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Practice

Handling Missing Values in Python

In this lesson, we'll delve deeper into how to handle missing values.

Missing values can lead to inaccurate AI model training and analysis results, so it's crucial to handle them properly during data preprocessing.


Why Do Missing Values Occur?​

Missing values can arise for a variety of reasons during dataset creation.

Here are some examples:

  • A respondent fails to answer some questions in a survey

  • An error occurs during the collection of sensor data

  • A specific field is empty in a database


Methods for Handling Missing Values​

There are several methods for handling missing values.

Here are some common approaches:


1. Removing Missing Values​

This method involves deleting rows or columns that contain missing values.

It's useful when there's enough data, though it risks discarding important information.

import pandas as pd

df = pd.DataFrame({'Name': ['John Doe', 'Jane Smith', None], 'Age': [25, None, 30]})

df_cleaned = df.dropna()  # Remove rows containing missing values

2. Replacing with Mean or Median​

For continuous data, missing values can be replaced with the mean or median.

df['Age'].fillna(df['Age'].mean(), inplace=True)  # Replace with the mean

3. Replacing with a Specific Value​

For categorical data, replacing missing values with a specific value like "Unknown" can be effective.

df['Name'].fillna('Unknown', inplace=True)  # Replace with a specific value

4. Imputing with Predicted Values Using AI Models​

You can use machine learning models to predict missing values.

This enables more sophisticated handling, though it requires additional computational resources.


Why Is Handling Missing Values Important?​

If not properly handled, missing values can lead to significant errors in analytical results.

For example, including missing values in average calculations can result in misleading outcomes.

In the next lesson, we'll review what we've learned so far with a simple quiz.

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