n","      <td>6.3917</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>minor</td>\n","      <td>76905.0</td>\n","      <td>76905.0</td>\n","    </tr>\n","    <tr>\n","      <th>107</th>\n","      <td>Marburg</td>\n","      <td>50.8167</td>\n","      <td>8.7667</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>76851.0</td>\n","      <td>76851.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>108 rows × 9 columns</p>\n","</div>"],"text/plain":["            city      lat      lng  country iso2              admin_name  \\\n","0         Berlin  52.5167  13.3833  Germany   DE                  Berlin   \n","1        Hamburg  53.5500  10.0000  Germany   DE                 Hamburg   \n","2         Munich  48.1372  11.5755  Germany   DE                 Bavaria   \n","3        Cologne  50.9422   6.9578  Germany   DE  North Rhine-Westphalia   \n","4      Frankfurt  50.1136   8.6797  Germany   DE                   Hesse   \n","..           ...      ...      ...      ...  ...                     ...   \n","103  Norderstedt  53.7064  10.0103  Germany   DE      Schleswig-Holstein   \n","104  Delmenhorst  53.0506   8.6317  Germany   DE            Lower Saxony   \n","105      Bamberg  49.8917  10.8917  Germany   DE                 Bavaria   \n","106      Viersen  51.2556   6.3917  Germany   DE  North Rhine-Westphalia   \n","107      Marburg  50.8167   8.7667  Germany   DE                   Hesse   \n","\n","     capital  population  population_proper  \n","0    primary   3644826.0          3644826.0  \n","1      admin   1841179.0          1841179.0  \n","2      admin   1471508.0          1471508.0  \n","3        NaN   1085664.0          1085664.0  \n","4      minor    753056.0           753056.0  \n","..       ...         ...                ...  \n","103      NaN     79159.0            79159.0  \n","104    minor     77607.0            77607.0  \n","105    minor     77592.0            77592.0  \n","106    minor     76905.0            76905.0  \n","107    minor     76851.0            76851.0  \n","\n","[108 rows x 9 columns]"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["df[df[\"population\"] > mean_population_of_dataset]"]},{"cell_type":"code","execution_count":34,"id":"42f61582","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.636077Z","iopub.status.busy":"2024-03-22T09:48:04.635632Z","iopub.status.idle":"2024-03-22T09:48:04.640799Z","shell.execute_reply":"2024-03-22T09:48:04.63947Z"},"papermill":{"duration":0.036715,"end_time":"2024-03-22T09:48:04.643444","exception":false,"start_time":"2024-03-22T09:48:04.606729","status":"completed"},"tags":[]},"outputs":[],"source":["import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":35,"id":"00a5dd4d","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:04.699485Z","iopub.status.busy":"2024-03-22T09:48:04.699062Z","iopub.status.idle":"2024-03-22T09:48:05.074111Z","shell.execute_reply":"2024-03-22T09:48:05.072914Z"},"papermill":{"duration":0.406206,"end_time":"2024-03-22T09:48:05.076672","exception":false,"start_time":"2024-03-22T09:48:04.670466","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABCsUlEQVR4nO3deVxUdf///+ewDSgCgiySivuCuWWmqKmZSS599dJKy1zKtMuwXFLTq3LrKsvLzDTS6jLQyssyl9TMJU0txQ2zTA3X0i4DvFLAFRDO7w9/zqcJUAYGB06P++02txvnfd7nnNd7jiNPzjYWwzAMAQAAmJSbqwsAAAAoSYQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdwMmqV6+uQYMGubqMUsFisWjy5MlOXWeHDh3UoUMHp66zpMTHx8tisejnn38u8W0NGjRI1atXt03//PPPslgsmjFjRolvW5ImT54si8VyS7YFOIqwAxTSsWPH9NRTT6lmzZry9vaWn5+f2rRpo7feekuXL18ucLmDBw9q8uTJTv+FN2jQIFksFtvLz89PTZo00RtvvKHMzEynbutWK6n3rDg2b95s935brVaFhoaqQ4cOevXVV3XmzBmnbOfSpUuaPHmyNm/e7JT1OVNprg24EQ9XFwCUBV988YUeeughWa1WDRgwQLfffruysrL07bffauzYsTpw4IDee+89SVJSUpLc3P7v74iDBw9qypQp6tChg91f3s5gtVr173//W5KUlpampUuXasyYMdq9e7cWL17s1G3dSjd6z9avX++aov5/zz77rFq0aKGcnBydOXNG27dv16RJkzRz5kx9+umn6tixo61v//791bdvX1mt1kKv/9KlS5oyZYokOXQE6/3331dubm6h+xfFjWp78cUXNX78+BLdPlBUhB3gJk6cOKG+ffsqIiJCmzZtUuXKlW3zYmJidPToUX3xxRe2Nkd+sRWXh4eHHnvsMdv0008/rZYtW+qTTz7RzJkzFR4efstquVW8vLxcuv27775bDz74oF3b999/r86dO6t37946ePCg7d+Iu7u73N3dS7Seixcvqnz58vL09CzR7dyMh4eHPDz4lYLSidNYwE1Mnz5dFy5c0Pz58+2CznW1a9fWiBEjbNN/vGYnPj5eDz30kCTpnnvusZ0C2bx5swYOHKhKlSopOzs7zzo7d+6sevXqOVyrm5ub7S/u66eAUlNTNXjwYIWGhsrb21tNmjTRggUL7Jb74/Udb775piIiIuTj46P27dvrxx9/tOtb0DUzf75mJD+//PKLnn76adWrV08+Pj4KCgrSQw89ZHe66kbvWUHbd3SM7733nmrVqiWr1aoWLVpo9+7dN6z7Zpo0aaJZs2YpLS1Nb7/9tt1Y/nzNzp49exQdHa1KlSrJx8dHNWrU0BNPPGGrMTg4WJI0ZcoU29ivX/c0aNAg+fr66tixY+ratasqVKigfv362eYV9P47Y5/erLb8rtm5evWqXn75Zdt7Xb16df3jH//Ic5q1evXq6t69u7799lvddddd8vb2Vs2aNbVw4cL833DAQcRw4CZWrVqlmjVrqnXr1g4v265dOz377LOaPXu2/vGPf6hBgwaSpAYNGqh///5auHCh1q1bp+7du9uWSU5O1qZNmzRp0qQi1Xvs2DFJUlBQkC5fvqwOHTro6NGjGj58uGrUqKElS5Zo0KBBSktLswtpkrRw4UKdP39eMTExunLlit566y117NhR+/fvV2hoaJHq+aPdu3dr+/bt6tu3r6pUqaKff/5Zc+fOVYcOHXTw4EGVK1fuhu9Zfhwd46JFi3T+/Hk99dRTslgsmj59unr16qXjx48X6+jIgw8+qMGDB2v9+vV65ZVX8u2Tmpqqzp07Kzg4WOPHj1dAQIB+/vlnLVu2TJIUHBysuXPnatiwYfrb3/6mXr16SZIaN25sW8fVq1cVHR2ttm3basaMGSpXrtwN63LWPi1MbX/25JNPasGCBXrwwQf13HPPaefOnZo2bZoOHTqk5cuX2/U9evSo7T0cOHCgPvjgAw0aNEjNmzdXw4YNC10nkC8DQIHS09MNSUaPHj0KvUxERIQxcOBA2/SSJUsMScbXX39t1y8nJ8eoUqWK0adPH7v2mTNnGhaLxTh+/PgNtzNw4ECjfPnyxpkzZ4wzZ84YR48eNV599VXDYrEYjRs3NgzDMGbNmmVIMj766CPbcllZWUZUVJTh6+trZGRkGIZhGCdOnDAkGT4+Psavv/5q67tz505DkjFq1ChbW/v27Y327dvnW09ERIRdmyRj0qRJtulLly7lWS4hIcGQZCxcuNDWVtB7lt/2HR1jUFCQcfbsWVvfzz//3JBkrFq1Ks+2/ujrr782JBlLliwpsE+TJk2MihUr2qbj4uIMScaJEycMwzCM5cuXG5KM3bt3F7iOM2fO5Hnfrhs4cKAhyRg/fny+8/74/pfEPr1RbZMmTTL++Ctl3759hiTjySeftOs3ZswYQ5KxadMmW1tERIQhydi6dautLTU11bBarcZzzz2XZ1uAoziNBdxARkaGJKlChQpOX7ebm5v69eunlStX6vz587b2jz/+WK1bt1aNGjVuuo6LFy8qODhYwcHBql27tv7xj38oKirK9lfzmjVrFBYWpkceecS2jKenp5599llduHBBW7ZssVtfz549ddttt9mm77rrLrVs2VJr1qwp7nAlST4+Prafs7Oz9fvvv6t27doKCAjQ3r17i7ROR8fYp08fVaxY0TZ99913S5KOHz9epO3/ka+vr92+/LOAgABJ0urVq/M9fVlYw4YNK3Tfkt6nBbm+/tGjR9u1P/fcc5Jkd52bJEVGRtr2hXTtSFK9evWcsl8Awg5wA35+fpJ0w19gxTFgwABdvnzZFk6SkpKUmJio/v37F2p5b29vbdiwQRs2bNDWrVt16tQpbdu2TTVr1pR07RqZOnXq2N0dJv3fKaFffvnFrr1OnTp5tlG3bl2n3QJ++fJlTZw4UVWrVpXValWlSpUUHBystLQ0paenF2mdjo6xWrVqdtPXg8+5c+eKtP0/unDhwg2Dcfv27dW7d29NmTJFlSpVUo8ePRQXF+fQowI8PDxUpUqVQvcv6X1akF9++UVubm6qXbu2XXtYWJgCAgJuul+ka/vGGfsF4Jod4Ab8/PwUHh6e54JOZ4mMjFTz5s310UcfacCAAfroo4/k5eWlhx9+uFDLu7u7q1OnTiVSW0EsFosMw8jTnpOTc9Nln3nmGcXFxWnkyJGKioqSv7+/LBaL+vbtW+K3TV9X0N1R+Y3JEdnZ2Tp8+LBuv/32AvtYLBZ99tln2rFjh1atWqV169bpiSee0BtvvKEdO3bI19f3ptuxWq15gl1xFWefFmbdhVFS+wWQOLID3FT37t117NgxJSQkFGn5m/1nP2DAAG3atEm//fabFi1apG7dutmdZimOiIgIHTlyJE+Q+Omnn2zz/+jIkSN51nH48GG7u3wqVqyotLS0PP3+/Jd6fj777DMNHDhQb7zxhh588EHdd999atu2bZ71OfIkXkfHWFI+++wzXb58WdHR0Tft26pVK73yyivas2ePPv74Yx04cMD2XCRnP4XYmfvU0f2Sm5ubZ/spKSlKS0u7ZfsFkAg7wE2NGzdO5cuX15NPPqmUlJQ8848dO6a33nqrwOXLly8vSfn+MpGkRx55RBaLRSNGjNDx48ftnptTXF27dlVycrI++eQTW9vVq1c1Z84c+fr6qn379nb9V6xYof/+97+26V27dmnnzp3q0qWLra1WrVr66aef7J4Y/P3332vbtm03rcfd3T3PX+pz5szJcwThZu9ZccZYEr7//nuNHDlSFStWVExMTIH9zp07l2f8TZs2lSTbqazrd1cVZuyF4cx96khtXbt2lSTNmjXLrn3mzJmSpG7dujk0DqA4OI0F3EStWrW0aNEi9enTRw0aNLB7gvL27dtttzkXpGnTpnJ3d9frr7+u9PR0Wa1WdezYUSEhIZKuXYh5//33a8mSJQoICHDqL4GhQ4fq3Xff1aBBg5SYmKjq1avrs88+07Zt2zRr1qw815fUrl1bbdu21bBhw5SZmalZs2YpKChI48aNs/V54oknNHPmTEVHR2vw4MFKTU3VvHnz1LBhQ9sF3QXp3r27PvzwQ/n7+ysyMlIJCQn66quvFBQUZNfvZu9ZccZYXN98842uXLminJwc/f7779q2bZtWrlwpf39/LV++XGFhYQUuu2DBAr3zzjv629/+plq1aun8+fN6//335efnZwsHPj4+ioyM1CeffKK6desqMDBQt99++w1Pj92IM/epI7U1adJEAwcO1Hvvvae0tDS1b99eu3bt0oIFC9SzZ0/dc889RRoPUCSuvBUMKEsOHz5sDBkyxKhevbrh5eVlVKhQwWjTpo0xZ84c48qVK7Z+f7713DAM4/333zdq1qxpuLu753tL9aeffmpIMoYOHVroeq7fen4zKSkpxuOPP25UqlTJ8PLyMho1amTExcXZ9bl+m/K//vUv44033jCqVq1qWK1W4+677za+//77POv86KOPjJo1axpeXl5G06ZNjXXr1hXq1vNz587ZavH19TWio6ONn376yaH3LL/bpB0d45/9uc78XL/1/PrL09PTCA4ONtq1a2e88sorRmpqap5l/nzr+d69e41HHnnEqFatmmG1Wo2QkBCje/fuxp49e+yW2759u9G8eXPDy8vLrrYb7fOCbj139j4tqLY/33puGIaRnZ1tTJkyxahRo4bh6elpVK1a1ZgwYYLd58Uwrn1munXrlqemgm6JBxxlMQyu/gJc7fPPP1fPnj21detWu9tvb5Wff/5ZNWrU0L/+9S+NGTPmlm8fAEoS1+wApcD777+vmjVrqm3btq4uBQBMh2t2ABdavHixfvjhB33xxRd66623nH4nDgCAsAO41COPPCJfX18NHjxYTz/9tKvLAQBT4podAABgalyzAwAATI2wAwAATI1rdiTl5ubq9OnTqlChAheIAgBQRhiGofPnzys8PPyG3xlH2JF0+vRpVa1a1dVlAACAIjh16pSqVKlS4HzCjmR7nPypU6fk5+fn4moAAEBhZGRkqGrVqjf9WhjCjv7vm3z9/PwIOwAAlDE3uwSFC5QBAICpEXYAAICpEXYAAICpuTzs/Pe//9Vjjz2moKAg+fj4qFGjRtqzZ49tvmEYmjhxoipXriwfHx916tRJR44csVvH2bNn1a9fP/n5+SkgIECDBw/WhQsXbvVQAABAKeTSsHPu3Dm1adNGnp6e+vLLL3Xw4EG98cYbqlixoq3P9OnTNXv2bM2bN087d+5U+fLlFR0drStXrtj69OvXTwcOHNCGDRu0evVqbd26VUOHDnXFkAAAQCnj0u/GGj9+vLZt26Zvvvkm3/mGYSg8PFzPPfecxowZI0lKT09XaGio4uPj1bdvXx06dEiRkZHavXu37rzzTknS2rVr1bVrV/36668KDw+/aR0ZGRny9/dXeno6d2MBAFBGFPb3t0uP7KxcuVJ33nmnHnroIYWEhKhZs2Z6//33bfNPnDih5ORkderUydbm7++vli1bKiEhQZKUkJCggIAAW9CRpE6dOsnNzU07d+7Md7uZmZnKyMiwewEAAHNyadg5fvy45s6dqzp16mjdunUaNmyYnn32WS1YsECSlJycLEkKDQ21Wy40NNQ2Lzk5WSEhIXbzPTw8FBgYaOvzZ9OmTZO/v7/txdOTAQAwL5eGndzcXN1xxx169dVX1axZMw0dOlRDhgzRvHnzSnS7EyZMUHp6uu116tSpEt0eAABwHZeGncqVKysyMtKurUGDBjp58qQkKSwsTJKUkpJi1yclJcU2LywsTKmpqXbzr169qrNnz9r6/JnVarU9LZmnJgMAYG4uDTtt2rRRUlKSXdvhw4cVEREhSapRo4bCwsK0ceNG2/yMjAzt3LlTUVFRkqSoqCilpaUpMTHR1mfTpk3Kzc1Vy5Ytb8EoAABAaebS78YaNWqUWrdurVdffVUPP/ywdu3apffee0/vvfeepGvfdTFy5Ej985//VJ06dVSjRg299NJLCg8PV8+ePSVdOxJ0//33205/ZWdna/jw4erbt2+h7sQCAADm5tJbzyVp9erVmjBhgo4cOaIaNWpo9OjRGjJkiG2+YRiaNGmS3nvvPaWlpalt27Z65513VLduXVufs2fPavjw4Vq1apXc3NzUu3dvzZ49W76+voWqgVvPAQAoewr7+9vlYac0IOwAAFD2FPb3t0tPY/0VnDlzpsw9x8fPz0/BwcGuLgMAAKcg7JSgM2fO6LHHn9TZ85dcXYpDAiuU00dx/ybwAABMgbBTgjIyMnT2/CUFR/VW+cDQmy9QClw8m6IzCUuVkZFB2AEAmAJh5xYoHxgqv5Aqri6j0M64ugAAAJzIpc/ZAQAAKGmEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGouDTuTJ0+WxWKxe9WvX982/8qVK4qJiVFQUJB8fX3Vu3dvpaSk2K3j5MmT6tatm8qVK6eQkBCNHTtWV69evdVDAQAApZSHqwto2LChvvrqK9u0h8f/lTRq1Ch98cUXWrJkifz9/TV8+HD16tVL27ZtkyTl5OSoW7duCgsL0/bt2/Xbb79pwIAB8vT01KuvvnrLxwIAAEofl4cdDw8PhYWF5WlPT0/X/PnztWjRInXs2FGSFBcXpwYNGmjHjh1q1aqV1q9fr4MHD+qrr75SaGiomjZtqpdfflnPP/+8Jk+eLC8vr1s9HAAAUMq4/JqdI0eOKDw8XDVr1lS/fv108uRJSVJiYqKys7PVqVMnW9/69eurWrVqSkhIkCQlJCSoUaNGCg0NtfWJjo5WRkaGDhw4UOA2MzMzlZGRYfcCAADm5NKw07JlS8XHx2vt2rWaO3euTpw4obvvvlvnz59XcnKyvLy8FBAQYLdMaGiokpOTJUnJycl2Qef6/OvzCjJt2jT5+/vbXlWrVnXuwAAAQKnh0tNYXbp0sf3cuHFjtWzZUhEREfr000/l4+NTYtudMGGCRo8ebZvOyMgg8AAAYFIuP431RwEBAapbt66OHj2qsLAwZWVlKS0tza5PSkqK7RqfsLCwPHdnXZ/O7zqg66xWq/z8/OxeAADAnEpV2Llw4YKOHTumypUrq3nz5vL09NTGjRtt85OSknTy5ElFRUVJkqKiorR//36lpqba+mzYsEF+fn6KjIy85fUDAIDSx6WnscaMGaMHHnhAEREROn36tCZNmiR3d3c98sgj8vf31+DBgzV69GgFBgbKz89PzzzzjKKiotSqVStJUufOnRUZGan+/ftr+vTpSk5O1osvvqiYmBhZrVZXDg0AAJQSLg07v/76qx555BH9/vvvCg4OVtu2bbVjxw4FBwdLkt588025ubmpd+/eyszMVHR0tN555x3b8u7u7lq9erWGDRumqKgolS9fXgMHDtTUqVNdNSQAAFDKuDTsLF68+Ibzvb29FRsbq9jY2AL7REREaM2aNc4uDQAAmESpumYHAADA2Qg7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1Ag7AADA1EpN2HnttddksVg0cuRIW9uVK1cUExOjoKAg+fr6qnfv3kpJSbFb7uTJk+rWrZvKlSunkJAQjR07VlevXr3F1QMAgNKqVISd3bt3691331Xjxo3t2keNGqVVq1ZpyZIl2rJli06fPq1evXrZ5ufk5Khbt27KysrS9u3btWDBAsXHx2vixIm3eggAAKCUcjjsLFiwQF988YVtety4cQoICFDr1q31yy+/OFzAhQsX1K9fP73//vuqWLGirT09PV3z58/XzJkz1bFjRzVv3lxxcXHavn27duzYIUlav369Dh48qI8++khNmzZVly5d9PLLLys2NlZZWVkO1wIAAMzH4bDz6quvysfHR5KUkJCg2NhYTZ8+XZUqVdKoUaMcLiAmJkbdunVTp06d7NoTExOVnZ1t116/fn1Vq1ZNCQkJtu03atRIoaGhtj7R0dHKyMjQgQMHCtxmZmamMjIy7F4AAMCcPBxd4NSpU6pdu7YkacWKFerdu7eGDh2qNm3aqEOHDg6ta/Hixdq7d692796dZ15ycrK8vLwUEBBg1x4aGqrk5GRbnz8Gnevzr88ryLRp0zRlyhSHagUAAGWTw0d2fH199fvvv0u6dhrpvvvukyR5e3vr8uXLhV7PqVOnNGLECH388cfy9vZ2tIximTBhgtLT022vU6dO3dLtAwCAW8fhIzv33XefnnzySTVr1kyHDx9W165dJUkHDhxQ9erVC72exMREpaam6o477rC15eTkaOvWrXr77be1bt06ZWVlKS0tze7oTkpKisLCwiRJYWFh2rVrl916r9+tdb1PfqxWq6xWa6FrBQAAZZfDR3ZiY2MVFRWlM2fOaOnSpQoKCpJ0Lbw88sgjhV7Pvffeq/3792vfvn2215133ql+/frZfvb09NTGjRttyyQlJenkyZOKioqSJEVFRWn//v1KTU219dmwYYP8/PwUGRnp6NAAAIAJOXxkJyAgQG+//XaedkevgalQoYJuv/12u7by5csrKCjI1j548GCNHj1agYGB8vPz0zPPPKOoqCi1atVKktS5c2dFRkaqf//+mj59upKTk/Xiiy8qJiaGIzcAAEBSEZ+z88033+ixxx5T69at9d///leS9OGHH+rbb791anFvvvmmunfvrt69e6tdu3YKCwvTsmXLbPPd3d21evVqubu7KyoqSo899pgGDBigqVOnOrUOAABQdjl8ZGfp0qXq37+/+vXrp7179yozM1PStefivPrqq1qzZk2Ri9m8ebPdtLe3t2JjYxUbG1vgMhEREcXaJgAAMDeHj+z885//1Lx58/T+++/L09PT1t6mTRvt3bvXqcUBAAAUl8NhJykpSe3atcvT7u/vr7S0NGfUBAAA4DQOh52wsDAdPXo0T/u3336rmjVrOqUoAAAAZ3E47AwZMkQjRozQzp07ZbFYdPr0aX388ccaM2aMhg0bVhI1AgAAFJnDFyiPHz9eubm5uvfee3Xp0iW1a9dOVqtVY8aM0TPPPFMSNQIAABSZw2HHYrHohRde0NixY3X06FFduHBBkZGR8vX1LYn6AAAAisXhsHOdl5cXTykGAAClXqHCTq9evRQfHy8/Pz/16tXrhn3/+NA/AAAAVytU2PH395fFYpEk+fn52X4GAAAo7QoVduLi4mw/x8fHl1QtAAAATufwrecdO3bM9+GBGRkZ6tixozNqAgAAcBqHw87mzZuVlZWVp/3KlSv65ptvnFIUAACAsxT6bqwffvjB9vPBgweVnJxsm87JydHatWt12223Obc6AACAYip02GnatKksFossFku+p6t8fHw0Z84cpxYHAABQXIUOOydOnJBhGKpZs6Z27dql4OBg2zwvLy+FhITI3d29RIoEAAAoqkKHnYiICElSbm5uiRUDAADgbIUKOytXrlSXLl3k6emplStX3rDv//t//88phQEAADhDocJOz549lZycrJCQEPXs2bPAfhaLRTk5Oc6qDQAAoNgKFXb+eOqK01gAAKAscfg5OwAAAGVJocPOpk2bFBkZqYyMjDzz0tPT1bBhQ23dutWpxQEAABRXocPOrFmzNGTIEPn5+eWZ5+/vr6eeekpvvvmmU4sDAAAorkKHne+//173339/gfM7d+6sxMREpxQFAADgLIUOOykpKfL09CxwvoeHh86cOeOUogAAAJyl0GHntttu048//ljg/B9++EGVK1d2SlEAAADOUuiw07VrV7300ku6cuVKnnmXL1/WpEmT1L17d6cWBwAAUFyF/rqIF198UcuWLVPdunU1fPhw1atXT5L0008/KTY2Vjk5OXrhhRdKrFAAAICiKHTYCQ0N1fbt2zVs2DBNmDBBhmFIuvbU5OjoaMXGxio0NLTECgUAACiKQocd6dqXga5Zs0bnzp3T0aNHZRiG6tSpo4oVK5ZUfQAAAMXiUNi5rmLFimrRooWzawEAAHA6vi4CAACYGmEHAACYGmEHAACYWqHCzh133KFz585JkqZOnapLly6VaFEAAADOUqiwc+jQIV28eFGSNGXKFF24cKFEiwIAAHCWQt2N1bRpUz3++ONq27atDMPQjBkz5Ovrm2/fiRMnOrVAAACA4ihU2ImPj9ekSZO0evVqWSwWffnll/LwyLuoxWIh7AAAgFKlUGGnXr16Wrx4sSTJzc1NGzduVEhISIkWBgAA4AwOP1QwNze3JOoAAAAoEUV6gvKxY8c0a9YsHTp0SJIUGRmpESNGqFatWk4tDgAAoLgcfs7OunXrFBkZqV27dqlx48Zq3Lixdu7cqYYNG2rDhg0lUSMAAECROXxkZ/z48Ro1apRee+21PO3PP/+87rvvPqcVBwAAUFwOH9k5dOiQBg8enKf9iSee0MGDB51SFAAAgLM4HHaCg4O1b9++PO379u3jDi0AAFDqOHwaa8iQIRo6dKiOHz+u1q1bS5K2bdum119/XaNHj3Z6gQAAAMXhcNh56aWXVKFCBb3xxhuaMGGCJCk8PFyTJ0/Ws88+6/QCAQAAisPhsGOxWDRq1CiNGjVK58+flyRVqFDB6YUBAAA4Q5Ges3MdIQcAAJR2Dl+gDAAAUJa4NOzMnTtXjRs3lp+fn/z8/BQVFaUvv/zSNv/KlSuKiYlRUFCQfH191bt3b6WkpNit4+TJk+rWrZvKlSunkJAQjR07VlevXr3VQwEAAKWUS8NOlSpV9NprrykxMVF79uxRx44d1aNHDx04cECSNGrUKK1atUpLlizRli1bdPr0afXq1cu2fE5Ojrp166asrCxt375dCxYsUHx8PN+8DgAAbBwKO9nZ2br33nt15MgRp2z8gQceUNeuXVWnTh3VrVtXr7zyinx9fbVjxw6lp6dr/vz5mjlzpjp27KjmzZsrLi5O27dv144dOyRJ69ev18GDB/XRRx+padOm6tKli15++WXFxsYqKyvLKTUCAICyzaGw4+npqR9++KFECsnJydHixYt18eJFRUVFKTExUdnZ2erUqZOtT/369VWtWjUlJCRIkhISEtSoUSOFhoba+kRHRysjI8N2dCg/mZmZysjIsHsBAABzcvg01mOPPab58+c7rYD9+/fL19dXVqtVf//737V8+XJFRkYqOTlZXl5eCggIsOsfGhqq5ORkSVJycrJd0Lk+//q8gkybNk3+/v62V9WqVZ02HgAAULo4fOv51atX9cEHH+irr75S8+bNVb58ebv5M2fOdGh99erV0759+5Senq7PPvtMAwcO1JYtWxwtyyETJkywe9pzRkYGgQcAAJNyOOz8+OOPuuOOOyRJhw8ftptnsVgcLsDLy0u1a9eWJDVv3ly7d+/WW2+9pT59+igrK0tpaWl2R3dSUlIUFhYmSQoLC9OuXbvs1nf9bq3rffJjtVpltVodrhUAAJQ9Doedr7/+uiTqsMnNzVVmZqaaN28uT09Pbdy4Ub1795YkJSUl6eTJk4qKipIkRUVF6ZVXXlFqaqrtS0g3bNggPz8/RUZGlmidAACgbCjyE5SPHj2qY8eOqV27dvLx8ZFhGA4f2ZkwYYK6dOmiatWq6fz581q0aJE2b96sdevWyd/fX4MHD9bo0aMVGBgoPz8/PfPMM4qKilKrVq0kSZ07d1ZkZKT69++v6dOnKzk5WS+++KJiYmI4cgMAACQVIez8/vvvevjhh/X111/LYrHoyJEjqlmzpgYPHqyKFSvqjTfeKPS6UlNTNWDAAP3222/y9/dX48aNtW7dOt13332SpDfffFNubm7q3bu3MjMzFR0drXfeece2vLu7u1avXq1hw4YpKipK5cuX18CBAzV16lRHhwUAAEzK4bAzatQoeXp66uTJk2rQoIGtvU+fPho9erRDYedmd3V5e3srNjZWsbGxBfaJiIjQmjVrCr1NAADw1+Jw2Fm/fr3WrVunKlWq2LXXqVNHv/zyi9MKAwAAcAaHn7Nz8eJFlStXLk/72bNnuU4GAACUOg6HnbvvvlsLFy60TVssFuXm5mr69Om65557nFocAABAcTl8Gmv69Om69957tWfPHmVlZWncuHE6cOCAzp49q23btpVEjQAAAEXm8JGd22+/XYcPH1bbtm3Vo0cPXbx4Ub169dJ3332nWrVqlUSNAAAARVak5+z4+/vrhRdecHYtAAAATleksHPu3DnNnz9fhw4dkiRFRkbq8ccfV2BgoFOLAwAAKC6HT2Nt3bpV1atX1+zZs3Xu3DmdO3dOs2fPVo0aNbR169aSqBEAAKDIHD6yExMToz59+mju3Llyd3eXJOXk5Ojpp59WTEyM9u/f7/QiAQAAisrhIztHjx7Vc889Zws60rWvbRg9erSOHj3q1OIAAACKy+Gwc8cdd9iu1fmjQ4cOqUmTJk4pCgAAwFkKdRrrhx9+sP387LPPasSIETp69Kjt28d37Nih2NhYvfbaayVTJQAAQBEVKuw0bdpUFotFhmHY2saNG5en36OPPqo+ffo4rzoAAIBiKlTYOXHiREnXAQAAUCIKFXYiIiJKug4AAIASUaSHCp4+fVrffvutUlNTlZubazfv2WefdUphAAAAzuBw2ImPj9dTTz0lLy8vBQUFyWKx2OZZLBbCDgAAKFUcDjsvvfSSJk6cqAkTJsjNzeE71wEAAG4ph9PKpUuX1LdvX4IOAAAoExxOLIMHD9aSJUtKohYAAACnc/g01rRp09S9e3etXbtWjRo1kqenp938mTNnOq04AACA4ipS2Fm3bp3q1asnSXkuUAYAAChNHA47b7zxhj744AMNGjSoBMoBAABwLoev2bFarWrTpk1J1AIAAOB0DoedESNGaM6cOSVRCwAAgNM5fBpr165d2rRpk1avXq2GDRvmuUB52bJlTisOAACguBwOOwEBAerVq1dJ1AIAAOB0DoeduLi4kqgDAACgRPAYZAAAYGoOH9mpUaPGDZ+nc/z48WIVBAAA4EwOh52RI0faTWdnZ+u7777T2rVrNXbsWGfVBQAA4BQOh50RI0bk2x4bG6s9e/YUuyAAAABncto1O126dNHSpUudtToAAACncFrY+eyzzxQYGOis1QEAADiFw6exmjVrZneBsmEYSk5O1pkzZ/TOO+84tTgAAIDicjjs9OzZ027azc1NwcHB6tChg+rXr++sugAAAJzC4bAzadKkkqgDAACgRPBQQQAAYGqFPrLj5uZ2w4cJSpLFYtHVq1eLXRQAAICzFDrsLF++vMB5CQkJmj17tnJzc51SFAAAgLMUOuz06NEjT1tSUpLGjx+vVatWqV+/fpo6dapTiwMAACiuIl2zc/r0aQ0ZMkSNGjXS1atXtW/fPi1YsEARERHOrg8AAKBYHAo76enpev7551W7dm0dOHBAGzdu1KpVq3T77beXVH0AAADFUujTWNOnT9frr7+usLAw/ec//8n3tBYAAEBpU+iwM378ePn4+Kh27dpasGCBFixYkG+/ZcuWOa04AACA4ip02BkwYMBNbz0HAAAobQodduLj40uwDAAAgJLBE5QBAICpEXYAAICpuTTsTJs2TS1atFCFChUUEhKinj17Kikpya7PlStXFBMTo6CgIPn6+qp3795KSUmx63Py5El169ZN5cqVU0hIiMaOHcvXVgAAAEkuDjtbtmxRTEyMduzYoQ0bNig7O1udO3fWxYsXbX1GjRqlVatWacmSJdqyZYtOnz6tXr162ebn5OSoW7duysrK0vbt27VgwQLFx8dr4sSJrhgSAAAoZQp9gXJJWLt2rd10fHy8QkJClJiYqHbt2ik9PV3z58/XokWL1LFjR0lSXFycGjRooB07dqhVq1Zav369Dh48qK+++kqhoaFq2rSpXn75ZT3//POaPHmyvLy8XDE0AABQSpSqa3bS09MlSYGBgZKkxMREZWdnq1OnTrY+9evXV7Vq1ZSQkCDp2peQNmrUSKGhobY+0dHRysjI0IEDB/LdTmZmpjIyMuxeAADAnEpN2MnNzdXIkSPVpk0b29dPJCcny8vLSwEBAXZ9Q0NDlZycbOvzx6Bzff71efmZNm2a/P39ba+qVas6eTQAAKC0KDVhJyYmRj/++KMWL15c4tuaMGGC0tPTba9Tp06V+DYBAIBruPSaneuGDx+u1atXa+vWrapSpYqtPSwsTFlZWUpLS7M7upOSkqKwsDBbn127dtmt7/rdWtf7/JnVapXVanXyKAAAQGnk0iM7hmFo+PDhWr58uTZt2qQaNWrYzW/evLk8PT21ceNGW1tSUpJOnjypqKgoSVJUVJT279+v1NRUW58NGzbIz89PkZGRt2YgAACg1HLpkZ2YmBgtWrRIn3/+uSpUqGC7xsbf318+Pj7y9/fX4MGDNXr0aAUGBsrPz0/PPPOMoqKi1KpVK0lS586dFRkZqf79+2v69OlKTk7Wiy++qJiYGI7eAAAA14aduXPnSpI6dOhg1x4XF6dBgwZJkt588025ubmpd+/eyszMVHR0tN555x1bX3d3d61evVrDhg1TVFSUypcvr4EDB2rq1Km3ahgAAKAUc2nYMQzjpn28vb0VGxur2NjYAvtERERozZo1ziwNAACYRKm5GwsAAKAkEHYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpuTTsbN26VQ888IDCw8NlsVi0YsUKu/mGYWjixImqXLmyfHx81KlTJx05csSuz9mzZ9WvXz/5+fkpICBAgwcP1oULF27hKAAAQGnm0rBz8eJFNWnSRLGxsfnOnz59umbPnq158+Zp586dKl++vKKjo3XlyhVbn379+unAgQPasGGDVq9era1bt2ro0KG3aggAAKCU83Dlxrt06aIuXbrkO88wDM2aNUsvvviievToIUlauHChQkNDtWLFCvXt21eHDh3S2rVrtXv3bt15552SpDlz5qhr166aMWOGwsPDb9lYAABA6VRqr9k5ceKEkpOT1alTJ1ubv7+/WrZsqYSEBElSQkKCAgICbEFHkjp16iQ3Nzft3LnzltcMAABKH5ce2bmR5ORkSVJoaKhde2hoqG1ecnKyQkJC7OZ7eHgoMDDQ1ic/mZmZyszMtE1nZGQ4q2wAAFDKlNojOyVp2rRp8vf3t72qVq3q6pIAAEAJKbVhJywsTJKUkpJi156SkmKbFxYWptTUVLv5V69e1dmzZ2198jNhwgSlp6fbXqdOnXJy9QAAoLQotWGnRo0aCgsL08aNG21tGRkZ2rlzp6KioiRJUVFRSktLU2Jioq3Ppk2blJubq5YtWxa4bqvVKj8/P7sXAAAwJ5des3PhwgUdPXrUNn3ixAnt27dPgYGBqlatmkaOHKl//vOfqlOnjmrUqKGXXnpJ4eHh6tmzpySpQYMGuv/++zVkyBDNmzdP2dnZGj58uPr27cudWAAAQJKLw86ePXt0zz332KZHjx4tSRo4cKDi4+M1btw4Xbx4UUOHDlVaWpratm2rtWvXytvb27bMxx9/rOHDh+vee++Vm5ubevfurdmzZ9/ysQAAgNLJpWGnQ4cOMgyjwPkWi0VTp07V1KlTC+wTGBioRYsWlUR5AADABErtNTsAAADOQNgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACm5uHqAlD6ZGdl6ZdffnF1GQ7x8/NTcHCwq8sAAJRChB3YybyQrp9PHNfIf0yW1Wp1dTmFFlihnD6K+zeBBwCQh2nCTmxsrP71r38pOTlZTZo00Zw5c3TXXXe5uqwyJzvzsnItHqrUqpeCwiNcXU6hXDybojMJS5WRkUHYAQDkYYqw88knn2j06NGaN2+eWrZsqVmzZik6OlpJSUkKCQlxdXllUrmKwfILqeLqMgrtjKsLAACUWqa4QHnmzJkaMmSIHn/8cUVGRmrevHkqV66cPvjgA1eXBgAAXKzMH9nJyspSYmKiJkyYYGtzc3NTp06dlJCQ4MLKcCtxUfWtc+bMGWVkZLi6DIdkZWXJy8vL1WU4pKz++0DJK4ufQVf/ey7zYed///ufcnJyFBoaatceGhqqn376Kd9lMjMzlZmZaZtOT0+XJKf/4zl//rxyrl5V2m8/K/vKJaeuu6RkpP4qIzdXGcmn5GFxdTWFc+70cZ04dlTPjHuxTF1U7Wv10OQXJygwMNDVpRTa2bNnNfmVabpw5aqrSym07Kwsnf71lG6rGiEPz7LzX15Z/PeBklcWP4OSVNHXR+/PfVuVKlVy6nqv/942DOOG/crOJ9+Jpk2bpilTpuRpr1q1aslscPvmkllvCfpm3oSbdyplDp457eoSHHbv11+5uoS/jLPJp1xdgsP49wEzqVWrVomt+/z58/L39y9wfpkPO5UqVZK7u7tSUlLs2lNSUhQWFpbvMhMmTNDo0aNt07m5uTp79qyCgoJksTjvcEZGRoaqVq2qU6dOyc/Pz2nrLc3+imOWGDfj/mtg3Iy7tDEMQ+fPn1d4ePgN+5X5sOPl5aXmzZtr48aN6tmzp6Rr4WXjxo0aPnx4vstYrdY8pzsCAgJKrEY/P79S+w+lpPwVxywx7r8axv3XwrhLpxsd0bmuzIcdSRo9erQGDhyoO++8U3fddZdmzZqlixcv6vHHH3d1aQAAwMVMEXb69OmjM2fOaOLEiUpOTlbTpk21du3aPBctAwCAvx5ThB1JGj58eIGnrVzFarVq0qRJZeoOoeL6K45ZYtyM+6+BcTPusspi3Ox+LQAAgDLMFE9QBgAAKAhhBwAAmBphBwAAmBphBwAAmBphpxhiY2NVvXp1eXt7q2XLltq1a9cN+y9ZskT169eXt7e3GjVqpDVr1tyiSp3LkXHHx8fLYrHYvby9vW9htc6xdetWPfDAAwoPD5fFYtGKFStuuszmzZt1xx13yGq1qnbt2oqPjy/xOp3N0XFv3rw5z/62WCxKTk6+NQU7wbRp09SiRQtVqFBBISEh6tmzp5KSkm66XFn/fBdl3Gb4fM+dO1eNGze2PTgvKipKX3755Q2XKev7WnJ83GV9XxN2iuiTTz7R6NGjNWnSJO3du1dNmjRRdHS0UlNT8+2/fft2PfLIIxo8eLC+++479ezZUz179tSPP/54iysvHkfHLV17+uZvv/1me5W1byeXpIsXL6pJkyaKjY0tVP8TJ06oW7duuueee7Rv3z6NHDlSTz75pNatW1fClTqXo+O+LikpyW6fh4SElFCFzrdlyxbFxMRox44d2rBhg7Kzs9W5c2ddvHixwGXM8Pkuyrilsv/5rlKlil577TUlJiZqz5496tixo3r06KEDBw7k298M+1pyfNxSGd/XBorkrrvuMmJiYmzTOTk5Rnh4uDFt2rR8+z/88MNGt27d7NpatmxpPPXUUyVap7M5Ou64uDjD39//FlV3a0gyli9ffsM+48aNMxo2bGjX1qdPHyM6OroEKytZhRn3119/bUgyzp07d0tquhVSU1MNScaWLVsK7GOWz/cfFWbcZvx8G4ZhVKxY0fj3v/+d7zwz7uvrbjTusr6vObJTBFlZWUpMTFSnTp1sbW5uburUqZMSEhLyXSYhIcGuvyRFR0cX2L80Ksq4JenChQuKiIhQ1apVb/qXg1mYYX8XR9OmTVW5cmXdd9992rZtm6vLKZb09HRJUmBgYIF9zLi/CzNuyVyf75ycHC1evFgXL15UVFRUvn3MuK8LM26pbO9rwk4R/O9//1NOTk6er6MIDQ0t8NqE5ORkh/qXRkUZd7169fTBBx/o888/10cffaTc3Fy1bt1av/76660o2WUK2t8ZGRm6fPmyi6oqeZUrV9a8efO0dOlSLV26VFWrVlWHDh20d+9eV5dWJLm5uRo5cqTatGmj22+/vcB+Zvh8/1Fhx22Wz/f+/fvl6+srq9Wqv//971q+fLkiIyPz7Wumfe3IuMv6vjbN10WgdIqKirL7S6F169Zq0KCB3n33Xb388ssurAwloV69eqpXr55tunXr1jp27JjefPNNffjhhy6srGhiYmL0448/6ttvv3V1KbdUYcdtls93vXr1tG/fPqWnp+uzzz7TwIEDtWXLlgJ/8ZuFI+Mu6/uasFMElSpVkru7u1JSUuzaU1JSFBYWlu8yYWFhDvUvjYoy7j/z9PRUs2bNdPTo0ZIosdQoaH/7+fnJx8fHRVW5xl133VUmw8Lw4cO1evVqbd26VVWqVLlhXzN8vq9zZNx/VlY/315eXqpdu7YkqXnz5tq9e7feeustvfvuu3n6mmlfOzLuPytr+5rTWEXg5eWl5s2ba+PGjba23Nxcbdy4scDznVFRUXb9JWnDhg03PD9a2hRl3H+Wk5Oj/fv3q3LlyiVVZqlghv3tLPv27StT+9swDA0fPlzLly/Xpk2bVKNGjZsuY4b9XZRx/5lZPt+5ubnKzMzMd54Z9nVBbjTuPytz+9rVV0iXVYsXLzasVqsRHx9vHDx40Bg6dKgREBBgJCcnG4ZhGP379zfGjx9v679t2zbDw8PDmDFjhnHo0CFj0qRJhqenp7F//35XDaFIHB33lClTjHXr1hnHjh0zEhMTjb59+xre3t7GgQMHXDWEIjl//rzx3XffGd99950hyZg5c6bx3XffGb/88othGIYxfvx4o3///rb+x48fN8qVK2eMHTvWOHTokBEbG2u4u7sba9euddUQisTRcb/55pvGihUrjCNHjhj79+83RowYYbi5uRlfffWVq4bgsGHDhhn+/v7G5s2bjd9++832unTpkq2PGT/fRRm3GT7f48ePN7Zs2WKcOHHC+OGHH4zx48cbFovFWL9+vWEY5tzXhuH4uMv6vibsFMOcOXOMatWqGV5eXsZdd91l7Nixwzavffv2xsCBA+36f/rpp0bdunUNLy8vo2HDhsYXX3xxiyt2DkfGPXLkSFvf0NBQo2vXrsbevXtdUHXxXL+l+s+v62MdOHCg0b59+zzLNG3a1PDy8jJq1qxpxMXF3fK6i8vRcb/++utGrVq1DG9vbyMwMNDo0KGDsWnTJtcUX0T5jVeS3f4z4+e7KOM2w+f7iSeeMCIiIgwvLy8jODjYuPfee22/8A3DnPvaMBwfd1nf1xbDMIxbdxwJAADg1uKaHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQCm1KFDB40cObLUrAf4K9q6daseeOABhYeHy2KxaMWKFQ6vwzAMzZgxQ3Xr1pXVatVtt92mV155xaF1EHYAON2gQYNksVhksVhsXzY4depUXb161dWlFWjz5s2yWCxKS0uza1+2bFmZ+FZnoDS6ePGimjRpotjY2CKvY8SIEfr3v/+tGTNm6KefftLKlSt11113ObQOvvUcQIm4//77FRcXp8zMTK1Zs0YxMTHy9PTUhAkTXF2aQwIDA11dAlBmdenSRV26dClwfmZmpl544QX95z//UVpamm6//Xa9/vrr6tChgyTp0KFDmjt3rn788UfVq1dPkor0JbUc2QFQIqxWq8LCwhQREaFhw4apU6dOWrlypc6dO6cBAwaoYsWKKleunLp06aIjR47YlouPj1dAQIBWrFihOnXqyNvbW9HR0Tp16pStz6BBg9SzZ0+77Y0cOdL2H2R+PvzwQ915552qUKGCwsLC9Oijjyo1NVWS9PPPP+uee+6RJFWsWFEWi0WDBg2SlPc0VmHrX7dunRo0aCBfX1/df//9+u2334r4TgLmNXz4cCUkJGjx4sX64Ycf9NBDD+n++++3faZWrVqlmjVravXq1apRo4aqV6+uJ598UmfPnnVoO4QdALeEj4+PsrKyNGjQIO3Zs0crV65UQkKCDMNQ165dlZ2dbet76dIlvfLKK1q4cKG2bdumtLQ09e3bt1jbz87O1ssvv6zvv/9eK1as0M8//2wLNFWrVtXSpUslSUlJSfrtt9/01ltv5buewtY/Y8YMffjhh9q6datOnjypMWPGFKt+wGxOnjypuLg4LVmyRHfffbdq1aqlMWPGqG3btoqLi5MkHT9+XL/88ouWLFmihQsXKj4+XomJiXrwwQcd2hansQCUKMMwtHHjRq1bt05dunTRihUrtG3bNrVu3VqS9PHHH6tq1apasWKFHnroIUnXgsnbb7+tli1bSpIWLFigBg0aaNeuXQ6fq7/uiSeesP1cs2ZNzZ49Wy1atNCFCxfk6+trO10VEhKigICAfNdx5MgRrVy5slD1z5s3T7Vq1ZJ07a/XqVOnFqluwKz279+vnJwc1a1b1649MzNTQUFBkqTc3FxlZmZq4cKFtn7z589X8+bNlZSUZDu1dTOEHQAlYvXq1fL19VV2drZyc3P16KOPqlevXlq9erUtxEhSUFCQ6tWrp0OHDtnaPDw81KJFC9t0/fr1FRAQoEOHDhU57CQmJmry5Mn6/vvvde7cOeXm5kq69tdlZGRkodZx6NAheXh43LT+cuXK2YKOJFWuXNl2ygzANRcuXJC7u7sSExPl7u5uN8/X11fStc+Oh4eHXSBq0KCBpGuf3cKGHU5jASgR99xzj/bt26cjR47o8uXLWrBggSwWi1PW7ebmJsMw7Nr+eBrpzy5evKjo6Gj5+fnp448/1u7du7V8+XJJUlZWllNq+iNPT0+7aYvFkqde4K+uWbNmysnJUWpqqmrXrm33CgsLkyS1adNGV69e1bFjx2zLHT58WJIUERFR6G0RdgCUiPLly6t27dqqVq2aPDyuHURu0KCBrl69qp07d9r6/f7770pKSrI7unL16lXt2bPHNp2UlKS0tDTbX3TBwcF5Lvjdt29fgbX89NNP+v333/Xaa6/p7rvvVv369fMcafHy8pIk5eTkFLiewtYP4JoLFy5o3759ts/niRMntG/fPp08eVJ169ZVv379NGDAAC1btkwnTpzQrl27NG3aNH3xxReSpE6dOumOO+7QE088oe+++06JiYl66qmndN999+U5/XUjhB0At0ydOnXUo0cPDRkyRN9++62+//57PfbYY7rtttvUo0cPWz9PT08988wz2rlzpxITEzVo0CC1atXKdgqrY8eO2rNnjxYuXKgjR45o0qRJ+vHHHwvcbrVq1eTl5aU5c+bo+PHjWrlyZZ5n50RERMhisWj16tU6c+aMLly4UOT6AVyzZ88eNWvWTM2aNZMkjR49Ws2aNdPEiRMlSXFxcRowYICee+451atXTz179tTu3btVrVo1SdeO4q5atUqVKlVSu3bt1K1bNzVo0ECLFy92qA7CDoBbKi4uTs2bN1f37t0VFRUlwzC0Zs0au1M/5cqV0/PPP69HH31Ubdq0ka+vrz755BPb/OjoaL300ksaN26cWrRoofPnz2vAgAEFbjM4OFjx8fFasmSJIiMj9dprr2nGjBl2fW677TZNmTJF48ePV2hoqIYPH17k+gFc06FDBxmGkecVHx8v6dofNlOmTNGJEyeUlZWl06dPa9myZWrUqJFtHeHh4Vq6dKnOnz+v5ORkxcXFOfz8K4vBiWQApUh8fLxGjhyZ50nGAFBUHNkBAACmRtgBAACmxmksAABgahzZAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApvb/AYscN9bwvTvkAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# What is the distribution of city populations? (Create a histogram)\n","import matplotlib.pyplot as plt\n","\n","# Assuming 'data' is your DataFrame with the 'population' column\n","\n","\n","# Create a histogram\n","plt.hist(df['population'], bins=10, edgecolor='k', alpha=0.7)\n","\n","# Add labels and title\n","plt.xlabel('Population')\n","plt.ylabel('Number of Cities')\n","plt.title('City Population Distribution')\n","\n","# Show the histogram\n","plt.show()\n","\n"]},{"cell_type":"code","execution_count":36,"id":"d670a5b0","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:05.137264Z","iopub.status.busy":"2024-03-22T09:48:05.136757Z","iopub.status.idle":"2024-03-22T09:48:05.144675Z","shell.execute_reply":"2024-03-22T09:48:05.143522Z"},"papermill":{"duration":0.03938,"end_time":"2024-03-22T09:48:05.147155","exception":false,"start_time":"2024-03-22T09:48:05.107775","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['Germany']"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["# Which country has the most cities in the dataset?\n","\n","list(df[\"country\"].unique())"]},{"cell_type":"code","execution_count":37,"id":"51d5bb41","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:05.204795Z","iopub.status.busy":"2024-03-22T09:48:05.204358Z","iopub.status.idle":"2024-03-22T09:48:05.529629Z","shell.execute_reply":"2024-03-22T09:48:05.528415Z"},"papermill":{"duration":0.357219,"end_time":"2024-03-22T09:48:05.532274","exception":false,"start_time":"2024-03-22T09:48:05.175055","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# What is the relationship between langitude (lat) and population?\n","\n","\n","\n","# Assuming you have lat_data and popul_data already defined\n","lag_data = df[\"lng\"]\n","popul_data = df[\"population\"]\n","\n","# Create a line chart\n","plt.plot(lag_data, popul_data, marker='o', linestyle='-', color='b', markersize=5)\n","\n","# Add labels and title\n","plt.xlabel('lng')\n","plt.ylabel('Population')\n","plt.title('Population vs. Longitude')\n","\n","# Show the line chart\n","plt.grid(True)\n","plt.show()\n"]},{"cell_type":"code","execution_count":38,"id":"e1339eec","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:05.591466Z","iopub.status.busy":"2024-03-22T09:48:05.591027Z","iopub.status.idle":"2024-03-22T09:48:05.600106Z","shell.execute_reply":"2024-03-22T09:48:05.598847Z"},"papermill":{"duration":0.041424,"end_time":"2024-03-22T09:48:05.602335","exception":false,"start_time":"2024-03-22T09:48:05.560911","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0.0651516024675942"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["df[\"lat\"].corr(df[\"population\"])\n","\n","# Answer : There is no correaltion"]},{"cell_type":"code","execution_count":39,"id":"776a5e1b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:05.66329Z","iopub.status.busy":"2024-03-22T09:48:05.662514Z","iopub.status.idle":"2024-03-22T09:48:06.002273Z","shell.execute_reply":"2024-03-22T09:48:06.001089Z"},"papermill":{"duration":0.373779,"end_time":"2024-03-22T09:48:06.005279","exception":false,"start_time":"2024-03-22T09:48:05.6315","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# What is the relationship between longitude (lng) and population?\n","\n","# Assuming you have lat_data and popul_data already defined\n","lat_data = df[\"lat\"]\n","popul_data = df[\"population\"]\n","\n","# Create a line chart\n","plt.plot(lat_data, popul_data, marker='o', linestyle='-', color='b', markersize=5)\n","\n","# Add labels and title\n","plt.xlabel('Latitude')\n","plt.ylabel('Population')\n","plt.title('Population vs. Latitude')\n","\n","# Show the line chart\n","plt.grid(True)\n","plt.show()"]},{"cell_type":"code","execution_count":40,"id":"61be5916","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.066183Z","iopub.status.busy":"2024-03-22T09:48:06.065543Z","iopub.status.idle":"2024-03-22T09:48:06.074494Z","shell.execute_reply":"2024-03-22T09:48:06.072966Z"},"papermill":{"duration":0.043945,"end_time":"2024-03-22T09:48:06.078273","exception":false,"start_time":"2024-03-22T09:48:06.034328","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["0.02406438435743983"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["df[\"lng\"].corr(df[\"population\"])\n","\n","# Answer : There is no correaltion"]},{"cell_type":"code","execution_count":41,"id":"a359523e","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.151298Z","iopub.status.busy":"2024-03-22T09:48:06.150572Z","iopub.status.idle":"2024-03-22T09:48:06.159254Z","shell.execute_reply":"2024-03-22T09:48:06.158139Z"},"papermill":{"duration":0.041825,"end_time":"2024-03-22T09:48:06.16156","exception":false,"start_time":"2024-03-22T09:48:06.119735","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["-0.009921762266200512"]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["\n","# Is there a correlation between latitude and longitude?\n","df[\"lng\"].corr(df[\"lat\"])\n","\n","# Answer : There is no correaltion"]},{"cell_type":"code","execution_count":42,"id":"82d55e7e","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.224678Z","iopub.status.busy":"2024-03-22T09:48:06.223904Z","iopub.status.idle":"2024-03-22T09:48:06.237024Z","shell.execute_reply":"2024-03-22T09:48:06.235755Z"},"papermill":{"duration":0.04881,"end_time":"2024-03-22T09:48:06.240065","exception":false,"start_time":"2024-03-22T09:48:06.191255","status":"completed"},"tags":[]},"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>admin_name</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>North Rhine-Westphalia</td>\n","      <td>169</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Bavaria</td>\n","      <td>85</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Baden-Württemberg</td>\n","      <td>79</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Lower Saxony</td>\n","      <td>66</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Hesse</td>\n","      <td>48</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["               admin_name  count\n","0  North Rhine-Westphalia    169\n","1                 Bavaria     85\n","2       Baden-Württemberg     79\n","3            Lower Saxony     66\n","4                   Hesse     48"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["# Which admin_name has the most cities?\n","df[\"admin_name\"].value_counts().reset_index().head()"]},{"cell_type":"code","execution_count":43,"id":"4a6d2012","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.319147Z","iopub.status.busy":"2024-03-22T09:48:06.318717Z","iopub.status.idle":"2024-03-22T09:48:06.336524Z","shell.execute_reply":"2024-03-22T09:48:06.335302Z"},"papermill":{"duration":0.058656,"end_time":"2024-03-22T09:48:06.339079","exception":false,"start_time":"2024-03-22T09:48:06.280423","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      city      lat      lng  country iso2              admin_name  capital  \\\n","0   Berlin  52.5167  13.3833  Germany   DE                  Berlin  primary   \n","1  Hamburg  53.5500  10.0000  Germany   DE                 Hamburg    admin   \n","2   Munich  48.1372  11.5755  Germany   DE                 Bavaria    admin   \n","3  Cologne  50.9422   6.9578  Germany   DE  North Rhine-Westphalia      NaN   \n","\n","   population  population_proper  \n","0   3644826.0          3644826.0  \n","1   1841179.0          1841179.0  \n","2   1471508.0          1471508.0  \n","3   1085664.0          1085664.0  "]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["# How many cities have a population greater than 1 million?\n","df[df[\"population\"] > 1000000]"]},{"cell_type":"code","execution_count":44,"id":"af368eae","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.403133Z","iopub.status.busy":"2024-03-22T09:48:06.4024Z","iopub.status.idle":"2024-03-22T09:48:06.767213Z","shell.execute_reply":"2024-03-22T09:48:06.765981Z"},"papermill":{"duration":0.400194,"end_time":"2024-03-22T09:48:06.770472","exception":false,"start_time":"2024-03-22T09:48:06.370278","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","# Assuming you have lat_data and popul_data already defined\n","lat_data = df[\"lat\"]\n","popul_data = df[\"population\"]\n","\n","# Create a scatter plot\n","plt.scatter(lat_data, popul_data, alpha=0.5, color='b')\n","\n","# Add labels and title\n","plt.xlabel('Latitude')\n","plt.ylabel('Population')\n","plt.title('City Populations vs. Latitude')\n","\n","# Show the scatter plot\n","plt.grid(True)\n","plt.show()\n"]},{"cell_type":"code","execution_count":45,"id":"17bba54b","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:06.8405Z","iopub.status.busy":"2024-03-22T09:48:06.839593Z","iopub.status.idle":"2024-03-22T09:48:07.175334Z","shell.execute_reply":"2024-03-22T09:48:07.174075Z"},"papermill":{"duration":0.372566,"end_time":"2024-03-22T09:48:07.178502","exception":false,"start_time":"2024-03-22T09:48:06.805936","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1M0lEQVR4nO3dd3wUZf4H8M8m2Wx6KCEFktAJoTdLRAWVoiAQvcN6AofluBMBubPgnQpnQVEEPO9AbKgnJ6IHnBUiCkhVqpQQOgkhCT29bLLz++P7ezK7ySbZJJvsZvN5v155JZmdmX1mnt2Z7zzVoGmaBiIiIiIP4eXqBBARERE5E4MbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDG6IKOnTogEmTJrk6GW7BYDBg9uzZTt3n0KFDMXToUKfukxrOhg0bYDAYsGHDhkZ5P34+yBkY3FCzcfz4cfzhD39Ap06d4Ofnh5CQEAwePBiLFi1CYWFhldsdOnQIs2fPxqlTp5yankmTJsFgMJT/hISEoG/fvpg/fz6Ki4ud+l6NraHOmSdatmwZDAYDdu7c6eqkOGz58uVYuHChq5NBVCUfVyeAqDF8/fXXGD9+PEwmEyZMmIBevXqhpKQEmzdvxhNPPIGDBw9i6dKlAICUlBR4eelx/6FDhzBnzhwMHToUHTp0cGq6TCYT3n33XQDAlStX8MUXX+Avf/kLfvnlF3z66adOfa/GVN05W7dunWsSRXVy4403orCwEL6+vuXLli9fjgMHDmDGjBmuSxhRNRjckMc7efIk7rnnHrRv3x4//PADoqKiyl979NFHcezYMXz99dfly0wmU6OlzcfHB7/73e/K///Tn/6Ea665BitWrMAbb7yBtm3bNlpaGov1TZLcn5eXF/z8/FydDKJaYbUUebx58+YhLy8P7733nk1go3Tp0gXTp08v/9+6zc2yZcswfvx4AMBNN91UXoW0YcMGTJw4EWFhYTCbzZX2OWLECMTFxdU6rV5eXuXtDVSVzrlz5/Dggw8iIiICfn5+6Nu3Lz788EOb7U6dOgWDwYDXX38dCxYsQPv27eHv748hQ4bgwIEDNutW1aZh0qRJNZZMnT59Gn/6058QFxcHf39/tG7dGuPHj7epfqrunFX1/rU9xqVLl6Jz584wmUy46qqr8Msvv9ism5mZid///veIjo6GyWRCVFQUxo0bV2012euvvw6DwYDTp09Xem3WrFnw9fXF5cuXAQBHjx7Fb37zG0RGRsLPzw/R0dG45557kJ2dXe35q489e/bgtttuQ0hICIKCgnDLLbdg+/btNuuoKq4tW7Zg5syZaNOmDQIDA3HHHXfg/PnzNutaLBbMnj0bbdu2RUBAAG666SYcOnSoUpuzim1uhg4diq+//hqnT58uz1v1uVHvX/E8V9VuR+Wjv78/rr76avz00092j724uBjPP/88unTpApPJhJiYGDz55JNNvvqWGg5Lbsjjffnll+jUqROuu+66Wm974403Ytq0aXjzzTfxzDPPID4+HgAQHx+PBx54AB999BHWrl2L22+/vXybzMxM/PDDD3j++efrlN7jx48DAFq3bo3CwkIMHToUx44dw9SpU9GxY0esXLkSkyZNwpUrV2yCMgD46KOPkJubi0cffRRFRUVYtGgRbr75Zuzfvx8RERF1So+1X375BVu3bsU999yD6OhonDp1CosXL8bQoUNx6NAhBAQEVHvO7KntMS5fvhy5ubn4wx/+AIPBgHnz5uHOO+/EiRMnYDQaAQC/+c1vcPDgQTz22GPo0KEDzp07h6SkJKSmplYZwN1111148skn8dlnn+GJJ56wee2zzz7DiBEj0LJlS5SUlGDkyJEoLi7GY489hsjISKSnp+Orr77ClStXEBoaWs+zXNnBgwdxww03ICQkBE8++SSMRiPefvttDB06FBs3bsQ111xjs/5jjz2Gli1b4vnnn8epU6ewcOFCTJ06FStWrChfZ9asWZg3bx7GjBmDkSNHYt++fRg5ciSKioqqTctf//pXZGdn48yZM1iwYAEAICgoqNbH9N577+EPf/gDrrvuOsyYMQMnTpzA2LFj0apVK8TExJSvZ7FYMHbsWGzevBmPPPII4uPjsX//fixYsABHjhzB6tWra/3e1AxoRB4sOztbA6CNGzfO4W3at2+vTZw4sfz/lStXagC0H3/80Wa9srIyLTo6Wrv77rttlr/xxhuawWDQTpw4Ue37TJw4UQsMDNTOnz+vnT9/Xjt27Jj28ssvawaDQevTp4+maZq2cOFCDYD273//u3y7kpISLSEhQQsKCtJycnI0TdO0kydPagA0f39/7cyZM+Xr7tixQwOgPf744+XLhgwZog0ZMsRuetq3b2+zDID2/PPPl/9fUFBQabtt27ZpALSPPvqofFlV58ze+9f2GFu3bq1dunSpfN01a9ZoALQvv/xS0zRNu3z5sgZAe+211yq9d00SEhK0gQMH2iz7+eefbY5vz549GgBt5cqVtd6/PR988IEGQPvll1+qXCcxMVHz9fXVjh8/Xr7s7NmzWnBwsHbjjTdW2tewYcM0i8VSvvzxxx/XvL29tStXrmiapmmZmZmaj4+PlpiYaPM+s2fP1gDYfP5//PHHSnk5evToSp8V6/c/efKkzfKK+ygpKdHCw8O1fv36acXFxeXrLV26VANg8/n4+OOPNS8vL+2nn36y2eeSJUs0ANqWLVvsnjNq3lgtRR4tJycHABAcHOz0fXt5eeH+++/H//73P+Tm5pYv/+STT3DdddehY8eONe4jPz8fbdq0QZs2bdClSxc888wzSEhIwKpVqwAA33zzDSIjI3HvvfeWb2M0GjFt2jTk5eVh48aNNvtLTExEu3btyv+/+uqrcc011+Cbb76p7+ECAPz9/cv/NpvNuHjxIrp06YIWLVpg9+7dddpnbY/x7rvvRsuWLcv/v+GGGwAAJ06cKE+jr68vNmzYUF6N5Ki7774bu3btKi89A4AVK1bAZDJh3LhxAFBeMrN27VoUFBTUav91UVZWhnXr1iExMRGdOnUqXx4VFYX77rsPmzdvLv+cK4888ggMBkP5/zfccAPKysrKq9zWr1+P0tJS/OlPf7LZ7rHHHmvAI9Ht3LkT586dw5QpU2zaYE2aNKlSydfKlSsRHx+P7t2748KFC+U/N998MwDgxx9/bJQ0U9PSrIObTZs2YcyYMWjbti0MBkOdijc1TcPrr7+Obt26wWQyoV27dnjppZecn1iqk5CQEACwCT6cacKECSgsLCwPRlJSUrBr1y488MADDm3v5+eHpKQkJCUlYdOmTUhLS8OWLVvKb2KnT59G165dbXpvAXoVT8X2IV27dq30Ht26dXNal+zCwkI899xziImJgclkQlhYGNq0aYMrV67Uub1JbY8xNjbW5n8V6KhAxmQy4dVXX8W3336LiIgI3HjjjZg3bx4yMzNrTMv48ePh5eVVXn2jaRpWrlxZ3tYFADp27IiZM2fi3XffRVhYGEaOHIl//vOfDdbe5vz58ygoKLDbhis+Ph4WiwVpaWk2y2s6R+qcdunSxWa9Vq1a2QSODUW9f8XPq9FotAngAGnfdPDgwfKHAPXTrVs3ANJei6iiZt3mJj8/H3379sXkyZNx55131mkf06dPx7p16/D666+jd+/euHTpEi5duuTklFJdhYSEoG3btpUa1TpLjx49MHDgQPz73//GhAkT8O9//xu+vr646667HNre29sbw4YNa5C0VcVgMEDTtErLy8rKatz2sccewwcffIAZM2YgISEBoaGhMBgMuOeee2CxWBoiuZV4e3vbXW59TDNmzMCYMWOwevVqrF27Fs8++yzmzp2LH374Af37969y323btsUNN9yAzz77DM888wy2b9+O1NRUvPrqqzbrzZ8/H5MmTcKaNWuwbt06TJs2DXPnzsX27dsRHR3tnAOtB0fOUUOwLi2y5shnqyoWiwW9e/fGG2+8Yfd16/Y5REqzLrm57bbb8OKLL+KOO+6w+3pxcTH+8pe/oF27dggMDMQ111xj09o/OTkZixcvxpo1azB27Fh07NgRAwcOxPDhwxvpCMgRt99+O44fP45t27bVafuqLtjKhAkT8MMPPyAjIwPLly/H6NGjnfb02759exw9erRS4HD48OHy160dPXq00j6OHDli04i2ZcuWuHLlSqX17PUSqujzzz/HxIkTMX/+fPz2t7/F8OHDcf3111faX03nzFptj9FRnTt3xp///GesW7cOBw4cQElJCebPn1/jdnfffTf27duHlJQUrFixAgEBARgzZkyl9Xr37o2//e1v2LRpE3766Sekp6djyZIldUprddq0aYOAgACkpKRUeu3w4cPw8vKq9Q1endNjx47ZLL948aJDVXlV5a/63Ff8PFT8bKn3r/h5NZvNOHnypM2yzp0749KlS7jlllswbNiwSj916ZVInq9ZBzc1mTp1KrZt24ZPP/0Uv/76K8aPH49bb721/AupeuF89dVX6NixIzp06ICHHnqIJTdu5sknn0RgYCAeeughZGVlVXr9+PHjWLRoUZXbBwYGAqh8wVbuvfdeGAwGTJ8+HSdOnLAZt6a+Ro0ahczMTJteLqWlpfjHP/6BoKAgDBkyxGb91atXIz09vfz/n3/+GTt27MBtt91Wvqxz5844fPiwTdfgffv2YcuWLTWmx9vbu9LT/z/+8Y9KT+Y1nbP6HGNNCgoKKvX46dy5M4KDgx3qOvyb3/wG3t7e+M9//oOVK1fi9ttvLz8eQNpxlZaW2mzTu3dveHl52ew/NTW1PECrD29vb4wYMQJr1qyxqV7MysrC8uXLcf3115dXmTnqlltugY+PDxYvXmyz/K233nJo+8DAQLvVcJ07dwYgVf5KWVlZ+QCZyqBBg9CmTRssWbIEJSUl5cuXLVtW6TNz1113IT09He+8806l9yssLER+fr5DaabmpVlXS1UnNTUVH3zwAVJTU8sHUvvLX/6C7777Dh988AFefvllnDhxAqdPn8bKlSvx0UcfoaysDI8//jh++9vf4ocffnDxEZDSuXNnLF++HHfffTfi4+NtRijeunVrebfjqvTr1w/e3t549dVXkZ2dDZPJhJtvvhnh4eEA5Mn61ltvxcqVK9GiRQuMHj3aaWl/5JFH8Pbbb2PSpEnYtWsXOnTogM8//xxbtmzBwoULKzWU7tKlC66//nr88Y9/RHFxMRYuXIjWrVvjySefLF9n8uTJeOONNzBy5Eg8+OCDOHfuHJYsWYKePXtWapha0e23346PP/4YoaGh6NGjB7Zt24bvv/8erVu3tlmvpnNWn2OsyZEjR3DLLbfgrrvuQo8ePeDj44NVq1YhKysL99xzT43bh4eH46abbsIbb7yB3Nxc3H333Tav//DDD5g6dSrGjx+Pbt26obS0FB9//DG8vb3xm9/8pny9CRMmYOPGjQ5XBb3//vv47rvvKi2fPn06XnzxRSQlJeH666/Hn/70J/j4+ODtt99GcXEx5s2b59D+rUVERGD69OmYP38+xo4di1tvvRX79u3Dt99+i7CwsBpL3gYOHIgVK1Zg5syZuOqqqxAUFIQxY8agZ8+euPbaazFr1ixcunQJrVq1wqefflopGDQajXjxxRfxhz/8ATfffDPuvvtunDx5Eh988EGlNjcPPPAAPvvsM0yZMgU//vgjBg8ejLKyMhw+fBifffYZ1q5di0GDBtX6HJCHc2FPLbcCQFu1alX5/1999ZUGQAsMDLT58fHx0e666y5N0zTt4Ycf1gBoKSkp5dvt2rVLA6AdPny4sQ+BanDkyBHt4Ycf1jp06KD5+vpqwcHB2uDBg7V//OMfWlFRUfl6FbuCa5qmvfPOO1qnTp00b29vu12cP/vsMw2A9sgjjzicHtUVvCZZWVna73//ey0sLEzz9fXVevfurX3wwQc266hu0q+99po2f/58LSYmRjOZTNoNN9yg7du3r9I+//3vf2udOnXSfH19tX79+mlr1651qCv45cuXy9MSFBSkjRw5Ujt8+HCtzpm9rui1PcaKrNN54cIF7dFHH9W6d++uBQYGaqGhodo111yjffbZZ1Wd4kreeecdDYAWHBysFRYW2rx24sQJbfLkyVrnzp01Pz8/rVWrVtpNN92kff/99zbrDRkyRHPkEqu6T1f1k5aWpmmapu3evVsbOXKkFhQUpAUEBGg33XSTtnXrVrv7qtit3F537tLSUu3ZZ5/VIiMjNX9/f+3mm2/WkpOTtdatW2tTpkypdtu8vDztvvvu01q0aKEBsPncHD9+XBs2bJhmMpm0iIgI7ZlnntGSkpLsfm/+9a9/aR07dtRMJpM2aNAgbdOmTXY/HyUlJdqrr76q9ezZUzOZTFrLli21gQMHanPmzNGys7NrPMfU/Bg0rYFbmDURBoMBq1atQmJiIgDp/nn//ffj4MGDlRrnBQUFITIyEs8//zxefvllmxFqCwsLERAQgHXr1rHtTTOyZs0aJCYmYtOmTeVdkxvTqVOn0LFjR7z22mv4y1/+0ujvT57hypUraNmyJV588UX89a9/dXVyiOqM1VJV6N+/P8rKynDu3Lkqb1aDBw9GaWkpjh8/Xl7XfOTIEQB1bwRJTdM777yDTp064frrr3d1UogcUlhYaDNuEYDymb7tTc9B1JQ06+AmLy/PprfAyZMnsXfvXrRq1QrdunXD/fffjwkTJmD+/Pno378/zp8/j/Xr16NPnz4YPXo0hg0bhgEDBmDy5MlYuHAhLBYLHn30UQwfPrx8DAbybKqx+ddff41FixbVqpcQkSutWLECy5Ytw6hRoxAUFITNmzfjP//5D0aMGIHBgwe7OnlE9ePqejFXUnXJFX9U24GSkhLtueee0zp06KAZjUYtKipKu+OOO7Rff/21fB/p6enanXfeqQUFBWkRERHapEmTtIsXL7roiKixAdCCgoK0Bx98UDObzS5LR3XtUYjs2bVrl3bLLbdorVu31oxGoxYdHa1Nnz5dy83NdXXSiOqNbW6IiIjIo3CcGyIiIvIoDG6IiIjIozS7BsUWiwVnz55FcHAwG38SERE1EZqmITc3F23btq000W5FzS64OXv2LCdaIyIiaqLS0tJqnKC22QU3aij3tLS0Ws/HUpHZbMa6deswYsQIGI1GZySP6ol54n6YJ+6HeeKemC/Vy8nJQUxMjENTsjS74EZVRYWEhDgluAkICEBISAg/iG6CeeJ+mCfuh3ninpgvjnGkSQkbFBMREZFHYXBDREREHoXBDREREXkUBjdERETkURjcEBERkUdhcENEREQehcENEREReRQGN0RERORRGNwQERGRR2l2IxQTERHZY7EAqalAbi4QHAzExgI1zM9IborBDRERNXvJycCqVcDhw0BREeDnB3TvDtxxBxAf7+rUUW0xuCEiomYtORl4803gwgUgJgYIDATy84E9e4C0NGDaNAY4TQ0L3IiIqNmyWKTE5sIFoEcPICQE8PaW3z16yPLVq2U9ajoY3BARUbOVmipVUTExQMXJpg0GIDpaSnZSU12TPqobBjdERNRs5eZKG5vAQPuvBwbK67m5jZsuqh8GN0RE1GwFB0vj4fx8+6/n58vrwcGNmy6qHwY3RETUbMXGSq+otDRA02xf0zTgzBlpTBwb65r0Ud0wuCEiombLy0u6e4eFAYcOAdnZQGmp/D50SJYnJnK8m6aG2UVERM1afLx09+7fH7h4EThyRH4PGMBu4E0Vx7khIqJmLz4eiIvjCMWegsENERERJJDp0MHVqSBnYExKREREHoXBDREREXkUlwY3ixcvRp8+fRASEoKQkBAkJCTg22+/rXL9ZcuWwWAw2Pz4+fk1YoqJiIjI3bm0zU10dDReeeUVdO3aFZqm4cMPP8S4ceOwZ88e9OzZ0+42ISEhSElJKf/fUHG8bCIiImrWXBrcjBkzxub/l156CYsXL8b27durDG4MBgMiIyMbI3lERETUBLlNb6mysjKsXLkS+fn5SEhIqHK9vLw8tG/fHhaLBQMGDMDLL79cZSAEAMXFxSguLi7/PycnBwBgNpthNpvrlWa1fX33Q87DPHE/zBP3wzxxT8yX6tXmvBg0reKA041r//79SEhIQFFREYKCgrB8+XKMGjXK7rrbtm3D0aNH0adPH2RnZ+P111/Hpk2bcPDgQURHR9vdZvbs2ZgzZ06l5cuXL0dAQIBTj4WIiIgaRkFBAe677z5kZ2cjJCSk2nVdHtyUlJQgNTUV2dnZ+Pzzz/Huu+9i48aN6NGjR43bms1mxMfH495778ULL7xgdx17JTcxMTG4cOFCjSfHkfdPSkrC8OHDYTQa67Uvcg7mifthnrgf5ol7Yr5ULycnB2FhYQ4FNy6vlvL19UWXLl0AAAMHDsQvv/yCRYsW4e23365xW6PRiP79++PYsWNVrmMymWAymexu66wPjzP3Rc7BPHE/zBP3wzxxT8wX+2pzTtxunBuLxWJT0lKdsrIy7N+/H1FRUQ2cKiIiImoqXFpyM2vWLNx2222IjY1Fbm4uli9fjg0bNmDt2rUAgAkTJqBdu3aYO3cuAODvf/87rr32WnTp0gVXrlzBa6+9htOnT+Ohhx5y5WEQERGRG3FpcHPu3DlMmDABGRkZCA0NRZ8+fbB27VoMHz4cAJCamgovq1nLLl++jIcffhiZmZlo2bIlBg4ciK1btzrUPoeIiIiaB5cGN++99161r2/YsMHm/wULFmDBggUNmCIiIiJq6tyuzQ0RERFRfTC4ISIiIo/C4IaIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPIpLg5vFixejT58+CAkJQUhICBISEvDtt99Wu83KlSvRvXt3+Pn5oXfv3vjmm28aKbVERETUFLg0uImOjsYrr7yCXbt2YefOnbj55psxbtw4HDx40O76W7duxb333osHH3wQe/bsQWJiIhITE3HgwIFGTjkRERG5K5cGN2PGjMGoUaPQtWtXdOvWDS+99BKCgoKwfft2u+svWrQIt956K5544gnEx8fjhRdewIABA/DWW281csqJiIjIXfm4OgFKWVkZVq5cifz8fCQkJNhdZ9u2bZg5c6bNspEjR2L16tVV7re4uBjFxcXl/+fk5AAAzGYzzGZzvdKstq/vfsh5mCfuh3nifpgn7on5Ur3anBeXBzf79+9HQkICioqKEBQUhFWrVqFHjx52183MzERERITNsoiICGRmZla5/7lz52LOnDmVlq9btw4BAQH1S/z/S0pKcsp+yHmYJ+6HeeJ+mCfuifliX0FBgcPrujy4iYuLw969e5GdnY3PP/8cEydOxMaNG6sMcGpr1qxZNqU9OTk5iImJwYgRIxASElKvfZvNZiQlJWH48OEwGo31TSo5AfPE/TBP3A/zxD0xX6qnal4c4fLgxtfXF126dAEADBw4EL/88gsWLVqEt99+u9K6kZGRyMrKslmWlZWFyMjIKvdvMplgMpkqLTcajU778DhzX+QczBP3wzxxP8wT98R8sa8258TtxrmxWCw2bWSsJSQkYP369TbLkpKSqmyjQ0RERM2PS0tuZs2ahdtuuw2xsbHIzc3F8uXLsWHDBqxduxYAMGHCBLRr1w5z584FAEyfPh1DhgzB/PnzMXr0aHz66afYuXMnli5d6srDICIiIjfi0uDm3LlzmDBhAjIyMhAaGoo+ffpg7dq1GD58OAAgNTUVXl564dJ1112H5cuX429/+xueeeYZdO3aFatXr0avXr1cdQhERETkZlwa3Lz33nvVvr5hw4ZKy8aPH4/x48c3UIqIiIioqXO7NjdERERE9cHghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8iguDW7mzp2Lq666CsHBwQgPD0diYiJSUlKq3WbZsmUwGAw2P35+fo2UYiIiInJ3Lg1uNm7ciEcffRTbt29HUlISzGYzRowYgfz8/Gq3CwkJQUZGRvnP6dOnGynFRERE5O58XPnm3333nc3/y5YtQ3h4OHbt2oUbb7yxyu0MBgMiIyMbOnlERETUBLlVm5vs7GwAQKtWrapdLy8vD+3bt0dMTAzGjRuHgwcPNkbyiIiIqAlwacmNNYvFghkzZmDw4MHo1atXlevFxcXh/fffR58+fZCdnY3XX38d1113HQ4ePIjo6OhK6xcXF6O4uLj8/5ycHACA2WyG2WyuV5rV9vXdDzkP88T9ME/cD/PEPTFfqleb82LQNE1rwLQ47I9//CO+/fZbbN682W6QUhWz2Yz4+Hjce++9eOGFFyq9Pnv2bMyZM6fS8uXLlyMgIKBeaSYiIqLGUVBQgPvuuw/Z2dkICQmpdl23CG6mTp2KNWvWYNOmTejYsWOttx8/fjx8fHzwn//8p9Jr9kpuYmJicOHChRpPTk3MZjOSkpIwfPhwGI3Geu2LnIN54n6YJ+6HeeKemC/Vy8nJQVhYmEPBjUurpTRNw2OPPYZVq1Zhw4YNdQpsysrKsH//fowaNcru6yaTCSaTqdJyo9HotA+PM/dFzsE8cT/ME/fDPHFPzBf7anNOXBrcPProo1i+fDnWrFmD4OBgZGZmAgBCQ0Ph7+8PAJgwYQLatWuHuXPnAgD+/ve/49prr0WXLl1w5coVvPbaazh9+jQeeughlx0HERERuQ+XBjeLFy8GAAwdOtRm+QcffIBJkyYBAFJTU+HlpXfqunz5Mh5++GFkZmaiZcuWGDhwILZu3YoePXo0VrKJiIjIjbm8WqomGzZssPl/wYIFWLBgQQOliIiIiJo6txrnhoiIiKi+GNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFHYXBDREREHoXBDREREXkUBjdERETkURjcEBERkUdhcENEREQehcENEREReRQGN0RERORRGNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFHYXBDREREHoXBDREREXkUBjdERETkURjcEBERkUdhcENEREQehcENEREReZQ6BTdZWVl44IEH0LZtW/j4+MDb29vmh4iIiMhVfOqy0aRJk5Camopnn30WUVFRMBgMzk4XERERUZ3UKbjZvHkzfvrpJ/Tr18/JySEiIiKqnzpVS8XExEDTNGenhYiIiKje6hTcLFy4EE8//TROnTrl5OQQERER1U+dqqXuvvtuFBQUoHPnzggICIDRaLR5/dKlS05JHBEREVFt1Sm4WbhwoZOTQUREROQcdQpuJk6c6Ox0EBERETlFnYIbACgrK8Pq1auRnJwMAOjZsyfGjh3LcW6IiIjIpeoU3Bw7dgyjRo1Ceno64uLiAABz585FTEwMvv76a3Tu3NmpiSQiIiJyVJ16S02bNg2dO3dGWloadu/ejd27dyM1NRUdO3bEtGnTnJ1GIiIiIofVKbjZuHEj5s2bh1atWpUva926NV555RVs3LjR4f3MnTsXV111FYKDgxEeHo7ExESkpKTUuN3KlSvRvXt3+Pn5oXfv3vjmm2/qchhERETkgeoU3JhMJuTm5lZanpeXB19fX4f3s3HjRjz66KPYvn07kpKSYDabMWLECOTn51e5zdatW3HvvffiwQcfxJ49e5CYmIjExEQcOHCgLodCREREHqZOwc3tt9+ORx55BDt27ICmadA0Ddu3b8eUKVMwduxYh/fz3XffYdKkSejZsyf69u2LZcuWITU1Fbt27apym0WLFuHWW2/FE088gfj4eLzwwgsYMGAA3nrrrbocChEREXmYOgU3b775Jjp37oyEhAT4+fnBz88PgwcPRpcuXbBo0aI6JyY7OxsAbKq7Ktq2bRuGDRtms2zkyJHYtm1bnd+XiIiIPEedeku1aNECa9aswdGjR3H48GEAQHx8PLp06VLnhFgsFsyYMQODBw9Gr169qlwvMzMTERERNssiIiKQmZlpd/3i4mIUFxeX/5+TkwMAMJvNMJvNdU6v2of1b3I95on7YZ64H+aJe2K+VK8256XO49wAQNeuXdG1a9f67KLco48+igMHDmDz5s1O2Z8yd+5czJkzp9LydevWISAgwCnvkZSU5JT9kPMwT9wP88T9ME/cE/PFvoKCAofXdTi4mTlzJl544QUEBgZi5syZ1a77xhtvOJwAAJg6dSq++uorbNq0CdHR0dWuGxkZiaysLJtlWVlZiIyMtLv+rFmzbNKbk5ODmJgYjBgxAiEhIbVKZ0VmsxlJSUkYPnx4pfm1yDWYJ+6HeeJ+mCfuiflSPVXz4giHg5s9e/aUFwnt2bOn9qmyQ9M0PPbYY1i1ahU2bNiAjh071rhNQkIC1q9fjxkzZpQvS0pKQkJCgt31TSYTTCZTpeVGo9FpHx5n7oucg3nifpgn7od54p6YL/bV5pw4HNz8+OOPdv+uj0cffRTLly/HmjVrEBwcXN5uJjQ0FP7+/gCACRMmoF27dpg7dy4AYPr06RgyZAjmz5+P0aNH49NPP8XOnTuxdOlSp6SJiIiImrY69ZaaPHmy3XFu8vPzMXnyZIf3s3jxYmRnZ2Po0KGIiooq/1mxYkX5OqmpqcjIyCj//7rrrsPy5cuxdOlS9O3bF59//jlWr15dbSNkIiIiaj7q1KD4ww8/xCuvvILg4GCb5YWFhfjoo4/w/vvvO7QfTdNqXGfDhg2Vlo0fPx7jx4936D2IiIioealVcJOTk1M+aF9ubi78/PzKXysrK8M333yD8PBwpyeSiIiIyFG1Cm5atGgBg8EAg8GAbt26VXrdYDDY7XZNRERE1FhqFdz8+OOP0DQNN998M7744gubkYR9fX3Rvn17tG3b1umJJCIiInJUrYKbIUOGAABOnjyJmJgYeHnVqT0yETUTFguQmip/p6YCHTsCvGwQUUOrU4Pi9u3bA5DRAlNTU1FSUmLzep8+feqfMiJq0pKTgVWrgGPHgHHjgJdeArp0Ae64A4iPd3XqiMiT1Sm4OX/+PH7/+9/j22+/tft6WVlZvRJFRE1bcjLw5pvAhQtAhw6yrHVrYM8eIC0NmDaNAQ4RNZw6FRDPmDEDV65cwY4dO+Dv74/vvvsOH374Ibp27Yr//e9/zk4jETUhFouU2Fy4APToAagRI4KD5f8LF4DVq2U9IqKGUKeSmx9++AFr1qzBoEGD4OXlhfbt22P48OEICQnB3LlzMXr0aGenk4iaiNRU4PBhICYGMBhsXzMYgOhoKdlJTdVLdYiInKlOJTf5+fnl49m0bNkS58+fBwD07t0bu3fvdl7qiKjJyc0FioqAwED7rwcGyut2BjknInKKOgU3cXFxSElJAQD07dsXb7/9NtLT07FkyRJERUU5NYFE1LQEBwN+fkB+vv3X8/Pl9QoDnBMROU2dqqWmT59ePt/T888/j1tvvRWffPIJfH19sWzZMmemj4iamNhYoHt3aTzco4fta5oGnDkDDBgg6xERNYQ6BTe/+93vyv8eOHAgTp8+jcOHDyM2NhZhYWFOSxwRNT1eXtLdOy0NOHQI+P+RI5CTA5w+DYSFAYmJHO+GiBpOnYKbigICAjBgwABn7IqIPEB8vHT3VuPcAMClS1Jik5jIbuBE1LAcDm5mzpzp8E7feOONOiWGiDxHfDwQFwecPAkcOAD89a8coZiIGofDwc2ePXscWs9Qse8nETVbXl7StubAAfnNwIaIGoPDwc2PP/7YkOkgIiIicgo+RxEREZFHqVOD4ptuuqna6qcffvihzgkiIiIiqo86BTf9+vWz+d9sNmPv3r04cOAAJk6c6Ix0EREREdVJnYKbBQsW2F0+e/Zs5OXl1StBRERERPXh1DY3v/vd7/D+++87c5dEREREteLU4Gbbtm3w8/Nz5i6JiIiIaqVO1VJ33nmnzf+apiEjIwM7d+7Es88+65SEEREREdVFnYKb0NBQm/+9vLwQFxeHv//97xgxYoRTEkZERERUF3UKbj744ANnp4OIiIjIKeo1cebOnTuRnJwMAOjRowcGDhzolEQRERER1VWdgpszZ87g3nvvxZYtW9CiRQsAwJUrV3Ddddfh008/RXR0tDPTSEREROSwOvWWeuihh2A2m5GcnIxLly7h0qVLSE5OhsViwUMPPeTsNBIRERE5rE4lNxs3bsTWrVsRFxdXviwuLg7/+Mc/cMMNNzgtcURERES1VaeSm5iYGJjN5krLy8rK0LZt23onioiIiKiu6hTcvPbaa3jsscewc+fO8mU7d+7E9OnT8frrrzstcURERES1VadqqUmTJqGgoADXXHMNfHxkF6WlpfDx8cHkyZMxefLk8nUvXbrknJQSEREROaBOwc3ChQudnAwiIiIi56hTcDNx4kRnp4OIiIjIKeo8iF9ZWRlWr15dPohfz549MXbsWHh7ezstcURERES1Vafg5tixYxg1ahTS09PLu4PPnTsXMTEx+Prrr9G5c2enJpKIiIjIUXXqLTVt2jR07twZaWlp2L17N3bv3o3U1FR07NgR06ZNc3g/mzZtwpgxY9C2bVsYDAasXr262vU3bNgAg8FQ6SczM7Muh0FEREQeqM6D+G3fvh2tWrUqX9a6dWu88sorGDx4sMP7yc/PR9++fTF58mTceeedDm+XkpKCkJCQ8v/Dw8Md3paIiIg8W52CG5PJhNzc3ErL8/Ly4Ovr6/B+brvtNtx22221fv/w8PDyOa2IiDyVxQKkpgK5uUBwMBAbC3jVqbydqHmpU3Bz++2345FHHsF7772Hq6++GgCwY8cOTJkyBWPHjnVqAu3p168fiouL0atXL8yePbva0qLi4mIUFxeX/5+TkwMAMJvNdkdZrg21fX33Q87DPHE/zJO6SUkBvvoKOHIEKCoC/PyAbt2A228HrGa+qRPmiXtivlSvNufFoGmaVts3uHLlCiZNmoQvv/zSZhC/sWPHYtmyZQgNDa3tLmEwGLBq1SokJiZWuU5KSgo2bNiAQYMGobi4GO+++y4+/vhj7NixAwMGDLC7zezZszFnzpxKy5cvX46AgIBap5OIiIgaX0FBAe677z5kZ2fbNE2xp1bBjcViwWuvvYb//e9/KCkpQWxsLCZOnAiDwYD4+Hh06dKlzol2JLixZ8iQIYiNjcXHH39s93V7JTcxMTG4cOFCjSenJmazGUlJSRg+fDiMRmO99kXOwTxxP8yT2rFYgAULgF9/lRIag0F/TdOkRKdvX2DGjLpXUTFP3BPzpXo5OTkICwtzKLipVbXUSy+9hNmzZ2PYsGHw9/fHN998g9DQULz//vv1SnB9XH311di8eXOVr5tMJphMpkrLjUaj0z48ztwXOQfzxP0wTxxz6hRw6BAQGSnBTMXHz4gI4OBBICMD6NChfu/FPHFPzBf7anNOahX3f/TRR/jXv/6FtWvXYvXq1fjyyy/xySefwGKx1DqRzrJ3715ERUW57P2JiJwpN1fa2AQG2n89MFBet9Ong4j+X61KblJTUzFq1Kjy/4cNGwaDwYCzZ88iOjq61m+el5eHY8eOlf9/8uRJ7N27F61atUJsbCxmzZqF9PR0fPTRRwBkTquOHTuiZ8+eKCoqwrvvvosffvgB69atq/V7ExG5o+BgaTycnw/YK3nPz5fXg4MbP21ETUWtgpvS0lL4+fnZLDMajXVu2b1z507cdNNN5f/PnDkTgMxdtWzZMmRkZCA1NbX89ZKSEvz5z39Geno6AgIC0KdPH3z//fc2+yAiaspiY4Hu3YE9e4AePSq3uTlzBhgwQNYjIvtqFdxomoZJkybZtGEpKirClClTEGhVhvrf//7Xof0NHToU1bVnXrZsmc3/Tz75JJ588snaJJmIqEnx8gLuuANIS5O2N9HRUhWVny+BTVgYkJjI8W6IqlOr4MbebOC/+93vnJYYIiIC4uOBadOAVauAw4eB9HSpihowQAKb+HhXp5DIvdUquPnggw8aKh1ERGQlPl66gnOEYqLaq9MIxURE1PC8vOrf3ZuoOeIzABEREXkUBjdERETkURjcEBERkUdhcENEREQehcENEREReRQGN0RERORRGNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFHYXBDREREHoXBDREREXkUBjdERETkURjcEBERkUdhcENEREQehcENEREReRQGN0RERORRGNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFH8XF1Asg1LBYgNRXIzQWCg4HYWMCLoS4REXkABjfNUHIysGoVcPgwUFQE+PkB3bsDd9wBxMe7OnVERET1w+CmmUlOBt58E7hwAYiJAQIDgfx8YM8eIC0NmDaNAQ4RETVtrIhoRiwWKbG5cAHo0QMICQG8veV3jx6yfPVqWY+IiKipYnDTjKSmSlVUTAxgMNi+ZjAA0dFSspOa6pr0EREROQODm2YkN1fa2AQG2n89MFBez81t3HQRERE5E4ObZiQ4WBoP5+fbfz0/X14PDm7cdBERETkTg5tmJDZWekWlpQGaZvuapgFnzkhj4thY16SPiIjIGRjcNCNeXtLdOywMOHQIyM4GSkvl96FDsjwxkePdEBFR0+bS29imTZswZswYtG3bFgaDAatXr65xmw0bNmDAgAEwmUzo0qULli1b1uDp9CTx8dLdu39/4OJF4MgR+T1gALuBExGRZ3DpODf5+fno27cvJk+ejDvvvLPG9U+ePInRo0djypQp+OSTT7B+/Xo89NBDiIqKwsiRIxshxZ4hPh6Ii+MIxURE5JlcGtzcdtttuO222xxef8mSJejYsSPmz58PAIiPj8fmzZuxYMECBje15OUFdOjg6lQQERE5X5MaoXjbtm0YNmyYzbKRI0dixowZVW5TXFyM4uLi8v9zcnIAAGazGWazuV7pUdvXdz/kPMwT98M8cT/ME/fEfKlebc5LkwpuMjMzERERYbMsIiICOTk5KCwshL+/f6Vt5s6dizlz5lRavm7dOgQEBDglXUlJSU7ZDzkP88T9ME/cD/PEPTFf7CsoKHB43SYV3NTFrFmzMHPmzPL/c3JyEBMTgxEjRiAkJKRe+zabzUhKSsLw4cNhNBrrm1RyAuaJ+2GeuB/miXtivlRP1bw4okkFN5GRkcjKyrJZlpWVhZCQELulNgBgMplgMpkqLTcajU778DhzX+QczBP3wzxxP8wT98R8sa8256RJ9Y9JSEjA+vXrbZYlJSUhISHBRSkiIiIid+PS4CYvLw979+7F3r17AUhX77179yL1/2dunDVrFiZMmFC+/pQpU3DixAk8+eSTOHz4MP71r3/hs88+w+OPP+6K5BMREZEbcmlws3PnTvTv3x/9+/cHAMycORP9+/fHc889BwDIyMgoD3QAoGPHjvj666+RlJSEvn37Yv78+Xj33XfZDZyIiIjKubTNzdChQ6FVnOTIir3Rh4cOHYo9e/Y0YKqIiIioKWtSbW6IiIiIasLghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPAqDGyIiIvIoDG6IiIjIozC4ISIiIo/C4IaIiIg8CoMbIiIi8igunVuKyBNZLEBqKpCbCwQHA7GxgBcfI4iIGg2DGyInSk4GVq0CDh8GiooAPz+ge3fgjjuA+HhXp46IqHlgcEPkJMnJwJtvAhcuADExQGAgkJ8P7NkDpKUBU6fKMpboEBE1LAY3RE5gsUiJzYULQI8egMEgy0NC5P/t24GZM4HwcKC4mCU6REQNic+NRE6QmipVUTExemCjXLgAZGTI676+QFwcEBYmJTpvviklPkRE5DwMboicIDdX2tgEBtou1zQJasxmec3XF/D21kt0LlwAVq+Wkh8iInIOBjdEThAcLFVN+fm2y7OzJYDx8wOMRsBk0l8zGIDoaCm5SU1t3PQSEXkyBjdEThAbK21o0tKktEYpLpZSm6IiqYoKDbXdLjBQXsvNbdz0EhF5MgY3RE7g5SWNg8PCgEOHpMSmtFSCm/x8KbXp3r1ye5z8fCnVCQ52TbqJiDwRgxsiJ4mPB6ZNA/r3By5eBI4ckVKb7t2BqCgJfKxpGnDmjGwXG+uaNBMReSJ2BSdyovh46Q1lPUJxfj7w1ltSohMdrY9/c+aMBDyJiRzvhojImRjcEDmZlxfQoYPtsmnT9JGL09OlKmrAAAls6jrODad5ICKyj8GNB+JNz/3YK9GpT75wmgcioqoxuPEwvOm5L3slOnVR0zQP06Yxr4moeePzvAdRN709e6QtB0fC9TwVp3kICeGggEREFTG48RDuetOzWIBTp4D9++U3b7r1U900DxwUkIhIsFrKQ9TmplexaqSh2uiwisz5qprmQQkMlAbLHBSQiJozBjceoq43vYYKQNgupGFYT/MQElL5dQ4KSETEaimPUdXcRoq9m15DtdFx1yoyT1DVNA8ABwUkcgesincPLLnxEOqmt2ePBBDWVVPqpjdggH7TqxiAqPVVAHLokAQgcXG1r6KqTxUZVU9N85CWxkEByT1w6Akdq+LdB4MbD1Hbm15DBiBsF9Kw1DQPzh4UsCa8iVFFvJnrWBXvXhjceJDa3PQaMgBhu5CG5+xBAWvCmxhVxJu5riFLwqluGNx4GEdveg0ZgNS2iozqxlmDAtaENzH7mnNJFm/mtlgV734Y3HgYRy+4DRmAsF2I5+BNzL7mXpLFm7ktVsW7H7e4HP3zn/9Ehw4d4Ofnh2uuuQY///xzlesuW7YMBoPB5sfPz68RU+u+kpOBV14BnnsOeOEF+f3KK/Z7PakAJCxMblDZ2UBpqfw+dKj+AYiqIuvfH7h4EThyRH4PGNB8n/SbIg4aWBlHAnfsZl5U1Hxu5nXprUoNy+UlNytWrMDMmTOxZMkSXHPNNVi4cCFGjhyJlJQUhIeH290mJCQEKSkp5f8bKl51m6G6VB00dMPUxm4XQs7HJ1JbLMkSbFdni1Xx7sflwc0bb7yBhx9+GL///e8BAEuWLMHXX3+N999/H08//bTdbQwGAyIjIxszmW6tPhfchg5AGqtdCDUM3sRssTpG8GZui1Xx7selwU1JSQl27dqFWbNmlS/z8vLCsGHDsG3btiq3y8vLQ/v27WGxWDBgwAC8/PLL6Nmzp911i4uLUVxcXP5/Tk4OAMBsNsNsNtcr/Wr7+u6nvlJTgWPH5GLq7V359fbtgaNHgZMnq77YtGun/11WJj9NkbvkiaeIipKb16+/AqGhlW9iWVlA376yXlWn3JPyRFXfBgfbv1EFB8s5yc6u+ny4A2fkydixwNmzcm1p2xYICAAKCmRZRAQwZkzTvpbUVpcuwKOPAl99JdXwWVkS+A8aBIweLa/XdLo96bvSEGpzXgyaVnGc08Zz9uxZtGvXDlu3bkVCQkL58ieffBIbN27Ejh07Km2zbds2HD16FH369EF2djZef/11bNq0CQcPHkR0dHSl9WfPno05c+ZUWr58+XIEBAQ494CIiIioQRQUFOC+++5DdnY2QuwVJVtxebVUbSUkJNgEQtdddx3i4+Px9ttv44UXXqi0/qxZszBz5szy/3NychATE4MRI0bUeHJqYjabkZSUhOHDh8NoNNZrX/WRmgq89BLQurX96oGcHODSJeCvf/X8YmJ3yRNPk5KiP5Gq3kFxcfJEGhdX/baelCcWC7BggZRkxcVVLslKSZGSrBkz3LsKwpl5YrFI1UteHhAUJFUy7nzs7syTvisNQdW8OMKlwU1YWBi8vb2RlZVlszwrK8vhNjVGoxH9+/fHsWPH7L5uMplgMpnsbuesD48z91UXHTtKkWdV9d+nT0v9d8eOzeei4+o88TS9eslnqz5tszwlT8aNk+/UgQP221aMHQvYueS4JWflSefOTkgMlfOU74qz1eacuPRW5+vri4EDB2L9+vXlyywWC9avX29TOlOdsrIy7N+/H1FRUQ2VTLfX0N26iQC9cXjv3vK7uX6eOMwBkftzebXUzJkzMXHiRAwaNAhXX301Fi5ciPz8/PLeUxMmTEC7du0wd+5cAMDf//53XHvttejSpQuuXLmC1157DadPn8ZDDz3kysNwOVfNN0TUHHGYAyL35vLg5u6778b58+fx3HPPITMzE/369cN3332HiIgIAEBqaiq8rK4Yly9fxsMPP4zMzEy0bNkSAwcOxNatW9GjRw9XHYLb4AWXqPFwmAMi9+Xy4AYApk6diqlTp9p9bcOGDTb/L1iwAAsWLGiEVDVNvOASEVFzx2d6IiIi8ihuUXJDTUNzngWZiIiaDgY35JCDB4Fly6SxssUCtGolbXyayyzIRETUdDC4oRp99ZXMMp6ZKeN3+PkBV64A585VPSknERGRqzC4oUqsq58yMoA5c+R3dDTg6yvzo1y+DKgpu5rDLMgVsYqOiMh9MbghG8nJ+lg5hYUyCGB6uky+6ecn65hMQJs2wPnz8vehQ3WfBbkpBgnW50hNRdC9O6voiIjcBYMbKpecDLz5JnDhAhATIyU0O3fKaMfnzkmpTWCgrGswACEh+rxVubl1ez93DhKsAy913Pv3A599JqVWsbH60Pt79rCKjojIXTC4IQByI1+1SgIbNT9VVhbg4wP4+wMlJfJaQIA+d5WvrwQ2Xl72J+ysTsVAyt2CBOvA69w5OReATA5YUiJz6URESIAXEiLn7NCh5llF54maYokiEekY3BAAuZAfPiyBhgpeTCYJbIqKJPAoKJASC1U9VVwsP/HxtZtt3F4gBbhPkGAdeAUESPVbfr6UZGVnS1CTmSmlVtdcI1V0BoO0SUpOrnsVHbkHdy9RJKKa8VmEAMgTalGRXv0CAKGhcuP29ZWfwkIJcCwW+Ts9HYiMBCZOrF0QYi+QUioGCQ3JYgFOnZKqplOn5H/rwCs+XmZ6LiwEoqKA1q2liq6oSCYjLSiQ49A02V9goLxWlyo6cg8qsN2zR/I4Lk5+79kjy5OTXZ1CInIES27cjKuKw4OD5Qk1P1/+zs6WUpm2baXbd2kpUFYmyzIz5Xe7dsCzzwI9e9buvewFUtYCAyVwasggoaqn86uv1gOvnBwJckJDJejy8ZEgT1VNhYTI69nZQIsWcu78/GpfRUfuwd1LFInIcQxu3Igri8NjY+W9Nm6U6peLFyWgUW1ufH1l4L6oKMDbW9IzcWLtAxvANpAKCan8ekMHCdW199m7V4Ka9u3l9dJSwGiU7UwmIChI2hmVlkqVVW6uBHqaJqU8AwbUroqOnKe+Dwa1KVFktSORe2Nw4yT1vbC6uoGtlxfQpw/wySdSEtGmDdCypaThzBkJQqZOlZKN+pYoqUBqzx7bJ2Sg4YOEmp7Of/lFGg/n5Ukw4+MjwZ7JJOuGhkoeZ2dLSZaXlwQ3hw5J9UViIp/qHeXMUkpnPBi4Q4lidZxxvtQ+srP1/5sSNvQmRzG4cYKUFOB//6v7hdWR4vD//he491692sjZX2qLBfj1V3k6jYiQkpvLl+Xm3q2blF6cOyclNfV9Xy8vOTdpaXJs0dF6MHfmTNVBgjMubDU9nXfrJgMWpqQAgwZJWjIyJNgDJNDp2lVKs06ckDwymyUYS0xkg1NHObOU0lkPBq4uUayOM86X9T5KS4G77wYWLADGjWsan1s29KbaYHDjBEuWyNN+XS+sNd1wAwKAzz8Hdu2SYKMhvtQqDT172ra5MZmktCInx7lF8vHxcm7UxSo9XY6rqiDBWRe2mp7Og4IkuAsKkveMjpYg7+xZeT04WAKgggJpczR+PNC3L58ga8OZpZS1aScDVB8cu7JEsTrOOF8V96ECtF9/BU6fdv3QCzVxdck2NT0MbupBFeleulS/BojV3XDPn5fePBcuAL16SWBR2y+1IyUe1mkwGKSBrLWGKJKPj5dzU1PanHlhc+TpPDwcmDAB+PlnCabCw/UeUeHh8nvgQJbU1IWzG+062k7mhx/0/KwqOK5riWJDcsb5srcPtW5cHHDggOMBoCuwoTfVBYObejhzRn63a1e/BohV3XA1TS7GubnS/qV1a2nMW5svtaMlHq4qkvfyqv7cOPvC5ujT+c03y0/FEYobqlrQFVzRfsHZjXYdaSeTnAwsXSr5W1NwXFOJYlycDBvQWOfMGefLmQGgK7ChN9UFg5t6yMuT3wEB9l93tLSjqhtudraU3ADS5iM0VN/GkS91bUo8GqpI3t16sNh7Og8IkHY1Z87IeR47Vk+jp14sXdV+wdmNdmsKyvPypMrYywu46irHguOqShRTUoBXXmncc+aM8+XsALCxuXtDb3JPDG7qIShIfhcU2P/iOVraUVVx+MWLUuUVHi4X0Yo39+q+1I6WeHTtKjf13FzpCZWa6rwieWf2YAkIkPF2rNsBGQx1u7BZP53v2CENg/PzZV/+/sCaNXKcnlrl5Mr2C84uIawpKD9yRP6Oi6tdcFyxRNFV58wZ56shAsDG5Og5CAxs3FI1cm8MbuohOlrqq9PTJUioT2mHveLw0lIpSejdW++tY626C1vFEg9Ns20k3K4dsG0b8Kc/SamFxSLj2LRuLa9dvFhzI9/qOLMHS3Ex8OOPchFWY++EhclNzdfX/jlITZWgs6qLXFyc9IbaskXaF111lQxYWFBQOY2e1P3U1e0XnF1CWFM7GesG4vY4Ehy78pw543w1VADYWBw5B9HRwH/+I6Vr7lSlRq7D4KYe1IWsVSvnlHZULA4PDJQv7N698iWu6sIWHV35icW6KPf8eQl01KB0Pj76DTsgQH8yunJFnuDatAGGDpWgpFs3uaB5eTl+k3fmzSA/X4Kvs2flOH19pet1RoYEa61bS1rVxT0lRX6/9JIEQ/YucsnJ0rX+88/l3LRqJT1G/P3l2K3TaLFISY6jpU/2ZhJ3p3Y6rm6/0BCNdqtrJ3PVVcBHH9Wv5MOV58wZ58vePtTxpqRI4BceLteGrCzbklHA9dU+NZ0Db2+9WtndqtTIdRjcOMGUKfo4N/Up7QAqF4ffead8aau6sPXuDcybZ38aAT8/ueAePCglEqGhMl5NdjZw9KhczNq2lRu62SzTKhQVSeD0669Av36S/jvukLQ4WsXkrJuBCixatpQB83Jy5Abl6ysX5/R0ubCpNjLJydItf/hwvQSq4kUOkBKl06flONq1k21VsKQmwoyOBrZvl3NXXOzYRbOqmcQjImSfkZHA9de7tuu4O7RfqO0wAI7u0147GUAaydan5MPV58wZ56viPrKypMdf375yHXnrLflMq2lGVMlomzbuMa1IVeegXz95QElPZ08qssXgxgni4oCnn26YqovqLmy9ewNff22/6ic1VapbfvhBggQ1c7Wm6dMH+PjIRdtgkP8LC6W0IzBQ1vP1lX39+qukpazMsZu8s24G1mPvFBfrpU+5uZL29u3lIhwYqJcWXbok2wYHyzLri9yqVbLswgXJn9On5SnVy0vOjyrhCguTEq0TJ2S6iYSEmi+aFWcSP3dOzqXBIMeRlQVs2iQlRr16SRDliiJzdxmoztFhAGqjqp539S35aMhz5mhpqDPOl/U+srOltHfUKOCf/5SHG7NZAvDSUj3Yv/pq+V64w7Qi9s6BxQLMns2eVFQZgxsnqalLc33Y+1JHR0uJTXVVPyEhEhQA8tvXV7a/ckVKPEJDpUSnqEj2YzbLvs1mff34eLkhA1KKpC6m1T0ZOetmYB0khYTIjci63VBgoJRA5ebqgVC7dpX3oy5yO3fK/x06yEWx4tQK1hNh5uRIOh25aMbG2s4kvmWLpDsqSs71qVNSYtaliz4Z5+7dzisyr02boMYYqK60VEq9VBVnRIQsT00FOnbU09aQ3xlr1T0gjB0r1ZH791d97mJjpXp261b5289Pr7apzzmrbYN7Z5wvtQ+zWT6XX38t7etuvFFKuC5elO9B69ZSkvvTT8Dgwe4zrUjFc7B/v+tLIsk9MbhpIip+qU+ckJt1UJDcjK3ryNXN99gxqXby8pISjdxcmc3a21supICUxhQUyI/JJK+p0hxfX7lw5+fLPtLSZJ2SEr0hb7t2lZ+MHLmB9usnN+XqbioVg6SKgwtmZ+tBknWvKnvU07r628vLdmoFdbxqP2fOyHpRUVXvLz1d0rB1qzTODg+X/y9ckH2lpclTb0mJ/Jw9K+178vLkvJ09W/8i84MHgWXL5AapGoWrqsSqbpANOVDdV19JCdaxY/KZKimRz+bChdIOqksX15RY2XtAyM93rD1VSork6dGjEpT6+0vA1q2blHbWtSehow3uG7JB+5Ej8v4hIVKaWLF01McH+O1v3bfNiruURJL7YXDTBB08CLz+utxQAwPlht6mjV5HDuhVNSEhckPRNHkSO3pULl7FxXJh9vWVvy0WCWxKS+UnMFACjzNnpIqlrEyeMr28ZF9Go7SFadtW9qEGG1QX35oaAJ4/L8XJ1d1U1DxX+/bJ8hYt9ECp4hNzaqp+kQMkzT4+etCnunoD+oWwe3d9LKGQENmnupGEhcn/qakSMFgHj2ofxcXAv/8tx753r5wPf3+ZrkGVfpWVyfkpK9N7ewUGyk2/vkXmX30FvPCC5KvJpDcKP3eu+lKhhmjzotLz1FNyToOC5PNQWiqlVYD8rmsjT2fc4K0fEJKTpZ1JTcGFCkJOnNCP6cIFKZU6dgwYPbpux+Jog/uUlIYdj8i61KNNG9vSUW9vOU5V8uaO3HXKDFfxpJ6d9cXgxk1V9SFVN7QzZ+RmaTbLjTI/37ZBbH6+3JQjI4GTJ+WiuH+/XLRUm5rCQtn+0iV5v+Jiedr285N9ZmbqAY/FIusbDHpd95UremnQ/Plyc7e++FrfQM+ckRt8YKBsd+aMHJO6qezeLUHbXXdJI0f1VH3kCHD8uFzko6OlnZG/f+VShthYOd4ffwRuuEFKU8rKZJ24OAlgBg2SdO/bJxfCNm30p9Xz5yUoCQuTbv2A3NiSk2W/1sGjpskN6MoVCfLCwyXw8vGRYv3z52W5r68+bYOXlwShhYV6eybr0p+qxueo6nNw8CAwZ47ciMPD5bx7eckxFBXJ+yxZAkyfrvd2s95faSlwzz2yzBm9uUpLJQjIzpbP3PHj8lny9pZjBeS833OPnO/alFg5e8DB2owBtWqVBDYXL8r3Q1VpFhVJEJmcXPuZta0b3AOVx2+yHjH4iy8admwd9UBQcT658HAJRv39617q0Rg3WnecMsNVOLGoLQY3bqDiRaCq4vJevYAXX5TqjJgYCT5ycuRGVloq+zp8WOrL1RNLz57Axo0SJJSWyg1Wdev29ZULmdo+L0+vby8rk79VUKJpcqMCJCBSk2kWFcmNvKBAAgU1TkxqqhRnjx4tF7Wff5YL0J49cpydO8sToWoXdOmS3BB//VXaZWRlScDQs6dUDalSpMxMOQ/XXmtbypCSIq8VFMj/AQGSztRUCRwGDZLGkfn5chE4eFDOYcuWEjAdOSL7GjFCT2tMjARuBQXS+DgzU9JtNksQ0bKlpA+Q9zh7Vs5RSYkck9Eo56601LYaUJ1PVfrz8ce2AwkOGAD85jeyvr2L1bhxUnKXkiJ5eeaMBFaBgfKTlib7P31a/h40qOYeb/Vty7F9u5RkhITIexYWyvFb31SysmTQxB49HC+xsld9k5cHbN4spWWPPCLTZDhz1Ot27YBffgE++UR+5+bKZ0BVXwLy+fL3l8/Ehx/KyMWOpkFVfRYWSsBnPUSDCq4LC4Evv2z4sXW6dZMHArNZAjiVjtatJf+GDrU/1ERN79mYN9qGKolsSjixaGUMblys4kWguFgumOrGbl2y8dln8rSonvgCA+WiW1oqN9SyMql2Ki6WnkQ9ewLffCPrqi6eZrPemNbbW9ZVVVFGo/xOS5PXzpzRgwVNk/fw8ZFtiorkR3UnP39eD7ratJGeQTt2yN9Hj8r+unSRfanGijk5suzgQf3GbrHIxSkrS46nuFhKAiIi5Ak3OVku6E88IWkB9CfxsjJg2DBZVlws6xsMsjwtTW5CxcV6ldyJE3p1zg03SOPSd96RG2dZmZwrddxFRXIu0tPl5mexyMVC3XTCwyUAu3xZf5JX2wOyfUmJHlhu3y7nW822XloqrxmNEmht2aKf65gYuaGowPG772SG+KIiPR+NRr00QbWVMBrlnNalx5vi6NN3Vpb+GSwp0c9LUZF+PkpLJdDOzJS0ffstMHKkbclSxff+4gsJ0mJj5f+LFyWoO39eAuKjRyWIvvPO2o96XdVEtYcOyWcjM1Peu6xMPoMVAyGTSX4cDdTUuUxPl89JcrLsWw3RoMZvOn9ePk9lZfI9ti5RUdWjzuoF1LOnBNfZ2fJdbdFC0rZvn5yfwYMlcDtyxPEgpSEC0prUpjeZp1XduHpgTnfF4KYRVDWwW1aWDCR38aJcBAIC5CkqPV2/sYeEyE+7dnJxKCiQfRUW6qUCpaX6ODBeXrJvb29g1izZt4+P3gjYaJSfc+ckHd7ekj5V3aS6hGuafmNWF15N07uMWvd4UU+eP/4oF+W8PL3E48IFPcg4f17er1MnuZCeOQOsW6e3S8nNlX0HBEgwk5+vd802GPSSksxM2VZVEVg36A0Lk3QFBuo3hZwcSUNUlHzZ8/PlHJpMwJAhem+YY8fkIqC6cGuapK2wUM5hZKSks1s3acx94IA+8u2RI3rPNEWdP0D2YzDIsanPgwoOvb31IMXPT7Y7flyWxcTI50SNO+LtLSVLxcVy7svK9M+YCiKMRvm7rEx+BwZKrxdvb+Duu+W3al/UurXcwP/7X/m8APpnNSMDWLtWjs1ikfMfFSVj9fTurX+Og4P1Uo3cXDkGFairkgClpETOm8Egba6WLpU8+MMfKo8GvW+ffD9KSiSNqnTRz0+visvNlUDwzJnajXptrxHq+fMSkGdnSz517SrnQLU5U70JVe+6khL522KpuTeO9UNMQYGcg4ICeQ+TSdYxmSSfVfseLy8JBqyHblCfg5YtndML6OBBvW1berqU0BQUyOcjNxeYO1dev+46uTnWFBDbu9GqIRbqE5A6QlVPq8+P6slofUOvqUSpKQY+dR1XrKGO1V3OIYMbJ1KZmp0tP3l5csFQg7qdPy83ZtWD5Px5uYgkJOh13nl58mHMydFv7IBc8FTxeHUXNF9f+SBt2yYXY0C/UVs/RSuqOgvQe7dUXEftx1phof63uolnZ8txenvLMaibQnCwXkqhqniCguQCqKZIsFj0oCgnR/4OCJAbZK9ecjEHZFlKijz1e3vLjffwYbkRtmwpF85+/SQtrVrJzam0VI5h2zbZX6dOciPZsUNviOjnJ6VjGRnymqpSKSmR7TVN0qTS6u8vF+qDB/UqtYrn18fH9vyWlkq6TCZJ28WLsrxVK72hdk6OXrqkbuY+PvI7I0MCHet8tX6/sjI5J/n5+kUuKUkvaTMagfXr9TF+MjNlGx8fKXWLjJT32L1b2mmdPKnnn8mkB9Offqr3OGvZUm6M/fpJoHThgl4CZ33s1jRNL6XKzZUAZs8eeZpXXefPndOrQjp0kO+L+l8F2Sp/WraU79J//yvB27Fj8j7dusmxqrnT1IXWXkN1QA88fHykNLJ9e3nfs2cl3woK5HMbECDfy/x8ee9Wrapvl1KxJMNsls9PQYF8bmJi5Pzm5uolmJomn2WjUa8ish6Zu2dPvRdQTTcTe68rR47IvoqK5BqjAhtvb3k/NeaN6plZcQTviqUBFW+0KmAsKNB7PF65Ig81hw7pbeyccQNUgUtysnwnvbwkcJk0SY4xORlYtEjvMBARIXmtgrXRo6WE0zrw6dZNqsAjItw32KnLuGINVW3oTu1+GNw4ieqS+8svcnHNztYb4vr66qMA5+XpNy71NJ+VJe1M2reXi5uPj1zszp+XC87+/XKhU1VE1VE3RutSA/V3TQ0fHdl/xX0C+o2msFAvvThxQo7NaLSt0iork4v8hQv69tnZlfetLvQGg7Q/6tRJbirHj8tNedcuvcFqu3Z6g9rjx2Uf6sntyhX9uDMyJCgyGuV/dYNVbYZOnJB1VbVKxZIX1ej44kX9Bnv6tO161uzd3NX+T53St7t0Sb/hqxIXtX1amvytSj9Uex31t/Xvin/n58vnSE23UVIin9Off9ZL69S+z5wB/vxnvddYZqa8v8Eg58a6l5x6D29vfbDIAwdkW29vOT/W6bDHYJBAzmyW/M/MlBIYLy+9IbYqjTpzRtYrKLANClRws3WrLN+1C1i+XO+l5e0tN+R27fTG7kajbJOVJftITpYApmVLyUvVCzAuToI7dUOwLhm7eFGCr5AQ2WdEhARMVX0G3n9f3is+Xm6QWVn65zY9Xb7bKt+9vPQpU4KCZP++vnK+TCZ9sMndu6Vxdm6u9FBTQwGo0rVrr9XP3aFDsk1hoXyHOnWSG054uBxLVJSc+6wsOe+qJEkF0SqoSk7WH7aCg+W8b90qpTrqs6tGPw4N1f9XEwtnZclnMj9fL5n75Rdpq1fd8AX22Gun+NZb8h3Oy9Nv+L/+KlXkf/2rNNDevFnSevq0XhLWrZt8LzZvlnzs3l3OfWqqBPIffSTnTE1gbJ1Oi0W+y6pkKjxcPjsVq1qrCkArLldDT6j9Vlf6YV3NqR6EQkNt19E0Cc4LC+V7ZrHIw2FDtM9xt3Y/Bk2r6tLsmXJychAaGors7GyE2BsYoRbMZjO++eYbeHmNwosvGnHihHyA7JV0KNa9VqypQACQC7Wvrz4VQkFB7XtkuBPr7tvOoHoeWQcPKhgxGMz4z3++wb33jkJhobHa/dgLGDyReup3NusA0GiU89mypV7ypvj7V50nBoPeI686qju9qopTpQuqbZH6joSGys1Z9UBS27ZvL+urxtaRkbKvzEy9GhaQm1r79vLdU6Wsqm2aehix/qz4+Ehwc8stUlV3+bK8fu21EpisWyfthtQ2qn1cdra8v9msH5fBoI8WDkjwFBoq2/r7y4/JJO9hMAAPPCC9JzMy9Orl0lI5F+qYVIDq66s33i8sBAIDzXj//W/wxz+OQsuWRqSn6w9W3t56tbjKWy8vOS9XX623ibtwQZbFx0vbnKwsCbp27ZIbW4sWEjz5+0swqD6DeXl6AOXlBdx0kx68qZKc6OjKJW7q2lmxdMBk0gMn1eNTtWMqKdHHrFLV9a1a6WlSpecquOzQQUrtwsMl6MzPl/OlJjDOyZG/1VQub78tVbcZGXJ8RqMEKCNH6lWt9tIbGSnnTpWgFherUg4zevb8Bhs2jMLp00Z4e0ta7c2PZ13NeeiQHOu110qnB1VqlpwsgXVIiJSudu9uf7oKQO8FOmCABMy1aa9ksUjbrKq65Ne0X0fV5v7NkhsnePVVibJV1+zqVBWkqNINNYieGhfFEzg7cFBVWPb4+zu+n5puqJ6iIQIboHLpndksNwLrPKjYBsDePhzJh5IS+W1dIqSqC1UPNEDvnm+9XkmJ3qg9MFBu4JcuyWfIYpGbisUi6c/JkZJSR45ZpSc9XZ7urambdWio3CDVQI5VsXddyMnRxwgyGOSm4OcnpbwhIXoHA19fPaBUpcUVFRbKj5eXHlSp41HzzKlzWfEY1brnzkn1lGrHlpsr77lvn5RwRERI76pOnSSgyMiQ4EYF1wEBertA1b7v8mWptmrVSoKlfftk+6IiPZizrh7KzwdWrJDt2rSRn5MnJaDSNDnfqtpNlZAHBuqlsiEhkqbCwsrnSQV1GRkSNHh56VXCly/L76goOe6lS6VEZMsW+d/bW45JDdb5xRcStPzud7ZT5BQUSGnVt9/K3wEBEuRcdZWkaflyGezyv/+VdEdEyPHYmx9PTfWihinIzJRS7i5d5Hyp6rlWraR0LSBA0nvsmO2UMtbslcgp1VU5+fu7dkJeexjc1IP6cpw7p48TUx/qg2T91ETU1Nhrj9VQrAMbpapgTjUOVyU9SkFB5RIZR1W1jWrjVlRU+31WtT9VOqPGRlIjiRcV6VXDNZXwqtcrVmWq47B3PGrZlSvyPj4++qjlqidUUZGUnuzcCfTpo5eK5OXpVa5qgM3gYNmX6hSRni4BgcoX1favVSspESoo0KuHiov1kckBPXBUQZ1qM2VdglVQoB9vTk7V50gFNh076lXlKsACJH0ZGXI8R47oeezrK4GDCmjVlDb790sbn9BQafNz5IhUkam8U43UjxyRwMV6uhw/P/k/K0uCJzXHl/X8eGFhUpWWny/vERoqJUEnT+pTvnToINVkERGSvthYSVdamrymAhHV6PvcOcmbV1+VAEiVFtVU5XTrre43DYZbNI365z//iQ4dOsDPzw/XXHMNfv7552rXX7lyJbp37w4/Pz/07t0b36j+zo3szBn5bTA4p5RFNRD15CoSIldSbXcqagrfObNZv9ZcvKhXaakqO0dLIq2vMaqBtiPUwJ85OXr7K9W2xWCQ9KSnSymJavtnTQWQqj1dxaBUjWZ97pw+geeePdIexmKRbVQvz7w8vR2RteJiubmrxvoqgLI+9uoUFEhAoTohqPZO3t56IJmeLjf0c+f00jDrqW9MJr0DQkqK3nlk0ya9DaB1sFpaqg9sqtKqguI2bSRNKSnSRmvnTqn6CwiQKk9VRXT2rJQuRUfrJUiq9P/XXyUIOn9egqagICnlUQGcavSdkSGBYYsWUi23Z48ENAcP2vaAU23rVFfzCxekRMhk0gPYilwxDYbLg5sVK1Zg5syZeP7557F792707dsXI0eOxDn1Ka5g69atuPfee/Hggw9iz549SExMRGJiIg4cONDIKbcNaGpqQOkI1f26pqJ8IndV8bPrbj1LmioVGKixklQ1oGqwr3o21VZtg7qKDdpVOyHV4LywUEoiLl+Wp/WWLfXgQN30Kz7AqTG4VHpKSyU4CQqSEojsbCmlUAGcv79ejaY6X1h/7qxHW1elRo5S3frVdVjTZF8+PnpJlI+Pnn4VLFuXFqpASI355O0tXfpV9ZVqzK/WU43Vy8r0oEYFPdYT+paW6kOI7NqlV0sFBMjnIC9PD3JU+lXvvowMCWBKSiRwVAOwaprebicsTI41PFyCJBW4fPihlNxUV+WUkSH7VW3arKlpMOLjG3caDJdfet544w08/PDD+P3vf48ePXpgyZIlCAgIwPvvv293/UWLFuHWW2/FE088gfj4eLzwwgsYMGAA3nrrrUZOuT7GSUCA7Qi+daEaR6qBwayfBIiaCm9v25uJjw8DHGeqeDNXN9W6PlxZlzpURd2Ira9v6nqnbtBqX6qEpaxMntT9/eW3uqlXfO+q0pCbqzd+9vfXq55U6YnqDaeOW1UdqfSWluq9Rh05RmvWVXdqjCtA78KvgjHV+FoFHdZBnzo/vr5SKnPhgh7w+PhYd4DQx52yHtpBtfcBZB9qGImAAH3aGzXFi0qPv7/e7lONl2Wx6L3sVAlQdLTsJzVVL4EymfRgqXt3PWhVbWUuXaq+yqm4WMa/CguTxsOqt3B2tvzvimkwXNrmpqSkBLt27cIsNXoYAC8vLwwbNgzbtm2zu822bdswc+ZMm2UjR47E6tWr7a5fXFyMYqvy0Zz/b6FnNpthrmdLy8hIMw4ckN+nT+vFxHWhgpuQEL3rrXX3U0d5eUnQpRoiNjf+/mab39R4VK8cwLbtgLc386Q+VEmBpsmYN8XFcp59ffXxaPz9a3etsP6eqJugGjLAegBPg0HyUN14VcmCl5d+o1dpsO59qNqhFBZK76OyMr09jLpGqoBDBU1qLCU1PINq7B0Sog+pocbK8fPT06va36g0qsBB7df6WlpVSZV1j05VwqTaFLVuLTf3oCC5YQcH64NxWk86bDTqAZe/v2wXGSkBh5pLTo0qrhpZq2NVwUxAgORLixZmBAfrwZxqjB0fL6Uz6hyq9wQkbb6+tvcR1fMWkAAjJ0eOY+xYadO0c6fsPzRUGjZ37ap39wfkWK1HQLdXrVRUJPvs1Ut6an31lZTeZWVJ3gwaJGMIdelS/84NtblnuzS4uXDhAsrKyhBRYdrZiIgIHD582O42mZmZdtfPzMy0u/7cuXMxZ86cSsvXrVuHgICAOqbc1pQpSZgyxSm7Iid5//0kVyeBKmCeuJ933mGeuKO//73qfElIcN77qLnxqjNwoGP7Ui1D4uMrj2dz/Lg+Bll9FNRiMDaP7y01a9Ysm5KenJwcxMTEYMSIEU4Z5yYpKQkdOw7Hhx8a8dVXUt9pPaotYDsztNGoD9munnaCgvQuqqooV80R5eurjzBr/WRmXU+txn1p0UJvVX/okP66GizNZJIovLDQ/mjF1qznonKmmsa8UaPDqlnOa1t65e9vxvvvJ2Hy5OE1jnPjzhzp+WJvG+t2W7VpT6HaTdS1Qbua2kM9xfbpo3cPNZvNWLQoCVOmDEdOjtGmON663UZ9262p70/Fxv2qVNS60a2fn+0giPaqTNxlbCl1rgIC5NoSHCwNPb28pFfL2bN6GwpH0xwYaMa77ybhj38cjoICY/nTvToX6nuvSgZUFQggpRgqn1X1DFB5qg2TSdpYqOoMi0WuS6rdihpgUo3JY512o1HaePj7SzWMt7c+Pk5oqHyuVFsYo1HOUWio3jYmLEzajpw/r4/qrj5n1j3YKuZzWJiMtTNkiIxfc/GibPPrr/rgkv7+UhoBSFsaNcCqqoaKjJQ57iZNkp5Khw8DTz8tx37lil5lZzLp13Z1vQ0LM2PevCS8885wnDljLG+IO3Kk7M/fX7qKGwzyvseP6w2f1XQkmibnvUcPOX41Iaoq5XrhBemyr/JkwQI5vri4yuPTpKTIuEOjRsmYPpcuyfU5IECO+exZKQGaMkW2b2g5taiScGlwExYWBm9vb2RlZdksz8rKQmRkpN1tIiMja7W+yWSCybpC9v8ZjUYYjc65+cXHG/Haa0bcdpuMM3D0qFxsLlzQq4kuX5ZlqjV9q1YyqNHtt0t9aHCwXuSXny+jyL79trRqDwqSH4tF9lFSIh9o9eVu0UKKBB97TD6Ep05JF8SjRyXqPn1aGqCp7pXnz8sFQQ16pS7yKtBSA3Pt3i37UpNt1uVmC+jFyGpiTjXgm+o5oOqcjUb5UqpZzVWx7OXLlS+6VVFfzsJCY43BTU2BlirureqGry6Wjp4XdQGrOLaLGsBRzQGmita9vPQAwLohpxrYS3V5tR5gTn3eBg+Wz8r27XqgrdKpbpZt2si4HXl5cqFSM5l7e8s5r2rwSG9v2e7WW2X/p0/LtmazFIePGCEXc0A+wxs2yN9FRUZomrE83Waz3uhUTbxZ1blWgZP1MVhXnagf1a1bNZYMCJD3UNUiFov87+8v76/GnjEY9Jnk1RgoFYOeiufPaNTbddhLsxrPJTBQbgoVRw5X6wQG6g2E1fGrdiLWgySWlcn39+mnZWZ4QMaE+eADqQrIytIn0q3uMxkUJF2DAaBlSyNatDDCbNbzXd0gVVdldQ4B/Tp1+bI+GayqhlHzZLVvL8ebnCztOVSj4kuX5D1atNDnnlNtdlQ7HTVCdIcOegNXNZZPr17SRuTCBX1uMHWcAQGyzqVL8n+bNnJuVHdw9R3x99cbZlvnm9Eo47r87W/6pJ5duujTORgMEiR07izXxlatZLuhQ+XchIfL35GRlUco7t0bmDdP5uk6cEB6W6lzp9rEREfLT9eusk1IiBGdOhkREwOMGaOnyWKRdO3ZI+/XoYMEIKoHm5pg9aqrJI9Vt/aiImljc/31MuCkdduXcePkO3zggKRBdfM+c0byYOxYOeZHH608zk3v3o0783pt7tkuDW58fX0xcOBArF+/HomJiQAAi8WC9evXY+rUqXa3SUhIwPr16zFjxozyZUlJSUhwZlldHXh5SbR+8832J8lU8+1Yz3tT1WzIgHxorr1WpnRQQ6u3aiWNvdq3ly/y+fPyJW7bVtZVT02dOkkk/eabsm10tMx6vX+/fGB9fOTDHxcnX6Z27eQG5+WlD93u5SXDlb/+ujxxqFKnoiL9otKxowRTvXvLF/bLLyVgadFCb+CXny8XnLAwCVzUzVrVYV+8qI8ompWlt/hv0ULSrSaQtB6YzcdHr39XQZ5q7a9i3B49pAV/SYneBdHLSx8ZVgVRRqOchzZt9OH8TSbpOpmRIcvUkOZqMC9Nk7T36SMlZaGhMm9VWpq87usr+w8J0RtBdu4s51bd7I4dk+PNypL8U/n9ww/Au+/KvgICJG+iomR7NWlkmzby+bl4UYKLDRuku6aa8XvQINn2zBm5cGmavE92th5gR0frT3djx8pF/8gR/bOZnw+88Qbw/fdyM1FBaOvWwPDhMlVDz55yfFu3yvsHBEhQ1amT/rl+4w3Jw0OHgN/8RtJcUqJ/htRn5fJleR8VaOXk6CUBwcGSt61by43OaJT0m81yQVbBr6bJuj16yO89e+Rzlp0taY+NBe69V/a9caMeUBmNsv8ePSQ/Y2IkfWoOqTVr5AZi7/wVFMj3Sj0oqMDIZJKb9LhxwMMPy/fj3/+W86RuQDExso7qHaRm5h43Ts7f2rVyTtSNuHt3YOpUfdJSQPJg3jwJNJculW7Y58/r87YpaoZ4VbqgAvvwcPm/Y0eZzDI3V3rHqNIVNfGtavcxfLjcHL/6Sm6Ifn56abO90XvVeQbk+nX77bL999/bjuzr5yfnOiEBmDhRbtxqhOKsLOnpk5KiB4hBQfK9OXtWL4kpLpZReBMT5TNx+LAcn6bpE+lqmlzz+veXPDl5Uj6Dzz0npSPW12Prmcb37QNWrtTHtVGNiNUEvjVNLdCzp0wBsWqVfGdTUvT7QlycHHdionx3vvtORvJVAxJap8nLS8aeSUuToCs6Wu47mZmyrGtXyScVAAYG6oFZ+/aybcV7Tny8pF8FLunpsu2AAbaBS21mXncHLp9+YcWKFZg4cSLefvttXH311Vi4cCE+++wzHD58GBEREZgwYQLatWuHuXPnApCu4EOGDMErr7yC0aNH49NPP8XLL7+M3bt3o1evXjW+X0NMvzBq1CinlQJVVNOkeNWxN+x3VJTcgBydrC45Wb9IqaeiVq3k4vPII7ZfaHsjWMbHy8W44oR08fHyxQH0bc6d0y+EERH6XC5XXy0XTXUOQkMrD8+eny83oWPHzBg37husXj0KbdoYMXiw7QzW1gFndeezqskGG+OLXZs8V+vu2ycBmfVQ7uocW1+QHD1+tW9H5s2pifqe3HrrKGRkGMuPq2IeRkdLOtUs5EFBet6qfFf5bP2ZVj08oqPlqbZlSzmu0lJZNy1NAolx4+TGpI7L+n3UIGjVzeFT1flLTrZ9CFHBrfUTd1X5ClSd17X97ltPHKnmGwoNle96167yvS8slMHwjh83Y+JE+Z5062a0uYlZf4/VODEVSxCq+q5b78f6PAO2D3S1/WxVlQf+/hLgnD8v1wz1kGdv/f37q/6OOFLy4Mgx10SlKzvb9nqm8tbRe0p1aQHqls763GsaS23u3y4PbgDgrbfewmuvvYbMzEz069cPb775Jq655hoAwNChQ9GhQwcsW7asfP2VK1fib3/7G06dOoWuXbti3rx5GDVqlEPv1dSCm/pyxge2uouUo+9XXTpqunk4ml6LBTh50owDB75Br16j0LGj0e2+nA3JXS9Ozv6euONxukuaHEmHI98TR/fjDsdcG/VNc0Mfc22+K45eU5tK3jiiyQU3jam5BTfNDfPE/TBP3A/zxD0xX6pXm/u3B8RyRERERDoGN0RERORRGNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFHYXBDREREHoXBDREREXkUl06c6QpqQObaTJ1eFbPZjIKCAuTk5HA0STfBPHE/zBP3wzxxT8yX6qn7tiMTKzS74CY3NxcAEBMT4+KUEBERUW3l5uYiNDS02nWa3dxSFosFZ8+eRXBwMAwGQ732lZOTg5iYGKSlpdV7nipyDuaJ+2GeuB/miXtivlRP0zTk5uaibdu28KphJtBmV3Lj5eWF6Ohop+4zJCSEH0Q3wzxxP8wT98M8cU/Ml6rVVGKjsEExEREReRQGN0RERORRGNzUg8lkwvPPPw+TyeTqpND/Y564H+aJ+2GeuCfmi/M0uwbFRERE5NlYckNEREQehcENEREReRQGN0RERORRGNwQERGRR2FwUwfp6en43e9+h9atW8Pf3x+9e/fGzp07XZ2sZqusrAzPPvssOnbsCH9/f3Tu3BkvvPCCQ/OPkPNs2rQJY8aMQdu2bWEwGLB69Wqb1zVNw3PPPYeoqCj4+/tj2LBhOHr0qGsS20xUlydmsxlPPfUUevfujcDAQLRt2xYTJkzA2bNnXZfgZqCm74m1KVOmwGAwYOHChY2WPk/B4KaWLl++jMGDB8NoNOLbb7/FoUOHMH/+fLRs2dLVSWu2Xn31VSxevBhvvfUWkpOT8eqrr2LevHn4xz/+4eqkNSv5+fno27cv/vnPf9p9fd68eXjzzTexZMkS7NixA4GBgRg5ciSKiooaOaXNR3V5UlBQgN27d+PZZ5/F7t278d///hcpKSkYO3asC1LafNT0PVFWrVqF7du3o23bto2UMg+jUa089dRT2vXXX+/qZJCV0aNHa5MnT7ZZduedd2r333+/i1JEALRVq1aV/2+xWLTIyEjttddeK1925coVzWQyaf/5z39ckMLmp2Ke2PPzzz9rALTTp083TqKauary5MyZM1q7du20AwcOaO3bt9cWLFjQ6Glr6lhyU0v/+9//MGjQIIwfPx7h4eHo378/3nnnHVcnq1m77rrrsH79ehw5cgQAsG/fPmzevBm33Xabi1NGysmTJ5GZmYlhw4aVLwsNDcU111yDbdu2uTBlZC07OxsGgwEtWrRwdVKaLYvFggceeABPPPEEevbs6erkNFnNbuLM+jpx4gQWL16MmTNn4plnnsEvv/yCadOmwdfXFxMnTnR18pqlp59+Gjk5OejevTu8vb1RVlaGl156Cffff7+rk0b/LzMzEwAQERFhszwiIqL8NXKtoqIiPPXUU7j33ns5aaMLvfrqq/Dx8cG0adNcnZQmjcFNLVksFgwaNAgvv/wyAKB///44cOAAlixZwuDGRT777DN88sknWL58OXr27Im9e/dixowZaNu2LfOEyAFmsxl33XUXNE3D4sWLXZ2cZmvXrl1YtGgRdu/eDYPB4OrkNGmslqqlqKgo9OjRw2ZZfHw8UlNTXZQieuKJJ/D000/jnnvuQe/evfHAAw/g8ccfx9y5c12dNPp/kZGRAICsrCyb5VlZWeWvkWuowOb06dNISkpiqY0L/fTTTzh37hxiY2Ph4+MDHx8fnD59Gn/+85/RoUMHVyevSWFwU0uDBw9GSkqKzbIjR46gffv2LkoRFRQUwMvL9qPs7e0Ni8XiohRRRR07dkRkZCTWr19fviwnJwc7duxAQkKCC1PWvKnA5ujRo/j+++/RunVrVyepWXvggQfw66+/Yu/eveU/bdu2xRNPPIG1a9e6OnlNCqulaunxxx/Hddddh5dffhl33XUXfv75ZyxduhRLly51ddKarTFjxuCll15CbGwsevbsiT179uCNN97A5MmTXZ20ZiUvLw/Hjh0r///kyZPYu3cvWrVqhdjYWMyYMQMvvvgiunbtio4dO+LZZ59F27ZtkZiY6LpEe7jq8iQqKgq//e1vsXv3bnz11VcoKysrb//UqlUr+Pr6uirZHq2m70nFANNoNCIyMhJxcXGNndSmzdXdtZqiL7/8UuvVq5dmMpm07t27a0uXLnV1kpq1nJwcbfr06VpsbKzm5+enderUSfvrX/+qFRcXuzppzcqPP/6oAaj0M3HiRE3TpDv4s88+q0VERGgmk0m75ZZbtJSUFNcm2sNVlycnT560+xoA7ccff3R10j1WTd+TitgVvG4MmsZhXImIiMhzsM0NEREReRQGN0RERORRGNwQERGRR2FwQ0RERB6FwQ0RERF5FAY3RERE5FEY3BAREZFHYXBDRB7j1KlTMBgM2Lt3b4Ps32AwYPXq1Q2ybyJyHgY3ROQ0kyZNcul0CjExMcjIyECvXr0AABs2bIDBYMCVK1dcliYianycW4qIPIa3tzdnGSciltwQUePYuHEjrr76aphMJkRFReHpp59GaWlp+etDhw7FtGnT8OSTT6JVq1aIjIzE7NmzbfZx+PBhXH/99fDz80OPHj3w/fff21QVWVdLnTp1CjfddBMAoGXLljAYDJg0aRIAoEOHDli4cKHNvvv162fzfkePHsWNN95Y/l5JSUmVjiktLQ133XUXWrRogVatWmHcuHE4depUfU8VEdUTgxsianDp6ekYNWoUrrrqKuzbtw+LFy/Ge++9hxdffNFmvQ8//BCBgYHYsWMH5s2bh7///e/lQUVZWRkSExMREBCAHTt2YOnSpfjrX/9a5XvGxMTgiy++AACkpKQgIyMDixYtcii9FosFd955J3x9fbFjxw4sWbIETz31lM06ZrMZI0eORHBwMH766Sds2bIFQUFBuPXWW1FSUlKb00NETsZqKSJqcP/6178QExODt956CwaDAd27d8fZs2fx1FNP4bnnnoOXlzxn9enTB88//zwAoGvXrnjrrbewfv16DB8+HElJSTh+/Dg2bNhQXvX00ksvYfjw4Xbf09vbG61atQIAhIeHo0WLFg6n9/vvv8fhw4exdu1atG3bFgDw8ssv47bbbitfZ8WKFbBYLHj33XdhMBgAAB988AFatGiBDRs2YMSIEbU7SUTkNAxuiKjBJScnIyEhoTwIAIDBgwcjLy8PZ86cQWxsLAAJbqxFRUXh3LlzAKT0JSYmxqZNzdVXX91g6Y2JiSkPbAAgISHBZp19+/bh2LFjCA4OtlleVFSE48ePN0i6iMgxDG6IyG0YjUab/w0GAywWi9Pfx8vLC5qm2Swzm8212kdeXh4GDhyITz75pNJrbdq0qVf6iKh+GNwQUYOLj4/HF198AU3TyktvtmzZguDgYERHRzu0j7i4OKSlpSErKwsREREAgF9++aXabXx9fQFIex1rbdq0QUZGRvn/OTk5OHnypE1609LSkJGRgaioKADA9u3bbfYxYMAArFixAuHh4QgJCXHoGIiocbBBMRE5VXZ2Nvbu3Wvz88gjjyAtLQ2PPfYYDh8+jDVr1uD555/HzJkzy9vb1GT48OHo3LkzJk6ciF9//RVbtmzB3/72NwCwqe6y1r59exgMBnz11Vc4f/488vLyAAA333wzPv74Y/z000/Yv38/Jk6cCG9v7/Lthg0bhm7dumHixInYt28ffvrpp0qNl++//36EhYVh3Lhx+Omnn3Dy5Els2LAB06ZNw5kzZ+py6ojISRjcEJFTbdiwAf3797f5eeGFF/DNN9/g559/Rt++fTFlyhQ8+OCD5cGJI7y9vbF69Wrk5eXhqquuwkMPPVQecPj5+dndpl27dpgzZw6efvppREREYOrUqQCAWbNmYciQIbj99tsxevRoJCYmonPnzuXbeXl5YdWqVSgsLMTVV1+Nhx56CC+99JLNvgMCArBp0ybExsbizjvvRHx8PB588EEUFRWxJIfIxQxaxYpnIqImYsuWLbj++utx7Ngxm+CEiJo3BjdE1GSsWrUKQUFB6Nq1K44dO4bp06ejZcuW2Lx5s6uTRkRuhA2KiajJyM3NxVNPPYXU1FSEhYVh2LBhmD9/vquTRURuhiU3RERE5FHYoJiIiIg8CoMbIiIi8igMboiIiMijMLghIiIij8LghoiIiDwKgxsiIiLyKAxuiIiIyKMwuCEiIiKPwuCGiIiIPMr/AV8U4WKXGL1HAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a scatter plot of city populations vs. longitude.\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming you have lat_data and popul_data already defined\n","lat_data = df[\"lng\"]\n","popul_data = df[\"population\"]\n","\n","# Create a scatter plot\n","plt.scatter(lat_data, popul_data, alpha=0.5, color='b')\n","\n","# Add labels and title\n","plt.xlabel('Longitude')\n","plt.ylabel('Population')\n","plt.title('City Populations vs. Longitude')\n","\n","# Show the scatter plot\n","plt.grid(True)\n","plt.show()\n"]},{"cell_type":"code","execution_count":46,"id":"5b0af8ac","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:07.249218Z","iopub.status.busy":"2024-03-22T09:48:07.247958Z","iopub.status.idle":"2024-03-22T09:48:07.496324Z","shell.execute_reply":"2024-03-22T09:48:07.495118Z"},"papermill":{"duration":0.288734,"end_time":"2024-03-22T09:48:07.499484","exception":false,"start_time":"2024-03-22T09:48:07.21075","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# Are there any outliers in terms of population? Identify and visualize them.\n","\n","import matplotlib.pyplot as plt\n","\n","# Assuming you have the 'population' column in a variable named popul_data\n","popul_data = df[\"population\"]\n","\n","# Create a box plot\n","plt.boxplot(popul_data)\n","\n","# Add labels and title\n","plt.ylabel('Population')\n","plt.title('Population Distribution with Outliers')\n","\n","# Show the box plot\n","plt.show()\n","\n","# I want to visulise the outlier of a column. What are different ways do it with Matplotlib or seaborn ?\n"]},{"cell_type":"code","execution_count":47,"id":"24c98e7f","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:07.564998Z","iopub.status.busy":"2024-03-22T09:48:07.56453Z","iopub.status.idle":"2024-03-22T09:48:07.812057Z","shell.execute_reply":"2024-03-22T09:48:07.810821Z"},"papermill":{"duration":0.283567,"end_time":"2024-03-22T09:48:07.814768","exception":false,"start_time":"2024-03-22T09:48:07.531201","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","# Assuming you have the 'population' column in a variable named popul_data\n","popul_data = df[\"population\"]\n","\n","# Create a box plot\n","plt.boxplot(popul_data)\n","\n","# Add labels and title\n","plt.ylabel('Population')\n","plt.title('Population Distribution with Outliers')\n","\n","# Show the box plot\n","plt.show()\n"]},{"cell_type":"code","execution_count":48,"id":"83bbb65c","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:07.882447Z","iopub.status.busy":"2024-03-22T09:48:07.882045Z","iopub.status.idle":"2024-03-22T09:48:11.20236Z","shell.execute_reply":"2024-03-22T09:48:11.200967Z"},"papermill":{"duration":3.357705,"end_time":"2024-03-22T09:48:11.205303","exception":false,"start_time":"2024-03-22T09:48:07.847598","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","# Using Matplotlib\n","plt.hist(df['population'], bins=20)\n","plt.title('Histogram of Population')\n","plt.show()\n","\n","# Using Seaborn\n","sns.histplot(df['population'], kde=True)\n","plt.title('Histogram and Density Plot of Population')\n","plt.show()\n"]},{"cell_type":"code","execution_count":49,"id":"72db266f","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:11.273079Z","iopub.status.busy":"2024-03-22T09:48:11.272605Z","iopub.status.idle":"2024-03-22T09:48:11.597328Z","shell.execute_reply":"2024-03-22T09:48:11.596115Z"},"papermill":{"duration":0.362103,"end_time":"2024-03-22T09:48:11.600048","exception":false,"start_time":"2024-03-22T09:48:11.237945","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Calculate and visualize the top 10 cities with the highest population.\n","updated_df = df.sort_values(by = \"population\", ascending = False).head(10)\n","cities = updated_df[\"city\"]\n","population = updated_df[\"population\"]\n","\n","\n","plt.barh(cities, population)\n","\n","# Add labels and title\n","plt.ylabel('Cities')\n","plt.xlabel('Population')\n","plt.title('City Population Distribution')\n","\n","# Show the histogram\n","plt.show()\n"]},{"cell_type":"code","execution_count":50,"id":"ad678bee","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:11.668965Z","iopub.status.busy":"2024-03-22T09:48:11.668538Z","iopub.status.idle":"2024-03-22T09:48:11.685221Z","shell.execute_reply":"2024-03-22T09:48:11.684223Z"},"papermill":{"duration":0.05426,"end_time":"2024-03-22T09:48:11.687407","exception":false,"start_time":"2024-03-22T09:48:11.633147","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Hamburg</td>\n","      <td>53.5500</td>\n","      <td>10.0000</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hamburg</td>\n","      <td>admin</td>\n","      <td>1841179.0</td>\n","      <td>1841179.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Munich</td>\n","      <td>48.1372</td>\n","      <td>11.5755</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Bavaria</td>\n","      <td>admin</td>\n","      <td>1471508.0</td>\n","      <td>1471508.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Cologne</td>\n","      <td>50.9422</td>\n","      <td>6.9578</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>North Rhine-Westphalia</td>\n","      <td>NaN</td>\n","      <td>1085664.0</td>\n","      <td>1085664.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Frankfurt</td>\n","      <td>50.1136</td>\n","      <td>8.6797</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Hesse</td>\n","      <td>minor</td>\n","      <td>753056.0</td>\n","      <td>753056.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        city      lat      lng  country iso2              admin_name  capital  \\\n","0     Berlin  52.5167  13.3833  Germany   DE                  Berlin  primary   \n","1    Hamburg  53.5500  10.0000  Germany   DE                 Hamburg    admin   \n","2     Munich  48.1372  11.5755  Germany   DE                 Bavaria    admin   \n","3    Cologne  50.9422   6.9578  Germany   DE  North Rhine-Westphalia      NaN   \n","4  Frankfurt  50.1136   8.6797  Germany   DE                   Hesse    minor   \n","\n","   population  population_proper  \n","0   3644826.0          3644826.0  \n","1   1841179.0          1841179.0  \n","2   1471508.0          1471508.0  \n","3   1085664.0          1085664.0  \n","4    753056.0           753056.0  "]},"execution_count":50,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":51,"id":"c87dab7a","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:11.756589Z","iopub.status.busy":"2024-03-22T09:48:11.756162Z","iopub.status.idle":"2024-03-22T09:48:11.910415Z","shell.execute_reply":"2024-03-22T09:48:11.908618Z"},"papermill":{"duration":0.194558,"end_time":"2024-03-22T09:48:11.915698","exception":false,"start_time":"2024-03-22T09:48:11.72114","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a pie chart to show the distribution of capital cities vs. non-capital cities.\n","\n","import matplotlib.pyplot as plt\n","\n","\n","# Create a pie chart\n","plt.pie(df[\"capital\"].value_counts(), autopct='%1.1f%%', startangle=90)\n","\n","# Add a title\n","plt.title('Distribution of Categories')\n","\n","# Show the pie chart\n","\n","plt.show()\n"]},{"cell_type":"code","execution_count":52,"id":"60fcaff8","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:12.01798Z","iopub.status.busy":"2024-03-22T09:48:12.016543Z","iopub.status.idle":"2024-03-22T09:48:12.436297Z","shell.execute_reply":"2024-03-22T09:48:12.434986Z"},"papermill":{"duration":0.458559,"end_time":"2024-03-22T09:48:12.439203","exception":false,"start_time":"2024-03-22T09:48:11.980644","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Calculate and visualize the total population of cities in each state.\n","updated_df = df.groupby(\"admin_name\")[\"population\"].sum().reset_index()\n","states = updated_df[\"admin_name\"]\n","population = updated_df[\"population\"]\n","plt.barh(states, population)\n","\n","# Add labels and title\n","plt.ylabel('Cities')\n","plt.xlabel('Population')\n","plt.title('State Population Distribution')\n","\n","# Show the histogram\n","plt.show()"]},{"cell_type":"code","execution_count":53,"id":"545eee01","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:12.512751Z","iopub.status.busy":"2024-03-22T09:48:12.512087Z","iopub.status.idle":"2024-03-22T09:48:12.578124Z","shell.execute_reply":"2024-03-22T09:48:12.576812Z"},"papermill":{"duration":0.108612,"end_time":"2024-03-22T09:48:12.582528","exception":false,"start_time":"2024-03-22T09:48:12.473916","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Province : Baden-Württemberg\n","                   city capital\n","0             Stuttgart   admin\n","1             Karlsruhe   minor\n","2              Mannheim   minor\n","3  Freiburg im Breisgau   minor\n","4            Heidelberg   minor\n","##################################################\n","Province : Bavaria\n","         city capital\n","0      Munich   admin\n","1   Nuremberg   minor\n","2    Augsburg   minor\n","3  Regensburg   minor\n","4  Ingolstadt   minor\n","##################################################\n","Province : Berlin\n","     city  capital\n","0  Berlin  primary\n","##################################################\n","Province : Brandenburg\n","               city capital\n","0           Potsdam   admin\n","1           Cottbus   minor\n","2       Brandenburg   minor\n","3  Frankfurt (Oder)   minor\n","4       Oranienburg   minor\n","##################################################\n","Province : Bremen\n","          city capital\n","0       Bremen   admin\n","1  Bremerhaven     NaN\n","##################################################\n","Province : Hamburg\n","      city capital\n","0  Hamburg   admin\n","##################################################\n","Province : Hesse\n","        city capital\n","0  Frankfurt   minor\n","1  Wiesbaden   admin\n","2     Kassel   minor\n","3  Darmstadt   minor\n","4      Hanau   minor\n","##################################################\n","Province : Lower Saxony\n","           city capital\n","0      Hannover   admin\n","1  Braunschweig   minor\n","2     Oldenburg   minor\n","3     Osnabrück   minor\n","4     Wolfsburg   minor\n","##################################################\n","Province : Mecklenburg-Western Pomerania\n","             city capital\n","0         Rostock     NaN\n","1        Schwerin   admin\n","2  Neubrandenburg   minor\n","3       Stralsund   minor\n","4      Greifswald   minor\n","##################################################\n","Province : North Rhine-Westphalia\n","         city capital\n","0     Cologne     NaN\n","1  Düsseldorf   admin\n","2    Dortmund   minor\n","3       Essen   minor\n","4    Duisburg   minor\n","##################################################\n","Province : Rhineland-Palatinate\n","             city capital\n","0           Mainz   admin\n","1    Ludwigshafen   minor\n","2         Koblenz   minor\n","3           Trier   minor\n","4  Kaiserslautern   minor\n","##################################################\n","Province : Saarland\n","            city capital\n","0    Saarbrücken   admin\n","1    Neunkirchen   minor\n","2        Homburg   minor\n","3     Völklingen     NaN\n","4  Sankt Ingbert     NaN\n","##################################################\n","Province : Saxony\n","       city capital\n","0   Dresden   admin\n","1   Leipzig   minor\n","2  Chemnitz   minor\n","3   Zwickau   minor\n","4    Plauen   minor\n","##################################################\n","Province : Saxony-Anhalt\n","            city capital\n","0          Halle   minor\n","1      Magdeburg   admin\n","2  Dessau-Roßlau   minor\n","3     Wittenberg   minor\n","4     Weißenfels     NaN\n","##################################################\n","Province : Schleswig-Holstein\n","          city capital\n","0         Kiel   admin\n","1       Lübeck   minor\n","2    Flensburg   minor\n","3   Neumünster   minor\n","4  Norderstedt     NaN\n","##################################################\n","Province : Thuringia\n","     city capital\n","0  Erfurt   admin\n","1    Jena   minor\n","2    Gera   minor\n","3  Weimar   minor\n","4   Gotha   minor\n","##################################################\n"]}],"source":["\n","# Is there a relationship between a city's status as a capital and its population?\n","all_data = df.groupby(\"admin_name\")\n","for group, data in all_data:\n","    group_data = df[df[\"admin_name\"] == group].sort_values(by = \"population\", ascending = False).head().reset_index()\n","    print(\"Province :\",group)\n","    print(group_data[[\"city\",\"capital\"]])\n","    print(\"#\"*50)\n"]},{"cell_type":"code","execution_count":54,"id":"091185e1","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:12.655469Z","iopub.status.busy":"2024-03-22T09:48:12.65501Z","iopub.status.idle":"2024-03-22T09:48:12.672983Z","shell.execute_reply":"2024-03-22T09:48:12.671763Z"},"papermill":{"duration":0.057547,"end_time":"2024-03-22T09:48:12.675555","exception":false,"start_time":"2024-03-22T09:48:12.618008","status":"completed"},"tags":[]},"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>city</th>\n","      <th>lat</th>\n","      <th>lng</th>\n","      <th>country</th>\n","      <th>iso2</th>\n","      <th>admin_name</th>\n","      <th>capital</th>\n","      <th>population</th>\n","      <th>population_proper</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Berlin</td>\n","      <td>52.5167</td>\n","      <td>13.3833</td>\n","      <td>Germany</td>\n","      <td>DE</td>\n","      <td>Berlin</td>\n","      <td>primary</td>\n","      <td>3644826.0</td>\n","      <td>3644826.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     city      lat      lng  country iso2 admin_name  capital  population  \\\n","0  Berlin  52.5167  13.3833  Germany   DE     Berlin  primary   3644826.0   \n","\n","   population_proper  \n","0          3644826.0  "]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["df[df['capital'] == \"primary\"]"]},{"cell_type":"code","execution_count":55,"id":"6897278c","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:12.748934Z","iopub.status.busy":"2024-03-22T09:48:12.748491Z","iopub.status.idle":"2024-03-22T09:48:13.134945Z","shell.execute_reply":"2024-03-22T09:48:13.133101Z"},"papermill":{"duration":0.427293,"end_time":"2024-03-22T09:48:13.13832","exception":false,"start_time":"2024-03-22T09:48:12.711027","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a bar chart showing the population of the capital cities for each states.\n","updtaed_df = df[(df[\"capital\"] == \"admin\") | (df[\"capital\"] == \"primary\")]\n","cities = updtaed_df[\"city\"]\n","population = updtaed_df[\"population\"]\n","    \n","plt.barh(cities, population)\n","\n","# Add labels and title\n","plt.ylabel('Cities')\n","plt.xlabel('Population')\n","plt.title('Capital Cities Population Distribution')\n","\n","# Show the histogram\n","plt.show()\n","\n"]},{"cell_type":"code","execution_count":null,"id":"590867ec","metadata":{"papermill":{"duration":0.035881,"end_time":"2024-03-22T09:48:13.210655","exception":false,"start_time":"2024-03-22T09:48:13.174774","status":"completed"},"tags":[]},"outputs":[],"source":[]},{"cell_type":"code","execution_count":56,"id":"5eaf4d01","metadata":{"execution":{"iopub.execute_input":"2024-03-22T09:48:13.284027Z","iopub.status.busy":"2024-03-22T09:48:13.28359Z","iopub.status.idle":"2024-03-22T09:48:13.323194Z","shell.execute_reply":"2024-03-22T09:48:13.321933Z"},"papermill":{"duration":0.079916,"end_time":"2024-03-22T09:48:13.326076","exception":false,"start_time":"2024-03-22T09:48:13.24616","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Province : Baden-Württemberg\n","0               Stuttgart\n","1               Karlsruhe\n","2                Mannheim\n","3    Freiburg im Breisgau\n","4              Heidelberg\n","Name: city, dtype: object\n","##################################################\n","Province : Bavaria\n","0        Munich\n","1     Nuremberg\n","2      Augsburg\n","3    Regensburg\n","4    Ingolstadt\n","Name: city, dtype: object\n","##################################################\n","Province : Berlin\n","0    Berlin\n","Name: city, dtype: object\n","##################################################\n","Province : Brandenburg\n","0             Potsdam\n","1             Cottbus\n","2         Brandenburg\n","3    Frankfurt (Oder)\n","4         Oranienburg\n","Name: city, dtype: object\n","##################################################\n","Province : Bremen\n","0         Bremen\n","1    Bremerhaven\n","Name: city, dtype: object\n","##################################################\n","Province : Hamburg\n","0    Hamburg\n","Name: city, dtype: object\n","##################################################\n","Province : Hesse\n","0    Frankfurt\n","1    Wiesbaden\n","2       Kassel\n","3    Darmstadt\n","4        Hanau\n","Name: city, dtype: object\n","##################################################\n","Province : Lower Saxony\n","0        Hannover\n","1    Braunschweig\n","2       Oldenburg\n","3       Osnabrück\n","4       Wolfsburg\n","Name: city, dtype: object\n","##################################################\n","Province : Mecklenburg-Western Pomerania\n","0           Rostock\n","1          Schwerin\n","2    Neubrandenburg\n","3         Stralsund\n","4        Greifswald\n","Name: city, dtype: object\n","##################################################\n","Province : North Rhine-Westphalia\n","0       Cologne\n","1    Düsseldorf\n","2      Dortmund\n","3         Essen\n","4      Duisburg\n","Name: city, dtype: object\n","##################################################\n","Province : Rhineland-Palatinate\n","0             Mainz\n","1      Ludwigshafen\n","2           Koblenz\n","3             Trier\n","4    Kaiserslautern\n","Name: city, dtype: object\n","##################################################\n","Province : Saarland\n","0      Saarbrücken\n","1      Neunkirchen\n","2          Homburg\n","3       Völklingen\n","4    Sankt Ingbert\n","Name: city, dtype: object\n","##################################################\n","Province : Saxony\n","0     Dresden\n","1     Leipzig\n","2    Chemnitz\n","3     Zwickau\n","4      Plauen\n","Name: city, dtype: object\n","##################################################\n","Province : Saxony-Anhalt\n","0            Halle\n","1        Magdeburg\n","2    Dessau-Roßlau\n","3       Wittenberg\n","4       Weißenfels\n","Name: city, dtype: object\n","##################################################\n","Province : Schleswig-Holstein\n","0           Kiel\n","1         Lübeck\n","2      Flensburg\n","3     Neumünster\n","4    Norderstedt\n","Name: city, dtype: object\n","##################################################\n","Province : Thuringia\n","0    Erfurt\n","1      Jena\n","2      Gera\n","3    Weimar\n","4     Gotha\n","Name: city, dtype: object\n","##################################################\n"]}],"source":["\n","# What are the five most populous cities in each states?\n","\n","all_data = df.groupby(\"admin_name\")\n","for group, data in all_data:\n","    group_data = df[df[\"admin_name\"] == group].sort_values(by = \"population\", ascending = False).head().reset_index()\n","    print(\"Province :\",group)\n","    print(group_data[\"city\"])\n","    print(\"#\"*50)"]},{"cell_type":"code","execution_count":null,"id":"1c3b9212","metadata":{"papermill":{"duration":0.036208,"end_time":"2024-03-22T09:48:13.398418","exception":false,"start_time":"2024-03-22T09:48:13.36221","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"kaggle":{"accelerator":"none","dataSources":[{"datasetId":1229045,"sourceId":2051456,"sourceType":"datasetVersion"}],"dockerImageVersionId":30558,"isGpuEnabled":false,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"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":"3.10.12"},"papermill":{"default_parameters":{},"duration":17.753445,"end_time":"2024-03-22T09:48:14.156018","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-03-22T09:47:56.402573","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}