vDataFrame.rolling

In [ ]:
vDataFrame.rolling(func: str,
                   window: (list, tuple),
                   columns: (str, list),
                   by: list = [],
                   order_by: (dict, list) = [],
                   name: str = "",)

Adds a new vcolumn to the vDataFrame by using an advanced analytical window function on one or two specific vcolumns.

⚠ Warning: Some window functions can significantly increase vDataFrame memory usage. You should always check the vDataFrame with the 'current_relation' method and save it with the 'to_db' method with the parameters 'inplace = True' and 'relation_type = table'

Parameters

Name Type Optional Description
func
str
Function to use.
  • aad : average absolute deviation
  • beta : Beta Coefficient between 2 vcolumns
  • count : number of non-missing elements
  • corr : Pearson correlation between 2 vcolumns
  • cov : covariance between 2 vcolumns
  • kurtosis : kurtosis
  • jb : Jarque Bera index
  • max : maximum
  • mean : average
  • min : min
  • prod : product
  • range : difference between the max and the min
  • sem : standard error of the mean
  • skewness : skewness
  • sum : sum
  • std : standard deviation
  • var : variance
  • Other aggregations could work if it is part of the DB version you are using.
window
list / tuple
Window Frame Range. If two integers, it will compute a Row Window, otherwise it will compute a Time Window. For example, if set to (-5, 1), the moving windows will take 5 rows preceding and one following. If set to ('- 5 minutes', '0 minutes'), the moving window will take all the elements of the last 5 minutes.
columns
str / list
Input vcolumns. It can be a list of one or two elements.
by
list
vcolumns used in the partition.
order_by
dict / list
List of the vcolumns to use to sort the data using asc order or dictionary of all the sorting methods. For example, to sort by "column1" ASC and "column2" DESC, write {"column1": "asc", "column2": "desc"}
name
str
Name of the new vcolumn. If empty a default name based on the other parameters will be generated.

Returns

vDataFrame : self

Example

In [1]:
from verticapy import vDataFrame
flights = vDataFrame("public.usa_flights")
display(flights)
Abc
destination_airport
Varchar(20)
📅
scheduled_departure
Timestamp
123
departure_delay
Int
123
arrival_delay
Int
Abc
origin_airport
Varchar(20)
Abc
airline
Varchar(20)
1101352015-10-01 10:09:00-9-211433EV
2101352015-10-01 10:27:00-3-1410397EV
3101352015-10-01 13:57:00-4613930EV
4101352015-10-01 14:02:00-3-811433EV
5101352015-10-01 14:44:000-110397EV
6101352015-10-01 20:02:00-3-1111433EV
7101352015-10-01 21:02:00-3413930EV
8101352015-10-01 21:06:00-3-1410397DL
9101352015-10-02 10:09:00-7-1411433EV
10101352015-10-02 10:27:0012410397EV
11101352015-10-02 13:57:00-3-213930EV
12101352015-10-02 14:02:00-15-1911433EV
13101352015-10-02 14:44:00-4-610397EV
14101352015-10-02 20:02:00-3-311433EV
15101352015-10-02 21:02:00585913930EV
16101352015-10-02 21:06:00-2-1410397DL
17101352015-10-03 10:27:00-4-210397EV
18101352015-10-03 21:02:00121113930EV
19101352015-10-03 21:06:00-2-1610397DL
20101352015-10-04 10:09:0042311433EV
21101352015-10-04 10:27:0001110397EV
22101352015-10-04 13:57:00141113930EV
23101352015-10-04 14:02:00-6-1011433EV
24101352015-10-04 14:44:00181810397EV
25101352015-10-04 20:02:00191211433EV
26101352015-10-04 21:02:00-2513930EV
27101352015-10-04 21:06:00-7-1310397DL
28101352015-10-05 10:09:00-8-311433EV
29101352015-10-05 13:57:00-10-2013930EV
30101352015-10-05 14:02:00-11-1711433EV
31101352015-10-05 14:44:00102010397EV
32101352015-10-05 20:02:00-3-1111433EV
33101352015-10-05 21:02:00-6-913930EV
34101352015-10-05 21:06:001110397DL
35101352015-10-06 10:09:00-8-111433EV
36101352015-10-06 10:27:00-8-210397EV
37101352015-10-06 13:57:00-8-1813930EV
38101352015-10-06 14:02:00-6-711433EV
39101352015-10-06 14:44:00-3-110397EV
40101352015-10-06 20:02:00-5-811433EV
41101352015-10-06 21:02:00-6-1513930EV
42101352015-10-06 21:06:00-12-1910397DL
43101352015-10-07 10:09:00-2211433EV
44101352015-10-07 11:30:00-5-610397EV
45101352015-10-07 13:57:00-7-1813930EV
46101352015-10-07 14:44:00-3-1210397EV
47101352015-10-07 20:02:00-5-1611433EV
48101352015-10-07 21:02:00-3213930EV
49101352015-10-07 21:06:00-3-2310397DL
50101352015-10-08 10:09:0010411433EV
51101352015-10-08 10:27:006-210397EV
52101352015-10-08 13:57:00-3113930EV
53101352015-10-08 14:02:0019420011433EV
54101352015-10-08 14:44:00-6-410397EV
55101352015-10-08 20:02:00-1311433EV
56101352015-10-08 21:02:00-4-613930EV
57101352015-10-08 21:06:00-2-1310397DL
58101352015-10-09 10:09:00-4-211433EV
59101352015-10-09 10:27:00-4-410397EV
60101352015-10-09 13:57:00-9113930EV
61101352015-10-09 14:02:0011610711433EV
62101352015-10-09 14:44:00404710397EV
63101352015-10-09 20:02:006211433EV
64101352015-10-09 21:02:00-3-413930EV
65101352015-10-09 21:06:00-11510397DL
66101352015-10-10 10:27:00-2-410397EV
67101352015-10-10 21:02:00-41013930EV
68101352015-10-10 21:06:00-3-1610397DL
69101352015-10-11 10:09:00-1-411433EV
70101352015-10-11 10:27:00-4010397EV
71101352015-10-11 13:57:00-4-213930EV
72101352015-10-11 14:02:00-8-1211433EV
73101352015-10-11 14:44:0013510397EV
74101352015-10-11 20:02:00-10-1611433EV
75101352015-10-11 21:02:00-7-1013930EV
76101352015-10-11 21:06:00-1-710397DL
77101352015-10-12 10:09:00-2111433EV
78101352015-10-12 10:27:00-2010397EV
79101352015-10-12 13:57:00-3213930EV
80101352015-10-12 14:02:00-2-511433EV
81101352015-10-12 14:44:00-7-1010397EV
82101352015-10-12 20:02:00-5-111433EV
83101352015-10-12 21:02:00382513930EV
84101352015-10-12 21:06:00-31110397DL
85101352015-10-13 10:09:000211433EV
86101352015-10-13 10:27:00-3-310397EV
87101352015-10-13 13:57:00-21113930EV
88101352015-10-13 14:02:00-3-511433EV
89101352015-10-13 14:44:00-2-810397EV
90101352015-10-13 20:02:0031611433EV
91101352015-10-13 21:02:00-5-313930EV
92101352015-10-13 21:06:00-6-210397DL
93101352015-10-14 10:09:00-3-311433EV
94101352015-10-14 11:30:00535410397EV
95101352015-10-14 13:57:00-3613930EV
96101352015-10-14 14:44:00-3-310397EV
97101352015-10-14 20:02:0033911433EV
98101352015-10-14 21:02:00-4-1013930EV
99101352015-10-14 21:06:009210397DL
100101352015-10-15 10:09:00-5-1611433EV
Rows: 1-100 | Columns: 6
In [2]:
# AVG delay using the 10 previous similar flights for the same airline
flights.rolling(func = "avg",
                columns = "departure_delay",
                window = (-10, -1),
                by = ["origin_airport", "destination_airport", "airline"],
                order_by = {"scheduled_departure": "asc"},)
Out[2]:
Abc
destination_airport
Varchar(20)
📅
scheduled_departure
Timestamp
123
departure_delay
Int
123
arrival_delay
Int
Abc
origin_airport
Varchar(20)
Abc
airline
Varchar(20)
123
Float
1101352015-10-01 21:06:00-3-1410397DL
2101352015-10-02 21:06:00-2-1410397DL
3101352015-10-03 21:06:00-2-1610397DL
4101352015-10-04 21:06:00-7-1310397DL
5101352015-10-05 21:06:001110397DL
6101352015-10-06 21:06:00-12-1910397DL
7101352015-10-07 21:06:00-3-2310397DL
8101352015-10-08 21:06:00-2-1310397DL
9101352015-10-09 21:06:00-11510397DL
10101352015-10-10 21:06:00-3-1610397DL
11101352015-10-11 21:06:00-1-710397DL
12101352015-10-12 21:06:00-31110397DL
13101352015-10-13 21:06:00-6-210397DL
14101352015-10-14 21:06:009210397DL
15101352015-10-15 21:06:000-710397DL
16101352015-10-16 21:06:00-1710397DL
17101352015-10-17 21:06:00-2-2110397DL
18101352015-10-18 21:06:00-2510397DL
19101352015-10-19 21:06:001410397DL
20101352015-10-20 21:06:009010397DL
21101352015-10-21 21:06:00-1-510397DL
22101352015-10-22 21:06:00-2-810397DL
23101352015-10-23 21:06:000-510397DL
24101352015-10-24 21:06:00-2010397DL
25101352015-10-25 21:06:000-1410397DL
26101352015-10-26 21:06:004-110397DL
27101352015-10-27 21:06:00-3-710397DL
28101352015-10-28 21:06:000-1210397DL
29101352015-10-29 21:06:00-1-1410397DL
30101352015-10-30 21:06:00-4-1410397DL
31101352015-10-31 21:06:00-3-2810397DL
32101352015-10-01 10:27:00-3-1410397EV
33101352015-10-01 14:44:000-110397EV
34101352015-10-02 10:27:0012410397EV
35101352015-10-02 14:44:00-4-610397EV
36101352015-10-03 10:27:00-4-210397EV
37101352015-10-04 10:27:0001110397EV
38101352015-10-04 14:44:00181810397EV
39101352015-10-05 14:44:00102010397EV
40101352015-10-06 10:27:00-8-210397EV
41101352015-10-06 14:44:00-3-110397EV
42101352015-10-07 11:30:00-5-610397EV
43101352015-10-07 14:44:00-3-1210397EV
44101352015-10-08 10:27:006-210397EV
45101352015-10-08 14:44:00-6-410397EV
46101352015-10-09 10:27:00-4-410397EV
47101352015-10-09 14:44:00404710397EV
48101352015-10-10 10:27:00-2-410397EV
49101352015-10-11 10:27:00-4010397EV
50101352015-10-11 14:44:0013510397EV
51101352015-10-12 10:27:00-2010397EV
52101352015-10-12 14:44:00-7-1010397EV
53101352015-10-13 10:27:00-3-310397EV
54101352015-10-13 14:44:00-2-810397EV
55101352015-10-14 11:30:00535410397EV
56101352015-10-14 14:44:00-3-310397EV
57101352015-10-15 10:27:00-5-1210397EV
58101352015-10-15 14:44:0092310397EV
59101352015-10-16 10:27:00-5-2210397EV
60101352015-10-16 14:44:00-2-410397EV
61101352015-10-17 10:27:00-1-1210397EV
62101352015-10-18 10:27:00556910397EV
63101352015-10-18 14:44:00-5-1010397EV
64101352015-10-19 10:27:00-5-510397EV
65101352015-10-19 14:44:00171710397EV
66101352015-10-20 10:27:00-2-210397EV
67101352015-10-20 14:44:00-5-1110397EV
68101352015-10-21 11:30:00-6-1710397EV
69101352015-10-21 14:44:00-2-1010397EV
70101352015-10-22 14:44:00242410397EV
71101352015-10-23 10:27:00-3510397EV
72101352015-10-23 14:44:00685510397EV
73101352015-10-24 10:27:00-1-210397EV
74101352015-10-25 10:27:00-6-510397EV
75101352015-10-25 14:44:00-5-1410397EV
76101352015-10-26 10:27:00625610397EV
77101352015-10-26 14:44:0017918010397EV
78101352015-10-27 10:27:00-2-910397EV
79101352015-10-27 14:44:00-4-2010397EV
80101352015-10-28 10:27:00586610397EV
81101352015-10-28 14:44:0010-110397EV
82101352015-10-29 10:27:00-3-210397EV
83101352015-10-29 14:44:000-1510397EV
84101352015-10-30 10:27:00292410397EV
85101352015-10-30 14:44:00-3-610397EV
86101352015-10-31 10:27:00-4-1310397EV
87101352015-10-01 10:09:00-9-211433EV
88101352015-10-01 14:02:00-3-811433EV
89101352015-10-01 20:02:00-3-1111433EV
90101352015-10-02 10:09:00-7-1411433EV
91101352015-10-02 14:02:00-15-1911433EV
92101352015-10-02 20:02:00-3-311433EV
93101352015-10-04 10:09:0042311433EV
94101352015-10-04 14:02:00-6-1011433EV
95101352015-10-04 20:02:00191211433EV
96101352015-10-05 10:09:00-8-311433EV
97101352015-10-05 14:02:00-11-1711433EV
98101352015-10-05 20:02:00-3-1111433EV
99101352015-10-06 10:09:00-8-111433EV
100101352015-10-06 14:02:00-6-711433EV
Rows: 1-100 | Columns: 7
In [3]:
# Corr between arrival_delay and departure_delay using the 100 previous flights
# and the 10 following flights at the same airport
flights.rolling(func = "corr",
                columns = ["departure_delay", "arrival_delay"],
                window = (-100, 10),
                by = ["origin_airport"],
                order_by = {"scheduled_departure": "asc"},)
Out[3]:
Abc
destination_airport
Varchar(20)
📅
scheduled_departure
Timestamp
123
departure_delay
Int
123
arrival_delay
Int
Abc
origin_airport
Varchar(20)
Abc
airline
Varchar(20)
123
Float
123
Float
1103972015-10-01 12:00:00-61010135EV
2139302015-10-01 12:05:00-8-310135EV
3114332015-10-01 12:55:00-7-1010135EV
4103972015-10-01 16:00:00-5-110135EV
5114332015-10-01 17:11:000110135EV
6139302015-10-01 17:22:0041210135EV
7103972015-10-02 12:00:00-3-310135EV
8139302015-10-02 12:05:00-4-410135EV
9114332015-10-02 12:55:007-310135EV
10103972015-10-02 16:00:00-5-510135EV
11114332015-10-02 17:11:00-1-210135EV
12139302015-10-02 17:22:001-410135EV
13103972015-10-03 14:00:00-9-1410135EV
14103972015-10-04 12:00:00211710135EV
15114332015-10-04 12:55:00201010135EV
16103972015-10-04 16:00:00-8-810135EV
17114332015-10-04 17:11:0013710135EV
18139302015-10-04 17:22:00121910135EV
19103972015-10-05 12:00:00-2810135EV
20139302015-10-05 12:05:000-110135EV
21114332015-10-05 12:55:0017315810135EV
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28103972015-10-06 16:00:002-1210135EV
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30139302015-10-06 17:22:00-10-1610135EV
31103972015-10-07 12:00:0011010135EV
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33103972015-10-07 15:48:0018410135EV
34114332015-10-07 17:11:00-6-1910135EV
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36103972015-10-08 12:00:00-1-110135EV
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39103972015-10-08 16:00:0019520210135EV
40114332015-10-08 17:11:0001010135EV
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42103972015-10-09 12:00:00-2-310135EV
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56114332015-10-12 12:55:00-1-110135EV
57103972015-10-12 16:00:00-1-1210135EV
58114332015-10-12 17:11:00-1-510135EV
59139302015-10-12 17:22:00-3-810135EV
60103972015-10-13 12:00:000-610135EV
61139302015-10-13 12:05:00364810135EV
62114332015-10-13 12:55:00-4-810135EV
63103972015-10-13 16:00:00-4-1510135EV
64114332015-10-13 17:11:00-2410135EV
65139302015-10-13 17:22:004-1010135EV
66103972015-10-14 12:00:00-3-710135EV
67139302015-10-14 12:05:00-12-2110135EV
68103972015-10-14 16:00:0031710135EV
69114332015-10-14 17:11:001-1010135EV
70139302015-10-14 17:22:001-110135EV
71103972015-10-15 12:00:00-4-1410135EV
72139302015-10-15 12:05:00-7-810135EV
73114332015-10-15 12:55:00-6510135EV
74103972015-10-15 16:00:00-3-810135EV
75114332015-10-15 17:11:00183310135EV
76139302015-10-15 17:22:0041241510135EV
77103972015-10-16 12:00:00768710135EV
78139302015-10-16 12:05:00-6-310135EV
79114332015-10-16 12:55:00-11-310135EV
80103972015-10-16 16:00:002510135EV
81114332015-10-16 17:11:00-2-210135EV
82139302015-10-16 17:22:00-11-1410135EV
83103972015-10-17 14:00:00-8-1710135EV
84103972015-10-18 12:00:00-5-710135EV
85114332015-10-18 12:55:00636210135EV
86103972015-10-18 16:00:00806710135EV
87114332015-10-18 17:11:00-5-1810135EV
88139302015-10-18 17:22:0018910135EV
89103972015-10-19 12:00:002010135EV
90139302015-10-19 12:05:003-1510135EV
91114332015-10-19 12:55:00-1-810135EV
92103972015-10-19 16:00:00-6-910135EV
93114332015-10-19 17:11:00252010135EV
94139302015-10-19 17:22:000-510135EV
95103972015-10-20 12:00:003110135EV
96139302015-10-20 12:05:00-3-1410135EV
97114332015-10-20 12:55:00-1010135EV
98103972015-10-20 16:00:00-5-1510135EV
99114332015-10-20 17:11:00-13-1210135EV
100139302015-10-20 17:22:004-1210135EV
Rows: 1-100 | Columns: 8
In [4]:
# Number of flights the airline has to manage 2 hours preceding 
# the flight and 1 hour following
flights.rolling(func = "count",
                columns = "scheduled_departure",
                window = ("- 2 hours", "1 hour"),
                by = ["origin_airport", "airline"],
                order_by = {"scheduled_departure": "asc"},)
Out[4]:
Abc
destination_airport
Varchar(20)
📅
scheduled_departure
Timestamp
123
departure_delay
Int
123
arrival_delay
Int
Abc
origin_airport
Varchar(20)
Abc
airline
Varchar(20)
123
Float
123
Float
123
Integer
1103972015-10-01 12:00:00-61010135EV
2139302015-10-01 12:05:00-8-310135EV
3114332015-10-01 12:55:00-7-1010135EV
4103972015-10-01 16:00:00-5-110135EV
5114332015-10-01 17:11:000110135EV
6139302015-10-01 17:22:0041210135EV
7103972015-10-02 12:00:00-3-310135EV
8139302015-10-02 12:05:00-4-410135EV
9114332015-10-02 12:55:007-310135EV
10103972015-10-02 16:00:00-5-510135EV
11114332015-10-02 17:11:00-1-210135EV
12139302015-10-02 17:22:001-410135EV
13103972015-10-03 14:00:00-9-1410135EV
14103972015-10-04 12:00:00211710135EV
15114332015-10-04 12:55:00201010135EV
16103972015-10-04 16:00:00-8-810135EV
17114332015-10-04 17:11:0013710135EV
18139302015-10-04 17:22:00121910135EV
19103972015-10-05 12:00:00-2810135EV
20139302015-10-05 12:05:000-110135EV
21114332015-10-05 12:55:0017315810135EV
22103972015-10-05 16:00:00505210135EV
23114332015-10-05 17:11:0022910135EV
24139302015-10-05 17:22:00-6-1210135EV
25103972015-10-06 12:00:00-1210135EV
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30139302015-10-06 17:22:00-10-1610135EV
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40114332015-10-08 17:11:0001010135EV
41139302015-10-08 17:22:00-2-1610135EV
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50114332015-10-11 12:55:00-2-810135EV
51103972015-10-11 16:00:00-10-2310135EV
52114332015-10-11 17:11:00312310135EV
53139302015-10-11 17:22:00-8410135EV
54103972015-10-12 12:00:00-2010135EV
55139302015-10-12 12:05:00-3-110135EV
56114332015-10-12 12:55:00-1-110135EV
57103972015-10-12 16:00:00-1-1210135EV
58114332015-10-12 17:11:00-1-510135EV
59139302015-10-12 17:22:00-3-810135EV
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61139302015-10-13 12:05:00364810135EV
62114332015-10-13 12:55:00-4-810135EV
63103972015-10-13 16:00:00-4-1510135EV
64114332015-10-13 17:11:00-2410135EV
65139302015-10-13 17:22:004-1010135EV
66103972015-10-14 12:00:00-3-710135EV
67139302015-10-14 12:05:00-12-2110135EV
68103972015-10-14 16:00:0031710135EV
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71103972015-10-15 12:00:00-4-1410135EV
72139302015-10-15 12:05:00-7-810135EV
73114332015-10-15 12:55:00-6510135EV
74103972015-10-15 16:00:00-3-810135EV
75114332015-10-15 17:11:00183310135EV
76139302015-10-15 17:22:0041241510135EV
77103972015-10-16 12:00:00768710135EV
78139302015-10-16 12:05:00-6-310135EV
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82139302015-10-16 17:22:00-11-1410135EV
83103972015-10-17 14:00:00-8-1710135EV
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85114332015-10-18 12:55:00636210135EV
86103972015-10-18 16:00:00806710135EV
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88139302015-10-18 17:22:0018910135EV
89103972015-10-19 12:00:002010135EV
90139302015-10-19 12:05:003-1510135EV
91114332015-10-19 12:55:00-1-810135EV
92103972015-10-19 16:00:00-6-910135EV
93114332015-10-19 17:11:00252010135EV
94139302015-10-19 17:22:000-510135EV
95103972015-10-20 12:00:003110135EV
96139302015-10-20 12:05:00-3-1410135EV
97114332015-10-20 12:55:00-1010135EV
98103972015-10-20 16:00:00-5-1510135EV
99114332015-10-20 17:11:00-13-1210135EV
100139302015-10-20 17:22:004-1210135EV
Rows: 1-100 | Columns: 9

See Also

vDataFrame.analytic Adds a new vcolumn to the vDataFrame by using an advanced analytical function on a specific vcolumn.
vDataFrame.eval Evaluates a customized expression.