# 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

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