How to Fix: ‘numpy.float64’ object cannot be interpreted as an integer


One error you may encounter when using NumPy is:

TypeError: 'numpy.float64' object cannot be interpreted as an integer

This error occurs when you supply a float to some function that expects an integer.

The following example shows how to fix this error in practice.

How to Reproduce the Error

Suppose we attempt to use the following for loop to print out various numbers in a NumPy array:

import numpy as np

#define array of values
data = np.array([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])

#use for loop to print out range of values at each index
for i in range(len(data)):
    print(range(data[i]))

TypeError: 'numpy.float64' object cannot be interpreted as an integer

We receive an error because the range() function expects an integer, but the values in the NumPy array are floats.

How to Fix the Error

There are two ways to quickly fix this error:

Method 1: Use the int() Function

One way to fix this error is to simply wrap the call with int() as follows:

import numpy as np

#define array of values
data = np.array([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])

#use for loop to print out range of values at each index
for i in range(len(data)):
    print(range(int(data[i])))

range(0, 3)
range(0, 4)
range(0, 5)
range(0, 7)
range(0, 10)
range(0, 11)

By using the int() function, we convert each float value in the NumPy array to an integer so we avoid the TypeError we encountered earlier.

Method 2: Use the .astype(int) Function

Another way to fix this error is to first convert the values in the NumPy array to integers:

import numpy as np

#define array of values
data = np.array([3.3, 4.2, 5.1, 7.7, 10.8, 11.4])

#convert array of floats to array of integers
data_int = data.astype(int)

#use for loop to print out range of values at each index
for i in range(len(data)):
    print(range(data[i]))

range(0, 3)
range(0, 4)
range(0, 5)
range(0, 7)
range(0, 10)
range(0, 11)

Using this method, we avoid the TypeError once again.

Additional Resources

The following tutorials explain how to fix other common errors in Python:

How to Fix KeyError in Pandas
How to Fix: ValueError: cannot convert float NaN to integer
How to Fix: ValueError: operands could not be broadcast together with shapes

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