week7_p53_exercise2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data array\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 30,  40,  50,  60,  40],\n",
       "       [200, 300, 500, 600, 300],\n",
       "       [ 10,  20,  20,  20,  20],\n",
       "       [  4,   4,   1,   2,   5]], dtype=int64)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age = np.array([30, 40, 50, 60, 40])\n",
    "income = np.array([200, 300, 500, 600, 300])\n",
    "worked = np.array([10, 20, 20, 20, 20])\n",
    "vacation = np.array([4, 4, 1, 2, 5])\n",
    "\n",
    "data = np.array([age, income, worked, vacation]).astype('int64')\n",
    "np.set_printoptions(precision=2, suppress=True)\n",
    "\n",
    "print(\"Data array\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Population covariance matrix\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  104.  ,  1480.  ,    28.  ,   -10.8 ],\n",
       "       [ 1480.  , 21600.  ,   360.  ,  -176.  ],\n",
       "       [   28.  ,   360.  ,    16.  ,    -1.6 ],\n",
       "       [  -10.8 ,  -176.  ,    -1.6 ,     2.16]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Population covariance matrix\n",
    "popCovMatrix = np.cov(data, bias=True)\n",
    "\n",
    "print(\"Population covariance matrix\")\n",
    "popCovMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(popCovMatrix, annot=True, fmt='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample covariance matrix\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[  130. ,  1850. ,    35. ,   -13.5],\n",
       "       [ 1850. , 27000. ,   450. ,  -220. ],\n",
       "       [   35. ,   450. ,    20. ,    -2. ],\n",
       "       [  -13.5,  -220. ,    -2. ,     2.7]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample covariance matrix\n",
    "sampleCovMatrix = np.cov(data, bias=False)\n",
    "\n",
    "print(\"Sample covariance matrix\")\n",
    "sampleCovMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(sampleCovMatrix, annot=True, fmt='g')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}