numpyの：条件付きnp.whereは

Question

を交換する私は、次のデータフレームがあります。

'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 

を私は次のプロセスを持っている：

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] 

はしかし、私はDFとの問題を解決するためにnp.whereを使用する必要があります[ 'window_start_dt']を次の条件で置き換えます。

'transaction_dt' <= 'window_start_dt'の場合は、前の日時の値をstart_date_rangeに設定します。

Answer 1

私はあなたが使用することができると思う：あなたが問題を解決する方法を知っているよう

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 

Answer 2

あなたは次のようにnumpy.where（）を使用することができます。このようなものについて

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

Answer 3

何？

# 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] 

出力：

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