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‎Hackforge_Demo_Election_Results.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Hackforge - Intro to Python for Data Exploration\n",
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"With opendata.citywindsor.ca\n",
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"\n",
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"December 2021"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Some Python Basics we'll use during this talk"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"c = 3\n",
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"d = 7.9\n",
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"e = \"2018 Windsor Election Results\"\n",
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"f = True\n",
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"g = 2021-12-31"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# What are the datatypes of our variables?\n",
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"type(g)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importing Python Libraries\n",
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"# Install your libraries using Anaconda\n",
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"\n",
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"import datetime\n",
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"\n",
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"g = datetime.datetime(2021, 12, 31)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(g)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Who won Windor's 2018 Election for Mayor?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"![title](https://upload.wikimedia.org/wikipedia/commons/thumb/e/ed/Pandas_logo.svg/450px-Pandas_logo.svg.png)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load and Explore the Election Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load Pandas libraries\n",
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"\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataframe = pd.read_csv(\"https://opendata.citywindsor.ca/Uploads/detailedresults-2018.csv\", encoding='unicode_escape')\n",
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"\n",
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"\n",
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"# dataframe = pd.read_excel(\"https://opendata.citywindsor.ca/Uploads/Election2014.xlsx\", encoding='unicode_escape')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# What type is our variable named dataframe?\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# How many records and attributes did we load?\n",
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"\n",
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"dataframe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# What types of data did Python auto-detect?\n",
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"dataframe.dtypes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# What does the top and bottom of my tabular DataFrame look like?\n",
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"\n",
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"dataframe."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Or, use Slicing\n",
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"\n",
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"dataframe"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dataframe[\"Contest Title\"].value_counts()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# selecting rows based on condition\n",
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"\n",
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"df = dataframe[dataframe['Contest Title'] == 'MAYOR']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df[560:565]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.shape\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Who are the canidates?\n",
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"pd.unique(df['Candidate Name'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Group the canidates and their votes together\n",
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"total_votes = df.groupby([\"Candidate Name\"])[\"Total\"].sum().sort_values(ascending = False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_votes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use Markdown to Create a Heading Two Section Title"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"total_votes.plot.bar()\n",
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"plt.ylabel('Total Votes')\n",
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"plt.title(e)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎Hackforge_Demo_Traffic.ipynb

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