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]

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

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.

Practice

1. What does the forward fill method do in pandas when handling missing data?

easy

A. It copies the next known value backward to fill missing spots.

B. It copies the last known value forward to fill missing spots.

C. It replaces missing values with zero.

D. It removes rows with missing values.

Solution

1. Step 1: Understand forward fill concept

   Forward fill means filling missing data by copying the last valid value forward.

2. Step 2: Compare options with definition

   It copies the last known value forward to fill missing spots. matches this exactly, while others describe different methods.

3. Final Answer:

   It copies the last known value forward to fill missing spots. -> Option B

4. Quick Check:

   Forward fill = copy last known value forward [OK]

Hint: Forward fill copies previous value downwards [OK]

Common Mistakes:

• Confusing forward fill with backward fill
• Thinking forward fill removes missing data
• Assuming forward fill replaces with zero

2. Which of the following is the correct pandas syntax to perform a backward fill on a DataFrame df?

easy

A. df.fillna(method='backfill')

B. df.fillna(method='ffill')

C. df.fillna(backward=True)

D. df.fillna(direction='backward')

Solution

1. Step 1: Recall pandas fillna syntax

   Backward fill uses method='backfill' or method='bfill' in fillna.

2. Step 2: Check options for correct syntax

   df.fillna(method='backfill') uses method='backfill', which is correct. Others are invalid or incorrect syntax.

3. Final Answer:

   df.fillna(method='backfill') -> Option A

4. Quick Check:

   Backward fill syntax = method='backfill' [OK]

Hint: Use method='backfill' for backward fill in fillna [OK]

Common Mistakes:

• Using method='ffill' for backward fill
• Passing invalid parameters like backward=True
• Confusing method names in fillna

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, None, None, 4, None]})
df_filled = df.fillna(method='ffill')

What is the content of df_filled?

medium

A. [1, None, None, 4, 1]

B. [1, 1, 1, 4, None]

C. [1, None, None, 4, None]

D. [1, 1, 1, 4, 4]

Solution

1. Step 1: Understand forward fill on the DataFrame

   Forward fill copies last valid value downwards to fill missing values.

2. Step 2: Apply forward fill step-by-step

   Index 0: 1 (valid)
   Index 1: filled with 1
   Index 2: filled with 1
   Index 3: 4 (valid)
   Index 4: filled with 4

3. Final Answer:

   [1, 1, 1, 4, 4] -> Option D

4. Quick Check:

   Forward fill propagates last valid value forward [OK]

Hint: Forward fill fills missing with last valid above [OK]

Common Mistakes:

• Thinking forward fill leaves trailing NAs unfilled
• Failing to propagate through consecutive NAs
• Confusing forward fill with backward fill

4. Consider this code snippet:

import pandas as pd
df = pd.DataFrame({'B': [None, 2, None, 4]})
df.fillna(method='bfill', inplace=True)

After running this, what is the content of column 'B' in df?

medium

A. [2, 2, 4, 4]

B. [None, 2, None, 4]

C. [2, 2, 2, 4]

D. [None, 2, 4, 4]

Solution

1. Step 1: Understand backward fill with inplace=True

   Backward fill copies next valid value upwards, modifying df directly.

2. Step 2: Apply backward fill to column 'B'

   Index 0: None filled with next valid 2
   Index 1: 2 (valid)
   Index 2: None filled with next valid 4
   Index 3: 4 (valid)

3. Final Answer:

   [2, 2, 4, 4] -> Option A

4. Quick Check:

   Backward fill copies next valid value up [OK]

Hint: Backward fill copies next valid value upward [OK]

Common Mistakes:

• Not using inplace=True and expecting df to change
• Confusing backward fill with forward fill
• Leaving None values unchanged

5. You have a DataFrame df with missing values scattered in multiple columns. You want to fill missing values by first forward filling, then backward filling any remaining missing values. Which code snippet correctly achieves this?

hard

A. df.fillna(method='ffill', method='bfill')

B. df.fillna(method='bfill').fillna(method='ffill')

C. df.fillna(method='ffill').fillna(method='bfill')

D. df.fillna(method=['ffill', 'bfill'])

Solution

1. Step 1: Understand chaining fillna methods

   First forward fill fills missing values from previous valid data, then backward fill fills remaining missing values from next valid data.

2. Step 2: Analyze options for correct chaining

   df.fillna(method='ffill').fillna(method='bfill') chains fillna with method='ffill' then method='bfill', which is correct.
   df.fillna(method='bfill').fillna(method='ffill') reverses order, which may fill differently.
   Options C and D use invalid syntax.

3. Final Answer:

   df.fillna(method='ffill').fillna(method='bfill') -> Option C

4. Quick Check:

   Chain forward then backward fill to cover all missing [OK]

Hint: Chain fillna with ffill then bfill to fill all gaps [OK]

Common Mistakes:

• Using invalid syntax with multiple methods in one fillna
• Reversing order and missing some fills
• Expecting one fillna call to do both fills