100 Days of Code: The Complete Python Pro Bootcamp for 2022 - Day 25 (US States Game)

Working with csv data and the Panda Libraries in python

main.py

# with open("weather_data.csv") as data_file:
#     data = data_file.readlines()
#     print(data)

# import csv

# with open("weather_data.csv") as data_file:
#     data = csv.reader(data_file)
#     for row in data:
#         print(row)

# with open("weather_data.csv") as data_file:
#     data = csv.reader(data_file)
#     temperatures = []
#     for row in data:
#         if row[1] != "temp":
#             temperatures.append(int(row[1]))
#
#     print(temperatures)

import pandas

# data = pandas.read_csv("weather_data.csv")
# print(type(data))
# print(type(data["temp"]))

# data_dict = data.to_dict()
# # print(data_dict)
#
# temp_list = data["temp"].to_list()
# # print(len(temp_list))
#
# average = sum(temp_list) / len(temp_list)
# print(average)
#
# print(data["temp"].mean())
#
# print(data["temp"].max())
#
# # Get data in columns
# print(data["condition"])
# print(data.condition)

# Get data in the row
# print(data[data.day == "Monday"])
# print(data[data.temp == data.temp.max()])

# monday = data[data.day == "Monday"]
# print(monday.condition)
#
# monday = data[data.day == "Monday"]
# print(monday.temp)
#
# monday = data[data.day == "Monday"]
# temp_conv = (monday.temp * 1.8 + 32)
# print(temp_conv)

# Create a data frame from scratch
data_dict = {
    "students": ["Amy", "James", "Angela"],
    "scores": [76, 56, 65]
}

data = pandas.DataFrame(data_dict)
# print(data)
data.to_csv("new_data.csv")

weather_data.csv

day,temp,condition
Monday,12,Sunny
Tuesday,14,Rain
Wednesday,15,Rain
Thursday,14,Cloudy
Friday,21,Sunny
Saturday,22,Sunny
Sunday,24,Sunny

new_data.csv

,students,scores
0,Amy,76
1,James,56
2,Angela,65

The Great Squirrel Census data with pandas

main.py

# How many coloured squirrels per color
# Create a new csv with this data (squirrel_count.csv)

import pandas

data = pandas.read_csv("2018_Central_Park_Squirrel_Census_Squirrel_Data.csv")
gray_squirrels_count = len(data[data["Primary Fur Color"] == "Gray"])
red_squirrels_count = len(data[data["Primary Fur Color"] == "Cinnamon"])
black_squirrels_count = len(data[data["Primary Fur Color"] == "Black"])
print(gray_squirrels_count)
print(red_squirrels_count)
print(black_squirrels_count)

data_dict = {
    "Fur Color": ["Gray", "Cinnamon", "Black"],
    "Count": [gray_squirrels_count, red_squirrels_count, black_squirrels_count]
}

data_frame = pandas.DataFrame(data_dict)
data_frame.to_csv("squirrel_count.csv")

squirrel_count.csv

,Fur Color,Count
0,Gray,2473
1,Cinnamon,392
2,Black,103

2018_Central_Park_Squirrel_Census_Squirrel_Data.csv

# download from https://data.cityofnewyork.us/Environment/2018-Central-Park-Squirrel-Census-Squirrel-Data/vfnx-vebw

US States Game

main.py

import turtle
import pandas

screen = turtle.Screen()
screen.title("U.S States Game")
image = "blank_states_img.gif"
screen.addshape(image)
turtle.shape(image)

data = pandas.read_csv("50_states.csv")
all_states = data.state.to_list()
guessed_states = []

while len(guessed_states) < 50:
    answer_state = screen.textinput(title=f"{len(guessed_states)}/50 States Correct",
                                    prompt="Whats another state's name?").title()
    if answer_state == "Exit":
        missing_states = []
        for state in all_states:
            if state not in guessed_states:
                missing_states.append(state)
        new_data = pandas.DataFrame(missing_states)
        new_data.to_csv("states_to_learn.csv")
        break
    if answer_state in all_states:
        guessed_states.append(answer_state)
        t = turtle.Turtle()
        t.hideturtle()
        t.penup()
        state_date = data[data.state == answer_state]
        t.goto(int(state_date.x), int(state_date.y))
        t.write(answer_state)

50_States.csv

state,x,y
Alabama,139,-77
Alaska,-204,-170
Arizona,-203,-40
Arkansas,57,-53
California,-297,13
Colorado,-112,20
Connecticut,297,96
Delaware,275,42
Florida,220,-145
Georgia,182,-75
Hawaii,-317,-143
Idaho,-216,122
Illinois,95,37
Indiana,133,39
Iowa,38,65
Kansas,-17,5
Kentucky,149,1
Louisiana,59,-114
Maine,319,164
Maryland,288,27
Massachusetts,312,112
Michigan,148,101
Minnesota,23,135
Mississippi,94,-78
Missouri,49,6
Montana,-141,150
Nebraska,-61,66
Nevada,-257,56
New Hampshire,302,127
New Jersey,282,65
New Mexico,-128,-43
New York,236,104
North Carolina,239,-22
North Dakota,-44,158
Ohio,176,52
Oklahoma,-8,-41
Oregon,-278,138
Pennsylvania,238,72
Rhode Island,318,94
South Carolina,218,-51
South Dakota,-44,109
Tennessee,131,-34
Texas,-38,-106
Utah,-189,34
Vermont,282,154
Virginia,234,12
Washington,-257,193
West Virginia,200,20
Wisconsin,83,113
Wyoming,-134,90