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verticapy.vDataColumn.isdate

vDataColumn.isdate() → bool

Returns True if the vDataColumn category is date, False otherwise.

Returns

bool

True if the vDataColumn category is date.

Examples

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the amazon dataset.

import verticapy.datasets as vpd

amazon = vpd.load_amazon()

	📅 date Date	Abc state Varchar(32)	123 number Integer
1	1998-01-01	AMAPÁ	0
2	1998-01-01	AMAZONAS	0
3	1998-01-01	DISTRITO FEDERAL	0
4	1998-01-01	ESPÍRITO SANTO	0
5	1998-01-01	MARANHÃO	0
6	1998-01-01	PARANÁ	0
7	1998-01-01	PIAUÍ	0
8	1998-01-01	RORAIMA	0
9	1998-01-01	SERGIPE	0
10	1998-01-01	SÃO PAULO	0
11	1998-02-01	GOIÁS	0
12	1998-02-01	MATO GROSSO DO SUL	0
13	1998-02-01	MINAS GERAIS	0
14	1998-02-01	PARAÍBA	0
15	1998-02-01	SANTA CATARINA	0
16	1998-02-01	SÃO PAULO	0
17	1998-03-01	AMAPÁ	0
18	1998-03-01	BAHIA	0
19	1998-03-01	MATO GROSSO DO SUL	0
20	1998-03-01	PARÁ	0
21	1998-03-01	PERNAMBUCO	0
22	1998-03-01	RIO GRANDE DO SUL	0
23	1998-04-01	CEARÁ	0
24	1998-04-01	PARANÁ	0
25	1998-04-01	PARAÍBA	0
26	1998-04-01	PARÁ	0
27	1998-05-01	ALAGOAS	0
28	1998-05-01	AMAPÁ	0
29	1998-05-01	MARANHÃO	0
30	1998-05-01	MATO GROSSO DO SUL	0
31	1998-05-01	RIO GRANDE DO SUL	0
32	1998-05-01	TOCANTINS	0
33	1998-06-01	ESPÍRITO SANTO	6
34	1998-06-01	RIO DE JANEIRO	3
35	1998-06-01	RIO GRANDE DO NORTE	1
36	1998-06-01	SÃO PAULO	451
37	1998-07-01	ESPÍRITO SANTO	37
38	1998-07-01	MARANHÃO	274
39	1998-07-01	MATO GROSSO	360
40	1998-07-01	MATO GROSSO DO SUL	3712
41	1998-07-01	PARAÍBA	0
42	1998-07-01	PARÁ	638
43	1998-07-01	RONDÔNIA	365
44	1998-07-01	SÃO PAULO	596
45	1998-08-01	ALAGOAS	1
46	1998-08-01	BAHIA	815
47	1998-08-01	DISTRITO FEDERAL	48
48	1998-08-01	ESPÍRITO SANTO	38
49	1998-08-01	MARANHÃO	1176
50	1998-08-01	MATO GROSSO	228
51	1998-08-01	MINAS GERAIS	875
52	1998-08-01	PIAUÍ	711
53	1998-08-01	RIO GRANDE DO SUL	9
54	1998-08-01	RORAIMA	0
55	1998-08-01	SERGIPE	0
56	1998-09-01	AMAPÁ	20
57	1998-09-01	DISTRITO FEDERAL	33
58	1998-09-01	PIAUÍ	1991
59	1998-09-01	RORAIMA	2
60	1998-10-01	AMAZONAS	83
61	1998-10-01	GOIÁS	1034
62	1998-10-01	MATO GROSSO	576
63	1998-10-01	PARAÍBA	179
64	1998-10-01	PARÁ	3665
65	1998-10-01	PIAUÍ	2586
66	1998-10-01	SERGIPE	0
67	1998-11-01	ALAGOAS	19
68	1998-11-01	AMAPÁ	131
69	1998-11-01	CEARÁ	575
70	1998-11-01	DISTRITO FEDERAL	0
71	1998-11-01	MARANHÃO	2237
72	1998-11-01	RIO DE JANEIRO	6
73	1998-11-01	RIO GRANDE DO SUL	28
74	1998-11-01	SÃO PAULO	488
75	1998-12-01	BAHIA	82
76	1998-12-01	MARANHÃO	1399
77	1998-12-01	MATO GROSSO	100
78	1998-12-01	PARAÍBA	51
79	1998-12-01	PERNAMBUCO	59
80	1998-12-01	RIO DE JANEIRO	1
81	1998-12-01	RONDÔNIA	33
82	1998-12-01	TOCANTINS	9
83	1999-01-01	ALAGOAS	58
84	1999-01-01	GOIÁS	14
85	1999-01-01	MATO GROSSO	239
86	1999-01-01	MINAS GERAIS	36
87	1999-01-01	PARÁ	87
88	1999-01-01	PERNAMBUCO	102
89	1999-01-01	RONDÔNIA	1
90	1999-01-01	SÃO PAULO	7
91	1999-01-01	TOCANTINS	36
92	1999-02-01	ACRE	0
93	1999-02-01	CEARÁ	16
94	1999-02-01	MATO GROSSO	69
95	1999-02-01	MATO GROSSO DO SUL	28
96	1999-02-01	PERNAMBUCO	13
97	1999-02-01	RONDÔNIA	1
98	1999-02-01	SANTA CATARINA	2
99	1999-02-01	TOCANTINS	1
100	1999-03-01	AMAPÁ	2

Rows: 1-100 | Columns: 3

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

Let’s check if the category of “date” vcolumn is date or not.

amazon["date"].isdate()
Out[2]: True

Let’s check if the category of “state” vcolumn is date or not

amazon["state"].isdate()
Out[3]: False