graph.py

"""
Data Visualization Project

Parse data from an ugly CSV or Excel file, and render it in
JSON-like form, visualize in graphs, and plot on Google Maps.

Part II: Take the data we just parsed and visualize it using popular
Python math libraries.
"""

from collections import Counter

import csv
import matplotlib.pyplot as plt
import numpy as np


MY_FILE = "../data/sample_sfpd_incident_all.csv"


def parse(raw_file, delimiter):
    """Parses a raw CSV file to a JSON-like object"""

    # Open CSV file, and safely close it when we're done
    opened_file = open(raw_file)

    # Read the CSV data
    csv_data = csv.reader(opened_file, delimiter=delimiter)

    # Setup an empty list
    parsed_data = []

    # Skip over the first line of the file for the headers
    fields = next(csv_data)

    # Iterate over each row of the csv file, zip together field -> value
    for row in csv_data:
        parsed_data.append(dict(zip(fields, row)))

    # Close the CSV file
    opened_file.close()

    return parsed_data


def visualize_days():
    """Visualize data by day of week"""
    data_file = parse(MY_FILE, ",")
    # Returns a dict where it sums the total values for each key.
    # In this case, the keys are the DaysOfWeek, and the values are
    # a count of incidents.
    counter = Counter(item["DayOfWeek"] for item in data_file)

    # Separate out the counter to order it correctly when plotting.
    data_list = [
                  counter["Monday"],
                  counter["Tuesday"],
                  counter["Wednesday"],
                  counter["Thursday"],
                  counter["Friday"],
                  counter["Saturday"],
                  counter["Sunday"]
                ]
    day_tuple = tuple(["Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"])

    # Assign the data to a plot
    plt.plot(data_list)

    # Assign labels to the plot from day_list
    plt.xticks(range(len(day_tuple)), day_tuple)

    # Save the graph!
    # If you look at new-coder/dataviz/tutorial_source, you should see
    # the PNG file, "Days.png".  This is our graph!
    plt.savefig("Days.png")

    # Close figure
    plt.clf()


def visualize_type():
    """Visualize data by category in a bar graph"""
    data_file = parse(MY_FILE, ",")
    # Same as before, this returns a dict where it sums the total
    # incidents per Category.
    counter = Counter(item["Category"] for item in data_file)

    # Set the labels which are based on the keys of our counter.
    labels = tuple(counter.keys())

    # Set where the labels hit the x-axis
    xlocations = np.arange(len(labels)) + 0.5

    # Width of each bar
    width = 0.5

    # Assign data to a bar plot
    plt.bar(xlocations, counter.values(), width=width)

    # Assign labels and tick location to x-axis
    plt.xticks(xlocations + width / 2, labels, rotation=90)

    # Give some more room so the labels aren't cut off in the graph
    plt.subplots_adjust(bottom=0.4)

    # Make the overall graph/figure larger
    plt.rcParams['figure.figsize'] = 12, 8

    # Save the graph!
    # If you look at new-coder/dataviz/tutorial_source, you should see
    # the PNG file, "Type.png".  This is our graph!
    plt.savefig("Type.png")

    # Close figure
    plt.clf()


def main():
    visualize_days()
    visualize_type()


if __name__ == "__main__":
    main()