Python的大熊貓：重新取樣多元時間序列與GROUPBY

Question

我有以下通用格式的數據，我想重新取樣到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天的窗口啓動於2014-01-01至2011年12月31日結束。不保證每個客戶都會在每個窗口中都有記錄。如果一個客戶在一個窗口中有多個交易，那麼它將對價格進行加權平均，對這些單位進行求和，然後對產品名稱進行拼接，爲每個客戶每個窗口創建一條記錄。

我有什麼到目前爲止是這樣的：

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來處理這個問題（特別是就地和/或矢量方法），我將不勝感激。理想情況下，我也想將窗口開始日期和停止日期作爲列添加到行中。

最後的輸出應該是這樣的理想：

'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

編輯的新的解決方案。我認爲你可以將每個transaction_dt轉換爲30天的Period對象，然後進行分組。

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 

我們現在可以使用這個數據框來獲得產品的連接，加權平均價格和單位總和。然後我們使用一些Period功能來獲得結束時間。

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)