Working with CSV files

I worked with txt files and py files the most but I extended my reach to csv files for data organization and learned about the pandas extension that helps a lot with organizing information.

data = pandas.read_csv('2018_Central_Park_Squirrel_Census_-_Squirrel_Data.csv')

squirrel_data = data.to_dict()

squirrel_color_dict = squirrel_data['Primary Fur Color']

squirrel_colors = []

squirrel_color_count = {}

for color in squirrel_color_dict.values():

        if color not in squirrel_colors:

                squirrel_colors.append(color)

for color in squirrel_colors:

        if color != 'NaN':

                if color not in squirrel_color_count.keys():

                        squirrel_color_count[color] = 0

for squirrel in squirrel_color_dict.values():

        squirrel_color_count[squirrel] += 1

squirrels = {'fur colors':list(squirrel_color_count.keys()),

             'number of':list(squirrel_color_count.values())}

print(squirrels)

print(pandas.DataFrame(squirrels))

With a piece of data that recorded the information of squirrels in New York parks, I tried to generate a code that would organize just the fur color of the squirrels. Although this code worked, it gave me information I didn't need and was too long and wordy. I didn't use the functions of the pandas library well either.

data = pandas.read_csv('2018_Central_Park_Squirrel_Census_-_Squirrel_Data.csv')

gray_squirrel_count = len(data[data['Primary Fur Color']=='Gray'])

red_squirrel_count = len(data[data['Primary Fur Color']=='Cinnamon'])

black_squirrel_count = len(data[data['Primary Fur Color']=='Black'])

Squirrel_data = {'Fur color':['Gray','Red','Black'],

                 'Number':[gray_squirrel_count,red_squirrel_count,black_squirrel_count]} #make it yourself

sql_dta = pandas.DataFrame(Squirrel_data)

print(sql_dta)

sql_dta.to_csv('Squirrel_fur_color.csv')

I thought being able to make the code ready for any amount of change in the information was the best code but I realized if I'm the one the read the code and the code has little information, I could just adjust and set the variables as I pleased. The code above assumes I know the categories in which the information is organized.

The harder, confusing part of this challenge was that I kept forgetting how to use the data[data[] ==_].

It really just means within the data, you have to find the column where the data of a column matches that column name.

Things I learned

• Can create a list specifically for csv files by importing csv

  Can list up all rows of a csv file with csv.reader(file_name)

import csv weather_list = []with open('weather_data.csv') as data:    read_data = csv.reader(data)    for row in read_data:        weather_list.append(row)print(weather_list)

• can read file in one short code using pandas library:

import pandas pandas.read_csv('weather_data.csv')

• can create a data table from dictionary by using DataFrame function preinstalled in pandas

food = {'food':['grapes','berries','apples'],        'price':[20, 10, 45]}food_data = pandas.DataFrame(food)print(food_data)

• can create a new csv file with pandas

food_data.to_csv('food_data.csv')