_doc/notebooks/population/seance5_cube_multidimensionnel_enonce.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Cube multidimensionnel - énoncé"
   ]
  },
  {
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   "execution_count": 1,
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   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
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       "<b>Plan</b>\n",
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   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import pyensae\n",
    "from pyquickhelper.helpgen import NbImage\n",
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Représentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le module [pandas](http://pandas.pydata.org/) manipule des tables et c'est la façon la plus commune de représenter les données. Lorsque les données sont multidimensionnelles, on distingue les coordonnées des valeurs :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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5khJt6M/wPGDs5KkzjEuwHaUEJ2uCuFP77P10zJFfUXgsIFtSog39GZ4HjJ08\ndYZxCbajlOBkTRD3ap8b9Y/LXvBzIo8CpExKtKE/w/OAsZOnzjAuwXaUEpysCeKu7fP20b8QK28V\nEztjLrajlOBkTRBmn6rRvxArbxUTO2MutqOU4GRNEGafqlG+EP/x638a5MEzjMOYfSpBap9/cwGN\nLSx2gxjJmuBcWFhY3FGgF3aD91i4gP/P4lFD+UJ4/8B/rBeLD99iepjwKgnhQph9qg6zT81h9mkx\nN0x4lYTZ5z2E2afmMPu0mBsmvErC7PMewuxTc5h9WswNE14lYfZ5D2H2qTnMPi3mhgmvkjD7vIcw\n+9QcZp8Wc8OEV0mYfd5DmH1qDrNPi7lhwqskzD7vIcw+NYfZp8XcMOFVEqKF+Pff/wdIN1XiuDSp\nlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NbImage(\"cube1.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Dans cet exemple, il y a :\n",
    "    \n",
    "* 3 coordonnées : Age, Profession, Annéee\n",
    "* 2 valeurs : Espérance de vie, Population\n",
    "\n",
    "On peut représenter les donnés également comme ceci :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IucSxWE4vq9PrYU/fJat7/YH6WbwlHAg4xBP9OYSWWaQrhSUI9U/UHzXGtDpp4VpJvnq3\n9J1tXWCd1TEzcCAGs1x9/ulLeElFwX0o98IExTvfv6Fy7AugsArI25/evwk2SSAUVq+QeENVwhrV\nmp+/VKxuQxMly+Rxzeo292ghFGQRfBwht6/+NyRmdTvgMsh3NdLv+aQOXkGKGpQ5hFOWJBbyPBHo\n7+WyUrC6rGmsM7tX1hZyU57f8VImdsH8FAU8ntAui/QjKnv+pOJHQE5oHAd+CUL04zEsWR12oXgO\n48tvwdLfcKCXC6Ot72zrAuOxiYGan1QuuItbnYc/24UyC6ioYcnMWYPykmCqkDul849MJcOXQFTN\n1XxCkGSYglSUXRPMKm4mLKk0R4ZaybaxCf3YJGbKV76/uUQoxiK8TqytZGZmdTvgMiBbOtIrCFUW\nlIzcsVBo1NNeYAerUyjVStbmI4o+ejUsxbVUDyc0yyL7iH2PKJh9m9zTj8ewZHXpFbRyXAPwKQvu\nvqBL39nWBdZYnS8j3hNGotKJAijHKb4cdJR6/AGIC/RKi5S+sGKo/FSBx6yk+2gMmfnlrE1YULRV\njG9tQjs5SRfxQ9GhuUgoxSJA8sAhJp3TrG4HXAZkSwdqWoCyQr2k+KxNeYzVSU2UtRXqJ7qp44pW\n51B5qitcghD9eAyLVqf3KCKfMlxyFLv7zrYu0NvYjcd5FOhSMWk2SCbHJVtFJJ0VAr3Gkr7LfFi1\nN0W5PmFOzYcW4okndW1zE8rhyUeUY2uP5gKhFIssxMFErDw9mNXtgcuAbGmkdzV+VkNjI4/VMqKw\nl9Vp/kQlSdZWqD+kXcQVrC4nDimd07LcD3mqW8gnDp6xxOoKZWQkVbul72zrAqusjkkkF8RKq8vV\nUCJVe8PqNL3ebnXlvtcnzOkzVEyUW932JuK9sRWtUb3LRaw1h4RSLILErgHWbcznYFa3CS4DsqU9\nqw89KCKHW11Uag4qSc1Wd5fv5EqEfFikN/1oxF6zj4Ry/BdCiH48hn5qSlan0K/V+F6htNhwSWN8\nS9/Z1gVWWt3CTCjJLW1WV9FW2eqSnjIcaHV1g5Ox1eo2NOHoT9t4hFJ73drl24TbWQQZjv6A1fp9\nDmZ1m+AyyHd1FIIyohJ1sTqha0JuUKeyU6bURF9bjdVxfRyBkA+L9CZOVug1+5iI88XP9GNaHVtC\nuHSVBZm9ft/Sd7Z1gS1Wh/S3kJdUJWsJJcNZoWgAnEygE0XTQCmdpXyadb86YU7W8URuddubWEh6\nmtkeK9CrOUe4nUUW+oae3rz1hqfMXaBZ3Q64DPJdXdrtC1FK+EO0Xhh5y+qEMPEvBVGY8mJSE6Wx\nyQiyJu2TCemwSG/y0S4MQtEGIMgiB7DN6kQXCisKmC+qDX1nWxfota/xMZ8JKMqcENAgf6FyPOSt\nfZHjyQU6UDWnzVZ3O5/qhAXxiMO7gBW25LDOaF2FeWzu8k3C3SwC9K0v1o5zVH4Xala3Ay6DbFcX\nd7sn14j4apE6mZMVolbcuhLDm9J0KUQAMYgpkSZQ0TZbXaqBiR3L/ERCLiyymcsYsl6HMaRxbe7k\njERClEUOYNnqvr3WnnV44ZB/XoPsaXvf2dYFVlid0yZqY0Iu0dIcUjBIXib3oSSTWmcM0qu4T2gy\nGpveYnXpdSjV5SyZyoQlw42p5jgggA1jUqD3VPU/13MUXd7ZnGp17KgaRrWwqMzqdsBlQHc1SkBZ\n7kUBND8OWkN0Jg9NbSne/bioGCmGZikhW6E6tWJ1lXWeR2iYRTaTPC4w8K4V5k47FR1pdeWEkxXV\npqquKES2Elr7zrYuEJ/HNQSpohqdQITMq+fLZzeKLfTSrIBqnxTowIbbpZgq1YZjFgO5t6pFhXrN\nbz8tg5l3bWsTC4N1AZSDlDaSu5qTVofLg0506Htqd2WZlc9/wxGSRcupw+FWh/ufPOoKasc+ri/C\nLWgB/taIGqG/0TeRW1TmTK7Yoo+0oUarc4wngwCe23mExllkF6Xil+aUlSyPAFxkkV6ssTplssqr\nVFaov6Vo6TvbusAKqwMSPXVgmkisJZNRzaschXeyE4Yq0JFZE67RpQs0JZJPvCvFRbXxbIeQN95M\nuMCsZl+tr0p2bWsTZAb5U4VjaSS3Nwdl6cfCG1GsP74MMKvrA5eB2NjG8zn+RMy8VKDvbOt2Ysla\njBfkYavoAejUowVmdZfl+BMx81I5TKTM6ibiYavoAejUowVmdZfl+BMx81I5TKTM6ibiYavoAejU\nowVmdZfl+BMx81I5TKTM6ibiYavoAejUowVmdZfl+BMx81I5TKTM6ibiYavoAejUowVmdZfl+BMx\n81KZWaSMvTjzKnLq0QKzusty/ImYeanMLFLGXpx5FTn1aIFZ3WU5/kTMvFRmFiljL868ipx6tODp\ne29ABgaDwWAwHAq0nDrYqe6yHH8iZl4q0Hf2lGo0tnLmVeTUowVmdZfl+BMx81KZWaSMvTjzKnLq\n0QKzusty/ImYeanMLFLGXpx5FTn1aIFZ3WU5/kTMvFRmFiljL868ipx6tMCs7rIcfyJmXiozi5Sx\nF2deRU49WmBWd1mOPxEzL5WZRcrYizOvIqceLTCruyzHn4iZl8rMImXsxZlXkVOPFpjVXZbjT8TM\nS2VmkTL24syryKlHC8zqLsvxJ2LmpfKwIuX/76RLv2luPJUPu4o60KlHC8zqLsvxJ2LmpfKwImVW\nNxAfdhV1oFOPFpxvdZ8/uK0CeP07vzQY//rxlcvzh1/+EpcegZA6i+zk7+/ccCS8+8wKBP76ywss\n4vHhGyuAhEss0p/fXi8plCcRZzmisCx5sVKnGvrOtu4///n+03N/U4Y3X1mx+7K31f356Zmr0H51\naAth4Fikkn98fOlGPeL5pz9EmcEJWaPl1OF0q0PpcSipwE3+/fMP7v6i1HaiWV2kEPoAYQyFkq9+\n/jUrthDiLNKV8aEKoE8iN28EL4zrTeLFj3+TYq19Z1s3uIiGgZRoo9Whtr79jcXN6vYQBo5FbhMH\nXOLBTuqQMVpOHc62ukVcfvjl58XwtnrVSVb34IQRYpHtXEQ8ezQJjyy5jgd3IR4Q7EQxG4iySC9G\nc/rwzR+zNKtTuqDkHw2M9pTUH4q1951t3eAimejHp+9nH7/H4F3Z2+qMOwgjyiK3+fU93JW9Klgi\nDg81O5AvWk4dzrU6rxpOC7wu6M/aN2lWV0MYIRbpS9RxOgtoHuyoF97pifmCGIt04tIiNle0OiV/\nYDC25GG+U+wAFzqV/K+972zrqlYHRJMY5WBnVjcQYURZZBvDE9UjHewgXbScOpxqdd6ivDp4ocnP\nBJT07RMgSFV4Es8R9cXfVXqrVpChAJGMrG2JLKoXHtjZXbzOzS9p9xNaZ5G+lA8cpTnFktwtDs9w\nYcnqcIGJCSp4WMHq4u0b+s62bsnqwkM3l6Evb5dwhOKFS4U+nr+2Et//FQwM76JxtST/ljE7g8ZD\nQ4ZYQ9E7azvo7806OMvrUOhqFsGhZt3H2Vn70rewxkYmpIuWU4czrS4XHV1ByCWOxXJ6WZ1eD3v6\nLlnd6w/Uz+It4UDAIZ7ozyG0zCJdKSxBqH+i/qgxptVJC9dK8tW7pe9s6wbhLlgdFfri1y1MzpYK\nn3/6Et6CUuRvRAt+U2N1upORA9wGq2vq4NP7N8wUHaZwO+gni+DzATk9V52n5RobnpAvWk4dTrQ6\nv+eTOngFKWpQ5hBOWZJYyPNEoL+Xy0rB6rKmsc7sXllbyE15fsdLmdgF81MU8HhCuyzSj6js+ZOK\nHwE5oXEc+CUI0Y/HsGR12IXiOYwvvwVLf8OBXi6Mtr6zrRuEm2u0EC9fDEDtIZ6rpC0tICoWTku0\nIc1vgLVWl+Wsvg0rC65semcHQ/ChhHsboZcsEruPzwQ4gzeM3y+JYb4PriIkjJZTh/OszisIVRaU\njNyxUGjU015gB6tTKNVK1uYjij56NSzFtVQPJzTLIvuIfY8omH2b3NOPx7BkdekVtHJcA/ApC+6+\noEvf2dYNIpWpUnyJl04zeEjKjGQhmgERLFQ9IWGlkqLOGqtTqHSkweraO8irLdZwNUInWQRIHjXk\niGl8zOGCjNFy6nCa1WlagLJCvaT4rE15jNVJTZS1FeonuqnjilbnUHmqK1yCEP14DItWp/coIp8y\nXHIUu/vOtm4SbgEqVSsP4MJOFMvxLJQUYrfR6rzCbrS6Hh2sSfIKhE6yyEJ0OMSN060fq9Vv8oYk\n5IyWU4ezrM67Gj+robGRx2oZUdjL6jR/opIkayvUH9Iu4gpWlxOHlM5pWe6HPNUt5BMHz1hidYUy\nMpKq3dJ3tnWj6ORgeo0qpguTfzznr/LOsLp4+iTYZnVdOljozuUInWQRJE4cQJl9Qj9Qt459QxLS\nRsupw0lWV33oQRE53OqiUnNQSWq2urt8J1ci5MMivelHI/aafSSU478QQvTjMfRTU7I6hX6txvcK\npcWGSxrjW/rOtm5ZuCnHs7qkqgxmdYcTOskiSHwn6VA+rvlRUiflAQiJo+XU4RyrQyEoIypRF6sT\nuibkBnUqO2VKTfS11Vgd18cRCPmwSG/iZIVes4+JOF/8TD+m1bElhEtXWZDZ6/ctfWdbtyzcGbu8\nwMRD2M2SFVaHVWVvybxdbbM6e4HZQOgkiyz03X9689YbnjoOWOZBfQ4IuaPl1OEMqyvt9oUoJfwh\nWi+MvGV1Qpj4l4IoTHkxqYnS2GQEWZP2yYR0WKQ3+WgXBqFoAxBkkQPYZnWiC4UVBcwX1Ya+s61b\nFu6MKPeZtXgGgUtP8RgRtlEsyU8AeDhYsTr1ELbL6to7aFaXkTzH4OyIYceRfFyfA0L6aDl1OMHq\nirvdk2tEfLVInczJClErbl2J4U1puhQigBjElEgTqGibrS7VwMSOZX4iIRcW2cxlDFmvwxjSuDZ3\nckYiIcoiB7Bsdd9ea886vHDIP69B9rS972zrloU7Z3xhmJlBEC81CEjxIH+5E6A+kmCIAGhJ5iJS\nTGOjeUcU1/RkFW7ooFkdITuIh8GUjzUP7XNA6AFaTh2OtzqUgLLciwJofhy0huhMHpraUrz7cVEx\nUgzNUkK2QnVqxeoq6zyP0DCLbCZ5XGDgXSvMnXYqOtLqygknK6pNVV1RiGwltPadbd2ycAuSb2Jy\nsHuxwmfobRn4AS4aDMXbT4uTUc8QLtKWTESsQVQIbOygWV2gcsjGU3IYjfCxgMfxP0gWLacOh1sd\n7n/yqCuoHfu4vgi3oAX4WyNqhP5G30RuUZkzuWKLPtKGGq3OMZ4MAnhu5xEaZ5FdlIpfmlNWsjwC\ncJFFerHG6pTJKq9SWaH+lqKl72zrloVbZTycIbQvt0iFmXmUPMCX9/Bp+FZoec1FssrdjcuJUHSE\numk6nJVsqbGDSrzUzesQOkk/Ft794kj6ATSr6waXgdjYxvM5/kTMvFSg72zrHsCSExgvwlNW0aB0\n6tECs7rLcvyJmHmpnCJSZnUX5ymraFA69WiBWd1lOf5EzLxUThEps7qL85RVNCiderTArO6yHH8i\nZl4qp4iUWd3FecoqGpROPVpgVndZjj8RMy+VU0TKrO7iPGUVDUqnHi0wq7ssx5+ImZfKzCJl7MWZ\nV5FTjxaY1V2W40/EzEtlZpEy9uLMq8ipRwvM6i7L8Sdi5qUys0gZe3HmVeTUowVP33sDMjAYDAaD\n4VCg5dTBTnWX5fgTMfNSgb6zp1SjsZUzryKnHi0wq7ssx5+ImZfKzCJl7MWZV5FTjxaY1V2W40/E\nzEtlZpEy9uLMq8ipRwvM6i7L8Sdi5qUys0gZe3HmVeTUowVmdZfl+BMx81KZWaSMvTjzKnLq0QKz\nusty/ImYeanMLFLGXpx5FTn1aIFZ3WU5/kTMvFRmFiljL868ipx6tMCs7rIcfyJmXiozi5SxF2de\nRU49WmBWd1mOPxEzL5UBRQp/n/pxfod6L/FX0R/4x8oHXEWnEfqOllMHs7rLcvyJmHmpDCdSj6/7\nzTSre2RC39Fy6nCS1f3+zq0pDT/88hcvfBL/+vHVfRM4mNA3FtlJPonvPrMC//73119e4EWGVz//\nKgqfYnXfXi/tl2cZl0HE699ZAc/KYmIEPnxjBZBwiW1d4Je3/iaJw3+IZ2l6sp/7OdnqsLmegwzV\nsUgl+Up7wKM8ZI2WUwezuoexur9//sHlqxiMTijLItvJhT6CKf5QVvf5g2/bQZ/lwrLkhSuLlUap\n1He2dYF3s7qv76GNZx+/8/i1OafVYRoSD3a6hYzRcupwqtWVnnCNNby71WXTh6clpuNodZUTDSVZ\npBdxrFwmPiXN6pQuBHd88ePfrcW0YPBIpXWIsq0L9Fb35iuPGw/hyVZ3ANVVdINLr7M1tjzoAB7r\nWQcSRsupg1ndw/CeVqcRdTzLZxCrW9LAxIpWp+WfTmaxC5XFgiMWjrli1iDGti7QrO5Uzml1GvF1\nwkO9xoR80XLqMJTV+efiRSyyd0HaI3kUEQ8hJbyA0jppjjAeCBjCQ/2OJJV3WXW1hYNFDp45I5Rg\nkb7UrBf7e2+ro/QpydnBQ5hIFbuQT/fNYrjCxfyGUcrOf44QY1sXWG1133967koiMoX67Q1Enn/6\nA/6dv6oqVJtXpRRLFcb3q+Txn98uTgbL7d5OsnxK7/F8+QhZ7GbCJWY1u7uKVtfWRPkvV7GecDsZ\nioybeySsDs9nbNBYGgof8Y9vIV+0nDqMaHUf/HN0BiZVKEAUmcpktkGQ6TKxGSR3Jopc+yqS1P1J\nz+FGbSNaHdd6Gnxkq2MWXlms3HFcinyyIMS2LrDO6pgZOBCDWa4+//TFi1cO7kO5FyYo3vn+DZVj\nXyC8+OKQtz+9fxNskkDYmF4h8YaqhDWqNT9/qVjdhiZKlsnjmtVt7tFCKMgi8nxWYWPohfYCsw0u\ng3xXA/EtkEAuDSgrDukpOATJc7E8T0D9RHM1eQrmR4K+GNEgdBThMepXMoDVJJd7M4XFtDPVq65N\n6/U6oSyL9GN4JsgzTPEMRUuGayxyAEtWh6kWz2G5h90q5qdMthIXHr8EIbZ1gfHYxEDNTyoX3MWt\nzsOf7UKZBdRdsGTmrEF5STBVyKXQ+UdmV5hbpuZqPiFIMkxBKsquCWYVNxOWVJojQ61k29iEfmwS\nM+Ur399cIhRjEV4n1iYPx5HhTJnNxQMQUkbLqcOQVsfUHB0oKWbpZRFyKa9c9fWkyn1z69VKoaxN\nUqMUxIba7m11IdUAbfxVq3NQJwviLHIAS1aX1mS2CHHk0zjXFZMzG9jb6nyZ8gM4Kp0oIJ7cl4OO\nUo8/AHGBXmmR0hdWDJWfKvCYlXQfjWFFcGsTFhRtFeNbm9BOTn7A5VB0aC4SSrEIkDxwlI5rYVIC\n6iZ3LELaaDl1ONXqcmOTLOmFjxPdD0KjVhi1SUeyCl5ti9VVJKlwV22jWZ0DP9VJxrtkH+9sdXqP\nIvg6KeCm1RUuQYhtXaC3sRuP8yjQpWLSbJBMjku2ikg6KwR6jSV9l/mwakuinFidMKfmQwvxxJO6\ntrkJ5fDkI8qxtUdzgVCKRRbiYCKUpwefSQ471bXCZUC2tGdnq2N+lql/8WCBKFudPEuhu2RNNyQJ\n1HyX3ttQ272tLmf8NrTa7eTBDoIscgBXrA7IVwskqY3zzWKledxyqrv55iqTSC6IJWthip+roUSq\nlgs0o6bX262u3Pf6hDl9hoqJcqvb3kS8N7aiNap3uYi15pBQikWQ2DWAshI4Y+GHcjvIFy2nDo9q\ndQszAQqVY/BWW0BZLVc0j1yjq5OMZsBB723o8lhW5+iTvD3U2nHZEYIscgD9nMoRLrKULWNerDwU\nuAz46oUQ27rASqtbmAkluaXN6iraKltd0lOGA62ubnAytlrdhiYc/Wkb3ULtdWuXbxNuZxFkOPoD\n6ur3ue1K5mRCtmg5dXhoq0P6yuPxolKqZLXeS169WBwFUUrmdpIsq4VSdhu6PJ7V4ZNBoyskHp8h\nsNXqSjPCyIoVhwJnTZx9Ica2LrDF6pD+FvI8XrKWUDKcFYoGwMkEOlE0DfTyfYTV1SfMyTqeyK1u\nexMLSU8z22MFejXnCLezyELf0NObt97wlLkTxPHfk8zJhGzRcupwBatjBUriIsiq9Wq10opnZZKo\nfXmXpexW1ub4qKe6cLqVxSDIIgewzeoqF48sVrixaIEQZFsX6LWv8cmaCSjKnNCsIH+hcjzk3X5n\nxQU6UDWnzVZ3O5/qhAXxiMO7gBW25LDOaF2FeWzu8k3C3SwC9K0v1o5zVPEulC+P8QnZouXU4RGt\nzskH1Q6hMijBXHfcV3HSZtbNSbIhSYfkTCErJYebtS0UXyWuE4qyyGYu08faDR1kXWZOvPqVHoRZ\n5AD6POUIu/HUHkRE4cpisadkBPBkr00ZRNnWBVZYnRMvamNCLlGzHFIwSF4m96Ekk1pnDNKruE9o\nMhqb3mJ16XUo1eUsmcqEJcONqeY4IIANY1Kg91T1P9dzFF3e2ZxqdeyoGkY1LqpljbHc1OUxOiFd\ntJw6nGp1KoiONLpIjkyP4t+Cc9DKhZ0U7sofyavNqSGHitpSMEIRUEoowSKbWZ4+mrk+Lwv0VOEC\ni/TiynqLVoRPSALsWaeyWFNJIMTZ1gXi87iGIFVUoxOIO3r1fPnsRrGFXpoVUJ2VAh3YcLsUbqXa\ncMxiIPdWtahQr/ntp2Uw865tbWJhsC6AcpDSRnJXc9LqcHnQiQ59x3bLa6yqxXEIGaPl1OERrS5G\nIuQtQKG8/GAhqi3+IYk8MtaZU+Z27pZlHOi9LbUBaYbaOYkSirDILsrB0V6lSrlXhd4TrrJIL9ZY\nnbJCtB5VF1vIRqk8R3CRbV1ghdUBiZ46MIUi1pLJqOZVjsI72QlDFejIrAnX6NIFmhLJJ96V4qLa\neLZDyBtvJlxgVrOv1lclu7a1CTKD/KnCsTSS25uDsvRj4Y0o1p9eBvBBVo15dELWaDl1OMnqHoHe\nXYQ0B/FakewxCTmzyGgcP8PjCH1nW7cTS9ZivCAPW0UPQKceLTCrC/QnMOUxHB/qzeq6c/wMjyP0\nnW3dTjSrm4iHraIHoFOPFpjVBeLLRv4uMbyRk+8YRyckzSKjcfwMjyP0nW3dTjSrm4iHraIHoFOP\nFpjVRa78VYXyx+LjE9JmkdE4fobHEfrOtm4nmtVNxMNW0QPQqUcLzOpyyj+bvPXXH8MScmeR0Th+\nhscR+s62biea1U3Ew1bRA9CpRwvM6i7L8Sdi5qUys0gZe3HmVeTUowVmdZfl+BMx81KZWaSMvTjz\nKnLq0QKzusty/ImYeanMLFLGXpx5FTn1aMHT996ADAwGg8FgOBRoOXWwU91lOf5EzLxUoO/sKdVo\nbOXMq8ipRwvM6i7L8Sdi5qUys0gZe3HmVeTUowVmdZfl+BMx81KZWaSMvTjzKnLq0QKzusty/ImY\neanMLFLGXpx5FTn1aIFZ3WU5/kTMvFRmFiljL868ipx6tMCs7rIcfyJmXiozi5SxF2deRU49WmBW\nd1mOPxEzL5WZRcrYizOvIqceLTCruyzHn4iZl8rMImXsxZlXkVOPFpjVXZbjT8TMS+VhRcr/30mX\nftPceCofdhV1oFOPFpjVXZbjT8TMS+VhRcqsbiA+7CrqQKceLTjJ6n5/5/aHhvv95OlfP766bwIH\nE/rGIjvJJ/HdZ1Zg5Tf/1B/8gziL9GfhF3cTcRlEvP6dFfCsLCZGoPTj9XCJbd1//vP9p+f+pgxv\nvrJi92Vvq/vz0zNXof3q0BbCwLFIM7++d8Pv8GCPL5AxWk4dzOoexurw99AVg9EJZVlkO7nQRzDF\nH8rqPn/wbTvos1xYlrxwZbHSKJX6zrZucBENzz/9wQvfixut7o+PL919b39jcbO6PYSBY5FG0iVn\nVtcIl4HY2F4sSk+4xhre3eqy6Qu/YZvrOFpd5URDSRbpRRwrl4lPSbM6pQvBHenv8VYW04LBI5XW\nIcq2btCdTPTRIZ6enn38HoN3ZW+rM+4gjCiLNPHLWzcnbz76pw2zuka4DPJdDTSr2897Wp1G1PEs\nn0GsbkkDEytanZZ/OpnFLlQWC45YOOaKWYMY27qq1QHRJEY52JnVDUQYURZpoH91CTOCB2uzuka4\nDPJdDayzOv9cvIhF9i5IeySPIuIhpIQXUFonzRHGAwFDeKjfkaTyLquutnCwyMEzZ4QSLNKXmvVi\nf+9tdZQ+JTk7eAgTqWIX8um+WQxXuJjfMErZ+c8RYmzrlqwufJvClcg/kicoXrhU6OMoZwjx/V/B\nwBQRVEvybxmzM2j6Nogi1lD0ztoO+nuzDs7yOhS6mkVwqFn3cXbySZdDp0zByISM0XLqMKLVffDP\n0RmYVKEAUWQqk9kGQabLxGaQ3Jkocu2rSFL3Jz2HG7WNaHVc62nwka2OWXhlsXLHcSnyyYIQ27pB\nfQpWR4U+9y0KRc6ef/oS3oJS5G9EC35TY3W6k5ED3Aara+rg0/s3zBQdpnA76CeL4PMBOT2r52lf\nDNeAWd02uAzyXQ3Et0ACuTSgrDikp+AQJM/F8jwB9RPN1eQpmB8J+mJEg9BRhMeoX8kAVpNc7s0U\nFtPOVK+6Nq3X64SyLNKP4ZkgzzDFMxQtGa6xyAEsWR2mWjyH5R52q5ifMtlKXHj8EoTY1g3CzTVa\niJcvBqDaFM9VNBhLZk4ZTku0Ic1vgLVWl+Ucvl/Maiu/wJRN7+xgCCqnwKsReskisfv4TIAzmC8q\n9vBkVrcNLgOypT3brI6pOTpQUszSyyLkUl656utJlfvm1quVQlmbpEYpiA213dvqQqoB2virVueg\nThbEWeQAlqwurclsEeLIp3GuKyZnNnCf1cWXeOk0g4ckKUxoBuS4hqon/qSlVFLUWWN1CpWONFhd\newd5tcUarkboJIsAyaOGHDGgD5LZMavbBpcB2dKeXi9yY5Ms6YWPE90PQqNWGLVJR7IKXm2L1VUk\nqXBXbaNZnQM/1UnGu2Qf72x1eo8i+Dop4KbVFS5BiG3dJNwCVKqyV085hZ0oluNZKCnEbqPVCTFt\nsboeHaxJ8gqETrLIQnQ4RH669WOYDa9Z3Ta4DMiW9uxsdczPMvUvHiwQZauTZyl0l6zphiSBmu/S\nextqu7fV5Yzfhla7nTzYQZBFDuCK1QH5aoEktXG+Waw0jxtOdQxMgFDF8q+sAtmLqaIT9Le6ePok\n2GZ1XTpY6M7lCJ1kESROHCAfHB9nr3bN6rbBZUC2tGd3q1uYCVCoHIO32gLKarmieeQaXZ1kNAMO\nem9Dl8eyOkef5O2h1o7LjhBkkQPo51SOcJGlbBnzYuWhwGXAVy+E2NYtCzfleFaXVJXBrO5wQidZ\nBImvcB3oSOIsrEIf+fEIqaLl1OGhrQ7pK4/Hi0qpktV6L3n1YnEURCmZ20myrBZK2W3o8nhWh08G\nja6QeHyGwFarK80IIytWHAqcNXH2hRjbumXhztjlBSYewm6WrLA6rCo7KHi72mZ19gKzgdBJFlno\nu//05q03vDQOZnU94TLIdzXwUKtjBUriIsiq9Wq10opnZZKofXmXpexW1ub4qKe6cLqVxSDIIgew\nzeoqF48sVrixaIEQZFu3LNwZUa3YOyjHIHBJqjAibKNYksscHg5WrE49hO2yuvYOmtVlJM8xODva\nsBPaC8xtcBnkuxrY1eqcfFDtECqDEsx1x30VJ21m3ZwkG5J0SM4UslJyuFnbQvFV4jqhKIts5jJ9\nrN3QQdZl5sSrX+lBmEUOoM9TjrAbT+1BRBSuLBZ7SkYAT/balEGUbd2ycOeMLwwzM0Ab0IOAFA/y\nl0sb6iMJhgiAlmQuIsU0Npp3RHFNT1bhhg6a1REy3wqDyR9iKM3qtsFlIDZ22PMKiI40ukiOTI/i\n34Jz0MqFnRTuyh/Jq82pIYeK2lIwQhFQSijBIptZnj6auT4vC/RU4QKL9OLKeotWhE9IAuxZp7JY\nU0kgxNnWLQu3IPkmJge7Fyt8ht6WgWtfNBiKt58WJ6MiKFykLZmIWINmS40dNKsLVA7Z4aVleTTM\n6rbBZSA2dleri5EIeQtQKC8/WIhqi39IIo+MdeaUuZ27ZRkHem9LbUCaoXZOooQiLLKLcnC0V6lS\n7lWh94SrLNKLNVanrBCtR9XFFrJRKs8RXGRbtyzcKuPhDKF9uUUqzMyjJGq+vIdPw7dCy2suklXu\nblxOhKIj1E3T4axkS40dVOIPpt0bCJ2kHwvvfnEk1a8/Hc3qtsFlIDb2I9C7i5DmIF4rkj0mIWcW\nGY3jZ3gcoe9s6x7AkhMYL8JTVtGgdOrRArO6QH8CUx7D8aHerK47x8/wOELf2dY9gGZ1F+cpq2hQ\nOvVogVldIL5s5O8Swxs5+Y5xdELSLDIax8/wOELf2dY9gGZ1F+cpq2hQOvVogVld5MpfVSh/LD4+\nIW0WGY3jZ3gcoe9s6x5As7qL85RVNCiderTArC6n/LPJW3/9MSwhdxYZjeNneByh72zrHkCzuovz\nlFU0KJ16tMCs7rIcfyJmXiozi5SxF2deRU49WmBWd1mOPxEzL5WZRcrYizOvIqceLTCruyzHn4iZ\nl8rMImXsxZlXkVOPFjx97w3IwGAwGAyGQ4GWU4f+pzqoFP9luCvGn4iZl4ptE8N+2A6qh1ndZTH+\nRNhGNRj2wHZQPczqLovxJ8I2qsGwB7aD6mFWd1mMPxG2UQ2GPbAdVA+zusti/ImwjWow7IHtoHqY\n1V0W40+EbVSDYQ9sB9XDrO6yGH8ibKMaDHtgO6geZnWXxfgTYRvVYNgD20H1MKu7LMafCNuoBsMe\n2A6qh1ndZTH+RNhGNRj2wHZQPczqLovxJ8I2qsGwB7aDavHvv/8PevMU6mf05csAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NbImage(\"cube2.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C'est assez simple. Prenons un exemple : [table de mortalité de 1960 à 2010](http://www.data-publica.com/opendata/7098--population-et-conditions-sociales-table-de-mortalite-de-1960-a-2010) qu'on récupère à l'aide de la fonction [table_mortalite_euro_stat](http://www.xavierdupre.fr/app/actuariat_python/helpsphinx/actuariat_python/data/population.html#actuariat_python.data.population.table_mortalite_euro_stat). C'est assez long (4-5 minutes) sur l'ensemble des données car elles doivent être prétraitées (voir la documentation de la fonction). Pour écouter, il faut utiliser le paramètre *stop_at*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mortalite.txt'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from actuariat_python.data import table_mortalite_euro_stat \n",
    "table_mortalite_euro_stat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "os.stat_result(st_mode=33206, st_ino=73183493945221205, st_dev=2797837379, st_nlink=1, st_uid=0, st_gid=0, st_size=104871002, st_atime=1445699900, st_mtime=1445699915, st_ctime=1445699900)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.stat(\"mortalite.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annee</th>\n",
       "      <th>valeur</th>\n",
       "      <th>age</th>\n",
       "      <th>age_num</th>\n",
       "      <th>indicateur</th>\n",
       "      <th>genre</th>\n",
       "      <th>pays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.00080</td>\n",
       "      <td>Y01</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>F</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.00067</td>\n",
       "      <td>Y01</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>F</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.00052</td>\n",
       "      <td>Y01</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>F</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.00123</td>\n",
       "      <td>Y01</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>F</td>\n",
       "      <td>AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.00016</td>\n",
       "      <td>Y01</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>F</td>\n",
       "      <td>AT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annee   valeur  age  age_num indicateur genre pays\n",
       "0   2009  0.00080  Y01        1  DEATHRATE     F   AM\n",
       "1   2008  0.00067  Y01        1  DEATHRATE     F   AM\n",
       "2   2007  0.00052  Y01        1  DEATHRATE     F   AM\n",
       "3   2006  0.00123  Y01        1  DEATHRATE     F   AM\n",
       "4   2013  0.00016  Y01        1  DEATHRATE     F   AT"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv(\"mortalite.txt\", sep=\"\\t\", encoding=\"utf8\", low_memory=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cubes de données avec xarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### création\n",
    "\n",
    "Cette réprésentation sous forme de table n'est pas toujours très lisible. Les informations sont souvent répétées et les données sont vraiment multidimensionnelles. Le module [xarray](http://xarray.pydata.org/en/stable/) propose de distinguer les coordonnées des valeurs pour proposer des manipulations plus intuitives. Le module propose un DataFrame multidimensionnelle [DataSet](http://xarray.pydata.org/en/stable/data-structures.html#dataset)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['annee', 'valeur', 'age', 'age_num', 'indicateur', 'genre', 'pays'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from actuariat_python.data import table_mortalite_euro_stat \n",
    "table_mortalite_euro_stat()\n",
    "import pandas\n",
    "df = pandas.read_csv(\"mortalite.txt\", sep=\"\\t\", encoding=\"utf8\", low_memory=False)\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dans ce cas-ci, pour reprendre la temrinologie du module [xarray](http://xarray.pydata.org/en/stable/), nous avons :\n",
    "\n",
    "* les **dimensions** : annee, age_num, pays, genre\n",
    "* les **valeurs** : une valeur par indicateur\n",
    "\n",
    "On peut passer d'un [DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) à un [DataSet](http://xarray.pydata.org/en/stable/data-structures.html#dataset) de la façon suivante :\n",
    "\n",
    "* les colonnes indéxées représentent les dimensions\n",
    "* les colonnes non indéxées sont les valeurs\n",
    "\n",
    "On garde supprime les colonnes qui ne nous intéressent pas et les valeurs manquantes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['annee', 'valeur', 'age', 'age_num', 'indicateur', 'genre', 'pays'], dtype='object'),\n",
       " Index(['annee', 'age_num', 'indicateur', 'pays', 'genre', 'valeur'], dtype='object'))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[[\"annee\", \"age_num\",\"indicateur\",\"pays\",\"genre\",\"valeur\"]].dropna().reset_index(drop=True)\n",
    "df.columns, df2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>annee</th>\n",
       "      <th>age_num</th>\n",
       "      <th>indicateur</th>\n",
       "      <th>pays</th>\n",
       "      <th>genre</th>\n",
       "      <th>valeur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>AM</td>\n",
       "      <td>F</td>\n",
       "      <td>0.00080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>AM</td>\n",
       "      <td>F</td>\n",
       "      <td>0.00067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>AM</td>\n",
       "      <td>F</td>\n",
       "      <td>0.00052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>AM</td>\n",
       "      <td>F</td>\n",
       "      <td>0.00123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>DEATHRATE</td>\n",
       "      <td>AT</td>\n",
       "      <td>F</td>\n",
       "      <td>0.00016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   annee  age_num indicateur pays genre   valeur\n",
       "0   2009        1  DEATHRATE   AM     F  0.00080\n",
       "1   2008        1  DEATHRATE   AM     F  0.00067\n",
       "2   2007        1  DEATHRATE   AM     F  0.00052\n",
       "3   2006        1  DEATHRATE   AM     F  0.00123\n",
       "4   2013        1  DEATHRATE   AT     F  0.00016"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2[\"indicateur\"] = df2[\"indicateur\"].astype(str)\n",
    "df2[\"genre\"] = df2[\"genre\"].astype(str)\n",
    "df2[\"pays\"] = df2[\"pays\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "annee           int64\n",
       "age_num       float64\n",
       "indicateur     object\n",
       "pays           object\n",
       "genre          object\n",
       "valeur        float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'opération complexe consiste à faire passer les valeurs de la colonne *indicateur* en tant que colonnes. C'est l'objet de la méthode [pivot_table](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.pivot_table.html?highlight=pivot#pandas.DataFrame.pivot_table) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>indicateur</th>\n",
       "      <th>DEATHRATE</th>\n",
       "      <th>LIFEXP</th>\n",
       "      <th>PROBDEATH</th>\n",
       "      <th>PROBSURV</th>\n",
       "      <th>PYLIVED</th>\n",
       "      <th>SURVIVORS</th>\n",
       "      <th>TOTPYLIVED</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annee</th>\n",
       "      <th>age_num</th>\n",
       "      <th>pays</th>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1960</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">BE</th>\n",
       "      <th>F</th>\n",
       "      <td>0.00159</td>\n",
       "      <td>73.7</td>\n",
       "      <td>0.00159</td>\n",
       "      <td>0.99841</td>\n",
       "      <td>97316</td>\n",
       "      <td>97393</td>\n",
       "      <td>7179465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.00207</td>\n",
       "      <td>68.2</td>\n",
       "      <td>0.00207</td>\n",
       "      <td>0.99793</td>\n",
       "      <td>96420</td>\n",
       "      <td>96520</td>\n",
       "      <td>6580601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>0.00184</td>\n",
       "      <td>70.9</td>\n",
       "      <td>0.00184</td>\n",
       "      <td>0.99816</td>\n",
       "      <td>96856</td>\n",
       "      <td>96945</td>\n",
       "      <td>6874529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">BG</th>\n",
       "      <th>F</th>\n",
       "      <td>0.00652</td>\n",
       "      <td>73.2</td>\n",
       "      <td>0.00650</td>\n",
       "      <td>0.99350</td>\n",
       "      <td>95502</td>\n",
       "      <td>95813</td>\n",
       "      <td>7017023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.00644</td>\n",
       "      <td>70.0</td>\n",
       "      <td>0.00642</td>\n",
       "      <td>0.99358</td>\n",
       "      <td>94801</td>\n",
       "      <td>95107</td>\n",
       "      <td>6653130</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "indicateur                DEATHRATE  LIFEXP  PROBDEATH  PROBSURV  PYLIVED  \\\n",
       "annee age_num pays genre                                                    \n",
       "1960  1       BE   F        0.00159    73.7    0.00159   0.99841    97316   \n",
       "                   M        0.00207    68.2    0.00207   0.99793    96420   \n",
       "                   T        0.00184    70.9    0.00184   0.99816    96856   \n",
       "              BG   F        0.00652    73.2    0.00650   0.99350    95502   \n",
       "                   M        0.00644    70.0    0.00642   0.99358    94801   \n",
       "\n",
       "indicateur                SURVIVORS  TOTPYLIVED  \n",
       "annee age_num pays genre                         \n",
       "1960  1       BE   F          97393     7179465  \n",
       "                   M          96520     6580601  \n",
       "                   T          96945     6874529  \n",
       "              BG   F          95813     7017023  \n",
       "                   M          95107     6653130  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv = df2.pivot_table(index=[\"annee\", \"age_num\",\"pays\",\"genre\"],\n",
    "                columns=[\"indicateur\"],\n",
    "               values=\"valeur\")\n",
    "piv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "indicateur\n",
       "DEATHRATE     float64\n",
       "LIFEXP        float64\n",
       "PROBDEATH     float64\n",
       "PROBSURV      float64\n",
       "PYLIVED       float64\n",
       "SURVIVORS     float64\n",
       "TOTPYLIVED    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "piv.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données sont maintenant prêtes à passer sous *xarray* :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (age_num: 84, annee: 54, genre: 3, pays: 54)\n",
       "Coordinates:\n",
       "  * annee       (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...\n",
       "  * age_num     (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...\n",
       "  * pays        (pays) object 'AM' 'AT' 'AZ' 'BE' 'BG' 'BY' 'CH' 'CY' 'CZ' ...\n",
       "  * genre       (genre) object 'F' 'M' 'T'\n",
       "Data variables:\n",
       "    DEATHRATE   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    LIFEXP      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBDEATH   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBSURV    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PYLIVED     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    SURVIVORS   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    TOTPYLIVED  (annee, age_num, pays, genre) float64 nan nan nan nan nan ..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xarray\n",
    "ds = xarray.Dataset.from_dataframe(piv)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sélection\n",
    "\n",
    "Il est facile ensuite d'extraire les données d'un pays avec la méthode [sel](http://xarray.pydata.org/en/stable/indexing.html#indexing-and-selecting-data) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (age_num: 84, annee: 54, genre: 3, pays: 1)\n",
       "Coordinates:\n",
       "  * annee       (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...\n",
       "  * age_num     (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...\n",
       "  * pays        (pays) object 'FR'\n",
       "  * genre       (genre) object 'F' 'M' 'T'\n",
       "Data variables:\n",
       "    DEATHRATE   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    LIFEXP      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBDEATH   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBSURV    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PYLIVED     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    SURVIVORS   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    TOTPYLIVED  (annee, age_num, pays, genre) float64 nan nan nan nan nan ..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(pays=[\"FR\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou de plusieurs pays :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (age_num: 84, annee: 54, genre: 3, pays: 2)\n",
       "Coordinates:\n",
       "  * annee       (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...\n",
       "  * age_num     (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...\n",
       "  * pays        (pays) object 'FR' 'BE'\n",
       "  * genre       (genre) object 'F' 'M' 'T'\n",
       "Data variables:\n",
       "    DEATHRATE   (annee, age_num, pays, genre) float64 nan nan nan 0.00159 ...\n",
       "    LIFEXP      (annee, age_num, pays, genre) float64 nan nan nan 73.7 68.2 ...\n",
       "    PROBDEATH   (annee, age_num, pays, genre) float64 nan nan nan 0.00159 ...\n",
       "    PROBSURV    (annee, age_num, pays, genre) float64 nan nan nan 0.9984 ...\n",
       "    PYLIVED     (annee, age_num, pays, genre) float64 nan nan nan 9.732e+04 ...\n",
       "    SURVIVORS   (annee, age_num, pays, genre) float64 nan nan nan 9.739e+04 ...\n",
       "    TOTPYLIVED  (annee, age_num, pays, genre) float64 nan nan nan 7.179e+06 ..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(pays=[\"FR\", \"BE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ou plusieurs dimensions :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (age_num: 84, genre: 3)\n",
       "Coordinates:\n",
       "    annee       int64 2000\n",
       "  * age_num     (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...\n",
       "    pays        object 'FR'\n",
       "  * genre       (genre) object 'F' 'M' 'T'\n",
       "Data variables:\n",
       "    DEATHRATE   (age_num, genre) float64 0.00043 0.00046 0.00044 0.00021 ...\n",
       "    LIFEXP      (age_num, genre) float64 82.3 74.7 78.5 81.3 73.7 77.6 80.3 ...\n",
       "    PROBDEATH   (age_num, genre) float64 0.00043 0.00046 0.00044 0.00021 ...\n",
       "    PROBSURV    (age_num, genre) float64 0.9996 0.9995 0.9996 0.9998 0.9997 ...\n",
       "    PYLIVED     (age_num, genre) float64 9.957e+04 9.944e+04 9.951e+04 ...\n",
       "    SURVIVORS   (age_num, genre) float64 9.959e+04 9.947e+04 9.953e+04 ...\n",
       "    TOTPYLIVED  (age_num, genre) float64 8.196e+06 7.432e+06 7.817e+06 ..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.sel(pays=\"FR\", annee=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour accéder à la série ``LIFEXP`` pour les hommes, les années 2000 et 2010, le pays ``FR``, on fait la différence, puis on la récupère sous forme de DataFrame :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIFEXP</th>\n",
       "      <th>genre</th>\n",
       "      <th>pays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.6</td>\n",
       "      <td>T</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.6</td>\n",
       "      <td>T</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.6</td>\n",
       "      <td>T</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.6</td>\n",
       "      <td>T</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.6</td>\n",
       "      <td>T</td>\n",
       "      <td>FR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         LIFEXP genre pays\n",
       "age_num                   \n",
       "1           2.6     T   FR\n",
       "2           2.6     T   FR\n",
       "3           2.6     T   FR\n",
       "4           2.6     T   FR\n",
       "5           2.6     T   FR"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ds.sel(pays=\"FR\", annee=2010, genre=\"T\")[\"LIFEXP\"] - \n",
    " ds.sel(pays=\"FR\", annee=2000, genre=\"T\")[\"LIFEXP\"]).to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a gagné presque deux ans et demi d'espérance de vie à la naissance en 10 ans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>LIFEXP</th>\n",
       "      <th>annee</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_num</th>\n",
       "      <th>genre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>F</th>\n",
       "      <td>2.6</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>F</th>\n",
       "      <td>2.7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>-0.4</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>F</th>\n",
       "      <td>2.7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               LIFEXP  annee\n",
       "age_num genre               \n",
       "1       F         2.6   2010\n",
       "        M        -0.3   2010\n",
       "2       F         2.7   2010\n",
       "        M        -0.4   2010\n",
       "3       F         2.7   2010"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ds.sel(pays=\"FR\", annee=2010, genre=[\"F\",\"M\"])[\"LIFEXP\"] - \n",
    " ds.sel(pays=\"UK\", annee=2010, genre=[\"F\",\"M\"])[\"LIFEXP\"]).to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quelques graphes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1bf22320>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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L5dLBgwe1fft2bd++XQcPHpTRCfD/8Y9/FLdESdKhQ4cK/c+IKVOmePzMDx8+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\n+Ph4dezYUW3atFGbNm0M9yD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ibTLBssbMttOK74NV5wGbeR/9+/fXlClTFBcXl2einitX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\nCQAAQO3eABjD0cGVyulcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1bf229b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sel(annee=2010,age_num=1,genre=\"T\")[\"LIFEXP\"].to_dataframe() \\\n",
    "            .sort_values(\"LIFEXP\", ascending=False) \\\n",
    "            .plot(y=\"LIFEXP\", kind=\"bar\", figsize=(16,6), ylim=[65,85])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1539ca58>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MfAnC+xHgaX8h572Z4Fd/kzoWIcQAVCiIUTG1DYTZ78Hi5ckQN4ZA3LcDvJi6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\nCNHr/wF95EtFT3uvYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14a06fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.sel(annee=2010,genre=\"T\",pays=\"FR\")[\"LIFEXP\"].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercice 1 : que font les lignes suivantes ?\n",
    "\n",
    "On pourra s'aider des pages :\n",
    "\n",
    "* [align nad reindex](http://xarray.pydata.org/en/stable/indexing.html#align-and-reindex)\n",
    "* [transforming datasets](http://xarray.pydata.org/en/stable/data-structures.html?highlight=assign#transforming-datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (age_num: 84, annee: 54, genre: 3, pays: 54)\n",
       "Coordinates:\n",
       "  * annee        (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...\n",
       "  * age_num      (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...\n",
       "  * pays         (pays) object 'AM' 'AT' 'AZ' 'BE' 'BG' 'BY' 'CH' 'CY' 'CZ' ...\n",
       "  * genre        (genre) object 'F' 'M' 'T'\n",
       "Data variables:\n",
       "    DEATHRATE    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    LIFEXP       (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBDEATH    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBSURV     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PYLIVED      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    SURVIVORS    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    TOTPYLIVED   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    LIFEEXP_add  (annee, age_num, pays, genre) float64 nan nan nan nan nan ..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.assign(LIFEEXP_add = ds.LIFEXP-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "meanp = ds.mean(dim=\"pays\")\n",
    "ds1, ds2 = xarray.align(ds, meanp, join='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "joined = ds1.assign(meanp = ds2[\"LIFEXP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>DEATHRATE</th>\n",
       "      <th>LIFEXP</th>\n",
       "      <th>PROBDEATH</th>\n",
       "      <th>PROBSURV</th>\n",
       "      <th>PYLIVED</th>\n",
       "      <th>SURVIVORS</th>\n",
       "      <th>TOTPYLIVED</th>\n",
       "      <th>meanp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age_num</th>\n",
       "      <th>annee</th>\n",
       "      <th>genre</th>\n",
       "      <th>pays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1960</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
       "      <th>AM</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BE</th>\n",
       "      <td>0.00159</td>\n",
       "      <td>73.7</td>\n",
       "      <td>0.00159</td>\n",
       "      <td>0.99841</td>\n",
       "      <td>97316</td>\n",
       "      <td>97393</td>\n",
       "      <td>7179465</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG</th>\n",
       "      <td>0.00652</td>\n",
       "      <td>73.2</td>\n",
       "      <td>0.00650</td>\n",
       "      <td>0.99350</td>\n",
       "      <td>95502</td>\n",
       "      <td>95813</td>\n",
       "      <td>7017023</td>\n",
       "      <td>73.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          DEATHRATE  LIFEXP  PROBDEATH  PROBSURV  PYLIVED  \\\n",
       "age_num annee genre pays                                                    \n",
       "1       1960  F     AM          NaN     NaN        NaN       NaN      NaN   \n",
       "                    AT          NaN     NaN        NaN       NaN      NaN   \n",
       "                    AZ          NaN     NaN        NaN       NaN      NaN   \n",
       "                    BE      0.00159    73.7    0.00159   0.99841    97316   \n",
       "                    BG      0.00652    73.2    0.00650   0.99350    95502   \n",
       "\n",
       "                          SURVIVORS  TOTPYLIVED  meanp  \n",
       "age_num annee genre pays                                \n",
       "1       1960  F     AM          NaN         NaN  73.52  \n",
       "                    AT          NaN         NaN  73.52  \n",
       "                    AZ          NaN         NaN  73.52  \n",
       "                    BE        97393     7179465  73.52  \n",
       "                    BG        95813     7017023  73.52  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined.to_dataframe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lire, écrire des datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le module [xarray](http://xarray.pydata.org/en/stable/) s'appuie sur le module [netCDF4](https://github.com/Unidata/netcdf4-python) qui lui-même est un [wrapper](https://fr.wikipedia.org/wiki/Adaptateur_(patron_de_conception)) poython de la libraire [netCDF-c](https://github.com/Unidata/netcdf-c). Celle-ci est spécialisée dans la lecture et l'écriture de données scientifiques. Concrètement, ce n'est pas [xarray](http://xarray.pydata.org/en/stable/) qui s'en occupe mais [netCDF4](https://github.com/Unidata/netcdf4-python). Cela explique la syntaxe décrite par [Serialization and IO](http://xarray.pydata.org/en/stable/io.html#serialization-and-io) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data variables:\n",
       "    DEATHRATE   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    LIFEXP      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBDEATH   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PROBSURV    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    PYLIVED     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    SURVIVORS   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...\n",
       "    TOTPYLIVED  (annee, age_num, pays, genre) float64 nan nan nan nan nan ..."
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.data_vars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l'écriture a échoué\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    ds.to_netcdf('mortalite.nc')\n",
    "except ValueError as e:\n",
    "    # it breaks with pandas 0.17\n",
    "    # xarray has to be updated\n",
    "    print(\"l'écriture a échoué\")\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "C'est un format binaire plus efficace que le format texte :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if os.path.exists(\"mortalite.nc\"):\n",
    "    os.stat('mortalite.nc').st_size, os.stat('mortalite.txt').st_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On relit :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if os.path.exists(\"mortalite.nc\"):\n",
    "    ds_lu = xarray.open_dataset('mortalite.nc')\n",
    "    ds_lu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le module [xarray](http://xarray.pydata.org/en/stable/) propose également de lire des données de plusieurs fichiers pour ne former qu'un seul dataset (voir [Combining multiple files](http://xarray.pydata.org/en/stable/io.html#combining-multiple-files)) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AM', 'AT', 'AZ', 'BE', 'BG']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pays = list(_.values for _ in ds[\"pays\"])\n",
    "pays[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enregistre AM\n",
      "l'écriture a échoué pour AM\n",
      "enregistre AT\n",
      "l'écriture a échoué pour AT\n",
      "enregistre AZ\n",
      "l'écriture a échoué pour AZ\n",
      "enregistre BE\n",
      "l'écriture a échoué pour BE\n",
      "enregistre BG\n",
      "l'écriture a échoué pour BG\n"
     ]
    }
   ],
   "source": [
    "for p in pays[:5]:\n",
    "    print(\"enregistre\", p)\n",
    "    d = ds.sel(pays=[p])\n",
    "    try:\n",
    "        d.to_netcdf(\"mortalite_pays_%s.nc\" % p)\n",
    "    except ValueError:\n",
    "        print(\"l'écriture a échoué pour\", p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On relit :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "if os.path.exists(\"mortalite_pays_AM.nc\"):\n",
    "    ds_lu2 = xarray.open_mfdataset('mortalite_pays*.nc')\n",
    "    ds_lu2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quoi ça sert ?\n",
    "\n",
    "Lorsqu'on travaille avec des gros fichiers de données, on cherche à retarder le plus possible l'exécution d'un calcul, on manipule les données sans les charger en mémoire. On appelle ceci [évaluation paresseuse](https://fr.wikipedia.org/wiki/%C3%89valuation_paresseuse) ou [lazy evaluation](https://en.wikipedia.org/wiki/Lazy_evaluation) en anglais. [xarray](http://xarray.pydata.org/en/stable/) et [dask](http://dask.pydata.org/en/latest/) permettent de faire cela (voir [Using dask with xarray](http://xarray.pydata.org/en/stable/dask.html#dask-io)). La logique qu'on suit est alors assez proche de la [programmation fonctionnelle](https://fr.wikipedia.org/wiki/Programmation_fonctionnelle)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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