[Python] How to Mark Repeated Entries as True Starting from the Second Occurrence Using NumPy?

Problem


I have a NumPy array and need to identify repeated elements, marking the second occurrence and beyond as True, while keeping the first occurrence as False.

For example, given the following array:

np.random.seed(100)
a = np.random.randint(0, 5, 10)
# Output: [0 0 3 0 2 4 2 2 2 2]


I want to get the following output:

[False True False True False False True True True True]


How can I achieve this using NumPy functions only, without using any loops or extra libraries?

What did you try and what were you expecting?


I was able to get it working with a loop, but I wanted to solve it using only NumPy functions. I tried implementing np.cumsum with masks, but I couldn’t make much progress.

Here's the solution I came up with using one loop:

np.random.seed(100)
a = np.random.randint(0, 5, 10)
print(a)
uniques, first_indices = np.unique(a, return_index=True)
all_occurrences = np.zeros_like(a, dtype=bool)
for i in range(len(a)):
all_occurrences = np.any(a[:i] == a)

all_occurrences[first_indices] = False
print(all_occurrences)

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