CODEWITHSUNDEEP
pythonpandasnumpyscipy
piRSquared
piRSquared

Reputation: 294218

How to check if 2-D array is in another 2-D array

consider the two dataframes df1 and df2

df1 = pd.DataFrame(np.zeros((6, 6)), list('abcdef'), list('abcdef'), dtype=int)
df1.iloc[2:4, 2:4] = np.array([[1, 2], [3, 4]])

df1

enter image description here

df2 = pd.DataFrame(np.array([[1, 2], [3, 4]]), list('CD'), list('CD'), dtype=int)

df2

enter image description here

It's clear that df2 is in df1. How do I test for this in general?

Upvotes: 2

Views: 108

Answers (2)

piRSquared
piRSquared

Reputation: 294218

Brute force

def isin2d(small_df, large_df):
    di, dj = small_df.shape
    mi, mj = large_df.shape
    for i in range(mi - di + 1):
        for j in range(mj - dj + 1):
            if (small_df.values == large_df.values[i:i + di, j:j + dj]).all():
                return True

    return False

isin2d(df2, df1)

True

Upvotes: 1

Divakar
Divakar

Reputation: 221504

Assuming the dataframes contain 0's and 1s only, you can use 2D convolution and look if any element in the convoluted output is equal to the number of elements in df2 -

from scipy.signal import convolve2d

out = (convolve2d(df1,df2)==df2.size).any()

For a generic case, let me use skimage module and this smart solution -

from skimage.util import view_as_windows as viewW

out = ((viewW(df1.values, df2.shape) == df2.values).all(axis=(2,3))).any()

This is basically a template-matching problem and it has been discussed and we have gotten really efficient solutions under this post : How can I check if one two-dimensional NumPy array contains a specific pattern of values inside it?. That post also gives us the indices of all places in df1 where df2 could be located.

Upvotes: 3

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