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