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Pandas apply multiple aggregations to multiple columns and rename columns appropriately
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| """ | |
| Groupby, apply aggregations and rename columns for a dataframe. When we apply multiple aggregations to a df, | |
| two level column names are created. Instead, this method returns a dataframe with format 'colname_agg' | |
| example: | |
| df = pd.DataFrame({'A': [1, 1, 1, 2, 2], | |
| 'B': range(5), | |
| 'C': range(5)}) | |
| groupByCols = ['A'] | |
| agg = {'B': ['sum','mean'], 'C': 'min'} | |
| agg_df(groupByCols,agg) returns column names [B_sum,B_mean,C_min] | |
| """ | |
| def agg_df(df, groupByCols, aggs): | |
| dfg = df.groupby(groupByCols).agg(aggs) | |
| dfg.columns = ["_".join(x) for x in dfg.columns.ravel()] | |
| return dfg |
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