在Python和PySpark中等效的R data.table滾動連接

Question

有誰知道如何在PySpark中進行R data.table滾動連接？

借用的例子，滾動距離Ben加入的很好解釋在這里 ;

sales<-data.table(saleID=c("S1","S2","S3","S4","S5"), 
              saleDate=as.Date(c("2014-2-20","2014-5-1","2014-6-15","2014-7- 1","2014-12-31")))

commercials<-data.table(commercialID=c("C1","C2","C3","C4"), 
                    commercialDate=as.Date(c("2014-1-1","2014-4-1","2014-7-1","2014-9-15")))

setkey(sales,"saleDate")
setkey(commercials,"commercialDate")

sales[commercials, roll=TRUE]

結果是;

saleDate saleID commercialID
1: 2014-01-01     NA    C1
2: 2014-04-01     S1    C2
3: 2014-07-01     S4    C3
4: 2014-09-15     S4    C4

非常感謝您的幫助。

Answer 1

滾動連接不是 join + fillna

首先滾動的加入是不一樣的join和fillna ！ 只有當連接的表的鍵（在data.table方面，即左表和右連接）在主表中具有等價物時才會出現這種情況。 data.table滾動連接不需要這樣。

據我所知，沒有直接的等價物，我搜索了很長一段時間。 甚至還有一個問題https://github.com/pandas-dev/pandas/issues/7546 。 然而：

大熊貓解決方案：

大熊貓有一個解決方案。 我們假設您的右側data​​.table是表A，而您的左側data​​.table是表B.

1. 按鍵對表A和B進行排序。
2. 向A添加列tag ，其全部為0，列tag為B，均為1。
3. 刪除除B中的鍵和tag之外的所有列（可以省略，但這樣更清楚）並調用表B'。 保持B作為原創 - 我們將在以后需要它。
4. 將A與B'連接到C並忽略B'中的行具有許多NA的事實。
5. 按鍵排序C.
6. 使用C = C.assign(groupNr = np.cumsum(C.tag))一個新的cumsum列C = C.assign(groupNr = np.cumsum(C.tag))
7. 在tag上使用過濾（ query ）去除所有B'行。
8. 將運行計數器列groupNr添加到原始B（整數從0到N-1或從1到N，具體取決於您是要進行前向還是后向滾動連接）。
9. 在groupNr上加入B和C.

編程代碼

#0. 'date' is the key for the rolling join. It does not have to be a date.
A = pd.DataFrame.from_dict(
    {'date': pd.to_datetime(["2014-3-1", "2014-5-1", "2014-6-1", "2014-7-1", "2014-12-1"]),
     'value': ["a1", "a2", "a3", "a4", "a5"]})
B = pd.DataFrame.from_dict(
    {'date': pd.to_datetime(["2014-1-15", "2014-3-15", "2014-6-15", "2014-8-15", "2014-11-15", "2014-12-15"]),
     'value': ["b1", "b2", "b3", "b4", "b5", "b6"]})

#1. Sort the table A and and B each by key.
A = A.sort_values('date')
B = B.sort_values('date')

#2. Add a column tag to A which are all 0 and a column tag to B that are all 1.
A['tag'] = 0
B['tag'] = 1

#3. Delete all columns except the key and tagfrom B (can be omitted, but it is clearer this way) and call the table B'. Keep B as an original - we are going to need it later.
B_ = B[['date','tag']] # You need two [], because you get a series otherwise.

#4. Concatenate A with B' to C and ignore the fact that the rows from B' has many NAs.
C = pd.concat([A, B_])

#5. Sort C by key.
C = C.sort_values('date')

#6. Make a new cumsum column with C = C.assign(groupNr = np.cumsum(C.tag))
C = C.assign(groupNr = np.cumsum(C.tag))

#7. Using filtering (query) on tag get rid of all B'-rows.
C = C[C.tag == 0]

#8. Add a running counter column groupNr to the original B (integers from 0 to N-1 or from 1 to N, depending on whether you want forward or backward rolling join).
B['groupNr'] = range(len(B)+1)[1:] # B's values are carried forward to A's values
B['groupNr'] = range(len(B))       # B's values are carried backward to A's values

#9. Join B with C on groupNr to D.
D = C.set_index('groupNr').join(B.set_index('groupNr'), lsuffix='_A', rsuffix='_B')

Answer 2

我也有類似的問題，用pandas.merge_asof解決了。

以下是暴露案例的快速解決方案：

sales = pd.DataFrame.from_dict(
    {'saleDate': pd.to_datetime(["2014-02-20","2014-05-01","2014-06-15","2014-07-01","2014-12-31"]),
     'saleID': ["S1","S2","S3","S4","S5"]})
commercials = pd.DataFrame.from_dict(
    {'commercialDate': pd.to_datetime(["2014-01-01","2014-04-01","2014-07-01","2014-09-15"]),
     'commercialID': ["C1","C2","C3","C4"]}

result = pd.merge_asof(commercials,
          sales,
          left_on='commercialDate', 
          right_on='saleDate')

# Ordering for easier comparison
result = result[['commercialDate','saleID','commercialID' ]]

結果與預期相同：

  commercialDate saleID commercialID
0     2014-01-01    NaN           C1
1     2014-04-01     S1           C2
2     2014-07-01     S4           C3
3     2014-09-15     S4           C4 

Answer 3

這可能是一個更簡單的解決方案。

sales.asfreq("D",method="ffill").join(commercials,how="outer").dropna(subset= ["commercialID"])

我在https://gormanalysis.com/r-data-table-rolling-joins/的第一個例子中對此進行了測試，它確實有效。 類似的方法可以用於其他滾動連接。