src/EBI_analysis/create_general_insights.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# EBI Metadata - General Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2831557 entries, 0 to 2831556\n",
      "Data columns (total 34 columns):\n",
      " #   Column                Dtype  \n",
      "---  ------                -----  \n",
      " 0   accession             object \n",
      " 1   base_count            int64  \n",
      " 2   collected_by          object \n",
      " 3   collection_date       object \n",
      " 4   country_and_region    object \n",
      " 5   culture_collection    object \n",
      " 6   dataclass             object \n",
      " 7   description           object \n",
      " 8   environmental_sample  bool   \n",
      " 9   first_public          object \n",
      " 10  germline              bool   \n",
      " 11  host                  object \n",
      " 12  identified_by         object \n",
      " 13  isolate               object \n",
      " 14  isolation_source      object \n",
      " 15  keywords              object \n",
      " 16  lab_host              object \n",
      " 17  last_updated          object \n",
      " 18  location              object \n",
      " 19  mol_type              object \n",
      " 20  sample_accession      object \n",
      " 21  scientific_name       object \n",
      " 22  sequence_md5          object \n",
      " 23  sequence_version      int64  \n",
      " 24  strain                object \n",
      " 25  study_accession       object \n",
      " 26  tax_division          object \n",
      " 27  tax_id                int64  \n",
      " 28  topology              object \n",
      " 29  gender                object \n",
      " 30  age                   object \n",
      " 31  country               object \n",
      " 32  n_inhabitants         float64\n",
      " 33  region                object \n",
      "dtypes: bool(2), float64(1), int64(3), object(28)\n",
      "memory usage: 696.7+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('../../data/metadata_EBI_preprocessed.csv',\n",
    "                 dtype={'collected_by': object,\n",
    "                        'collection_date': object,\n",
    "                        'culture_collection': object,\n",
    "                        'identified_by': object,\n",
    "                        'isolate': object,\n",
    "                        'isolation_source': object,\n",
    "                        'keywords': object,\n",
    "                        'lab_host': object,\n",
    "                        'location': object,\n",
    "                        'sample_accession': object,\n",
    "                        'strain': object,\n",
    "                        'study_accession': object})\n",
    "df.info()  # 29 + 5 columns, 2.8 mio entries"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Descriptive Statistics"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "baa49aca16134dfdb94f6fb194aea4bf"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "f175b97140bd470aadba8432e9e14cc5"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "e3a3a364e6854b76a4de116bfc47bb6d"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "4bc58befc23f482fba7cfa1e490f4016"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas_profiling\n",
    "import json\n",
    "\n",
    "# Create fancy profile report\n",
    "# See https://pandas-profiling.github.io/pandas-profiling/docs/master/rtd/pages/advanced_usage.html for advanced usage\n",
    "profile_ebi = pandas_profiling.ProfileReport(df,\n",
    "                                             title='EBI Metadata Profiling',\n",
    "                                             minimal=True,\n",
    "                                             correlations={'pearson': {'calculate': True},\n",
    "                                                           'spearman': {'calculate': True},\n",
    "                                                           'kendall': {'calculate': True},\n",
    "                                                           'phi_k': {'calculate': True},\n",
    "                                                           'cramers': {'calculate': False}})\n",
    "\n",
    "# Add definitions\n",
    "with open('../helper_dicts/definitions_EBI_metadata.json') as f:\n",
    "    definitions_ebi_metadata = json.load(f)\n",
    "profile_ebi.config.variables.descriptions = definitions_ebi_metadata\n",
    "\n",
    "# Save as html\n",
    "profile_ebi.to_file(output_file='../../data/metadata_EBI_profile.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Investigation of National Bias"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Separate dataframe into countries appearing often and rarely\n",
    "threshold = 1000\n",
    "\n",
    "rare_countries = df[df['country'].map(df['country'].value_counts()) < threshold]\n",
    "rare_countries['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "popular_countries = df[df['country'].map(df['country'].value_counts()) >= threshold]\n",
    "popular_countries['country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('../helper_dicts/country_inhabitants.json') as f:\n",
    "    country_inhabitants_map = json.load(f)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.isdir('../../plots/general_country_counts'):\n",
    "    os.mkdir('../../plots/general_country_counts')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Plot distribution of countries appearing more than 1,000 times\n",
    "for hue in ['dataclass', 'mol_type', 'keywords', 'gender']:\n",
    "    fig, ax = plt.subplots(figsize=(18, 16))\n",
    "    sns.countplot(data=popular_countries,\n",
    "                  x='country',\n",
    "                  hue=hue,\n",
    "                  order=popular_countries['country'].value_counts().index)\n",
    "\n",
    "    fig.suptitle('Distribution of Countries with >= 1,000 Samples', fontsize=22)\n",
    "    plt.xlabel('Country', fontsize=18)\n",
    "    plt.ylabel('Count Normalized by Number of Inhabitants', fontsize=18)\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "    plt.legend(loc='upper right', title=f'{hue.capitalize()}:', fontsize=14)\n",
    "    plt.rcParams['legend.title_fontsize'] = 14\n",
    "\n",
    "    # Normalize height of bars with number of inhabitants\n",
    "    max_y = 0\n",
    "    n_countries = len(popular_countries['country'].unique())\n",
    "\n",
    "    for i, patch in enumerate(ax.patches):\n",
    "        country = ax.get_xticklabels()[i % n_countries].get_text()\n",
    "        new_height = patch.get_height() / country_inhabitants_map[country]\n",
    "        patch.set_height(new_height)\n",
    "\n",
    "        max_y = max(max_y, new_height)\n",
    "    plt.gca().set_ylim([0, max_y + (max_y / 20)])\n",
    "\n",
    "    fig.savefig(f'../../plots/general_country_counts/popular_countries_count_by_{hue}_normalized.png', dpi=300)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Plot distribution of countries appearing less than 1,000 times\n",
    "for hue in ['dataclass', 'mol_type', 'keywords', 'gender']:\n",
    "    fig, ax = plt.subplots(figsize=(36, 18))\n",
    "    sns.countplot(data=rare_countries,\n",
    "                  x='country',\n",
    "                  hue=hue,\n",
    "                  order=rare_countries['country'].value_counts().index)\n",
    "\n",
    "    fig.suptitle('Distribution of Countries with < 1,000 Samples', fontsize=22)\n",
    "    plt.xlabel('Country', fontsize=18)\n",
    "    plt.ylabel('Count Normalized by Number of Inhabitants', fontsize=18)\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "    plt.legend(loc='upper right', title=f'{hue.capitalize()}:', fontsize=14)\n",
    "    plt.rcParams['legend.title_fontsize'] = 14\n",
    "\n",
    "    # Normalize height of bars with number of inhabitants\n",
    "    max_y = 0\n",
    "    n_countries = len(popular_countries['country'].unique())\n",
    "\n",
    "    for i, patch in enumerate(ax.patches):\n",
    "        country = ax.get_xticklabels()[i % n_countries].get_text()\n",
    "        if country == 'Unknown':\n",
    "            new_height = 0.0\n",
    "        else:\n",
    "            new_height = patch.get_height() / country_inhabitants_map[country]\n",
    "        patch.set_height(new_height)\n",
    "\n",
    "        max_y = max(max_y, new_height)\n",
    "    plt.gca().set_ylim([0, max_y + (max_y / 20)])\n",
    "\n",
    "    fig.savefig(f'../../plots/general_country_counts/rare_countries_count_by_{hue}_normalized.png', dpi=300)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2304x1152 with 1 Axes>",
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plotdata = df.copy(deep=True)\n",
    "# only take countries with at least 200 samples\n",
    "plotdata = plotdata[plotdata['country'].map(plotdata['country'].value_counts()) > 200]\n",
    "# exclude 3 countries that are not in list(country_inhabitants_map.keys())\n",
    "plotdata = plotdata[~plotdata['country'].isin(['Malawi', 'Comoros', 'Lesotho'])]\n",
    "\n",
    "# Plot distribution of selected countries\n",
    "sns.set_style('whitegrid')\n",
    "fig, ax = plt.subplots(figsize=(32, 16))\n",
    "sns.countplot(data=plotdata,\n",
    "              x='country',\n",
    "              color='#9B2D1F',  # \"#D34817\" for custom orange\n",
    "              order=plotdata['country'].value_counts().index)\n",
    "\n",
    "fig.suptitle('Number of Samples per Country - Scaled by Number of Inhabitants', fontsize=28)\n",
    "plt.xlabel('Country', fontsize=18)\n",
    "plt.ylabel('Scaled Sample Count', fontsize=18)\n",
    "plt.xticks(fontsize=14, rotation=90)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Normalize height of bars with number of inhabitants\n",
    "max_y = 0\n",
    "for i, patch in enumerate(ax.patches):\n",
    "    country = ax.get_xticklabels()[i].get_text()\n",
    "    new_height = patch.get_height() / country_inhabitants_map[country]\n",
    "    max_y = max(max_y, new_height)\n",
    "    patch.set_height(new_height)\n",
    "plt.gca().set_ylim([0, max_y + (max_y / 20)])\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(f'../../plots/general_country_counts/selected_countries_counts_scaled.png', dpi=300)\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "{'Unknown': 0,\n 'Palau': 17907,\n 'Liechtenstein': 38896,\n 'Seychelles': 98462,\n 'Guam': 168801,\n 'Iceland': 371580,\n 'Belize': 419199,\n 'Malta': 514564,\n 'Djibouti': 921804,\n 'Estonia': 1330068,\n 'Timor-Leste': 1340513,\n 'Bahrain': 1569446,\n 'Gabon': 2119275,\n 'Gambia': 2173999,\n 'Jamaica': 2726667,\n 'Qatar': 2795484,\n 'Armenia': 2963900,\n 'Puerto Rico': 3285874,\n 'West Bank': 3340143,\n 'Uruguay': 3518552,\n 'Croatia': 3901165,\n 'New Zealand': 5133320,\n 'Singapore': 5453600,\n 'Slovakia': 5457873,\n 'Finland': 5536146,\n 'Denmark': 5850189,\n 'Lebanon': 6859408,\n 'Serbia': 6871547,\n 'Libya': 6992701,\n 'Hong Kong': 7500700,\n 'Sierra Leone': 8059155,\n 'Switzerland': 8606033,\n 'Togo': 8608444,\n 'Austria': 8935112,\n 'Belarus': 9349645,\n 'Israel': 9442940,\n 'Hungary': 9730000,\n 'Portugal': 10347892,\n 'Sweden': 10402070,\n 'Georgia': 10620000,\n 'Czech Republic': 10701777,\n 'Greece': 10718565,\n 'Dominican Republic': 10878246,\n 'Jordan': 11042719,\n 'Cuba': 11181595,\n 'Belgium': 11492641,\n 'Tunisia': 11708370,\n 'Benin': 11733059,\n 'Guinea': 12414293,\n 'Zimbabwe': 15092171,\n 'Cambodia': 15552211,\n 'Somalia': 15893219,\n 'Guatemala': 17263239,\n 'Zambia': 17351708,\n 'Chile': 17574003,\n 'Netherlands': 17667700,\n 'Ecuador': 17715822,\n 'Kazakhstan': 19082467,\n 'Romania': 19186201,\n 'Mali': 20250833,\n 'Sri Lanka': 22156000,\n 'Taiwan': 23451837,\n 'Australia': 25700000,\n 'Cameroon': 26545864,\n 'Nepal': 28095714,\n 'Venezuela': 28887118,\n 'Ghana': 31072940,\n 'Malaysia': 32730000,\n 'Saudi Arabia': 34218169,\n 'Peru': 34294231,\n 'Uzbekistan': 35011180,\n 'Morocco': 37112080,\n 'Canada': 38246108,\n 'Poland': 38268000,\n 'Iraq': 40222503,\n 'Uganda': 42729036,\n 'Argentina': 45605826,\n 'Colombia': 50372424,\n 'South Korea': 51709098,\n 'Myanmar': 53582855,\n 'Kenya': 54985698,\n 'South Africa': 60142978,\n 'Italy': 60317116,\n 'United Kingdom': 67081000,\n 'France': 67413000,\n 'Thailand': 69950850,\n 'Germany': 83129285,\n 'Iran': 83183741,\n 'Turkey': 83614362,\n 'Viet Nam': 96208984,\n 'Egypt': 101478581,\n 'Philippines': 109035343,\n 'Ethiopia': 117876227,\n 'Japan': 125708382,\n 'Mexico': 126014024,\n 'Russia': 146171015,\n 'Bangladesh': 164700000,\n 'Brazil': 210147125,\n 'Nigeria': 211400708,\n 'Pakistan': 226992332,\n 'Indonesia': 270203917,\n 'Spain': 331449281,\n 'USA': 331449281,\n 'India': 1352642280,\n 'China': 1418120000}"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_inhabitants = {country: n_inhabitants for country, n_inhabitants in sorted(country_inhabitants_map.items(), key=lambda pair: pair[1])}\n",
    "sorted_inhabitants"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Number of Infections per Country\n",
    "### Preprocess Time Series Data by JHU"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 280 entries, 0 to 279\n",
      "Columns: 723 entries, Province/State to 1/9/22\n",
      "dtypes: float64(2), int64(719), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "# Taken from https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\n",
    "countries_ts = pd.read_csv('../../data/JHU_countries_time_series.csv')\n",
    "countries_ts.info()  # 280 entries, 723 columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "[nan,\n 'Viet Nam',\n 'Czech Republic',\n 'Guam',\n 'South Korea',\n 'Hong Kong',\n 'West Bank',\n 'USA',\n 'Puerto Rico',\n 'Myanmar',\n 'Taiwan']"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check which countries from EBI metadata do not exist in JHU data\n",
    "countries_JHU = countries_ts['Country/Region'].unique()\n",
    "countries_ebi = df['country'].unique()\n",
    "\n",
    "list(set(countries_ebi) - set(countries_JHU))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# Change countries in JHU data to EBI metadata wording\n",
    "countries_ts['Country/Region'].replace(['Vietnam', 'West Bank and Gaza', 'US'], ['Viet Nam', 'West Bank', 'USA'], inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# Only keep TS data of countries that can be found in EBI metadata too (keep 184 of 280 samples)\n",
    "countries_ts = countries_ts[countries_ts['Country/Region'].isin(countries_ebi)]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "China             34\nCanada            16\nFrance            12\nUnited Kingdom    12\nAustralia          8\n                  ..\nGreece             1\nGhana              1\nGermany            1\nGeorgia            1\nZimbabwe           1\nName: Country/Region, Length: 100, dtype: int64"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_ts['Country/Region'].value_counts()  # -> some countries occur several times and have to be summed up first"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "    Province/State Country/Region      Lat      Long  1/22/20  1/23/20  \\\n58           Anhui          China  31.8257  117.2264        1        9   \n59         Beijing          China  40.1824  116.4142       14       22   \n60       Chongqing          China  30.0572  107.8740        6        9   \n61          Fujian          China  26.0789  117.9874        1        5   \n62           Gansu          China  35.7518  104.2861        0        2   \n63       Guangdong          China  23.3417  113.4244       26       32   \n64         Guangxi          China  23.8298  108.7881        2        5   \n65         Guizhou          China  26.8154  106.8748        1        3   \n66          Hainan          China  19.1959  109.7453        4        5   \n67           Hebei          China  39.5490  116.1306        1        1   \n68    Heilongjiang          China  47.8620  127.7615        0        2   \n69           Henan          China  37.8957  114.9042        5        5   \n70       Hong Kong          China  22.3000  114.2000        0        2   \n71           Hubei          China  30.9756  112.2707      444      444   \n72           Hunan          China  27.6104  111.7088        4        9   \n73  Inner Mongolia          China  44.0935  113.9448        0        0   \n74         Jiangsu          China  32.9711  119.4550        1        5   \n75         Jiangxi          China  27.6140  115.7221        2        7   \n76           Jilin          China  43.6661  126.1923        0        1   \n77        Liaoning          China  41.2956  122.6085        2        3   \n78           Macau          China  22.1667  113.5500        1        2   \n79         Ningxia          China  37.2692  106.1655        1        1   \n80         Qinghai          China  35.7452   95.9956        0        0   \n81         Shaanxi          China  35.1917  108.8701        0        3   \n82        Shandong          China  36.3427  118.1498        2        6   \n83        Shanghai          China  31.2020  121.4491        9       16   \n84          Shanxi          China  37.5777  112.2922        1        1   \n85         Sichuan          China  30.6171  102.7103        5        8   \n86         Tianjin          China  39.3054  117.3230        4        4   \n87           Tibet          China  31.6927   88.0924        0        0   \n88         Unknown          China      NaN       NaN        0        0   \n89        Xinjiang          China  41.1129   85.2401        0        2   \n90          Yunnan          China  24.9740  101.4870        1        2   \n91        Zhejiang          China  29.1832  120.0934       10       27   \n\n    1/24/20  1/25/20  1/26/20  1/27/20  ...  12/31/21  1/1/22  1/2/22  1/3/22  \\\n58       15       39       60       70  ...      1009    1009    1009    1009   \n59       36       41       68       80  ...      1211    1211    1215    1215   \n60       27       57       75      110  ...       611     611     611     611   \n61       10       18       35       59  ...      1363    1372    1372    1383   \n62        2        4        7       14  ...       356     356     356     356   \n63       53       78      111      151  ...      3457    3467    3467    3486   \n64       23       23       36       46  ...       622     630     637     641   \n65        3        4        5        7  ...       160     160     160     160   \n66        8       19       22       33  ...       190     190     190     190   \n67        2        8       13       18  ...      1458    1458    1458    1458   \n68        4        9       15       21  ...      2035    2035    2035    2035   \n69        9       32       83      128  ...      1641    1642    1642    1650   \n70        2        5        8        8  ...     12649   12667   12664   12677   \n71      549      761     1058     1423  ...     68317   68317   68317   68317   \n72       24       43       69      100  ...      1222    1222    1222    1223   \n73        1        7        7       11  ...      1186    1186    1186    1191   \n74        9       18       33       47  ...      1625    1625    1625    1626   \n75       18       18       36       72  ...       959     959     959     959   \n76        3        4        4        6  ...       589     589     589     589   \n77        4       17       21       27  ...       795     796     796     796   \n78        2        2        5        6  ...        79      79      79      79   \n79        2        3        4        7  ...       122     122     122     122   \n80        0        1        1        6  ...        30      30      30      30   \n81        5       15       22       35  ...      2219    2342    2342    2530   \n82       15       27       46       75  ...      1043    1045    1046    1047   \n83       20       33       40       53  ...      3103    3121    3147    3176   \n84        1        6        9       13  ...       266     266     266     266   \n85       15       28       44       69  ...      1321    1325    1326    1329   \n86        8       10       14       23  ...       589     594     596     597   \n87        0        0        0        0  ...         1       1       1       1   \n88        0        0        0        0  ...         0       0     105       0   \n89        2        3        4        5  ...       981     981     981     981   \n90        5       11       16       26  ...      1817    1818    1820    1823   \n91       43       62      104      128  ...      2016    2025    2038    2044   \n\n    1/4/22  1/5/22  1/6/22  1/7/22  1/8/22  1/9/22  \n58    1009    1009    1009    1009    1009    1009  \n59    1216    1216    1217    1217    1218    1218  \n60     611     611     611     611     611     611  \n61    1390    1396    1397    1401    1410    1415  \n62     356     356     356     356     356     356  \n63    3493    3503    3512    3521    3530    3539  \n64     650     652     656     662     665     668  \n65     160     160     160     160     160     160  \n66     190     190     190     190     190     190  \n67    1458    1458    1458    1458    1458    1458  \n68    2035    2035    2035    2035    2035    2035  \n69    1654    1719    1777    1821    1880    1941  \n70   12690   12708   12732   12749   12770   12786  \n71   68319   68319   68320   68320   68320   68320  \n72    1223    1223    1225    1228    1228    1229  \n73    1191    1191    1191    1191    1191    1191  \n74    1626    1627    1627    1627    1627    1627  \n75     959     959     959     959     959     959  \n76     589     590     590     590     590     590  \n77     796     796     798     799     800     802  \n78      79      79      79      79      79      79  \n79     122     122     122     122     122     122  \n80      30      30      30      30      30      30  \n81    2566    2629    2686    2732    2762    2777  \n82    1048    1049    1049    1049    1050    1050  \n83    3193    3213    3237    3269    3299    3325  \n84     266     266     266     266     266     266  \n85    1329    1331    1332    1333    1341    1342  \n86     602     606     615     621     625     650  \n87       1       1       1       1       1       1  \n88       0       0       0       0       0       0  \n89     981     981     981     981     981     981  \n90    1823    1825    1826    1829    1830    1836  \n91    2046    2058    2062    2066    2075    2078  \n\n[34 rows x 723 columns]",
      "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>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>12/31/21</th>\n      <th>1/1/22</th>\n      <th>1/2/22</th>\n      <th>1/3/22</th>\n      <th>1/4/22</th>\n      <th>1/5/22</th>\n      <th>1/6/22</th>\n      <th>1/7/22</th>\n      <th>1/8/22</th>\n      <th>1/9/22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>58</th>\n      <td>Anhui</td>\n      <td>China</td>\n      <td>31.8257</td>\n      <td>117.2264</td>\n      <td>1</td>\n      <td>9</td>\n      <td>15</td>\n      <td>39</td>\n      <td>60</td>\n      <td>70</td>\n      <td>...</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n      <td>1009</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>Beijing</td>\n      <td>China</td>\n      <td>40.1824</td>\n      <td>116.4142</td>\n      <td>14</td>\n      <td>22</td>\n      <td>36</td>\n      <td>41</td>\n      <td>68</td>\n      <td>80</td>\n      <td>...</td>\n      <td>1211</td>\n      <td>1211</td>\n      <td>1215</td>\n      <td>1215</td>\n      <td>1216</td>\n      <td>1216</td>\n      <td>1217</td>\n      <td>1217</td>\n      <td>1218</td>\n      <td>1218</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Chongqing</td>\n      <td>China</td>\n      <td>30.0572</td>\n      <td>107.8740</td>\n      <td>6</td>\n      <td>9</td>\n      <td>27</td>\n      <td>57</td>\n      <td>75</td>\n      <td>110</td>\n      <td>...</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n      <td>611</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>Fujian</td>\n      <td>China</td>\n      <td>26.0789</td>\n      <td>117.9874</td>\n      <td>1</td>\n      <td>5</td>\n      <td>10</td>\n      <td>18</td>\n      <td>35</td>\n      <td>59</td>\n      <td>...</td>\n      <td>1363</td>\n      <td>1372</td>\n      <td>1372</td>\n      <td>1383</td>\n      <td>1390</td>\n      <td>1396</td>\n      <td>1397</td>\n      <td>1401</td>\n      <td>1410</td>\n      <td>1415</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Gansu</td>\n      <td>China</td>\n      <td>35.7518</td>\n      <td>104.2861</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>7</td>\n      <td>14</td>\n      <td>...</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n      <td>356</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>Guangdong</td>\n      <td>China</td>\n      <td>23.3417</td>\n      <td>113.4244</td>\n      <td>26</td>\n      <td>32</td>\n      <td>53</td>\n      <td>78</td>\n      <td>111</td>\n      <td>151</td>\n      <td>...</td>\n      <td>3457</td>\n      <td>3467</td>\n      <td>3467</td>\n      <td>3486</td>\n      <td>3493</td>\n      <td>3503</td>\n      <td>3512</td>\n      <td>3521</td>\n      <td>3530</td>\n      <td>3539</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>Guangxi</td>\n      <td>China</td>\n      <td>23.8298</td>\n      <td>108.7881</td>\n      <td>2</td>\n      <td>5</td>\n      <td>23</td>\n      <td>23</td>\n      <td>36</td>\n      <td>46</td>\n      <td>...</td>\n      <td>622</td>\n      <td>630</td>\n      <td>637</td>\n      <td>641</td>\n      <td>650</td>\n      <td>652</td>\n      <td>656</td>\n      <td>662</td>\n      <td>665</td>\n      <td>668</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>Guizhou</td>\n      <td>China</td>\n      <td>26.8154</td>\n      <td>106.8748</td>\n      <td>1</td>\n      <td>3</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>7</td>\n      <td>...</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n      <td>160</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Hainan</td>\n      <td>China</td>\n      <td>19.1959</td>\n      <td>109.7453</td>\n      <td>4</td>\n      <td>5</td>\n      <td>8</td>\n      <td>19</td>\n      <td>22</td>\n      <td>33</td>\n      <td>...</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>Hebei</td>\n      <td>China</td>\n      <td>39.5490</td>\n      <td>116.1306</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>8</td>\n      <td>13</td>\n      <td>18</td>\n      <td>...</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n      <td>1458</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>Heilongjiang</td>\n      <td>China</td>\n      <td>47.8620</td>\n      <td>127.7615</td>\n      <td>0</td>\n      <td>2</td>\n      <td>4</td>\n      <td>9</td>\n      <td>15</td>\n      <td>21</td>\n      <td>...</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n      <td>2035</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>Henan</td>\n      <td>China</td>\n      <td>37.8957</td>\n      <td>114.9042</td>\n      <td>5</td>\n      <td>5</td>\n      <td>9</td>\n      <td>32</td>\n      <td>83</td>\n      <td>128</td>\n      <td>...</td>\n      <td>1641</td>\n      <td>1642</td>\n      <td>1642</td>\n      <td>1650</td>\n      <td>1654</td>\n      <td>1719</td>\n      <td>1777</td>\n      <td>1821</td>\n      <td>1880</td>\n      <td>1941</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>Hong Kong</td>\n      <td>China</td>\n      <td>22.3000</td>\n      <td>114.2000</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>8</td>\n      <td>8</td>\n      <td>...</td>\n      <td>12649</td>\n      <td>12667</td>\n      <td>12664</td>\n      <td>12677</td>\n      <td>12690</td>\n      <td>12708</td>\n      <td>12732</td>\n      <td>12749</td>\n      <td>12770</td>\n      <td>12786</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>Hubei</td>\n      <td>China</td>\n      <td>30.9756</td>\n      <td>112.2707</td>\n      <td>444</td>\n      <td>444</td>\n      <td>549</td>\n      <td>761</td>\n      <td>1058</td>\n      <td>1423</td>\n      <td>...</td>\n      <td>68317</td>\n      <td>68317</td>\n      <td>68317</td>\n      <td>68317</td>\n      <td>68319</td>\n      <td>68319</td>\n      <td>68320</td>\n      <td>68320</td>\n      <td>68320</td>\n      <td>68320</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>Hunan</td>\n      <td>China</td>\n      <td>27.6104</td>\n      <td>111.7088</td>\n      <td>4</td>\n      <td>9</td>\n      <td>24</td>\n      <td>43</td>\n      <td>69</td>\n      <td>100</td>\n      <td>...</td>\n      <td>1222</td>\n      <td>1222</td>\n      <td>1222</td>\n      <td>1223</td>\n      <td>1223</td>\n      <td>1223</td>\n      <td>1225</td>\n      <td>1228</td>\n      <td>1228</td>\n      <td>1229</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>Inner Mongolia</td>\n      <td>China</td>\n      <td>44.0935</td>\n      <td>113.9448</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>7</td>\n      <td>7</td>\n      <td>11</td>\n      <td>...</td>\n      <td>1186</td>\n      <td>1186</td>\n      <td>1186</td>\n      <td>1191</td>\n      <td>1191</td>\n      <td>1191</td>\n      <td>1191</td>\n      <td>1191</td>\n      <td>1191</td>\n      <td>1191</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Jiangsu</td>\n      <td>China</td>\n      <td>32.9711</td>\n      <td>119.4550</td>\n      <td>1</td>\n      <td>5</td>\n      <td>9</td>\n      <td>18</td>\n      <td>33</td>\n      <td>47</td>\n      <td>...</td>\n      <td>1625</td>\n      <td>1625</td>\n      <td>1625</td>\n      <td>1626</td>\n      <td>1626</td>\n      <td>1627</td>\n      <td>1627</td>\n      <td>1627</td>\n      <td>1627</td>\n      <td>1627</td>\n    </tr>\n    <tr>\n      <th>75</th>\n      <td>Jiangxi</td>\n      <td>China</td>\n      <td>27.6140</td>\n      <td>115.7221</td>\n      <td>2</td>\n      <td>7</td>\n      <td>18</td>\n      <td>18</td>\n      <td>36</td>\n      <td>72</td>\n      <td>...</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n      <td>959</td>\n    </tr>\n    <tr>\n      <th>76</th>\n      <td>Jilin</td>\n      <td>China</td>\n      <td>43.6661</td>\n      <td>126.1923</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>4</td>\n      <td>6</td>\n      <td>...</td>\n      <td>589</td>\n      <td>589</td>\n      <td>589</td>\n      <td>589</td>\n      <td>589</td>\n      <td>590</td>\n      <td>590</td>\n      <td>590</td>\n      <td>590</td>\n      <td>590</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>Liaoning</td>\n      <td>China</td>\n      <td>41.2956</td>\n      <td>122.6085</td>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>17</td>\n      <td>21</td>\n      <td>27</td>\n      <td>...</td>\n      <td>795</td>\n      <td>796</td>\n      <td>796</td>\n      <td>796</td>\n      <td>796</td>\n      <td>796</td>\n      <td>798</td>\n      <td>799</td>\n      <td>800</td>\n      <td>802</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>Macau</td>\n      <td>China</td>\n      <td>22.1667</td>\n      <td>113.5500</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>5</td>\n      <td>6</td>\n      <td>...</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>Ningxia</td>\n      <td>China</td>\n      <td>37.2692</td>\n      <td>106.1655</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>7</td>\n      <td>...</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n      <td>122</td>\n    </tr>\n    <tr>\n      <th>80</th>\n      <td>Qinghai</td>\n      <td>China</td>\n      <td>35.7452</td>\n      <td>95.9956</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n      <td>...</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>81</th>\n      <td>Shaanxi</td>\n      <td>China</td>\n      <td>35.1917</td>\n      <td>108.8701</td>\n      <td>0</td>\n      <td>3</td>\n      <td>5</td>\n      <td>15</td>\n      <td>22</td>\n      <td>35</td>\n      <td>...</td>\n      <td>2219</td>\n      <td>2342</td>\n      <td>2342</td>\n      <td>2530</td>\n      <td>2566</td>\n      <td>2629</td>\n      <td>2686</td>\n      <td>2732</td>\n      <td>2762</td>\n      <td>2777</td>\n    </tr>\n    <tr>\n      <th>82</th>\n      <td>Shandong</td>\n      <td>China</td>\n      <td>36.3427</td>\n      <td>118.1498</td>\n      <td>2</td>\n      <td>6</td>\n      <td>15</td>\n      <td>27</td>\n      <td>46</td>\n      <td>75</td>\n      <td>...</td>\n      <td>1043</td>\n      <td>1045</td>\n      <td>1046</td>\n      <td>1047</td>\n      <td>1048</td>\n      <td>1049</td>\n      <td>1049</td>\n      <td>1049</td>\n      <td>1050</td>\n      <td>1050</td>\n    </tr>\n    <tr>\n      <th>83</th>\n      <td>Shanghai</td>\n      <td>China</td>\n      <td>31.2020</td>\n      <td>121.4491</td>\n      <td>9</td>\n      <td>16</td>\n      <td>20</td>\n      <td>33</td>\n      <td>40</td>\n      <td>53</td>\n      <td>...</td>\n      <td>3103</td>\n      <td>3121</td>\n      <td>3147</td>\n      <td>3176</td>\n      <td>3193</td>\n      <td>3213</td>\n      <td>3237</td>\n      <td>3269</td>\n      <td>3299</td>\n      <td>3325</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>Shanxi</td>\n      <td>China</td>\n      <td>37.5777</td>\n      <td>112.2922</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n      <td>9</td>\n      <td>13</td>\n      <td>...</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n      <td>266</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>Sichuan</td>\n      <td>China</td>\n      <td>30.6171</td>\n      <td>102.7103</td>\n      <td>5</td>\n      <td>8</td>\n      <td>15</td>\n      <td>28</td>\n      <td>44</td>\n      <td>69</td>\n      <td>...</td>\n      <td>1321</td>\n      <td>1325</td>\n      <td>1326</td>\n      <td>1329</td>\n      <td>1329</td>\n      <td>1331</td>\n      <td>1332</td>\n      <td>1333</td>\n      <td>1341</td>\n      <td>1342</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>Tianjin</td>\n      <td>China</td>\n      <td>39.3054</td>\n      <td>117.3230</td>\n      <td>4</td>\n      <td>4</td>\n      <td>8</td>\n      <td>10</td>\n      <td>14</td>\n      <td>23</td>\n      <td>...</td>\n      <td>589</td>\n      <td>594</td>\n      <td>596</td>\n      <td>597</td>\n      <td>602</td>\n      <td>606</td>\n      <td>615</td>\n      <td>621</td>\n      <td>625</td>\n      <td>650</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>Tibet</td>\n      <td>China</td>\n      <td>31.6927</td>\n      <td>88.0924</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>Unknown</td>\n      <td>China</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>105</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>Xinjiang</td>\n      <td>China</td>\n      <td>41.1129</td>\n      <td>85.2401</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>5</td>\n      <td>...</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n      <td>981</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>Yunnan</td>\n      <td>China</td>\n      <td>24.9740</td>\n      <td>101.4870</td>\n      <td>1</td>\n      <td>2</td>\n      <td>5</td>\n      <td>11</td>\n      <td>16</td>\n      <td>26</td>\n      <td>...</td>\n      <td>1817</td>\n      <td>1818</td>\n      <td>1820</td>\n      <td>1823</td>\n      <td>1823</td>\n      <td>1825</td>\n      <td>1826</td>\n      <td>1829</td>\n      <td>1830</td>\n      <td>1836</td>\n    </tr>\n    <tr>\n      <th>91</th>\n      <td>Zhejiang</td>\n      <td>China</td>\n      <td>29.1832</td>\n      <td>120.0934</td>\n      <td>10</td>\n      <td>27</td>\n      <td>43</td>\n      <td>62</td>\n      <td>104</td>\n      <td>128</td>\n      <td>...</td>\n      <td>2016</td>\n      <td>2025</td>\n      <td>2038</td>\n      <td>2044</td>\n      <td>2046</td>\n      <td>2058</td>\n      <td>2062</td>\n      <td>2066</td>\n      <td>2075</td>\n      <td>2078</td>\n    </tr>\n  </tbody>\n</table>\n<p>34 rows × 723 columns</p>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_ts[countries_ts['Country/Region'] == 'China']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "0             China\n1            Canada\n2            France\n3    United Kingdom\n4         Australia\n5       Netherlands\n6           Denmark\n7       New Zealand\nName: index, dtype: object"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get countries whose time series values have to be summed up\n",
    "countries_to_sum = countries_ts['Country/Region'].value_counts().reset_index(name=\"count\").query(\"count > 1\")[\"index\"]\n",
    "countries_to_sum"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Exclude first 4 columns (meaning 'Province/State', 'Country/Region', 'Lat' and 'Long')\n",
    "date_cols = countries_ts.columns.tolist()[4:]\n",
    "date_cols"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "for country in countries_to_sum:\n",
    "    country_ts = countries_ts[countries_ts['Country/Region'] == country]\n",
    "    # Sum up column values for each date column\n",
    "    country_ts.loc['Country_Total'] = country_ts[date_cols].sum(axis=0)\n",
    "    # Mark \"special\" country with suffix\n",
    "    country_ts.loc['Country_Total', 'Country/Region'] = f'{country}_Sum'\n",
    "    # Append new row to existing time series data frame\n",
    "    countries_ts = pd.concat([countries_ts, country_ts], axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "              Province/State Country/Region  Lat  Long  1/22/20  1/23/20  \\\nCountry_Total            NaN      China_Sum  NaN   NaN    548.0    643.0   \n\n               1/24/20  1/25/20  1/26/20  1/27/20  ...  12/31/21    1/1/22  \\\nCountry_Total    920.0   1406.0   2075.0   2877.0  ...  115042.0  115251.0   \n\n                 1/2/22    1/3/22    1/4/22    1/5/22    1/6/22    1/7/22  \\\nCountry_Total  115409.0  115597.0  115701.0  115908.0  116106.0  116282.0   \n\n                 1/8/22    1/9/22  \nCountry_Total  116468.0  116641.0  \n\n[1 rows x 723 columns]",
      "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>Province/State</th>\n      <th>Country/Region</th>\n      <th>Lat</th>\n      <th>Long</th>\n      <th>1/22/20</th>\n      <th>1/23/20</th>\n      <th>1/24/20</th>\n      <th>1/25/20</th>\n      <th>1/26/20</th>\n      <th>1/27/20</th>\n      <th>...</th>\n      <th>12/31/21</th>\n      <th>1/1/22</th>\n      <th>1/2/22</th>\n      <th>1/3/22</th>\n      <th>1/4/22</th>\n      <th>1/5/22</th>\n      <th>1/6/22</th>\n      <th>1/7/22</th>\n      <th>1/8/22</th>\n      <th>1/9/22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Country_Total</th>\n      <td>NaN</td>\n      <td>China_Sum</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>548.0</td>\n      <td>643.0</td>\n      <td>920.0</td>\n      <td>1406.0</td>\n      <td>2075.0</td>\n      <td>2877.0</td>\n      <td>...</td>\n      <td>115042.0</td>\n      <td>115251.0</td>\n      <td>115409.0</td>\n      <td>115597.0</td>\n      <td>115701.0</td>\n      <td>115908.0</td>\n      <td>116106.0</td>\n      <td>116282.0</td>\n      <td>116468.0</td>\n      <td>116641.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1 rows × 723 columns</p>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show example of new row\n",
    "countries_ts[countries_ts['Country/Region'] == 'China_Sum']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# Filter for countries that appear only one time (which is also true for countries that appeared several times before because suffix \"_Sum\" makes country name unique)\n",
    "relevant_countries = countries_ts['Country/Region'].value_counts().reset_index(name=\"count\").query(\"count == 1\")[\"index\"]\n",
    "countries_ts = countries_ts[countries_ts['Country/Region'].isin(relevant_countries)]\n",
    "\n",
    "# Remove suffix after filtering of relevant countries and reset index\n",
    "countries_ts['Country/Region'] = countries_ts['Country/Region'].str.replace('_Sum', '')\n",
    "countries_ts.reset_index(drop=True, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "100"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_ts['Country/Region'].nunique()  # -> EBI metadata contains 7 countries more"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "countries_ts.to_csv('../../data/JHU_countries_time_series_preprocessed.csv', index=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Number of Samples in Relation to Current Number of Infections per Country"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [],
   "source": [
    "countries_ts = pd.read_csv('../../data/JHU_countries_time_series_preprocessed.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "       Country  Count\n0    Argentina     43\n1      Armenia     48\n2    Australia  13311\n3      Austria    261\n4      Bahrain   3304\n..         ...    ...\n102  Venezuela     40\n103   Viet Nam     11\n104  West Bank     63\n105     Zambia      1\n106   Zimbabwe     98\n\n[107 rows x 2 columns]",
      "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>Country</th>\n      <th>Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Australia</td>\n      <td>13311</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Austria</td>\n      <td>261</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bahrain</td>\n      <td>3304</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Venezuela</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>Viet Nam</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>West Bank</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Zambia</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Zimbabwe</td>\n      <td>98</td>\n    </tr>\n  </tbody>\n</table>\n<p>107 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count of each country\n",
    "country_counts = pd.DataFrame(df['country'].value_counts().sort_index().reset_index())\n",
    "country_counts.rename(columns={'index': 'Country', 'country': 'Count'}, inplace=True)\n",
    "country_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "       Country  Count  Infections\n0    Argentina     43   6310844.0\n1      Armenia     48    345855.0\n2    Australia  13311   1045291.0\n3      Austria    261   1339421.0\n4      Bahrain   3304    292572.0\n..         ...    ...         ...\n102  Venezuela     40    447288.0\n103   Viet Nam     11   1899575.0\n104  West Bank     63    471414.0\n105     Zambia      1    282904.0\n106   Zimbabwe     98    221918.0\n\n[107 rows x 3 columns]",
      "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>Country</th>\n      <th>Count</th>\n      <th>Infections</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>43</td>\n      <td>6310844.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>48</td>\n      <td>345855.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Australia</td>\n      <td>13311</td>\n      <td>1045291.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Austria</td>\n      <td>261</td>\n      <td>1339421.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bahrain</td>\n      <td>3304</td>\n      <td>292572.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Venezuela</td>\n      <td>40</td>\n      <td>447288.0</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>Viet Nam</td>\n      <td>11</td>\n      <td>1899575.0</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>West Bank</td>\n      <td>63</td>\n      <td>471414.0</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Zambia</td>\n      <td>1</td>\n      <td>282904.0</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Zimbabwe</td>\n      <td>98</td>\n      <td>221918.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>107 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def add_infection(country):\n",
    "    if country in countries_ts['Country/Region'].unique():\n",
    "        return countries_ts[countries_ts['Country/Region'] == country]['1/9/22'].iloc[0] \\\n",
    "               - countries_ts[countries_ts['Country/Region'] == country]['1/8/22'].iloc[0]\n",
    "    else:\n",
    "        return np.nan\n",
    "\n",
    "country_counts['Infections'] = country_counts.apply(lambda row: add_infection(str(row['Country'])), axis=1)\n",
    "country_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "       Country  Count  Infections  Count_Infection_Ratio\n0    Argentina     43   6310844.0               0.000007\n1      Armenia     48    345855.0               0.000139\n2    Australia  13311   1045291.0               0.012734\n3      Austria    261   1339421.0               0.000195\n4      Bahrain   3304    292572.0               0.011293\n..         ...    ...         ...                    ...\n102  Venezuela     40    447288.0               0.000089\n103   Viet Nam     11   1899575.0               0.000006\n104  West Bank     63    471414.0               0.000134\n105     Zambia      1    282904.0               0.000004\n106   Zimbabwe     98    221918.0               0.000442\n\n[107 rows x 4 columns]",
      "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>Country</th>\n      <th>Count</th>\n      <th>Infections</th>\n      <th>Count_Infection_Ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>43</td>\n      <td>6310844.0</td>\n      <td>0.000007</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>48</td>\n      <td>345855.0</td>\n      <td>0.000139</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Australia</td>\n      <td>13311</td>\n      <td>1045291.0</td>\n      <td>0.012734</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Austria</td>\n      <td>261</td>\n      <td>1339421.0</td>\n      <td>0.000195</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bahrain</td>\n      <td>3304</td>\n      <td>292572.0</td>\n      <td>0.011293</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>102</th>\n      <td>Venezuela</td>\n      <td>40</td>\n      <td>447288.0</td>\n      <td>0.000089</td>\n    </tr>\n    <tr>\n      <th>103</th>\n      <td>Viet Nam</td>\n      <td>11</td>\n      <td>1899575.0</td>\n      <td>0.000006</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>West Bank</td>\n      <td>63</td>\n      <td>471414.0</td>\n      <td>0.000134</td>\n    </tr>\n    <tr>\n      <th>105</th>\n      <td>Zambia</td>\n      <td>1</td>\n      <td>282904.0</td>\n      <td>0.000004</td>\n    </tr>\n    <tr>\n      <th>106</th>\n      <td>Zimbabwe</td>\n      <td>98</td>\n      <td>221918.0</td>\n      <td>0.000442</td>\n    </tr>\n  </tbody>\n</table>\n<p>107 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column showing #samples/#infections ratio per country\n",
    "country_counts['Count_Infection_Ratio'] = country_counts['Count'] / country_counts['Infections']\n",
    "country_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [],
   "source": [
    "# Remove countries that did not occur in time series data from JHU\n",
    "plotdata = country_counts[country_counts['Infections'] != np.nan]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1728x576 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate bar plots with #samples/#infections on y-axis and countries on x-axis\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, _ = plt.subplots(figsize=(24, 8))\n",
    "plt.bar(plotdata['Country'].tolist(), plotdata['Count_Infection_Ratio'].tolist())\n",
    "\n",
    "fig.suptitle('Sample Count per Country - Scaled by Number of Infections\\n (Numbers from 9th January 2022)', fontsize=22)\n",
    "plt.xlabel('Country', fontsize=18)\n",
    "plt.ylabel('Count Scaled by Number of Infections', fontsize=18)\n",
    "plt.xticks(fontsize=14, rotation=90)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../../plots/country_counts_by_infections.png', dpi=300)\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Number of Samples in Relation to Number of Infections per Country: Week-wise over Time"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '2/3/20', '2/4/20',\n       '2/5/20', '2/6/20', '2/7/20', '2/8/20',\n       ...\n       '12/31/21', '1/1/22', '1/2/22', '1/3/22', '1/4/22', '1/5/22', '1/6/22',\n       '1/7/22', '1/8/22', '1/9/22'],\n      dtype='object', length=711)"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove some date columns to start on first Monday of February 2020\n",
    "countries_ts = countries_ts.drop(['1/22/20', '1/23/20', '1/24/20', '1/25/20', '1/26/20',                       # remove fourth week\n",
    "                                  '1/27/20', '1/28/20', '1/29/20', '1/30/20', '1/31/20', '2/1/20', '2/2/20'],  # remove fifth week\n",
    "                                 axis=1)\n",
    "countries_ts.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Calculate number of new cases instead of cumulative number of cases & overwrite with new values\n",
    "new_cases = countries_ts[['Province/State', 'Country/Region', 'Lat', 'Long']].copy()\n",
    "\n",
    "for i, col in enumerate(countries_ts.columns[5:]):\n",
    "    new_cases[col] = countries_ts[col] - countries_ts.iloc[:, i+4]\n",
    "\n",
    "countries_ts = new_cases.copy()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "# Sum up infection count per week (starting with week from 2/3/20)\n",
    "for i, date_col in enumerate(countries_ts.columns[4:711:7]):\n",
    "    countries_ts[f'{date_col}_Week'] = countries_ts.iloc[:, i*7 : i*7 + 7].sum(axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "    Country/Region      1/3/22      1/4/22      1/5/22      1/6/22  \\\n0        Argentina   5739326.0   5820536.0   5915695.0   6025303.0   \n1          Armenia    345036.0    345126.0    345255.0    345389.0   \n2          Austria   1288829.0   1294325.0   1304086.0   1312939.0   \n3          Bahrain    284224.0    285448.0    286529.0    287939.0   \n4       Bangladesh   1587140.0   1587915.0   1588807.0   1589947.0   \n..             ...         ...         ...         ...         ...   \n95  United Kingdom  13497515.0  13723275.0  13918510.0  14100750.0   \n96       Australia    537308.0    608803.0    684229.0    762458.0   \n97     Netherlands   3234143.0   3254947.0   3282146.0   3309168.0   \n98         Denmark    849334.0    874890.0    903652.0    929647.0   \n99     New Zealand     14367.0     14407.0     14469.0     14527.0   \n\n        1/7/22      1/8/22      1/9/22  1/3/22_Week  \n0    6135836.0   6237525.0   6310844.0   40106068.0  \n1     345518.0    345713.0    345855.0    2415160.0  \n2    1321725.0   1329130.0   1339421.0    9008591.0  \n3     289454.0    290878.0    292572.0    1985690.0  \n4    1591093.0   1592209.0   1593700.0   11106803.0  \n..         ...         ...         ...          ...  \n95  14280816.0  14422067.0  14563769.0   93455720.0  \n96    860550.0    972457.0   1045291.0    3583940.0  \n97   3346598.0   3376251.0   3410288.0   22538032.0  \n98    948499.0    961591.0    982189.0    5902650.0  \n99     14527.0     14675.0     14735.0     100048.0  \n\n[100 rows x 9 columns]",
      "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>Country/Region</th>\n      <th>1/3/22</th>\n      <th>1/4/22</th>\n      <th>1/5/22</th>\n      <th>1/6/22</th>\n      <th>1/7/22</th>\n      <th>1/8/22</th>\n      <th>1/9/22</th>\n      <th>1/3/22_Week</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>5739326.0</td>\n      <td>5820536.0</td>\n      <td>5915695.0</td>\n      <td>6025303.0</td>\n      <td>6135836.0</td>\n      <td>6237525.0</td>\n      <td>6310844.0</td>\n      <td>40106068.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>345036.0</td>\n      <td>345126.0</td>\n      <td>345255.0</td>\n      <td>345389.0</td>\n      <td>345518.0</td>\n      <td>345713.0</td>\n      <td>345855.0</td>\n      <td>2415160.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Austria</td>\n      <td>1288829.0</td>\n      <td>1294325.0</td>\n      <td>1304086.0</td>\n      <td>1312939.0</td>\n      <td>1321725.0</td>\n      <td>1329130.0</td>\n      <td>1339421.0</td>\n      <td>9008591.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bahrain</td>\n      <td>284224.0</td>\n      <td>285448.0</td>\n      <td>286529.0</td>\n      <td>287939.0</td>\n      <td>289454.0</td>\n      <td>290878.0</td>\n      <td>292572.0</td>\n      <td>1985690.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bangladesh</td>\n      <td>1587140.0</td>\n      <td>1587915.0</td>\n      <td>1588807.0</td>\n      <td>1589947.0</td>\n      <td>1591093.0</td>\n      <td>1592209.0</td>\n      <td>1593700.0</td>\n      <td>11106803.0</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>95</th>\n      <td>United Kingdom</td>\n      <td>13497515.0</td>\n      <td>13723275.0</td>\n      <td>13918510.0</td>\n      <td>14100750.0</td>\n      <td>14280816.0</td>\n      <td>14422067.0</td>\n      <td>14563769.0</td>\n      <td>93455720.0</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>Australia</td>\n      <td>537308.0</td>\n      <td>608803.0</td>\n      <td>684229.0</td>\n      <td>762458.0</td>\n      <td>860550.0</td>\n      <td>972457.0</td>\n      <td>1045291.0</td>\n      <td>3583940.0</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>Netherlands</td>\n      <td>3234143.0</td>\n      <td>3254947.0</td>\n      <td>3282146.0</td>\n      <td>3309168.0</td>\n      <td>3346598.0</td>\n      <td>3376251.0</td>\n      <td>3410288.0</td>\n      <td>22538032.0</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>Denmark</td>\n      <td>849334.0</td>\n      <td>874890.0</td>\n      <td>903652.0</td>\n      <td>929647.0</td>\n      <td>948499.0</td>\n      <td>961591.0</td>\n      <td>982189.0</td>\n      <td>5902650.0</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>New Zealand</td>\n      <td>14367.0</td>\n      <td>14407.0</td>\n      <td>14469.0</td>\n      <td>14527.0</td>\n      <td>14527.0</td>\n      <td>14675.0</td>\n      <td>14735.0</td>\n      <td>100048.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>100 rows × 9 columns</p>\n</div>"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries_ts[['Country/Region', '1/3/22', '1/4/22', '1/5/22', '1/6/22', '1/7/22', '1/8/22', '1/9/22', '1/3/22_Week']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "  Country/Region      2/3/20  2/10/20  2/17/20  2/24/20  3/2/20  3/9/20  \\\n0      Argentina -102.032800      0.0      0.0      0.0     2.0    71.0   \n1        Armenia   85.107300      0.0      0.0      0.0     4.0     7.0   \n2        Austria   62.066300      0.0      0.0      4.0    97.0   838.0   \n3        Bahrain   76.577500      0.0      0.0     57.0   307.0   685.0   \n4     Bangladesh  114.041300      0.0      0.0      0.0     0.0    12.0   \n5        Belarus   81.663200      0.0      0.0      0.0    11.0    48.0   \n6        Belgium   57.303236      7.0      7.0      7.0    49.0  1348.0   \n7         Belize  -71.307700      0.0      0.0      0.0     0.0     0.0   \n8          Benin   11.623500      0.0      0.0      0.0     0.0     0.0   \n9         Brazil  -66.160300      0.0      0.0      1.0    14.0   144.0   \n\n   3/16/20  3/23/20  3/30/20  ...      11/1/21      11/8/21     11/15/21  \\\n0    332.0   1724.0   5454.0  ...   37024366.0   37080690.0   37143671.0   \n1    270.0   1354.0   3035.0  ...    2154761.0    2238938.0    2299544.0   \n2   6317.0  26142.0  62134.0  ...    5812621.0    6183629.0    6715615.0   \n3   1512.0   2390.0   3550.0  ...    1937837.0    1939465.0    1940563.0   \n4     46.0    200.0    342.0  ...   10986859.0   10997149.0   11007709.0   \n5    216.0    520.0    835.0  ...    4200873.0    4297236.0    4391570.0   \n6   6235.0  23217.0  72127.0  ...    9548521.0    9977230.0   10498319.0   \n7      0.0      4.0     17.0  ...     188996.0     196247.0     203206.0   \n8      4.0     25.0     52.0  ...     173298.0     173657.0     173831.0   \n9   1409.0  10706.0  31694.0  ...  152693304.0  153198822.0  153722438.0   \n\n      11/22/21     11/29/21      12/6/21     12/13/21     12/20/21  \\\n0   37209235.0   37289967.0   37390578.0   37520477.0   37750017.0   \n1    2337248.0    2364446.0    2384018.0    2397172.0    2405199.0   \n2    7383571.0    7988277.0    8380428.0    8598674.0    8746229.0   \n3    1941709.0    1943222.0    1944843.0    1946479.0    1949664.0   \n4   11019082.0   11030833.0   11042429.0   11055707.0   11068227.0   \n5    4480242.0    4561773.0    4643556.0    4719578.0    4788894.0   \n6   11222758.0   12086708.0   12939146.0   13611425.0   14027694.0   \n7     207514.0     211841.0     215300.0     217462.0     218958.0   \n8     173926.0     173963.0     174041.0     174289.0     174377.0   \n9  154156179.0  154605797.0  155038486.0  155359228.0  155528102.0   \n\n      12/27/21       1/3/22  \n0   38337405.0   40106068.0  \n1    2411173.0    2415160.0  \n2    8852909.0    9008591.0  \n3    1959123.0    1985690.0  \n4   11083483.0   11106803.0  \n5    4851581.0    4905903.0  \n6   14336699.0   14870594.0  \n7     221257.0     233180.0  \n8     174545.0     175719.0  \n9  155725648.0  156132121.0  \n\n[10 rows x 102 columns]",
      "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>Country/Region</th>\n      <th>2/3/20</th>\n      <th>2/10/20</th>\n      <th>2/17/20</th>\n      <th>2/24/20</th>\n      <th>3/2/20</th>\n      <th>3/9/20</th>\n      <th>3/16/20</th>\n      <th>3/23/20</th>\n      <th>3/30/20</th>\n      <th>...</th>\n      <th>11/1/21</th>\n      <th>11/8/21</th>\n      <th>11/15/21</th>\n      <th>11/22/21</th>\n      <th>11/29/21</th>\n      <th>12/6/21</th>\n      <th>12/13/21</th>\n      <th>12/20/21</th>\n      <th>12/27/21</th>\n      <th>1/3/22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>-102.032800</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>71.0</td>\n      <td>332.0</td>\n      <td>1724.0</td>\n      <td>5454.0</td>\n      <td>...</td>\n      <td>37024366.0</td>\n      <td>37080690.0</td>\n      <td>37143671.0</td>\n      <td>37209235.0</td>\n      <td>37289967.0</td>\n      <td>37390578.0</td>\n      <td>37520477.0</td>\n      <td>37750017.0</td>\n      <td>38337405.0</td>\n      <td>40106068.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>85.107300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>270.0</td>\n      <td>1354.0</td>\n      <td>3035.0</td>\n      <td>...</td>\n      <td>2154761.0</td>\n      <td>2238938.0</td>\n      <td>2299544.0</td>\n      <td>2337248.0</td>\n      <td>2364446.0</td>\n      <td>2384018.0</td>\n      <td>2397172.0</td>\n      <td>2405199.0</td>\n      <td>2411173.0</td>\n      <td>2415160.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Austria</td>\n      <td>62.066300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>97.0</td>\n      <td>838.0</td>\n      <td>6317.0</td>\n      <td>26142.0</td>\n      <td>62134.0</td>\n      <td>...</td>\n      <td>5812621.0</td>\n      <td>6183629.0</td>\n      <td>6715615.0</td>\n      <td>7383571.0</td>\n      <td>7988277.0</td>\n      <td>8380428.0</td>\n      <td>8598674.0</td>\n      <td>8746229.0</td>\n      <td>8852909.0</td>\n      <td>9008591.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bahrain</td>\n      <td>76.577500</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>307.0</td>\n      <td>685.0</td>\n      <td>1512.0</td>\n      <td>2390.0</td>\n      <td>3550.0</td>\n      <td>...</td>\n      <td>1937837.0</td>\n      <td>1939465.0</td>\n      <td>1940563.0</td>\n      <td>1941709.0</td>\n      <td>1943222.0</td>\n      <td>1944843.0</td>\n      <td>1946479.0</td>\n      <td>1949664.0</td>\n      <td>1959123.0</td>\n      <td>1985690.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bangladesh</td>\n      <td>114.041300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>46.0</td>\n      <td>200.0</td>\n      <td>342.0</td>\n      <td>...</td>\n      <td>10986859.0</td>\n      <td>10997149.0</td>\n      <td>11007709.0</td>\n      <td>11019082.0</td>\n      <td>11030833.0</td>\n      <td>11042429.0</td>\n      <td>11055707.0</td>\n      <td>11068227.0</td>\n      <td>11083483.0</td>\n      <td>11106803.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Belarus</td>\n      <td>81.663200</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>48.0</td>\n      <td>216.0</td>\n      <td>520.0</td>\n      <td>835.0</td>\n      <td>...</td>\n      <td>4200873.0</td>\n      <td>4297236.0</td>\n      <td>4391570.0</td>\n      <td>4480242.0</td>\n      <td>4561773.0</td>\n      <td>4643556.0</td>\n      <td>4719578.0</td>\n      <td>4788894.0</td>\n      <td>4851581.0</td>\n      <td>4905903.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Belgium</td>\n      <td>57.303236</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>49.0</td>\n      <td>1348.0</td>\n      <td>6235.0</td>\n      <td>23217.0</td>\n      <td>72127.0</td>\n      <td>...</td>\n      <td>9548521.0</td>\n      <td>9977230.0</td>\n      <td>10498319.0</td>\n      <td>11222758.0</td>\n      <td>12086708.0</td>\n      <td>12939146.0</td>\n      <td>13611425.0</td>\n      <td>14027694.0</td>\n      <td>14336699.0</td>\n      <td>14870594.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Belize</td>\n      <td>-71.307700</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>17.0</td>\n      <td>...</td>\n      <td>188996.0</td>\n      <td>196247.0</td>\n      <td>203206.0</td>\n      <td>207514.0</td>\n      <td>211841.0</td>\n      <td>215300.0</td>\n      <td>217462.0</td>\n      <td>218958.0</td>\n      <td>221257.0</td>\n      <td>233180.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Benin</td>\n      <td>11.623500</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>25.0</td>\n      <td>52.0</td>\n      <td>...</td>\n      <td>173298.0</td>\n      <td>173657.0</td>\n      <td>173831.0</td>\n      <td>173926.0</td>\n      <td>173963.0</td>\n      <td>174041.0</td>\n      <td>174289.0</td>\n      <td>174377.0</td>\n      <td>174545.0</td>\n      <td>175719.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Brazil</td>\n      <td>-66.160300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>144.0</td>\n      <td>1409.0</td>\n      <td>10706.0</td>\n      <td>31694.0</td>\n      <td>...</td>\n      <td>152693304.0</td>\n      <td>153198822.0</td>\n      <td>153722438.0</td>\n      <td>154156179.0</td>\n      <td>154605797.0</td>\n      <td>155038486.0</td>\n      <td>155359228.0</td>\n      <td>155528102.0</td>\n      <td>155725648.0</td>\n      <td>156132121.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 102 columns</p>\n</div>"
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keep weekly data and country\n",
    "plotdata_infections = countries_ts.filter(regex='_Week$|Country/Region', axis=1)\n",
    "# Remove suffix from date column names for interpretability on x-axis later on\n",
    "plotdata_infections.columns = plotdata_infections.columns.str.replace('_Week', '')\n",
    "\n",
    "plotdata_infections.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "  Country/Region      2/3/20  2/10/20  2/17/20  2/24/20  3/2/20  3/9/20  \\\n0      Argentina    0.000000      0.0      0.0      0.0     2.0    71.0   \n1        Armenia   85.107300      0.0      0.0      0.0     4.0     7.0   \n2        Austria   62.066300      0.0      0.0      4.0    97.0   838.0   \n3        Bahrain   76.577500      0.0      0.0     57.0   307.0   685.0   \n4     Bangladesh  114.041300      0.0      0.0      0.0     0.0    12.0   \n5        Belarus   81.663200      0.0      0.0      0.0    11.0    48.0   \n6        Belgium   57.303236      7.0      7.0      7.0    49.0  1348.0   \n7         Belize    0.000000      0.0      0.0      0.0     0.0     0.0   \n8          Benin   11.623500      0.0      0.0      0.0     0.0     0.0   \n9         Brazil    0.000000      0.0      0.0      1.0    14.0   144.0   \n\n   3/16/20  3/23/20  3/30/20  ...      11/1/21      11/8/21     11/15/21  \\\n0    332.0   1724.0   5454.0  ...   37024366.0   37080690.0   37143671.0   \n1    270.0   1354.0   3035.0  ...    2154761.0    2238938.0    2299544.0   \n2   6317.0  26142.0  62134.0  ...    5812621.0    6183629.0    6715615.0   \n3   1512.0   2390.0   3550.0  ...    1937837.0    1939465.0    1940563.0   \n4     46.0    200.0    342.0  ...   10986859.0   10997149.0   11007709.0   \n5    216.0    520.0    835.0  ...    4200873.0    4297236.0    4391570.0   \n6   6235.0  23217.0  72127.0  ...    9548521.0    9977230.0   10498319.0   \n7      0.0      4.0     17.0  ...     188996.0     196247.0     203206.0   \n8      4.0     25.0     52.0  ...     173298.0     173657.0     173831.0   \n9   1409.0  10706.0  31694.0  ...  152693304.0  153198822.0  153722438.0   \n\n      11/22/21     11/29/21      12/6/21     12/13/21     12/20/21  \\\n0   37209235.0   37289967.0   37390578.0   37520477.0   37750017.0   \n1    2337248.0    2364446.0    2384018.0    2397172.0    2405199.0   \n2    7383571.0    7988277.0    8380428.0    8598674.0    8746229.0   \n3    1941709.0    1943222.0    1944843.0    1946479.0    1949664.0   \n4   11019082.0   11030833.0   11042429.0   11055707.0   11068227.0   \n5    4480242.0    4561773.0    4643556.0    4719578.0    4788894.0   \n6   11222758.0   12086708.0   12939146.0   13611425.0   14027694.0   \n7     207514.0     211841.0     215300.0     217462.0     218958.0   \n8     173926.0     173963.0     174041.0     174289.0     174377.0   \n9  154156179.0  154605797.0  155038486.0  155359228.0  155528102.0   \n\n      12/27/21       1/3/22  \n0   38337405.0   40106068.0  \n1    2411173.0    2415160.0  \n2    8852909.0    9008591.0  \n3    1959123.0    1985690.0  \n4   11083483.0   11106803.0  \n5    4851581.0    4905903.0  \n6   14336699.0   14870594.0  \n7     221257.0     233180.0  \n8     174545.0     175719.0  \n9  155725648.0  156132121.0  \n\n[10 rows x 102 columns]",
      "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>Country/Region</th>\n      <th>2/3/20</th>\n      <th>2/10/20</th>\n      <th>2/17/20</th>\n      <th>2/24/20</th>\n      <th>3/2/20</th>\n      <th>3/9/20</th>\n      <th>3/16/20</th>\n      <th>3/23/20</th>\n      <th>3/30/20</th>\n      <th>...</th>\n      <th>11/1/21</th>\n      <th>11/8/21</th>\n      <th>11/15/21</th>\n      <th>11/22/21</th>\n      <th>11/29/21</th>\n      <th>12/6/21</th>\n      <th>12/13/21</th>\n      <th>12/20/21</th>\n      <th>12/27/21</th>\n      <th>1/3/22</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2.0</td>\n      <td>71.0</td>\n      <td>332.0</td>\n      <td>1724.0</td>\n      <td>5454.0</td>\n      <td>...</td>\n      <td>37024366.0</td>\n      <td>37080690.0</td>\n      <td>37143671.0</td>\n      <td>37209235.0</td>\n      <td>37289967.0</td>\n      <td>37390578.0</td>\n      <td>37520477.0</td>\n      <td>37750017.0</td>\n      <td>38337405.0</td>\n      <td>40106068.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Armenia</td>\n      <td>85.107300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>270.0</td>\n      <td>1354.0</td>\n      <td>3035.0</td>\n      <td>...</td>\n      <td>2154761.0</td>\n      <td>2238938.0</td>\n      <td>2299544.0</td>\n      <td>2337248.0</td>\n      <td>2364446.0</td>\n      <td>2384018.0</td>\n      <td>2397172.0</td>\n      <td>2405199.0</td>\n      <td>2411173.0</td>\n      <td>2415160.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Austria</td>\n      <td>62.066300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>97.0</td>\n      <td>838.0</td>\n      <td>6317.0</td>\n      <td>26142.0</td>\n      <td>62134.0</td>\n      <td>...</td>\n      <td>5812621.0</td>\n      <td>6183629.0</td>\n      <td>6715615.0</td>\n      <td>7383571.0</td>\n      <td>7988277.0</td>\n      <td>8380428.0</td>\n      <td>8598674.0</td>\n      <td>8746229.0</td>\n      <td>8852909.0</td>\n      <td>9008591.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Bahrain</td>\n      <td>76.577500</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>57.0</td>\n      <td>307.0</td>\n      <td>685.0</td>\n      <td>1512.0</td>\n      <td>2390.0</td>\n      <td>3550.0</td>\n      <td>...</td>\n      <td>1937837.0</td>\n      <td>1939465.0</td>\n      <td>1940563.0</td>\n      <td>1941709.0</td>\n      <td>1943222.0</td>\n      <td>1944843.0</td>\n      <td>1946479.0</td>\n      <td>1949664.0</td>\n      <td>1959123.0</td>\n      <td>1985690.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Bangladesh</td>\n      <td>114.041300</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>12.0</td>\n      <td>46.0</td>\n      <td>200.0</td>\n      <td>342.0</td>\n      <td>...</td>\n      <td>10986859.0</td>\n      <td>10997149.0</td>\n      <td>11007709.0</td>\n      <td>11019082.0</td>\n      <td>11030833.0</td>\n      <td>11042429.0</td>\n      <td>11055707.0</td>\n      <td>11068227.0</td>\n      <td>11083483.0</td>\n      <td>11106803.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Belarus</td>\n      <td>81.663200</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>11.0</td>\n      <td>48.0</td>\n      <td>216.0</td>\n      <td>520.0</td>\n      <td>835.0</td>\n      <td>...</td>\n      <td>4200873.0</td>\n      <td>4297236.0</td>\n      <td>4391570.0</td>\n      <td>4480242.0</td>\n      <td>4561773.0</td>\n      <td>4643556.0</td>\n      <td>4719578.0</td>\n      <td>4788894.0</td>\n      <td>4851581.0</td>\n      <td>4905903.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Belgium</td>\n      <td>57.303236</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>49.0</td>\n      <td>1348.0</td>\n      <td>6235.0</td>\n      <td>23217.0</td>\n      <td>72127.0</td>\n      <td>...</td>\n      <td>9548521.0</td>\n      <td>9977230.0</td>\n      <td>10498319.0</td>\n      <td>11222758.0</td>\n      <td>12086708.0</td>\n      <td>12939146.0</td>\n      <td>13611425.0</td>\n      <td>14027694.0</td>\n      <td>14336699.0</td>\n      <td>14870594.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Belize</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>17.0</td>\n      <td>...</td>\n      <td>188996.0</td>\n      <td>196247.0</td>\n      <td>203206.0</td>\n      <td>207514.0</td>\n      <td>211841.0</td>\n      <td>215300.0</td>\n      <td>217462.0</td>\n      <td>218958.0</td>\n      <td>221257.0</td>\n      <td>233180.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Benin</td>\n      <td>11.623500</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>4.0</td>\n      <td>25.0</td>\n      <td>52.0</td>\n      <td>...</td>\n      <td>173298.0</td>\n      <td>173657.0</td>\n      <td>173831.0</td>\n      <td>173926.0</td>\n      <td>173963.0</td>\n      <td>174041.0</td>\n      <td>174289.0</td>\n      <td>174377.0</td>\n      <td>174545.0</td>\n      <td>175719.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Brazil</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>144.0</td>\n      <td>1409.0</td>\n      <td>10706.0</td>\n      <td>31694.0</td>\n      <td>...</td>\n      <td>152693304.0</td>\n      <td>153198822.0</td>\n      <td>153722438.0</td>\n      <td>154156179.0</td>\n      <td>154605797.0</td>\n      <td>155038486.0</td>\n      <td>155359228.0</td>\n      <td>155528102.0</td>\n      <td>155725648.0</td>\n      <td>156132121.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>10 rows × 102 columns</p>\n</div>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove invalid weekly sum of infections\n",
    "plotdata_infections._get_numeric_data()[plotdata_infections._get_numeric_data() < 0] = 0\n",
    "plotdata_infections.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "Country/Region  Argentina  Armenia  Austria   Bahrain  Bangladesh  Belarus  \\\n2/3/20                0.0  85.1073  62.0663   76.5775    114.0413  81.6632   \n2/10/20               0.0   0.0000   0.0000    0.0000      0.0000   0.0000   \n2/17/20               0.0   0.0000   0.0000    0.0000      0.0000   0.0000   \n2/24/20               0.0   0.0000   4.0000   57.0000      0.0000   0.0000   \n3/2/20                2.0   4.0000  97.0000  307.0000      0.0000  11.0000   \n\nCountry/Region    Belgium  Belize    Benin  Brazil  ...     Zambia   Zimbabwe  \\\n2/3/20          57.303236     0.0  11.6235     0.0  ...  14.715435  10.139419   \n2/10/20          7.000000     0.0   0.0000     0.0  ...   0.000000   0.000000   \n2/17/20          7.000000     0.0   0.0000     0.0  ...   0.000000   0.000000   \n2/24/20          7.000000     0.0   0.0000     1.0  ...   0.000000   0.000000   \n3/2/20          49.000000     0.0   0.0000    14.0  ...   0.000000   0.000000   \n\nCountry/Region     China  Canada  France  United Kingdom  Australia  \\\n2/3/20           70863.0    13.0    18.0            25.0       38.0   \n2/10/20         272839.0    47.0    67.0            90.0      104.0   \n2/17/20         486443.0    52.0    82.0           129.0      105.0   \n2/24/20         537811.0    69.0    92.0           197.0      105.0   \n3/2/20          557599.0   187.0  1016.0           824.0      203.0   \n\nCountry/Region  Netherlands  Denmark  New Zealand  \n2/3/20                  0.0      0.0          0.0  \n2/10/20                 0.0      0.0          0.0  \n2/17/20                 0.0      0.0          0.0  \n2/24/20                 0.0      0.0          0.0  \n3/2/20                 98.0     30.0          8.0  \n\n[5 rows x 100 columns]",
      "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>Country/Region</th>\n      <th>Argentina</th>\n      <th>Armenia</th>\n      <th>Austria</th>\n      <th>Bahrain</th>\n      <th>Bangladesh</th>\n      <th>Belarus</th>\n      <th>Belgium</th>\n      <th>Belize</th>\n      <th>Benin</th>\n      <th>Brazil</th>\n      <th>...</th>\n      <th>Zambia</th>\n      <th>Zimbabwe</th>\n      <th>China</th>\n      <th>Canada</th>\n      <th>France</th>\n      <th>United Kingdom</th>\n      <th>Australia</th>\n      <th>Netherlands</th>\n      <th>Denmark</th>\n      <th>New Zealand</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2/3/20</th>\n      <td>0.0</td>\n      <td>85.1073</td>\n      <td>62.0663</td>\n      <td>76.5775</td>\n      <td>114.0413</td>\n      <td>81.6632</td>\n      <td>57.303236</td>\n      <td>0.0</td>\n      <td>11.6235</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>14.715435</td>\n      <td>10.139419</td>\n      <td>70863.0</td>\n      <td>13.0</td>\n      <td>18.0</td>\n      <td>25.0</td>\n      <td>38.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2/10/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>272839.0</td>\n      <td>47.0</td>\n      <td>67.0</td>\n      <td>90.0</td>\n      <td>104.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2/17/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>486443.0</td>\n      <td>52.0</td>\n      <td>82.0</td>\n      <td>129.0</td>\n      <td>105.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2/24/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>4.0000</td>\n      <td>57.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>537811.0</td>\n      <td>69.0</td>\n      <td>92.0</td>\n      <td>197.0</td>\n      <td>105.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3/2/20</th>\n      <td>2.0</td>\n      <td>4.0000</td>\n      <td>97.0000</td>\n      <td>307.0000</td>\n      <td>0.0000</td>\n      <td>11.0000</td>\n      <td>49.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>14.0</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>557599.0</td>\n      <td>187.0</td>\n      <td>1016.0</td>\n      <td>824.0</td>\n      <td>203.0</td>\n      <td>98.0</td>\n      <td>30.0</td>\n      <td>8.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 100 columns</p>\n</div>"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transpose data frame with country being index\n",
    "plotdata = plotdata_infections.set_index('Country/Region').T\n",
    "plotdata.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata['Germany'].plot(legend=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [],
   "source": [
    "# Remove data before 2/3/20 from EBI metadata\n",
    "df['collection_date'] = pd.to_datetime(df['collection_date'])\n",
    "df_relevant_dates = df[~(df['collection_date'] < '2020-02-03')]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "data": {
      "text/plain": "        country collection_date  count\n0     Argentina      2020-05-16      1\n1     Argentina      2020-06-04     23\n2     Argentina      2020-09-13      2\n3     Argentina      2020-09-14      8\n4     Argentina      2020-09-15      2\n...         ...             ...    ...\n8892     Zambia      2020-03-16      1\n8893   Zimbabwe      2020-03-01      8\n8894   Zimbabwe      2020-04-01     14\n8895   Zimbabwe      2020-05-01     35\n8896   Zimbabwe      2020-06-01     41\n\n[8897 rows x 3 columns]",
      "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>country</th>\n      <th>collection_date</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Argentina</td>\n      <td>2020-05-16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Argentina</td>\n      <td>2020-06-04</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Argentina</td>\n      <td>2020-09-13</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Argentina</td>\n      <td>2020-09-14</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Argentina</td>\n      <td>2020-09-15</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>8892</th>\n      <td>Zambia</td>\n      <td>2020-03-16</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8893</th>\n      <td>Zimbabwe</td>\n      <td>2020-03-01</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>8894</th>\n      <td>Zimbabwe</td>\n      <td>2020-04-01</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>8895</th>\n      <td>Zimbabwe</td>\n      <td>2020-05-01</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>8896</th>\n      <td>Zimbabwe</td>\n      <td>2020-06-01</td>\n      <td>41</td>\n    </tr>\n  </tbody>\n</table>\n<p>8897 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get count per country over time\n",
    "country_counts = df_relevant_dates.groupby(['country', 'collection_date']).size().reset_index()\n",
    "country_counts.rename(columns={0 : 'count'}, inplace=True)\n",
    "country_counts"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "Empty DataFrame\nColumns: []\nIndex: [2/3/20, 2/10/20, 2/17/20, 2/24/20, 3/2/20, 3/9/20, 3/16/20, 3/23/20, 3/30/20, 4/6/20, 4/13/20, 4/20/20, 4/27/20, 5/4/20, 5/11/20, 5/18/20, 5/25/20, 6/1/20, 6/8/20, 6/15/20, 6/22/20, 6/29/20, 7/6/20, 7/13/20, 7/20/20, 7/27/20, 8/3/20, 8/10/20, 8/17/20, 8/24/20, 8/31/20, 9/7/20, 9/14/20, 9/21/20, 9/28/20, 10/5/20, 10/12/20, 10/19/20, 10/26/20, 11/2/20, 11/9/20, 11/16/20, 11/23/20, 11/30/20, 12/7/20, 12/14/20, 12/21/20, 12/28/20, 1/4/21, 1/11/21, 1/18/21, 1/25/21, 2/1/21, 2/8/21, 2/15/21, 2/22/21, 3/1/21, 3/8/21, 3/15/21, 3/22/21, 3/29/21, 4/5/21, 4/12/21, 4/19/21, 4/26/21, 5/3/21, 5/10/21, 5/17/21, 5/24/21, 5/31/21, 6/7/21, 6/14/21, 6/21/21, 6/28/21, 7/5/21, 7/12/21, 7/19/21, 7/26/21, 8/2/21, 8/9/21, 8/16/21, 8/23/21, 8/30/21, 9/6/21, 9/13/21, 9/20/21, 9/27/21, 10/4/21, 10/11/21, 10/18/21, 10/25/21, 11/1/21, 11/8/21, 11/15/21, 11/22/21, 11/29/21, 12/6/21, 12/13/21, 12/20/21, 12/27/21, ...]\n\n[101 rows x 0 columns]",
      "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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2/3/20</th>\n    </tr>\n    <tr>\n      <th>2/10/20</th>\n    </tr>\n    <tr>\n      <th>2/17/20</th>\n    </tr>\n    <tr>\n      <th>2/24/20</th>\n    </tr>\n    <tr>\n      <th>3/2/20</th>\n    </tr>\n    <tr>\n      <th>...</th>\n    </tr>\n    <tr>\n      <th>12/6/21</th>\n    </tr>\n    <tr>\n      <th>12/13/21</th>\n    </tr>\n    <tr>\n      <th>12/20/21</th>\n    </tr>\n    <tr>\n      <th>12/27/21</th>\n    </tr>\n    <tr>\n      <th>1/3/22</th>\n    </tr>\n  </tbody>\n</table>\n<p>101 rows × 0 columns</p>\n</div>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prepare counts: add wanted dates as index\n",
    "country_counts_prepared = pd.DataFrame(index=plotdata.index.tolist())\n",
    "country_counts_prepared"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "# Add column per country holding country count of week (according to start day of week in index)\n",
    "for country in country_counts['country'].unique():\n",
    "    country_counts_prepared[str(country)] = 0\n",
    "    country_rows = country_counts[country_counts['country'] == country]\n",
    "\n",
    "    for date in country_counts_prepared.index:\n",
    "        date_dt = pd.to_datetime(date)\n",
    "        country_rows_week = country_rows[(country_rows['collection_date'] >= date_dt)\n",
    "                                         & (country_rows['collection_date'] < date_dt + pd.DateOffset(7))]\n",
    "        country_counts_prepared[country][date] = country_rows_week['count'].sum()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "         Argentina  Armenia  Australia  Austria  Bahrain  Bangladesh  Belarus  \\\n2/3/20           0        0          1        0        0           0        0   \n2/10/20          0        0          0        0        0           0        0   \n2/17/20          0        0          3        0        0           0        0   \n2/24/20          0        0          1        0        0           0        0   \n3/2/20           0        0          9        0        0           0        0   \n3/9/20           0        0         77        0        0           0        0   \n3/16/20          0        0        280        0        0           0        0   \n3/23/20          0        0        434        0        0           0        0   \n3/30/20          0        0        226        0        0           0        0   \n4/6/20           0        0        183        0        0           0        0   \n4/13/20          0        0        111        0        0           1        0   \n4/20/20          0        0         27        0        0           2        0   \n4/27/20          0        0         51        0        0          34        0   \n5/4/20           0        0         53        0        0          15        0   \n5/11/20          1        0         46        0        0          16        0   \n5/18/20          0        0         22        0        0          38        0   \n5/25/20          0        0         51        0        1          13        0   \n6/1/20          23        0         12        0        0          41        0   \n6/8/20           0        0         52        0        0          23        0   \n6/15/20          0        0         80        0        2          48        0   \n6/22/20          0        0        261        0       11          24        0   \n6/29/20          0        0        472        0        1          57        0   \n7/6/20           0        0        936        0        1          47        0   \n7/13/20          0        0       1535        0        0          37        0   \n7/20/20          0        0       1762        0        3          11        0   \n7/27/20          0       11       1803        0        0           0        0   \n8/3/20           0        5       1349        0        0           0        0   \n8/10/20          0        2        951        0        0           0        0   \n8/17/20          0        8        851        0        2           0        0   \n8/24/20          0        6        466        0        3           0        0   \n\n         Belgium  Belize  Benin  ...   USA  Uganda  United Kingdom  Uruguay  \\\n2/3/20         3       0      0  ...     1       0               0        0   \n2/10/20        0       0      0  ...     2       0               0        0   \n2/17/20        0       0      0  ...    28       0               0        0   \n2/24/20        0       1      0  ...   299       0              10        0   \n3/2/20         0       0      0  ...   346       0              71        0   \n3/9/20         1       0      1  ...  2289       0             209        1   \n3/16/20        0       1      5  ...  3415       0             518        0   \n3/23/20        0       1      2  ...  2513       0            1428        0   \n3/30/20        0       0      1  ...  2845       0            2185        0   \n4/6/20         0       1      1  ...  1729       0            2060        0   \n4/13/20        0       0      2  ...  1462       0            1465        0   \n4/20/20        0       0      0  ...  1502       0            1331        0   \n4/27/20        0       0      0  ...  2279       0            1216        0   \n5/4/20         0       0      0  ...  1308       0            1116        0   \n5/11/20        0       0      0  ...  1289       0             693        0   \n5/18/20        0       0      0  ...  1041       0             378        0   \n5/25/20        0       0      0  ...  1005       0             154        0   \n6/1/20         0       0      0  ...  1034       0             568        0   \n6/8/20         0       0      0  ...   898       0            2225        0   \n6/15/20        0       0      0  ...  1676       0            1651        0   \n6/22/20        0       0      0  ...  1556       0             607        0   \n6/29/20        0       0      0  ...  1228       0            1132        0   \n7/6/20         0       0      0  ...  1572       0             101        0   \n7/13/20        0       0      0  ...  1434       0             774        7   \n7/20/20        0       0      0  ...  1113       0             461        0   \n7/27/20        0       0      0  ...  1147       0             393        0   \n8/3/20         0       0      0  ...   991       0             744        0   \n8/10/20        0       0      0  ...  1048       0            1295        0   \n8/17/20        0       0      0  ...  1001       0            1515        0   \n8/24/20        0       0      0  ...   974       0            1547        0   \n\n         Uzbekistan  Venezuela  Viet Nam  West Bank  Zambia  Zimbabwe  \n2/3/20            0          0         0          0       0         0  \n2/10/20           0          0         5          0       0         0  \n2/17/20           0          0         0          0       0         0  \n2/24/20           0          0         0          0       0         8  \n3/2/20            2          0         0          0       0         0  \n3/9/20            0          1         0          0       0         0  \n3/16/20           0          2         0          0       1         0  \n3/23/20           0          0         0          0       0         0  \n3/30/20           0          0         0          0       0        14  \n4/6/20            0          1         0          0       0         0  \n4/13/20           0          1         0          0       0         0  \n4/20/20           0          0         0          0       0         0  \n4/27/20           0          0         0          0       0        35  \n5/4/20            0          0         0          0       0         0  \n5/11/20           0          0         0          0       0         0  \n5/18/20           8          8         0          0       0         0  \n5/25/20           0          2         0          0       0         0  \n6/1/20            0          0         0          0       0        41  \n6/8/20            0          0         0          0       0         0  \n6/15/20           0          0         0          0       0         0  \n6/22/20           0          0         0          0       0         0  \n6/29/20           0          0         0          0       0         0  \n7/6/20            0          0         0          0       0         0  \n7/13/20           0          0         0          0       0         0  \n7/20/20           0          0         0          0       0         0  \n7/27/20           0          0         0          0       0         0  \n8/3/20            0          0         3          0       0         0  \n8/10/20           0          0         0          0       0         0  \n8/17/20           0          5         0          0       0         0  \n8/24/20           0          0         0          0       0         0  \n\n[30 rows x 107 columns]",
      "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>Argentina</th>\n      <th>Armenia</th>\n      <th>Australia</th>\n      <th>Austria</th>\n      <th>Bahrain</th>\n      <th>Bangladesh</th>\n      <th>Belarus</th>\n      <th>Belgium</th>\n      <th>Belize</th>\n      <th>Benin</th>\n      <th>...</th>\n      <th>USA</th>\n      <th>Uganda</th>\n      <th>United Kingdom</th>\n      <th>Uruguay</th>\n      <th>Uzbekistan</th>\n      <th>Venezuela</th>\n      <th>Viet Nam</th>\n      <th>West Bank</th>\n      <th>Zambia</th>\n      <th>Zimbabwe</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2/3/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2/10/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2/17/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2/24/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>...</td>\n      <td>299</td>\n      <td>0</td>\n      <td>10</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>3/2/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>9</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>346</td>\n      <td>0</td>\n      <td>71</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3/9/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>77</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>2289</td>\n      <td>0</td>\n      <td>209</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3/16/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>280</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>5</td>\n      <td>...</td>\n      <td>3415</td>\n      <td>0</td>\n      <td>518</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3/23/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>434</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>...</td>\n      <td>2513</td>\n      <td>0</td>\n      <td>1428</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3/30/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>226</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>2845</td>\n      <td>0</td>\n      <td>2185</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>4/6/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>183</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>...</td>\n      <td>1729</td>\n      <td>0</td>\n      <td>2060</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4/13/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>111</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>...</td>\n      <td>1462</td>\n      <td>0</td>\n      <td>1465</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4/20/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1502</td>\n      <td>0</td>\n      <td>1331</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4/27/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>51</td>\n      <td>0</td>\n      <td>0</td>\n      <td>34</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>2279</td>\n      <td>0</td>\n      <td>1216</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>35</td>\n    </tr>\n    <tr>\n      <th>5/4/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>53</td>\n      <td>0</td>\n      <td>0</td>\n      <td>15</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1308</td>\n      <td>0</td>\n      <td>1116</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5/11/20</th>\n      <td>1</td>\n      <td>0</td>\n      <td>46</td>\n      <td>0</td>\n      <td>0</td>\n      <td>16</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1289</td>\n      <td>0</td>\n      <td>693</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5/18/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>22</td>\n      <td>0</td>\n      <td>0</td>\n      <td>38</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1041</td>\n      <td>0</td>\n      <td>378</td>\n      <td>0</td>\n      <td>8</td>\n      <td>8</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5/25/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>51</td>\n      <td>0</td>\n      <td>1</td>\n      <td>13</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1005</td>\n      <td>0</td>\n      <td>154</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6/1/20</th>\n      <td>23</td>\n      <td>0</td>\n      <td>12</td>\n      <td>0</td>\n      <td>0</td>\n      <td>41</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1034</td>\n      <td>0</td>\n      <td>568</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>6/8/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>52</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>898</td>\n      <td>0</td>\n      <td>2225</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6/15/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>80</td>\n      <td>0</td>\n      <td>2</td>\n      <td>48</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1676</td>\n      <td>0</td>\n      <td>1651</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6/22/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>261</td>\n      <td>0</td>\n      <td>11</td>\n      <td>24</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1556</td>\n      <td>0</td>\n      <td>607</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6/29/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>472</td>\n      <td>0</td>\n      <td>1</td>\n      <td>57</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1228</td>\n      <td>0</td>\n      <td>1132</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7/6/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>936</td>\n      <td>0</td>\n      <td>1</td>\n      <td>47</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1572</td>\n      <td>0</td>\n      <td>101</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7/13/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1535</td>\n      <td>0</td>\n      <td>0</td>\n      <td>37</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1434</td>\n      <td>0</td>\n      <td>774</td>\n      <td>7</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7/20/20</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1762</td>\n      <td>0</td>\n      <td>3</td>\n      <td>11</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1113</td>\n      <td>0</td>\n      <td>461</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7/27/20</th>\n      <td>0</td>\n      <td>11</td>\n      <td>1803</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1147</td>\n      <td>0</td>\n      <td>393</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8/3/20</th>\n      <td>0</td>\n      <td>5</td>\n      <td>1349</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>991</td>\n      <td>0</td>\n      <td>744</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8/10/20</th>\n      <td>0</td>\n      <td>2</td>\n      <td>951</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1048</td>\n      <td>0</td>\n      <td>1295</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8/17/20</th>\n      <td>0</td>\n      <td>8</td>\n      <td>851</td>\n      <td>0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>1001</td>\n      <td>0</td>\n      <td>1515</td>\n      <td>0</td>\n      <td>0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>8/24/20</th>\n      <td>0</td>\n      <td>6</td>\n      <td>466</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>974</td>\n      <td>0</td>\n      <td>1547</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>30 rows × 107 columns</p>\n</div>"
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_counts_prepared.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "country_counts_prepared['Germany'].plot(legend=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [],
   "source": [
    "# Add column for each country holding weekly #samples/#infections sum ratio\n",
    "for country in countries_ts['Country/Region'].unique():\n",
    "    if country in country_counts_prepared.columns:\n",
    "        plotdata[f'{country}_Ratio'] = country_counts_prepared[[country]] / plotdata[[country]]\n",
    "    else:\n",
    "        plotdata[f'{country}_Ratio'] = 0"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "outputs": [
    {
     "data": {
      "text/plain": "Country/Region  USA_Ratio\n2/3/20                inf\n2/10/20          0.023256\n2/17/20          0.285714\n2/24/20          2.718182\n3/2/20           1.058104\n...                   ...\n12/6/21          0.000005\n12/13/21         0.000000\n12/20/21         0.000000\n12/27/21         0.000000\n1/3/22           0.000000\n\n[101 rows x 1 columns]",
      "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>Country/Region</th>\n      <th>USA_Ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2/3/20</th>\n      <td>inf</td>\n    </tr>\n    <tr>\n      <th>2/10/20</th>\n      <td>0.023256</td>\n    </tr>\n    <tr>\n      <th>2/17/20</th>\n      <td>0.285714</td>\n    </tr>\n    <tr>\n      <th>2/24/20</th>\n      <td>2.718182</td>\n    </tr>\n    <tr>\n      <th>3/2/20</th>\n      <td>1.058104</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>12/6/21</th>\n      <td>0.000005</td>\n    </tr>\n    <tr>\n      <th>12/13/21</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>12/20/21</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>12/27/21</th>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>1/3/22</th>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>101 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotdata[['USA_Ratio']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Replace inf values with NaN values -> occur if #infection was zero\n",
    "plotdata.replace([np.inf, -np.inf], 0, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "data": {
      "text/plain": "Country/Region  Argentina  Armenia  Austria   Bahrain  Bangladesh  Belarus  \\\n2/3/20                0.0  85.1073  62.0663   76.5775    114.0413  81.6632   \n2/10/20               0.0   0.0000   0.0000    0.0000      0.0000   0.0000   \n2/17/20               0.0   0.0000   0.0000    0.0000      0.0000   0.0000   \n2/24/20               0.0   0.0000   4.0000   57.0000      0.0000   0.0000   \n3/2/20                2.0   4.0000  97.0000  307.0000      0.0000  11.0000   \n\nCountry/Region    Belgium  Belize    Benin  Brazil  ...  Zambia_Ratio  \\\n2/3/20          57.303236     0.0  11.6235     0.0  ...           0.0   \n2/10/20          7.000000     0.0   0.0000     0.0  ...           NaN   \n2/17/20          7.000000     0.0   0.0000     0.0  ...           NaN   \n2/24/20          7.000000     0.0   0.0000     1.0  ...           NaN   \n3/2/20          49.000000     0.0   0.0000    14.0  ...           NaN   \n\nCountry/Region  Zimbabwe_Ratio  China_Ratio  Canada_Ratio  France_Ratio  \\\n2/3/20                     0.0     0.000550           0.0      0.000000   \n2/10/20                    NaN     0.000066           0.0      0.000000   \n2/17/20                    NaN     0.000023           0.0      0.000000   \n2/24/20                    0.0     0.000035           0.0      0.880435   \n3/2/20                     NaN     0.000011           0.0      0.000984   \n\nCountry/Region  United Kingdom_Ratio  Australia_Ratio  Netherlands_Ratio  \\\n2/3/20                      0.000000         0.026316                NaN   \n2/10/20                     0.000000         0.000000                NaN   \n2/17/20                     0.000000         0.028571                NaN   \n2/24/20                     0.050761         0.009524           0.000000   \n3/2/20                      0.086165         0.044335           1.214286   \n\nCountry/Region  Denmark_Ratio  New Zealand_Ratio  \n2/3/20                    NaN                NaN  \n2/10/20                   NaN                NaN  \n2/17/20                   NaN                NaN  \n2/24/20                   NaN                0.0  \n3/2/20                    0.0                0.5  \n\n[5 rows x 200 columns]",
      "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>Country/Region</th>\n      <th>Argentina</th>\n      <th>Armenia</th>\n      <th>Austria</th>\n      <th>Bahrain</th>\n      <th>Bangladesh</th>\n      <th>Belarus</th>\n      <th>Belgium</th>\n      <th>Belize</th>\n      <th>Benin</th>\n      <th>Brazil</th>\n      <th>...</th>\n      <th>Zambia_Ratio</th>\n      <th>Zimbabwe_Ratio</th>\n      <th>China_Ratio</th>\n      <th>Canada_Ratio</th>\n      <th>France_Ratio</th>\n      <th>United Kingdom_Ratio</th>\n      <th>Australia_Ratio</th>\n      <th>Netherlands_Ratio</th>\n      <th>Denmark_Ratio</th>\n      <th>New Zealand_Ratio</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2/3/20</th>\n      <td>0.0</td>\n      <td>85.1073</td>\n      <td>62.0663</td>\n      <td>76.5775</td>\n      <td>114.0413</td>\n      <td>81.6632</td>\n      <td>57.303236</td>\n      <td>0.0</td>\n      <td>11.6235</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.000550</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.026316</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2/10/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000066</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2/17/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000023</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.028571</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2/24/20</th>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>4.0000</td>\n      <td>57.0000</td>\n      <td>0.0000</td>\n      <td>0.0000</td>\n      <td>7.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>0.000035</td>\n      <td>0.0</td>\n      <td>0.880435</td>\n      <td>0.050761</td>\n      <td>0.009524</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3/2/20</th>\n      <td>2.0</td>\n      <td>4.0000</td>\n      <td>97.0000</td>\n      <td>307.0000</td>\n      <td>0.0000</td>\n      <td>11.0000</td>\n      <td>49.000000</td>\n      <td>0.0</td>\n      <td>0.0000</td>\n      <td>14.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000011</td>\n      <td>0.0</td>\n      <td>0.000984</td>\n      <td>0.086165</td>\n      <td>0.044335</td>\n      <td>1.214286</td>\n      <td>0.0</td>\n      <td>0.5</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 200 columns</p>\n</div>"
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove columns where all values are NaNs\n",
    "plotdata = plotdata.dropna(axis=1, how='all')\n",
    "plotdata.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.isdir('../../plots/n_samples_by_infections_weekly'):\n",
    "    os.mkdir('../../plots/n_samples_by_infections_weekly')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "outputs": [],
   "source": [
    "plotdata.index = pd.to_datetime(plotdata.index)\n",
    "\n",
    "# Remove even more weeks\n",
    "plotdata = plotdata[plotdata.index > '2020-03-30']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Generate one line plot per country showing #samples/#infections week-wise over time\n",
    "sns.set_style('whitegrid')\n",
    "for country in countries_ts['Country/Region'].unique():\n",
    "    if country not in country_counts_prepared.columns: continue\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(26, 12))\n",
    "    plotdata[f'{country}_Ratio'].plot(style='-o')\n",
    "\n",
    "    fig.suptitle(f'Weekly Sample Count of {country} - Scaled by Weekly Number of Infections', fontsize=22)\n",
    "    plt.xlabel('Start Date of Week', fontsize=18)\n",
    "    plt.ylabel('Scaled Count', fontsize=18)\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'../../plots/n_samples_by_infections_weekly/weekly_count_by_infections_{country}.png', dpi=300)\n",
    "    plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Number of Samples in Relation to Number of Infections per Country: Shifted Week-wise over Time"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Argentina_Infections', 'Armenia_Infections', 'Austria_Infections',\n       'Bahrain_Infections', 'Bangladesh_Infections', 'Belarus_Infections',\n       'Belgium_Infections', 'Belize_Infections', 'Benin_Infections',\n       'Brazil_Infections',\n       ...\n       'Zambia_Ratio', 'Zimbabwe_Ratio', 'China_Ratio', 'Canada_Ratio',\n       'France_Ratio', 'United Kingdom_Ratio', 'Australia_Ratio',\n       'Netherlands_Ratio', 'Denmark_Ratio', 'New Zealand_Ratio'],\n      dtype='object', name='Country/Region', length=200)"
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infection_cols = plotdata.columns[~plotdata.columns.str.endswith('_Ratio')]\n",
    "infection_cols_new = [(col, col + '_Infections') for col in infection_cols]\n",
    "plotdata.rename(columns=dict(infection_cols_new), inplace=True)\n",
    "plotdata.columns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "outputs": [
    {
     "data": {
      "text/plain": "Country/Region  USA_Infections  USA_Infections_Shifted\n2020-04-06           2457384.0               3987594.0\n2020-04-13           3987594.0               5389279.0\n2020-04-20           5389279.0               6812573.0\n2020-04-27           6812573.0               8158092.0\n2020-05-04           8158092.0               9363735.0\n...                        ...                     ...\n2021-12-06         344219054.0             350134363.0\n2021-12-13         350134363.0             356965259.0\n2021-12-20         356965259.0             367941229.0\n2021-12-27         367941229.0             390034885.0\n2022-01-03         390034885.0                     NaN\n\n[92 rows x 2 columns]",
      "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>Country/Region</th>\n      <th>USA_Infections</th>\n      <th>USA_Infections_Shifted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-04-06</th>\n      <td>2457384.0</td>\n      <td>3987594.0</td>\n    </tr>\n    <tr>\n      <th>2020-04-13</th>\n      <td>3987594.0</td>\n      <td>5389279.0</td>\n    </tr>\n    <tr>\n      <th>2020-04-20</th>\n      <td>5389279.0</td>\n      <td>6812573.0</td>\n    </tr>\n    <tr>\n      <th>2020-04-27</th>\n      <td>6812573.0</td>\n      <td>8158092.0</td>\n    </tr>\n    <tr>\n      <th>2020-05-04</th>\n      <td>8158092.0</td>\n      <td>9363735.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-12-06</th>\n      <td>344219054.0</td>\n      <td>350134363.0</td>\n    </tr>\n    <tr>\n      <th>2021-12-13</th>\n      <td>350134363.0</td>\n      <td>356965259.0</td>\n    </tr>\n    <tr>\n      <th>2021-12-20</th>\n      <td>356965259.0</td>\n      <td>367941229.0</td>\n    </tr>\n    <tr>\n      <th>2021-12-27</th>\n      <td>367941229.0</td>\n      <td>390034885.0</td>\n    </tr>\n    <tr>\n      <th>2022-01-03</th>\n      <td>390034885.0</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>92 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column with shifted number of infections for all countries (shifts one week \"to the front\")\n",
    "cols_to_shift = plotdata.loc[:, plotdata.columns.str.endswith('_Infections')]\n",
    "\n",
    "for col in cols_to_shift:\n",
    "    plotdata[f'{col}_Shifted'] = plotdata[col].shift(periods=-1)\n",
    "\n",
    "plotdata[['USA_Infections', 'USA_Infections_Shifted']]  # -> infections numbers are shifted by one row from now on"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "country_counts_prepared.index = pd.to_datetime(country_counts_prepared.index)\n",
    "\n",
    "# Add columns with sample count per country\n",
    "for country in country_counts_prepared.columns:\n",
    "    plotdata[f'{country}_Count'] = country_counts_prepared[country]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": true
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": "Country/Region  Germany_Count  Germany_Infections  Germany_Infections_Shifted  \\\n2020-04-06                  2            696501.0                    889298.0   \n2020-04-13                  0            889298.0                   1013625.0   \n2020-04-20                  0           1013625.0                   1102620.0   \n2020-04-27                  0           1102620.0                   1159038.0   \n2020-05-04                  0           1159038.0                   1203066.0   \n...                       ...                 ...                         ...   \n2021-12-06                  5          43457153.0                  45919559.0   \n2021-12-13                  0          45919559.0                  47803360.0   \n2021-12-20                  0          47803360.0                  49186678.0   \n2021-12-27                  0          49186678.0                  50737108.0   \n2022-01-03                  0          50737108.0                         NaN   \n\nCountry/Region  Germany_Ratio  Germany_Ratio_Shifted  \n2020-04-06       2.871496e-06           2.248965e-06  \n2020-04-13       0.000000e+00           0.000000e+00  \n2020-04-20       0.000000e+00           0.000000e+00  \n2020-04-27       0.000000e+00           0.000000e+00  \n2020-05-04       0.000000e+00           0.000000e+00  \n...                       ...                    ...  \n2021-12-06       1.150559e-07           1.088861e-07  \n2021-12-13       0.000000e+00           0.000000e+00  \n2021-12-20       0.000000e+00           0.000000e+00  \n2021-12-27       0.000000e+00           0.000000e+00  \n2022-01-03       0.000000e+00                    NaN  \n\n[92 rows x 5 columns]",
      "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>Country/Region</th>\n      <th>Germany_Count</th>\n      <th>Germany_Infections</th>\n      <th>Germany_Infections_Shifted</th>\n      <th>Germany_Ratio</th>\n      <th>Germany_Ratio_Shifted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-04-06</th>\n      <td>2</td>\n      <td>696501.0</td>\n      <td>889298.0</td>\n      <td>2.871496e-06</td>\n      <td>2.248965e-06</td>\n    </tr>\n    <tr>\n      <th>2020-04-13</th>\n      <td>0</td>\n      <td>889298.0</td>\n      <td>1013625.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2020-04-20</th>\n      <td>0</td>\n      <td>1013625.0</td>\n      <td>1102620.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2020-04-27</th>\n      <td>0</td>\n      <td>1102620.0</td>\n      <td>1159038.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2020-05-04</th>\n      <td>0</td>\n      <td>1159038.0</td>\n      <td>1203066.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2021-12-06</th>\n      <td>5</td>\n      <td>43457153.0</td>\n      <td>45919559.0</td>\n      <td>1.150559e-07</td>\n      <td>1.088861e-07</td>\n    </tr>\n    <tr>\n      <th>2021-12-13</th>\n      <td>0</td>\n      <td>45919559.0</td>\n      <td>47803360.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2021-12-20</th>\n      <td>0</td>\n      <td>47803360.0</td>\n      <td>49186678.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2021-12-27</th>\n      <td>0</td>\n      <td>49186678.0</td>\n      <td>50737108.0</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>2022-01-03</th>\n      <td>0</td>\n      <td>50737108.0</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>92 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add column for each country holding SHIFTED weekly #samples/#infections sum ratio\n",
    "for country in countries_ts['Country/Region'].unique():\n",
    "    if country in country_counts_prepared.columns:\n",
    "        plotdata[f'{country}_Ratio_Shifted'] = plotdata[f'{country}_Count'] / plotdata[f'{country}_Infections_Shifted']\n",
    "    else:\n",
    "        plotdata[f'{country}_Ratio_Shifted'] = 0\n",
    "\n",
    "plotdata[['Germany_Count', 'Germany_Infections', 'Germany_Infections_Shifted', 'Germany_Ratio', 'Germany_Ratio_Shifted']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "outputs": [],
   "source": [
    "# Replace inf values with NaN values -> occur if #infection was zero\n",
    "plotdata.replace([np.inf, -np.inf], 0, inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [],
   "source": [
    "if not os.path.isdir('../../plots/n_samples_by_infections_weekly_shifted'):\n",
    "    os.mkdir('../../plots/n_samples_by_infections_weekly_shifted')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "# Generate one line plot per country showing #samples/#infections week-wise shifted over time\n",
    "for country in countries_ts['Country/Region'].unique():\n",
    "    if country not in country_counts_prepared.columns: continue\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(24, 12))\n",
    "    plotdata[f'{country}_Ratio_Shifted'].plot(style='-o')\n",
    "\n",
    "    fig.suptitle(f'Weekly Sample Count of {country} - Scaled by Weekly Number of Infections from Week Before', fontsize=22)\n",
    "    plt.xlabel('Start Date of Week', fontsize=18)\n",
    "    plt.ylabel('Scaled Count', fontsize=18)\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'../../plots/n_samples_by_infections_weekly_shifted/weekly_count_by_last_weeks_infections_{country}.png', dpi=300)\n",
    "    plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [],
   "source": [
    "plotdata.to_csv('../../data/weekly_sample_and_infection_counts_of_countries.csv')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Insert Incidence as Second Line with Extra Y-Axis\n",
    "\n",
    "incidence = ((n_infections_today - n_infections_one_week_ago) * 100,000)) / n_inhabitants"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "            Argentina_Infections  Armenia_Infections  Austria_Infections  \\\n2020-04-06               10197.0              5558.0             84363.0   \n2020-04-13               14815.0              7055.0             97153.0   \n2020-04-20               19953.0              9112.0            103084.0   \n2020-04-27               27129.0             12149.0            106479.0   \n2020-05-04               33539.0             16781.0            109093.0   \n\n            Bahrain_Infections  Bangladesh_Infections  Belarus_Infections  \\\n2020-04-06              5093.0                  780.0              4284.0   \n2020-04-13              8548.0                 4903.0             18199.0   \n2020-04-20             13001.0                18112.0             40355.0   \n2020-04-27             18425.0                38767.0             73526.0   \n2020-05-04             24064.0                66941.0            116571.0   \n\n            Belgium_Infections  Belize_Infections  Benin_Infections  \\\n2020-04-06            136651.0               38.0             145.0   \n2020-04-13            204596.0              100.0             236.0   \n2020-04-20            269454.0              126.0             302.0   \n2020-04-27            320429.0              126.0             418.0   \n2020-05-04            348531.0              126.0             622.0   \n\n            Brazil_Infections  ...  Zambia_Ratio_Shifted  \\\n2020-04-06            80955.0  ...                   0.0   \n2020-04-13           157661.0  ...                   0.0   \n2020-04-20           268998.0  ...                   0.0   \n2020-04-27           446869.0  ...                   0.0   \n2020-05-04           729001.0  ...                   0.0   \n\n            Zimbabwe_Ratio_Shifted  China_Ratio_Shifted  Canada_Ratio_Shifted  \\\n2020-04-06                    0.00             0.000022                   0.0   \n2020-04-13                    0.00             0.000000                   0.0   \n2020-04-20                    0.00             0.000000                   0.0   \n2020-04-27                    0.14             0.000000                   0.0   \n2020-05-04                    0.00             0.000000                   0.0   \n\n            France_Ratio_Shifted  United Kingdom_Ratio_Shifted  \\\n2020-04-06          1.532865e-06                      0.003117   \n2020-04-13          0.000000e+00                      0.001649   \n2020-04-20          1.748617e-06                      0.001190   \n2020-04-27          8.342768e-07                      0.000915   \n2020-05-04          8.003419e-07                      0.000749   \n\n            Australia_Ratio_Shifted  Netherlands_Ratio_Shifted  \\\n2020-04-06                 0.004145                   0.000747   \n2020-04-13                 0.002409                   0.000422   \n2020-04-20                 0.000575                   0.000333   \n2020-04-27                 0.001067                   0.000281   \n2020-05-04                 0.001089                   0.000140   \n\n            Denmark_Ratio_Shifted  New Zealand_Ratio_Shifted  \n2020-04-06                    0.0                   0.005828  \n2020-04-13                    0.0                   0.002000  \n2020-04-20                    0.0                   0.002335  \n2020-04-27                    0.0                   0.000577  \n2020-05-04                    0.0                   0.000860  \n\n[5 rows x 507 columns]",
      "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>Argentina_Infections</th>\n      <th>Armenia_Infections</th>\n      <th>Austria_Infections</th>\n      <th>Bahrain_Infections</th>\n      <th>Bangladesh_Infections</th>\n      <th>Belarus_Infections</th>\n      <th>Belgium_Infections</th>\n      <th>Belize_Infections</th>\n      <th>Benin_Infections</th>\n      <th>Brazil_Infections</th>\n      <th>...</th>\n      <th>Zambia_Ratio_Shifted</th>\n      <th>Zimbabwe_Ratio_Shifted</th>\n      <th>China_Ratio_Shifted</th>\n      <th>Canada_Ratio_Shifted</th>\n      <th>France_Ratio_Shifted</th>\n      <th>United Kingdom_Ratio_Shifted</th>\n      <th>Australia_Ratio_Shifted</th>\n      <th>Netherlands_Ratio_Shifted</th>\n      <th>Denmark_Ratio_Shifted</th>\n      <th>New Zealand_Ratio_Shifted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-04-06</th>\n      <td>10197.0</td>\n      <td>5558.0</td>\n      <td>84363.0</td>\n      <td>5093.0</td>\n      <td>780.0</td>\n      <td>4284.0</td>\n      <td>136651.0</td>\n      <td>38.0</td>\n      <td>145.0</td>\n      <td>80955.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000022</td>\n      <td>0.0</td>\n      <td>1.532865e-06</td>\n      <td>0.003117</td>\n      <td>0.004145</td>\n      <td>0.000747</td>\n      <td>0.0</td>\n      <td>0.005828</td>\n    </tr>\n    <tr>\n      <th>2020-04-13</th>\n      <td>14815.0</td>\n      <td>7055.0</td>\n      <td>97153.0</td>\n      <td>8548.0</td>\n      <td>4903.0</td>\n      <td>18199.0</td>\n      <td>204596.0</td>\n      <td>100.0</td>\n      <td>236.0</td>\n      <td>157661.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>0.000000e+00</td>\n      <td>0.001649</td>\n      <td>0.002409</td>\n      <td>0.000422</td>\n      <td>0.0</td>\n      <td>0.002000</td>\n    </tr>\n    <tr>\n      <th>2020-04-20</th>\n      <td>19953.0</td>\n      <td>9112.0</td>\n      <td>103084.0</td>\n      <td>13001.0</td>\n      <td>18112.0</td>\n      <td>40355.0</td>\n      <td>269454.0</td>\n      <td>126.0</td>\n      <td>302.0</td>\n      <td>268998.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>1.748617e-06</td>\n      <td>0.001190</td>\n      <td>0.000575</td>\n      <td>0.000333</td>\n      <td>0.0</td>\n      <td>0.002335</td>\n    </tr>\n    <tr>\n      <th>2020-04-27</th>\n      <td>27129.0</td>\n      <td>12149.0</td>\n      <td>106479.0</td>\n      <td>18425.0</td>\n      <td>38767.0</td>\n      <td>73526.0</td>\n      <td>320429.0</td>\n      <td>126.0</td>\n      <td>418.0</td>\n      <td>446869.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.14</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>8.342768e-07</td>\n      <td>0.000915</td>\n      <td>0.001067</td>\n      <td>0.000281</td>\n      <td>0.0</td>\n      <td>0.000577</td>\n    </tr>\n    <tr>\n      <th>2020-05-04</th>\n      <td>33539.0</td>\n      <td>16781.0</td>\n      <td>109093.0</td>\n      <td>24064.0</td>\n      <td>66941.0</td>\n      <td>116571.0</td>\n      <td>348531.0</td>\n      <td>126.0</td>\n      <td>622.0</td>\n      <td>729001.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.00</td>\n      <td>0.000000</td>\n      <td>0.0</td>\n      <td>8.003419e-07</td>\n      <td>0.000749</td>\n      <td>0.001089</td>\n      <td>0.000140</td>\n      <td>0.0</td>\n      <td>0.000860</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 507 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotdata = pd.read_csv('../../data/weekly_sample_and_infection_counts_of_countries.csv', index_col=0)\n",
    "plotdata.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "['Argentina',\n 'Armenia',\n 'Austria',\n 'Bahrain',\n 'Bangladesh',\n 'Belarus',\n 'Belgium',\n 'Belize',\n 'Benin',\n 'Brazil',\n 'Cambodia',\n 'Cameroon',\n 'Chile',\n 'Colombia',\n 'Comoros',\n 'Croatia',\n 'Cuba',\n 'Djibouti',\n 'Dominican Republic',\n 'Ecuador',\n 'Egypt',\n 'Estonia',\n 'Ethiopia',\n 'Finland',\n 'Gabon',\n 'Gambia',\n 'Georgia',\n 'Germany',\n 'Ghana',\n 'Greece',\n 'Guatemala',\n 'Guinea',\n 'Hungary',\n 'Iceland',\n 'India',\n 'Indonesia',\n 'Iran',\n 'Iraq',\n 'Israel',\n 'Italy',\n 'Jamaica',\n 'Japan',\n 'Jordan',\n 'Kazakhstan',\n 'Kenya',\n 'Lebanon',\n 'Lesotho',\n 'Libya',\n 'Liechtenstein',\n 'Malawi',\n 'Malaysia',\n 'Mali',\n 'Malta',\n 'Mexico',\n 'Morocco',\n 'Nepal',\n 'Nigeria',\n 'Pakistan',\n 'Palau',\n 'Peru',\n 'Philippines',\n 'Poland',\n 'Portugal',\n 'Qatar',\n 'Romania',\n 'Russia',\n 'Saudi Arabia',\n 'Serbia',\n 'Seychelles',\n 'Sierra Leone',\n 'Singapore',\n 'Slovakia',\n 'Somalia',\n 'South Africa',\n 'Spain',\n 'Sri Lanka',\n 'Sweden',\n 'Switzerland',\n 'Thailand',\n 'Timor-Leste',\n 'Togo',\n 'Tunisia',\n 'Turkey',\n 'USA',\n 'Uganda',\n 'Uruguay',\n 'Uzbekistan',\n 'Venezuela',\n 'Viet Nam',\n 'West Bank',\n 'Zambia',\n 'Zimbabwe',\n 'China',\n 'Canada',\n 'France',\n 'United Kingdom',\n 'Australia',\n 'Netherlands',\n 'Denmark',\n 'New Zealand']"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = plotdata.columns[plotdata.columns.str.endswith('_Infections')]\n",
    "countries = [country.split('_')[0] for country in countries]\n",
    "countries"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('../helper_dicts/country_inhabitants.json') as f:\n",
    "    country_inhabitants_map = json.load(f)\n",
    "\n",
    "# Exclude countries without inhabitants information\n",
    "for non_existent_country in ['Malawi', 'Comoros', 'Lesotho']:\n",
    "    countries.remove(non_existent_country)\n",
    "\n",
    "# Add incidence per country and week\n",
    "for country in countries:\n",
    "    plotdata[f'{country}_Incidence'] = 0\n",
    "    n_inhabitants_country = country_inhabitants_map[country]\n",
    "\n",
    "    for i, date in enumerate(plotdata.index):\n",
    "        # Exclude first week\n",
    "        if i == 0: continue\n",
    "        # ((n_infections_today - n_infections_one_week_ago) * 100,000)) / n_inhabitants\n",
    "        plotdata[f'{country}_Incidence'][date] = ((plotdata[f'{country}_Infections'][i] - plotdata[f'{country}_Infections'][i-1]) * 100000) / n_inhabitants_country"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "\n",
    "# Create extra folder\n",
    "if not os.path.isdir('../../plots/country_sampling_strategy_incidence_over_time'):\n",
    "    os.mkdir('../../plots/country_sampling_strategy_incidence_over_time')\n",
    "\n",
    "# Generate one line plot per country showing #samples/#infections week-wise over time\n",
    "sns.set_style('whitegrid')\n",
    "for country in countries:\n",
    "    fig, ax1 = plt.subplots(figsize=(32, 12))\n",
    "    ax2 = ax1.twinx()\n",
    "    plotdata[f'{country}_Ratio'].plot(style='-o', ax=ax1, color='#9B2D1F')\n",
    "    plotdata[f'{country}_Incidence'].plot(style='-', ax=ax2, color='#D34817')\n",
    "\n",
    "    fig.suptitle(f'{country}: Weekly Incidence and Sample Count Scaled by Number of Infections', fontsize=28)\n",
    "    ax1.set_xlabel('Start Date of Week', fontsize=18)\n",
    "    ax1.set_ylabel('Scaled Weekly Sample Count', fontsize=18, color='#9B2D1F')\n",
    "    ax2.set_ylabel('Weekly Incidence', fontsize=18, color='#D34817')\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "    # align ticks of both y-axes\n",
    "    ax2.grid(None)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'../../plots/country_sampling_strategy_incidence_over_time/sampling_strategy_incidence_{country}.png', dpi=300)\n",
    "    plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Investigation of Temporal Bias"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "plotdata = df.copy(deep=True)\n",
    "plotdata['collection_date'] = pd.to_datetime(plotdata['collection_date'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.isdir('../../plots/n_samples_over_time'):\n",
    "    os.mkdir('../../plots/n_samples_over_time')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1728x864 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A) Plot density\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, _ = plt.subplots(figsize=(24, 12))\n",
    "sns.kdeplot(data=plotdata,\n",
    "            x='collection_date') #,\n",
    "            #hue='keywords')\n",
    "fig.suptitle('Density of Collection Date Over Time', fontsize=22)\n",
    "plt.xlabel('Collection Date', fontsize=18)\n",
    "plt.ylabel('Density', fontsize=18)\n",
    "plt.xticks(fontsize=14, rotation=90)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../../plots/n_samples_over_time/collection_date_density.png', dpi=300)\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Count\ncollection_date        \n2021-08-31       443839\n2021-09-07       414717\n2021-08-03       405730\n2021-08-02       394096\n2021-08-05       391909\n...                 ...\n2020-01-09           23\n2019-09-12           23\n2020-01-11           22\n2020-02-14           22\n2019-12-01           21\n\n[724 rows x 1 columns]",
      "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>Count</th>\n    </tr>\n    <tr>\n      <th>collection_date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-08-31</th>\n      <td>443839</td>\n    </tr>\n    <tr>\n      <th>2021-09-07</th>\n      <td>414717</td>\n    </tr>\n    <tr>\n      <th>2021-08-03</th>\n      <td>405730</td>\n    </tr>\n    <tr>\n      <th>2021-08-02</th>\n      <td>394096</td>\n    </tr>\n    <tr>\n      <th>2021-08-05</th>\n      <td>391909</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2020-01-09</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2019-09-12</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2020-01-11</th>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2020-02-14</th>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2019-12-01</th>\n      <td>21</td>\n    </tr>\n  </tbody>\n</table>\n<p>724 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B) Plot absolute count\n",
    "date_counts = plotdata.groupby(['collection_date']).count().sum(axis=1)\n",
    "# date_counts = plotdata.groupby(['collection_date', 'keywords']).count().sum(axis=1)\n",
    "date_counts = pd.DataFrame(date_counts, columns=['Count'])\n",
    "date_counts.sort_values(by='Count', ascending=False)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1728x864 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, _ = plt.subplots(figsize=(24, 12))\n",
    "sns.lineplot(x=date_counts.index, #.get_level_values(0),\n",
    "             y=date_counts['Count']) #,\n",
    "             #hue=date_counts.index.get_level_values(1))\n",
    "fig.suptitle('Amount of Samples per Collection Date Over Time', fontsize=22)\n",
    "plt.xlabel('Collection Date', fontsize=18)\n",
    "plt.ylabel('Count', fontsize=18)\n",
    "plt.xticks(fontsize=14, rotation=90)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../../plots/n_samples_over_time/collection_date_count.png', dpi=300)\n",
    "plt.close()\n",
    "\n",
    "# -> temporal bias around April 2021 and Winter 2021\n",
    "# -> amount of sequences was doubled in Winter in comparison to warmer months\n",
    "# -> amount of sequences decreases in the end probably because original TSV is not up-to-date"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "                  Count\ncollection_date        \n2020-01-01       370504\n2020-01-02           67\n2020-01-03           23\n2020-01-04          105\n2020-01-05           23\n2020-01-07           23\n2020-01-08           66\n2020-01-09           23\n2020-01-10           47\n2020-01-11           22\n2020-01-13           44\n2020-01-14           67\n2020-01-17           46\n2020-01-19          180\n2020-01-20           90\n2020-01-21          161\n2020-01-22          574\n2020-01-23          373\n2020-01-24          225\n2020-01-25          483\n2020-01-26          338\n2020-01-27          248\n2020-01-28          251\n2020-01-29          696\n2020-01-30          360\n2020-01-31          351",
      "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>Count</th>\n    </tr>\n    <tr>\n      <th>collection_date</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-01</th>\n      <td>370504</td>\n    </tr>\n    <tr>\n      <th>2020-01-02</th>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2020-01-04</th>\n      <td>105</td>\n    </tr>\n    <tr>\n      <th>2020-01-05</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>2020-01-09</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>2020-01-10</th>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>2020-01-11</th>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>2020-01-13</th>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>2020-01-14</th>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>2020-01-17</th>\n      <td>46</td>\n    </tr>\n    <tr>\n      <th>2020-01-19</th>\n      <td>180</td>\n    </tr>\n    <tr>\n      <th>2020-01-20</th>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>2020-01-21</th>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>2020-01-22</th>\n      <td>574</td>\n    </tr>\n    <tr>\n      <th>2020-01-23</th>\n      <td>373</td>\n    </tr>\n    <tr>\n      <th>2020-01-24</th>\n      <td>225</td>\n    </tr>\n    <tr>\n      <th>2020-01-25</th>\n      <td>483</td>\n    </tr>\n    <tr>\n      <th>2020-01-26</th>\n      <td>338</td>\n    </tr>\n    <tr>\n      <th>2020-01-27</th>\n      <td>248</td>\n    </tr>\n    <tr>\n      <th>2020-01-28</th>\n      <td>251</td>\n    </tr>\n    <tr>\n      <th>2020-01-29</th>\n      <td>696</td>\n    </tr>\n    <tr>\n      <th>2020-01-30</th>\n      <td>360</td>\n    </tr>\n    <tr>\n      <th>2020-01-31</th>\n      <td>351</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter for January 2020\n",
    "date_counts[(date_counts.index.month == 1) & (date_counts.index.year == 2020)]\n",
    "\n",
    "# -> a lot of sequences on New Year's Day 2020 in UK and Switzerland\n",
    "# -> probably typo and 2021 is meant: UK had deflection around 9th January 2021 regarding number of cases\n",
    "# -> but not explainable at all for Switzerland"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "Timestamp('2019-09-12 00:00:00')"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(date_counts.index)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "Timestamp('2021-12-12 00:00:00')"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(date_counts.index)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plot collection data count per country\n",
    "for country in plotdata['country'].unique():\n",
    "    plotdata_country = plotdata[plotdata['country'] == country]\n",
    "    date_counts_country = plotdata_country.groupby(['collection_date']).count().sum(axis=1)\n",
    "    date_counts_country = pd.DataFrame(date_counts_country, columns=['Count'])\n",
    "\n",
    "    sns.set_style('whitegrid')\n",
    "    fig, _ = plt.subplots(figsize=(24, 12))\n",
    "    sns.lineplot(x=date_counts_country.index,\n",
    "                 y=date_counts_country['Count'])\n",
    "    fig.suptitle(f'Amount of Samples per Collection Date Over Time in {country}', fontsize=22)\n",
    "    plt.xlabel('Collection Date', fontsize=18)\n",
    "    plt.ylabel('Count', fontsize=18)\n",
    "    plt.xticks(fontsize=14, rotation=90)\n",
    "    plt.yticks(fontsize=14)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'../../plots/n_samples_over_time/collection_date_count_{str(country).replace(\" \", \"_\")}.png', dpi=300)\n",
    "    plt.close()\n",
    "\n",
    "# -> USA: raise of cases from end of October 2020 until February 2021 not recognisable\n",
    "# -> UK: curve fits well\n",
    "# -> Germany: same like for the USA + number of tests should be much higher since November 2021"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Investigation of Existing Ages"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "outputs": [
    {
     "data": {
      "text/plain": "2520583"
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_len = len(df)\n",
    "original_len  # 2,520,583"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [
    {
     "data": {
      "text/plain": "9948"
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count how many samples do contain (valuable) information about age\n",
    "df_with_ages = df[df['age'].notna()]\n",
    "len(df_with_ages)  # 9,948"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "          age                                host\n993150   0.67     Homo sapiens; Male age 8 months\n993264   0.33   Homo sapiens; Female age 4 months\n993296   0.25     Homo sapiens; Male age 3 months\n993677   0.14      Homo sapiens; Male age 50 days\n993775   0.08   Homo sapiens; Female age 1 months\n993802   0.83  Homo sapiens; Female age 10 months\n993970   0.92  Homo sapiens; Female age 11 months\n994025   0.58   Homo sapiens; Female age 7 months\n994601   0.92  Homo sapiens; Female age 11 months\n994679   0.42   Homo sapiens; Female age 5 months\n994681   0.50     Homo sapiens; Male age 6 months\n994890   0.67   Homo sapiens; Female age 8 months\n994918   0.17     Homo sapiens; Male age 2 months\n994919   0.58     Homo sapiens; Male age 7 months\n995028   0.58   Homo sapiens; Female age 7 months\n995042   0.50     Homo sapiens; Male age 6 months\n1334892  0.67              Homo sapiens; age 8MOS\n1334903  1.92            Homo sapiens; age 23 MOS\n1335000  0.00             Homo sapiens; age 1 day\n1335356  0.83            Homo sapiens; age 10 mos\n1335383  0.07           Homo sapiens; age 25 days\n1335439  0.25             Homo sapiens; age 3 mos\n1335765  0.00             Homo sapiens; age 1 day\n1335776  0.75             Homo sapiens; age 9 mos",
      "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>age</th>\n      <th>host</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>993150</th>\n      <td>0.67</td>\n      <td>Homo sapiens; Male age 8 months</td>\n    </tr>\n    <tr>\n      <th>993264</th>\n      <td>0.33</td>\n      <td>Homo sapiens; Female age 4 months</td>\n    </tr>\n    <tr>\n      <th>993296</th>\n      <td>0.25</td>\n      <td>Homo sapiens; Male age 3 months</td>\n    </tr>\n    <tr>\n      <th>993677</th>\n      <td>0.14</td>\n      <td>Homo sapiens; Male age 50 days</td>\n    </tr>\n    <tr>\n      <th>993775</th>\n      <td>0.08</td>\n      <td>Homo sapiens; Female age 1 months</td>\n    </tr>\n    <tr>\n      <th>993802</th>\n      <td>0.83</td>\n      <td>Homo sapiens; Female age 10 months</td>\n    </tr>\n    <tr>\n      <th>993970</th>\n      <td>0.92</td>\n      <td>Homo sapiens; Female age 11 months</td>\n    </tr>\n    <tr>\n      <th>994025</th>\n      <td>0.58</td>\n      <td>Homo sapiens; Female age 7 months</td>\n    </tr>\n    <tr>\n      <th>994601</th>\n      <td>0.92</td>\n      <td>Homo sapiens; Female age 11 months</td>\n    </tr>\n    <tr>\n      <th>994679</th>\n      <td>0.42</td>\n      <td>Homo sapiens; Female age 5 months</td>\n    </tr>\n    <tr>\n      <th>994681</th>\n      <td>0.50</td>\n      <td>Homo sapiens; Male age 6 months</td>\n    </tr>\n    <tr>\n      <th>994890</th>\n      <td>0.67</td>\n      <td>Homo sapiens; Female age 8 months</td>\n    </tr>\n    <tr>\n      <th>994918</th>\n      <td>0.17</td>\n      <td>Homo sapiens; Male age 2 months</td>\n    </tr>\n    <tr>\n      <th>994919</th>\n      <td>0.58</td>\n      <td>Homo sapiens; Male age 7 months</td>\n    </tr>\n    <tr>\n      <th>995028</th>\n      <td>0.58</td>\n      <td>Homo sapiens; Female age 7 months</td>\n    </tr>\n    <tr>\n      <th>995042</th>\n      <td>0.50</td>\n      <td>Homo sapiens; Male age 6 months</td>\n    </tr>\n    <tr>\n      <th>1334892</th>\n      <td>0.67</td>\n      <td>Homo sapiens; age 8MOS</td>\n    </tr>\n    <tr>\n      <th>1334903</th>\n      <td>1.92</td>\n      <td>Homo sapiens; age 23 MOS</td>\n    </tr>\n    <tr>\n      <th>1335000</th>\n      <td>0.00</td>\n      <td>Homo sapiens; age 1 day</td>\n    </tr>\n    <tr>\n      <th>1335356</th>\n      <td>0.83</td>\n      <td>Homo sapiens; age 10 mos</td>\n    </tr>\n    <tr>\n      <th>1335383</th>\n      <td>0.07</td>\n      <td>Homo sapiens; age 25 days</td>\n    </tr>\n    <tr>\n      <th>1335439</th>\n      <td>0.25</td>\n      <td>Homo sapiens; age 3 mos</td>\n    </tr>\n    <tr>\n      <th>1335765</th>\n      <td>0.00</td>\n      <td>Homo sapiens; age 1 day</td>\n    </tr>\n    <tr>\n      <th>1335776</th>\n      <td>0.75</td>\n      <td>Homo sapiens; age 9 mos</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show how age is set for patients with age information given in days or months\n",
    "df_with_ages[df_with_ages['host'].str.contains('day|months|mos|MOS')][['age', 'host']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "outputs": [
    {
     "data": {
      "text/plain": "         age                  host\n460      nan  Homo sapiens, female\n461      nan    Homo sapiens, male\n462      nan    Homo sapiens, male\n463      nan  Homo sapiens, female\n464      nan    Homo sapiens, male\n...      ...                   ...\n2242394  nan                   NaN\n2252312  nan                   NaN\n2474640  nan          not provided\n2496096  nan          not provided\n2498810  nan           Felis catus\n\n[4773 rows x 2 columns]",
      "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>age</th>\n      <th>host</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>460</th>\n      <td>nan</td>\n      <td>Homo sapiens, female</td>\n    </tr>\n    <tr>\n      <th>461</th>\n      <td>nan</td>\n      <td>Homo sapiens, male</td>\n    </tr>\n    <tr>\n      <th>462</th>\n      <td>nan</td>\n      <td>Homo sapiens, male</td>\n    </tr>\n    <tr>\n      <th>463</th>\n      <td>nan</td>\n      <td>Homo sapiens, female</td>\n    </tr>\n    <tr>\n      <th>464</th>\n      <td>nan</td>\n      <td>Homo sapiens, male</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2242394</th>\n      <td>nan</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2252312</th>\n      <td>nan</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2474640</th>\n      <td>nan</td>\n      <td>not provided</td>\n    </tr>\n    <tr>\n      <th>2496096</th>\n      <td>nan</td>\n      <td>not provided</td>\n    </tr>\n    <tr>\n      <th>2498810</th>\n      <td>nan</td>\n      <td>Felis catus</td>\n    </tr>\n  </tbody>\n</table>\n<p>4773 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check how host is set if age in not numeric and host is not equal to main value \"homo sapiens\" (for debugging and to finally check all set ages)\n",
    "df['age'] = df['age'].astype(str)\n",
    "non_numeric_ages = df[df['age'].apply(lambda x: not x.isnumeric())]\n",
    "non_numeric_ages[non_numeric_ages['host'].str.lower() != 'homo sapiens'][['age', 'host']]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Convert ages to integers for simpler plotting\n",
    "df_with_ages['age'] = df_with_ages['age'].astype(int)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [],
   "source": [
    "# Sort by age\n",
    "plotdata = df_with_ages.sort_values(by=['age'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1728x864 with 1 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "fig, _ = plt.subplots(figsize=(24, 12))\n",
    "sns.countplot(data=plotdata,\n",
    "              x='age',\n",
    "              hue='gender')\n",
    "fig.suptitle('Distribution of Existing Ages', fontsize=22)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Count', fontsize=18)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Show only every 5th x-tick (aka every 5th age on x-axis)\n",
    "for i, label in enumerate(ax.get_xticklabels()):\n",
    "    if i % 5 == 0:\n",
    "        label.set_visible(True)\n",
    "    else:\n",
    "        label.set_visible(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "fig.savefig('../../plots/age_distribution.png', dpi=300)\n",
    "plt.close()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "USA               9656\nRussia             265\nItaly               10\nCzech Republic       6\nGermany              5\nChina                5\nPakistan             1\nName: country, dtype: int64"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotdata['country'].value_counts()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "if not os.path.isdir('../../plots/age_distribution'):\n",
    "    os.mkdir('../../plots/age_distribution')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# Ensure that age distribution won't change for USA and check if other countries show interesting distributions\n",
    "for country, age_count in plotdata['country'].value_counts().iteritems():\n",
    "    fig, ax = plt.subplots(figsize=(24, 12))\n",
    "    sns.countplot(data=plotdata[plotdata['country'] == country],\n",
    "                  x='age',\n",
    "                  hue='gender')\n",
    "    fig.suptitle(f'Distribution of Existing Ages in {country}\\n ({age_count} ages available)', fontsize=22)\n",
    "    plt.xlabel('Age', fontsize=18)\n",
    "    plt.ylabel('Count', fontsize=18)\n",
    "    plt.xticks(fontsize=14)\n",
    "    plt.yticks(fontsize=14)\n",
    "\n",
    "    # Show only every 5th x-tick\n",
    "    for i, label in enumerate(ax.get_xticklabels()):\n",
    "        if i % 5 == 0:\n",
    "            label.set_visible(True)\n",
    "        else:\n",
    "            label.set_visible(False)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    fig.savefig(f'../../plots/age_distribution/age_distribution_{country}.png', dpi=300)\n",
    "    plt.close()\n",
    "\n",
    "# USA:\n",
    "# -> similiar to real-world age distribution, especially regarding rise around 20\n",
    "# -> see e.g. https://www.statista.com/statistics/241488/population-of-the-us-by-sex-and-age/\n",
    "# -> a bit more female samples are also quite realistic\n",
    "\n",
    "# RUSSIA:\n",
    "# -> russian ages are mostly between 63 and 83"
   ],
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