It is always super hyper important to know how to deal with NaN values.

It can have a huge impact on your algorithm performance, especially in Machine Learning.

You will always need to think twice about how to deal with those NaN values.

The Pandas library has few methods that you can use to deal with those NaN values, such as:

1. Backward fill
2. Forward fill
3. Interpolate value
4. Replace

Backward fill

This method will take the next available value as present value if exists.

How to backward fill in Python

How to replace NaN values by the next available value. The simplest method using the fillna Pandas method.

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Forward fill

This method will take the previous available value as present value if exists.

How to forward fill in Python

How to replace NaN values by the latest available value. The simplest method using Pandas fillna method.

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Interpolate value

This method will try to estimate what is the value in-between two observations.

How to interpolate values with Pandas

How to interpolate values using Pandas library. All the methods you need.

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Replace

This method will replace NaN values with a specific value.

How to replace NaN values in Python

How to replace NaN values by a specific value. The simplest method using the Pandas library.

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Here you are, all those articles will teach you how to apply a specific method to deal with NaN values.