vDataFrame.at_time¶

In [ ]:

vDataFrame.at_time(ts: (str, datetime.timedelta),
                   time: str)

Filters the vDataFrame by only keeping the records at the input time.

Parameters¶

Name	Type	Optional	Description
ts	str	❌	TS (Time Series) vcolumn to use to filter the data. The vcolumn type must be date like (date, datetime, timestamp...)
time	str / time	❌	Input Time. For example, time = '12:00' will filter the data when time('ts') is equal to 12:00.

Returns¶

vDataFrame : self

Example¶

In [120]:

from verticapy.datasets import load_smart_meters
sm = load_smart_meters()
display(sm)

	123 val Numeric(11,7)	📅 time Timestamp	123 id Int
1	0.0370000	2014-01-01 01:15:00	2
2	0.0800000	2014-01-01 02:30:00	5
3	0.0810000	2014-01-01 03:00:00	1
4	1.4890000	2014-01-01 05:00:00	3
5	0.0720000	2014-01-01 06:00:00	5
6	2.3060000	2014-01-01 07:15:00	9
7	0.1020000	2014-01-01 07:45:00	4
8	0.0970000	2014-01-01 10:45:00	8
9	0.0290000	2014-01-01 11:00:00	0
10	0.5060000	2014-01-01 11:00:00	6
11	0.1290000	2014-01-01 11:15:00	5
12	0.6220000	2014-01-01 13:00:00	4
13	0.2770000	2014-01-01 13:45:00	0
14	0.2350000	2014-01-01 15:30:00	9
15	0.6230000	2014-01-01 16:45:00	7
16	1.3500000	2014-01-01 17:00:00	5
17	0.5590000	2014-01-01 17:15:00	1
18	0.3750000	2014-01-01 19:15:00	1
19	0.5400000	2014-01-01 22:30:00	9
20	0.3580000	2014-01-02 00:30:00	2
21	0.1390000	2014-01-02 01:30:00	3
22	0.0550000	2014-01-02 02:45:00	3
23	0.0860000	2014-01-02 03:00:00	6
24	0.0440000	2014-01-02 03:30:00	1
25	0.0730000	2014-01-02 03:45:00	8
26	0.1000000	2014-01-02 04:45:00	7
27	0.0440000	2014-01-02 05:30:00	1
28	0.0480000	2014-01-02 06:45:00	1
29	0.0550000	2014-01-02 06:45:00	5
30	0.0820000	2014-01-02 10:15:00	1
31	0.3210000	2014-01-02 10:45:00	0
32	0.3050000	2014-01-02 11:15:00	0
33	0.3970000	2014-01-02 12:30:00	5
34	0.3580000	2014-01-02 13:45:00	0
35	0.2540000	2014-01-02 14:30:00	4
36	0.1150000	2014-01-02 15:30:00	0
37	0.1850000	2014-01-02 15:30:00	7
38	0.5240000	2014-01-02 16:00:00	8
39	0.8710000	2014-01-02 17:45:00	4
40	1.0380000	2014-01-02 19:30:00	9
41	1.4780000	2014-01-02 19:45:00	6
42	1.7760000	2014-01-02 20:15:00	8
43	0.0940000	2014-01-03 00:30:00	8
44	0.3130000	2014-01-03 00:45:00	6
45	0.1330000	2014-01-03 01:45:00	9
46	0.0600000	2014-01-03 02:45:00	6
47	0.0850000	2014-01-03 03:15:00	9
48	0.0660000	2014-01-03 04:30:00	3
49	0.0680000	2014-01-03 04:30:00	1
50	0.0670000	2014-01-03 05:45:00	8
51	0.0320000	2014-01-03 06:30:00	7
52	0.0840000	2014-01-03 07:45:00	9
53	0.2720000	2014-01-03 07:45:00	2
54	0.0710000	2014-01-03 08:30:00	0
55	1.5060000	2014-01-03 09:15:00	4
56	0.0740000	2014-01-03 10:30:00	9
57	2.1080000	2014-01-03 11:00:00	4
58	0.1030000	2014-01-03 12:15:00	8
59	0.4890000	2014-01-03 19:45:00	7
60	0.6720000	2014-01-03 21:30:00	7
61	0.5910000	2014-01-03 22:15:00	5
62	1.9380000	2014-01-03 22:15:00	1
63	0.2840000	2014-01-03 23:30:00	4
64	0.1310000	2014-01-04 01:15:00	6
65	1.5460000	2014-01-04 01:30:00	1
66	0.3610000	2014-01-04 01:45:00	6
67	0.3830000	2014-01-04 02:15:00	6
68	0.1850000	2014-01-04 02:45:00	5
69	0.0620000	2014-01-04 05:45:00	8
70	0.2670000	2014-01-04 06:00:00	5
71	0.0770000	2014-01-04 06:45:00	8
72	0.0680000	2014-01-04 07:30:00	2
73	0.3090000	2014-01-04 07:45:00	4
74	0.1530000	2014-01-04 10:00:00	8
75	0.5450000	2014-01-04 10:45:00	7
76	1.2680000	2014-01-04 11:45:00	8
77	0.0760000	2014-01-04 12:00:00	2
78	1.3600000	2014-01-04 13:30:00	8
79	0.2850000	2014-01-04 17:15:00	2
80	0.4470000	2014-01-04 17:15:00	9
81	0.6410000	2014-01-04 18:00:00	4
82	0.8270000	2014-01-04 22:30:00	4
83	0.3230000	2014-01-04 23:45:00	0
84	0.3050000	2014-01-05 02:15:00	6
85	0.1110000	2014-01-05 04:00:00	9
86	0.0750000	2014-01-05 06:30:00	9
87	0.0900000	2014-01-05 08:00:00	4
88	0.1600000	2014-01-05 08:45:00	9
89	0.2810000	2014-01-05 10:00:00	3
90	0.5800000	2014-01-05 10:15:00	6
91	1.1320000	2014-01-05 11:30:00	6
92	0.6250000	2014-01-05 17:30:00	4
93	0.5370000	2014-01-05 19:45:00	4
94	0.5460000	2014-01-05 19:45:00	1
95	0.5390000	2014-01-05 23:30:00	9
96	0.0850000	2014-01-06 01:15:00	0
97	0.0870000	2014-01-06 02:45:00	8
98	0.0690000	2014-01-06 05:00:00	3
99	0.0270000	2014-01-06 07:45:00	3
100	0.5300000	2014-01-06 07:45:00	2

Rows: 1-100 of 11844 | Columns: 3

In [121]:

sm.at_time(ts = "time", time = "12:00")

11704 element(s) was/were filtered

	123 val Numeric(11,7)	📅 time Timestamp	123 id Int
1	0.0760000	2014-01-04 12:00:00	2
2	0.1610000	2014-01-09 12:00:00	6
3	0.0490000	2014-01-15 12:00:00	9
4	0.0200000	2014-01-19 12:00:00	4
5	0.1440000	2014-01-21 12:00:00	7
6	0.3230000	2014-01-27 12:00:00	4
7	0.1040000	2014-02-01 12:00:00	7
8	0.1870000	2014-02-04 12:00:00	0
9	0.0490000	2014-02-11 12:00:00	9
10	0.2900000	2014-02-19 12:00:00	9
11	0.1740000	2014-02-23 12:00:00	0
12	0.1240000	2014-02-26 12:00:00	8
13	0.1180000	2014-02-28 12:00:00	6
14	0.2990000	2014-02-28 12:00:00	2
15	0.0780000	2014-03-09 12:00:00	3
16	0.0660000	2014-03-10 12:00:00	7
17	0.0870000	2014-03-12 12:00:00	3
18	0.6840000	2014-03-17 12:00:00	6
19	0.0540000	2014-03-18 12:00:00	0
20	0.0210000	2014-03-22 12:00:00	5
21	0.1650000	2014-03-24 12:00:00	9
22	0.1520000	2014-03-27 12:00:00	1
23	1.2980000	2014-03-28 12:00:00	2
24	0.0390000	2014-03-31 12:00:00	1
25	0.1590000	2014-04-02 12:00:00	7
26	5.4240000	2014-04-05 12:00:00	9
27	0.0540000	2014-04-14 12:00:00	4
28	0.1370000	2014-04-22 12:00:00	2
29	0.9540000	2014-04-22 12:00:00	4
30	0.2010000	2014-04-24 12:00:00	6
31	0.3460000	2014-04-24 12:00:00	8
32	0.0610000	2014-04-25 12:00:00	3
33	0.2790000	2014-05-06 12:00:00	7
34	0.0210000	2014-05-13 12:00:00	4
35	0.8310000	2014-05-14 12:00:00	1
36	0.1200000	2014-05-15 12:00:00	9
37	0.0510000	2014-05-26 12:00:00	8
38	5.0990000	2014-05-27 12:00:00	6
39	0.0970000	2014-06-04 12:00:00	1
40	1.0130000	2014-06-04 12:00:00	9
41	0.0990000	2014-06-08 12:00:00	4
42	0.0800000	2014-06-09 12:00:00	7
43	0.0200000	2014-06-11 12:00:00	0
44	0.3100000	2014-06-14 12:00:00	1
45	0.1250000	2014-06-23 12:00:00	8
46	0.0580000	2014-07-07 12:00:00	0
47	0.4980000	2014-07-11 12:00:00	7
48	1.5410000	2014-07-13 12:00:00	9
49	0.2830000	2014-07-16 12:00:00	1
50	0.0550000	2014-07-24 12:00:00	3
51	0.2890000	2014-08-04 12:00:00	2
52	0.1040000	2014-08-07 12:00:00	4
53	0.0870000	2014-08-09 12:00:00	2
54	0.1500000	2014-08-11 12:00:00	1
55	0.3180000	2014-08-11 12:00:00	6
56	0.0950000	2014-08-13 12:00:00	5
57	0.0930000	2014-08-22 12:00:00	7
58	0.0920000	2014-08-26 12:00:00	6
59	0.4450000	2014-08-28 12:00:00	6
60	0.0740000	2014-09-05 12:00:00	4
61	1.0690000	2014-09-06 12:00:00	2
62	0.0490000	2014-09-09 12:00:00	8
63	0.0750000	2014-09-09 12:00:00	5
64	0.0440000	2014-09-13 12:00:00	8
65	0.0250000	2014-09-14 12:00:00	4
66	0.1090000	2014-09-17 12:00:00	3
67	0.0800000	2014-09-26 12:00:00	7
68	0.1640000	2014-10-10 12:00:00	6
69	0.0040000	2014-10-16 12:00:00	6
70	0.1210000	2014-10-21 12:00:00	0
71	2.1400000	2014-10-22 12:00:00	9
72	0.2420000	2014-11-04 12:00:00	9
73	1.0510000	2014-11-04 12:00:00	7
74	0.1470000	2014-11-08 12:00:00	1
75	0.0700000	2014-11-19 12:00:00	9
76	0.1190000	2014-11-22 12:00:00	7
77	0.1210000	2014-11-22 12:00:00	9
78	0.4230000	2014-11-26 12:00:00	0
79	0.2105000	2014-11-28 12:00:00	0
80	0.7550000	2014-11-29 12:00:00	9
81	0.3310000	2014-12-02 12:00:00	4
82	0.1690000	2014-12-09 12:00:00	8
83	0.1330000	2014-12-10 12:00:00	7
84	0.1700000	2014-12-12 12:00:00	3
85	0.0415000	2014-12-23 12:00:00	8
86	0.0630000	2014-12-28 12:00:00	1
87	0.2850000	2014-12-29 12:00:00	8
88	0.5380000	2014-12-30 12:00:00	8
89	1.1790000	2014-12-30 12:00:00	6
90	0.1770000	2015-01-07 12:00:00	8
91	1.3120000	2015-01-10 12:00:00	3
92	0.2460000	2015-01-19 12:00:00	6
93	0.4800000	2015-01-19 12:00:00	1
94	0.1550000	2015-01-23 12:00:00	0
95	0.4770000	2015-02-01 12:00:00	3
96	0.2140000	2015-02-02 12:00:00	5
97	0.1370000	2015-02-15 12:00:00	4
98	0.3180000	2015-02-15 12:00:00	6
99	0.0360000	2015-02-28 12:00:00	2
100	0.1340000	2015-03-11 12:00:00	4

Out[121]:

Rows: 1-100 of 140 | Columns: 3

vDataFrame.between_time	Filters the data between two time ranges.
vDataFrame.first	Filters the data by only keeping the first records.
vDataFrame.filter	Filters the data using the input expression.
vDataFrame.last	Filters the data by only keeping the last records.

vDataFrame.at_time¶

Parameters¶

Returns¶

Example¶

See Also¶