pandas_intro_answers.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a simple Python notebook demonstrating the power of pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported 392744 rows\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('2018_General_Election_Returns.csv')\n",
    "\n",
    "len_df = len(df)\n",
    "\n",
    "print(f'Imported {len_df} rows')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 173 offices\n"
     ]
    }
   ],
   "source": [
    "#printing all offices\n",
    "offices = list(df['Office'].drop_duplicates())\n",
    "num_offices = len(offices)\n",
    "print(f'Found {num_offices} offices')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total votes: Cruz, O'Rourke, Dikeman\n",
      "Total votes: 8369175\n"
     ]
    }
   ],
   "source": [
    "#Finding results for first office\n",
    "first_office = offices[0]\n",
    "\n",
    "office_df = df[df['Office'] == first_office]\n",
    "\n",
    "#Printing who ran in that office\n",
    "candidates = ', '.join(list(office_df['Name'].drop_duplicates()))\n",
    "print(f'Total votes: {candidates}')\n",
    "\n",
    "#Printing total votes\n",
    "total_votes = office_df['Votes'].sum()\n",
    "print(f'Total votes: {total_votes}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print list of all of the offices\n",
    "print(', '.join(offices))\n",
    "\n",
    "#Pick an office and list the candidates running for that office\n",
    "my_office_df = df[df['Office'] == 'U.S. Rep 32']\n",
    "print(', '.join(list(my_office_df['Name'].drop_duplicates())))"
   ]
  }
 ],
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