vDataFrame.interpolate¶

In [ ]:

vDataFrame.interpolate(ts: str,
                  rule: (str, datetime.timedelta),
                  method: dict,
                  by: list = [])

Computes a regular time interval vDataFrame by interpolating the missing values using different techniques.

Parameters¶

Name	Type	Optional	Description
ts	str	❌	TS (Time Series) vcolumn to use to order the data. The vcolumn type must be date like (date, datetime, timestamp...)
rule	str / time	❌	Interval used to create the time slices. The final interpolation is divided by these intervals. For example, specifying '5 minutes' creates records separated by time intervals of '5 minutes'.
method	dict	❌	Dictionary, with the following format, of interpolation methods: {"column1": "interpolation1" ..., "columnk": "interpolationk"}. Interpolation methods must be one of the following: bfill : Interpolates with the final value of the time slice. With gaps, the behavior is similar to ffill. ffill : Interpolates with the first value of the time slice. linear : Linear Interpolation.
by	list	✓	vcolumns used in the partition.

Returns¶

vDataFrame : object result of the interpolation.

Example¶

In [26]:

from verticapy import tablesample
ts = tablesample({"datetime": ["1993-11-03 00:00:00", 
                               "1993-11-03 00:00:01", 
                               "1993-11-03 00:00:02",
                               "1993-11-03 00:00:03",
                               "1993-11-03 00:00:04",
                               "1993-11-03 00:00:05",
                               "1993-11-03 00:01:01",
                               "1993-11-03 00:01:02",
                               "1993-11-03 00:01:03",
                               "1993-11-03 00:01:04",
                               "1993-11-03 00:01:05",],
                  "val": [0., 1., 2., 3., 4., 5., 61., 62., 63., 64., 65.,]})
ts = ts.to_vdf()
ts["datetime"].astype("datetime")
display(ts)

	📅 datetime Datetime	123 val Numeric(3,1)
1	1993-11-03 00:00:00	0.0
2	1993-11-03 00:00:01	1.0
3	1993-11-03 00:00:02	2.0
4	1993-11-03 00:00:03	3.0
5	1993-11-03 00:00:04	4.0
6	1993-11-03 00:00:05	5.0
7	1993-11-03 00:01:01	61.0
8	1993-11-03 00:01:02	62.0
9	1993-11-03 00:01:03	63.0
10	1993-11-03 00:01:04	64.0
11	1993-11-03 00:01:05	65.0

Rows: 1-11 | Columns: 2

In [36]:

# Linear interpolation by second
ts.interpolate(ts = "datetime",
          rule = "1 second",
          method = {"val": "linear"},)

Out[36]:

	📅 datetime Timestamp	123 val Float
1	1993-11-03 00:00:00	0.0
2	1993-11-03 00:00:01	1.0
3	1993-11-03 00:00:02	2.0
4	1993-11-03 00:00:03	3.0
5	1993-11-03 00:00:04	4.0
6	1993-11-03 00:00:05	5.0
7	1993-11-03 00:00:06	6.0
8	1993-11-03 00:00:07	7.0
9	1993-11-03 00:00:08	8.0
10	1993-11-03 00:00:09	9.0
11	1993-11-03 00:00:10	10.0
12	1993-11-03 00:00:11	11.0
13	1993-11-03 00:00:12	12.0
14	1993-11-03 00:00:13	13.0
15	1993-11-03 00:00:14	14.0
16	1993-11-03 00:00:15	15.0
17	1993-11-03 00:00:16	16.0
18	1993-11-03 00:00:17	17.0
19	1993-11-03 00:00:18	18.0
20	1993-11-03 00:00:19	19.0
21	1993-11-03 00:00:20	20.0
22	1993-11-03 00:00:21	21.0
23	1993-11-03 00:00:22	22.0
24	1993-11-03 00:00:23	23.0
25	1993-11-03 00:00:24	24.0
26	1993-11-03 00:00:25	25.0
27	1993-11-03 00:00:26	26.0
28	1993-11-03 00:00:27	27.0
29	1993-11-03 00:00:28	28.0
30	1993-11-03 00:00:29	29.0
31	1993-11-03 00:00:30	30.0
32	1993-11-03 00:00:31	31.0
33	1993-11-03 00:00:32	32.0
34	1993-11-03 00:00:33	33.0
35	1993-11-03 00:00:34	34.0
36	1993-11-03 00:00:35	35.0
37	1993-11-03 00:00:36	36.0
38	1993-11-03 00:00:37	37.0
39	1993-11-03 00:00:38	38.0
40	1993-11-03 00:00:39	39.0
41	1993-11-03 00:00:40	40.0
42	1993-11-03 00:00:41	41.0
43	1993-11-03 00:00:42	42.0
44	1993-11-03 00:00:43	43.0
45	1993-11-03 00:00:44	44.0
46	1993-11-03 00:00:45	45.0
47	1993-11-03 00:00:46	46.0
48	1993-11-03 00:00:47	47.0
49	1993-11-03 00:00:48	48.0
50	1993-11-03 00:00:49	49.0
51	1993-11-03 00:00:50	50.0
52	1993-11-03 00:00:51	51.0
53	1993-11-03 00:00:52	52.0
54	1993-11-03 00:00:53	53.0
55	1993-11-03 00:00:54	54.0
56	1993-11-03 00:00:55	55.0
57	1993-11-03 00:00:56	56.0
58	1993-11-03 00:00:57	57.0
59	1993-11-03 00:00:58	58.0
60	1993-11-03 00:00:59	59.0
61	1993-11-03 00:01:00	60.0
62	1993-11-03 00:01:01	61.0
63	1993-11-03 00:01:02	62.0
64	1993-11-03 00:01:03	63.0
65	1993-11-03 00:01:04	64.0
66	1993-11-03 00:01:05	65.0

Rows: 1-66 | Columns: 2

In [37]:

# First fill interpolation by second
ts.interpolate / asfreq(ts = "datetime",
          rule = "1 second",
          method = {"val": "ffill"},)

Out[37]:

	📅 datetime Timestamp	123 val Numeric(3,1)
1	1993-11-03 00:00:00	0.0
2	1993-11-03 00:00:01	1.0
3	1993-11-03 00:00:02	2.0
4	1993-11-03 00:00:03	3.0
5	1993-11-03 00:00:04	4.0
6	1993-11-03 00:00:05	5.0
7	1993-11-03 00:00:06	5.0
8	1993-11-03 00:00:07	5.0
9	1993-11-03 00:00:08	5.0
10	1993-11-03 00:00:09	5.0
11	1993-11-03 00:00:10	5.0
12	1993-11-03 00:00:11	5.0
13	1993-11-03 00:00:12	5.0
14	1993-11-03 00:00:13	5.0
15	1993-11-03 00:00:14	5.0
16	1993-11-03 00:00:15	5.0
17	1993-11-03 00:00:16	5.0
18	1993-11-03 00:00:17	5.0
19	1993-11-03 00:00:18	5.0
20	1993-11-03 00:00:19	5.0
21	1993-11-03 00:00:20	5.0
22	1993-11-03 00:00:21	5.0
23	1993-11-03 00:00:22	5.0
24	1993-11-03 00:00:23	5.0
25	1993-11-03 00:00:24	5.0
26	1993-11-03 00:00:25	5.0
27	1993-11-03 00:00:26	5.0
28	1993-11-03 00:00:27	5.0
29	1993-11-03 00:00:28	5.0
30	1993-11-03 00:00:29	5.0
31	1993-11-03 00:00:30	5.0
32	1993-11-03 00:00:31	5.0
33	1993-11-03 00:00:32	5.0
34	1993-11-03 00:00:33	5.0
35	1993-11-03 00:00:34	5.0
36	1993-11-03 00:00:35	5.0
37	1993-11-03 00:00:36	5.0
38	1993-11-03 00:00:37	5.0
39	1993-11-03 00:00:38	5.0
40	1993-11-03 00:00:39	5.0
41	1993-11-03 00:00:40	5.0
42	1993-11-03 00:00:41	5.0
43	1993-11-03 00:00:42	5.0
44	1993-11-03 00:00:43	5.0
45	1993-11-03 00:00:44	5.0
46	1993-11-03 00:00:45	5.0
47	1993-11-03 00:00:46	5.0
48	1993-11-03 00:00:47	5.0
49	1993-11-03 00:00:48	5.0
50	1993-11-03 00:00:49	5.0
51	1993-11-03 00:00:50	5.0
52	1993-11-03 00:00:51	5.0
53	1993-11-03 00:00:52	5.0
54	1993-11-03 00:00:53	5.0
55	1993-11-03 00:00:54	5.0
56	1993-11-03 00:00:55	5.0
57	1993-11-03 00:00:56	5.0
58	1993-11-03 00:00:57	5.0
59	1993-11-03 00:00:58	5.0
60	1993-11-03 00:00:59	5.0
61	1993-11-03 00:01:00	5.0
62	1993-11-03 00:01:01	61.0
63	1993-11-03 00:01:02	62.0
64	1993-11-03 00:01:03	63.0
65	1993-11-03 00:01:04	64.0
66	1993-11-03 00:01:05	65.0

Rows: 1-66 | Columns: 2

In [42]:

# Back fill interpolation by 3 seconds
# Back fill uses the final value of each block. With gaps, the behavior
# can be similar to first fill
ts.interpolate(ts = "datetime",
          rule = "2 seconds",
          method = {"val": "bfill"},)

Out[42]:

	📅 datetime Timestamp	123 val Numeric(3,1)
1	1993-11-03 00:00:00	1.0
2	1993-11-03 00:00:02	3.0
3	1993-11-03 00:00:04	5.0
4	1993-11-03 00:00:06	5.0
5	1993-11-03 00:00:08	5.0
6	1993-11-03 00:00:10	5.0
7	1993-11-03 00:00:12	5.0
8	1993-11-03 00:00:14	5.0
9	1993-11-03 00:00:16	5.0
10	1993-11-03 00:00:18	5.0
11	1993-11-03 00:00:20	5.0
12	1993-11-03 00:00:22	5.0
13	1993-11-03 00:00:24	5.0
14	1993-11-03 00:00:26	5.0
15	1993-11-03 00:00:28	5.0
16	1993-11-03 00:00:30	5.0
17	1993-11-03 00:00:32	5.0
18	1993-11-03 00:00:34	5.0
19	1993-11-03 00:00:36	5.0
20	1993-11-03 00:00:38	5.0
21	1993-11-03 00:00:40	5.0
22	1993-11-03 00:00:42	5.0
23	1993-11-03 00:00:44	5.0
24	1993-11-03 00:00:46	5.0
25	1993-11-03 00:00:48	5.0
26	1993-11-03 00:00:50	5.0
27	1993-11-03 00:00:52	5.0
28	1993-11-03 00:00:54	5.0
29	1993-11-03 00:00:56	5.0
30	1993-11-03 00:00:58	5.0
31	1993-11-03 00:01:00	61.0
32	1993-11-03 00:01:02	63.0
33	1993-11-03 00:01:04	65.0

Rows: 1-33 | Columns: 2

vDataFrame[].fillna	Fills the vcolumn missing values.
vDataFrame[].slice	Slices the vcolumn.

vDataFrame.interpolate¶

Parameters¶

Returns¶

Example¶

See Also¶