Why end-to-end analysis matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load data from a CSV file using pandas.

Pandas

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

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

Aread_csv

Bread_excel

Cto_csv

Dread_json

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'to_csv' instead of 'read_csv' which saves data instead of loading it.

Using 'read_excel' when the file is a CSV.

The read_csv function loads data from a CSV file into a DataFrame.

2fill in blank

medium

Complete the code to display the first 5 rows of the DataFrame.

Pandas

print(data.[1]())

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

Ahead

Binfo

Csample

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tail()' which shows the last rows instead of the first.

Using 'info()' which shows summary info, not data rows.

The head() method shows the first 5 rows of the DataFrame by default.

3fill in blank

hard

Fix the error in the code to filter rows where the 'score' column is greater than 50.

Pandas

filtered = data[data['score'] [1] 50]

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

A<

B<=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' or '<' which select smaller or equal values.

Using '==' which selects only values exactly equal to 50.

To select rows where 'score' is greater than 50, use the '>' operator.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if [2]

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

Alen(word)

Blen(words) > 3

Clen(word) > 3

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using len(words) which is the total number of words, not length of each word.

Using word > 3 which compares a string to a number and causes error.

The key is the word, the value is its length using len(word). The condition filters words longer than 3 characters with len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores if the score is above 70.

Pandas

result = { [1]: [2] for word, score in data.items() if score [3] 70 }

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

Aword.upper()

Bscore

C>

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.lower() instead of uppercase.

Using '<' or '==' instead of '>' for filtering scores.

The dictionary keys are uppercase words using word.upper(). The values are the scores. The filter keeps scores greater than 70.