CO Sin Tax Revenue 2015-2018.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np    # linear algebra\n",
    "import pandas as pd   # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "import matplotlib.pyplot as plt         #plotting functions\n",
    "import matplotlib.ticker as mtick       #tick locating and formatting\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data was retrieved from:\n",
    "\n",
    "\n",
    "References:\n",
    "https://matplotlib.org/3.1.1/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"Year\": [2015, 2016, 2017, 2018],\n",
    "                  \"Marijuana\": [102395175, 156701018, 223465054, 263766809],\n",
    "                  \"Alcohol\": [42074458, 44116397, 45671281, 46671281],\n",
    "                  \"Tobacco\": [197016599, 201209069, 199998395, 193880659],\n",
    "                  \"Lottery\": [128000000, 143600000, 133500000, 140700000],\n",
    "                  \"Gambling\": [110251037, 116993800, 117217775, 124714406]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Marijuana</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Tobacco</th>\n",
       "      <th>Lottery</th>\n",
       "      <th>Gambling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>102395175</td>\n",
       "      <td>42074458</td>\n",
       "      <td>197016599</td>\n",
       "      <td>128000000</td>\n",
       "      <td>110251037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>156701018</td>\n",
       "      <td>44116397</td>\n",
       "      <td>201209069</td>\n",
       "      <td>143600000</td>\n",
       "      <td>116993800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>223465054</td>\n",
       "      <td>45671281</td>\n",
       "      <td>199998395</td>\n",
       "      <td>133500000</td>\n",
       "      <td>117217775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>263766809</td>\n",
       "      <td>46671281</td>\n",
       "      <td>193880659</td>\n",
       "      <td>140700000</td>\n",
       "      <td>124714406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Marijuana   Alcohol    Tobacco    Lottery   Gambling\n",
       "0  2015  102395175  42074458  197016599  128000000  110251037\n",
       "1  2016  156701018  44116397  201209069  143600000  116993800\n",
       "2  2017  223465054  45671281  199998395  133500000  117217775\n",
       "3  2018  263766809  46671281  193880659  140700000  124714406"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ax = plt.gca()\n",
    "\n",
    "#ax.get_yaxis().get_major_formatter().set_scientific(False)\n",
    "\n",
    "#df.plot(x=\"Year\", y=[\"Marijuana\", \"Alcohol\", \"Tobacco\", \"Lottery\", \"Gambling\"], color=['red', 'orange', 'yellow', 'green', 'blue'])"
   ]
  },
  {
   "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": [
    "ax = plt.gca()\n",
    "ax.get_yaxis().get_major_formatter().set_scientific(False)\n",
    "\n",
    "df.plot(kind='line',x='Year',y='Marijuana', color='red', ax=ax)\n",
    "df.plot(kind='line',x='Year',y='Alcohol', color='orange', ax=ax)\n",
    "df.plot(kind='line',x='Year',y='Tobacco', color='yellow', ax=ax)\n",
    "df.plot(kind='line',x='Year',y='Lottery', color='green', ax=ax)\n",
    "df.plot(kind='line',x='Year',y='Gambling', color='blue', ax=ax)\n",
    "\n",
    "plt.title(\"Colorado 'Sin Tax' Revenue 2015-2018\")\n",
    "plt.xlabel(\"Year\")\n",
    "plt.ylabel(\"Dollars\")\n",
    "\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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}