03 - Bar chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -------------------------------------------------------------\n",
    "# Create the bar chart for the temperature variation\n",
    "# -------------------------------------------------------------\n",
    "\n",
    "# Import pyplot\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of cities and temperatures\n",
    "x_cities = ['NewYork', 'London', 'Dubai', 'New Delhi','Tokyo']\n",
    "y_temp = [75,65,105,98,90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temperature')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Change the chart labels\n",
    "plt.title(\"Temperaure Variation\")\n",
    "plt.xlabel(\"Cities\")\n",
    "plt.ylabel(\"Temperature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "# Create the Bar chart\n",
    "plt.bar(x_cities,y_temp)\n",
    "\n",
    "# Show the Plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}