Introduction¶

Welcome to the Learn Pandas track. These hands-on exercises are targeted for someone who has worked with Pandas a little before. Each page has a list of relevant resources you can use if you get stumped. The top item in each list has been custom-made to help you with the exercises on that page.

The first step in most data analytics projects is reading the data file. In this section, you'll create Series and DataFrame objects, both by hand and by reading data files.

Relevant Resources¶

Set Up¶

Run the code cell below to load libraries you will need (including coad to check your answers).

import pandas as pd
pd.set_option('max_rows', 5)
from learntools.advanced_pandas.creating_reading_writing import *

Checking Answers¶

You can check your answers in each of the exercises that follow using the check_qN function provided in the code cell above (replacing N with the number of the exercise). For example here's how you would check an incorrect answer to exercise 1:

check_q1(pd.DataFrame())

False

For the questions that follow, if you use check_qN on your answer, and your answer is right, a simple True value will be returned.

If you get stuck, you may run the print(answer_qN()) function to print the answer outright.

Exercises¶

Exercise 1: Create a DataFrame that looks like this:

pd.DataFrame({'Apples': [30], 'Bananas': [21]})

print(check_q1(pd.DataFrame({'Apples': [30], 'Bananas': [21]})))
print('-------')
print(answer_q1())

True
-------
pd.DataFrame({'Apples': [30], 'Bananas': [21]})
None

Exercise 2: Create the following DataFrame:

pd.DataFrame({'Apples': [35, 41], 
              'Bananas': [21, 34]},
             index=['2017 Sales', '2018 Sales'])

print(check_q2(pd.DataFrame({'Apples': [35, 41], 
              'Bananas': [21, 34]},
             index=['2017 Sales', '2018 Sales'])))
print('-------')
print(answer_q2())

True
-------
pd.DataFrame(
    {'Apples': [35, 41], 'Bananas': [21, 34]},
    index=['2017 Sales', '2018 Sales']
)
None

Exercise 3: Create a Series that looks like this:

Flour     4 cups
Milk       1 cup
Eggs     2 large
Spam       1 can
Name: Dinner, dtype: object

pd.Series(['4 cups', '1 cup', '2 large', '1 can'], index=['Flour', 'Milk', 'Eggs', 'Spam'], name='Dinner')

Flour     4 cups
Milk       1 cup
Eggs     2 large
Spam       1 can
Name: Dinner, dtype: object

print(check_q3(pd.Series(['4 cups', '1 cup', '2 large', '1 can'], index=['Flour', 'Milk', 'Eggs', 'Spam'], name='Dinner')))
print('-------')
print(answer_q3())

True
-------
pd.Series(['4 cups', '1 cup', '2 large', '1 can'], 
index=['Flour', 'Milk', 'Eggs', 'Spam'], 
name='Dinner')
None

Exercise 4: Read the following csv dataset on wine reviews into the a DataFrame:

The filepath to the CSV file is ../input/wine-reviews/winemag-data_first150k.csv.

pd.read_csv("../input/wine-reviews/winemag-data_first150k.csv", index_col=0)

print(check_q4(pd.read_csv("../input/wine-reviews/winemag-data_first150k.csv", index_col=0)))
print('-------')
print(answer_q4())

True
-------
pd.read_csv("../input/wine-reviews/winemag-data_first150k.csv", index_col=0)
None

Exercise 6**: Suppose we have the following DataFrame:

q6_df = pd.DataFrame({'Cows': [12, 20], 'Goats': [22, 19]}, index=['Year 1', 'Year 2'])

Save this DataFrame to disc as a csv file with the name cows_and_goats.csv.

q6_df.to_csv("cows_and_goats.csv")

print(check_q6(q6_df.to_csv("cows_and_goats.csv")))
print('-------')
print(answer_q6())

True
-------
q6_df.to_csv("cows_and_goats.csv")
None

Exercise 7: This exercise is optional. Read the following SQL data into a DataFrame:

The filepath is ../input/pitchfork-data/database.sqlite. Hint: use the sqlite3 library. The name of the table is artists.

import sqlite3
conn = sqlite3.connect("../input/pitchfork-data/database.sqlite")
pd.read_sql_query("SELECT * FROM artists", conn)

print(check_q7(pd.read_sql_query("SELECT * FROM artists", conn)))
print('-------')
print(answer_q7())

True
-------
import sqlite3
conn = sqlite3.connect("../input/pitchfork-data/database.sqlite")
pd.read_sql_query("SELECT * FROM artists", conn)
None

Keep going¶

Move on to the indexing, selecting and assigning workbook

This is part of the Learn Pandas series.

	country	description	designation	points	price	province	region_1	region_2	variety	winery
0	US	This tremendous 100% varietal wine hails from ...	Martha's Vineyard	96	235.0	California	Napa Valley	Napa	Cabernet Sauvignon	Heitz
1	Spain	Ripe aromas of fig, blackberry and cassis are ...	Carodorum Selección Especial Reserva	96	110.0	Northern Spain	Toro	NaN	Tinta de Toro	Bodega Carmen Rodríguez
...	...	...	...	...	...	...	...	...	...	...
150928	France	A perfect salmon shade, with scents of peaches...	Grand Brut Rosé	90	52.0	Champagne	Champagne	NaN	Champagne Blend	Gosset
150929	Italy	More Pinot Grigios should taste like this. A r...	NaN	90	15.0	Northeastern Italy	Alto Adige	NaN	Pinot Grigio	Alois Lageder

	reviewid	artist
0	22703	massive attack
1	22721	krallice
...	...	...
18829	2413	don caballero
18830	3723	neil hamburger

	Apples	Bananas
2017 Sales	35	21
2018 Sales	41	34