Forward fill and backward fill in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of forward fill on this DataFrame?

Given the DataFrame below, what is the result after applying df.ffill()?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, np.nan, 4, np.nan], 'B': [np.nan, 2, np.nan, np.nan, 5]})
result = df.ffill()

A {'A': [1.0, 1.0, 1.0, 4.0, 4.0], 'B': [nan, 2.0, 2.0, 2.0, 5.0]}

B {'A': [1.0, nan, nan, 4.0, nan], 'B': [nan, 2.0, nan, nan, 5.0]}

C {'A': [1.0, 1.0, 1.0, 4.0, nan], 'B': [nan, 2.0, 2.0, 2.0, 5.0]}

D {'A': [1.0, 1.0, 1.0, 4.0, 4.0], 'B': [nan, 2.0, nan, nan, 5.0]}

💡 Hint

Forward fill replaces NaN with the last known non-NaN value going down the column.

Forward fill fills missing values by propagating the last valid observation forward. So NaNs after a number get replaced by that number until a new number appears.

❓ Predict Output

intermediate

What is the output of backward fill on this DataFrame?

Given the DataFrame below, what is the result after applying df.bfill()?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [np.nan, 3, np.nan, 7, np.nan], 'Y': [1, np.nan, np.nan, 4, 5]})
result = df.bfill()

A {'X': [3.0, 3.0, 7.0, 7.0, 7.0], 'Y': [1.0, 4.0, 4.0, 4.0, 5.0]}

B {'X': [3.0, 3.0, 7.0, 7.0, nan], 'Y': [1.0, 4.0, 4.0, 4.0, 5.0]}

C {'X': [nan, 3.0, 7.0, 7.0, nan], 'Y': [1.0, nan, nan, 4.0, 5.0]}

D {'X': [nan, 3.0, nan, 7.0, nan], 'Y': [1.0, nan, nan, 4.0, 5.0]}

💡 Hint

Backward fill replaces NaN with the next known non-NaN value going down the column.

Backward fill fills missing values by propagating the next valid observation backward. So NaNs before a number get replaced by that number until a new number appears.

❓ data_output

advanced

How many missing values remain after forward fill with limit?

Given the DataFrame below, after applying df.ffill(limit=1), how many NaN values remain?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [np.nan, np.nan, 2, np.nan, np.nan, 5]})
result = df.ffill(limit=1)

A 0

B 2

C 1

D 3

💡 Hint

Limit controls how many consecutive NaNs get filled forward.

With limit=1, only one NaN after a valid value is filled. The first two NaNs remain because no previous value exists. The NaN after 2 is filled once, but the next NaN remains.

❓ visualization

advanced

Which plot correctly shows the effect of forward fill on missing data?

You have a time series with missing values. Which plot below correctly shows the original data and the data after forward fill?

Pandas

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

dates = pd.date_range('2024-01-01', periods=6)
values = [1, np.nan, np.nan, 4, np.nan, 6]
df = pd.DataFrame({'Value': values}, index=dates)
df_ffill = df.ffill()

plt.figure(figsize=(8,4))
plt.plot(df.index, df['Value'], 'o-', label='Original')
plt.plot(df_ffill.index, df_ffill['Value'], 's--', label='Forward Fill')
plt.legend()
plt.title('Forward Fill Effect')
plt.xlabel('Date')
plt.ylabel('Value')
plt.grid(True)
plt.tight_layout()
plt.show()

A Plot with original line having gaps at NaNs and forward fill line connecting those gaps with previous values

B Plot with original line continuous and forward fill line with gaps at NaNs

C Plot with both lines continuous and identical

D Plot with original line flat and forward fill line increasing linearly

💡 Hint

Forward fill fills gaps by extending last known value, so forward fill line has no gaps.

Original data has gaps where NaNs are. Forward fill replaces NaNs with last known value, so line is continuous without gaps at those points.

🧠 Conceptual

expert

What happens if you apply forward fill then backward fill on a DataFrame with missing values?

Consider a DataFrame with missing values scattered. If you apply df.ffill() followed by df.bfill(), what is the resulting effect on missing data?

A All missing values are filled except those at the start and end of the DataFrame

B Only missing values between two valid values are filled; missing values at edges remain

C All missing values are filled because forward fill fills down and backward fill fills up

D No missing values are filled because the two methods cancel each other out

💡 Hint

Forward fill fills down, backward fill fills up, so combined they fill all gaps.

Forward fill fills missing values by propagating last valid value downward. Backward fill fills missing values by propagating next valid value upward. Together, they fill all missing values except if entire column is NaN.

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