In this video, dive deep into handling missing data as part of the exploratory data analysis (EDA) process. You’ll learn how to manage missing values effectively using various Pandas functions and methods, including:

Setting an index with set_index()
Using fillna() to replace missing data
Filling NA values with dictionaries
Applying forward and backward fill methods
Understanding how to use the axis parameter in fillna()
Interpolating data, especially when working with time series
Dropping missing data with dropna()
Replacing data with the replace() function
Using regular expressions with replace()

This tutorial will help you learn missing data handling techniques to ensure your dataset is clean and ready for analysis!

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