pandas 在相同的 table （相同的数据帧）中，如何使用新名称和其他行值的总和对不同的行进行分组

Question

below dataframe is the output of below code i want to group rows further
train=pd.read_excel("monthly_report.xlsx", sheet_name="xy12",sep=r'\s*,\s*')
train['Date/Time Opened']=train['Date/Time Opened'].dt.month_name()
train=train.groupby(['col1', 'Date/Time Opened'])['Date/Time Opened'].count()

col1         Date/Time Opened    number
abc          April               40
             August              30
             December            25
             February            30
             January             45

xyz          April                1
             August               1
             November             3
             October              2
             September            3
pqr          March                2
             May                  4
             November             5
             October              2

现在我希望上面的格式如下所示。此后，基于此我想构建图表

abcxyz(new name)  April               41
                  August              31
                  December            25
                  February            30
                  January             45
                  September            3
                  November             3
                  October              2

pqr(new name)           
                 March                2
                 May                  4
                 November             5
                 October              2

有人可以告诉我如何将新行中的行与 diffrenet 值和其余行值的总和连接起来吗

Answer 1

您可以使用 Series.mask with Series.isin 来设置相同的类别：

train['col1'] = train['col1'].mask(train['col1'].isin(['abc','xyz']), 'abcxyz')

或使用 Series.replace 与字典:

train['col1'] = train['col1'].replace({'abc':'abcxyz','xyz':'abcxyz'})

...然后使用您的解决方案：

train['Date/Time Opened']=train['Date/Time Opened'].dt.month_name()
train=train.groupby(['col1', 'Date/Time Opened'])['Date/Time Opened'].count()

pandas 在相同的 table （相同的数据帧）中，如何使用新名称和其他行值的总和对不同的行进行分组

pandas in same table (same dataframe), how to group different rows with new name and with sum of other row values

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