I’m getting a chained assignment warning with the following code:

file_2 = pd.read_csv(data_list[place_holder2])
file_2.drop(file_2.index[0:3], inplace = True)
file_2.iloc[:,8] = pd.to_numeric(file_2.iloc[:,8])
file_2.iloc[:,8] = file_2.iloc[:,8].multiply(time_adjust)
file_2_sorted = file_2.sort_values(by = ["TRACK_ID","EDGE_TIME"], ascending = [True,True])
file_2_grouped = file_2_sorted.groupby("TRACK_ID", sort = False)

track_id_2 = file_2.iloc[:,1]
track_id_2.drop_duplicates(inplace = True)

for index_2 in track_id_2:
        group_2 = file_2_grouped.get_group(index_2)
        first_row = group_2.iloc[0,:]
        compare_x_2 = float(first_row.iloc[9])
        compare_y_2 = float(first_row.iloc[10])
        
        if ((compare_x_1 == compare_x_2) and (compare_y_1 == compare_y_2)):
            file_2_grouped.get_group(index_2).iloc[:,1] = track_number_1
            file_2_grouped.get_group(index_2).iloc[:, 8] += time_interval
        else:
            file_2_grouped.get_group(index_2).iloc[:,1] = track_number_2

I’ve tried using .loc and the warning persists. How can I correct this?

The warning is with the last line of this bloc:

“FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df[“col”][row_indexer] = value

Use df.loc[row_indexer, "col"] = values instead, to perform the assignment in a single step and ensure this keeps updating the original df.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

file_2_grouped.get_group(index_2).iloc[:,1] = track_number_2″