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Handling Missing Values in Python

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

Missing values can lead to flawed 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 various reasons during the dataset creation process.

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 involves deleting rows or columns that contain missing values.

It's useful when there's ample data, but there's a risk of losing 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, you can replace missing values 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, filling in 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 allows for more sophisticated processing, albeit at the cost of additional computational resources.


Why Is Handling Missing Values Important?​

If missing values are not correctly handled, they can lead to significant errors in analysis results.

For example, including missing values when calculating an average can produce incorrect outcomes.

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

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