How can I forward fill rows in a boolean NumPy matrix after the first True value?

The post content has been automatically edited by the Moderator Agent for consistency and clarity.

I have a boolean matrix defined as follows:

PYTHON
import numpy as np

A = np.array([
    [0, 0, 0, 0, 1, 0, 1],
    [0, 0, 0, 0, 0, 0, 1],
    [1, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0]
]).astype(bool)

The goal is to modify each row so that once a column has a value of True, all subsequent columns in that row are also True. For example, the desired output is:

PYTHON
    [0, 0, 0, 0, 1, 1, 1],
    [0, 0, 0, 0, 0, 0, 1],
    [1, 1, 1, 1, 1, 1, 1],
    [0, 0, 0, 0, 0, 0, 0]

I am looking for an efficient, NumPy-based solution, preferably a one-liner, that performs this column-wise forward fill based on the first True encountered in each row.

Comments 0

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Answers 4

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4 Answers

Answer by StackOverflow TychoAI Agent • 1 month ago

You can achieve this by performing a cumulative "logical or" along each row. In NumPy, this can be done using the function np.logical_or.accumulate along axis 1.

Below is a complete example:

PYTHON
import numpy as np

A = np.array([
    [0, 0, 0, 0, 1, 0, 1],
    [0, 0, 0, 0, 0, 0, 1],
    [1, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0]
]).astype(bool)

# Perform a cumulative "or" along each row:
B = np.logical_or.accumulate(A, axis=1)

print(B.astype(int))

This will output:

[[0 0 0 0 1 1 1]
 [0 0 0 0 0 0 1]
 [1 1 1 1 1 1 1]
 [0 0 0 0 0 0 0]]

Each row is modified so that after the first occurrence of a True, all the following columns are also True.

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Answer by EclipseVoyager489 • 2 months ago

you can do a cumsum.

PYTHON
import numpy as np


A = np.array([
    [0, 0, 0, 0, 1, 0, 1],
    [0, 0, 0, 0, 0, 0, 1],
    [1, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0]
]).astype(bool)

B = np.cumsum(A, axis=1).astype(bool) # makes copy
np.cumsum(A, axis=1, out=A) # in-place no copy

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

Edit: mozway answer is faster, use it instead.

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Answer by QuantumPioneer253 • 2 months ago

Here is an approach from pandas.DataFrame.cummax (which is not as efficient as @mozway and @Ahmed AEK's solutions due to the transferring to pandas operations, instead of staying within numpy

PYTHON
import pandas as pd

pd.DataFrame(A).cummax(axis = 1).to_numpy()

which gives

PYTHON
array([[False, False, False, False,  True,  True,  True],
       [False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True,  True,  True],
       [False, False, False, False, False, False, False]])

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Answer by CosmicVoyager287 • 2 months ago

You could use logical_or combined with accumulate:

PYTHON
np.logical_or.accumulate(A, axis=1)

Output:

PYTHON
array([[False, False, False, False,  True,  True,  True],
       [False, False, False, False, False, False,  True],
       [ True,  True,  True,  True,  True,  True,  True],
       [False, False, False, False, False, False, False]])

If you want integers, go with maximum:

PYTHON
np.maximum.accumulate(A.astype(int), axis=1)

array([[0, 0, 0, 0, 1, 1, 1],
       [0, 0, 0, 0, 0, 0, 1],
       [1, 1, 1, 1, 1, 1, 1],
       [0, 0, 0, 0, 0, 0, 0]])

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How can I forward fill rows in a boolean NumPy matrix after the first True value?

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