numpy:有条件的np.where替换 [英] Numpy: conditional np.where replace

问题描述

我有以下数据框:

'customer_id','transaction_dt','product','price','units'
1,2004-01-02 00:00:00,thing1,25,47
1,2004-01-17 00:00:00,thing2,150,8
2,2004-01-29 00:00:00,thing2,150,25
3,2017-07-15 00:00:00,thing3,55,17
3,2016-05-12 00:00:00,thing3,55,47
4,2012-02-23 00:00:00,thing2,150,22
4,2009-10-10 00:00:00,thing1,25,12
4,2014-04-04 00:00:00,thing2,150,2
5,2008-07-09 00:00:00,thing2,150,43
5,2004-01-30 00:00:00,thing1,25,40
5,2004-01-31 00:00:00,thing1,25,22
5,2004-02-01 00:00:00,thing1,25,2

我有以下过程:

start_date_range = pd.date_range('2004-01-01 00:00:00', '12-31-2017 00:00:00', freq='30D')
end_date_range = pd.date_range('2004-01-30 23:59:59', '12-31-2017 23:59:59', freq='30D')

tra = df['transaction_dt'].values[:, None]
idx = np.argmax(end_date_range.values > tra, axis=1)

df['window_start_dt'] = np.take(start_date_range, idx)
df['window_end_dt'] = end_date_range[idx]

但是，我需要使用np.where来解决df ['window_start_dt']在以下情况下的问题:

However, I need to use np.where to fix an issue with df['window_start_dt'] with the following condition:

如果'transaction_dt' <= 'window_start_dt'，则选择start_date_range中的上一个日期时间值.

If 'transaction_dt' <= 'window_start_dt' then select the previous datetime value in start_date_range.

推荐答案

我认为您可以使用:

tra = df['transaction_dt'].values[:, None]
idx = np.argmax(end_date_range.values > tra, axis=1)

sdr = start_date_range[idx]
m = df['transaction_dt'] < sdr
#change value by condition with previous
df["window_start_dt"] = np.where(m, start_date_range[idx - 1], sdr)

df['window_end_dt'] = end_date_range[idx]
print (df)
    customer_id transaction_dt product  price  units window_start_dt  \
0             1     2004-01-02  thing1     25     47      2004-01-01   
1             1     2004-01-17  thing2    150      8      2004-01-01   
2             2     2004-01-29  thing2    150     25      2004-01-01   
3             3     2017-07-15  thing3     55     17      2017-06-21   
4             3     2016-05-12  thing3     55     47      2016-04-27   
5             4     2012-02-23  thing2    150     22      2012-02-18   
6             4     2009-10-10  thing1     25     12      2009-10-01   
7             4     2014-04-04  thing2    150      2      2014-03-09   
8             5     2008-07-09  thing2    150     43      2008-07-08   
9             5     2004-01-30  thing1     25     40      2004-01-01   
10            5     2004-01-31  thing1     25     22      2004-01-01   
11            5     2004-02-01  thing1     25      2      2004-01-31  

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