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 |
Solution 1:
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()
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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