Скачать или смотреть Pandas Fundamentals | Handling Missing Data

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In this video, you’ll learn how to handle missing data in real-world datasets using Python and the Pandas library. We explore various techniques to identify, analyze, and handle missing values, such as using isnull(), notnull(), dropping rows or columns, and filling missing values with substitutes like mean, median, mode, or custom values. This tutorial includes practical examples to ensure you can apply these techniques to your own datasets.

To start learning without installing Python locally, use Google Colab.
Introduction to Google Colab:    • Google Colab Basics  
Create a new Google Colab Notebook: https://colab.research.google.com/#cr...

Table of Contents:

0:00 Introduction to handling missing values in data sets
0:09 Pandas methods for handling missing data
0:13 Creating a DataFrame with missing values
0:39 Filling missing values randomly with NaN
0:49 Displaying a DataFrame with missing values
1:02 Identifying missing values using isnull()
1:22 Using notnull() to find non-missing values
1:32 Counting total missing values in each column using isnull() and sum()
2:07 Removing rows with missing values using dropna()
3:11 Dropping columns with missing values by setting the axis parameter
3:56 Filling missing values with specified values using fillna()
5:16 Forward filling missing values using fillna(method='ffill')
6:06 Backward filling missing values using fillna(method='bfill')
6:57 Filling missing values in numerical columns with mean, median, or mode
7:46 Using a dictionary to specify fill values for different columns
8:18 Creating a new DataFrame filled using the specified dictionary
10:15 Joining AI Learning Hub
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