我有以下格式的pandas dataframe:
'customer_id','transaction_dt','product','price','units'
1,2004-01-02,thing1,25,47
1,2004-01-17,thing2,150,8
2,2004-01-29,thing2,150,25
3,2017-07-15,thing3,55,17
3,2016-05-12,thing3,55,47
4,2012-02-23,thing2,150,22
4,2009-10-10,thing1,25,12
4,2014-04-04,thing2,150,2
5,2008-07-09,thing2,150,43
我已经写了以下内容,以创建两个新字段,指示30天的窗口:
import numpy as np
import pandas as pd
start_date_period = pd.period_range('2004-01-01', '12-31-2017', freq='30D')
end_date_period = pd.period_range('2004-01-30', '12-31-2017', freq='30D')
def find_window_start_date(x):
window_start_date_idx = np.argmax(x < start_date_period.end_time)
return start_date_period[window_start_date_idx]
df['window_start_dt'] = df['transaction_dt'].apply(find_window_start_date)
def find_window_end_date(x):
window_end_date_idx = np.argmin(x > end_date_period.start_time)
return end_date_period[window_end_date_idx]
df['window_end_dt'] = df['transaction_dt'].apply(find_window_end_date)
不幸的是,对于我的应用程序,这件行列适用太慢。我将非常感谢有关矢量化这些功能的任何提示。
编辑:
最终的数据框应具有此布局:
'customer_id','transaction_dt','product','price','units','window_start_dt','window_end_dt'
它不需要以形式的意义重新采样或窗口。它只需要添加'window_start_dt'和'window_end_dt'列。当前的代码有效,只需要在可能的情况下进行矢量。
编辑2 :pandas.cut是内置的:
tt=[[1,'2004-01-02',0.1,25,47],
[1,'2004-01-17',0.2,150,8],
[2,'2004-01-29',0.2,150,25],
[3,'2017-07-15',0.3,55,17],
[3,'2016-05-12',0.3,55,47],
[4,'2012-02-23',0.2,150,22],
[4,'2009-10-10',0.1,25,12],
[4,'2014-04-04',0.2,150,2],
[5,'2008-07-09',0.2,150,43]]
start_date_period = pd.date_range('2004-01-01', '12-01-2017', freq='MS')
end_date_period = pd.date_range('2004-01-30', '12-31-2017', freq='M')
df = pd.DataFrame(tt,columns=['customer_id','transaction_dt','product','price','units'])
df['transaction_dt'] = pd.Series([pd.to_datetime(sub_t[1],format='%Y-%m-%d') for sub_t in tt])
the_cut = pd.cut(df['transaction_dt'],bins=start_date_period,right=True,labels=False,include_lowest=True)
df['win_start_test'] = pd.Series([start_date_period[int(x)] if not np.isnan(x) else 0 for x in the_cut])
df['win_end_test'] = pd.Series([end_date_period[int(x)] if not np.isnan(x) else 0 for x in the_cut])
print(df.head())
win_start_test
和 win_end_test
应该等于使用您的功能计算的对应物。
ValueError
是从相关行中的不施放x
到int
的。我还添加了NaN
检查,尽管这个玩具示例不需要。
请注意,更改为pd.date_range
以及使用月底和月底标志M
和MS
的使用,以及将日期字符串转换为datetime
。