Skip to content
  • YouTube
  • FaceBook
  • Twitter
  • Instagram

Data Analytics Ireland

Data Analytics and Video Tutorials

  • Home
  • Contact
  • About Us
    • Latest
    • Write for us
    • Learn more information about our website
  • Useful Links
  • Glossary
  • All Categories
  • Faq
  • Livestream
  • Toggle search form
  • How to Create an XML file from Excel using Python Python
  • How do I fix TypeError: unhashable type: ‘list’ Error? Articles
  • How to Generate Random Integers Between 0 and 9 Python
  • Welcome to Data Analytics Ireland Livestream
  • Python tutorial: Pandas groupby ( Video 1) Python
  • How to Group By in a Select Statement SQL
  • What is The Julia Programming Language Julia programming
  • hide a column from a data frame Python Dataframe

ValueError: cannot convert float NaN to integer

Posted on October 16, 2021December 16, 2021 By admin

Estimated reading time: 2 minutes

Sometimes in your data analytics project, you will be working with float data types and integers, but the value NaN may also appear, which will give you headaches if you don’t know how to fix the problem at hand.

A NaN is defined as “Not a Number” and represents missing values in the data. If you are familiar with SQL, it is a similar concept to NULLS.

So how does this error occur in Python?

Let’s look at some logic below:

NaN =float('NaN')
print(type(NaN))
print(NaN)

Result:
<class 'float'>
nan

As can be seen, we have a variable called ‘NaN’, and it is a data type ‘Float’

One of the characteristics of NaN is that it is a special floating-point value and cannot be converted to any other type than float; thus, when you look at the example below, it shows us this exactly and why you would get the error message we are trying to solve.

NaN =float('NaN')
print(type(NaN))
print(NaN)

a= int(NaN)

print(a)

Result:

Traceback (most recent call last):
  File "ValueError_cannot_convert_float_NaN_to_integer.py", line 5, in <module>
    a= int(NaN)
ValueError: cannot convert float NaN to integer

In the variable ‘a’ we are trying to make that an integer number from the NaN variable, which, as we know, is a floating-point value and cannot be converted to any other type than float.

How do we fix this problem?

The easiest way to fix this is to change the ‘NaN’ actual value to an integer as per the below:

NaN =float(1)
print(type(NaN))
print(NaN)

a= int(NaN)

print(a)
print(type(a))

Result:
<class 'float'>
1.0
1
<class 'int'>

So, in summary, if you come across this error:

  1. Check to see if you have any ‘Nan’ values in your data.
  2. If you do replace them with an integer value, or a value that you need for your project, that should solve your problem.

Null values, SQL, Value Error Tags:convert floats, Data Analysis, Data Analytics, floating-point value, integer, NaN value, null values, Python

Post navigation

Previous Post: TypeError: List Indices Must Be Integers Or Slices, Not Tuple
Next Post: How to Automate Testing With Python | unittest

Related Posts

  • how to insert data into a table in SQL SQL
  • TypeError: ‘NoneType’ object is not iterable class
  • how to select all records with SQL SQL
  • How to Group By in a Select Statement SQL
  • What is Query Optimization in SQL? SQL
  • ValueError: invalid literal for int() with base 10 Value Error

Select your language!

  • हिंदी
  • Español
  • Português
  • Français
  • Italiano
  • Deutsch
  • How to pass multiple lists to a function and compare Python Functions
  • supervised machine learning vs unsupervised machine learning? machine learning
  • how to copy/paste special a range of cells with xlwings Python
  • How to add a date when a record is created SQL
  • TypeError: ‘NoneType’ object is not iterable class
  • YouTube channel lists – Python Data Cleansing Python Data Cleansing
  • How to use wildcards in SQL SQL
  • IndexError: list index out of range Index Error

Copyright © 2023 Data Analytics Ireland.

Powered by PressBook Premium theme

This website uses cookies to improve your experience. We'll assume you're ok with this, but you can opt-out if you wish. Cookie settingsACCEPT
Privacy & Cookies Policy

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Non-necessary
Any cookies that may not be particularly necessary for the website to function and is used specifically to collect user personal data via analytics, ads, other embedded contents are termed as non-necessary cookies. It is mandatory to procure user consent prior to running these cookies on your website.
SAVE & ACCEPT