How Can I Effectively Use The Pandas Library To Handle Missing Values In A Real-world Dataset, Specifically When Dealing With A Mix Of Numerical And Categorical Features, And Then Integrate This Cleaned Dataset Into A Jupyter Notebook For A Hands-on Exercise With My Beginner Python Students?

Apr 29, 2025 by ADMIN 293 views

Handling Missing Values with Pandas and Integrating into a Jupyter Notebook

Here's a step-by-step guide to effectively handle missing values using Pandas and integrate the cleaned dataset into a Jupyter notebook for a hands-on exercise with your students:

Step 1: Import Necessary Libraries

import pandas as pd
import numpy as np

Step 2: Load the Dataset

Load a sample dataset that contains both numerical and categorical features. For this example, we'll create a sample dataset:

# Sample dataset with missing values
data = {
    'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'Age': [25, 30, np.nan, 35, 40, np.nan, 45, 50, 55, np.nan],
    'Gender': ['Male', 'Female', 'Male', np.nan, 'Female', 'Male', 'Female', 'Male', np.nan, 'Female'],
    'City': ['New York', 'Los Angeles', np.nan, 'Chicago', 'Boston', 'Dallas', 'San Francisco', np.nan, 'Miami', 'New York'],
    'Salary': [50000, 60000, 70000, np.nan, 80000, 90000, 100000, np.nan, 110000, 120000]
}
df = pd.DataFrame(data)

Step 3: Identify Missing Values

Before handling missing values, it's essential to identify where they are located in the dataset.

# Check for missing values
print("Missing Values Count:")
print(df.isnull().sum())

Step 4: Handle Missing Values

a) For Numerical Features:

Strategy 1: Fill with Mean/Median

For numerical features, a common approach is to fill missing values with the mean or median of the respective column.

# Fill missing 'Age' with the median of the 'Age' column
df['Age'].fillna(df['Age'].median(), inplace=True)
df['Salary'].fillna(df['Salary'].mean(), inplace=True)

Strategy 2: Interpolation

For time-series or sequential data, you can use interpolation.

# Assuming 'Age' is sequential, fill missing values with interpolation
df['Age'].interpolate(method='linear', inplace=True)

b) For Categorical Features:

Strategy 1: Fill with Mode

For categorical features, you can fill missing values with the mode (most frequent category).

# Fill missing 'Gender' with the mode of the 'Gender' column
df['Gender'].fillna(df['Gender'].mode()[0], inplace=True)

Strategy 2: Create a New Category

Alternatively, you can create a new category for missing values.
```
# Fill missing 'City' with 'Unknown'
df['City'].fillna('Unknown', inplace=True)
```

Step 5: Verify the Changes

After handling the missing values, verify that the changes have been applied correctly.

# Re-check for missing values
print("\nMissing Values Count After Handling:")
print(df.isnull().sum())

print("\nUpdated DataFrame:")
print(df.head())

Step 6: Save the Cleaned Dataset

Once you're satisfied with the handling of missing values, save the cleaned dataset for future use.

# Save the cleaned DataFrame to a CSV file
df.to_csv('cleaned_dataset.csv', index=False)

Step 7: Integrate into a Jupyter Notebook

Create a Jupyter notebook that guides your students through the process of handling missing values. Here's a suggested structure for the notebook:

Introduction to Missing Values:
- Briefly explain why missing values are important in data analysis.
- Discuss common strategies for handling missing values.
Loading the Dataset:
- Provide code to load the dataset.
- Include a section to display the first few rows of the dataset.
Identifying Missing Values:
- Teach students how to check for missing values using isnull().sum().
Handling Missing Values:
- Numerical Features:
  - Demonstrate filling with mean, median, or interpolation.
- Categorical Features:
  - Show how to fill with mode or create a new category.
Verification:
- Include steps to re-check for missing values after handling them.
Exercises:
- Provide exercises where students can practice handling missing values on their own.
- Example exercises:
  - Fill missing values in a numerical column using the median.
  - Fill missing values in a categorical column using the mode.
Visualization (Optional):
- Include visualizations to show the impact of handling missing values (e.g., before and after plots).
Conclusion:
- Summarize the key points.
- Encourage students to think critically about the appropriate strategy for different scenarios.

Example Jupyter Notebook Code

Here's an example of how you can structure the Jupyter notebook:

# Step 1: Import Libraries
import pandas as pd
import numpy as np

data = 
'ID' [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'Age': [25, 30, np.nan, 35, 40, np.nan, 45, 50, 55, np.nan],
'Gender': ['Male', 'Female', 'Male', np.nan, 'Female', 'Male', 'Female', 'Male', np.nan, 'Female'],
'City': ['New York', 'Los Angeles', np.nan, 'Chicago', 'Boston', 'Dallas', 'San Francisco', np.nan, 'Miami', 'New York'],
'Salary': [50000, 60000, 70000, np.nan, 80000, 90000, 100000, np.nan, 110000, 120000]

df = pd.DataFrame(data)

print("Missing Values Count:")
print(df.isnull().sum())

Numerical Features

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

df['Salary'].fillna(df['Salary'].mean(), inplace=True)
Categorical Features

df['Gender'].fillna(df['Gender'].mode()[0], inplace=True)

df['City'].fillna('Unknown', inplace=True)

print("\nMissing Values Count After Handling:")
print(df.isnull().sum())
print("\nUpdated DataFrame:")
print(df.head())

df.to_csv('cleaned_dataset.csv', index=False)

Exercise 1: Handle missing values in the 'Age' column using median
Exercise 2: Handle missing values in the 'City' column using mode
Exercise 3: Visualize the dataset before and after handling missing values

Tips for Teaching

Encourage Exploration:
- Teach students to use df.head(), df.info(), and df.describe() to understand the dataset.
- Encourage them to visualize the data using plots to identify patterns and outliers.
Hands-On Practice:
- Provide plenty of exercises where students can practice different strategies for handling missing values.
- Encourage them to experiment with different techniques and observe the impact on the dataset.
Real-World Context:
- Emphasize the importance of handling missing values in real-world scenarios.
- Discuss how different industries (e.g., healthcare, finance) might approach missing data differently.
Collaboration:
- Encourage students to work in pairs or small groups to discuss and implement different strategies.
- Facilitate class discussions to share insights and learn from each other's approaches.
Assessment:
- Review their notebooks to ensure they've correctly implemented the strategies.
- Ask them to present their findings and explain their chosen methods.

By following this guide, your students will gain hands-on experience in handling missing values, which is an essential skill in data analysis and machine learning.