vDataFrame[].astype¶

In [ ]:

vDataFrame[].astype(dtype)

Converts the vcolumn to the input type.

Parameters¶

Name	Type	Optional	Description
dtype	str or Python data type	❌	New type. One of the following values: 'json': Converts to a JSON string. 'array': Converts to an array. 'vmap': Converts to a VMap. If converting a delimited string, you can add the header_names as follows: dtype = 'vmap(age,name,date)', where the header_names are age, name, and date. It can also be any Vertica type or Python standard types.

Returns¶

vDataFrame : self.parent

Example¶

In [14]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic)

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc Varchar(100)
1	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124
2	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]
3	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]
4	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148
5	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]
6	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]
7	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]
8	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]
9	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133
10	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]
11	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]
12	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]
13	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]
14	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45
15	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]
16	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]
17	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]
18	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]
19	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]
20	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263
21	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189
22	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]
23	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]
24	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]
25	1	0		male	49.0	1	1	17421	110.8833	C68	C	[null]	[null]
26	1	0		male	47.0	0	0	36967	34.0208	D46	S	[null]	[null]
27	1	0		male	64.0	1	0	110813	75.25	D37	C	[null]	[null]
28	1	1		male	0.92	1	2	113781	151.55	C22 C26	S	11	[null]
29	1	1		female	53.0	2	0	11769	51.4792	C101	S	D	[null]
30	1	1		female	18.0	1	0	PC 17757	227.525	C62 C64	C	4	[null]
31	1	1		male	80.0	0	0	27042	30.0	A23	S	B	[null]
32	1	1		male	37.0	1	1	11751	52.5542	D35	S	5	[null]
33	1	1		male	26.0	0	0	111369	30.0	C148	C	5	[null]
34	1	1		female	42.0	0	0	PC 17757	227.525	[null]	C	4	[null]
35	1	1		male	25.0	1	0	11967	91.0792	B49	C	7	[null]
36	1	1		female	35.0	0	0	PC 17760	135.6333	C99	S	8	[null]
37	1	1		female	45.0	0	0	PC 17608	262.375	[null]	C	4	[null]
38	1	1		female	22.0	0	1	113505	55.0	E33	S	6	[null]
39	1	1		female	60.0	0	0	11813	76.2917	D15	C	8	[null]
40	1	1		female	14.0	1	2	113760	120.0	B96 B98	S	4	[null]
41	1	1		female	[null]	0	0	17770	27.7208	[null]	C	5	[null]
42	1	1		male	45.0	0	0	PC 17594	29.7	A9	C	7	[null]
43	1	1		female	22.0	0	0	113781	151.55	[null]	S	11	[null]
44	1	1		female	64.0	0	2	PC 17756	83.1583	E45	C	14	[null]
45	1	1		female	36.0	0	2	WE/P 5735	71.0	B22	S	7	[null]
46	1	1		female	27.0	1	1	PC 17558	247.5208	B58 B60	C	6	[null]
47	1	1		female	54.0	1	0	36947	78.2667	D20	C	4	[null]
48	1	1		male	43.0	1	0	17765	27.7208	D40	C	5	[null]
49	1	1		female	22.0	0	2	13568	49.5	B39	C	5	[null]
50	1	1		female	35.0	1	0	113803	53.1	C123	S	D	[null]
51	1	1		male	53.0	0	0	113780	28.5	C51	C	B	[null]
52	1	1		female	19.0	0	0	112053	30.0	B42	S	3	[null]
53	1	1		female	58.0	0	1	PC 17582	153.4625	C125	S	3	[null]
54	1	1		male	23.0	0	1	PC 17759	63.3583	D10 D12	C	7	[null]
55	1	1		male	25.0	1	0	11765	55.4417	E50	C	5	[null]
56	1	1		male	[null]	0	0	16988	30.0	D45	S	3	[null]
57	1	1		female	35.0	1	0	113789	52.0	[null]	S	8	[null]
58	1	1		female	[null]	1	0	17464	51.8625	D21	S	8	[null]
59	1	1		male	42.0	1	0	11753	52.5542	D19	S	5	[null]
60	1	1		female	45.0	1	0	11753	52.5542	D19	S	5	[null]
61	1	1		female	39.0	0	0	24160	211.3375	[null]	S	2	[null]
62	1	1		female	16.0	0	1	PC 17592	39.4	D28	S	9	[null]
63	1	1		female	21.0	0	0	13502	77.9583	D9	S	10	[null]
64	1	1		female	18.0	1	0	113773	53.1	D30	S	10	[null]
65	1	1		male	36.0	0	0	PC 17473	26.2875	E25	S	7	[null]
66	1	1		female	37.0	1	0	19928	90.0	C78	Q	14	[null]
67	1	1		male	[null]	0	0	F.C. 12998	25.7417	[null]	C	7	[null]
68	1	1		female	22.0	1	0	113776	66.6	C2	S	8	[null]
69	1	1		female	30.0	0	0	12749	93.5	B73	S	3	[null]
70	1	1		female	33.0	0	0	PC 17613	27.7208	A11	C	11	[null]
71	1	1		female	54.0	1	0	PC 17603	59.4	[null]	C	6	[null]
72	1	1		female	18.0	2	2	PC 17608	262.375	B57 B59 B63 B66	C	4	[null]
73	1	1		female	48.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	4	[null]
74	1	1		male	35.0	0	0	PC 17475	26.2875	E24	S	5	[null]
75	1	1		female	23.0	1	0	21228	82.2667	B45	S	7	[null]
76	1	1		female	43.0	1	0	11778	55.4417	C116	C	5	[null]
77	1	1		female	39.0	1	1	110413	79.65	E67	S	8	[null]
78	1	1		female	39.0	1	1	17421	110.8833	C68	C	4	[null]
79	1	1		female	55.0	0	0	PC 17760	135.6333	C32	C	8	[null]
80	1	1		female	31.0	0	2	36928	164.8667	C7	S	8	[null]
81	2	0		male	30.0	1	0	P/PP 3381	24.0	[null]	C	[null]	[null]
82	2	0		male	30.0	0	0	248744	13.0	[null]	S	[null]	[null]
83	2	0		male	57.0	0	0	244346	13.0	[null]	S	[null]	[null]
84	2	0		male	51.0	0	0	S.O.P. 1166	12.525	[null]	S	[null]	174
85	2	0		male	[null]	0	0	239853	0.0	[null]	S	[null]	[null]
86	2	0		male	52.0	0	0	248731	13.5	[null]	S	[null]	130
87	2	0		male	37.0	1	0	SC/AH 29037	26.0	[null]	S	[null]	17
88	2	0		female	29.0	1	0	SC/AH 29037	26.0	[null]	S	[null]	[null]
89	2	0		male	29.0	0	0	W./C. 14263	10.5	[null]	S	[null]	[null]
90	2	0		female	30.0	0	0	237249	13.0	[null]	S	[null]	[null]
91	2	0		male	[null]	0	0	239853	0.0	[null]	S	[null]	[null]
92	2	0		male	17.0	0	0	S.O.C. 14879	73.5	[null]	S	[null]	[null]
93	2	0		male	18.0	0	0	C.A. 15185	10.5	[null]	S	[null]	[null]
94	2	0		male	24.0	0	0	248726	13.5	[null]	S	[null]	297
95	2	0		male	30.0	0	0	250646	13.0	[null]	S	[null]	305
96	2	0		male	52.0	0	0	250647	13.0	[null]	S	[null]	19
97	2	0		female	18.0	1	1	250650	13.0	[null]	S	[null]	[null]
98	2	0		male	23.0	2	1	29104	11.5	[null]	S	[null]	[null]
99	2	0		male	36.0	0	0	242963	13.0	[null]	S	[null]	[null]
100	2	0		male	44.0	1	0	26707	26.0	[null]	S	[null]	[null]

Rows: 1-100 | Columns: 14

In [15]:

titanic["fare"].dtype()

Out[15]:

'numeric(10,5)'

In [16]:

titanic["fare"].astype(int)

Out[16]:

	123 pclass Int	123 survived Int	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Int	123 parch Int	Abc ticket Varchar(36)	123 fare Integer	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Int	Abc Varchar(100)
1	1	0		male	47.0	1	0	PC 17757	228	C62 C64	C	[null]	124
2	1	0		male	[null]	0	0	PC 17318	26	[null]	S	[null]	[null]
3	1	0		male	24.0	0	1	PC 17558	248	B58 B60	C	[null]	[null]
4	1	0		male	25.0	0	0	13905	26	[null]	C	[null]	148
5	1	0		male	42.0	0	0	110489	27	D22	S	[null]	[null]
6	1	0		male	45.0	0	0	113050	27	B38	S	[null]	[null]
7	1	0		male	46.0	1	0	W.E.P. 5734	61	E31	S	[null]	[null]
8	1	0		male	[null]	0	0	113791	27	[null]	S	[null]	[null]
9	1	0		male	39.0	0	0	PC 17580	30	A18	C	[null]	133
10	1	0		male	64.0	1	4	19950	263	C23 C25 C27	S	[null]	[null]
11	1	0		male	[null]	0	0	113778	27	D34	S	[null]	[null]
12	1	0		male	71.0	0	0	PC 17754	35	A5	C	[null]	[null]
13	1	0		male	[null]	0	0	113796	42	[null]	S	[null]	[null]
14	1	0		male	32.5	0	0	113503	212	C132	C	[null]	45
15	1	0		male	41.0	1	0	17464	52	D21	S	[null]	[null]
16	1	0		male	[null]	0	0	113028	27	C124	S	[null]	[null]
17	1	0		male	28.0	1	0	PC 17604	82	[null]	C	[null]	[null]
18	1	0		male	55.0	0	0	113787	31	C30	S	[null]	[null]
19	1	0		male	37.0	0	1	PC 17596	30	C118	C	[null]	[null]
20	1	0		male	64.0	0	0	693	26	[null]	S	[null]	263
21	1	0		male	28.5	0	0	PC 17562	28	D43	C	[null]	189
22	1	0		male	[null]	0	0	PC 17757	228	[null]	C	[null]	[null]
23	1	0		male	56.0	0	0	113792	27	[null]	S	[null]	[null]
24	1	0		male	24.0	1	0	13695	60	C31	S	[null]	[null]
25	1	0		male	49.0	1	1	17421	111	C68	C	[null]	[null]
26	1	0		male	47.0	0	0	36967	34	D46	S	[null]	[null]
27	1	0		male	64.0	1	0	110813	75	D37	C	[null]	[null]
28	1	1		male	0.92	1	2	113781	152	C22 C26	S	11	[null]
29	1	1		female	53.0	2	0	11769	51	C101	S	D	[null]
30	1	1		female	18.0	1	0	PC 17757	228	C62 C64	C	4	[null]
31	1	1		male	80.0	0	0	27042	30	A23	S	B	[null]
32	1	1		male	37.0	1	1	11751	53	D35	S	5	[null]
33	1	1		male	26.0	0	0	111369	30	C148	C	5	[null]
34	1	1		female	42.0	0	0	PC 17757	228	[null]	C	4	[null]
35	1	1		male	25.0	1	0	11967	91	B49	C	7	[null]
36	1	1		female	35.0	0	0	PC 17760	136	C99	S	8	[null]
37	1	1		female	45.0	0	0	PC 17608	262	[null]	C	4	[null]
38	1	1		female	22.0	0	1	113505	55	E33	S	6	[null]
39	1	1		female	60.0	0	0	11813	76	D15	C	8	[null]
40	1	1		female	14.0	1	2	113760	120	B96 B98	S	4	[null]
41	1	1		female	[null]	0	0	17770	28	[null]	C	5	[null]
42	1	1		male	45.0	0	0	PC 17594	30	A9	C	7	[null]
43	1	1		female	22.0	0	0	113781	152	[null]	S	11	[null]
44	1	1		female	64.0	0	2	PC 17756	83	E45	C	14	[null]
45	1	1		female	36.0	0	2	WE/P 5735	71	B22	S	7	[null]
46	1	1		female	27.0	1	1	PC 17558	248	B58 B60	C	6	[null]
47	1	1		female	54.0	1	0	36947	78	D20	C	4	[null]
48	1	1		male	43.0	1	0	17765	28	D40	C	5	[null]
49	1	1		female	22.0	0	2	13568	50	B39	C	5	[null]
50	1	1		female	35.0	1	0	113803	53	C123	S	D	[null]
51	1	1		male	53.0	0	0	113780	29	C51	C	B	[null]
52	1	1		female	19.0	0	0	112053	30	B42	S	3	[null]
53	1	1		female	58.0	0	1	PC 17582	153	C125	S	3	[null]
54	1	1		male	23.0	0	1	PC 17759	63	D10 D12	C	7	[null]
55	1	1		male	25.0	1	0	11765	55	E50	C	5	[null]
56	1	1		male	[null]	0	0	16988	30	D45	S	3	[null]
57	1	1		female	35.0	1	0	113789	52	[null]	S	8	[null]
58	1	1		female	[null]	1	0	17464	52	D21	S	8	[null]
59	1	1		male	42.0	1	0	11753	53	D19	S	5	[null]
60	1	1		female	45.0	1	0	11753	53	D19	S	5	[null]
61	1	1		female	39.0	0	0	24160	211	[null]	S	2	[null]
62	1	1		female	16.0	0	1	PC 17592	39	D28	S	9	[null]
63	1	1		female	21.0	0	0	13502	78	D9	S	10	[null]
64	1	1		female	18.0	1	0	113773	53	D30	S	10	[null]
65	1	1		male	36.0	0	0	PC 17473	26	E25	S	7	[null]
66	1	1		female	37.0	1	0	19928	90	C78	Q	14	[null]
67	1	1		male	[null]	0	0	F.C. 12998	26	[null]	C	7	[null]
68	1	1		female	22.0	1	0	113776	67	C2	S	8	[null]
69	1	1		female	30.0	0	0	12749	94	B73	S	3	[null]
70	1	1		female	33.0	0	0	PC 17613	28	A11	C	11	[null]
71	1	1		female	54.0	1	0	PC 17603	59	[null]	C	6	[null]
72	1	1		female	18.0	2	2	PC 17608	262	B57 B59 B63 B66	C	4	[null]
73	1	1		female	48.0	1	3	PC 17608	262	B57 B59 B63 B66	C	4	[null]
74	1	1		male	35.0	0	0	PC 17475	26	E24	S	5	[null]
75	1	1		female	23.0	1	0	21228	82	B45	S	7	[null]
76	1	1		female	43.0	1	0	11778	55	C116	C	5	[null]
77	1	1		female	39.0	1	1	110413	80	E67	S	8	[null]
78	1	1		female	39.0	1	1	17421	111	C68	C	4	[null]
79	1	1		female	55.0	0	0	PC 17760	136	C32	C	8	[null]
80	1	1		female	31.0	0	2	36928	165	C7	S	8	[null]
81	2	0		male	30.0	1	0	P/PP 3381	24	[null]	C	[null]	[null]
82	2	0		male	30.0	0	0	248744	13	[null]	S	[null]	[null]
83	2	0		male	57.0	0	0	244346	13	[null]	S	[null]	[null]
84	2	0		male	51.0	0	0	S.O.P. 1166	13	[null]	S	[null]	174
85	2	0		male	[null]	0	0	239853	0	[null]	S	[null]	[null]
86	2	0		male	52.0	0	0	248731	14	[null]	S	[null]	130
87	2	0		male	37.0	1	0	SC/AH 29037	26	[null]	S	[null]	17
88	2	0		female	29.0	1	0	SC/AH 29037	26	[null]	S	[null]	[null]
89	2	0		male	29.0	0	0	W./C. 14263	11	[null]	S	[null]	[null]
90	2	0		female	30.0	0	0	237249	13	[null]	S	[null]	[null]
91	2	0		male	[null]	0	0	239853	0	[null]	S	[null]	[null]
92	2	0		male	17.0	0	0	S.O.C. 14879	74	[null]	S	[null]	[null]
93	2	0		male	18.0	0	0	C.A. 15185	11	[null]	S	[null]	[null]
94	2	0		male	24.0	0	0	248726	14	[null]	S	[null]	297
95	2	0		male	30.0	0	0	250646	13	[null]	S	[null]	305
96	2	0		male	52.0	0	0	250647	13	[null]	S	[null]	19
97	2	0		female	18.0	1	1	250650	13	[null]	S	[null]	[null]
98	2	0		male	23.0	2	1	29104	12	[null]	S	[null]	[null]
99	2	0		male	36.0	0	0	242963	13	[null]	S	[null]	[null]
100	2	0		male	44.0	1	0	26707	26	[null]	S	[null]	[null]

Rows: 1-100 | Columns: 14

In [17]:

titanic["fare"].dtype()

Out[17]:

'integer'

In [18]:

from verticapy.utilities import tablesample

# str -> array
dataset = tablesample({"artists": ["Inna, Alexandra, Reea", "Rihanna, Beyonce"]}).to_vdf()
dataset["artists"].astype("array")

Out[18]:

	🛠 artists Array
1	b'["Inna","Alexandra","Reea"]'
2	b'["Rihanna","Beyonce"]'

Rows: 1-2 | Column: artists | Type: array

In [19]:

# array -> json
dataset["artists"].astype("json")

Out[19]:

	Abc artists Varchar
1	["Inna","Alexandra","Reea"]
2	["Rihanna","Beyonce"]

Rows: 1-2 | Column: artists | Type: varchar