vDataFrame[].div¶

In [ ]:

vDataFrame[].div(x: float)

Divides the vcolumn by the input element.

Parameters¶

Name	Type	Optional	Description
x	float	❌	Input number.

Returns¶

vDataFrame : self.parent

Example¶

In [53]:

from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic["age"])

	123 age Numeric(6,3)
1	2.000
2	30.000
3	25.000
4	39.000
5	71.000
6	47.000
7	[null]
8	24.000
9	36.000
10	25.000
11	45.000
12	42.000
13	41.000
14	48.000
15	[null]
16	45.000
17	[null]
18	33.000
19	28.000
20	17.000
21	49.000
22	36.000
23	46.000
24	[null]
25	27.000
26	[null]
27	47.000
28	37.000
29	[null]
30	70.000
31	39.000
32	31.000
33	50.000
34	39.000
35	36.000
36	[null]
37	30.000
38	19.000
39	64.000
40	[null]
41	[null]
42	37.000
43	47.000
44	24.000
45	71.000
46	38.000
47	46.000
48	[null]
49	45.000
50	40.000
51	55.000
52	42.000
53	[null]
54	55.000
55	42.000
56	[null]
57	50.000
58	46.000
59	50.000
60	32.500
61	58.000
62	41.000
63	[null]
64	[null]
65	29.000
66	30.000
67	30.000
68	19.000
69	46.000
70	54.000
71	28.000
72	65.000
73	44.000
74	55.000
75	47.000
76	37.000
77	58.000
78	64.000
79	65.000
80	28.500
81	[null]
82	45.500
83	23.000
84	29.000
85	18.000
86	47.000
87	38.000
88	22.000
89	[null]
90	31.000
91	[null]
92	36.000
93	55.000
94	33.000
95	61.000
96	50.000
97	56.000
98	56.000
99	24.000
100	[null]

Rows: 1-100 of 1234 | Column: age | Type: numeric(6,3)

In [54]:

titanic["age"].div(2)

	123 age Numeric(24,21)
1	1.000000000000000000000
2	15.000000000000000000000
3	12.500000000000000000000
4	19.500000000000000000000
5	35.500000000000000000000
6	23.500000000000000000000
7	[null]
8	12.000000000000000000000
9	18.000000000000000000000
10	12.500000000000000000000
11	22.500000000000000000000
12	21.000000000000000000000
13	20.500000000000000000000
14	24.000000000000000000000
15	[null]
16	22.500000000000000000000
17	[null]
18	16.500000000000000000000
19	14.000000000000000000000
20	8.500000000000000000000
21	24.500000000000000000000
22	18.000000000000000000000
23	23.000000000000000000000
24	[null]
25	13.500000000000000000000
26	[null]
27	23.500000000000000000000
28	18.500000000000000000000
29	[null]
30	35.000000000000000000000
31	19.500000000000000000000
32	15.500000000000000000000
33	25.000000000000000000000
34	19.500000000000000000000
35	18.000000000000000000000
36	[null]
37	15.000000000000000000000
38	9.500000000000000000000
39	32.000000000000000000000
40	[null]
41	[null]
42	18.500000000000000000000
43	23.500000000000000000000
44	12.000000000000000000000
45	35.500000000000000000000
46	19.000000000000000000000
47	23.000000000000000000000
48	[null]
49	22.500000000000000000000
50	20.000000000000000000000
51	27.500000000000000000000
52	21.000000000000000000000
53	[null]
54	27.500000000000000000000
55	21.000000000000000000000
56	[null]
57	25.000000000000000000000
58	23.000000000000000000000
59	25.000000000000000000000
60	16.250000000000000000000
61	29.000000000000000000000
62	20.500000000000000000000
63	[null]
64	[null]
65	14.500000000000000000000
66	15.000000000000000000000
67	15.000000000000000000000
68	9.500000000000000000000
69	23.000000000000000000000
70	27.000000000000000000000
71	14.000000000000000000000
72	32.500000000000000000000
73	22.000000000000000000000
74	27.500000000000000000000
75	23.500000000000000000000
76	18.500000000000000000000
77	29.000000000000000000000
78	32.000000000000000000000
79	32.500000000000000000000
80	14.250000000000000000000
81	[null]
82	22.750000000000000000000
83	11.500000000000000000000
84	14.500000000000000000000
85	9.000000000000000000000
86	23.500000000000000000000
87	19.000000000000000000000
88	11.000000000000000000000
89	[null]
90	15.500000000000000000000
91	[null]
92	18.000000000000000000000
93	27.500000000000000000000
94	16.500000000000000000000
95	30.500000000000000000000
96	25.000000000000000000000
97	28.000000000000000000000
98	28.000000000000000000000
99	12.000000000000000000000
100	[null]

Out[54]:

Rows: 1-100 of 1234 | Column: age | Type: numeric(24,21)

vDataFrame[].div¶

Parameters¶

Returns¶

Example¶

See Also¶