Filling missing values with fillna() in Pandas - Mini Project: Build & Apply

Filling missing values with fillna()

📖 Scenario: You work in a small shop that keeps track of daily sales in a table. Sometimes, the sales data is missing for some days. You want to fill those missing sales numbers with zero so you can calculate total sales easily.

🎯 Goal: Build a small program that creates a sales data table with some missing values, sets a fill value, fills the missing values using fillna(), and then shows the updated table.

📋 What You'll Learn

Create a pandas DataFrame called sales_data with the exact data given.

Create a variable called fill_value and set it to 0.

Use fillna() on sales_data with fill_value to fill missing values and save it as filled_sales.

Print the filled_sales DataFrame.

💡 Why This Matters

🌍 Real World

In real life, sales or sensor data often have missing values. Filling them with a default value helps keep calculations accurate and avoids errors.

💼 Career

Data analysts and scientists frequently clean data by filling missing values before analysis or building models.

1

Create the sales data with missing values

Create a pandas DataFrame called sales_data with these exact values: the column 'Day' with values ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] and the column 'Sales' with values [200, None, 150, None, 300].

Pandas

import pandas as pd
# Create the sales_data DataFrame with missing values
# Your code here

Hint

Use pd.DataFrame with a dictionary for columns. Use None for missing values.

2

Set the fill value

Create a variable called fill_value and set it to 0.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})

# Create fill_value variable and set it to 0
# Your code here

Hint

Just create a variable named fill_value and assign 0 to it.

3

Fill missing values using fillna()

Use fillna() on sales_data with fill_value to fill missing values. Save the result in a new variable called filled_sales.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})
fill_value = 0

# Fill missing values in sales_data using fill_value and save as filled_sales
# Your code here

Hint

Use sales_data.fillna(fill_value) and assign it to filled_sales.

4

Print the filled sales data

Print the filled_sales DataFrame to see the missing values replaced with zero.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, None, 150, None, 300]
})
fill_value = 0
filled_sales = sales_data.fillna(fill_value)

# Print the filled_sales DataFrame
# Your code here

Hint

Use print(filled_sales) to show the DataFrame.

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