How to Remove NaN Values from a List in Python

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Python is a powerful and versatile programming language that is widely used for data analysis and scientific computing.

One of the challenges that data analysts and scientists often face is dealing with missing or NaN (Not a Number) values in their data.

In this guide, we will explore how to remove NaN values from a list in Python.

What is NaN?

NaN stands for Not a Number, and it is a special value used to represent undefined or unrepresentable values in numerical computations.

NaN values can occur when performing mathematical operations that result in undefined values, such as dividing by zero or taking the square root of a negative number.

How to remove NaN from a list in Python?

To remove NaN values from a list in Python, we can use a combination of the math.isnan() function and list comprehension.

The math.isnan() function returns True if the value passed to it is NaN, and False otherwise.

We can use this function to filter out NaN values from a list.

Here is an example of how to remove NaN values from a list:

import math

# Create a list with NaN values
data = [1.2, 2.3, float('NaN'), 4.5, float('NaN'), 6.7]

# Remove NaN values from the list using list comprehension and math.isnan()
data = [x for x in data if not math.isnan(x)]

print(data)

Output:

[1.2, 2.3, 4.5, 6.7]

In this example, we first create a list called data that contains some NaN values.

We then use list comprehension to iterate over the elements of the list and remove any element that is NaN using the math.isnan() function.

The resulting list data contains only the non-NaN elements.

Note that the float('NaN') expression is used to create a NaN value in the list.

In practice, NaN values can occur when reading data from files or databases, or when performing calculations that result in undefined values.

Conclusion

Removing NaN values from a list in Python is a common task in data analysis and scientific computing.

By using the math.isnan() function and list comprehension, we can easily filter out NaN values from a list and obtain a clean dataset.

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