'numpy.float64' Error Resolution

Introduction

The 'numpy.float64' object cannot be interpreted as an integer typeerror occurs when we try to pass some float value to a function that expects integer values.

For demonstration purposes we can use the range() function. We pass three parameters in the range() function: start-value, end-value and jump-value.

for i in range(2, 12, 3):
  print(i)

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The start-value and jump-value are optional parameters; by default, the start-value is 0, and the jump-value is 1.

for i in range(5):
  print(i)

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import numpy as np
arr = np.array([1.2, 2.4, 4.8, 9.6, 19.2, 38.4])

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for i in range(len(arr)):
  print(arr[i])

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The range() function expects integer values in parameter. If we try to pass float values, we’ll get the error.

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

Resolving the Error

The above TypeError can be easily resolved by using either of the following two methods:

astype() Method

As the name suggests, we use astype() function to convert one data type to other.

We can modify the array to convert all the float values to integer values by using astype() function.

import numpy as np
arr = np.array([1.2, 2.4, 4.8, 9.6, 19.2, 38.4])

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arr = arr.astype(int)

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for i in range(len(arr)):
  print(arr[i])

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for i in range(len(arr)):
  print(range(arr[i]))

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int() Method

By using int() function, we can explicitly typecast each float datatype to int without changing the array's contents.

import numpy as np
arr = np.array([1.2, 2.4, 4.8, 9.6, 19.2, 38.4])

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for i in range(len(arr)):
  print(range(int(arr[i])))

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