Pyhon uses .groupby () as the example code of grouping operation.
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1. Construct the data source and give it a try.
Import pandas as pd df = pd.DataFrame ({"category": ["vegetables", "vegetables", "fruits", "fruits", "vegetables", "vegetables", "fruits", "aquatic products"], "quantity": [10, 20, 30, 40, 50, 60, 70, 80, 90100] df
two。 Practice, confirm whether the method is feasible or not
Df.sort_values (["category", "quantity"], ascending= [1d0], inplace=True) df_grouped = df.groupby (["category"]) .head (2) df_grouped
It's obviously feasible.
2.1 | 0Jing True or False, "true" or "false"
Import pandas as pd df1 = pd.DataFrame ({"category": ["vegetables", "vegetables", "fruits", "fruits", "vegetables", "vegetables", "fruits", "aquatic products"], "quantity": [10, 20, 30, 40, 50, 60, 70, 80, 90100] df1
Df.sort_values ([category "," quantity "], ascending= [True, False], inplace=True) df1_grouped = df.groupby ([" category "]) .head (3) df1_grouped
3. Add a little more hierarchical index
Import pandas as pd df2 = pd.read_excel (r "D:\ my documents\ jupyter.xlsx", sheet_name = 1) df2
Df2.sort_values ([True, False], inplace=True) df2_grouped = df2.groupby (["category"]) .head (3) df2_groupeddf2.sort_values (["city", "category", "sales quantity"], ascending= [True,True, False], inplace=True) df2_grouped = df2.groupby (["category"]) .head (3) df2_grouped
Df2.sort_values (["city", "category", "sales quantity"], ascending= [True,False, False], inplace=True) df2_grouped = df2.groupby (["category", "city"]) .head (3) df2_grouped
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