matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9dd7f01550>]"
      ]
     },
     "execution_count": 7,
     "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "under_18_users = [10, 100, 190, 300]\n",
    "above_40_users = [20, 50, 90, 40]\n",
    "\n",
    "year = [2008, 2009, 2010, 2011]\n",
    "\n",
    "plt.plot(year, under_18_users)\n",
    "plt.plot(year,above_40_users)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "under_18_users = [10, 100, 190, 300]\n",
    "above_40_users = [20, 50, 90, 40]\n",
    "\n",
    "year_one = [2008, 2009, 2010, 2011]\n",
    "year_two = [2007, 2006, 2012, 2015]\n",
    "\n",
    "plt.bar(year_one, under_18_users)\n",
    "plt.bar(year_two, above_40_users)\n",
    "\n",
    "plt.ioff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "pie() got an unexpected keyword argument 'label'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-8c81b772effe>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'car_name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mpg'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpie\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcars\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mcars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: pie() got an unexpected keyword argument 'label'"
     ]
    },
    {
     "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": [
    "file_path = '/home/azi/Desktop/Ex_Files_Python_Data_Science_EssT_Pt_1/Exercise Files/Data/mtcars.csv'\n",
    "\n",
    "cars = pd.read_csv(file_path)\n",
    "cars.columns = ['car_name', 'mpg', 'cyl', 'disp', 'hp', 'drat', 'wt', 'qsec', 'vs', 'am', 'gear', 'carb']\n",
    "cars.head()\n",
    "cars = cars[0:5]\n",
    "import matplotlib.pyplot as plt\n",
    "plt.bar(cars['car_name'], cars['mpg'])\n",
    "\n",
    "plt.pie(cars['hp'])\n",
    "cars.plot()"
   ]
  }
 ],
 "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
}