Say I have an array:
import numpy as np
arr = np.random.randint(0, 5, 20)
then arr>3
results in an array of type bool with shape (20,). How can I most efficiently do the same thing with the "contains" operator? The simple
arr in [2, 4]
will result in "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()". Is there another way than
np.array([ x in [2, 4] for x in arr])
?
CodePudding user response:
You can use np.isin
:
import numpy as np
arr = np.random.randint(0, 5, 20)
np.isin(arr, [2, 4])
Output:
array([False, True, False, False, False, True, False, False, True,
False, False, False, True, False, False, True, False, True,
True, True])
The function returns a boolean array of the same shape as your input array named arr
that is True
where an element of arr
is in your second list argument [2, 4]
and False
otherwise
CodePudding user response:
pandas offer this via, pd.Series, or np.ndarray, but so far I don't know any other array module provide this.
a = pd.Series([0,1,2,3,4,5,6,7,8,9])
print(a.isin([0,3]).any()) # returns True
print(a.values.isin([0,3]).any()) # returns True (a.values is np.ndarray)