I have a function in a script that I am testing and the df.drop() function is not working as expected.
def area(df,value): df["area"] = df['geo'].apply(lambda row:to_area(row)) df["area"] = df["area"].apply(lambda row: abs(row - mean)) df = df.filter(pl.col("area") < value) df = df.drop("area") return df
def test(): df = some df res = area(df,2) res_2 = area(df,4)
At res_2, I keep getting the "area" column back in the dataframe, which is causing me problems with type checking. Any ideas on what might be causing this? I know that using df.clone() works, but I don't understand what is causing this issue with how things are set up.