What are NaN values?

NaN (Not a Number) is a unique floating-point value that is frequently used to indicate missing, undefined or unrepresentable results in numerical computations.

Why remove NaN values?

Data integrity is paramount in any analytical endeavor, and NaNs pose a threat to the seamless flow of data analysis and computations.

• NaNs can disrupt data analysis and computations.
• Algorithm Compatibility
• NaNs can affect data visualization.
• They can lead to errors in machine learning model training.

How to remove NaN values in Python pandas?

There are various ways to get rid of NaN values from dataset using Python pandas. The most popular techniques are:

• dropna(): eliminates columns and rows containing NaN values.
• fillna(value): Fills NaN values with the specified value..
• interpolate(): interpolates values to fill in NaN values

NaN stands for Not A Number and is one of the common ways to represent the missing value in the data. It is a special floating-point value and cannot be converted to any other type than float. NaN value is one of the major problems in Data Analysis. It is very essential to deal with NaN in order to get the desired results. In this article, we will discuss how to drop rows with NaN values.