[FIXED] Numpy: Drop rows with all nan or 0 values

Issue

I'd like to drop all values from a table if the rows = nan or 0.

I know there's a way to do this using pandas i.e pandas.dropna(how = 'all') but I'd like a numpy method to remove rows with all nan or 0.

Is there an efficient implementation of this?

Solution

import numpy as np

a = np.array([
    [1, 0, 0],
    [0, np.nan, 0],
    [0, 0, 0],
    [np.nan, np.nan, np.nan],
    [2, 3, 4]
])

mask = np.all(np.isnan(a) | np.equal(a, 0), axis=1)
a[~mask]

Answered By - HYRY