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.

Practice

1. What does the fillna() function do in a pandas DataFrame?

easy

A. It converts all values to strings.

B. It replaces missing values with a specified value or method.

C. It sorts the DataFrame by missing values.

D. It deletes rows with missing values.

Solution

1. Step 1: Understand the purpose of fillna()

   The fillna() function is designed to handle missing data by replacing NaN values.

2. Step 2: Identify what fillna() replaces missing values with

   It can replace missing values with a fixed value or use methods like forward fill or backward fill.

3. Final Answer:

   It replaces missing values with a specified value or method. -> Option B

4. Quick Check:

   fillna() replaces NaN values = D [OK]

Hint: fillna() fills missing spots, not deletes or sorts [OK]

Common Mistakes:

• Confusing fillna() with dropna() which deletes rows
• Thinking fillna() sorts data
• Assuming fillna() changes data types

2. Which of the following is the correct syntax to fill missing values in column 'Age' with 30 in a pandas DataFrame df?

easy

A. df['Age'].fill(30)

B. df.fillna['Age'](30)

C. df.fillna('Age', 30)

D. df['Age'].fillna(30)

Solution

1. Step 1: Identify the correct method call on a DataFrame column

   To fill missing values in a specific column, call fillna() on that column like df['Age'].fillna(30).

2. Step 2: Check syntax correctness

   df['Age'].fillna(30) uses correct syntax: column selection with brackets and method call with parentheses and argument.

3. Final Answer:

   df['Age'].fillna(30) -> Option D

4. Quick Check:

   Correct method call syntax = A [OK]

Hint: Use brackets for column, parentheses for fillna() [OK]

Common Mistakes:

• Using square brackets instead of parentheses for fillna()
• Calling fillna on DataFrame with wrong syntax
• Using fill() instead of fillna()

3. Given the DataFrame df below, what will be the output of df['Score'].fillna(0)?

import pandas as pd
df = pd.DataFrame({'Score': [10, None, 20, None, 30]})
print(df['Score'].fillna(0))

medium

A. [0, 0, 0, 0, 0]

B. [10, None, 20, None, 30]

C. [10, 0, 20, 0, 30]

D. [10, 10, 20, 20, 30]

Solution

1. Step 1: Understand fillna(0) effect on NaN values

   Calling fillna(0) replaces all missing (NaN) values with 0.

2. Step 2: Apply fillna(0) to the 'Score' column

   The original column has values [10, NaN, 20, NaN, 30]. After fillna(0), NaNs become 0, so result is [10, 0, 20, 0, 30].

3. Final Answer:

   [10, 0, 20, 0, 30] -> Option C

4. Quick Check:

   NaN replaced by 0 = C [OK]

Hint: fillna(value) replaces NaN with value directly [OK]

Common Mistakes:

• Expecting fillna() to remove rows
• Thinking fillna() copies previous values by default
• Confusing fillna(0) with fillna(method='ffill')

4. What is wrong with this code snippet to fill missing values in DataFrame df?

df.fillna(method='forward')

medium

A. The method name should be 'ffill', not 'forward'.

B. fillna() cannot use methods to fill missing values.

C. The method argument should be a number, not a string.

D. fillna() must be called on a column, not the whole DataFrame.

Solution

1. Step 1: Check valid method names for fillna()

   fillna() supports method='ffill' for forward fill and method='bfill' for backward fill.

2. Step 2: Identify the incorrect method name

   The code uses method='forward', which is invalid. The correct name is 'ffill'.

3. Final Answer:

   The method name should be 'ffill', not 'forward'. -> Option A

4. Quick Check:

   Correct method name = 'ffill' [OK]

Hint: Use 'ffill' or 'bfill' for method in fillna() [OK]

Common Mistakes:

• Using 'forward' instead of 'ffill'
• Thinking fillna() can't use methods
• Calling fillna() only on columns, not DataFrame

5. You have a DataFrame df with missing values in multiple columns. You want to fill missing values in column 'A' with the mean of 'A', and in column 'B' with the previous valid value. Which code correctly does this?

hard

A. df['A'] = df['A'].fillna(df['A'].mean())
df['B'] = df['B'].fillna(method='ffill')

B. df.fillna({'A': df['A'].mean(), 'B': 'ffill'})

C. df['A'].fillna(df['A'].mean())
df['B'].fillna(method='bfill')

D. df.fillna(method={'A': 'mean', 'B': 'ffill'})

Solution

1. Step 1: Fill column 'A' with its mean

   Calculate mean with df['A'].mean() and fill missing values using fillna() on column 'A'. Assign back to df['A'].

2. Step 2: Fill column 'B' with previous valid value

   Use fillna(method='ffill') on column 'B' to forward fill missing values. Assign back to df['B'].

3. Final Answer:

   df['A'] = df['A'].fillna(df['A'].mean()) df['B'] = df['B'].fillna(method='ffill') -> Option A

4. Quick Check:

   Mean fill + forward fill = B [OK]

Hint: Fill columns separately with correct methods, assign back [OK]

Common Mistakes:

• Using fillna() without assignment, so no change saved
• Passing 'ffill' as string value instead of method argument
• Trying to use method='mean' which is invalid