Filling missing values with fillna() in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of fillna() with a scalar value?

Given the DataFrame below, what will be the result after filling missing values with 0 using fillna(0)?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 5, np.nan]})
result = df.fillna(0)
print(result)

A {'A': [1.0, 0.0, 3.0], 'B': [4.0, 5.0, 0.0]}

B {'A': [1, nan, 3], 'B': [4, 5, nan]}

C {'A': [1.0, nan, 3.0], 'B': [4.0, 5.0, nan]}

D {'A': [1, 0, 3], 'B': [4, 5, 0]}

💡 Hint

Remember that fillna replaces all NaN values with the given scalar.

fillna(0) replaces all missing values (NaN) with 0. The resulting DataFrame has no NaNs and numeric columns become floats because of NaN replacement.

❓ data_output

intermediate

How many missing values remain after fillna() with method='ffill'?

Consider the DataFrame below. After applying fillna(method='ffill'), how many missing values remain?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [np.nan, 2, np.nan, 4, np.nan]})
filled = df.fillna(method='ffill')
missing_count = filled.isna().sum().iloc[0]
print(missing_count)

A 2

B 0

C 3

D 1

💡 Hint

Forward fill cannot fill NaN at the start of the column.

Forward fill replaces NaNs with the last valid value above. The first NaN remains because there is no previous value to fill from.

🔧 Debug

advanced

Why does this fillna() call raise an error?

What error will this code raise and why?

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3]})
df.fillna(inplace=True, value=[0, 0])

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3]})
df.fillna(inplace=True, value=[0, 0])

A ValueError: Length of values does not match length of index

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

C AttributeError: 'NoneType' object has no attribute 'fillna'

D No error, fills NaN with 0

💡 Hint

Check if the length of the list matches the DataFrame length.

The value parameter expects a scalar or dict matching columns. Passing a list of length 2 for a DataFrame with 3 rows causes a ValueError due to length mismatch.

🚀 Application

advanced

Which fillna() call fills missing values with column means?

You want to fill missing values in each column with that column's mean. Which code does this correctly?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3], 'B': [4, 5, np.nan]})

A df.fillna(df.median())

B df.fillna(df.mean())

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

D df.fillna(0)

💡 Hint

Use a Series with means for each column as the fill value.

df.mean() returns a Series with mean of each column. Passing it to fillna fills NaNs per column with that mean.

🧠 Conceptual

expert

What is the effect of fillna() with limit parameter?

Given a column with consecutive NaNs, what does fillna(method='bfill', limit=1) do?

A Fills NaNs only if they are at the start of the column

B Fills all NaNs in the column with the next valid value

C Fills only the first NaN in each consecutive group, leaving others as NaN

D Raises an error because limit cannot be used with method

💡 Hint

Limit controls how many consecutive NaNs get filled.

The limit parameter restricts the number of consecutive NaNs filled. With limit=1, only the first NaN in each consecutive group is filled.

Practice

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

easy

A. It changes the data type of columns.

B. It removes all rows with missing values.

C. It sorts the DataFrame by column values.

D. It replaces missing values with specified values.

Solution

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

   The fillna() function is designed to handle missing data by replacing NaN values with a specified value or method.

2. Step 2: Compare options with fillna() behavior

   Options B, C, and D describe other DataFrame operations, but only It replaces missing values with specified values. correctly describes filling missing values.

3. Final Answer:

   It replaces missing values with specified values. -> Option D

4. Quick Check:

   fillna() = replace missing values [OK]

Hint: fillna() fills missing spots with your chosen value [OK]

Common Mistakes:

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

2. Which of the following is the correct syntax to fill missing values in a DataFrame df with zero using fillna()?

easy

A. df.fillna(na=0)

B. df.fillna = 0

C. df.fillna(0)

D. df.fillna(0, value=0)

Solution

1. Step 1: Recall fillna() method syntax

   The correct way to fill missing values is calling fillna() with the value as the first argument, like df.fillna(0).

2. Step 2: Evaluate each option

   df.fillna = 0 tries to assign to fillna which is incorrect. df.fillna(na=0) uses a wrong keyword argument. df.fillna(0, value=0) passes 0 both positionally (as value) and explicitly as value=0 keyword, causing TypeError: multiple values for argument 'value'. Only C is correct.

3. Final Answer:

   df.fillna(0) -> Option C

4. Quick Check:

   fillna(value) fills missing with value [OK]

Hint: Use fillna(value) to fill missing values simply [OK]

Common Mistakes:

• Assigning fillna instead of calling it as a method
• Using wrong argument names like na=0
• Confusing inplace parameter usage

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, None, 3], 'B': [None, 2, None]})
filled = df.fillna({'A': 0, 'B': 5})
print(filled)

What is the output?

medium

A. A B
0 1 5
1 0 2
2 3 5

B. A B
0 1 NaN
1 0 2
2 3 NaN

C. A B
0 1 0
1 0 2
2 3 0

D. A B
0 0 5
1 0 2
2 0 5

Solution

1. Step 1: Understand fillna() with dict argument

   Using a dictionary with fillna() fills missing values in each column with the corresponding value from the dict. Here, column 'A' missing values become 0, and column 'B' missing values become 5.

2. Step 2: Apply fillna() to each column

   Original df has NaN at df.loc[1, 'A'] and df.loc[0, 'B'], df.loc[2, 'B']. After fillna, these become 0 for 'A' and 5 for 'B'. The rest of the values remain unchanged.

3. Final Answer:

   A B 0 1 5 1 0 2 2 3 5 -> Option A

4. Quick Check:

   fillna(dict) fills columns separately [OK]

Hint: Use dict in fillna() to fill columns differently [OK]

Common Mistakes:

• Expecting fillna() to fill all columns with same value
• Confusing NaN replacement with zero by default
• Misreading which values are replaced

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'X': [None, 2, None]})
df.fillna(inplace=True, 0)
print(df)

medium

A. fillna() keyword arguments are incorrectly used.

B. The arguments to fillna() are in wrong order.

C. The value argument should come before inplace.

D. fillna() does not have an inplace parameter.

Solution

1. Step 1: Check fillna() parameter names and order

   In Python functions, all positional arguments must come before any keyword arguments.

2. Step 2: Identify incorrect argument order

   The code uses inplace=True (keyword) followed by 0 (positional for value), which raises SyntaxError: positional argument follows keyword argument. Correct is df.fillna(0, inplace=True).

3. Final Answer:

   The arguments to fillna() are in wrong order. -> Option B

4. Quick Check:

   Positional args before keywords [OK]

Hint: Pass value first, then inplace as keyword in fillna() [OK]

Common Mistakes:

• Swapping argument order without keywords
• Using inplace without value argument
• Confusing positional and keyword arguments

5. You have a DataFrame df with columns 'Score' and 'Grade'. 'Score' has missing values. You want to fill missing 'Score' values with the mean of 'Score' and then fill missing 'Grade' values with 'Unknown'. Which code correctly does this?

hard

A. df.fillna({'Score': df['Score'].mean(), 'Grade': 'Unknown'}, inplace=True)

B. df['Score'].fillna(df['Score'].mean())
df['Grade'].fillna('Unknown')

C. df.fillna({'Score': 'Unknown', 'Grade': df['Grade'].mean()}, inplace=True)

D. df.fillna(df.mean(), inplace=True)
df['Grade'].fillna('Unknown')

Solution

1. Step 1: Calculate mean and prepare fill dictionary

   We calculate the mean of 'Score' using df['Score'].mean() and want to fill missing 'Score' with this mean. For 'Grade', missing values should be filled with 'Unknown'.

2. Step 2: Use fillna() with a dictionary and inplace=True

   Using df.fillna({'Score': mean_value, 'Grade': 'Unknown'}, inplace=True) fills missing values in both columns correctly in one step.

3. Step 3: Evaluate other options

   df['Score'].fillna(df['Score'].mean()) df['Grade'].fillna('Unknown') does not assign back the filled columns, so missing values remain. df.fillna({'Score': 'Unknown', 'Grade': df['Grade'].mean()}, inplace=True) swaps fill values incorrectly. df.fillna(df.mean(), inplace=True) df['Grade'].fillna('Unknown') fills 'Score' with its column mean (assuming 'Score' numeric) but the second line returns a new Series without assignment or inplace=True, leaving 'Grade' unchanged.

4. Final Answer:

   df.fillna({'Score': df['Score'].mean(), 'Grade': 'Unknown'}, inplace=True) -> Option A

5. Quick Check:

   Use dict with fillna() and inplace=True for multiple columns [OK]

Hint: Fill multiple columns at once with dict and inplace=True [OK]

Common Mistakes:

• Not assigning back after fillna() without inplace
• Mixing fill values between columns
• Using fillna() separately without inplace or assignment