Pandas: Apply function over each pair of columns under constraints

As the title says, I'm trying to apply a function over each pair of columns of a dataframe under some conditions. I'm going to try to illustrate this. My df is of the form:

Code | 14 | 17 | 19 | ... w1 | 0 | 5 | 3 | ... w2 | 2 | 5 | 4 | ... w3 | 0 | 0 | 5 | ...

The Code corresponds to a determined location in a rectangular grid and the ws are different words. I would like to apply cosine similarity measure between each pair of columns only <strong>(EDITED!)</strong> <strong>if the sum of items in one of the columns of the pair is greater thah 5</strong>.

The desired output would be something like:

| [14,17] | [14,19] | [14,...] | [17,19] | ... Sim |cs(14,17) |cs(14,19) |cs(14,...) |cs(17,19)..| ...

cs is the result of the cosine similarity for each pair of columns. Is there any suitable method to do this?

Any help would be appreciated :-)

Answer1:

To apply the cosine metric to each pair from two collections of inputs, you could use scipy.spatial.distance.cdist. This will be much much faster than using a double Python loop.

Let one collection be all the columns of df. Let the other collection be only those columns where the sum is greater than 5:

import pandas as pd df = pd.DataFrame({'14':[0,2,0], '17':[5,5,0], '19':[3,4,5]}) mask = df.sum(axis=0) > 5 df2 = df.loc[:, mask]

Then all the cosine similarities can be computed with one call to cdist:

import scipy.spatial.distance as SSD values = SSD.cdist(df2.T, df.T, metric='cosine') # array([[ 2.92893219e-01, 1.11022302e-16, 3.00000000e-01], # [ 4.34314575e-01, 3.00000000e-01, 1.11022302e-16]])

The values can be wrapped in a new DataFrame and reshaped:

result = pd.DataFrame(values, columns=df.columns, index=df2.columns) result = result.stack() <hr> import pandas as pd import scipy.spatial.distance as SSD df = pd.DataFrame({'14':[0,2,0], '17':[5,5,0], '19':[3,4,5]}) mask = df.sum(axis=0) > 5 df2 = df.loc[:, mask] values = SSD.cdist(df2.T, df.T, metric='cosine') result = pd.DataFrame(values, columns=df.columns, index=df2.columns) result = result.stack() mask = result.index.get_level_values(0) != result.index.get_level_values(1) result = result.loc[mask] print(result)

yields the Series

17 14 0.292893 19 0.300000 19 14 0.434315 17 0.300000

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