我需要计算pandas DataFrame中每个产品的activity_months数.到目前为止,这是我的数据和代码：
from pandas import DataFrame
from datetime import datetime
data = [
('product_a','08/31/2013'),('product_b',('product_c',('product_a','09/30/2013'),'10/31/2013'),'10/31/2013')
]
product_df = DataFrame( data,columns=['prod_desc','activity_month'])
for index,row in product_df.iterrows():
row['activity_month']= datetime.strptime(row['activity_month'],'%m/%d/%Y')
product_df.loc[index,'activity_month'] = datetime.strftime(row['activity_month'],'%Y-%m-%d')
product_df = product_df.sort(['prod_desc','activity_month'])
product_df['month_num'] = product_df.groupby(['prod_desc']).size()
但是,这会返回month_num的NaN.
这是我想要的：
prod_desc activity_month month_num product_a 2014-08-31 1 product_a 2014-09-30 2 product_a 2014-10-31 3 product_b 2014-08-31 1 product_b 2014-09-30 2 product_b 2014-10-31 3 product_c 2014-08-31 1 product_c 2014-09-30 2 product_c 2014-10-31 3
解决方法
groupby是正确的想法,但正确的方法是cumcount：
>>> product_df['month_num'] = product_df.groupby('product_desc').cumcount()
>>> product_df
product_desc activity_month prod_count pct_ch month_num
0 product_a 2014-01-01 53 NaN 0
3 product_a 2014-02-01 52 -0.018868 1
6 product_a 2014-03-01 50 -0.038462 2
1 product_b 2014-01-01 44 NaN 0
4 product_b 2014-02-01 43 -0.022727 1
7 product_b 2014-03-01 41 -0.046512 2
2 product_c 2014-01-01 36 NaN 0
5 product_c 2014-02-01 35 -0.027778 1
8 product_c 2014-03-01 34 -0.028571 2
如果你真的希望它从1开始,那么就这样做：
>>> product_df['month_num'] = product_df.groupby('product_desc').cumcount() + 1
product_desc activity_month prod_count pct_ch month_num
0 product_a 2014-01-01 53 NaN 1
3 product_a 2014-02-01 52 -0.018868 2
6 product_a 2014-03-01 50 -0.038462 3
1 product_b 2014-01-01 44 NaN 1
4 product_b 2014-02-01 43 -0.022727 2
7 product_b 2014-03-01 41 -0.046512 3
2 product_c 2014-01-01 36 NaN 1
5 product_c 2014-02-01 35 -0.027778 2
8 product_c 2014-03-01 34 -0.028571 3