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Commit dd21e15f authored by 2582446175ee82f7405c2fa41d1f47f7's avatar 2582446175ee82f7405c2fa41d1f47f7
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v2

parent 500ae52b
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Showing with 40 additions and 17 deletions
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ dd21e15f
...@@ -2226,24 +2226,30 @@ ...@@ -2226,24 +2226,30 @@
"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 0x7f5740833208>"
"traceback": [ ]
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", },
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "execution_count": 10,
"\u001b[0;32m<ipython-input-10-0966cd984262>\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'].plot()" "sorted_data['inc'].astype(int).plot()"
] ]
}, },
{ {
...@@ -2255,9 +2261,26 @@ ...@@ -2255,9 +2261,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-495b7092a92e>\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",
"\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",
"\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",
"\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",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [ "source": [
"sorted_data['inc'][-200:].plot()" "sorted_data['inc'][-200:].plot()"
] ]
......
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