Replace NaN values with 0
We can replace NaN values with 0 to get rid of NaN values. This is done by using fillna() function. This function will check the NaN values in the dataframe columns and fill the given value.
Syntax:
dataframe.fillna(0)
Example: Dealing with the error
Python3
# import pandasimport pandas# import numpyimport numpy# create a dataframedataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh', 'sridevi', 'vijay', 'sreemukhi'], 'marks': [90.3, numpy.nan, 67.8, 89, numpy.nan]})# display data typeprint(dataframe['marks'] .dtype)# replace NaN values with 0dataframe = dataframe.fillna(0)# displayprint(dataframe)# convert to integer type for marks columndataframe['marks'] = dataframe['marks'].astype(int)# display data typedataframe['marks'] .dtype |
Output:
How to Fix: ValueError: cannot convert float NaN to integer
In this article we will discuss how to fix the value error – cannot convert float NaN to integer in Python.
In Python, NaN stands for Not a Number. This error will occur when we are converting the dataframe column of the float type that contains NaN values to an integer.
Let’s see the error and explore the methods to deal with it.
Dataset in use:
Let’s check the error when converting from float type (marks column) to integer type. We can convert by using astype() function
Example: Depicting the error
Python3
# import pandasimport pandas# import numpyimport numpy# create a dataframedataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh', 'sridevi', 'vijay', 'sreemukhi'], 'marks': [90.3, numpy.nan, 67.8, 89, numpy.nan]})# convert to integer typedataframe['marks'].astype(int) |
Output:
ValueError: Cannot convert non-finite values (NA or inf) to integer
Because the NaN values are not possible to convert the dataframe. So in order to fix this issue, we have to remove NaN values