vDataFrame.analytic¶

In [ ]:

vDataFrame.analytic(func: str,
                    columns: (str, list) = [],
                    by: list = [],
                    order_by: (dict, list) = [],
                    name: str = "",
                    offset: int = 1,
                    x_smoothing: float = 0.5,
                    add_count: bool = True,)

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

⚠ Warning: Some analytical 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 ema : exponential moving average first_value : first non null lead iqr : interquartile range dense_rank : dense rank kurtosis : kurtosis jb : Jarque Bera index lead : next element lag : previous element last_value : first non null lag mad : median absolute deviation max : maximum mean : average median : median min : min mode : most occurent element q% : q quantile (ex: 50% for the median) pct_change : ratio between the current value and the previous one percent_rank : percent rank prod : product range : difference between the max and the min rank : rank row_number : row number sem : standard error of the mean skewness : skewness sum : sum std : standard deviation unique : cardinality (count distinct) var : variance Other analytical functions could work if it is part of the DB version you are using.
columns	str	✓	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.
offset	int	✓	Lead/Lag offset if parameter 'func' is the function 'lead'/'lag'.
x_smoothing	float	✓	The smoothing parameter of the 'ema' if the function is 'ema'. It must be in [0;1]
add_count	bool	✓	If the function is the 'mode' and this parameter is True then another column will be added to the vDataFrame with the mode number of occurences.

Returns¶

vDataFrame : self

Example¶

In [1]:

from verticapy import vDataFrame
flights = vDataFrame("public.usa_flights")
flights.eval(name = "week", expr = "WEEK(scheduled_departure)")
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)	123 week Integer
1	10135	2015-10-01 10:09:00	-9	-2	11433	EV	40
2	10135	2015-10-01 10:27:00	-3	-14	10397	EV	40
3	10135	2015-10-01 13:57:00	-4	6	13930	EV	40
4	10135	2015-10-01 14:02:00	-3	-8	11433	EV	40
5	10135	2015-10-01 14:44:00	0	-1	10397	EV	40
6	10135	2015-10-01 20:02:00	-3	-11	11433	EV	40
7	10135	2015-10-01 21:02:00	-3	4	13930	EV	40
8	10135	2015-10-01 21:06:00	-3	-14	10397	DL	40
9	10135	2015-10-02 10:09:00	-7	-14	11433	EV	40
10	10135	2015-10-02 10:27:00	12	4	10397	EV	40
11	10135	2015-10-02 13:57:00	-3	-2	13930	EV	40
12	10135	2015-10-02 14:02:00	-15	-19	11433	EV	40
13	10135	2015-10-02 14:44:00	-4	-6	10397	EV	40
14	10135	2015-10-02 20:02:00	-3	-3	11433	EV	40
15	10135	2015-10-02 21:02:00	58	59	13930	EV	40
16	10135	2015-10-02 21:06:00	-2	-14	10397	DL	40
17	10135	2015-10-03 10:27:00	-4	-2	10397	EV	40
18	10135	2015-10-03 21:02:00	12	11	13930	EV	40
19	10135	2015-10-03 21:06:00	-2	-16	10397	DL	40
20	10135	2015-10-04 10:09:00	4	23	11433	EV	41
21	10135	2015-10-04 10:27:00	0	11	10397	EV	41
22	10135	2015-10-04 13:57:00	14	11	13930	EV	41
23	10135	2015-10-04 14:02:00	-6	-10	11433	EV	41
24	10135	2015-10-04 14:44:00	18	18	10397	EV	41
25	10135	2015-10-04 20:02:00	19	12	11433	EV	41
26	10135	2015-10-04 21:02:00	-2	5	13930	EV	41
27	10135	2015-10-04 21:06:00	-7	-13	10397	DL	41
28	10135	2015-10-05 10:09:00	-8	-3	11433	EV	41
29	10135	2015-10-05 13:57:00	-10	-20	13930	EV	41
30	10135	2015-10-05 14:02:00	-11	-17	11433	EV	41
31	10135	2015-10-05 14:44:00	10	20	10397	EV	41
32	10135	2015-10-05 20:02:00	-3	-11	11433	EV	41
33	10135	2015-10-05 21:02:00	-6	-9	13930	EV	41
34	10135	2015-10-05 21:06:00	1	1	10397	DL	41
35	10135	2015-10-06 10:09:00	-8	-1	11433	EV	41
36	10135	2015-10-06 10:27:00	-8	-2	10397	EV	41
37	10135	2015-10-06 13:57:00	-8	-18	13930	EV	41
38	10135	2015-10-06 14:02:00	-6	-7	11433	EV	41
39	10135	2015-10-06 14:44:00	-3	-1	10397	EV	41
40	10135	2015-10-06 20:02:00	-5	-8	11433	EV	41
41	10135	2015-10-06 21:02:00	-6	-15	13930	EV	41
42	10135	2015-10-06 21:06:00	-12	-19	10397	DL	41
43	10135	2015-10-07 10:09:00	-2	2	11433	EV	41
44	10135	2015-10-07 11:30:00	-5	-6	10397	EV	41
45	10135	2015-10-07 13:57:00	-7	-18	13930	EV	41
46	10135	2015-10-07 14:44:00	-3	-12	10397	EV	41
47	10135	2015-10-07 20:02:00	-5	-16	11433	EV	41
48	10135	2015-10-07 21:02:00	-3	2	13930	EV	41
49	10135	2015-10-07 21:06:00	-3	-23	10397	DL	41
50	10135	2015-10-08 10:09:00	10	4	11433	EV	41
51	10135	2015-10-08 10:27:00	6	-2	10397	EV	41
52	10135	2015-10-08 13:57:00	-3	1	13930	EV	41
53	10135	2015-10-08 14:02:00	194	200	11433	EV	41
54	10135	2015-10-08 14:44:00	-6	-4	10397	EV	41
55	10135	2015-10-08 20:02:00	-1	3	11433	EV	41
56	10135	2015-10-08 21:02:00	-4	-6	13930	EV	41
57	10135	2015-10-08 21:06:00	-2	-13	10397	DL	41
58	10135	2015-10-09 10:09:00	-4	-2	11433	EV	41
59	10135	2015-10-09 10:27:00	-4	-4	10397	EV	41
60	10135	2015-10-09 13:57:00	-9	1	13930	EV	41
61	10135	2015-10-09 14:02:00	116	107	11433	EV	41
62	10135	2015-10-09 14:44:00	40	47	10397	EV	41
63	10135	2015-10-09 20:02:00	6	2	11433	EV	41
64	10135	2015-10-09 21:02:00	-3	-4	13930	EV	41
65	10135	2015-10-09 21:06:00	-1	15	10397	DL	41
66	10135	2015-10-10 10:27:00	-2	-4	10397	EV	41
67	10135	2015-10-10 21:02:00	-4	10	13930	EV	41
68	10135	2015-10-10 21:06:00	-3	-16	10397	DL	41
69	10135	2015-10-11 10:09:00	-1	-4	11433	EV	42
70	10135	2015-10-11 10:27:00	-4	0	10397	EV	42
71	10135	2015-10-11 13:57:00	-4	-2	13930	EV	42
72	10135	2015-10-11 14:02:00	-8	-12	11433	EV	42
73	10135	2015-10-11 14:44:00	1	35	10397	EV	42
74	10135	2015-10-11 20:02:00	-10	-16	11433	EV	42
75	10135	2015-10-11 21:02:00	-7	-10	13930	EV	42
76	10135	2015-10-11 21:06:00	-1	-7	10397	DL	42
77	10135	2015-10-12 10:09:00	-2	1	11433	EV	42
78	10135	2015-10-12 10:27:00	-2	0	10397	EV	42
79	10135	2015-10-12 13:57:00	-3	2	13930	EV	42
80	10135	2015-10-12 14:02:00	-2	-5	11433	EV	42
81	10135	2015-10-12 14:44:00	-7	-10	10397	EV	42
82	10135	2015-10-12 20:02:00	-5	-1	11433	EV	42
83	10135	2015-10-12 21:02:00	38	25	13930	EV	42
84	10135	2015-10-12 21:06:00	-3	11	10397	DL	42
85	10135	2015-10-13 10:09:00	0	2	11433	EV	42
86	10135	2015-10-13 10:27:00	-3	-3	10397	EV	42
87	10135	2015-10-13 13:57:00	-2	11	13930	EV	42
88	10135	2015-10-13 14:02:00	-3	-5	11433	EV	42
89	10135	2015-10-13 14:44:00	-2	-8	10397	EV	42
90	10135	2015-10-13 20:02:00	3	16	11433	EV	42
91	10135	2015-10-13 21:02:00	-5	-3	13930	EV	42
92	10135	2015-10-13 21:06:00	-6	-2	10397	DL	42
93	10135	2015-10-14 10:09:00	-3	-3	11433	EV	42
94	10135	2015-10-14 11:30:00	53	54	10397	EV	42
95	10135	2015-10-14 13:57:00	-3	6	13930	EV	42
96	10135	2015-10-14 14:44:00	-3	-3	10397	EV	42
97	10135	2015-10-14 20:02:00	33	9	11433	EV	42
98	10135	2015-10-14 21:02:00	-4	-10	13930	EV	42
99	10135	2015-10-14 21:06:00	9	2	10397	DL	42
100	10135	2015-10-15 10:09:00	-5	-16	11433	EV	42

Rows: 1-100 | Columns: 7

In [2]:

# LAG of departure_delay for the same flight 
# (same airline and same origin / destination airports)
flights.analytic(func = "lag",
                 columns = "departure_delay",
                 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 week Integer	123 Integer
1	10135	2015-10-01 21:06:00	-3	-14	10397	DL	40
2	10135	2015-10-02 21:06:00	-2	-14	10397	DL	40
3	10135	2015-10-03 21:06:00	-2	-16	10397	DL	40
4	10135	2015-10-04 21:06:00	-7	-13	10397	DL	41
5	10135	2015-10-05 21:06:00	1	1	10397	DL	41
6	10135	2015-10-06 21:06:00	-12	-19	10397	DL	41
7	10135	2015-10-07 21:06:00	-3	-23	10397	DL	41
8	10135	2015-10-08 21:06:00	-2	-13	10397	DL	41
9	10135	2015-10-09 21:06:00	-1	15	10397	DL	41
10	10135	2015-10-10 21:06:00	-3	-16	10397	DL	41
11	10135	2015-10-11 21:06:00	-1	-7	10397	DL	42
12	10135	2015-10-12 21:06:00	-3	11	10397	DL	42
13	10135	2015-10-13 21:06:00	-6	-2	10397	DL	42
14	10135	2015-10-14 21:06:00	9	2	10397	DL	42
15	10135	2015-10-15 21:06:00	0	-7	10397	DL	42
16	10135	2015-10-16 21:06:00	-1	7	10397	DL	42
17	10135	2015-10-17 21:06:00	-2	-21	10397	DL	42
18	10135	2015-10-18 21:06:00	-2	5	10397	DL	43
19	10135	2015-10-19 21:06:00	1	4	10397	DL	43
20	10135	2015-10-20 21:06:00	9	0	10397	DL	43
21	10135	2015-10-21 21:06:00	-1	-5	10397	DL	43
22	10135	2015-10-22 21:06:00	-2	-8	10397	DL	43
23	10135	2015-10-23 21:06:00	0	-5	10397	DL	43
24	10135	2015-10-24 21:06:00	-2	0	10397	DL	43
25	10135	2015-10-25 21:06:00	0	-14	10397	DL	44
26	10135	2015-10-26 21:06:00	4	-1	10397	DL	44
27	10135	2015-10-27 21:06:00	-3	-7	10397	DL	44
28	10135	2015-10-28 21:06:00	0	-12	10397	DL	44
29	10135	2015-10-29 21:06:00	-1	-14	10397	DL	44
30	10135	2015-10-30 21:06:00	-4	-14	10397	DL	44
31	10135	2015-10-31 21:06:00	-3	-28	10397	DL	44
32	10135	2015-10-01 10:27:00	-3	-14	10397	EV	40
33	10135	2015-10-01 14:44:00	0	-1	10397	EV	40
34	10135	2015-10-02 10:27:00	12	4	10397	EV	40
35	10135	2015-10-02 14:44:00	-4	-6	10397	EV	40
36	10135	2015-10-03 10:27:00	-4	-2	10397	EV	40
37	10135	2015-10-04 10:27:00	0	11	10397	EV	41
38	10135	2015-10-04 14:44:00	18	18	10397	EV	41
39	10135	2015-10-05 14:44:00	10	20	10397	EV	41
40	10135	2015-10-06 10:27:00	-8	-2	10397	EV	41
41	10135	2015-10-06 14:44:00	-3	-1	10397	EV	41
42	10135	2015-10-07 11:30:00	-5	-6	10397	EV	41
43	10135	2015-10-07 14:44:00	-3	-12	10397	EV	41
44	10135	2015-10-08 10:27:00	6	-2	10397	EV	41
45	10135	2015-10-08 14:44:00	-6	-4	10397	EV	41
46	10135	2015-10-09 10:27:00	-4	-4	10397	EV	41
47	10135	2015-10-09 14:44:00	40	47	10397	EV	41
48	10135	2015-10-10 10:27:00	-2	-4	10397	EV	41
49	10135	2015-10-11 10:27:00	-4	0	10397	EV	42
50	10135	2015-10-11 14:44:00	1	35	10397	EV	42
51	10135	2015-10-12 10:27:00	-2	0	10397	EV	42
52	10135	2015-10-12 14:44:00	-7	-10	10397	EV	42
53	10135	2015-10-13 10:27:00	-3	-3	10397	EV	42
54	10135	2015-10-13 14:44:00	-2	-8	10397	EV	42
55	10135	2015-10-14 11:30:00	53	54	10397	EV	42
56	10135	2015-10-14 14:44:00	-3	-3	10397	EV	42
57	10135	2015-10-15 10:27:00	-5	-12	10397	EV	42
58	10135	2015-10-15 14:44:00	9	23	10397	EV	42
59	10135	2015-10-16 10:27:00	-5	-22	10397	EV	42
60	10135	2015-10-16 14:44:00	-2	-4	10397	EV	42
61	10135	2015-10-17 10:27:00	-1	-12	10397	EV	42
62	10135	2015-10-18 10:27:00	55	69	10397	EV	43
63	10135	2015-10-18 14:44:00	-5	-10	10397	EV	43
64	10135	2015-10-19 10:27:00	-5	-5	10397	EV	43
65	10135	2015-10-19 14:44:00	17	17	10397	EV	43
66	10135	2015-10-20 10:27:00	-2	-2	10397	EV	43
67	10135	2015-10-20 14:44:00	-5	-11	10397	EV	43
68	10135	2015-10-21 11:30:00	-6	-17	10397	EV	43
69	10135	2015-10-21 14:44:00	-2	-10	10397	EV	43
70	10135	2015-10-22 14:44:00	24	24	10397	EV	43
71	10135	2015-10-23 10:27:00	-3	5	10397	EV	43
72	10135	2015-10-23 14:44:00	68	55	10397	EV	43
73	10135	2015-10-24 10:27:00	-1	-2	10397	EV	43
74	10135	2015-10-25 10:27:00	-6	-5	10397	EV	44
75	10135	2015-10-25 14:44:00	-5	-14	10397	EV	44
76	10135	2015-10-26 10:27:00	62	56	10397	EV	44
77	10135	2015-10-26 14:44:00	179	180	10397	EV	44
78	10135	2015-10-27 10:27:00	-2	-9	10397	EV	44
79	10135	2015-10-27 14:44:00	-4	-20	10397	EV	44
80	10135	2015-10-28 10:27:00	58	66	10397	EV	44
81	10135	2015-10-28 14:44:00	10	-1	10397	EV	44
82	10135	2015-10-29 10:27:00	-3	-2	10397	EV	44
83	10135	2015-10-29 14:44:00	0	-15	10397	EV	44
84	10135	2015-10-30 10:27:00	29	24	10397	EV	44
85	10135	2015-10-30 14:44:00	-3	-6	10397	EV	44
86	10135	2015-10-31 10:27:00	-4	-13	10397	EV	44
87	10135	2015-10-01 10:09:00	-9	-2	11433	EV	40
88	10135	2015-10-01 14:02:00	-3	-8	11433	EV	40
89	10135	2015-10-01 20:02:00	-3	-11	11433	EV	40
90	10135	2015-10-02 10:09:00	-7	-14	11433	EV	40
91	10135	2015-10-02 14:02:00	-15	-19	11433	EV	40
92	10135	2015-10-02 20:02:00	-3	-3	11433	EV	40
93	10135	2015-10-04 10:09:00	4	23	11433	EV	41
94	10135	2015-10-04 14:02:00	-6	-10	11433	EV	41
95	10135	2015-10-04 20:02:00	19	12	11433	EV	41
96	10135	2015-10-05 10:09:00	-8	-3	11433	EV	41
97	10135	2015-10-05 14:02:00	-11	-17	11433	EV	41
98	10135	2015-10-05 20:02:00	-3	-11	11433	EV	41
99	10135	2015-10-06 10:09:00	-8	-1	11433	EV	41
100	10135	2015-10-06 14:02:00	-6	-7	11433	EV	41

Rows: 1-100 | Columns: 8

In [3]:

# Airlines having the biggest number of flights to manage in the week
flights.analytic(func = "mode",
                 columns = "airline",
                 by = ["origin_airport", "week"],
                 add_count = True)

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 week Integer	123 Integer	Abc Varchar(20)	123 Integer
1	10397	2015-10-01 12:00:00	-6	10	10135	EV	40
2	10397	2015-10-01 16:00:00	-5	-1	10135	EV	40
3	10397	2015-10-02 12:00:00	-3	-3	10135	EV	40
4	10397	2015-10-02 16:00:00	-5	-5	10135	EV	40
5	10397	2015-10-03 14:00:00	-9	-14	10135	EV	40
6	11433	2015-10-01 12:55:00	-7	-10	10135	EV	40
7	11433	2015-10-01 17:11:00	0	1	10135	EV	40
8	11433	2015-10-02 12:55:00	7	-3	10135	EV	40
9	11433	2015-10-02 17:11:00	-1	-2	10135	EV	40
10	13930	2015-10-01 12:05:00	-8	-3	10135	EV	40
11	13930	2015-10-01 17:22:00	4	12	10135	EV	40
12	13930	2015-10-02 12:05:00	-4	-4	10135	EV	40
13	13930	2015-10-02 17:22:00	1	-4	10135	EV	40
14	10397	2015-10-04 12:00:00	21	17	10135	EV	41
15	10397	2015-10-04 16:00:00	-8	-8	10135	EV	41
16	10397	2015-10-05 12:00:00	-2	8	10135	EV	41
17	10397	2015-10-05 16:00:00	50	52	10135	EV	41
18	10397	2015-10-06 12:00:00	-1	2	10135	EV	41
19	10397	2015-10-06 16:00:00	2	-12	10135	EV	41
20	10397	2015-10-07 12:00:00	1	10	10135	EV	41
21	10397	2015-10-07 15:48:00	18	4	10135	EV	41
22	10397	2015-10-08 12:00:00	-1	-1	10135	EV	41
23	10397	2015-10-08 16:00:00	195	202	10135	EV	41
24	10397	2015-10-09 12:00:00	-2	-3	10135	EV	41
25	10397	2015-10-09 16:00:00	104	104	10135	EV	41
26	10397	2015-10-10 14:00:00	-1	-19	10135	EV	41
27	11433	2015-10-04 12:55:00	20	10	10135	EV	41
28	11433	2015-10-04 17:11:00	13	7	10135	EV	41
29	11433	2015-10-05 12:55:00	173	158	10135	EV	41
30	11433	2015-10-05 17:11:00	22	9	10135	EV	41
31	11433	2015-10-06 12:55:00	1	7	10135	EV	41
32	11433	2015-10-06 17:11:00	-3	5	10135	EV	41
33	11433	2015-10-07 17:11:00	-6	-19	10135	EV	41
34	11433	2015-10-08 12:55:00	-2	-5	10135	EV	41
35	11433	2015-10-08 17:11:00	0	10	10135	EV	41
36	11433	2015-10-09 12:55:00	5	-6	10135	EV	41
37	11433	2015-10-09 17:11:00	45	40	10135	EV	41
38	13930	2015-10-04 17:22:00	12	19	10135	EV	41
39	13930	2015-10-05 12:05:00	0	-1	10135	EV	41
40	13930	2015-10-05 17:22:00	-6	-12	10135	EV	41
41	13930	2015-10-06 12:05:00	-7	0	10135	EV	41
42	13930	2015-10-06 17:22:00	-10	-16	10135	EV	41
43	13930	2015-10-07 12:05:00	175	166	10135	EV	41
44	13930	2015-10-07 17:22:00	-10	16	10135	EV	41
45	13930	2015-10-08 12:05:00	-2	-10	10135	EV	41
46	13930	2015-10-08 17:22:00	-2	-16	10135	EV	41
47	13930	2015-10-09 12:05:00	-10	-6	10135	EV	41
48	13930	2015-10-09 17:22:00	3	16	10135	EV	41
49	10397	2015-10-11 12:00:00	-1	-9	10135	EV	42
50	10397	2015-10-11 16:00:00	-10	-23	10135	EV	42
51	10397	2015-10-12 12:00:00	-2	0	10135	EV	42
52	10397	2015-10-12 16:00:00	-1	-12	10135	EV	42
53	10397	2015-10-13 12:00:00	0	-6	10135	EV	42
54	10397	2015-10-13 16:00:00	-4	-15	10135	EV	42
55	10397	2015-10-14 12:00:00	-3	-7	10135	EV	42
56	10397	2015-10-14 16:00:00	3	17	10135	EV	42
57	10397	2015-10-15 12:00:00	-4	-14	10135	EV	42
58	10397	2015-10-15 16:00:00	-3	-8	10135	EV	42
59	10397	2015-10-16 12:00:00	76	87	10135	EV	42
60	10397	2015-10-16 16:00:00	2	5	10135	EV	42
61	10397	2015-10-17 14:00:00	-8	-17	10135	EV	42
62	11433	2015-10-11 12:55:00	-2	-8	10135	EV	42
63	11433	2015-10-11 17:11:00	31	23	10135	EV	42
64	11433	2015-10-12 12:55:00	-1	-1	10135	EV	42
65	11433	2015-10-12 17:11:00	-1	-5	10135	EV	42
66	11433	2015-10-13 12:55:00	-4	-8	10135	EV	42
67	11433	2015-10-13 17:11:00	-2	4	10135	EV	42
68	11433	2015-10-14 17:11:00	1	-10	10135	EV	42
69	11433	2015-10-15 12:55:00	-6	5	10135	EV	42
70	11433	2015-10-15 17:11:00	18	33	10135	EV	42
71	11433	2015-10-16 12:55:00	-11	-3	10135	EV	42
72	11433	2015-10-16 17:11:00	-2	-2	10135	EV	42
73	13930	2015-10-11 17:22:00	-8	4	10135	EV	42
74	13930	2015-10-12 12:05:00	-3	-1	10135	EV	42
75	13930	2015-10-12 17:22:00	-3	-8	10135	EV	42
76	13930	2015-10-13 12:05:00	36	48	10135	EV	42
77	13930	2015-10-13 17:22:00	4	-10	10135	EV	42
78	13930	2015-10-14 12:05:00	-12	-21	10135	EV	42
79	13930	2015-10-14 17:22:00	1	-1	10135	EV	42
80	13930	2015-10-15 12:05:00	-7	-8	10135	EV	42
81	13930	2015-10-15 17:22:00	412	415	10135	EV	42
82	13930	2015-10-16 12:05:00	-6	-3	10135	EV	42
83	13930	2015-10-16 17:22:00	-11	-14	10135	EV	42
84	10397	2015-10-18 12:00:00	-5	-7	10135	EV	43
85	10397	2015-10-18 16:00:00	80	67	10135	EV	43
86	10397	2015-10-19 12:00:00	2	0	10135	EV	43
87	10397	2015-10-19 16:00:00	-6	-9	10135	EV	43
88	10397	2015-10-20 12:00:00	3	1	10135	EV	43
89	10397	2015-10-20 16:00:00	-5	-15	10135	EV	43
90	10397	2015-10-21 12:00:00	-3	-11	10135	EV	43
91	10397	2015-10-21 16:00:00	-4	-17	10135	EV	43
92	10397	2015-10-22 12:00:00	-1	-8	10135	EV	43
93	10397	2015-10-22 16:00:00	-7	-16	10135	EV	43
94	10397	2015-10-23 12:00:00	19	15	10135	EV	43
95	10397	2015-10-23 16:00:00	-3	-4	10135	EV	43
96	10397	2015-10-24 14:00:00	-5	-11	10135	EV	43
97	11433	2015-10-18 12:55:00	63	62	10135	EV	43
98	11433	2015-10-18 17:11:00	-5	-18	10135	EV	43
99	11433	2015-10-19 12:55:00	-1	-8	10135	EV	43
100	11433	2015-10-19 17:11:00	25	20	10135	EV	43

Rows: 1-100 | Columns: 10

In [4]:

# Correlation between the arrival delay and departure delay for the 
# same origin and destination airports
flights.analytic(func = "corr",
                 columns = ["departure_delay", "arrival_delay"],
                 by = ["origin_airport", "destination_airport"])

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 week Integer	123 Integer	Abc Varchar(20)	123 Integer	123 Float
1	10397	2015-10-01 12:00:00	-6	10	10135	EV	40
2	10397	2015-10-01 16:00:00	-5	-1	10135	EV	40
3	10397	2015-10-02 12:00:00	-3	-3	10135	EV	40
4	10397	2015-10-02 16:00:00	-5	-5	10135	EV	40
5	10397	2015-10-03 14:00:00	-9	-14	10135	EV	40
6	11433	2015-10-01 12:55:00	-7	-10	10135	EV	40
7	11433	2015-10-01 17:11:00	0	1	10135	EV	40
8	11433	2015-10-02 12:55:00	7	-3	10135	EV	40
9	11433	2015-10-02 17:11:00	-1	-2	10135	EV	40
10	13930	2015-10-01 12:05:00	-8	-3	10135	EV	40
11	13930	2015-10-01 17:22:00	4	12	10135	EV	40
12	13930	2015-10-02 12:05:00	-4	-4	10135	EV	40
13	13930	2015-10-02 17:22:00	1	-4	10135	EV	40
14	10397	2015-10-04 12:00:00	21	17	10135	EV	41
15	10397	2015-10-04 16:00:00	-8	-8	10135	EV	41
16	10397	2015-10-05 12:00:00	-2	8	10135	EV	41
17	10397	2015-10-05 16:00:00	50	52	10135	EV	41
18	10397	2015-10-06 12:00:00	-1	2	10135	EV	41
19	10397	2015-10-06 16:00:00	2	-12	10135	EV	41
20	10397	2015-10-07 12:00:00	1	10	10135	EV	41
21	10397	2015-10-07 15:48:00	18	4	10135	EV	41
22	10397	2015-10-08 12:00:00	-1	-1	10135	EV	41
23	10397	2015-10-08 16:00:00	195	202	10135	EV	41
24	10397	2015-10-09 12:00:00	-2	-3	10135	EV	41
25	10397	2015-10-09 16:00:00	104	104	10135	EV	41
26	10397	2015-10-10 14:00:00	-1	-19	10135	EV	41
27	11433	2015-10-04 12:55:00	20	10	10135	EV	41
28	11433	2015-10-04 17:11:00	13	7	10135	EV	41
29	11433	2015-10-05 12:55:00	173	158	10135	EV	41
30	11433	2015-10-05 17:11:00	22	9	10135	EV	41
31	11433	2015-10-06 12:55:00	1	7	10135	EV	41
32	11433	2015-10-06 17:11:00	-3	5	10135	EV	41
33	11433	2015-10-07 17:11:00	-6	-19	10135	EV	41
34	11433	2015-10-08 12:55:00	-2	-5	10135	EV	41
35	11433	2015-10-08 17:11:00	0	10	10135	EV	41
36	11433	2015-10-09 12:55:00	5	-6	10135	EV	41
37	11433	2015-10-09 17:11:00	45	40	10135	EV	41
38	13930	2015-10-04 17:22:00	12	19	10135	EV	41
39	13930	2015-10-05 12:05:00	0	-1	10135	EV	41
40	13930	2015-10-05 17:22:00	-6	-12	10135	EV	41
41	13930	2015-10-06 12:05:00	-7	0	10135	EV	41
42	13930	2015-10-06 17:22:00	-10	-16	10135	EV	41
43	13930	2015-10-07 12:05:00	175	166	10135	EV	41
44	13930	2015-10-07 17:22:00	-10	16	10135	EV	41
45	13930	2015-10-08 12:05:00	-2	-10	10135	EV	41
46	13930	2015-10-08 17:22:00	-2	-16	10135	EV	41
47	13930	2015-10-09 12:05:00	-10	-6	10135	EV	41
48	13930	2015-10-09 17:22:00	3	16	10135	EV	41
49	10397	2015-10-11 12:00:00	-1	-9	10135	EV	42
50	10397	2015-10-11 16:00:00	-10	-23	10135	EV	42
51	10397	2015-10-12 12:00:00	-2	0	10135	EV	42
52	10397	2015-10-12 16:00:00	-1	-12	10135	EV	42
53	10397	2015-10-13 12:00:00	0	-6	10135	EV	42
54	10397	2015-10-13 16:00:00	-4	-15	10135	EV	42
55	10397	2015-10-14 12:00:00	-3	-7	10135	EV	42
56	10397	2015-10-14 16:00:00	3	17	10135	EV	42
57	10397	2015-10-15 12:00:00	-4	-14	10135	EV	42
58	10397	2015-10-15 16:00:00	-3	-8	10135	EV	42
59	10397	2015-10-16 12:00:00	76	87	10135	EV	42
60	10397	2015-10-16 16:00:00	2	5	10135	EV	42
61	10397	2015-10-17 14:00:00	-8	-17	10135	EV	42
62	11433	2015-10-11 12:55:00	-2	-8	10135	EV	42
63	11433	2015-10-11 17:11:00	31	23	10135	EV	42
64	11433	2015-10-12 12:55:00	-1	-1	10135	EV	42
65	11433	2015-10-12 17:11:00	-1	-5	10135	EV	42
66	11433	2015-10-13 12:55:00	-4	-8	10135	EV	42
67	11433	2015-10-13 17:11:00	-2	4	10135	EV	42
68	11433	2015-10-14 17:11:00	1	-10	10135	EV	42
69	11433	2015-10-15 12:55:00	-6	5	10135	EV	42
70	11433	2015-10-15 17:11:00	18	33	10135	EV	42
71	11433	2015-10-16 12:55:00	-11	-3	10135	EV	42
72	11433	2015-10-16 17:11:00	-2	-2	10135	EV	42
73	13930	2015-10-11 17:22:00	-8	4	10135	EV	42
74	13930	2015-10-12 12:05:00	-3	-1	10135	EV	42
75	13930	2015-10-12 17:22:00	-3	-8	10135	EV	42
76	13930	2015-10-13 12:05:00	36	48	10135	EV	42
77	13930	2015-10-13 17:22:00	4	-10	10135	EV	42
78	13930	2015-10-14 12:05:00	-12	-21	10135	EV	42
79	13930	2015-10-14 17:22:00	1	-1	10135	EV	42
80	13930	2015-10-15 12:05:00	-7	-8	10135	EV	42
81	13930	2015-10-15 17:22:00	412	415	10135	EV	42
82	13930	2015-10-16 12:05:00	-6	-3	10135	EV	42
83	13930	2015-10-16 17:22:00	-11	-14	10135	EV	42
84	10397	2015-10-18 12:00:00	-5	-7	10135	EV	43
85	10397	2015-10-18 16:00:00	80	67	10135	EV	43
86	10397	2015-10-19 12:00:00	2	0	10135	EV	43
87	10397	2015-10-19 16:00:00	-6	-9	10135	EV	43
88	10397	2015-10-20 12:00:00	3	1	10135	EV	43
89	10397	2015-10-20 16:00:00	-5	-15	10135	EV	43
90	10397	2015-10-21 12:00:00	-3	-11	10135	EV	43
91	10397	2015-10-21 16:00:00	-4	-17	10135	EV	43
92	10397	2015-10-22 12:00:00	-1	-8	10135	EV	43
93	10397	2015-10-22 16:00:00	-7	-16	10135	EV	43
94	10397	2015-10-23 12:00:00	19	15	10135	EV	43
95	10397	2015-10-23 16:00:00	-3	-4	10135	EV	43
96	10397	2015-10-24 14:00:00	-5	-11	10135	EV	43
97	11433	2015-10-18 12:55:00	63	62	10135	EV	43
98	11433	2015-10-18 17:11:00	-5	-18	10135	EV	43
99	11433	2015-10-19 12:55:00	-1	-8	10135	EV	43
100	11433	2015-10-19 17:11:00	25	20	10135	EV	43

Rows: 1-100 | Columns: 11

vDataFrame.eval	Evaluates a customized expression.
vDataFrame.rolling	Computes a customized moving window.

vDataFrame.analytic¶

Parameters¶

Returns¶

Example¶

See Also¶