Hackforge_Demo_Election_Results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hackforge - Intro to Python for Data Exploration\n",
    "With opendata.citywindsor.ca\n",
    "\n",
    "December 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some Python Basics we'll use during this talk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 3\n",
    "d = 7.9\n",
    "e = \"2018 Windsor Election Results\"\n",
    "f = True\n",
    "g = 2021-12-31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What are the datatypes of our variables?\n",
    "type(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Python Libraries\n",
    "# Install your libraries using Anaconda\n",
    "\n",
    "import datetime\n",
    "\n",
    "g = datetime.datetime(2021, 12, 31)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Who won Windor's 2018 Election for Mayor?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/450px-Pandas_logo.svg.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Explore the Election Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Pandas libraries\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe = pd.read_csv(\"https://opendata.citywindsor.ca/Uploads/detailedresults-2018.csv\", encoding='unicode_escape')\n",
    "\n",
    "\n",
    "# dataframe = pd.read_excel(\"https://opendata.citywindsor.ca/Uploads/Election2014.xlsx\", encoding='unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What type is our variable named dataframe?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# How many records and attributes did we load?\n",
    "\n",
    "dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What types of data did Python auto-detect?\n",
    "dataframe.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# What does the top and bottom of my tabular DataFrame look like?\n",
    "\n",
    "dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Or, use Slicing\n",
    "\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe[\"Contest Title\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# selecting rows based on condition\n",
    "\n",
    "df = dataframe[dataframe['Contest Title'] == 'MAYOR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[560:565]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Who are the canidates?\n",
    "pd.unique(df['Candidate Name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group the canidates and their votes together\n",
    "total_votes = df.groupby([\"Candidate Name\"])[\"Total\"].sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use Markdown to Create a Heading Two Section Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_votes.plot.bar()\n",
    "plt.ylabel('Total Votes')\n",
    "plt.title(e)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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