Why data I/O matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file into a pandas DataFrame.

Pandas

import pandas as pd
data = pd.[1]('data.csv')

Drag options to blanks, or click blank then click option'

Aread_csv

Bto_csv

Cread_excel

Dto_excel

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_csv instead of read_csv

Using read_excel for CSV files

Use pd.read_csv to load CSV data into a DataFrame.

2fill in blank

medium

Complete the code to save a DataFrame to an Excel file.

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2]})
df.[1]('output.xlsx')

Drag options to blanks, or click blank then click option'

Aread_excel

Bto_excel

Cread_csv

Dto_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel instead of to_excel

Using to_csv for Excel files

Use to_excel to save a DataFrame as an Excel file.

3fill in blank

hard

Fix the error in the code to correctly read a JSON file into a DataFrame.

Pandas

import pandas as pd
data = pd.[1]('data.json')

Drag options to blanks, or click blank then click option'

Ato_json

Bread_csv

Cread_json

Dto_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using to_json instead of read_json

Using read_csv for JSON files

Use read_json to load JSON data into a DataFrame.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

Pandas

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Bword

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the value

Using word > 3 which compares strings to numbers

The dictionary maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 letters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their lengths only if length is less than 5.

Pandas

words = ['apple', 'bat', 'carrot', 'dog']
result = { [1]: [2] for w in words if [3] }

Drag options to blanks, or click blank then click option'

Aw.upper()

Blen(w)

Clen(w) < 5

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper()

Using len(w) > 5 instead of less than 5

The keys are uppercase words using w.upper(). The values are their lengths with len(w). The condition filters words shorter than 5 letters.