Скачать или смотреть Handle Missing Data in Pandas | fillna, dropna & replace Explained

Missing data is one of the most common challenges in real-world datasets. In this lesson, you’ll learn how Pandas identifies, processes, and fills missing values.

💡 What You’ll Learn:

Types of missing data: at random vs completely random

How Pandas detects NaN, NULL, None, or custom placeholders

Using .isnull() to generate Boolean masks

Dropping missing values with .dropna()

Filling missing data with .fillna(), forward fill (ffill), and backward fill (bfill)

Sorting data for time-based fills

Replacing values using .replace() with scalars, lists, dicts, and regex

How statistical functions handle missing values

By the end, you’ll know how to clean datasets with missing values and when to drop, fill, or treat missing data as informative.

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