Why data exploration matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the CSV file into a DataFrame.

Pandas

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

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Aread_csv

Bread_excel

Cto_csv

Dread_json

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using 'read_excel' which is for Excel files, not CSV.

We use pd.read_csv to load CSV files into a DataFrame.

2fill in blank

medium

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

Pandas

print(data.[1]())

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Ahead

Bsample

Cinfo

Dtail

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'tail' which shows the last rows.

Using 'info' which shows summary info, not rows.

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

3fill in blank

hard

Fix the error in the code to get summary statistics of the DataFrame.

Pandas

summary = data.[1]()

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Asummary

Bdescribe

Cstats

Dinfo

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'summary' or 'stats' which are not pandas methods.

Using 'info' which gives data types, not statistics.

The describe() method returns summary statistics like mean and count.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using 'word' instead of 'len(word)' for the value.

We want the length of each word (len(word)) and only words longer than 3 (len(word) > 3).

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Pandas

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k.lower()' instead of 'k.upper()' for keys.

Using '<' instead of '>' causing wrong filtering.

Keys are converted to uppercase with k.upper(), values are v, and we filter values greater than 0 using >.