import pandas as pd categories = [] values = [] for i in range(0,100): categories.append(chr(i%10+65)) values.append(i) df = pd.DataFrame({'category': categories, 'value':values}) print(df) # Aggregate df_agg = df.groupby(by=['category']) \ .aggregate({"value": ['max', 'min', 'mean', 'median']}) # Flatten DataFrame df_agg.columns = ['_'.join(col) for col in df_agg.columns] df_agg = df_agg.reset_index() print(df_agg)
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access_time 6 months ago
language English
codeFlatten Pandas DataFrame after Aggregate
In code snippet Pandas DataFrame Group by one Column and Aggregate using MAX, MIN, MEAN and MEDIAN, it shows how to do aggregations in a pandas DataFrame. This code snippet shows you how to flatten the DataFrame (multiindex) after aggregations.
Sample output:
category value_max value_min value_mean value_median
0 A 90 0 45 45
1 B 91 1 46 46
2 C 92 2 47 47
3 D 93 3 48 48
4 E 94 4 49 49
5 F 95 5 50 50
6 G 96 6 51 51
7 H 97 7 52 52
8 I 98 8 53 53
9 J 99 9 54 54
Code snippet
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