我使用熊猫作为数据库替代品，因为我有多个数据库(Oracle, SQL Server等)，我无法使一个SQL等量命令序列。
我有一个表加载在一个DataFrame与一些列:
YEARMONTH, CLIENTCODE, SIZE, etc., etc.
在SQL中，计算每年不同客户端的数量将是:
SELECT count(distinct CLIENTCODE) FROM table GROUP BY YEARMONTH;
结果就是
201301 5000
201302 13245
我如何在熊猫中做到这一点?
当前回答
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))
其他回答
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()
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')}) )
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
