Filling missing values (fillna) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of fillna with forward fill method

What is the output of the following code snippet?

Data Analysis Python

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

A

     A
0  1.0
1  NaN
2  NaN
3  4.0
4  5.0

B

     A
0  1.0
1  1.0
2  1.0
3  4.0
4  5.0

C

     A
0  1.0
1  2.0
2  3.0
3  4.0
4  5.0

D

     A
0  NaN
1  NaN
2  NaN
3  4.0
4  5.0

💡 Hint

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

The fillna method with method='ffill' fills missing values by propagating the last valid observation forward. So the NaNs at index 1 and 2 become 1.0.

❓ data_output

intermediate

Number of missing values after fillna with a constant

After running the code below, how many missing values remain in the DataFrame?

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({'B': [np.nan, 2, np.nan, 4, 5]})
df_filled = df.fillna(0)
missing_count = df_filled.isna().sum().sum()
print(missing_count)

A 3

B 1

C 2

D 0

💡 Hint

Filling missing values with 0 replaces all NaNs.

fillna(0) replaces all NaN values with 0, so no missing values remain.

🔧 Debug

advanced

Identify the error in fillna usage

What error will this code raise?

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({'C': [1, np.nan, 3]})
df.fillna(inplace=True, value=5)

A No error, DataFrame is updated in place with NaNs replaced by 5

B TypeError: fillna() missing 1 required positional argument: 'value'

C TypeError: fillna() got multiple values for argument 'value'

D AttributeError: 'DataFrame' object has no attribute 'fillna'

💡 Hint

Check the order and naming of arguments in fillna method.

The fillna method expects the first argument to be the value to fill. Using both positional and keyword arguments for 'value' causes a TypeError.

🚀 Application

advanced

Fill missing values with column mean

Given the DataFrame below, which code correctly fills missing values in column 'D' with the mean of that column?

Data Analysis Python

import pandas as pd
import numpy as np
df = pd.DataFrame({'D': [1, np.nan, 3, np.nan, 5]})

A df['D'].fillna(df['D'].median(), inplace=True)

B df.fillna(df.mean(), inplace=True)

C df['D'] = df['D'].fillna(df['D'].mean())

D df.fillna(df['D'].mean())

💡 Hint

You want to fill only column 'D' with its mean, not the whole DataFrame.

Option C fills only column 'D' with its mean and assigns back to the column. Option C fills all columns with their means, which is not asked. Option C uses median, not mean. Option C does not assign back or use inplace, so no change.

🧠 Conceptual

expert

Effect of fillna with limit parameter

Consider this DataFrame and code: import pandas as pd import numpy as np df = pd.DataFrame({'E': [np.nan, np.nan, 3, np.nan, 5]}) result = df.fillna(method='bfill', limit=1) What is the content of 'result'?

A

     E
0  NaN
1  3.0
2  3.0
3  5.0
4  5.0

B

     E
0  3.0
1  3.0
2  3.0
3  5.0
4  5.0

C

     E
0  3.0
1  3.0
2  3.0
3  NaN
4  5.0

D

     E
0  3.0
1  NaN
2  3.0
3  5.0
4  5.0

💡 Hint

The limit parameter restricts how many consecutive NaNs are filled in one go.

The fillna method with method='bfill' fills missing values by propagating the next valid observation backward. With limit=1, it fills only one consecutive NaN preceding each valid value. So the NaN at index 1 becomes 3.0, index 0 remains NaN, and index 3 becomes 5.0.