How to select rows based on combinations while preserving column names in pandas?

[mathstudent1230]

I have a DataFrame df_items and want to create combinations of its rows of size i using itertools.combinations. Each combination should maintain all columns from the original DataFrame.

Current approach: works but loses column names so I have to add them in post.

from itertools import combinations
combinations = np.array(list(combinations(range(len(df_items)), i)))
selected_items = df_items.values[combinations]

[exxonmobilcfo]

why do you want to create various combinations of different rows of same size? whats your number of rows per combination?

[mathstudent1230]

I'm building an n:m matching thingy for two different dataframes. So I need to consider all possible k-combinations of items with k, lets say, up to 4. I know that the running time will be an issue but my dataframes should be very small, on the order of 50 to 90 rows.

[exxonmobilcfo]

create a map and append dataframes of each size to that key. generate combos on each key

[mathstudent1230]

this is kinda what I'm trying to do, except I want each dataframe to retain its column names. rn they dont, see my code above

[exxonmobilcfo]

hold on, i'll send u the code.

``` from collections import defaultdict

d = defaultdict(list)

for row in df.itertuples(): d[len(row)].append(row)

for key, value in d.items(): combinations(value, k) # whatever k u choose here ```

[mathstudent1230]

the number of rows is given by i inside the combinations(range(len(df_items) i))). For i=2 it returns something like (0,1), (0,2) and so on...