From 821bf9c644b1093e939f1f52cd0cad794e05e756 Mon Sep 17 00:00:00 2001 From: fc4409bf899a7e72cc05b9f79ff24087 Date: Tue, 10 Oct 2023 15:57:43 +0000 Subject: [PATCH] exo pas fini --- module3/exo1/analyse-syndrome-grippal.ipynb | 125 +++++++++++--------- 1 file changed, 72 insertions(+), 53 deletions(-) diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 9bc0884..0e5cc86 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1024,7 +1024,7 @@ "[2031 rows x 10 columns]" ] }, - "execution_count": 6, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1043,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1105,7 +1105,7 @@ "1794 FR France " ] }, - "execution_count": 7, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1123,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2088,7 +2088,7 @@ "[2030 rows x 10 columns]" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -2118,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2148,7 +2148,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -2173,7 +2173,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -2199,30 +2199,43 @@ "Un premier regard sur les données !" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "La j'ai un bug et je sais pas pourquoi\n" + ] + }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 48, "metadata": {}, "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\u001b[0m in \u001b[0;36m\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", - "\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 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\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" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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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 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\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" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - " sorted_data['inc'][-200:].plot()" + "sorted_data[-200:].plot()" ] }, { @@ -2288,11 +2307,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ - "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] @@ -2308,7 +2327,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -2332,7 +2351,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -2342,7 +2361,7 @@ "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[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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 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"\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", 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