NumPy DeprecationWarning: Using a non-integer array as obj in insert will result in an error in the future

Understanding the Issue

NumPy is a fundamental package for numerical computations in Python. It provides a high-performance multidimensional array object and tools for working with arrays. A deprecation warning in NumPy should be taken seriously, as it indicates that certain behavior will change in future releases and that users should update their code accordingly.

The DeprecationWarning: Using a non-integer array as obj in insert suggests that the current usage of NumPy insert function with non-integer values is slated for change. Below are solutions to address this warning.

Solution 1: Use Explicit Integer Conversion

Convert any non-integer arrays or values to integers before using them with the insert function.

• Identify places in the code where np.insert is called with non-integer indices.
• Explicitly convert the indices to an integer type such as int or use np.floor, np.ceil, or np.round functions to achieve an array of integers.
• Run the code to ensure that deprecation warnings are gone.

Example:

import numpy as np

# Assuming 'arr' is an array and 'values' are to be inserted
# 'indices' might be a float or non-integer array
indices = np.array([1.8, 2.2, 3.5])

# Convert 'indices' to an integer array
int_indices = indices.astype(int)

# Now using integer indices
arr = np.insert(arr, int_indices, values)
print(arr)

Notes: This method ensures that the code will work with future versions of NumPy. The downside is that fractional indices result in truncation when using astype(int).

Solution 2: Use Floor Division for Calculation of Indices

Before using a calculated index, apply floor division if the source values are floats. Floor division // will ensure the result is always an integer.

• Review calculations producing indices for np.insert.
• Replace regular division / with floor division //.
• Verify the change by running the code and checking outputs.

Example:

import numpy as np

# Assume 'divisor' is used to calculate indices for insertion
indices = (array_length // divisor)

arr = np.insert(arr, indices, values)
print(arr)

Notes: This solution applies only when indices are derived from calculations. It may not be suitable if the original indices need precision and are not meant to be integers.