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Converts a numpy array to int64 dtype while suppressing FutureWarning about datetime64 to int64 casting that is deprecated in newer numpy/pandas versions.

Purpose

This function provides a safe way to cast numpy arrays (particularly datetime64[ns] arrays) to int64 without triggering deprecation warnings. It addresses a known issue in pandas/numpy where direct .astype('int64') on datetime arrays raises FutureWarning. The function is designed as a temporary workaround until the pandas community settles on the final approach for datetime to integer conversion.

Source Code

def cast_array_to_int64(array):
    """
    Convert a numpy array  to `int64`. Suppress the following warning
    emitted by Numpy, which as of 12/2021 has been extensively discussed
    (https://github.com/pandas-dev/pandas/issues/22384)
    and whose fate (possible revert) has not yet been settled:

        FutureWarning: casting datetime64[ns] values to int64 with .astype(...)
        is deprecated and will raise in a future version. Use .view(...) instead.

    """
    with warnings.catch_warnings():
        warnings.filterwarnings(
            action='ignore',
            message='casting datetime64',
            category=FutureWarning,
        )
        return array.astype('int64')

Parameters

Parameter Details

array: A numpy array of any dtype that can be cast to int64. Most commonly used with datetime64[ns] arrays where the conversion would normally trigger a FutureWarning. Can also be used with other numeric or compatible array types.

Return Value

Returns a numpy array with dtype int64. For datetime64 arrays, this represents the datetime as nanoseconds since epoch. For other numeric types, returns the integer representation of the values. The shape and dimensions of the input array are preserved.

Dependencies

• warnings
• numpy

Required Imports

import warnings
import numpy as np

Usage Example

import warnings
import numpy as np

def cast_array_to_int64(array):
    with warnings.catch_warnings():
        warnings.filterwarnings(
            action='ignore',
            message='casting datetime64',
            category=FutureWarning,
        )
        return array.astype('int64')

# Example 1: Convert datetime64 array to int64
datetime_array = np.array(['2021-01-01', '2021-12-31'], dtype='datetime64[ns]')
int_array = cast_array_to_int64(datetime_array)
print(int_array)
# Output: [1609459200000000000 1640908800000000000]

# Example 2: Convert float array to int64
float_array = np.array([1.5, 2.7, 3.9])
int_result = cast_array_to_int64(float_array)
print(int_result)
# Output: [1 2 3]

# Example 3: With pandas datetime series
import pandas as pd
date_series = pd.date_range('2021-01-01', periods=3)
int_values = cast_array_to_int64(date_series.values)
print(int_values)

Best Practices

• This function is a temporary workaround for a known pandas/numpy issue. Monitor the referenced GitHub issue (pandas-dev/pandas/issues/22384) for updates on the final resolution.
• Be aware that casting datetime64 to int64 converts timestamps to nanoseconds since Unix epoch, which may cause overflow for dates far from 1970.
• Consider using .view('int64') instead of .astype('int64') for datetime arrays in future code, as this is the recommended approach going forward.
• This function suppresses warnings globally within its context, so use it specifically for datetime64 conversions where the warning is expected and understood.
• The function does not validate input types, so ensure the input array can be safely cast to int64 to avoid unexpected behavior or data loss.

Tags

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