Update toy_notebook_fr.ipynb

parent 7a91ce37
{
"cells": [
{
"cell_type": "markdown",
"cells": [],
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Titre du document"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"hideCode": false,
"hidePrompt": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"x=10\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20\n"
]
}
],
"source": [
"x = x + 10\n",
"print(x)"
]
},
{
"cell_type": "markdown",
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"### Completion"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"mu, sigma = 100, 15"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"source": [
"x = np.random.normal(loc=mu, scale=sigma, size =10000)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"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": [
"%matplotlib inline\n",
"plt.hist(x)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Utilisation d'autres langages"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The rpy2.ipython extension is already loaded. To reload it, use:\n",
" %reload_ext rpy2.ipython\n"
]
}
],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2FXSgeSG7FvOLcRndHQFVV35Jc9Hc668yGZrJ8zkMcasyNHar1KyYZlA6inC6DnafF3ffey4imgedlNlpIn95R+66s8D189opRuWjhGbBR1zUPnLEcwXhQ/QU6bt62eb2Ct2OTuWsNFH4xKLBoFlwtTrPStzz7LOeKwgLLu/vaBO5133JrLD2zy8xKh1FNAve+vfRNxmr472CsGDpqtCzEmvFefHFvINC+9/g4jENN4WcYOvA4yQrO7pTbe+lEGwK+JxFL0zyXgbBpgCPC31QKGTi0SJDng9DsBcHo1s0HsRd8enmCbGddRiq4gkEe1IivyT94WTeYZseZGz1q7yj+geCPflxMLs9CThHrr0gtxi6YgIODJCakiaco+a77sxDsAkobZzDnOPUzbodAK12Mfb5G7yj+oeY4DV9hqq9I3wluUGzKk9us1tE297mfl/qQnuHvZ/4SXW8oCV49Os/74n/TtUm+Q/0+GZwzarP5sWUZzCm6AMpwUXy37j8OFXbDJAHZQ9oJSQfM0BKcLarcoa6M5kEeZLYVV4vHJGBlODSsDLGcluo2masTWo6dBKTkAkgJZjNb7ViYbi6s6yKx58fE1v7hsodXTnm8Re18Ij6MpMXj4uf+4WYYJY1fa7a6o0VE1uOUHmXuLRLyx7h29yXTIrsHTFTXZAbycmvROxXt00QEBOsD+PnM1ZgK7yz4Lue0v8o7X5UFaT/OunaySb8OwzBQRBXKjUp6XcWjNgtNesnqQriOhfsle2vm1YgOAhayH+yB2bcWTBevvZO+1hVEIf85e0ofPYXCA6CJb1L2P4otxGVWXF57Jz9tKogU9+qYFuacc7MGwgOhrl2R6dc9wXbmjia7b1bb984UyMdPYXPJgzB1IFg4kAwcSCYONQFl2fs8XgIm7P9ijGpGANxwWftr71p2+22oKzDS2Mc8w3LR3+IC26XydgVm9uCqbMYq0gwxdh7fSAu2HU/sLPbHYhk+fg880uD0jEA6oLl+4HN3B4G9pTrk72t7q0eS0Nc8LRBJc753dwW7G52me2OVP/s1rLQElw0PqZplXHYznmxjnFVTqO3trD3Pue+4ES3qA5V3hHIHxHdfK37gvKP4+KmCy9wJAhagjvNLy/srW7e/Cu2AyynsduU787mK51X265265IytqR0ohmmJQwGUoKvy29IlkWr2mbhp1LzjVt59mPdpSbf/cWuSLmxm3QEvz9ICT4uu1H5VuUU+Wh88PU7C3aNlBqn486CUtd7uAkGvLTOA1KCK54vYCzjJVXbfP+K1IxOu7OgIKKEsY0D3brE/8rYGd6j0fSClGC21TZ6kP28YhfnzmVVZ6Qb22pC277uC1ZEvt0vPt9twWHbmyNth7glqS+0BLMbW/cpjxApShj0cVLVGctyv/GoqnNtc2bVP7jFu3YWc0nPAIgJ9stE+SrqBTNMA6sToSY4SZ5mf7Y55ywTQqgJ7ntQakZuNzoN/Qg1wYfsPxeviLXqbakgMLHgWVH2Xp4zBmrnp1eajsn3340M5hU87/VStq2JRe8fmQfzltWJk0eEdc/x2w8oYt6yOg75yztI/PA74pi3rM6QlYxdDzdFeTgrY96yOoXJbfradmqL4YOSZdN/ULvN2blzz3BPRCfMXFbnbFap1hBeXLdPWd5jqLpttkR+tijKq6qMRQi1sjop8pP8l35WtY39GmMFjcXkIxwBZXWWPueiTozG1ISQKJ8xzFmsZpPK58GuUjoWhNt1sNfUGOb8Br+xR2oG7vLbz50I6SKwPFxMPsLRXlbHUbeDPEy9hucKcwo+Ebb51IxW6m6fzOt4OKuLyilWTINmwbaUjEn1c0wi+FzbqIjhymdmuSO7zlR77ra1T+/NwSdlLJoF31fB2IYG580hOF66Apo+Wv/9mhjNghvIl6qLG500g+AL7eW2ykt35ft3W/TsiBOaBa+ouUxu61b3XGGA4NOd5dZd8Bn762+Gf697IiZC+1l0rusmzxWvIZlGHKLt0n/NV+6zbrc7IKVm1RNgLpj3caE3RXNHfqk8M9yxuKS4Lu6HZK/RhSGHhQQXRM7Z9W6Sn7n/rlUdV+Y1ujDksJDgVPlVuZT1qraZNrDY+Wk3//3oYiHBPeWXXVe/r2ob56dxUeMtOuiEDxYSnCqPyx+9jnNU6lhIcEHEpz9MaaV5/t2K6zySsQwWEsx+mzl0sebSJePDkhqH0GvRlhLMg8+HOVl+pPg5QE1DqAnuII89/HCl0WnoR6gJds0CnLrG6DT0I9QEr+pVxs5HXDM6Df0INcFsRnh0k1B62TrkBDMWWqNhQlBwaAHBxIFg4ugluDCr8G4dNVCcFVr3HYNAJ8EfRvaNVPccKBDSbL2bDAitcybV6CN4V2cnc76cfvfOQfGrXGJu3ELOUYmhj2BX6bf0lOCD+WTJHKk5q25iu5BDH8FTV0nNOnXFG/3ztXzQP/oq56jE0EfwiagL7GK08q7Uk2/LYTeSeR/4iaHTSdbuZo4EdSO+AuHoi/bYr7lHpQWug4kDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxDGx4BUJjlGhPLKXD+YVnNYp37nMaw5boBLzCo6Xh/V2DqFa3WIwr2DX9BpD9mkLAswruO8Wxm6Gh/TofB7oJThzSabK7a/G9hvTWN2MHMAbnQT36j6zR3eV7z86M7cV+O8FlNFH8Oo3pSZlRfDBQLDoI3i0PGnCbqtWSbc0+gie9ZnUfPFR8MFAsOgj+Gr49pvp4V6TwgPx6FT57NzAhAFnVSUG+GDeymeACwIqn1360UWHZK25AQ4IqHy2fbiL2B7aMgNcEFf5bOknQaYEeCKu8hkEmwIBlc9uAcGmQNx1MASbAggmDgQTR5zgjX+LVeRPDzwogHvvFxG15n0iot5/r4ioD9Su8mtuoHwDUYNgf4gpdjNmq4ioi+aKiPr9myKiXmyrpjcEu4DgIIBgCA4CCIbgIIDgIIBg4oK7nhERdZyQabOWeNVW5cGeUSKiXm6vprdAwWJeiS3UXDjLFyXFIqI6RUzAq/IXK1AwMAMQTBwIJg4EEweCiQPBxIFg4kAwcUQJLq5Wo0aNNnxjlg2tLtcF3tDwwfjzvKPyzvfrZ+63Z3PPtTKqqlxFCT5fm3/MxDG/k1Rcr/192agXeUflnG/u/TsrRjl453orqqpcRQk+Wp9/zEwmq0iLlyTX4HdrsTIq53xz0xjLeJR3rreiqspVlOA9f3Y8HJvNO6qsYsJr0g91eIaWowrId1I7AbnKUVXlKkrw4V5Hbo7wGu2iFVnFyGHSD0+qnfHFX1T++W58MldArnJUVbmKPIsu/QPvEcOyion9pB8ezuEcVYZrvosb5AjI1RVVJuBcRQk+d1g6M739m+OGHHB5JGNn/uhjQLqmqLzzXdVIPnnmnWtlVFW5ihK8/vGT5W/9H++o8n9VQe3NZX268I7KOd+rdU/KH5xzvRVVVa7CDtHvPvpQ/EmuEfNq1JAuAC+wb//6YAt+s4Hcjso33/nVpUvVGnmcc70dVU2uuJNFHAgmDgQTB4KJA8HEgWDiQDBxIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBbmQKGG9jNBDsBgRbnZJOf3miY9Hehv0d/0hnbE2jv8Rc+tfHO3X/NgGCLc6yOGfF4J2Z1daz9U+zMw8eZKlJtz+yap2r6AjBFmfHo2tvSkfiWoyVVb80O46xG78vvfUxsxVjGyHY6qTZa75cmFlP+une7En31qtXr9a5Wx8TuzK2F4KtT17M5Mz/cLKb1S4vqqzGeOtjRpL01xiCLc60MU5ntymZ96Sxz/7KLvwpm+3tf/vjQK2zZckQbHEuNX/siTY3Mp8a/HSDnYytbfTUczv+9ZHyyDMfPGF0gvwJLcGVULzcvSsQTBwIJk4oCg4pIJg4EEwcCCYOBBMHgokDwcSBYOJAMHEgmDgQTBwIJg4EEweCiQPBxIFg4vw/C6hxe08+0jwAAAAASUVORK5CYII=\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"plot(cars)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"hide_code_all_hidden": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
......@@ -218,9 +16,10 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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