pandas 如何在DataFrame中增加groupby中的行数
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How to increment a row count in groupby in DataFrame
提问by analyticsPierce
I need to calculate the number of activity_months for each product in a pandas DataFrame. Here is my data and code so far:
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','08/31/2013')
,('product_c','08/31/2013')
,('product_a','09/30/2013')
,('product_b','09/30/2013')
,('product_c','09/30/2013')
,('product_a','10/31/2013')
,('product_b','10/31/2013')
,('product_c','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()
However, this returns NaNs for month_num.
但是，这会为month_num 返回NaN。
Here is what I want to get:
这是我想要得到的：
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
回答by Karl D.
The groupby is the right idea, but the right method is cumcount:
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
If your really want it to start with 1 then just do this instead:
如果您真的希望它以 1 开头，则只需执行以下操作：
>>> 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
