How to Fix: ValueError: cannot convert float NaN to integer

Last Updated : 24 Dec, 2021

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 pandas import pandas  # import numpy import numpy  # create a dataframe dataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh',                                         'sridevi', 'vijay', 'sreemukhi'],                               'marks': [90.3, numpy.nan, 67.8, 89, numpy.nan]})  # convert to integer type dataframe['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

Method 1: Drop rows with NaN values

Here we are going to remove NaN values from the dataframe column by using dropna() function. This function will remove the rows that contain NaN values.

Syntax:

dataframe.dropna()

Example: Dealing with error

Python3

# import pandas import pandas  # import numpy import numpy  # create a dataframe dataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh',                                        'sridevi', 'vijay',                                         'sreemukhi'],                               'marks': [90.3, numpy.nan, 67.8, 89, numpy.nan]}) # display data type print(dataframe['marks'] .dtype)   # drop the NaN values dataframe = dataframe.dropna()  # display print(dataframe)  # convert to integer type for marks column dataframe['marks'] = dataframe['marks'].astype(int)  # display data type dataframe['marks'] .dtype

Output:

Method 2: 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 pandas import pandas  # import numpy import numpy  # create a dataframe dataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh',                                         'sridevi', 'vijay',                                         'sreemukhi'],                               'marks': [90.3, numpy.nan, 67.8, 89, numpy.nan]}) # display data type print(dataframe['marks'] .dtype)  # replace NaN values with 0 dataframe = dataframe.fillna(0)  # display print(dataframe)  # convert to integer type for marks column dataframe['marks'] = dataframe['marks'].astype(int)  # display data type dataframe['marks'] .dtype

Output:

Method 3: Using numpy.nan_to_num()

Here we are using NumPy to convert NaN values to 0 numbers.

Syntax:

numpy.nan_to_num(numpy.nal)

Example: Dealing with the error

Python3

# import modules import numpy  # create an nan value data = numpy.nan  # display print(data)  # convert man to value final = numpy.nan_to_num(data)  # display final

Output:

nan 0.0

Method 4: Use Nullable

We can create nan value as NaN, this does not create any error while converting float to integer.

Syntax:

numpy.NaN

Example: Dealing with the error

Python3

# import pandas import pandas  # import numpy import numpy  # create a dataframe dataframe = pandas.DataFrame({'name': ['sireesha', 'gnanesh',                                         'sridevi', 'vijay',                                        'sreemukhi'],                               'marks': [90.3, numpy.NaN, 67.8, 89, numpy.NaN]}) # display data type print(dataframe['marks'] .dtype)  # replace NaN values with 0 dataframe = dataframe.fillna(0)  # display print(dataframe)  # convert to integer type for marks column dataframe['marks'] = dataframe['marks'].astype(int)  # display data type dataframe['marks'] .dtype

Output:

float64         name  marks 0   sireesha   90.3 1    gnanesh    0.0 2    sridevi   67.8 3      vijay   89.0 4  sreemukhi    0.0 dtype('int64')

How to convert Float to Int in Python?

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How to Fix: ValueError: cannot convert float NaN to integer

Method 1: Drop rows with NaN values

Method 2: Replace NaN values with 0

Method 3: Using numpy.nan_to_num()

Method 4: Use Nullable

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