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Commit 1204f687 authored by be338296bfc44819a17966a724334dd4's avatar be338296bfc44819a17966a724334dd4
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Transformation des données de la colonne inc en valeurs numériques pour que ça marche

parent 14eff789
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Showing with 148 additions and 41 deletions
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 1204f687
...@@ -2201,27 +2201,34 @@ ...@@ -2201,27 +2201,34 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "TypeError", "data": {
"evalue": "Empty 'DataFrame': no numeric data to plot", "text/plain": [
"output_type": "error", "<matplotlib.axes._subplots.AxesSubplot at 0x7f80e9251358>"
"traceback": [ ]
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", },
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "execution_count": 28,
"\u001b[0;32m<ipython-input-25-0966cd984262>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "metadata": {},
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "output_type": "execute_result"
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", },
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", {
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "data": {
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "image/png": 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\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" "text/plain": [
] "<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
"sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce')\n",
"sorted_data['inc'].plot()" "sorted_data['inc'].plot()"
] ]
}, },
...@@ -2234,24 +2241,30 @@ ...@@ -2234,24 +2241,30 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"ename": "TypeError", "data": {
"evalue": "Empty 'DataFrame': no numeric data to plot", "text/plain": [
"output_type": "error", "<matplotlib.axes._subplots.AxesSubplot at 0x7f80e915afd0>"
"traceback": [ ]
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", },
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "execution_count": 29,
"\u001b[0;32m<ipython-input-26-495b7092a92e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "metadata": {},
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "output_type": "execute_result"
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", },
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\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[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\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 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", {
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\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 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "data": {
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "image/png": 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\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot" "text/plain": [
] "<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
} }
], ],
"source": [ "source": [
...@@ -2288,10 +2301,8 @@ ...@@ -2288,10 +2301,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 31,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
...@@ -2310,7 +2321,7 @@ ...@@ -2310,7 +2321,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 32,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -2334,9 +2345,32 @@ ...@@ -2334,9 +2345,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 33,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f80e90d2828>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"yearly_incidence.plot(style='*')" "yearly_incidence.plot(style='*')"
] ]
...@@ -2350,9 +2384,59 @@ ...@@ -2350,9 +2384,59 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 34,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"2021 743449\n",
"2014 1600941\n",
"1991 1659249\n",
"1995 1840410\n",
"2020 2010315\n",
"2022 2060304\n",
"2012 2175217\n",
"2003 2234584\n",
"2019 2254386\n",
"2006 2307352\n",
"2017 2321583\n",
"2001 2529279\n",
"1992 2574578\n",
"1993 2703886\n",
"2018 2705325\n",
"1988 2765617\n",
"2007 2780164\n",
"1987 2855570\n",
"2016 2856393\n",
"2011 2857040\n",
"2023 2873501\n",
"2008 2973918\n",
"1998 3034904\n",
"2002 3125418\n",
"2009 3444020\n",
"1994 3514763\n",
"1996 3539413\n",
"2004 3567744\n",
"1997 3620066\n",
"2015 3654892\n",
"2024 3670417\n",
"2000 3826372\n",
"2005 3835025\n",
"1999 3908112\n",
"2010 4111392\n",
"2013 4182691\n",
"1986 5115251\n",
"1990 5235827\n",
"1989 5466192\n",
"dtype: int64"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"yearly_incidence.sort_values()" "yearly_incidence.sort_values()"
] ]
...@@ -2367,9 +2451,32 @@ ...@@ -2367,9 +2451,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f80e903b7b8>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [ "source": [
"yearly_incidence.hist(xrot=20)" "yearly_incidence.hist(xrot=20)"
] ]
......
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