I have 2 dataframes:
df1 :
Id purchase_count purchase_sim
12 100 1500
13 1020 1300
14 1010 1100
20 1090 1400
21 1300 1600
df2:
Id click_count click_sim
12 1030 2500
13 1020 1300
24 1010 1100
30 1090 1400
31 1300 1600
I need to get the combined data frame with results as :
Id click_count click_sim purchase_count purchase_sim
12 1030 2500 100 1500
13 1020 1300 1020 1300
14 null null 1010 1100
24 1010 1100 null null
30 1090 1400 null null
31 1300 1600 null null
20 null null 1090 1400
21 null null 1300 1600
I can't use union because of different column names. Can some one suggest me a better way to do this ?
All you require a full outer join on ID column.
df1.join(df2, Seq("Id"), "full_outer")
// Since the Id column name is same in both the dataframes, if you use comparison like
df1($"Id") === df2($"Id"), you will get duplicate ID columns
Please refer the below documentation for future references.
https://docs.databricks.com/spark/latest/faq/join-two-dataframes-duplicated-column.html
Related
I have sensors data as below wherein under Data Column, there are 6rows containing value 45 in between preceding and following rows containing value 50. The requirement is to clean this data and impute with 50 (prev value) in the new_data column. Moreover, the no of noise records (shown as 45 in table) might either vary in number or with level of rows.
Case 1 (sample data) :-
Sl.no
Timestamp
Data
New_data
1
1/1/2021 0:00:00
50
50
2
1/1/2021 0:15:00
50
50
3
1/1/2021 0:30:00
50
50
4
1/1/2021 0:45:00
50
50
5
1/1/2021 1:00:00
50
50
6
1/1/2021 1:15:00
50
50
7
1/1/2021 1:30:00
50
50
8
1/1/2021 1:45:00
50
50
9
1/1/2021 2:00:00
50
50
10
1/1/2021 2:15:00
50
50
11
1/1/2021 2:30:00
45
50
12
1/1/2021 2:45:00
45
50
13
1/1/2021 3:00:00
45
50
14
1/1/2021 3:15:00
45
50
15
1/1/2021 3:30:00
45
50
16
1/1/2021 3:45:00
45
50
17
1/1/2021 4:00:00
50
50
18
1/1/2021 4:15:00
50
50
19
1/1/2021 4:30:00
50
50
20
1/1/2021 4:45:00
50
50
21
1/1/2021 5:00:00
50
50
22
1/1/2021 5:15:00
50
50
23
1/1/2021 5:30:00
50
50
I am thinking of a need to group these data ordered by timestamp asc (like below) and then could have a condition in place where it will have to check group by group in large sample data and if group 1 is same as group 3 , replace group 2 with group 1 values.
Sl.no
Timestamp
Data
New_data
group
1
1/1/2021 0:00:00
50
50
1
2
1/1/2021 0:15:00
50
50
1
3
1/1/2021 0:30:00
50
50
1
4
1/1/2021 0:45:00
50
50
1
5
1/1/2021 1:00:00
50
50
1
6
1/1/2021 1:15:00
50
50
1
7
1/1/2021 1:30:00
50
50
1
8
1/1/2021 1:45:00
50
50
1
9
1/1/2021 2:00:00
50
50
1
10
1/1/2021 2:15:00
50
50
1
11
1/1/2021 2:30:00
45
50
2
12
1/1/2021 2:45:00
45
50
2
13
1/1/2021 3:00:00
45
50
2
14
1/1/2021 3:15:00
45
50
2
15
1/1/2021 3:30:00
45
50
2
16
1/1/2021 3:45:00
45
50
2
17
1/1/2021 4:00:00
50
50
3
18
1/1/2021 4:15:00
50
50
3
19
1/1/2021 4:30:00
50
50
3
20
1/1/2021 4:45:00
50
50
3
21
1/1/2021 5:00:00
50
50
3
22
1/1/2021 5:15:00
50
50
3
23
1/1/2021 5:30:00
50
50
3
Moreover, there is also a need to add an exception like, if the next group is having similar pattern, not to change but to retain the data as it is.
Ex below : If group 1 and group 3 are same , impute group 2 with group 1 value.
But if group 2 and group 4 are same, do not change group 3 , retain same data in New_data.
Case 2:-
Sl.no
Timestamp
Data
New_data
group
1
1/1/2021 0:00:00
50
50
1
2
1/1/2021 0:15:00
50
50
1
3
1/1/2021 0:30:00
50
50
1
4
1/1/2021 0:45:00
50
50
1
5
1/1/2021 1:00:00
50
50
1
6
1/1/2021 1:15:00
50
50
1
7
1/1/2021 1:30:00
50
50
1
8
1/1/2021 1:45:00
50
50
1
9
1/1/2021 2:00:00
50
50
1
10
1/1/2021 2:15:00
50
50
1
11
1/1/2021 2:30:00
45
50
2
12
1/1/2021 2:45:00
45
50
2
13
1/1/2021 3:00:00
45
50
2
14
1/1/2021 3:15:00
45
50
2
15
1/1/2021 3:30:00
45
50
2
16
1/1/2021 3:45:00
45
50
2
17
1/1/2021 4:00:00
50
50
3
18
1/1/2021 4:15:00
50
50
3
19
1/1/2021 4:30:00
50
50
3
20
1/1/2021 4:45:00
50
50
3
21
1/1/2021 5:00:00
50
50
3
22
1/1/2021 5:15:00
50
50
3
23
1/1/2021 5:30:00
50
50
3
24
1/1/2021 5:45:00
45
45
4
25
1/1/2021 6:00:00
45
45
4
26
1/1/2021 6:15:00
45
45
4
27
1/1/2021 6:30:00
45
45
4
28
1/1/2021 6:45:00
45
45
4
29
1/1/2021 7:00:00
45
45
4
30
1/1/2021 7:15:00
45
45
4
31
1/1/2021 7:30:00
45
45
4
Reaching out for help in coding in postgresql to address above scenario. Please feel free to suggest any alternative approaches to solve above problem.
The query below should answer the need.
The first query identifies the rows which correspond to a change of
data.
The second query groups the rows between two successive changes of data and set up the corresponding range of timestamp
The third query is a recursive query which calculates the new_data in an
iterative way according to the timestamp order.
The last query display the expected result.
WITH RECURSIVE list As
(
SELECT no
, timestamp
, lag(data) OVER w AS previous
, data
, lead(data) OVER w AS next
, data IS DISTINCT FROM lag(data) OVER w AS first
, data IS DISTINCT FROM lead(data) OVER w AS last
FROM sensors
WINDOW w AS (ORDER BY timestamp ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING)
), range_list AS
(
SELECT tsrange(timestamp, lead(timestamp) OVER w, '[]') AS range
, previous
, data
, lead(next) OVER w AS next
, first
FROM list
WHERE first OR last
WINDOW w AS (ORDER BY timestamp ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING)
), rec_list (range, previous, data, next, new_data, arr) AS
(
SELECT range
, previous
, data
, next
, data
, array[range]
FROM range_list
WHERE previous IS NULL
UNION ALL
SELECT c.range
, p.data
, c.data
, c.next
, CASE
WHEN p.new_data IS NOT DISTINCT FROM c.next
THEN p.data
ELSE c.data
END
, p.arr || c.range
FROM rec_list AS p
INNER JOIN range_list AS c
ON lower(c.range) = upper(p.range) + interval '15 minutes'
WHERE NOT array[c.range] <# p.arr
AND first
)
SELECT s.*, r.new_data
FROM sensors AS s
INNER JOIN rec_list AS r
ON r.range #> s.timestamp
ORDER BY timestamp
see the test result in dbfiddle
I am new to Python and Spark Programming.
I have data in Below given format-1, which will have data captured for different fields based on timestamp and trigger.
I need to convert this data into format-2, i.e, based on timestamp and Key, need to group all the fields given in format-1 and created records as per Format-2. In Format-1, there are field that does not have any key value (timestamp and Trigger), these fields should be populated for all the records in format-2
Can you please suggest me the best approach to perform this in pyspark.
Format-1:
Event time (key-1) trig (key-2) data field_Name
------------------------------------------------------
2021-05-01T13:57:29Z 30Sec 10 A
2021-05-01T13:57:59Z 30Sec 11 A
2021-05-01T13:58:29Z 30Sec 12 A
2021-05-01T13:58:59Z 30Sec 13 A
2021-05-01T13:59:29Z 30Sec 14 A
2021-05-01T13:59:59Z 30Sec 15 A
2021-05-01T14:00:29Z 30Sec 16 A
2021-05-01T14:00:48Z OFF 17 A
2021-05-01T13:57:29Z 30Sec 110 B
2021-05-01T13:57:59Z 30Sec 111 B
2021-05-01T13:58:29Z 30Sec 112 B
2021-05-01T13:58:59Z 30Sec 113 B
2021-05-01T13:59:29Z 30Sec 114 B
2021-05-01T13:59:59Z 30Sec 115 B
2021-05-01T14:00:29Z 30Sec 116 B
2021-05-01T14:00:48Z OFF 117 B
2021-05-01T14:00:48Z OFF 21 C
2021-05-01T14:00:48Z OFF 31 D
Null Null 41 E
Null Null 51 F
Format-2:
Event Time Trig A B C D E F
--------------------------------------------------------------
2021-05-01T13:57:29Z 30Sec 10 110 Null Null 41 51
2021-05-01T13:57:59Z 30Sec 11 111 Null Null 41 51
2021-05-01T13:58:29Z 30Sec 12 112 Null Null 41 51
2021-05-01T13:58:59Z 30Sec 13 113 Null Null 41 51
2021-05-01T13:59:29Z 30Sec 14 114 Null Null 41 51
2021-05-01T13:59:59Z 30Sec 15 115 Null Null 41 51
2021-05-01T14:00:29Z 30Sec 16 116 Null Null 41 51
2021-05-01T14:00:48Z OFF 17 117 21 31 41 51
Hi Can anyone help me please to get unique group number?
I need to give unique rows for each group even when same group repeats after some groups.
I have following data:
id version product startdate enddate
123 0 2443 2010/09/01 2011/01/02
123 1 131 2011/01/03 2011/03/09
123 2 131 2011/08/10 2012/09/10
123 3 3009 2012/09/11 2014/03/31
123 4 668 2014/04/01 2014/04/30
123 5 668 2014/05/01 2016/01/01
123 6 668 2016/01/02 2017/09/08
123 7 131 2017/09/09 2017/10/10
123 8 131 2018/10/11 2019/01/01
123 9 550 2019/01/02 2099/01/01
select *,
dense_rank()over(partition by id order by id,product)
from table
Expected results:
id version product startdate enddate count
123 0 2443 2010/09/01 2011/01/02 1
123 1 131 2011/01/03 2011/03/09 2
123 2 131 2011/08/10 2012/09/10 2
123 3 3009 2012/09/11 2014/03/31 3
123 4 668 2014/04/01 2014/04/30 4
123 5 668 2014/05/01 2016/01/01 4
123 6 668 2016/01/02 2017/09/08 4
123 7 131 2017/09/09 2017/10/10 5
123 8 131 2018/10/11 2019/01/01 5
123 9 550 2019/01/02 2099/01/01 6
Try the following
SELECT
id,version,product,startdate,enddate,
1+SUM(v)OVER(PARTITION BY id ORDER BY version) n
FROM
(
SELECT
*,
IIF(LAG(product)OVER(PARTITION BY id ORDER BY version)<>product,1,0) v
FROM TestTable
) q
I have 2 dataframes. I want to take distinct values of 1 column and link it with all the rows of another dataframe. For e.g -
Dataframe 1 : df1 contains
scenarioId
---------------
101
102
103
Dataframe 2 : df2 contains columns
trades
-------------------------------------
isin price
ax11 111
re32 909
erre 445
Expected output
trades
----------------
isin price scenarioid
ax11 111 101
re32 909 101
erre 445 101
ax11 111 102
re32 909 102
erre 445 102
ax11 111 103
re32 909 103
erre 445 103
Note that i dont have a possibility to join the 2 dataframes on a common column. Please suggest.
What you need is cross join or cartessian product:
val result = df1.crossJoin(df2)
although I do not recommend it as the amount of data rises very fast. You'll get all possible pairs - elements of cartessian product (the number will be number of rows in df1 times number of rows in df2).
I have two lists:
data:
dt sym bid ask
2017.01.01D05:00:09.140745000 AAPL 101.20 101.30
2017.01.01D05:00:09.284281800 GOOG 801.00 802.00
2017.01.02D05:00:09.824847299 AAPL 101.30 101.40
info:
date sym shares divisor
2017.01.01 AAPL 500 2
2017.01.01 GOOG 100 1
2017.01.02 AAPL 200 2
I need to append from "info" the shares and divisor values for each ticker based on the date. How can I achieve this? Below is an example:
result:
dt sym bid ask shares divisor
2017.01.01D05:00:09.140745000 AAPL 101.20 101.30 500 2
2017.01.01D05:00:09.284281800 GOOG 801.00 802.00 100 1
2017.01.02D05:00:09.824847299 AAPL 101.30 101.40 200 2
If matching based on an exact date match then you can use lj. For this to work you will need to create a date column in the data table and key info by date and sym. Like so:
(update date:`date$dt from data)lj 2!info
dt sym price date shares divisor
---------------------------------------------------------------------
2018.02.04D17:25:06.658216000 AAPL 103.9275 2018.02.04 500 2
2018.02.04D17:25:06.658216000 GOOG 105.1709 2018.02.04 100 1
2018.02.05D17:25:06.658217000 AAPL 105.1598 2018.02.05 200 2
2018.02.05D17:25:06.658217000 GOOG 104.0666 2018.02.05
You can then delete the date column from this output.
It might be useful for you to use the stepped attribute [ http://code.kx.com/q/cookbook/temporal-data/#stepped-attribute ]
This will allow you to have e.g. missing dates from the info table and use the "most recent" date instead (so you don't have to have data for every sym every day). For example, without stepped attribute:
q)data:([] dt:(10?2017.01.01+til 2)+10?.z.t;sym:10?`AAPL`GOOG;bid:100+10?5;ask:105+10?5)
q)info:([] date:2017.01.01 2017.01.01 2017.01.02;sym:`AAPL`GOOG`AAPL;shares:500 100 200;divisor:2 1 2)
q)(update date:`date$dt from data) lj 2!info
dt sym bid ask date shares divisor
--------------------------------------------------------------------
2017.01.01D04:04:03.440000000 GOOG 104 105 2017.01.01 100 1
2017.01.01D14:00:02.748000000 GOOG 104 105 2017.01.01 100 1
2017.01.02D09:34:52.869000000 GOOG 102 106 2017.01.02
2017.01.02D16:44:16.648000000 AAPL 100 107 2017.01.02 200 2
2017.01.01D08:48:23.285000000 AAPL 102 108 2017.01.01 500 2
2017.01.02D02:31:11.038000000 AAPL 104 109 2017.01.02 200 2
2017.01.01D05:50:50.463000000 GOOG 104 109 2017.01.01 100 1
2017.01.02D02:13:45.275000000 AAPL 101 107 2017.01.02 200 2
2017.01.01D10:25:30.322000000 AAPL 104 109 2017.01.01 500 2
2017.01.01D14:51:12.687000000 AAPL 103 109 2017.01.01 500 2
Note the nulls for GOOG on 2017.01.02. With stepped attribute:
q)(update date:`date$dt from data) lj `s#2!`sym xasc `sym`date xcols info
dt sym bid ask date shares divisor
--------------------------------------------------------------------
2017.01.01D04:04:03.440000000 GOOG 104 105 2017.01.01 100 1
2017.01.01D14:00:02.748000000 GOOG 104 105 2017.01.01 100 1
2017.01.02D09:34:52.869000000 GOOG 102 106 2017.01.02 100 1
2017.01.02D16:44:16.648000000 AAPL 100 107 2017.01.02 200 2
2017.01.01D08:48:23.285000000 AAPL 102 108 2017.01.01 500 2
2017.01.02D02:31:11.038000000 AAPL 104 109 2017.01.02 200 2
2017.01.01D05:50:50.463000000 GOOG 104 109 2017.01.01 100 1
2017.01.02D02:13:45.275000000 AAPL 101 107 2017.01.02 200 2
2017.01.01D10:25:30.322000000 AAPL 104 109 2017.01.01 500 2
2017.01.01D14:51:12.687000000 AAPL 103 109 2017.01.01 500 2
Here, GOOG gets the values for 2017.01.01 as there is no new value on 2017.01.02
Could possibly use an aj as well.
q)aj[`date`sym;update date:`date$dt from data;info]
dt sym bid ask date shares divisor
--------------------------------------------------------------------
2017.01.02D07:57:14.764000000 GOOG 101 109 2017.01.02 200 2
2017.01.02D02:31:39.330000000 AAPL 100 105 2017.01.02 200 2
2017.01.02D04:25:17.604000000 AAPL 102 107 2017.01.02 200 2
2017.01.01D01:47:51.333000000 GOOG 104 106 2017.01.01 100 1
2017.01.02D15:50:12.140000000 AAPL 101 107 2017.01.02 200 2
2017.01.01D02:59:16.636000000 GOOG 102 106 2017.01.01 100 1
2017.01.01D14:35:31.860000000 AAPL 100 107 2017.01.01 500 2
2017.01.01D16:36:29.214000000 GOOG 101 108 2017.01.01 100 1
2017.01.01D14:01:18.498000000 GOOG 101 107 2017.01.01 100 1
2017.01.02D08:31:52.958000000 AAPL 102 109 2017.01.02 200 2