prise_en_main_jupyter.ipynb

{
 "cells": [
  {
   "attachments": {
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     "image/png": 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"
    },
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"
    },
    "image-4.png": {
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3bkRmZiY2btyIjRs3YvPmzfjwww9RU1MDAOjt7cW8efPw/vvvA/BiRVpxldAlSyQHlc2Y1MwoGSH5sfMqwzIVzsmk1WhqSUOpHmMzmvIRuVqNpnzIrtuIptu29MzYhs4qDWUWr5FHVV2ud2oDMSqbHSYMnV2iMGTY40q7LkZTHr0SojFhMqPRnB2FqKgohIWl4EhzNqKiohxRUbspUIrquElH5RqXsroxwdL7w8Q6OdVPNAMyUyAxsMplaEZ2lHg8BSmSe5XyVK+X+r3KJk7NZHmOaB5JkUR7FfpYtb2cEdtGLdLWnI2olBSkSOphT1fpWVB7XlT7SV4+xTazGWHNEW5pXprKr95Wyn3rqK/0/BENnwmhSFETLlJPCCGB4tSlHsS8fwqzM4/g8Q2HMSOjVDCYfz+EmRllWL37DLIOnsPm4n9i04EfsOnAD8jcX4OsohocO9cI9H8O9KQAHc8BrQuBlkSgYwHQvhTA+KOZwPiNZpUxHOHhNhmrxBtQkCQeN8JoTEJBu5vr2wuQZL8XqLJdb79Wcm5c9XDCb0ZTbTsQ0WjW19dDr9fjq6++sp87dOgQEhISUFdXB4vFArPZDKvVap+jKY1o7t69G/Hx8faIpnTfy5ycHMTGxspWqT179iz0ej3q6+uxZcsWvPjii7L9LgFhhVqdTodDhw7BYrHYFxTKy8tDXFycxzr39fXJjKbmLV527BCMptEoOegLoylNTjKnL6YQahFT79CSRpCMpmTeqNpw2tA3mtJ6em4DMVppFlcwtkX0IbvFe6NpH753JEX2Ui2+ADdnR8mNnP2FXBphlLxUa0lH5RpXjthNgbqhlJRDxcBI87Pn5aacDiMgifiq5KleL8m9muur1gYehs5KyqaYtlp7qeXjzmjKyuIwew6z62w0FZ4XtX6Smm2VNpN+aSB+aekWaV20lN8pX+Xj0pEAav3j+TNhLxOdJiGEKFLzy6948r3vMGvDYTxmPITojFJEGw/hudwTuNbTDNw5IEQsb/xD+PfXXKAnB+jeAnS8BtxMgbVtEdBiAC4vAC7HAS1zgctJgNWLaXBuGJfRrDLKTGCVMRzGKqC9IAlJorNEFYzhNqOpcr2a0Qx+RNPHczTdRTSrq6uh1+tx8OBB2T2ZmZnQ6/WyrUsKCwuRkJCA/fv3y669c+cOFi1aZF8xVjR7w8PDSE5ORkZGBv72t7+huLjYXh7xmo6ODixYsABFRUWyNOvq6qDT6XDgwAHFOolGta+vD7Gxsd4bTTGiqWA0PQ6d9dYc2c3JGOZjuqAljeAYTcV2cBpKHHCjqZS/u6Gz9rpPwerVCivMKkQ0Y8PChWdp2jTFMo41omk3gk6R66jsZrnxcmcQbC/VWtJRu8YTR1LCVNLQYGCcokxqZZBFB+FkbBTydK6X0r1jra8tFQ1zNMUhmOpDNxXbS/W8ShldDKs8YqdoNJX6Q4PRVC2PZJ6xljZUN3XK5VebR6nWt1qMptu2pdEkhBBVmrv78cSGcjxm/ALRGSWIzijBTOOXuNJ5Gfh1DWAxAnffAu69Lawq+9sq4M5KYahs9wtA6yIhinl5PvDLM0DT00DbU0DLywjoqrMqRrO9IAmyYGOVEUkF7Q4DaT8sGEe160POaFoH78A6YNvA1GrxetVZS2+H4mXOEU3RaFqtVrtJTEhIwNGjR1FbW4vMzEy8+OKLmDdvnn2FWECIaM6fPx+JiYn45JNP0NDQgB9++AGvvvoq5s+fj/r6eqEctvmbAPDZZ58hPj4eiYmJ6O3ttecrcufOHftWKd9//z0aGxthtVphtVqxZcsW6HQ6fPLJJ7h48SKamppw6tQp+3xOQDCazzzzDLZs2WLPWxNujKbHxYDcmSOFOZoyo2GLcrosJuTNaqse0wii0ZSet0c4A2Q0xf5zXkXWi8WAHFVVGvoMVzM7+Cmu2IbNoqxMVm9Hl3trND1Hsbw1mprS0bIIypEU2XBG4VCYQtTRKVImG8IpN4kpKSkey6AW0VTL07leYTIj44ho+mZxHfdGUlY/1TppmOvqduisj4ymYj/JI5pu28xlDq+Gumgpv6aIpko0301EU7UuTvkRQghx0H9vGDGbvxKM5trP8ZjxEF7IPQncKQeGM4Drq4Hr6UD3SuDacqBzKdDxItCeDLQsBFr0gslsfgZoegq4NAe4Nhvo2mrLYQwBNydCJqJpT19yfdAimiNmDH31DkYvnZAft1oF02nfQ1PeAdZhM8yHN2P0l1OO6yWI5uvnn3+2G0rAMU+ytrYWS5cuRWxsLGJjY7Flyxa0tbXh+eefR3V1tT2dvLw8xMbG4vTp01i5ciViY2Mxb948pKam4qeffrJl7Rgaa7FYcPXqVeh0OphMJtl56c8VFRUwGAyIj4/Hhg0b7OdGRkZQWFgIg8GAefPmYf78+dDpdNi6davdPN++fRvJycnYuXOnS/puEY3mihWSg+pbeohoMUey7T/C1CJo0m/SvdvSw3MawZujaTdo9raTGzn/Gk04bfsyBasLPW9vojgMWcHMS+tsvy8rS7KtiUPyaZ0ajKbT/fIojes5r42mlnRUrnEtrjwSJB8C6YjOpaSI0Sb1+ZVKcz6Vy6CShkqe6vUS5m4qzu9UrO/Y52g6rlMxkKrtpYK/jaZqP9mGSivMoRXbTHpsbHM0PZVfyxxNad9qMJoqdRGKxDmahBCihPia/da+GszceBSP//1zPL7+C6wuOAP07QMG1wLdaUBXqmAwr74EtD8PtD0LtCQBV3RAc5wtkvlX4NJs4OJMoOUvwIBoqgJoNMMlcysl8yZ9MkdT9foqGMPDEa7BbPp86KylqxEDb/87RotWYrT5e8Csviek5UYrhqsLMbr1MQx9Jhgmq0JEb2RkBFarFTU1NUhISLBHKaXRv+HhYXR0dMjmWA4NDcmik/n5+YiLi8ONGzcAAJ2dneju7rZfLzV5ooktLi7G/Pnz0dLS4pKnlL6+PnR0dMi2PpGWrbOzE9euXVM8bzabta82KyJub5KYKDnorRkjwcZbY+rR9Dku9PwFQGsr8MADghobNZaAjAXXiOYEWRVUa4TvviOUV3YN5bIRQkhwsViEd/2W6/2Y/24F/rK2DI9tOIKlO04CfZ8Bd9cA11KBqy8LBvNqMnBjMfBrEnBdD3THAdfmAp1PAR2zgNZooGsG0LFOyMAHK84C/tlH0zWiaYTn5XzGh2+Npji38VIVLBlTgXVTMZA1A4O7noe5ZC2GyjfD/OVGmPe/jnsfzsfA2v8HZP07zLueg3V02GPnbNu2DXFxcfa9KZ0X5nEUwyozjeJ5ceisuOqsFCUDWVtbi/j4eGRnZ8vyc6225z1AtV6vmdZWhXl1NJoTDc3G0cvrFbeQcWbOHIXh18Q/SKNtwdrmwzuEyO/E2voloPhhH02fwH00CSFEEz237+LIj23YW9mAqvrrwO3PgN9WAleXCCbz2mLg6grgdjlw+7CgvkPArRKgtxi4eQC4uR/47aRP9s6U4g+jKY9QhktMp//w36qzXRcxtEMPrJsKbHhY0PqHHT9vfBhDGf8Xw8eyYbUod45oyM6dO4d169ZBr9djx44dsnNSLBaL6nEA+Pjjj6HT6dDRIcwFFSOlzpjNZuzduxfx8fFIS0vD3bt3PZpDq9Xqdn6lWDafmEyRhx4SjII9GhVkoykb9qms8a1YKyAf3qqkMQzl9TUa2uJ/KAxt9oRHoynm6ylKKu7DOnUqYFspmRBCCCHkfkDxfbyvELj9imAyW/8GtCYCLX8DOlYBHSuB1heBlheBKy8Dwz1+LZ9/jGbg8bnRvH79On46dw79d80AAEv9MYwUvY57257C3awZuPfuTAxuT8DwsWxYetx/6yoat507d2Lp0qWoqKjwurDSdL755hts2LABN2/eBKAeWTSbzUhOTkZWVhZu377t9tqgsmaNwvBZQjQwbZp8ASBCCCGEkPsIqxUYtVgxahkVhtT2fgrcekkwmVd0woI/l58Brs4FOuYAHW8AzYnA1RnA3XpbFHPY59FMgEZTkf7+fnya9ykO7D+AA5/tx8iopOEto7De7QPMd+U3adgKJRgm77fffgtq/pro7gZ+/3vBMCQnB7s0ZKIgRjNVtjMhhBBCCLl/sHmRW3uBW88K+2M2xwK/PC2sKtvypGAwAcFgXn4UGOq03esfj0CjqUBvby8++fgT7CnYgz0FezAyPKy4yiwAwXhq3cpDvMXL65XwxjT6fKirP2htdZjNOXOAvr5gl4iEOoxmEkIIIYTYsL3rD7UCVxcC1+cDzX8FGp8QdDEaaJwBXNsCtCwCWl+33eM/j0CjqcKlS5dQdaIKVzuEBXdkRi3UTZuEkDeYUrq7HeZh2jTBfBKihNEoPCeRkcEuCSGEEEJIaHHvEtCdIUQ0L84A2mcArX8BLj4K/PIk0JMDWIf8XgwaTRJa9PU5VhF98EFh+xNCpFRUcDsTQgghhBBFJEGm0dvAvYtA/7dA/3fAYCNg9XIrwnFQV1eHc+fOTXjV1dV5XXcazVBmxQrBbIaFCYsFSfYJJfcx2dmO52L//mCXhhBCCCEkBLHA7ZBYPyz8Q+TQaIY6+/cLUU3RWCQmAvn5NJ33I5WVjmHVDzxAk0kIIYQQQkIWGs2JQHe3sBLtAw84DKe4b2JiojBXr7ISqKkJdkmJr2lsFPpXXCRK7Pfa2mCXjBBCCCGEEFVoNCcSfX1CFCs5WTAbUtPprMhIIDpaGHKblSUYUc71nBhUVgrDpqXmUjSY+fnBLh0hhBBCCCEeodGc6FRWAjt2CFGv6GhB7gyoqGnThGuTk4V7nVVW5jCnlZUcqhsIysocQ2Ol/WQ0csEfQgghhBAyoaDRnMxUVgrmxWgUDGV0tBDp1GJE1fTgg0BsrLAgDYdv+gbp3EvRXO7Ywa1tCCGEEELIhIVG836mpkYwOfn5ylFNqUGNjpYvSkTjOX5aW4W2kw6NLSsLdqkIIYQQQggZN2Hd3d2gKG90s7wcd956C+bHHoP1f/5PmfG0/Mu/YOjPf8bASy/hzoYN6C0pQW9JCa63tQW93KGi3pISDEoMpuVf/xV3NmwIerkoiqIoiqIoyleyRzS7u7thNpspymsNnzyJkY0bYZk923VlXDey/vGPsEyfLtPo2rUyjXzyCYa//tquofb2oNfXK12/juEvv8ToqlWwTJ8ur//vfoeRLVtgvn07+OWkKIqiKIqiKI3q7va8fguNJuVzDTU1YfjrrwWzuGiRzEha//CH8c0RdRq2KzOpy5c7DOrGjTKD6lejev26kP6ePbI6W3/3O+UyP/MMhvfsCXo/URRFURRFUdRYRKNJhbyG2ttdzKBzVNPZrHoTNR2LYfUk67/9m/ao7R/+gNFFizDyyScYunAh6O1NURRFURRFUeMVjSY16TV04YJbk2qZPdvVKCpFGn1gUkdXrcLo2rVC5PTMmaC3DUVRFEVR1HjU1taGy5cvh6Ta2tqC3j73s2g0KcoLKQ21dZb5+vWgl5OiKIqiKCoQunz5MiwWS0jq8uXLQW+f+1k0mhRFURRFURRFjUk0mnLp9fqg90moiEaToiiKoiiKoqgxiUZTLhpNh2g0KYqiKIqiKIoak2g05aLRdMj/RrOlGGl6PfQSpRW3wGyuhklvQrXZ7PRzkKRQTr1eD1O167XVpjQUt7iWu7q4GC1+L2sLitMUylVtgj6tGC2h0JZu5b/yBab9lZ8X4ZnWWu8A9lFLMdLS5O1SbdJDb6qecP3r0t/2z6HYH06/e/g8yO4P5WfNuT+d+q7apPx3Khh9QlEURd1/mqxGc6ChCGtXmXDqV+/um9RGc6ALP3z2LlYtfQEvvPAClq4y4avmftXrA2M0Pb7UhsCLkMILuZqUX1CrYdJ4/7hVbXIxCp5fNkNF/urrALa/uRomqalRM/+q9Q6e0fT/cxK4urUUp9nr0lKcBpPJ5DD81SaP5n/sRjOQz5pCf9JoUhRFUSEkJaPZ09ODNWvWyHTixAqzIFUAABMuSURBVAlYLBasWbPGrTns6elBUVGR4jlP9/rKaA70X8K+1w3Q6/UwPJ+F416YTUWjqRTQ8vAuYf9SW5OX8l7VJveBNRcNNGDPyoXQL3wFG/JLUVFRivwNr2ChPhlvfH5J8Z4gGk31F29pxT1HinwkD0bTUSYTTAoRTft5v0WKpGpBcZpS9EZLW7agOE3a7pK6BOTlWVK+lmKkpaU5Pnhi/tUmlwdf6ZloKU5DWloa9HoT9gSw/VuK09w+l8rPb/CNppIh8dSu1Wp9pKmeAaibrb8F0+h4hlVHHSh+joUvClyPK9dR9llXeFYDVWdpH7r7nLh85kwmmPSB/HtFURRFTWapGU01s+hJwTaaAw1FWJOSgo17DyNnuQF6vQFvfN6p+X5Vo+n0nu3+fVLyXu4PoykLWml7d/v5oxTokzOdTPcAur5ah0WGdBS1ud4ThKGzSi/bkp+donXSqIVfpTh0VqlMTuZMWocARjmkD6f8QfXclvY2rTYhLU3yc0BMvbPRFNvMYZ6lkaaWlha39ZD1S4Da3+0zqfr8BtdoFpsUzJCWdlXpI231DMCzJA6PTStGi/1LFOcvU9x/jl0/Pxrq6GJobc9qIPpTbYi/lj5pKUaak/GeGCMhKIqiqFCVt0ZTahaLiorsEU/xevFe5+Pu7hWjpeM1mgMNRViz/C1krkmGXp+MjL0HsbekDv1epOGt0ZT9X2wzlbIvtZ2/JFb50l/+7qYcJFCWlne3n/CPF/R464tehXOXkL9Mj+V7XNs65CKaLcVpLi9RATFAbiKazsZCOVoS4OF09vJKX6o1tqWtT1qKTSiuFh/osc9X807O0RXHs2Evg+RlOq24RbUeLgYhgEOXXZ7JlmpUt7h7foNoNO0GU/6saGpXlT7SVk//q9pkQrWkP4TPajVMCt/SqX2Onc2WpjpKv2UM9N8plYimpj5xvj9gXzBRFEVRk1Vah846m0VnM5qTk4Oenh77vVJDWV9fL7u3vr5edu+JEyfs14zVaMpNph56vQHLd3lnMs3mMQydlfzfrPSltvxLYvUv/e3vM2pBAoWyikbV87vAcWTq9cg8rnz+eKYey/IvuRwPOaMZtBcfd0NnZR0p7bAgGk2z7YW42Hm+ppa2FOqQZrIZVZMJJlMg5zd6MJrOfaJSj6AZTZU5mmnFLW7aPPhDZ13aSUu7qvWRpnr6X+IwX+k3gaY0LRF+x+fYXURTtY7Oz5oXc7zH3Z9qQ2e19AkjmhRFUZSPNZ6I5okTJ2RmVDSa0ghlfX29y/xO5/vUoppajeZAQxHeeHkV1o3TZJrN2iOaDomBABVf4eZLf+eRatKAkvP17srsaVqYGLVc+Vmbwrmf8VGKHulFrsOLQ89omp3CwIGc+6QwJM1lXpZeeLFVNJqawtM+VLUJer3zw6OtLaXDI6tNAZwLq8FoKk1OVqqHq0EIYPu7WXVWuc1DxWiaJSsUa2hXN3+sPNczUP3gHNGXfibUPg/Sz7G2OZryOgrP2mFvJ9L7or4a52gq9gnnaFIURVE+1liN5okTJ2TmUBrRzMnJ8RjRVBsu673RPAPTGzko+WglFo7TZJrNYzGaCosaajCabiOanoym05fTno2mGW1F6TAkZ+CLlgHJ8X7U7VoOgyED5b2u93AfTYqi7mupznGejPLTynUURVHU/auxGk3n4bWioRSNpjdzNNesWTOOobPHkblwMd480IBvNy0el8k0m8dmNJW/KLcFULR+6S+do6khoql4r1v9iuNZz8NgWITlb7+Dd955B2+/shB6w8t4/9SvivfQaFIUdZ9LGtEMZHQ/CKLRpCiKonysib+PZhVMLwj7Qq7OH5/JNJvHuI9moKbgjFv96PixBDvWr8LS197GOztKcKFrQPV6Gk2KoiiKoiiKosakiW80fStvjaawmF+gFuUMrGg0KYqiKIqiKIoak2g05RpTRHOSikaToiiKoiiKoqgxiUZTLhpNh2g0KYqiKIqiKIoak9ra2nD58uWQVFub0nYcVKBEo0lRFEVRFEVRFEX5VDSaFEVRFEVRFEVRlE/lldHs6enBvXv3gl5oiqIoiqIoiqIoKjR179499PT0aDea/f396OnpQXd3N0VRFEVRFEVRFEW5qKenB/39/dqNJiGEEEIIIYQQ4gtoNAkhhBBCCCGE+BQaTUIIIYQQQgghPoVGkxBCCCGEEEKIT7Ebza6uLtTV1eHcuXM+VV1dHbq6ulwy9kd+gcxrMmmytdtkqw8VGKk9N4QEC3//zeIzTwghxJ/YjWZdXR3MZrPPMzCbzairq3M57o/8ApmXlHPnzvkt7UCkH4x282edgvUcTDT81QfiS+xES1vpueGXE5S/pMXk+ftvltrfSkIIIcQX2I3muXP+e/FXSttf+QUyr8mSvloegX4m/J1+INpxIuHPz+BETFtMX0pdXR16enpw8+ZNivKpenp6PJq8YP3tJ4QQQnwBjaaf8pxI6avlQaM5uZmIZjDQRpPPDPEnnp4vGk1CCCETGRpNP+U5kdJXy4NGc3IzEc0gjSaZTNBoEkIImczQaPopT1cG0VyShbS0NEHZ36LXJ+lfRXlWmiNdqTL2oWF47HnQaE5uJqIZpNEkkwkaTUIIIZMZTUaze98yzNXFIWbWLMya9STm6gzQzY3Bk/NeRJ4GJzPml/7ufVgyaxZmxWxGteerx5jXMO709OCORkOmNU85g7iwzYBHIl/G3q4udHVVY1NMKsp9kn45UsPD8eSbh/Djjz86lLsY4eFzYWoaex7aX0B6UVNSggtanbNXaQMdBS9i1qxZWLKve1zpu8vz2JuxWFXSjEHNOYyD9gIkhRtRFYi83OCuPY5nzMKsWW60OA9X3KTrsX+He/BTYQYWxgjpxSzMQEnzINBbg6/P9bsts3rax/GR6YKsD49/lKv5yxYxfXe/E+JLaDQJIYRMZjQZzSZTKkxNgqEJDxcMUpMpFaaaGmQmJ3s0m2M2MU0mzJXkqQXv8upFeepMPPLYLDw2/Qkk5zVAXpNh3GmsQlWze/vhvi6CyTTkNuBQqliPJpjmPot3j5aitFRQdZt6G2oxmuGKisG2BrdFd5uHtheQXpSnzkB4eDgiZq5EuUaz6c3LTXmqUJ+5Wl2zSvru8hTziHx6OXae7oEmb9JegCSFdjd6cJDtBUker3FHVUEB2sd+ux0t7aGquSao9YZHozncgFxdJMLDI/HEwmVIS1uGhU9EIjx8BmbOjHDbz+7TLkdqeATitjnMZnlqOCJ12s0mjSYJJOMymr0XcLTU8X+Iqo7+hB43zz+fcUIIIf5iDEbzFRwaFo8B6K2BcVGG24hj6BrNY1j56Os4NgxguAHb5s7GO+eFM8M9Ndi3fR9Ot7fjq615qPUyT5H+gy8hIuIRPDZrFqY/shLHAGC4BsbZEYh4eS+6urrQVb0Jr3h4uVbH1Wi+vLcLXV1dqN40F5G6TNRoMH9j6yOHyRQV8dSb+HaM+akRSKMpKAIzk9/BEQ9fMKC9AElJvjF92qmC0Ud5Bstonn9nNsIj4rDtgqR9e0vwcpTnfvZsNIX+E82mWI+IOJMms+nRaHaWIl2nQ3ppp/z42RzodOlwPqxGZ2k6cs6qnz+boz0tMnEZj9FsMr2CTdXC33p3qt70ituRLTSahBBC/IUXRrMW78dLhs7FvIZS20jG8lT3RtBr8xfh5gU3Lld1yJ73eQ2jIVeH6U+/gCyTCS/NnoHk3C9wcLsJ+2p6MNhTg315x+Am2OghfYkht2fZgFyDAdsulCA1tVy8CKk+NJqOZFOx7VAekpM9m03vjWYvylfORIRCH2kxm+7T7kdVxlOIiNQht2FYZjSHG0yIi4hAnMk5+jy++igbq0g8vaoEjWrjqt0YzSpjOMJtYUuXn23pJxW0S5JKcuSbVID29gIkScKeVcYkFLRL7neTXntBEpKSkhDuYXhucIzmOWQ+Ho7HjTWy/vv5A739b4u7IdLajKbDbJZI6qHFbGoymuk5yEnPgdQnns1JR3o6jSYRWLx4MVpbW+2/t7a2YvHixS7Xjc9opsLUdBwZGcdxPCMDx6/kISPvOPKWbEb18QxkHC9H6lwTyp3/D/KyDIQQQshY8cJoqicSUKPp5gXX+7wEBjvP4GhpKY5+kYelf38XJeVN6K3JxdaDbZqGUGpvu36ULY3EDN1ypKUtxFMrj4kX+cloGhC3JA2vGWYiwpCHDi/r4C7f6g02kxkRh9RXYoS8nzUiMy7C9lKf63U/OejHsZW2SGmkDrq5Qr1iXklFnO3ZmLHyGNRn8vnKaNoMyhNpOHJD4SbFobMOc1dlDIfRaLSbQlRJfoZkCK2TYW0vSEJSTo6i0ZRFNFXSay9Ikh33po20tMf4jKbwvHoTnfY2bUc5Z2LmTHm5Z2444zF9d78LRrMUZ6VGsbMUOaVnUSoxmmdzdNDpbLJd2FkqmFGdLgcVtvs7S9Pt5x335CAnx11anSi1G92zyLFHUs8iJ70UnZ2lSE9PR7p4T3op6FkDS2VlJRYvXoyBgQEMDAxg8eLFqKysdLlu/EazFrvT0rC7tha73z2K2qO7cbS7Frt316J290LMptEkhBASRLwwmseRERMHg8EgURziNlf72GgCg7e67ENKY8LDER4uLqLThR4Pq/Z4bTR7a7BzebywIMnc1/BO/nt4Iy0D+35sx7c7duBbd5NbNKQvN5rlSI3ZhGpbXW4N2i/yi9HE8B302PLa+7Jv+6i3fCVmRgrDH5tMc4W8U8uB3hpkxkVi5spyt6vqel4spg37X5qhaHJmpB5yO+doLPVRNFaRT2P5ztPqeXkaOttegKRw0SA6RS2lUcgqoyy6ab/Xg9FUS6+9IMk1PQWCYzSbYJobjkdXf6ttHqxXaUs/CzPw0v42HEqVPjfun0kxfXe/i0azs7MU6XYDmYPSzk6H0ZScE0yhcFxqKjtL05FTKrnubI79nMw8yo4LplM0qDlnhfPp6ZKfhYyEMjrlTwJLbm4uVq1ahVWrViE3N1fxmvEbzVu40nAFXV3taGpowpWmBjS1X0GDeKypCxdoNAkhhAQJ7+ZopjpbFeGYr42mJGM/z9HsQJ5hJl4qbMSd4UGUvByF1d+K8zPz8HXzHbSVfYi8c+5fT9233fNYtkNcmGE99Eov6P4ymtKr/NVHgNxoakRT2gpmU4vJVEtfs7GKeASxq0rgaYqme6PZjoKkJBRUSa5RMpRK6VQZEZ6+TnKsCsZw5YimUnqhbTSBjjwDIsJnIPWQZNGlwWYULn8RuRoWFvNsNAWTOSyphxaTKabv7neHiROjip0ozRF/dxi6ztJ0RxRS5zCa4txO+3mp8ZQMpRWHzroMsZWayZyzgsk9K/xsH24rM7ochhtM8vPzkZ+fr3reJxHN3bWo3e26vZUY5ayi0SSEEBIkQttoXslFXHg4wiNsi+howOthujNewYErXfi5ZCWemrkSx+zjMYdxp7Ec27fnIXf97jEvBoTBW5KFGfbiZZ8bTdehxpPGaAIys6nVZKql79lYRWBmsgmntWaisupsUkENCpLkcybDbeZQOqcyPNyxQq08OmlEFdpRkOT43SiNaIrzOFXSC3WjKZ3fGzldmJc5PTIc4RFP4U0Pk3s9G02HyRTrodVkium7+10aLRSjkjmCu5NFLh2LBSkfFw2k3QTKIpcS06oS0RSvSRdNbk4OcnJsUUwazQnDeIxmd0Eypj/mYRuiWbPw2HQDPnKzsAGNJiGEEH+hyWj2H3vTtoemiuLf99qI+es/N6+HgNbsxPL4eLyQsVO7wfAifTnyobN2jWvVWTlNprn2VWel8vXQWSnD//wQC2Ni8GLB+FaFVc+gDdUelujXkr67PI+/vwoljXfGNJxzouJ2Du7mGPcvsEv2QW3JHs9GEwCG0fNTIbKWLURM/AtIyyrETxqHqaunXY2D++XzqqvLtZtMMX13v8uGpXaWIt0+P9Jp6KwkmpmT45iP6Ww0pek55mLKFxZSmu8ppiGd35nuCKfSaE4QxmM0A1UGQgghZKxoMprjJZSNpr/yVGYYbdXKe52NfR9NJ9T2Vjt6wes5k4F+JvydPl+o5PjzMzgR0xbTd/c7Ib6ERpMQQshkhkbTT3lOpPTV8qDRnNxMRDNIo0kmEzSahBBCJjM0mn7KcyKlr5YHjebkZiKaQRpNMpmg0SSEEDKZodH0U54TKX21PGg0JzcT0QwG2mjW1dXBbDb7LT9y/2I2m1FXV+f2GhpNQgghExm70fTXC5Xaf6b+yC+QeUmZ6EYzGO3mzzoF6zmYaExEM+jPtJWem66uLtTV1dnzpShfqa6uDl1dXW6fSX//zdJidgkhhJCxYjea/nqhUvvP1B/5BTKvyaTJ1m6TrT5UYKTlxZ+QQOLvv1l85gkhhPiTMM+XEEIIIYQQQggh2qHRJIQQQgghhBDiU2g0CSGEEEIIIYT4FBpNQgghhBBCCCE+JaylpQUURVEURVEURVEU5SuFtbW1gaIoiqIoiqIoiqJ8pbCOjg5QFEVRFEVRFEVRlK8U1tnZCVFXr16lKIqiKIqiKIqiKFVJPaSa/j+UvIyDaBGxLAAAAABJRU5ErkJggg=="
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "baec2ed3",
   "metadata": {},
   "source": [
    "# Prise en main du notebook Jupyter\n",
    "\n",
    "Pour créer de nouvelles cellules (texte ou code)\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "Pour choisir entre une cellule de texte ou de code (la cellule doit être sélectionnée avant)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "Pour executer la cellule (texte ou code)\n",
    "\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "Pour renommer le notebook\n",
    "\n",
    "![image-4.png](attachment:image-4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b4892ce",
   "metadata": {},
   "source": [
    "## Exercices\n",
    "\n",
    "\n",
    "***Exercice 1***  \n",
    "Tapez le code suivant dans la cellule suivante (vous pouvez sélectionner le code, le copier avec la commande windows Ctrl + c, cliquer dans la cellule suivante et utiliser la commande windows Ctrl + v) :\n",
    "    \n",
    "```\n",
    "a = 2\n",
    "b = 5\n",
    "print(a+b)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a368a2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "546c5681",
   "metadata": {},
   "source": [
    "Remarquez qu'un espace apparaît sous la cellule avec le résultat de l'addition. C'est l'effet de la commande `print()` qui renvoie sous la cellule le contenu qui est mis entre parenthèses. Essayez successivement les commandes dans la cellule suivante :\n",
    "`print(a)`\n",
    "puis\n",
    "`print(b)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f97cea5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a9b60454",
   "metadata": {},
   "source": [
    "Vous remarquez que les valeurs affectées à `a` et `b` restent en mémoire et qu'on peut les afficher dans n'importe quelle cellule du document. Maintenant regardons comment changer la valeur attribuée à `b` en recopiant le code qui suit dans la cellule suivante :\n",
    "```\n",
    "print(b)\n",
    "b = 7\n",
    "print(b)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e21f7a4e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a673d05",
   "metadata": {},
   "source": [
    "Vous remarquez que la commande `print(b)` donne deux résultats différents. C'est étrange une commande unique (c'est la même écriture `print(b)` dans les deux cas) qui donne des résultats différents, non ? \n",
    "\n",
    "C'est normal vu que `b` n'est pas interprété comme la lettre 'b' mais bien comme une variable qui contient une information que la commande `print()` affiche. \n",
    "\n",
    "Une analogie : vous vous trouvez face à un rayon de boîtes de conserve au supermarché. Sur une étiquette vous lisez \"haricots\" et sur une autre \"carottes\". Le trublion qui est en vous s'amuse à échanger les étiquettes. Plus tard, une personne inconnue achète deux boîtes de carottes dont une qui contient en fait... des haricots (vous suivez toujours ??). Il ouvre les deux boîtes successivement en disant \"J'ouvre une boîte de carottes !\" avant de verser le contenu dans une assiette : l'action et le message sont identiques et pourtant le résultat dans son assiette sera, lui, bien différent !\n",
    "\n",
    "Revenons à l'informatique : lorsque vous écrivez `b=7`, vous choisissez dans un premier temps d'attribuer \"l'étiquette b\" à la valeur 7. \"b\" représente donc automatiquement un objet, ici un nombre, avec un contenu, ici 7 (dans l'analogie, l'étiquette était posée sur une boîte de conserve, c'est l'objet, avec un contenu, un aliment). \n",
    "\n",
    "Si vous avez tout compris, la commande `print(b)` correspond à afficher le contenu de l'objet qui a pour étiquette `b`.\n",
    "\n",
    "Exercice suivant : recopiez la commande `print(c)` dans la cellule suivante. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeec70e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "673cc07e",
   "metadata": {},
   "source": [
    "Un message d'erreur apparaît ? C'est normal et vous en verrez beaucoup dans votre vie ;-p \n",
    "\n",
    "Pas de panique, l'ordinateur ne va pas s'auto-détruire, il vous indique juste qu'il ne peut pas interpréter la commande `print(c)` tout simplement parce que vous n'avez jamais attribué de valeur à `c` ! Vous l'avez fait pour les variables notées a et b en écrivant `a = 2` et `b = 5` **avant** d'afficher la variable avec la commande `print()`.\n",
    "\n",
    "En Anglais (eh oui il faut s'y mettre) ça donne `NameError: name 'c' is not defined` qui est l'erreur la plus commune à notre niveau. Tentez votre chance une deuxième fois en affectant à la variable notée c la valeur 7 **puis** en l'affichant avec la commande `print()` dans la cellule suivante : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8c0ec01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "abaf33be",
   "metadata": {},
   "source": [
    " Bravo si ça marche ! Vous venez de résoudre votre première erreur. Comme les erreurs reviennent souvent, pensez à les noter sur un carnet avec la solution pour éviter de les refaire. \n",
    " \n",
    "Recopiez maintenant dans la cellule suivante les commandes :\n",
    "```\n",
    "print(b*2)\n",
    "print(b/2)\n",
    "print(b//2)\n",
    "print(b%2)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b4e168e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1f943c3f",
   "metadata": {},
   "source": [
    "À quoi correspondent les deux dernières opérations \"//\" et \"%\" ?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "314d3b8c",
   "metadata": {},
   "source": [
    "Notez votre réponse ici "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e260254",
   "metadata": {},
   "outputs": [],
   "source": [
    "Notez votre réponse ici "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2371d808",
   "metadata": {},
   "source": [
    "Encore un message d'erreur ? `SyntaxError: invalid syntax` ? Eh oui vous n'avez pas utilisé le bon mode pour la cellule : deuxième erreur ! Une piqure de rappel ? Sélectionnez \"Markdown\" pour écrire du texte et \"Code\" pour coder. Autre solution, vous pouvez écrire des commentaires dans le mode \"Code\" de deux manières :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a46abf",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(a) # Comme ça, le texte écrit dans une cellule \"Code\" ne génère pas d'erreur\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "458da539",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(a)\n",
    "'''\n",
    "Où comme ça \n",
    "pour pouvoir écrire \n",
    "de manière \n",
    "lisible sur plusieurs\n",
    "lignes.\n",
    "'''\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8a27e1",
   "metadata": {},
   "source": [
    "***Exercice 2***  \n",
    "Tapez le code suivant :\n",
    "```\n",
    "a = 'Hello'\n",
    "b = ' world'\n",
    "print(a + b)\n",
    "```\n",
    "Remarquez que l'addition (attention à l'ordre) peut être utilisée pour les chaînes de caractères (abréviation \"str\" pour \"string\" en anglais). Pour définir une chaîne de caractères, il faut placer le texte entre guillemets (3 possibilités pour des raisons de conflits avec la chaîne de caractères) : \n",
    "```\n",
    "'texte' \n",
    "\"texte\" \n",
    "\"\"\"texte\"\"\" \n",
    "```\n",
    "Si vous voulez enregistrer une chaîne de caractères comme << ma citation : \"l'avion vole, les mouches aussi\" >> dans la variable (l'étiquette) `a` quelle serait la bonne manière d'écrire ?\n",
    "\n",
    "`a = 'ma citation : \"l'avion vole, les mouches aussi\"'`\n",
    "\n",
    "ou\n",
    "\n",
    "`a = \"ma citation : \"l'avion vole, les mouches aussi\"\"`\n",
    "\n",
    "ou\n",
    "\n",
    "`a = \"\"\"ma citation : \"l'avion vole, les mouches aussi\" \"\"\"`\n",
    "\n",
    "Normalement si vous testez dans la cellule suivante ces lignes, vous verrez la chaîne de caractères en rouge. Si elle n'est pas complète, vous verrez une erreur apparaître à l'exécution de la cellule.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "688a7766",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cae2bbb3",
   "metadata": {},
   "source": [
    "Remarquez que les variables a et b n'ont plus les valeurs 2 et 5. Ce sont des variables dites ré-assignables. Voyez ces lettres comme des étiquettes sur des objets (un nombre, un texte, une liste, un dictionnaire, ... la liste des objets sur Python est grande !). Deux objets différents ne peuvent pas avoir la même étiquette. Vous pouvez donner un nom plus explicite pour repérer vos objets comme :\n",
    "```\n",
    "g = 9.81\n",
    "c = 3e8\n",
    "mon_titre = 'Voici mon titre'\n",
    "\n",
    "print(g)\n",
    "print(c)\n",
    "print(mon_titre)\n",
    "```\n",
    "Pour les récalcitrants à la physique, g est la lettre qui désigne (comme une étiquette), l'accélération de la pesanteur, et c la vitesse de la lumière. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17ef99e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc5b8027",
   "metadata": {},
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  {
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   "id": "d35e0722",
   "metadata": {},
   "source": [
    "***Exercice 3***  \n",
    "Tapez le code suivant\n",
    "```\n",
    "a = [1,2,3,4]\n",
    "b = [5,6,7,8]\n",
    "\n",
    "print(a[0], b[0], a[1], a[-1])\n",
    "print(b)\n",
    "print(b[1:3])\n",
    "print(a*2+b)\n",
    "```\n",
    "Le type d'objet `[nombre 1, nombre 2,...]` est une liste. Chaque valeur est séparée par des virgules. Pour afficher une valeur de la liste il faut utiliser un indice. Le premier élément de la liste est repéré par l'indice 0. a[0] correspond au chiffre 1 et b[0] au chiffre 5. Le dernier élément de la liste s'obtient avec l'indice \"-1\". Pour ne prendre qu'une partie de la liste, utilisez l'opérateur \":\" [indice début : indice fin] (appelé slice en Anglais).\n",
    "Remarquez que l'on peut \"sommer\" des listes = mettre des listes bout à bout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7e87063",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e6b8a54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0be77943",
   "metadata": {},
   "source": [
    "***Exercice 4***  \n",
    "Tapez le code suivant :\n",
    "```\n",
    "a = [1,2,3,4,5,7,10]\n",
    "for v in a:\n",
    "    print(v)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b06a8a47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7beec7b7",
   "metadata": {},
   "source": [
    "Voici une \"boucle for\" utilisée sur les valeurs d'une liste. \n",
    "\n",
    "La notation est importante pour ne pas générer d'erreur (ce n'est pas forcément le cas dans d'autres languages informatiques). Respectez la notation \":\" après la liste qui indique à Python le début du code à exécuter en boucle, puis le retour à la ligne avec incrément (apuyer sur la touche tab) pour décaler le texte à exécuter dans la boucle sur la droite. \n",
    "\n",
    "A quoi sert la \"boucle for\" ? Ici elle permet de parcourir la liste `a` et d'afficher chaque valeur. L'étiquette `v` va être réaffectée une fois l'opération contenue dans la boucle terminée (ici il n'y en a qu'une : `print(v)`) : elle contiendra la valeur 1 puis 2 puis 3 puis 4 puis 5 puis 7 et enfin 10. La liste étant entièrement parcourue, la \"boucle for\" s'arrête.\n",
    "\n",
    "Remarquez que c'est bien la valeur qu'on affiche, pas l'indice affecté à cette valeur. Pour afficher l'indice en même temps on peut utiliser la fonction `enumerate()`. Recopiez et testez : \n",
    "\n",
    "```\n",
    "a = [1,2,3,4,5,7,10]\n",
    "for i,v in enumerate(a):\n",
    "    print(i,v)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164b0308",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "05881b7c",
   "metadata": {},
   "source": [
    "`i` représente l'indice où la valeur `v` est rangée dans la liste `a`. Autrement dit, le code suivant est équivalent au précédent :\n",
    "```\n",
    "for i,v in enumerate(a):\n",
    "    print(i,a[i])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb22afd3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ab98f4e6",
   "metadata": {},
   "source": [
    "Autre solution pour avoir l'indice de la valeur :\n",
    "```\n",
    "j = 0\n",
    "for v in a:\n",
    "    print(j,v)\n",
    "    j = j + 1\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99feec75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9ce2bd14",
   "metadata": {},
   "source": [
    "Là on commence à faire de l'informatique :-p On crée une variable `j` et on initialise sa valeur à `0`. Puis commence la boucle :\n",
    "\n",
    "**Premier round** : la variable v contient la première valeur de la liste autrement dit la valeur `a[j]`, on affiche j (qui vaut `0`) et `v` (qui vaut `1`). Ensuite l'opération `j = j + 1` est effectuée. Un problème mathématique majeur ?? 0 est différent de 0 + 1 non ??? Pas de problème si vous avez compris à quoi le signe \"=\" fait référence : ce n'est pas une égalité mais une affectation (toujours coller une étiquette sur une valeur). Il faut lire la ligne `j = j + 1` comme suit : j'affecte la valeur de `j + 1` à la variable `j` ou autrement dit je réassigne la variable `j` à la valeur `j + 1`. Comme la valeur de `j` est de `0`, la valeur après réaffectation sera de `0 + 1`. Après la ligne `j = j + 1`, la valeur de `j` vaut ... `1` (vous suivez toujours ?).\n",
    "\n",
    "**Deuxième round** : Comme il n'y a plus de lignes après `j = j + 1`, la valeur de `v` change et passe à la deuxième valeur du tableau, soit `2`. La valeur de `j` est de `1` ce qui correspond bien à l'indice du tableau `a` où la valeur `2` est stockée. Autrement dit on a toujours `a[j]` qui vaut `v`. Vous aurez compris la suite : l'opération `j = j + 1` fait passer la valeur de `j` à `2` qui correspond à l'indice du tableau où on trouvera la prochaine valeur de `v`.\n",
    "\n",
    "**Troisième round** : ...\n",
    "\n",
    "**Dernier round** : début `v` vaut 10 et `j` vaut 6 (nombre de valeur de la liste - 1), puis on affiche les valeurs de `j` et `v`, puis `j` augmente de 1.\n",
    "\n",
    "Il intéressant de remarquer que les valeurs de j et i sont différentes en fin de boucle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "675c8d6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58107899",
   "metadata": {},
   "source": [
    "***Exercice 5***\n",
    "\n",
    "Ecrire un code permettant de multiplier par deux les valeurs d'une liste (par exemple a = [1, 2, 3] qui devient a = [2, 4, 6]) sans changer à la main chaque valeur de la liste, évidemment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57b9a1af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c4de6d8a",
   "metadata": {},
   "source": [
    "***Exercice 6***  \n",
    "Tapez le code suivant :\n",
    "```\n",
    "print(sin(90))\n",
    "```\n",
    "La fonction sinus n'est pas reconnue par Python. Il faut importer cette fonction à l'aide d'une bibliothèque. Il existe plusieurs bibliothèques disponibles comportant cette fonction comme \"math\" ou \"numpy\". Vous pouvez soit importer la fonction désirée à l'aide de la commande :\n",
    "```\n",
    "from math import sin\n",
    "\n",
    "print(sin(90))\n",
    "```\n",
    "soit importer l'ensemble des fonctions contenues dans la bibliothèque \n",
    "\n",
    "```\n",
    "import numpy\n",
    "\n",
    "print(numpy.sin(90))\n",
    "a = numpy.radians(90)\n",
    "print(numpy.sin(a))\n",
    "```\n",
    "\n",
    "l'appel de la fonction se fait par \"bibliothèque.nom_fonction\". Pour éviter les noms trops longs, il est possible d'utiliser une étiquette pour nommer la bibliothèque :\n",
    "\n",
    "```\n",
    "import numpy as np\n",
    "\n",
    "print(np.sin(90))\n",
    "```\n",
    "\n",
    "Nous utiliserons toujours cette dernière notation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d0e860a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d3aa451",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e13b7ebe",
   "metadata": {},
   "source": [
    "***Exercice 7***  \n",
    "Tapez le code suivant :\n",
    "```\n",
    "import matplotlib.pyplot as plt #voici une nouvelle bibliothèque très utile en Physique-Chimie\n",
    "a = [1,2,3,4,5,6]\n",
    "b = [4,2,1,3,6,10]\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(a,b)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(b,a)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(a,b,'r+')\n",
    "plt.show()\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1294618",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "67ac07be",
   "metadata": {},
   "source": [
    "Bienvenue dans le monde des graphiques ! Vous pouvez tracer une liste de points en fonction d'une autre. Vérifier l'ordre des listes. Quelle est la liste qui correspond aux abscisses ? "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e726251",
   "metadata": {},
   "source": [
    "Notez votre réponse ici"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8801fd6",
   "metadata": {},
   "source": [
    "***Exercice 8***  \n",
    "Il est possible d'utiliser plusieurs bibliothèques en même temps. Par exemple, créer des histogrammes avec matplotlib.pyplot de nombres aléatoires distribués selon une \"loi gaussienne\" avec numpy.random.normal(centre,écart type,nombre de valeurs).\n",
    "```\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "plt.hist(np.random.normal(0,1,1000), bins = 50)\n",
    "plt.show()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5a89b15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d7e50735",
   "metadata": {},
   "source": [
    "***Exercice 9***\n",
    "\n",
    "Ecrire un code permettant de **tracer** k! (exemple : 4! = 4 x 3 x 2 x 1, 6! = 6 x 5 x 4 x 3 x 2 x 1) en fonction de k. On pourra s'aider d'une \"boucle for\". On tracera par exemple une liste [1!, 2!, 3!, ..., 10!] en fonction d'une autre liste [1, 2, 3, ..., 10].  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff01a61b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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