numpy.in1d#

numpy.in1d(ar1, ar2, assume_unique=False, invert=False, *, kind=None)[source]#

Test whether each element of a 1-D array is also present in a second array.

Deprecated since version 2.0: Use isin instead of in1d for new code.

Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise.

Parameters:
ar1(M,) array_like

Input array.

ar2array_like

The values against which to test each value of ar1.

assume_uniquebool, optional

If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

invertbool, optional

If True, the values in the returned array are inverted (that is, False where an element of ar1 is in ar2 and True otherwise). Default is False. np.in1d(a, b, invert=True) is equivalent to (but is faster than) np.invert(in1d(a, b)).

kind{None, ‘sort’, ‘table’}, optional

The algorithm to use. This will not affect the final result, but will affect the speed and memory use. The default, None, will select automatically based on memory considerations.

  • If ‘sort’, will use a mergesort-based approach. This will have a memory usage of roughly 6 times the sum of the sizes of ar1 and ar2, not accounting for size of dtypes.

  • If ‘table’, will use a lookup table approach similar to a counting sort. This is only available for boolean and integer arrays. This will have a memory usage of the size of ar1 plus the max-min value of ar2. assume_unique has no effect when the ‘table’ option is used.

  • If None, will automatically choose ‘table’ if the required memory allocation is less than or equal to 6 times the sum of the sizes of ar1 and ar2, otherwise will use ‘sort’. This is done to not use a large amount of memory by default, even though ‘table’ may be faster in most cases. If ‘table’ is chosen, assume_unique will have no effect.

Returns:
in1d(M,) ndarray, bool

The values ar1[in1d] are in ar2.

See also

isin

Version of this function that preserves the shape of ar1.

Notes

in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences. in1d(a, b) is roughly equivalent to np.array([item in b for item in a]). However, this idea fails if ar2 is a set, or similar (non-sequence) container: As ar2 is converted to an array, in those cases asarray(ar2) is an object array rather than the expected array of contained values.

Using kind='table' tends to be faster than kind=’sort’ if the following relationship is true: log10(len(ar2)) > (log10(max(ar2)-min(ar2)) - 2.27) / 0.927, but may use greater memory. The default value for kind will be automatically selected based only on memory usage, so one may manually set kind='table' if memory constraints can be relaxed.

Examples

>>> import numpy as np
>>> test = np.array([0, 1, 2, 5, 0])
>>> states = [0, 2]
>>> mask = np.in1d(test, states)
>>> mask
array([ True, False,  True, False,  True])
>>> test[mask]
array([0, 2, 0])
>>> mask = np.in1d(test, states, invert=True)
>>> mask
array([False,  True, False,  True, False])
>>> test[mask]
array([1, 5])
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