Python的大熊猫：重新取样多元时间序列与GROUPBY

Question

我有以下通用格式的数据，我想重新取样到30天有一系列窗口：

'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 

我想在30天的窗口启动于2014-01-01至2011年12月31日结束。不保证每个客户都会在每个窗口中都有记录。如果一个客户在一个窗口中有多个交易，那么它将对价格进行加权平均，对这些单位进行求和，然后对产品名称进行拼接，为每个客户每个窗口创建一条记录。

我有什么到目前为止是这样的：

wa = lambda x:np.average(x, weights=df.loc[x.index, 'units']) 
con = lambda x: '/'.join(x)) 

agg_funcs = {'customer_id':'first', 
      'product':'con', 
      'price':'wa', 
      'transaction_dt':'first', 
      'units':'sum'} 

df_window = df.groupby(['customer_id', pd.Grouper(freq='30D')]).agg(agg_funcs) 
df_window_final = df_window.unstack('customer_id', fill_value=0) 

如果有人知道一些更好的方式来处理这个问题（特别是就地和/或矢量方法），我将不胜感激。理想情况下，我也想将窗口开始日期和停止日期作为列添加到行中。

最后的输出应该是这样的理想：

'customer_id','transaction_dt','product','price','units','window_start_dt','window_end_dt' 
1,2004-01-02,thing1/thing2,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
2,2004-01-29,thing2,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
3,2017-07-15,thing3,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
3,2016-05-12,thing3,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
4,2012-02-23,thing2,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
4,2009-10-10,thing1,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
4,2014-04-04,thing2,(weighted average price),(total units),(window_start_dt),(window_end_dt) 
5,2008-07-09,thing2,(weighted average price),(total units),(window_start_dt),(window_end_dt) 

Answer 1

编辑的新的解决方案。我认为你可以将每个transaction_dt转换为30天的Period对象，然后进行分组。

p = pd.period_range('2004-1-1', '12-31-2018',freq='30D') 
def find_period(v): 
    p_idx = np.argmax(v < p.end_time) 
    return p[p_idx] 
df['period'] = df['transaction_dt'].apply(find_period) 
df 

    customer_id transaction_dt product price units  period 
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 

我们现在可以使用这个数据框来获得产品的连接，加权平均价格和单位总和。然后我们使用一些Period功能来获得结束时间。

def my_funcs(df): 
    data = {} 
    data['product'] = '/'.join(df['product'].tolist()) 
    data['units'] = df.units.sum() 
    data['price'] = np.average(df['price'], weights=df['units']) 
    data['transaction_dt'] = df['transaction_dt'].iloc[0] 
    data['window_start_time'] = df['period'].iloc[0].start_time 
    data['window_end_time'] = df['period'].iloc[0].end_time 
    return pd.Series(data, index=['transaction_dt', 'product', 'price','units', 
            'window_start_time', 'window_end_time']) 

df.groupby(['customer_id', 'period']).apply(my_funcs).reset_index('period', drop=True)