Forward fill and backward fill in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to fill missing values forward in the DataFrame column.

Pandas

df['A'] = df['A'].[1]()

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

Afillna

Bffill

Cbfill

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'bfill' which fills missing values backward.

Using 'fillna' without specifying method.

Using 'dropna' which removes missing values instead of filling.

The ffill() method fills missing values by propagating the last valid observation forward.

2fill in blank

medium

Complete the code to fill missing values backward in the DataFrame column.

Pandas

df['B'] = df['B'].[1]()

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

Afillna

Bdropna

Cbfill

Dffill

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ffill' which fills missing values forward.

Using 'fillna' without specifying method.

Using 'dropna' which removes missing values instead of filling.

The bfill() method fills missing values by using the next valid observation backward.

3fill in blank

hard

Fix the error in the code to forward fill missing values in the DataFrame.

Pandas

df.fillna(method=[1], inplace=True)

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

A'ffill'

B'backward'

C'forward'

D'bfill'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'forward' instead of 'ffill'.

Using 'backward' or 'bfill' which fills backward.

Using method names without quotes.

The correct method name for forward fill in fillna is 'ffill'.

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

{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 the word itself as the value instead of its length.

Checking if the word is greater than 3 instead of its length.

Using incorrect comparison operators.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than 0.

Pandas

{ [1]: [2] for [3], [2] in data.items() if [2] > 0 }

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

Ak.upper()

Bv

Ck

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original key instead of uppercase.

Using wrong variable names in the loop.

Not filtering values greater than 0.

The comprehension maps the uppercase key k.upper() to its value v. It iterates over k, v in data.items() and filters values greater than 0.