05. Libraries/1. Numpy/07. Standard Deviation and Variance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8442854683830507"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard Devation = a measure of how spread out a group of number is from the Mean\n",
    "np.std(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4013888888888886"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Variance = Measure of the average degree to which each number is Different to the mean\n",
    "# Higher variance = wider range of numbers\n",
    "# Lower variance = Lower rage of Numbers\n",
    "np.var(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8442854683830507"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standard devaition = square root of Variance\n",
    "np.sqrt(np.var(a2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Demo of Standard Deviation and Variance\n",
    "high_var_array = np.array([1,100,200,300,4000,5000])\n",
    "low_var_array = np.array([2,4,6,8,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4296133.472222221, 8.0)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(high_var_array), np.var(low_var_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2072.711623024829, 2.8284271247461903)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(high_var_array), np.std(low_var_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600.1666666666667, 6.0)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(high_var_array), np.mean(low_var_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.hist(high_var_array)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PB97V/WcgjwL2VtWKeYvgCvQ04IPdR+avBt5bVR/pd6TveRXwnu7wxmHgFT3PA0B3LPiFwG/0PcsJVXVbkpuATzM4pHEnK+dU+/cneQrwXeCqqnqgjyGS3ABcCqxJsgC8EXgTsDfJlQz+Ufzlqd2fp/5LUhvOqkMukqRTM+iS1AiDLkmNMOiS1AiDLkmNMOiS1AiDLkmN+H9/vrL5GK9SygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(low_var_array)\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}