Forward fill and backward fill in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of forward fill on missing data?

Given the following pandas DataFrame with missing values, what will be the result after applying forward fill (ffill)?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, None, None, 4, None, 6]})
result = df.ffill()
print(result)

A

     A
0  1.0
1  1.0
2  NaN
3  4.0
4  4.0
5  6.0

B

     A
0  1.0
1  NaN
2  NaN
3  4.0
4  NaN
5  6.0

C

     A
0  1.0
1  1.0
2  1.0
3  4.0
4  4.0
5  6.0

D

     A
0  NaN
1  NaN
2  NaN
3  4.0
4  4.0
5  6.0

💡 Hint

Forward fill replaces missing values with the last known non-missing value above it.

Forward fill (ffill) fills missing values by propagating the last valid observation forward. So, missing values after 1.0 become 1.0 until a new non-missing value appears.

❓ Predict Output

intermediate

What is the output of backward fill on missing data?

Given the following pandas DataFrame with missing values, what will be the result after applying backward fill (bfill)?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'A': [1, None, None, 4, None, 6]})
result = df.bfill()
print(result)

A

     A
0  1.0
1  NaN
2  NaN
3  4.0
4  NaN
5  6.0

B

     A
0  1.0
1  4.0
2  4.0
3  4.0
4  6.0
5  6.0

C

     A
0  1.0
1  1.0
2  1.0
3  4.0
4  4.0
5  6.0

D

     A
0  NaN
1  4.0
2  4.0
3  4.0
4  6.0
5  NaN

💡 Hint

Backward fill replaces missing values with the next known non-missing value below it.

Backward fill (bfill) fills missing values by propagating the next valid observation backward. So, missing values before 4.0 become 4.0, and the missing value before 6.0 becomes 6.0.

❓ data_output

advanced

How many missing values remain after forward fill?

Given this DataFrame, how many missing values remain after applying forward fill on column 'B' only?

Data Analysis Python

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [np.nan, 5, np.nan, 7]})
result = df.copy()
result['B'] = result['B'].ffill()
missing_count = result['B'].isna().sum()
print(missing_count)

A 1

B 0

C 2

D 3

💡 Hint

Forward fill cannot fill missing values at the start if no previous value exists.

The first value in column 'B' is NaN and has no previous value to fill from, so it remains NaN. The other NaN at index 2 is filled with 5. So, one missing value remains.

❓ visualization

advanced

Which plot shows the effect of backward fill on missing data?

You have a time series with missing values. Which plot correctly shows the data after applying backward fill?

Data Analysis Python

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

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_bfill = df.bfill()

plt.figure(figsize=(8,4))
plt.plot(df.index, df['Value'], marker='o', label='Original')
plt.plot(df_bfill.index, df_bfill['Value'], marker='x', label='Backward Fill')
plt.legend()
plt.title('Backward Fill Effect')
plt.xlabel('Date')
plt.ylabel('Value')
plt.grid(True)
plt.tight_layout()
plt.show()

A A line plot where missing points are replaced by the next known value, showing steps upward at missing points.

B A line plot where missing points remain as gaps, no fill applied.

C A line plot where missing points are replaced by the previous known value, showing flat steps backward.

D A scatter plot with random points unrelated to the data.

💡 Hint

Backward fill uses the next valid value to fill missing points, so the line jumps up at missing points.

Backward fill replaces missing values with the next valid value, so the plot shows the line jumping up at missing points to the next known value.

🧠 Conceptual

expert

What is the difference between forward fill and backward fill in time series data?

Choose the best explanation of how forward fill and backward fill handle missing values differently in time series data.

A Forward fill replaces missing values with zeros; backward fill replaces missing values with the mean of the column.

B Forward fill replaces missing values with the next known value after them; backward fill replaces missing values with the last known value before them.

C Both forward fill and backward fill replace missing values with the average of previous and next known values.

D Forward fill replaces missing values with the last known value before them; backward fill replaces missing values with the next known value after them.

💡 Hint

Think about the direction in which each fill method propagates values.

Forward fill propagates the last valid observation forward to fill missing values. Backward fill propagates the next valid observation backward to fill missing values.