I am trying to very efficiently chain a variable amount of boolean pandas Series, to be used as a filter on a DataFrame through boolean indexing.
Normally when dealing with multiple boolean conditions, one chains them like this
condition_1 = (df.A > some_value)
condition_2 = (df.B <= other_value)
condition_3 = (df.C == another_value)
full_indexer = condition_1 & condition_2 & condition_3
but this becomes a problem with a variable amount of conditions.
bool_indexers = [
condition_1,
condition_2,
...,
condition_N,
]
I have tried out some possible solutions, but I am convinced it can be done more efficiently.
Option 1
Loop over the indexers and apply consecutively.
full_indexer = bool_indexers[0]
for indexer in bool_indexers[1:]:
full_indexer &= indexer
Option 2
Put into a DataFrame and calculate the row product.
full_indexer = pd.DataFrame(bool_indexers).product(axis=0)
Option 3
Use numpy.product (like in this answer) and create a new Series out of the result.
full_indexer = pd.Series(np.prod(np.vstack(bool_indexers), axis=0))
All three solutions are somewhat inefficient because they rely on looping or force you to create a new object (which can be slow if repeated many times).
Can it be done more efficiently or is this it?
Use np.logical_and:
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [0, 1, 2], 'B': [0, 1, 2], 'C': [0, 1, 2]})
m1 = df.A > 0
m2 = df.B <= 1
m3 = df.C == 1
m = np.logical_and.reduce([m1, m2, m3])
# OR m = np.all([m1, m2, m3], axis=0)
out = df[np.logical_and.reduce([m1, m2, m3])]
Output:
>>> pd.concat([m1, m2, m3], axis=1)
A B C
0 False True False
1 True True True
2 True False False
>>> m
array([False, True, False])
>>> out
A B C
1 1 1 1