Handling Missing Data

Missing data is common in datasets and can impact analysis and model performance. In this lesson, we’ll explore techniques to identify and handle missing values effectively.

Identifying Missing Data

You can identify missing data in Python using Pandas. The isnull() method detects missing values in a dataset.

Example:

import pandas as pd data = {'Age': [25, None, 30], 'Name': ['Alice', 'Bob', None]} df = pd.DataFrame(data) print(df.isnull())

Counting Missing Values

The isnull() method can be combined with sum() to count missing values in each column.

Example:

print(df.isnull().sum())

Dropping Missing Data

You can remove rows or columns with missing data using the dropna() method.

Example:

df_cleaned = df.dropna()

Filling Missing Data

Use the fillna() method to replace missing values with specific values, like a mean or median.

Example:

df['Age'] = df['Age'].fillna(df['Age'].mean())

Forward and Backward Filling

Fill missing values using the values before or after them with method='ffill' or method='bfill'.

Example:

df['Age'] = df['Age'].fillna(method='ffill')

Choosing the Right Technique

Consider the dataset and analysis goals when handling missing data:

• Use dropna() for rows with many missing values.
• Use fillna() with mean/median for numerical data.
• Use forward/backward filling for time-series data.

Recap

In this lesson, you learned how to:

• Identify missing values using isnull().
• Count missing values with sum().
• Handle missing data by dropping or filling values.
• Choose the right technique based on your dataset.

Congratulations!

You’ve completed the lesson on Handling Missing Data. Continue to the next lesson to learn about Data Normalization techniques.