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Commit 06430d44 authored by 2582446175ee82f7405c2fa41d1f47f7's avatar 2582446175ee82f7405c2fa41d1f47f7
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v3

parent dd21e15f
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Showing with 128 additions and 26 deletions
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 06430d44
...@@ -1051,7 +1051,7 @@ ...@@ -1051,7 +1051,7 @@
} }
], ],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data = pd.read_csv(data_file, skiprows=1)\n",
"raw_data" "raw_data"
] ]
}, },
...@@ -2228,7 +2228,7 @@ ...@@ -2228,7 +2228,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f5740833208>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f9f9d8ebb38>"
] ]
}, },
"execution_count": 10, "execution_count": 10,
...@@ -2265,24 +2265,30 @@ ...@@ -2265,24 +2265,30 @@
"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 0x7f9f9b629be0>"
"traceback": [ ]
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", },
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "execution_count": 11,
"\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", "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'][-200:].plot()" "sorted_data['inc'][-200:].astype(int).plot()"
] ]
}, },
{ {
...@@ -2315,7 +2321,7 @@ ...@@ -2315,7 +2321,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -2335,7 +2341,7 @@ ...@@ -2335,7 +2341,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -2343,7 +2349,7 @@ ...@@ -2343,7 +2349,7 @@
"yearly_incidence = []\n", "yearly_incidence = []\n",
"for week1, week2 in zip(first_august_week[:-1],\n", "for week1, week2 in zip(first_august_week[:-1],\n",
" first_august_week[1:]):\n", " first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n", " one_year = sorted_data['inc'].astype(int)[week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n", " assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n", " yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n", " year.append(week2.year)\n",
...@@ -2359,9 +2365,32 @@ ...@@ -2359,9 +2365,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9f9b5bcd68>"
]
},
"execution_count": 14,
"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='*')"
] ]
...@@ -2375,9 +2404,59 @@ ...@@ -2375,9 +2404,59 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 15,
"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": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"yearly_incidence.sort_values()" "yearly_incidence.sort_values()"
] ]
...@@ -2392,9 +2471,32 @@ ...@@ -2392,9 +2471,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9f9b585978>"
]
},
"execution_count": 16,
"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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