透视 table 并按月分组

Question

我有一个 DataFrame df 如下：

df = pd.DataFrame([["12", "10-01-2022", 'boot', "shoe", 100, 50],
                   ["211", "10-01-2022", 'sandal', "shoe", 210, 20],
                   ["321", "10-02-2022", 'boot', "shoe", 100, 45],
                   ["413", "10-02-2022", 'boot', "shoe", 100, 45],
                   ["15", "10-02-2022", 'dress', "cloth", 155, 95],
                   ["633", "10-03-2022", 'boot', "shoe", 75, 30],
                   ["247", "10-03-2022", 'boot', "shoe", 75, 30],
                   ["8787", "10-04-2022", 'boot', "shoe", 120, 45],
                   ["9232", "10-05-2022", 'shirt', "cloth", 75, 30],
                   ["12340", "10-05-2022", 'dress', "cloth", 175, 95 ]],
                  columns=["count", "date", "name", "category", "price", "revenue"])

我需要按月汇总以查看计数、价格和收入的总和，如下所示：

|name  | category |Count                        | price                       | revenue            |      
|      |          | Jan | Feb | Mar | Apr | Mai | Jan | Feb | Mar | Apr | Mai |Jan | Feb | Mar | Apr | Mai |
|boot  | shoe     | 12  | 734 | 880 | 8787|  -  | 100 | 100 | 75  | 120 | -   | 50 | 45  | 30  | 45 |-|
|sandal| shoe     | 211 | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
|dress | cloth    | -   | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
|shirt | cloth    | -   | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |

我该怎么做？

Answer 1

试试这个：

df = pd.DataFrame([["12", "10-01-2022", 'boot', "shoe", 100, 50],
                   ["211", "10-01-2022", 'sandal', "shoe", 210, 20],
                   ["321", "10-02-2022", 'boot', "shoe", 100, 45],
                   ["413", "10-02-2022", 'boot', "shoe", 100, 45],
                   ["15", "10-02-2022", 'dress', "cloth", 155, 95],
                   ["633", "10-03-2022", 'boot', "shoe", 75, 30],
                   ["247", "10-03-2022", 'boot', "shoe", 75, 30],
                   ["8787", "10-04-2022", 'boot', "shoe", 120, 45],
                   ["9232", "10-05-2022", 'shirt', "cloth", 75, 30],
                   ["12340", "10-05-2022", 'dress', "cloth", 175, 95 ]],
                  columns=["count", "date", "name", "category", "price", "revenue"])

['count'] = df['count'].astype(int)
df['month'] = pd.to_datetime(df['date']).dt.strftime('%b')
df.groupby(['category', 'name', 'month'])[['count', 'revenue', 'price']].sum().unstack(fill_value=0)

输出：

                 count revenue price
month              Oct     Oct   Oct
category name                       
cloth    dress   12355     190   330
         shirt    9232      30    75
shoe     boot    10413     245   570
         sandal    211      20   210

透视 table 并按月分组

Pivot table and group by month

python

pandas

dataframe

pivot-table