Handling Missing Values in Pandas Dataframe | GeeksforGeeks

In this video, we're going to discuss how to handle missing values in Pandas. In Pandas DataFrame sometimes many datasets simply arrive with missing data, either because it exists and was not collected or it never existed. And as we can't provide null values to our Machine Learning model, we need to handle them properly. Now, let's get started.

00:00 Let's Start
01:44 Checking for Missing Values using isnull()
03:31 Filling Null Values Using fillna()
05:35 Filling Null Values Using fillna(method = 'pad')
07:05 Filling Null Values Using fillna(method = 'bfill')
11:41 Filling Null Values with the Mean, Max or Min of a Column
13:38 Dropping Null Values Using dropna()
16:12 Filling Null Values Using replace()
18:47 Filling Null Values Using interpolate()
21:50 Closing Notes

Download Dataset From [https://www.kaggle.com/amitabhajoy/bengaluru-house-price-data?select=Bengaluru_House_Data.csv]

Check Out the Related Article:

Working with Missing Data in Pandas [https://www.geeksforgeeks.org/working-with-missing-data-in-pandas/]
Complete Pandas Tutorial [https://www.geeksforgeeks.org/pandas-tutorial/]

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This video is contributed by Akshit Madan.

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Видео Handling Missing Values in Pandas Dataframe | GeeksforGeeks канала GeeksforGeeks