Filtering And Filling In Missing Data : Python Pandas

pandas: powerful Python data analysis toolkit.
In this video we are going to learn on how to handle missing data by using two methods
1. dropna()
2. fillna()

Filtering And Filling In Missing Data
Missing data occur commonly in many data analysis work. One of the greatest function of python pandas is to provide a way on how to handle such situation and make data analysis easy. Handling missing value can be grouped into two ways
1. Filtering In Missing Data
This is the way to drop all missing data by method called dropna().
2. Filling In Missing Data
This is the way to fill all missing data with a specific value. Method being used in fillna() where by you can have ffill (forward fill) or bfill(backward fill)
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