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import pandas as pd
import numpy as np
import timeit
np.random.seed(1)
column_names_example = [i for i in range(10000)]
index = pd.MultiIndex.from_tuples(
[("left", c) for c in column_names_example]
+ [("right", c) for c in column_names_example]
)
df = pd.DataFrame(np.random.rand(1000, 20000), columns=index)
def keep_column(left_col, right_col):
return (
left_col[left_col.first_valid_index()] > right_col[right_col.last_valid_index()]
)
def do_it_original():
v = [c for c in column_names_example if keep_column(df["left"][c], df["right"][c])]
return v
# Proposed optimization: Replaced for loop with vectorized operations, eliminating the need to create a list comprehension.
def do_it():
left_cols = df["left"].loc[:, column_names_example]
right_cols = df["right"].loc[:, column_names_example]
v = left_cols.columns[left_cols.iloc[0] > right_cols.iloc[-1]]
return v
do_it()
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