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mooc-rr
Commit a9c10f19 authored by 9c46a7ac8e859b7599715f55b09fc2a6's avatar 9c46a7ac8e859b7599715f55b09fc2a6
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first graph belgium

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Showing with 47 additions and 8 deletions
module3/exo3/exercice.ipynb
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...@@ -1814,25 +1814,64 @@ ...@@ -1814,25 +1814,64 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 79,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"<class 'str'>\n", "[datetime.date(2020, 1, 22), datetime.date(2020, 1, 23), datetime.date(2020, 1, 24), datetime.date(2020, 1, 25), datetime.date(2020, 1, 26), datetime.date(2020, 1, 27), datetime.date(2020, 1, 28), datetime.date(2020, 1, 29), datetime.date(2020, 1, 30), datetime.date(2020, 1, 31), datetime.date(2020, 2, 1), datetime.date(2020, 2, 2), datetime.date(2020, 2, 3), datetime.date(2020, 2, 4), datetime.date(2020, 2, 5), datetime.date(2020, 2, 6), datetime.date(2020, 2, 7), datetime.date(2020, 2, 8), datetime.date(2020, 2, 9), datetime.date(2020, 2, 10), datetime.date(2020, 2, 11), datetime.date(2020, 2, 12), datetime.date(2020, 2, 13), datetime.date(2020, 2, 14), datetime.date(2020, 2, 15), datetime.date(2020, 2, 16), datetime.date(2020, 2, 17), datetime.date(2020, 2, 18), datetime.date(2020, 2, 19), datetime.date(2020, 2, 20), datetime.date(2020, 2, 21), datetime.date(2020, 2, 22), datetime.date(2020, 2, 23), datetime.date(2020, 2, 24), datetime.date(2020, 2, 25), datetime.date(2020, 2, 26), datetime.date(2020, 2, 27), datetime.date(2020, 2, 28), datetime.date(2020, 2, 29), datetime.date(2020, 3, 1), datetime.date(2020, 3, 2), datetime.date(2020, 3, 3), datetime.date(2020, 3, 4), datetime.date(2020, 3, 5), datetime.date(2020, 3, 6), datetime.date(2020, 3, 7), datetime.date(2020, 3, 8), datetime.date(2020, 3, 9), datetime.date(2020, 3, 10), datetime.date(2020, 3, 11), datetime.date(2020, 3, 12), datetime.date(2020, 3, 13), datetime.date(2020, 3, 14), datetime.date(2020, 3, 15), datetime.date(2020, 3, 16), datetime.date(2020, 3, 17), datetime.date(2020, 3, 18), datetime.date(2020, 3, 19), datetime.date(2020, 3, 20), datetime.date(2020, 3, 21), datetime.date(2020, 3, 22), datetime.date(2020, 3, 23), datetime.date(2020, 3, 24), datetime.date(2020, 3, 25), datetime.date(2020, 3, 26), datetime.date(2020, 3, 27), datetime.date(2020, 3, 28), datetime.date(2020, 3, 29), datetime.date(2020, 3, 30), datetime.date(2020, 3, 31), datetime.date(2020, 4, 1), datetime.date(2020, 4, 2), datetime.date(2020, 4, 3), datetime.date(2020, 4, 4), datetime.date(2020, 4, 5), datetime.date(2020, 4, 6), datetime.date(2020, 4, 7), datetime.date(2020, 4, 8), datetime.date(2020, 4, 9), datetime.date(2020, 4, 10), datetime.date(2020, 4, 11), datetime.date(2020, 4, 12), datetime.date(2020, 4, 13), datetime.date(2020, 4, 14), datetime.date(2020, 4, 15), datetime.date(2020, 4, 16), datetime.date(2020, 4, 17), datetime.date(2020, 4, 18), datetime.date(2020, 4, 19), datetime.date(2020, 4, 20), datetime.date(2020, 4, 21), datetime.date(2020, 4, 22), datetime.date(2020, 4, 23), datetime.date(2020, 4, 24), datetime.date(2020, 4, 25), datetime.date(2020, 4, 26), datetime.date(2020, 4, 27), datetime.date(2020, 4, 28), datetime.date(2020, 4, 29), datetime.date(2020, 4, 30), datetime.date(2020, 5, 1), datetime.date(2020, 5, 2), datetime.date(2020, 5, 3), datetime.date(2020, 5, 4), datetime.date(2020, 5, 5), datetime.date(2020, 5, 6), datetime.date(2020, 5, 7), datetime.date(2020, 5, 8), datetime.date(2020, 5, 9), datetime.date(2020, 5, 10), datetime.date(2020, 5, 11), datetime.date(2020, 5, 12), datetime.date(2020, 5, 13), datetime.date(2020, 5, 14), datetime.date(2020, 5, 15), datetime.date(2020, 5, 16), datetime.date(2020, 5, 17), datetime.date(2020, 5, 18), datetime.date(2020, 5, 19), datetime.date(2020, 5, 20), datetime.date(2020, 5, 21), datetime.date(2020, 5, 22), datetime.date(2020, 5, 23), datetime.date(2020, 5, 24), datetime.date(2020, 5, 25), datetime.date(2020, 5, 26), datetime.date(2020, 5, 27), datetime.date(2020, 5, 28), datetime.date(2020, 5, 29), datetime.date(2020, 5, 30), datetime.date(2020, 5, 31), datetime.date(2020, 6, 1), datetime.date(2020, 6, 2), datetime.date(2020, 6, 3), datetime.date(2020, 6, 4), datetime.date(2020, 6, 5), datetime.date(2020, 6, 6), datetime.date(2020, 6, 7), datetime.date(2020, 6, 8), datetime.date(2020, 6, 9), datetime.date(2020, 6, 10), datetime.date(2020, 6, 11), datetime.date(2020, 6, 12), datetime.date(2020, 6, 13), datetime.date(2020, 6, 14), datetime.date(2020, 6, 15), datetime.date(2020, 6, 16), datetime.date(2020, 6, 17), datetime.date(2020, 6, 18), datetime.date(2020, 6, 19), datetime.date(2020, 6, 20), datetime.date(2020, 6, 21), datetime.date(2020, 6, 22), datetime.date(2020, 6, 23), datetime.date(2020, 6, 24), datetime.date(2020, 6, 25), datetime.date(2020, 6, 26), datetime.date(2020, 6, 27), datetime.date(2020, 6, 28), datetime.date(2020, 6, 29), datetime.date(2020, 6, 30), datetime.date(2020, 7, 1), datetime.date(2020, 7, 2), datetime.date(2020, 7, 3), datetime.date(2020, 7, 4), datetime.date(2020, 7, 5), datetime.date(2020, 7, 6), datetime.date(2020, 7, 7), datetime.date(2020, 7, 8), datetime.date(2020, 7, 9), datetime.date(2020, 7, 10), datetime.date(2020, 7, 11), datetime.date(2020, 7, 12), datetime.date(2020, 7, 13), datetime.date(2020, 7, 14), datetime.date(2020, 7, 15), datetime.date(2020, 7, 16), datetime.date(2020, 7, 17), datetime.date(2020, 7, 18), datetime.date(2020, 7, 19), datetime.date(2020, 7, 20), datetime.date(2020, 7, 21), datetime.date(2020, 7, 22), datetime.date(2020, 7, 23), datetime.date(2020, 7, 24), datetime.date(2020, 7, 25), datetime.date(2020, 7, 26), datetime.date(2020, 7, 27), datetime.date(2020, 7, 28), datetime.date(2020, 7, 29), datetime.date(2020, 7, 30), datetime.date(2020, 7, 31), datetime.date(2020, 8, 1), datetime.date(2020, 8, 2), datetime.date(2020, 8, 3), datetime.date(2020, 8, 4), datetime.date(2020, 8, 5), datetime.date(2020, 8, 6), datetime.date(2020, 8, 7), datetime.date(2020, 8, 8), datetime.date(2020, 8, 9), datetime.date(2020, 8, 10), datetime.date(2020, 8, 11), datetime.date(2020, 8, 12), datetime.date(2020, 8, 13), datetime.date(2020, 8, 14), datetime.date(2020, 8, 15), datetime.date(2020, 8, 16), datetime.date(2020, 8, 17), datetime.date(2020, 8, 18), datetime.date(2020, 8, 19), datetime.date(2020, 8, 20), datetime.date(2020, 8, 21), datetime.date(2020, 8, 22), datetime.date(2020, 8, 23), datetime.date(2020, 8, 24), datetime.date(2020, 8, 25), datetime.date(2020, 8, 26), datetime.date(2020, 8, 27), datetime.date(2020, 8, 28), datetime.date(2020, 8, 29), datetime.date(2020, 8, 30), datetime.date(2020, 8, 31), datetime.date(2020, 9, 1), datetime.date(2020, 9, 2), datetime.date(2020, 9, 3), datetime.date(2020, 9, 4), datetime.date(2020, 9, 5), datetime.date(2020, 9, 6), datetime.date(2020, 9, 7), datetime.date(2020, 9, 8), datetime.date(2020, 9, 9), datetime.date(2020, 9, 10), datetime.date(2020, 9, 11), datetime.date(2020, 9, 12), datetime.date(2020, 9, 13), datetime.date(2020, 9, 14), datetime.date(2020, 9, 15)]\n"
"2020-01-22 00:00:00\n",
"<class 'pandas._libs.tslib.Timestamp'>\n"
] ]
},
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f9e55550f98>]"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"print(type(raw_data.keys()[4]))\n", "date1=[]\n",
"a=pd.to_datetime(raw_data.keys()[4])\n", "date2=[]\n",
"print(a)\n", "belgium_cumulated_list = []\n",
"print(type(a))\n" "\n",
"for i in range(4, len(raw_data.keys())):\n",
" date1.append(raw_data.keys()[i])\n",
" date2.append(pd.to_datetime(raw_data.keys()[i]).date())\n",
"print(date2)\n",
"b=0\n",
"for date in date1:\n",
" raw_data.loc[raw_data['Country/Region'] == 'Belgium'][date].values[0]\n",
" belgium_cumulated_list.append(a)\n",
" \n",
"plt.plot(date2, belgium_cumulated_list);\n",
" \n",
"\n"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
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