Diagrams and graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import matplotlib for graphs\n",
    "import  matplotlib\n",
    "from matplotlib import pyplot\n",
    "\n",
    "# set matplotlib to work inline\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import data file\n",
    "import pandas as pd\n",
    "\n",
    "data_unscaled = pd.read_csv('data_processed_unscaled.csv')\n",
    "data_scaled = pd.read_csv('data_processed_scaled.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "averages_unscaled = data_unscaled['AVG']\n",
    "averages_scaled = data_scaled['AVG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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w0fVN6BHxq8BLmXl89eYuu3ZdtjEi7omIuYiYW1hYuMQwJa3XRrhPsNHVqaHfDLw/In4Z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\nCBO6JBXiHwHUuIsgq+0THwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115aec438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# frequency by age\n",
    "pyplot.scatter(averages_unscaled, data_unscaled['AGE'])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JiK9FxB9HxDvLD1Uaj/XQzmjpqf2GJveImAKeBO4FdgD7I2JHz7AzwFxm/n3gS8Anyw5U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\nLWRyl6QWMrlLUguZ3CWphf4/tqxm/iHOMl8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c87278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.scatter(averages_scaled, data_scaled['AGE'])\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}