average_height_US_President.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[189 170 189 163 183 171 185 168 173 183 173 173 175 178 183 193 178 173\n",
      " 174 183 183 168 170 178 182 180 183 178 182 188 175 179 183 193 182 183\n",
      " 177 185 188 188 182 185]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv(r'C:\\Users\\sneha\\average_height_of_US_President\\president_heights.csv')\n",
    "heights = np.array(data['height(cm)'])\n",
    "print(heights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have this data array, we can compute a variety of summary statistics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean height:        179.73809523809524\n",
      "Standard deviation: 6.931843442745892\n",
      "Minimum height:     163\n",
      "Maximum height:     193\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean height:       \", heights.mean())\n",
    "print(\"Standard deviation:\", heights.std())\n",
    "print(\"Minimum height:    \", heights.min())\n",
    "print(\"Maximum height:    \", heights.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25th percentile:    174.25\n",
      "Median:             182.0\n",
      "75th percentile:    183.0\n"
     ]
    }
   ],
   "source": [
    "print(\"25th percentile:   \", np.percentile(heights, 25))\n",
    "print(\"Median:            \", np.median(heights))\n",
    "print(\"75th percentile:   \", np.percentile(heights, 75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn; seaborn.set()  # set plot style"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(heights)\n",
    "plt.title('Height Distribution of US Presidents')\n",
    "plt.xlabel('height (cm)')\n",
    "plt.ylabel('number');"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}