vDataFrame[].apply¶

In [ ]:

vDataFrame[].apply(func: str, 
                   copy_name: str = "")

Applies a function to the vcolumn.

Parameters¶

Name	Type	Optional	Description
func	str	❌	Function to use to transform the vcolumn. It must be pure SQL. The function variable must be composed of two flower brackets {}. For example to apply the function: x -> x^2 + 2 use "POWER({}, 2) + 2".
copy_name	str	✓	Name of the copy if the 'copy' parameter is set to True.

Returns¶

vDataFrame : self.parent

Example¶

In [16]:

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 [15]:

# the variable must be composed of two flower brackets '{}' and only pure SQL works
titanic["age"].apply(func = "POWER({}, 2)")

	123 age Float
1	4.0
2	900.0
3	625.0
4	1521.0
5	5041.0
6	2209.0
7	[null]
8	576.0
9	1296.0
10	625.0
11	2025.0
12	1764.0
13	1681.0
14	2304.0
15	[null]
16	2025.0
17	[null]
18	1089.0
19	784.0
20	289.0
21	2401.0
22	1296.0
23	2116.0
24	[null]
25	729.0
26	[null]
27	2209.0
28	1369.0
29	[null]
30	4900.0
31	1521.0
32	961.0
33	2500.0
34	1521.0
35	1296.0
36	[null]
37	900.0
38	361.0
39	4096.0
40	[null]
41	[null]
42	1369.0
43	2209.0
44	576.0
45	5041.0
46	1444.0
47	2116.0
48	[null]
49	2025.0
50	1600.0
51	3025.0
52	1764.0
53	[null]
54	3025.0
55	1764.0
56	[null]
57	2500.0
58	2116.0
59	2500.0
60	1056.25
61	3364.0
62	1681.0
63	[null]
64	[null]
65	841.0
66	900.0
67	900.0
68	361.0
69	2116.0
70	2916.0
71	784.0
72	4225.0
73	1936.0
74	3025.0
75	2209.0
76	1369.0
77	3364.0
78	4096.0
79	4225.0
80	812.25
81	[null]
82	2070.25
83	529.0
84	841.0
85	324.0
86	2209.0
87	1444.0
88	484.0
89	[null]
90	961.0
91	[null]
92	1296.0
93	3025.0
94	1089.0
95	3721.0
96	2500.0
97	3136.0
98	3136.0
99	576.0
100	[null]

Out[15]:

Rows: 1-100 of 1234 | Column: age | Type: float

In [17]:

# creating a new vcolumn to compute the result of the transformation
titanic["age"].apply(func = "POWER({}, 2)",
                     copy_name = "age_pow_2")

	123 age Numeric(6,3)	123 age_pow_2 Float
1	2.000	4.0
2	30.000	900.0
3	25.000	625.0
4	39.000	1521.0
5	71.000	5041.0
6	47.000	2209.0
7	[null]	[null]
8	24.000	576.0
9	36.000	1296.0
10	25.000	625.0
11	45.000	2025.0
12	42.000	1764.0
13	41.000	1681.0
14	48.000	2304.0
15	[null]	[null]
16	45.000	2025.0
17	[null]	[null]
18	33.000	1089.0
19	28.000	784.0
20	17.000	289.0
21	49.000	2401.0
22	36.000	1296.0
23	46.000	2116.0
24	[null]	[null]
25	27.000	729.0
26	[null]	[null]
27	47.000	2209.0
28	37.000	1369.0
29	[null]	[null]
30	70.000	4900.0
31	39.000	1521.0
32	31.000	961.0
33	50.000	2500.0
34	39.000	1521.0
35	36.000	1296.0
36	[null]	[null]
37	30.000	900.0
38	19.000	361.0
39	64.000	4096.0
40	[null]	[null]
41	[null]	[null]
42	37.000	1369.0
43	47.000	2209.0
44	24.000	576.0
45	71.000	5041.0
46	38.000	1444.0
47	46.000	2116.0
48	[null]	[null]
49	45.000	2025.0
50	40.000	1600.0
51	55.000	3025.0
52	42.000	1764.0
53	[null]	[null]
54	55.000	3025.0
55	42.000	1764.0
56	[null]	[null]
57	50.000	2500.0
58	46.000	2116.0
59	50.000	2500.0
60	32.500	1056.25
61	58.000	3364.0
62	41.000	1681.0
63	[null]	[null]
64	[null]	[null]
65	29.000	841.0
66	30.000	900.0
67	30.000	900.0
68	19.000	361.0
69	46.000	2116.0
70	54.000	2916.0
71	28.000	784.0
72	65.000	4225.0
73	44.000	1936.0
74	55.000	3025.0
75	47.000	2209.0
76	37.000	1369.0
77	58.000	3364.0
78	64.000	4096.0
79	65.000	4225.0
80	28.500	812.25
81	[null]	[null]
82	45.500	2070.25
83	23.000	529.0
84	29.000	841.0
85	18.000	324.0
86	47.000	2209.0
87	38.000	1444.0
88	22.000	484.0
89	[null]	[null]
90	31.000	961.0
91	[null]	[null]
92	36.000	1296.0
93	55.000	3025.0
94	33.000	1089.0
95	61.000	3721.0
96	50.000	2500.0
97	56.000	3136.0
98	56.000	3136.0
99	24.000	576.0
100	[null]	[null]

Out[17]:

Rows: 1-100 of 1234 | Columns: 2

vDataFrame.apply	Applies functions to the input vcolumns.
vDataFrame.applymap	Applies a function to all the vcolumns.
vDataFrame.eval	Evaluates a customized expression.

vDataFrame[].apply¶

Parameters¶

Returns¶

Example¶

See Also¶