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mooc-rr
Commit cfccbffb authored by 75c80a38547344d7d76c3a6fb64a936b's avatar 75c80a38547344d7d76c3a6fb64a936b
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module3/exo1/analyse-syndrome-grippal.ipynb
View file @ cfccbffb
...@@ -2221,8 +2221,10 @@ ...@@ -2221,8 +2221,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 13,
"metadata": {}, "metadata": {
"scrolled": true
},
"outputs": [ "outputs": [
{ {
"ename": "TypeError", "ename": "TypeError",
...@@ -2231,7 +2233,7 @@ ...@@ -2231,7 +2233,7 @@
"traceback": [ "traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\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", "\u001b[0;32m<ipython-input-13-0966cd984262>\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",
"\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;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;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;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",
...@@ -2245,6 +2247,47 @@ ...@@ -2245,6 +2247,47 @@
"sorted_data['inc'].plot()" "sorted_data['inc'].plot()"
] ]
}, },
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"sorted_data['inc']=sorted_data['inc'].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0e52e122e8>"
]
},
"execution_count": 15,
"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": [
"sorted_data['inc'].plot()"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -2254,9 +2297,32 @@ ...@@ -2254,9 +2297,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0e50c60d30>"
]
},
"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": [
"sorted_data['inc'][-200:].plot()" "sorted_data['inc'][-200:].plot()"
] ]
...@@ -2291,7 +2357,7 @@ ...@@ -2291,7 +2357,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -2311,7 +2377,7 @@ ...@@ -2311,7 +2377,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -2335,9 +2401,32 @@ ...@@ -2335,9 +2401,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0e50bf6a58>"
]
},
"execution_count": 19,
"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='*')"
] ]
...@@ -2351,9 +2440,59 @@ ...@@ -2351,9 +2440,59 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 20,
"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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"yearly_incidence.sort_values()" "yearly_incidence.sort_values()"
] ]
...@@ -2368,9 +2507,32 @@ ...@@ -2368,9 +2507,32 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0e50bc9e10>"
]
},
"execution_count": 21,
"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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