example from Lynn Root's workshop.

pyladies_practice_parsing

from collections import Counter   #collections is a standard library, Counter is capitalized
import csv

import matplotlib.pyplot as plt   #renames the pyplot function from third party library
import numpy.numarray as na             # best practices: import in alphabetical order

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

def parse(raw_file, delimiter):
        """ csv file -> JSON-like object"""


        # open csv file
        opened_file = open(raw_file) #open is a built-in function in python

        # read the csv data
        csv_data = csv.reader(opened_file, delimiter=delimiter) #using the csv module we imported

        # parse data into python data type
        parsed_data = [] #create an empty list (actually will be a dictionary, see below)

        fields = csv_data.next()  # reads the first line to get the header row

        #iterate over the rest of the rows

        for row in csv_data:
                parsed_data.append(dict(zip(fields, row)))   #makes pairs of field:row --> into a dictionary

        # close the csv file

        opened_file.close()  #just to be safe

        # return the parsed data

        return parsed_data


def visualize_days():
        """ visualize data by day of week
        for each row of the data file, sum the values for each key -> return a new dict

        i.e. how many Mondays are there, put those together --> line graph"""

        data_file = parse(MY_FILE, ",")

        counter = Counter(item["DayOfWeek"] for item in data_file) #called a set comprehension because it's inside of a co$

        #want to get the days of the week in order

        data_list = [counter["Monday"], counter["Tuesday"], counter["Wednesday"],
                                counter["Thursday"], counter["Friday"],
                                counter["Saturday"], counter["Sunday"]]


        #make x-axis labels, preserve order, immutable format

        day_tuple = tuple(["Mon", "Tues", "Wed", "Thurs", "Fri", "Sat", "Sun"])

        plt.plot(data_list)  #use the plot function from matplotlib

        plt.xticks(range(len(day_tuple)), day_tuple) #number of ticks, did this in the most flexible way, labels

        plt.savefig("Days.png")   #filename

        plt.clf() #close figure
        
               #should go back and try to add y-axis labels

def visualize_type():
        """what kind of crime -> bar graph"""

        data_file = parse(MY_FILE, ",")

        counter = Counter(item["Category"] for item in data_file)

        labels = tuple(counter.keys())   #this way you don't have to know what they are ahead of time

        xlocations = na.array(range(len(labels))) + 0.5  #na is numpy, 0.5 is the offset she determine empirically

        width = 0.5  #units are not known, see docs if you really want to know

        plt.bar(xlocations, counter.values(), width=width) #left, height, width

        plt.xticks(xlocations + width/2, labels, rotation=90) #centered labels and make vertical

        plt.subplots_adjust(bottom=0.4)   #just so it's not cut off

        plt.rcParams["figure.figsize"] = 12, 8   #plot scale, units unknown

        plt.savefig("Type.png")

        plt.clf()

        #should go back and try to add y-axis labels

def main():
        #visualize_days()
        visualize_type()
if __name__== "__main__":
        main()