我相信这就是你想要的:

table.groupby('YEARMONTH').CLIENTCODE.nunique()

例子:

In [2]: table
Out[2]: 
   CLIENTCODE  YEARMONTH
0           1     201301
1           1     201301
2           2     201301
3           1     201302
4           2     201302
5           2     201302
6           3     201302

In [3]: table.groupby('YEARMONTH').CLIENTCODE.nunique()
Out[3]: 
YEARMONTH
201301       2
201302       3

有趣的是，len(unique())通常比nunique()快几倍(3 -15倍)。

这是另一种简单得多的方法。假设你的数据框架名为daat，列名为YEARMONTH:

daat.YEARMONTH.value_counts()

使用crosstab，这将返回比groupby nunique更多的信息:

pd.crosstab(df.YEARMONTH,df.CLIENTCODE)
Out[196]:
CLIENTCODE  1  2  3
YEARMONTH
201301      2  1  0
201302      1  2  1

稍加修改后，得到如下结果:

pd.crosstab(df.YEARMONTH,df.CLIENTCODE).ne(0).sum(1)
Out[197]:
YEARMONTH
201301    2
201302    3
dtype: int64

我也使用nunique，但如果你必须使用'min'， 'max'， 'count'或'mean'等聚合函数，这将是非常有用的。

df.groupby('YEARMONTH')['CLIENTCODE'].transform('nunique') #count(distinct)
df.groupby('YEARMONTH')['CLIENTCODE'].transform('min')     #min
df.groupby('YEARMONTH')['CLIENTCODE'].transform('max')     #max
df.groupby('YEARMONTH')['CLIENTCODE'].transform('mean')    #average
df.groupby('YEARMONTH')['CLIENTCODE'].transform('count')   #count

使用新的Pandas版本，可以很容易地获得数据帧:

unique_count = pd.groupby(['YEARMONTH'], as_index=False).agg(uniq_CLIENTCODE=('CLIENTCODE', pd.Series.count))

这里有一种方法可以使多个列上的计数不同。让我们看一些数据:

data = {'CLIENT_CODE':[1,1,2,1,2,2,3],
        'YEAR_MONTH':[201301,201301,201301,201302,201302,201302,201302],
        'PRODUCT_CODE': [100,150,220,400,50,80,100]
       }
table = pd.DataFrame(data)
table

CLIENT_CODE YEAR_MONTH  PRODUCT_CODE
0   1       201301      100
1   1       201301      150
2   2       201301      220
3   1       201302      400
4   2       201302      50
5   2       201302      80
6   3       201302      100

现在，列出感兴趣的列，并在稍微修改的语法中使用groupby:

columns = ['YEAR_MONTH', 'PRODUCT_CODE']
table[columns].groupby(table['CLIENT_CODE']).nunique()

我们获得:

YEAR_MONTH  PRODUCT_CODE CLIENT_CODE
1           2            3
2           2            3
3           1            1

不同的列以及其他列上的聚合

要获得任何列(在您的例子中是CLIENTCODE)的不同值数量，我们可以使用nunique。我们可以在agg函数中传递输入作为字典，以及其他列上的聚合:

grp_df = df.groupby('YEARMONTH').agg({'CLIENTCODE': ['nunique'],
                                      'other_col_1': ['sum', 'count']})

# to flatten the multi-level columns
grp_df.columns = ["_".join(col).strip() for col in grp_df.columns.values]

# if you wish to reset the index
grp_df.reset_index(inplace=True)

现在你也可以在Python中使用dplyr语法来做到这一点:

>>> from datar.all import f, tibble, group_by, summarise, n_distinct
>>>
>>> data = tibble(
...     CLIENT_CODE=[1,1,2,1,2,2,3],
...     YEAR_MONTH=[201301,201301,201301,201302,201302,201302,201302]
... )
>>>
>>> data >> group_by(f.YEAR_MONTH) >> summarise(n=n_distinct(f.CLIENT_CODE))
   YEAR_MONTH       n
      <int64> <int64>
0      201301       2
1      201302       3

创建一个数据透视表并使用非唯一级数函数:

ID = [ 123, 123, 123, 456, 456, 456, 456, 789, 789]
domain = ['vk.com', 'vk.com', 'twitter.com', 'vk.com', 'facebook.com',
          'vk.com', 'google.com', 'twitter.com', 'vk.com']
df = pd.DataFrame({'id':ID, 'domain':domain})
fp = pd.pivot_table(data=df, index='domain', aggfunc=pd.Series.nunique)
print(fp)

输出:

               id
domain
facebook.com   1
google.com     1
twitter.com    2
vk.com         3

To obtain the number of different clients and sizes per year (i.e. number of unique values of multiple columns), use a list of columns: df.groupby('YEARMONTH')[['CLIENTCODE', 'SIZE']].nunique() Actually, the result from the above code can be obtained using SQL syntax on df using pandasql (a module built on pandas that lets you query pandas DataFrames using SQL syntax). #! pip install pandasql from pandasql import sqldf sqldf(""" SELECT COUNT(DISTINCT CLIENTCODE), COUNT(DISTINCT SIZE) FROM df GROUP BY YEARMONTH """) If you want to keep YEARMONTH as a column, i.e. the analog of the following SQL query SELECT YEARMONTH, COUNT(DISTINCT CLIENTCODE), COUNT(DISTINCT SIZE) FROM df GROUP BY YEARMONTH in pandas is the following (set as_index to False): df.groupby('YEARMONTH', as_index=False)[['CLIENTCODE', 'SIZE']].nunique() If you need to set custom names to the aggregated columns, i.e. the analog of the following SQL query: SELECT YEARMONTH, COUNT(DISTINCT CLIENTCODE) AS `No. clients`, COUNT(DISTINCT SIZE) AS `No. size` FROM df GROUP BY YEARMONTH use named aggregation in pandas: ( df.groupby('YEARMONTH', as_index=False) .agg(**{'No. clients':('CLIENTCODE', 'nunique'), 'No. size':('SIZE', 'nunique')}) )