Label vs Position Indexing: .loc and .iloc

easypandas_basics

Implement two selection functions.

loc_select(df, row_labels, col_labels) Return the sub-DataFrame obtained by selecting row_labels and col_labels using label-based indexing (.loc[]).

iloc_select(df, row_positions, col_positions) Return the sub-DataFrame obtained by selecting row_positions and col_positions using integer-position indexing (.iloc[]).

df = pd.DataFrame({"A":[1,2,3],"B":[4,5,6],"C":[7,8,9]}, index=["r1","r2","r3"])
loc_select(df, ["r1","r3"], ["A","C"])
#    A  C
# r1 1  7
# r3 3  9

iloc_select(df, [0, 2], [0, 2])
#    A  C
# r1 1  7
# r3 3  9

Your solution

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Hints

Hint 1

df.loc[row_labels, col_labels] selects by label — the index values and column names you see.

Hint 2

df.iloc[row_positions, col_positions] selects by integer position (0-based), regardless of the index.

Hint 3

Both accept Python lists, so df.loc[['r1','r3'], ['A','C']] returns a 2×2 DataFrame.