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Python 3
Handling Missing Values

Handling Missing Values

Handling Missing Values

Handling Missing Values

There are several ways to handle missing data.

Common approaches include:

Removing records
Replacing with mean values
Replacing with median values
Replacing with default values

For this project, we use simple imputation methods suitable for beginners.

Filling Missing Humidity Values

df['humidity'] = df['humidity'].fillna(

df['humidity'].mean()

)

Filling Missing Wind Speed Values

df['wind_speed'] = df['wind_speed'].fillna(

df['wind_speed'].mean()

)

Filling Missing Rainfall Values

df['precipitation'] = df['precipitation'].fillna(0)

Explanation

Humidity and wind speed use the column average.
Missing rainfall values are replaced with 0 because no recorded rainfall often indicates no precipitation.

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Handling Missing Values

Contents

Handling Missing Values

Filling Missing Humidity Values

Filling Missing Wind Speed Values

Filling Missing Rainfall Values

Explanation

Weather Data Analysis and Visualization Using Python

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Docker Tutorial

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GIT Guide

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Spring Boot Tutorial

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Apache Cordova Tutorial

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