From 5f6187c4a3b3b90bb127e059fa5509310ef1d779 Mon Sep 17 00:00:00 2001 From: 082aa0aa9507b80b621099010e33de9d <082aa0aa9507b80b621099010e33de9d@app-learninglab.inria.fr> Date: Fri, 28 Jun 2024 14:22:52 +0000 Subject: [PATCH] test --- module3/exo3/exercice_en.ipynb | 1925 +++++++++++++++++++++++++++++++- 1 file changed, 1906 insertions(+), 19 deletions(-) diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb index 60c5e5a..7ed69dd 100644 --- a/module3/exo3/exercice_en.ipynb +++ b/module3/exo3/exercice_en.ipynb @@ -3,7 +3,8 @@ { "cell_type": "markdown", "metadata": { - "hideCode": true + "hideCode": true, + "hidePrompt": true }, "source": [ "# Title of document" @@ -12,7 +13,10 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -1801,7 +1805,10 @@ { "cell_type": "code", "execution_count": 6, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -3592,7 +3599,10 @@ { "cell_type": "code", "execution_count": 7, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -5378,7 +5388,10 @@ { "cell_type": "code", "execution_count": 8, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -5848,7 +5861,10 @@ { "cell_type": "code", "execution_count": 14, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -5886,7 +5902,10 @@ { "cell_type": "code", "execution_count": 15, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -5931,7 +5950,10 @@ { "cell_type": "code", "execution_count": 19, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -5977,7 +5999,10 @@ { "cell_type": "code", "execution_count": 24, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -6439,7 +6464,10 @@ { "cell_type": "code", "execution_count": 25, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -6486,7 +6514,10 @@ { "cell_type": "code", "execution_count": 45, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -7722,7 +7753,10 @@ { "cell_type": "code", "execution_count": 49, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -8528,7 +8562,10 @@ { "cell_type": "code", "execution_count": 50, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -9014,7 +9051,10 @@ { "cell_type": "code", "execution_count": 53, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -9060,7 +9100,10 @@ { "cell_type": "code", "execution_count": 52, - "metadata": {}, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, "outputs": [ { "data": { @@ -9101,15 +9144,1859 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "source": [ + "## 1st: fr_dead" + ] + }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 122, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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121French PolynesiaFrance-17.679700149.406800000000...649649649649649649649649649649
122GuadeloupeFrance16.265000-61.551000000000...1010101010101010101010101010101010101010
123MartiniqueFrance14.641500-61.024200000000...1092109210921092109210921092109210921092
124MayotteFrance-12.82750045.166244000000...187187187187187187187187187187
125New CaledoniaFrance-20.904305165.618042000000...314314314314314314314314314314
126ReunionFrance-21.11510055.536400000000...921921921921921921921921921921
127Saint BarthelemyFrance17.900000-62.833300000000...6666666666
128Saint Pierre and MiquelonFrance46.885200-56.315900000000...2222222222
129St MartinFrance18.070800-63.050100000000...63636363636363636363
130Wallis and FutunaFrance-14.293800-178.116500000000...7777777777
131NaNFrance46.2276002.213700000000...161340161365161386161407161407161407161450161474161501161512
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1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/20...2/28/233/1/233/2/233/3/233/4/233/5/233/6/233/7/233/8/233/9/23
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1 rows × 1143 columns

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" + ], + "text/plain": [ + " 1/22/20 1/23/20 1/24/20 1/25/20 1/26/20 1/27/20 1/28/20 \\\n", + "Country/Region \n", + "France 0 0 0 0 0 0 0 \n", + "\n", + " 1/29/20 1/30/20 1/31/20 ... 2/28/23 3/1/23 3/2/23 \\\n", + "Country/Region ... \n", + "France 0 0 0 ... 166004 166029 166050 \n", + "\n", + " 3/3/23 3/4/23 3/5/23 3/6/23 3/7/23 3/8/23 3/9/23 \n", + "Country/Region \n", + "France 166071 166071 166071 166114 166138 166165 166176 \n", + "\n", + "[1 rows x 1143 columns]" + ] + }, + "execution_count": 134, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove unuse col\n", + "filtered_france_data = filtered_france_data.drop(columns=['Province/State', 'Lat', 'Long'])\n", + "\n", + "grouped_france_data = filtered_france_data.groupby('Country/Region').sum()\n", + "grouped_france_data" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(grouped_france_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Resample the data to get the last available value for each month\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmonthly_france_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrouped_france_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mffill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mmonthly_france_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base, on, level)\u001b[0m\n\u001b[1;32m 5520\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5521\u001b[0m \u001b[0mconvention\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvention\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5522\u001b[0;31m base=base, key=on, level=level)\n\u001b[0m\u001b[1;32m 5523\u001b[0m return _maybe_process_deprecations(r,\n\u001b[1;32m 5524\u001b[0m \u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36mresample\u001b[0;34m(obj, kind, **kwds)\u001b[0m\n\u001b[1;32m 997\u001b[0m \u001b[0;34m\"\"\" create a TimeGrouper and return our resampler \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 998\u001b[0m \u001b[0mtg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTimeGrouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 999\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_resampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1000\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1001\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/resample.py\u001b[0m in \u001b[0;36m_get_resampler\u001b[0;34m(self, obj, kind)\u001b[0m\n\u001b[1;32m 1114\u001b[0m raise TypeError(\"Only valid with DatetimeIndex, \"\n\u001b[1;32m 1115\u001b[0m \u001b[0;34m\"TimedeltaIndex or PeriodIndex, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1116\u001b[0;31m \"but got an instance of %r\" % type(ax).__name__)\n\u001b[0m\u001b[1;32m 1117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_grouper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'" + ] + } + ], + "source": [ + "# Resample the data to get the last available value for each month\n", + "monthly_france_data = grouped_france_data.resample('M').ffill()\n", + "monthly_france_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "source": [ + "## 2nd: france_dead I GIVE UP???? help me huhuhu. why i can not see only france. i try filter etc,. and only loc() is work. but I hate this visualize" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "hideCode": true, + "hidePrompt": 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2020-01-31 00:00:002020-02-29 00:00:002020-03-31 00:00:002020-04-30 00:00:002020-05-31 00:00:002020-06-30 00:00:002020-07-31 00:00:002020-08-31 00:00:002020-09-30 00:00:002020-10-31 00:00:00...2022-06-30 00:00:002022-07-31 00:00:002022-08-31 00:00:002022-09-30 00:00:002022-10-31 00:00:002022-11-30 00:00:002022-12-31 00:00:002023-01-31 00:00:002023-02-28 00:00:002023-03-31 00:00:00
Country/Region
Belgium00705759494679747984198951001611625...31918322283251632673329023306133228335573371733814
China213283733234697470847134733479648134814...1492715046152401571315952159861710297663101048101056
France02352624349288052984630268306463197836827...150572153023155133156152158034159990163003165274166004166176
Germany0058362888500897391419298948810452...141105143855147404149948153544157791161465165711168086168935
Iran0432898602877971081716766215712616934864...141389141998143867144426144576144633144685144749144845144933
Italy0291242827967334153476735141354833589438618...168353172086175595177092179101181098184642186833188094188322
Japan06674818989741013130015751770...31281326133994244918467654965257274680997239572997
Korea, South016162248271282301324415466...24555250682687628445292093056832219334863398834093
Netherlands0010404811597561326166625264577459...23000231222323923292234592356823697237022370523705
Portugal00160989141015761735182219712507...24149245922485525031252282545025714260222611726266
Spain00846424543271272835528445290943179135878...107906110719112600114179115078115901117095118434119380119479
US01535966638107857127432153969182794205957230545...1017377103008810462401059507107037610804661092764110868811199171123836
United Kingdom13514539062525695628457434579845887564308...201790205481207831209268212350214179217054220064220721220721
\n", + "

13 rows × 39 columns

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" + ], + "text/plain": [ + " 2020-01-31 2020-02-29 2020-03-31 2020-04-30 2020-05-31 \\\n", + "Country/Region \n", + "Belgium 0 0 705 7594 9467 \n", + "China 213 2837 3323 4697 4708 \n", + "France 0 2 3526 24349 28805 \n", + "Germany 0 0 583 6288 8500 \n", + "Iran 0 43 2898 6028 7797 \n", + "Italy 0 29 12428 27967 33415 \n", + "Japan 0 6 67 481 898 \n", + "Korea, South 0 16 162 248 271 \n", + "Netherlands 0 0 1040 4811 5975 \n", + "Portugal 0 0 160 989 1410 \n", + "Spain 0 0 8464 24543 27127 \n", + "US 0 1 5359 66638 107857 \n", + "United Kingdom 1 3 5145 39062 52569 \n", + "\n", + " 2020-06-30 2020-07-31 2020-08-31 2020-09-30 2020-10-31 \\\n", + "Country/Region \n", + "Belgium 9747 9841 9895 10016 11625 \n", + "China 4713 4733 4796 4813 4814 \n", + "France 29846 30268 30646 31978 36827 \n", + "Germany 8973 9141 9298 9488 10452 \n", + "Iran 10817 16766 21571 26169 34864 \n", + "Italy 34767 35141 35483 35894 38618 \n", + "Japan 974 1013 1300 1575 1770 \n", + "Korea, South 282 301 324 415 466 \n", + "Netherlands 6132 6166 6252 6457 7459 \n", + "Portugal 1576 1735 1822 1971 2507 \n", + "Spain 28355 28445 29094 31791 35878 \n", + "US 127432 153969 182794 205957 230545 \n", + "United Kingdom 56284 57434 57984 58875 64308 \n", + "\n", + " ... 2022-06-30 2022-07-31 2022-08-31 2022-09-30 \\\n", + "Country/Region ... \n", + "Belgium ... 31918 32228 32516 32673 \n", + "China ... 14927 15046 15240 15713 \n", + "France ... 150572 153023 155133 156152 \n", + "Germany ... 141105 143855 147404 149948 \n", + "Iran ... 141389 141998 143867 144426 \n", + "Italy ... 168353 172086 175595 177092 \n", + "Japan ... 31281 32613 39942 44918 \n", + "Korea, South ... 24555 25068 26876 28445 \n", + "Netherlands ... 23000 23122 23239 23292 \n", + "Portugal ... 24149 24592 24855 25031 \n", + "Spain ... 107906 110719 112600 114179 \n", + "US ... 1017377 1030088 1046240 1059507 \n", + "United Kingdom ... 201790 205481 207831 209268 \n", + "\n", + " 2022-10-31 2022-11-30 2022-12-31 2023-01-31 2023-02-28 \\\n", + "Country/Region \n", + "Belgium 32902 33061 33228 33557 33717 \n", + "China 15952 15986 17102 97663 101048 \n", + "France 158034 159990 163003 165274 166004 \n", + "Germany 153544 157791 161465 165711 168086 \n", + "Iran 144576 144633 144685 144749 144845 \n", + "Italy 179101 181098 184642 186833 188094 \n", + "Japan 46765 49652 57274 68099 72395 \n", + "Korea, South 29209 30568 32219 33486 33988 \n", + "Netherlands 23459 23568 23697 23702 23705 \n", + "Portugal 25228 25450 25714 26022 26117 \n", + "Spain 115078 115901 117095 118434 119380 \n", + "US 1070376 1080466 1092764 1108688 1119917 \n", + "United Kingdom 212350 214179 217054 220064 220721 \n", + "\n", + " 2023-03-31 \n", + "Country/Region \n", + "Belgium 33814 \n", + "China 101056 \n", + "France 166176 \n", + "Germany 168935 \n", + "Iran 144933 \n", + "Italy 188322 \n", + "Japan 72997 \n", + "Korea, South 34093 \n", + "Netherlands 23705 \n", + "Portugal 26266 \n", + "Spain 119479 \n", + "US 1123836 \n", + "United Kingdom 220721 \n", + "\n", + "[13 rows x 39 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "monthly_death_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2020-01-31 0\n", + "2020-02-29 2\n", + "2020-03-31 3526\n", + "2020-04-30 24349\n", + "2020-05-31 28805\n", + "2020-06-30 29846\n", + "2020-07-31 30268\n", + "2020-08-31 30646\n", + "2020-09-30 31978\n", + "2020-10-31 36827\n", + "2020-11-30 52818\n", + "2020-12-31 64758\n", + "2021-01-31 76200\n", + "2021-02-28 86579\n", + "2021-03-31 95798\n", + "2021-04-30 104676\n", + "2021-05-31 109693\n", + "2021-06-30 111259\n", + "2021-07-31 112061\n", + "2021-08-31 114926\n", + "2021-09-30 117474\n", + "2021-10-31 118625\n", + "2021-11-30 120112\n", + "2021-12-31 124729\n", + "2022-01-31 131937\n", + "2022-02-28 139382\n", + "2022-03-31 143307\n", + "2022-04-30 146967\n", + "2022-05-31 149366\n", + "2022-06-30 150572\n", + "2022-07-31 153023\n", + "2022-08-31 155133\n", + "2022-09-30 156152\n", + "2022-10-31 158034\n", + "2022-11-30 159990\n", + "2022-12-31 163003\n", + "2023-01-31 165274\n", + "2023-02-28 166004\n", + "2023-03-31 166176\n", + "Name: France, dtype: int64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "france_dead_data = monthly_death_data.loc['France']\n", + "france_dead_data" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(france_dead_data) #covid death, 2nd way extract" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(monthly_death_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(filtered_france_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(fr_death) #normal death" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(grouped_france_data) #covid death, 1st way extract" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "source": [ + "## Plot compare *dead covid and dead daily" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": { + "hideCode": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "\n", + "# Ensure that the date indices/columns are in datetime format\n", + "monthly_death_data.columns = pd.to_datetime(monthly_death_data.columns)\n", + "france_dead_data.index = pd.to_datetime(france_dead_data.index)\n", + "\n", + "plt.figure(figsize=(14, 8))\n", + "\n", + "# Plotting cumulative cases for France from monthly_death_data\n", + "if 'France' in monthly_death_data.index:\n", + " plt.plot(monthly_death_data.columns, monthly_death_data.loc['France'], label='Cumulative Cases - France')\n", + "\n", + "# Plotting death data for France from france_dead_data\n", + "plt.plot(france_dead_data.index, france_dead_data, label='Deaths - France', linestyle='--', color='red')\n", + "\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Number of Cases/Deaths')\n", + "plt.title('COVID-19 Cases and Deaths Over Time in France - Linear')\n", + "\n", + "# Format x-axis to show month and year\n", + "plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%b %Y'))\n", + "plt.gca().xaxis.set_major_locator(plt.matplotlib.dates.MonthLocator(interval=1))\n", + "\n", + "plt.xticks(rotation=45)\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] } ], "metadata": { + "hide_code_all_hidden": true, "kernelspec": { "display_name": "Python 3", "language": "python", -- 2.18.1