update codes_various_exercises

module2/exo1/toy_notebook_en.ipynb

@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -46,7 +46,7 @@
        "3.128911138923655"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "df =  [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, \n",
+    "               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, \n",
+    "               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, \n",
+    "               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, \n",
+    "               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,\n",
+    "               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]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "avg = round(np.average(df),2)\n",
+    "print(avg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = round(np.mean(df),2)\n",
+    "print(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "standard_Dev = round(np.std(df, ddof=1),2)\n",
+    "print(standard_Dev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "minimum = round(np.min(df),2)\n",
+    "print(minimum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "maximum = round(np.max(df),2)\n",
+    "print(maximum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = round(np.median(df),2)\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +151,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo2/exercise2_2-3.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "df =  [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, \n",
+    "               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, \n",
+    "               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, \n",
+    "               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, \n",
+    "               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,\n",
+    "               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]\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.11\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean = round(np.mean(df),2)\n",
+    "print(mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.33\n"
+     ]
+    }
+   ],
+   "source": [
+    "standard_Dev = round(np.std(df, ddof=1),2)\n",
+    "print(standard_Dev)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "minimum = round(np.min(df),2)\n",
+    "print(minimum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "maximum = round(np.max(df),2)\n",
+    "print(maximum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "median = round(np.median(df),2)\n",
+    "print(median)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df, color='r', linewidth=2.0)\n",
+    "plt.axis([0,100,0,25])\n",
+    "plt.title('sequence plot of the Dataframe')\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(*args, **kw)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df)\n",
+    "plt.title('Histogram of the Dataframe')\n",
+    "plt.show"
+   ]
+  },
+  {
+   "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
+}