Numpy: Conditional Np.where Replace

I have the following dataframe: '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

Solution 1:

I think you can use:

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  

Solution 2:

You can use numpy.where() like :

numpy.where(df['transaction_dt'] <= df['window_start_dt'], *operation when True*, *operation when False*)

Solution 3:

What about something like this?

# get argmax indices
idx = df.transaction_dt.apply(lambda x: np.argmax(end_date_range > x)).values
# define window_start_dt
df = df.assign(window_start_dt = start_date_range[idx])

# identify exceptions
mask = df.transaction_dt.le(df.window_start_dt)
# replace with shifted start_date_rage
df.loc[mask, "window_start_dt"] = start_date_range[idx - 1][mask]

Output:

    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