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# Test array values for NaT and store the result in a new location in Numpy

To test array values for NaT, use the **numpy.isnat()** method in Python Numpy. The new location where we will store the result is a new array.

The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

## Steps

At first, import the required library −

import numpy as np

Create an array with some NaT and date values −

arr = np.array(["2021-12-22", "NaT", "NAT", "nAt", '2021-12'], dtype="datetime64[ns]")

Display the array −

print("Array...\n", arr)

Get the type of the array −

print("\nOur Array type...\n", arr.dtype)

Get the dimensions of the Array −

print("\nOur Array Dimensions...\n",arr.ndim)

Get the number of elements in the Array −

print("\nNumber of elements...\n", arr.size)

Create another array with the same shape to store the result −

arrRes = np.array([5, 5, 5, 5, 5])

To test array values for NaT, use the numpy.isnat() method in Python Numpy. The new location where we will store the result is arrRes −

print("\nTest array for NaT...\n",np.isnat(arr, arrRes))

Check the value of the new array where our result is stored −

print("\nResult...\n",arrRes)

## Example

import numpy as np # Create an array with some NaT and date values arr = np.array(["2021-12-22", "NaT", "NAT", "nAt", '2021-12'], dtype="datetime64[ns]") # Display the array print("Array...\n", arr) # Get the type of the array print("\nOur Array type...\n", arr.dtype) # Get the dimensions of the Array print("\nOur Array Dimensions...\n",arr.ndim) # Get the number of elements in the Array print("\nNumber of elements...\n", arr.size) # Create another array with the same shape to store the result arrRes = np.array([5, 5, 5, 5, 5]) # To test array values for NaT, use the numpy.isnat() method in Python Numpy # The new location where we will store the result is arrRes print("\nTest array for NaT...\n",np.isnat(arr, arrRes)) # Check the value of the new array where our result is stored print("\nResult...\n",arrRes)

## Output

Array... ['2021-12-22T00:00:00.000000000' 'NaT' 'NaT' 'NaT' '2021-12-01T00:00:00.000000000'] Our Array type... datetime64[ns] Our Array Dimensions... 1 Number of elements... 5 Test array for NaT... [0 1 1 1 0] Result... [0 1 1 1 0]

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