Matplotlib_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy  as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median height of goalkeepers: 191.0\n",
      "Median height of other players: 184.0\n"
     ]
    }
   ],
   "source": [
    "positions = ['GK', 'M', 'A', 'D']\n",
    "heights = [191, 184, 185, 180]\n",
    "\n",
    "np_heights = np.array(heights)\n",
    "np_positions = np.array(positions)\n",
    "\n",
    "gk_heights = np_heights[np_positions == \"GK\"]\n",
    "other_heights = np_heights[np_positions != \"GK\"]\n",
    "\n",
    "print(\"Median height of goalkeepers: \" + str(np.median(gk_heights)))\n",
    "print(\"Median height of other players: \" + str(np.median(other_heights)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "year = [1950, 1970, 1990, 2010]\n",
    "pop = [2.519, 3.692, 5.263, 6.972]\n",
    "plt.plot(year, pop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(year, pop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "values = [0, 0.6, 1.4, 1.6, 2.2, 2.5, 2.6, 3.2, 3.5, 3.9, 4.2, 6]\n",
    "plt.hist(values, bins=3) #default value for bins= 10\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "year = [1950, 1951, 1952, 2100]\n",
    "pop = [2.538, 2.57, 2.62, 10.85]\n",
    "plt.plot(year, pop)\n",
    "\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Population\")\n",
    "plt.title(\"World Population Projections\")\n",
    "plt.yticks([0, 2, 4, 6, 8, 10],\n",
    "           [\"0\", \"2B\", \"4B\", \"6B\", \"8B\", \"10B\"])\n",
    "plt.show()\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "232e827413a02f55ab2a4e0202aa953f4c3a3c8f9ad034ca3a58e95699cce70a"
  },
  "kernelspec": {
   "display_name": "Python 3.10.1 64-bit",
   "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"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}