exercise homework

module2/exo4/data.csv

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module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "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,\n",
+    "                 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,\n",
+    "                 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,\n",
+    "                 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,\n",
+    "                 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,\n",
+    "                 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,\n",
+    "                 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": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"data.csv\", data, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compute statistics:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "average:  14.113000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"average: \", np.mean(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mininum:  2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"mininum: \", np.min(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "maximum:  23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"maximum: \", np.max(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "std:  4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"std: \", np.std(data, ddof=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_style(\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +200,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-