Data validation checks in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to check if there are any missing values in the DataFrame.

Pandas

missing = df.[1]()

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Afillna

Bdropna

Cisnull

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows instead of checking for missing values.

Using fillna() replaces missing values but does not check for them.

The isnull() function returns a DataFrame of booleans indicating missing values.

2fill in blank

medium

Complete the code to count the number of missing values in each column.

Pandas

missing_counts = df.isnull().[1]()

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Asum

Bcount

Cmean

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using count() counts non-missing values, not missing ones.

Using mean() gives proportion, not count.

The sum() function adds up True values (missing) per column.

3fill in blank

hard

Fix the error in the code to check if all values in column 'age' are positive.

Pandas

all_positive = (df['age'] [1] 0).all()

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A<

B<=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' checks for negative values.

Using '==' checks for equality, not positivity.

We want to check if all ages are greater than 0, so use the '>' operator.

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 '<' includes shorter words, not longer.

Using 'word' as value stores the word, not its length.

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 dictionary of uppercase keys and values greater than zero.

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() changes keys to lowercase.

Using '<' filters for negative values.

Keys are uppercase (k.upper()), values are v, and filter values greater than zero (v > 0).