Hello Guys, How are you all? Hope You all Are Fine. Today I get the following error **Scikit-learn : Input contains NaN, infinity or a value too large for dtype (‘float64’)** **in python**. So Here I am Explain to you all the possible solutions here.

Without wasting your time, Let’s start This Article to Solve This Error.

Table of Contents

## How Scikit-learn : Input contains NaN, infinity or a value too large for dtype (‘float64’) Error Occurs?

Today I get the following error **Scikit-learn : Input contains NaN, infinity or a value too large for dtype (‘float64’)** **in python**.

## How To Solve Scikit-learn : Input contains NaN, infinity or a value too large for dtype (‘float64’) Error ?

**How To Solve Scikit-learn : Input contains NaN, infinity or a value too large for dtype ('float64') Error ?**To Solve Scikit-learn : Input contains NaN, infinity or a value too large for dtype ('float64') Error I got the same error message when using

**sklearn**with**pandas**. My solution is to reset the index of my dataframe`df`

before running any sklearn code:**Scikit-learn : Input contains NaN, infinity or a value too large for dtype ('float64')**To Solve Scikit-learn : Input contains NaN, infinity or a value too large for dtype ('float64') Error I got the same error message when using

**sklearn**with**pandas**. My solution is to reset the index of my dataframe`df`

before running any sklearn code:

## Solution 1

This might happen inside scikit, and it depends on what you’re doing. I recommend reading the documentation for the functions you’re using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria.

**EDIT**: How could I miss that:

np.isnan(mat.any()) #and gets False np.isfinite(mat.all()) #and gets True

is obviously wrong. Right would be:

np.any(np.isnan(mat))

and

np.all(np.isfinite(mat))

You want to check wheter any of the element is NaN, and not whether the return value of the `any`

function is a number…

## Solution 2

I got the same error message when using **sklearn** with **pandas**. My solution is to reset the index of my dataframe `df`

before running any sklearn code:

df = df.reset_index()

I encountered this issue many times when I removed some entries in my `df`

, such as

df = df[df.label=='desired_one']

**Summery**

It’s all About this issue. Hope all solution helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which solution worked for you? Thank You.

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