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I have a very large NumPy array
1 40 3 4 50 4 5 60 7 5 49 6 6 70 8 8 80 9 8 72 1 9 90 7 .... I want to check to see if a value exists in the 1st column of the array. I've got a bunch of homegrown ways (e.g. iterating through each row and checking), but given the size of the array I'd like to find the most efficient method.
Thanks!
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Check if List Contains Element With not in Operator. By contrast, we can use the not in operator, which is the logical opposite of the in operator. It returns True if the element is not present in a sequence. Running this code won't produce anything, since the Bird is present in our list.
You can search an array for a certain value, and return the indexes that get a match. To search an array, use the where() method.
Steps to find the most frequency value in a NumPy array:Create a NumPy array. Apply bincount() method of NumPy to get the count of occurrences of each element in the array. The n, apply argmax() method to get the value having a maximum number of occurrences(frequency).
The variable is_in_list indicates if there is any array within he list of numpy arrays which is equal to the array to check.
How about
if value in my_array[:, col_num]: do_whatever Edit: I think __contains__ is implemented in such a way that this is the same as @detly's version
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The most obvious to me would be:
np.any(my_array[:, 0] == value) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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