unique and other set logic

NumPy has some basic set operations for one-dimensional ndarray. A commonly used one is numpy.unique, which returns the sorted unique values in an array:

[1]:

import numpy as np


names = np.array(
    [
        "Andrea",
        "Tim",
        "Veit",
        "Tim",
    ],
)

[2]:

np.unique(names)

[2]:

array(['Andrea', 'Tim', 'Veit'], dtype='<U6')

With numpy.in1d you can check the membership of the values in a one-dimensional array to another array and a boolean array is returned:

[3]:

np.isin(names, ["Emma", "Ava", "Charlotte"])

[3]:

array([False, False, False, False])

Array set operations:

Method

Description

unique(x)

calculates the sorted, unique elements in x

intersect1d(x, y)

calculates the sorted common elements x and y

union1d(x, y)

calculates the sorted union of elements

in1d(x, y)

computes a boolean array indicating whether each element of x is contained in y

setdiff1d(x, y)

sets the difference of the elements in x that are not contained in y

setxor1d(x, y)

sets symmetric differences; elements contained in one of the arrays but not in both