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Learn AI with Python · Lesson

Handling Missing Data

Techniques to deal with missing values.

1

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.

Handling Missing Data — illustration 1

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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())

All lessons in this course

  1. Types of Data
  2. Handling Missing Data
  3. Data Normalization
  4. Data Merging in Python
  5. Feature Extraction from Data
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