modifié par LL

module2/exo1/toy_notebook_fr.ipynb

@@ -4,8 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 1 À propos du calcul de $\\pi$\n",
-    "\n"
+    "# 1 À propos du calcul de $\\pi$"
    ]
   },
   {
@@ -19,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mon ordinateur m’indique que p vaut approximativement"
+    "Mon ordinateur m’indique que $\\pi$ vaut approximativement"
    ]
   },
   {
@@ -37,7 +36,7 @@
    ],
    "source": [
     "from math import *\n",
-    "print(pi)\n"
+    "print(pi)"
    ]
   },
   {

module2/exo2/exercice-02-2.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 2 2ème partie - LL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "essais avec des données simples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = np.arange(1, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.5"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.average(data1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(data1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(data1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "données exercice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14. ,  7.6, 11.2, 12.8, 12.5,  9.9, 14.9,  9.4, 16.9, 10.2, 14.9,\n",
+       "       18.1,  7.3,  9.8, 10.9, 12.2,  9.9,  2.9,  2.8, 15.4, 15.7,  9.7,\n",
+       "       13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2, 16.2, 18.7,  8.9,\n",
+       "       11.9, 12.1, 14.6, 12.1,  4.7,  3.9, 16.9, 16.8, 11.3, 14.4, 15.7,\n",
+       "       14. , 13.6, 18. , 13.6, 19.9, 13.7, 17. , 20.5,  9.9, 12.5, 13.2,\n",
+       "       16.1, 13.5,  6.3,  6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6,\n",
+       "       19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15. , 14.3, 16.8, 14. ,\n",
+       "        6.8,  8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8,\n",
+       "       22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2,  8.9,\n",
+       "       21. ])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Moyenne"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.average(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Minimum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maximum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ecart-Type avec **numpy**, avec ddof = 0 puis ddof = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.312369534258399"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.334094455301447"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.std(data,ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Médiane et écrat-type avec le module **statistics**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.median(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.334094455301447"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "statistics.stdev(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "np.array([1, 4, 2, 5, 3])"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +27,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo3/exercice 2-3.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 2 3ème partie - LL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14. ,  7.6, 11.2, 12.8, 12.5,  9.9, 14.9,  9.4, 16.9, 10.2, 14.9,\n",
+       "       18.1,  7.3,  9.8, 10.9, 12.2,  9.9,  2.9,  2.8, 15.4, 15.7,  9.7,\n",
+       "       13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2, 16.2, 18.7,  8.9,\n",
+       "       11.9, 12.1, 14.6, 12.1,  4.7,  3.9, 16.9, 16.8, 11.3, 14.4, 15.7,\n",
+       "       14. , 13.6, 18. , 13.6, 19.9, 13.7, 17. , 20.5,  9.9, 12.5, 13.2,\n",
+       "       16.1, 13.5,  6.3,  6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6,\n",
+       "       19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15. , 14.3, 16.8, 14. ,\n",
+       "        6.8,  8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8,\n",
+       "       22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2,  8.9,\n",
+       "       21. ])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3caf2f9ef0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "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": [
+    "plt.hist(data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exercice 2 3ème partie - LL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('seaborn-white')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14. ,  7.6, 11.2, 12.8, 12.5,  9.9, 14.9,  9.4, 16.9, 10.2, 14.9,\n",
+       "       18.1,  7.3,  9.8, 10.9, 12.2,  9.9,  2.9,  2.8, 15.4, 15.7,  9.7,\n",
+       "       13.1, 13.2, 12.3, 11.7, 16. , 12.4, 17.9, 12.2, 16.2, 18.7,  8.9,\n",
+       "       11.9, 12.1, 14.6, 12.1,  4.7,  3.9, 16.9, 16.8, 11.3, 14.4, 15.7,\n",
+       "       14. , 13.6, 18. , 13.6, 19.9, 13.7, 17. , 20.5,  9.9, 12.5, 13.2,\n",
+       "       16.1, 13.5,  6.3,  6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6,\n",
+       "       19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15. , 14.3, 16.8, 14. ,\n",
+       "        6.8,  8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8,\n",
+       "       22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2,  8.9,\n",
+       "       21. ])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f3caf2f9ef0>]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "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": [
+    "plt.hist(data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +135,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Journal de bord accessible à tout le monde"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "exercice 1 3ème partie"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "exercice 2 4ème partie"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +45,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-