Why Filling missing values with fillna() in Pandas? - Purpose & Use Cases

The Big Idea

What if you could fix all missing data in your table with just one simple command?

The Scenario

Imagine you have a big table of data about your friends' favorite movies, but some spots are empty because they forgot to tell you. You want to fill those empty spots so you can understand the list better.

The Problem

Trying to fill those empty spots by hand is slow and easy to mess up. You might miss some, fill wrong values, or spend hours checking each spot. This makes your work frustrating and full of mistakes.

The Solution

The fillna() function in pandas quickly fills all empty spots with a value you choose or a smart guess. It saves time, avoids errors, and makes your data ready for analysis in seconds.

Before vs After

✗ Before

for i in range(len(data)):
    if pd.isna(data['movie'][i]):
        data['movie'][i] = 'Unknown'

✓ After

data['movie'] = data['movie'].fillna('Unknown')

What It Enables

With fillna(), you can clean messy data fast and focus on finding cool patterns or answers.

Real Life Example

A teacher has a spreadsheet of student test scores but some scores are missing. Using fillna(), the teacher fills missing scores with the class average to fairly compare all students.

Key Takeaways

Manually filling missing data is slow and error-prone.

fillna() fills missing values quickly and correctly.

This helps prepare data for better analysis and decisions.

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