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

TypeError: ‘numpy.float64’ object cannot be interpreted as an integer error typically occurs when “you try to use a floating-point number where an integer is expected.” Many functions in the numpy library expect integer arguments for certain parameters. When you mistakenly provide a floating-point value (like numpy.float64) to such functions, you encounter the error.

Common scenarios

Here are a few scenarios where this error might occur:

Scenario 1: Reshaping an array

The numpy.reshape() function changes the shape of an array without changing its data. If you provide a floating-point number as the new shape, you’ll get the error.

import numpy as np

arr = np.array([1, 2, 3, 4])

reshaped_arr = arr.reshape(2, 2.0)

print(reshaped_arr)

Output

TypeError: 'float' object cannot be interpreted as an integer

Scenario 2: While using for loop and range()

import numpy as np

# Define array of values
data = np.array([1.1, 1.9, 2.1, 4.6])

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

Output

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

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

How to fix the error

Here are three ways to fix this error:

1. Using the “int()” function
2. Using the “.astype(int)” function
3. Using numpy.linspace() instead of numpy.arange()

Solution 1: Using the “int()” function

import numpy as np

# Define array of values
data = np.array([1.1, 1.9, 2.1, 4.6])

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

Output

range(0, 1)
range(0, 1)
range(0, 2)
range(0, 4)

Solution 2: Using the “.astype(int)” function

import numpy as np

# Define array of values
data = np.array([1.1, 1.9, 2.1, 4.6])

# 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_int[i]))

Output

range(0, 1)
range(0, 1)
range(0, 2)
range(0, 4)

Solution 3: Using numpy.linspace() Instead of numpy.arange()

To generate an array with floating-point increments, use the “numpy.linspace()” function instead of “numpy.arange()”. The numpy.linspace() method returns evenly spaced numbers over a specified range and accepts floating-point increments.

import numpy as np

data = np.linspace(0, 10, num=int(10/0.5))

print(data)

Output

[0. 0.52631579 1.05263158 1.57894737 2.10526316 2.63157895
   3.15789474 3.68421053 4.21052632 4.73684211 5.26315789 5.78947368
   6.31578947 6.84210526 7.36842105 7.89473684 8.42105263 8.94736842
   9.47368421 10.]

And we fixed the error!

Related posts

How to Fix ‘numpy.ndarray’ object is not callable

How to Fix ‘numpy.float64’ object is not callable

How to Fix numpy.linalg.linalgerror: singular matrix

Krunal

Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.