vDataFrame.apply¶

In [ ]:

vDataFrame.apply(func: dict)

Applies each function of the dictionary to the input vcolumns.

Parameters¶

Name	Type	Optional	Description
func	dict	❌	Dictionary of functions. The dictionary must be like the following: {column1: func1, ..., columnk: funck}. Each function variable must be composed of two flower brackets {}. For example to apply the function: x -> x^2 + 2 use "POWER({}, 2) + 2".

Returns¶

vDataFrame : self

Example¶

In [110]:

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

	123 fare Numeric(10,5)	123 survived Int	Abc sex Varchar(20)	Abc boat Varchar(100)	123 pclass Int	123 age Numeric(6,3)	Abc ticket Varchar(36)	Abc Varchar(164)	Abc embarked Varchar(20)	Abc cabin Varchar(30)	123 body Int	123 parch Int	Abc home.dest Varchar(100)	123 sibsp Int
1	151.55000	0	female	[null]	1	2.000	113781		S	C22 C26	[null]	2	Montreal, PQ / Chesterville, ON	1
2	151.55000	0	male	[null]	1	30.000	113781		S	C22 C26	135	2	Montreal, PQ / Chesterville, ON	1
3	151.55000	0	female	[null]	1	25.000	113781		S	C22 C26	[null]	2	Montreal, PQ / Chesterville, ON	1
4	0.00000	0	male	[null]	1	39.000	112050		S	A36	[null]	0	Belfast, NI	0
5	49.50420	0	male	[null]	1	71.000	PC 17609		C	[null]	22	0	Montevideo, Uruguay	0
6	227.52500	0	male	[null]	1	47.000	PC 17757		C	C62 C64	124	0	New York, NY	1
7	25.92500	0	male	[null]	1	[null]	PC 17318		S	[null]	[null]	0	New York, NY	0
8	247.52080	0	male	[null]	1	24.000	PC 17558		C	B58 B60	[null]	1	Montreal, PQ	0
9	75.24170	0	male	A	1	36.000	13050		C	C6	[null]	0	Winnipeg, MN	0
10	26.00000	0	male	[null]	1	25.000	13905		C	[null]	148	0	San Francisco, CA	0
11	35.50000	0	male	[null]	1	45.000	113784		S	T	[null]	0	Trenton, NJ	0
12	26.55000	0	male	[null]	1	42.000	110489		S	D22	[null]	0	London / Winnipeg, MB	0
13	30.50000	0	male	[null]	1	41.000	113054		S	A21	[null]	0	Pomeroy, WA	0
14	50.49580	0	male	[null]	1	48.000	PC 17591		C	B10	208	0	Omaha, NE	0
15	39.60000	0	male	[null]	1	[null]	112379		C	[null]	[null]	0	Philadelphia, PA	0
16	26.55000	0	male	[null]	1	45.000	113050		S	B38	[null]	0	Washington, DC	0
17	31.00000	0	male	[null]	1	[null]	113798		S	[null]	[null]	0	[null]	0
18	5.00000	0	male	[null]	1	33.000	695		S	B51 B53 B55	[null]	0	New York, NY	0
19	47.10000	0	male	[null]	1	28.000	113059		S	[null]	[null]	0	Montevideo, Uruguay	0
20	47.10000	0	male	[null]	1	17.000	113059		S	[null]	[null]	0	Montevideo, Uruguay	0
21	26.00000	0	male	[null]	1	49.000	19924		S	[null]	[null]	0	Ascot, Berkshire / Rochester, NY	0
22	78.85000	0	male	[null]	1	36.000	19877		S	C46	172	0	Little Onn Hall, Staffs	1
23	61.17500	0	male	[null]	1	46.000	W.E.P. 5734		S	E31	[null]	0	Amenia, ND	1
24	0.00000	0	male	[null]	1	[null]	112051		S	[null]	[null]	0	Liverpool, England / Belfast	0
25	136.77920	0	male	[null]	1	27.000	13508		C	C89	[null]	0	Los Angeles, CA	1
26	52.00000	0	male	[null]	1	[null]	110465		S	A14	[null]	0	Stoughton, MA	0
27	25.58750	0	male	[null]	1	47.000	5727		S	E58	[null]	0	Victoria, BC	0
28	83.15830	0	male	[null]	1	37.000	PC 17756		C	E52	[null]	1	Lakewood, NJ	1
29	26.55000	0	male	[null]	1	[null]	113791		S	[null]	[null]	0	Roachdale, IN	0
30	71.00000	0	male	[null]	1	70.000	WE/P 5735		S	B22	269	1	Milwaukee, WI	1
31	71.28330	0	male	[null]	1	39.000	PC 17599		C	C85	[null]	0	New York, NY	1
32	52.00000	0	male	[null]	1	31.000	F.C. 12750		S	B71	[null]	0	Montreal, PQ	1
33	106.42500	0	male	[null]	1	50.000	PC 17761		C	C86	62	0	Deephaven, MN / Cedar Rapids, IA	1
34	29.70000	0	male	[null]	1	39.000	PC 17580		C	A18	133	0	Philadelphia, PA	0
35	31.67920	0	female	[null]	1	36.000	PC 17531		C	A29	[null]	0	New York, NY	0
36	221.77920	0	male	[null]	1	[null]	PC 17483		S	C95	[null]	0	[null]	0
37	27.75000	0	male	[null]	1	30.000	113051		C	C111	[null]	0	New York, NY	0
38	263.00000	0	male	[null]	1	19.000	19950		S	C23 C25 C27	[null]	2	Winnipeg, MB	3
39	263.00000	0	male	[null]	1	64.000	19950		S	C23 C25 C27	[null]	4	Winnipeg, MB	1
40	26.55000	0	male	[null]	1	[null]	113778		S	D34	[null]	0	Westcliff-on-Sea, Essex	0
41	0.00000	0	male	[null]	1	[null]	112058		S	B102	[null]	0	[null]	0
42	53.10000	0	male	[null]	1	37.000	113803		S	C123	[null]	0	Scituate, MA	1
43	38.50000	0	male	[null]	1	47.000	111320		S	E63	275	0	St Anne's-on-Sea, Lancashire	0
44	79.20000	0	male	[null]	1	24.000	PC 17593		C	B86	[null]	0	[null]	0
45	34.65420	0	male	[null]	1	71.000	PC 17754		C	A5	[null]	0	New York, NY	0
46	153.46250	0	male	[null]	1	38.000	PC 17582		S	C91	147	1	Winnipeg, MB	0
47	79.20000	0	male	[null]	1	46.000	PC 17593		C	B82 B84	[null]	0	New York, NY	0
48	42.40000	0	male	[null]	1	[null]	113796		S	[null]	[null]	0	[null]	0
49	83.47500	0	male	[null]	1	45.000	36973		S	C83	[null]	0	New York, NY	1
50	0.00000	0	male	[null]	1	40.000	112059		S	B94	110	0	[null]	0
51	93.50000	0	male	[null]	1	55.000	12749		S	B69	307	1	Montreal, PQ	1
52	42.50000	0	male	[null]	1	42.000	113038		S	B11	[null]	0	London / Middlesex	0
53	51.86250	0	male	[null]	1	[null]	17463		S	E46	[null]	0	Brighton, MA	0
54	50.00000	0	male	[null]	1	55.000	680		S	C39	[null]	0	London / Birmingham	0
55	52.00000	0	male	[null]	1	42.000	113789		S	[null]	38	0	New York, NY	1
56	30.69580	0	male	14	1	[null]	PC 17600		C	[null]	[null]	0	New York, NY	0
57	28.71250	0	female	[null]	1	50.000	PC 17595		C	C49	[null]	0	Paris, France New York, NY	0
58	26.00000	0	male	[null]	1	46.000	694		S	[null]	80	0	Bennington, VT	0
59	26.00000	0	male	[null]	1	50.000	113044		S	E60	[null]	0	London	0
60	211.50000	0	male	[null]	1	32.500	113503		C	C132	45	0	[null]	0
61	29.70000	0	male	[null]	1	58.000	11771		C	B37	258	0	Buffalo, NY	0
62	51.86250	0	male	[null]	1	41.000	17464		S	D21	[null]	0	Southington / Noank, CT	1
63	26.55000	0	male	[null]	1	[null]	113028		S	C124	[null]	0	Portland, OR	0
64	27.72080	0	male	[null]	1	[null]	PC 17612		C	[null]	[null]	0	Chicago, IL	0
65	30.00000	0	male	[null]	1	29.000	113501		S	D6	126	0	Springfield, MA	0
66	45.50000	0	male	[null]	1	30.000	113801		S	[null]	[null]	0	London / New York, NY	0
67	26.00000	0	male	[null]	1	30.000	110469		S	C106	[null]	0	Brockton, MA	0
68	53.10000	0	male	[null]	1	19.000	113773		S	D30	[null]	0	New York, NY	1
69	75.24170	0	male	[null]	1	46.000	13050		C	C6	292	0	Vancouver, BC	0
70	51.86250	0	male	[null]	1	54.000	17463		S	E46	175	0	Dorchester, MA	0
71	82.17080	0	male	[null]	1	28.000	PC 17604		C	[null]	[null]	0	New York, NY	1
72	26.55000	0	male	[null]	1	65.000	13509		S	E38	249	0	East Bridgewater, MA	0
73	90.00000	0	male	[null]	1	44.000	19928		Q	C78	230	0	Fond du Lac, WI	2
74	30.50000	0	male	[null]	1	55.000	113787		S	C30	[null]	0	Montreal, PQ	0
75	42.40000	0	male	[null]	1	47.000	113796		S	[null]	[null]	0	Washington, DC	0
76	29.70000	0	male	[null]	1	37.000	PC 17596		C	C118	[null]	1	Brooklyn, NY	0
77	113.27500	0	male	[null]	1	58.000	35273		C	D48	122	2	Lexington, MA	0
78	26.00000	0	male	[null]	1	64.000	693		S	[null]	263	0	Isle of Wight, England	0
79	61.97920	0	male	[null]	1	65.000	113509		C	B30	234	1	Providence, RI	0
80	27.72080	0	male	[null]	1	28.500	PC 17562		C	D43	189	0	?Havana, Cuba	0
81	0.00000	0	male	[null]	1	[null]	112052		S	[null]	[null]	0	Belfast	0
82	28.50000	0	male	[null]	1	45.500	113043		S	C124	166	0	Surbiton Hill, Surrey	0
83	93.50000	0	male	[null]	1	23.000	12749		S	B24	[null]	0	Montreal, PQ	0
84	66.60000	0	male	[null]	1	29.000	113776		S	C2	[null]	0	Isleworth, England	1
85	108.90000	0	male	[null]	1	18.000	PC 17758		C	C65	[null]	0	Madrid, Spain	1
86	52.00000	0	male	[null]	1	47.000	110465		S	C110	207	0	Worcester, MA	0
87	0.00000	0	male	[null]	1	38.000	19972		S	[null]	[null]	0	Rotterdam, Netherlands	0
88	135.63330	0	male	[null]	1	22.000	PC 17760		C	[null]	232	0	[null]	0
89	227.52500	0	male	[null]	1	[null]	PC 17757		C	[null]	[null]	0	[null]	0
90	50.49580	0	male	[null]	1	31.000	PC 17590		S	A24	[null]	0	Trenton, NJ	0
91	50.00000	0	male	[null]	1	[null]	113767		S	A32	[null]	0	Seattle, WA	0
92	40.12500	0	male	[null]	1	36.000	13049		C	A10	[null]	0	Winnipeg, MB	0
93	59.40000	0	male	[null]	1	55.000	PC 17603		C	[null]	[null]	0	New York, NY	1
94	26.55000	0	male	[null]	1	33.000	113790		S	[null]	109	0	London	0
95	262.37500	0	male	[null]	1	61.000	PC 17608		C	B57 B59 B63 B66	[null]	3	Haverford, PA / Cooperstown, NY	1
96	55.90000	0	male	[null]	1	50.000	13507		S	E44	[null]	0	Duluth, MN	1
97	26.55000	0	male	[null]	1	56.000	113792		S	[null]	[null]	0	New York, NY	0
98	30.69580	0	male	[null]	1	56.000	17764		C	A7	[null]	0	St James, Long Island, NY	0
99	60.00000	0	male	[null]	1	24.000	13695		S	C31	[null]	0	Huntington, WV	1
100	26.00000	0	male	[null]	1	[null]	113056		S	A19	[null]	0	Streatham, Surrey	0

Rows: 1-100 of 1234 | Columns: 14

In [111]:

# the variable must be composed of two flower brackets '{}' and only pure SQL works
titanic.apply(func = {"boat": "DECODE({}, NULL, 0, 1)",
                      "age" : "COALESCE(age, AVG({}) OVER (PARTITION BY pclass, sex))",
                      "name": "REGEXP_SUBSTR({}, ' ([A-Za-z])+\.')"})

	123 fare Numeric(10,5)	123 survived Int	Abc sex Varchar(20)	123 boat Integer	123 pclass Int	123 age Float	Abc ticket Varchar(36)	Abc name Varchar(164)	Abc embarked Varchar(20)	Abc cabin Varchar(30)	123 body Int	123 parch Int	Abc Varchar(100)	123 sibsp Int
1	135.63330	1	female	1	1	36.0	PC 17760	Miss.	C	C32	[null]	0		0
2	134.50000	1	female	1	1	31.0	16966	Miss.	C	E39 E41	[null]	0		0
3	26.55000	1	female	1	1	21.0	113795	Miss.	S	[null]	[null]	0		0
4	211.50000	1	female	1	1	50.0	113503	Mrs.	C	C80	[null]	1		1
5	164.86670	1	female	1	1	45.0	36928	Mrs.	S	[null]	[null]	1		1
6	164.86670	1	female	1	1	31.0	36928	Miss.	S	C7	[null]	2		0
7	135.63330	1	female	1	1	55.0	PC 17760	Mrs.	C	C32	[null]	0		0
8	75.25000	1	female	1	1	60.0	110813	Mrs.	C	D37	[null]	0		1
9	512.32920	1	female	1	1	35.0	PC 17755	Miss.	C	[null]	[null]	0		0
10	79.20000	1	female	1	1	37.2635658914729	PC 17585	Mrs.	C	[null]	[null]	0		0
11	110.88330	1	female	1	1	39.0	17421	Mrs.	C	C68	[null]	1		1
12	52.00000	1	female	1	1	37.2635658914729	19996	Mrs.	S	C126	[null]	0		1
13	79.65000	1	female	1	1	39.0	110413	Mrs.	S	E67	[null]	1		1
14	79.65000	1	female	1	1	18.0	110413	Miss.	S	E68	[null]	2		0
15	25.92920	1	female	1	1	48.0	17466	Mrs.	S	D17	[null]	0		0
16	80.00000	1	female	1	1	62.0	113572	Mrs.	[null]	B28	[null]	0		0
17	78.26670	1	female	1	1	52.0	36947	Mrs.	C	D20	[null]	0		1
18	55.44170	1	female	1	1	43.0	11778	Mrs.	C	C116	[null]	0		1
19	146.52080	1	female	1	1	37.2635658914729	PC 17569	Mrs.	C	B78	[null]	0		1
20	134.50000	1	female	1	1	40.0	16966	Mrs.	C	E34	[null]	1		1
21	82.26670	1	female	1	1	23.0	21228	Mrs.	S	B45	[null]	0		1
22	60.00000	1	female	1	1	18.0	13695	Mrs.	S	C31	[null]	0		1
23	55.90000	1	female	1	1	39.0	13507	Mrs.	S	E44	[null]	0		1
24	153.46250	1	female	1	1	40.0	PC 17582	Miss.	S	C125	[null]	0		0
25	31.00000	1	female	1	1	30.0	113798	Miss.	C	[null]	[null]	0		0
26	57.75000	1	female	1	1	35.0	13236	Mrs.	C	C28	[null]	0		1
27	69.30000	1	female	1	1	24.0	PC 17477	Mlle.	C	B35	[null]	0		0
28	262.37500	1	female	1	1	48.0	PC 17608	Mrs.	C	B57 B59 B63 B66	[null]	3		1
29	262.37500	1	female	1	1	18.0	PC 17608	Miss.	C	B57 B59 B63 B66	[null]	2		2
30	59.40000	1	female	1	1	54.0	PC 17603	Mrs.	C	[null]	[null]	0		1
31	86.50000	1	female	1	1	33.0	110152	Countess.	S	B77	[null]	0		0
32	27.72080	1	female	1	1	33.0	PC 17613	Miss.	C	A11	[null]	0		0
33	211.33750	1	female	1	1	43.0	24160	Mrs.	S	B3	[null]	1		0
34	83.15830	1	female	1	1	56.0	11767	Mrs.	C	C50	[null]	1		0
35	93.50000	1	female	1	1	30.0	12749	Miss.	S	B73	[null]	0		0
36	108.90000	1	female	1	1	17.0	PC 17758	Mrs.	C	C65	[null]	0		1
37	66.60000	1	female	1	1	22.0	113776	Mrs.	S	C2	[null]	0		1
38	61.97920	1	female	1	1	22.0	113509	Miss.	C	B36	[null]	1		0
39	108.90000	1	female	1	1	39.0	PC 17758	Dona.	C	C105	[null]	0		0
40	26.28330	1	female	1	1	19.0	11752	Miss.	S	D47	[null]	2		0
41	113.27500	1	female	1	1	23.0	35273	Miss.	C	D36	[null]	0		1
42	113.27500	1	female	1	1	31.0	35273	Miss.	C	D36	[null]	0		1
43	90.00000	1	female	1	1	37.0	19928	Mrs.	Q	C78	[null]	0		1
44	90.00000	1	female	1	1	33.0	19928	Miss.	Q	C78	[null]	0		1
45	82.17080	1	female	1	1	37.2635658914729	PC 17604	Mrs.	C	[null]	[null]	0		1
46	53.10000	1	female	1	1	18.0	113773	Mrs.	S	D30	[null]	0		1
47	86.50000	1	female	1	1	16.0	110152	Miss.	S	B79	[null]	0		0
48	211.33750	1	female	1	1	15.0	24160	Miss.	S	B5	[null]	1		0
49	146.52080	1	female	0	1	58.0	PC 17569	Miss.	C	B80	[null]	0		0
50	77.95830	1	female	1	1	21.0	13502	Miss.	S	D9	[null]	0		0
51	39.40000	1	female	1	1	51.0	PC 17592	Mrs.	S	D28	[null]	1		0
52	39.40000	1	female	1	1	16.0	PC 17592	Miss.	S	D28	[null]	1		0
53	27.72080	1	female	1	1	55.0	112377	Mrs.	C	[null]	[null]	0		0
54	25.92920	1	female	1	1	49.0	17465	Dr.	S	D17	[null]	0		0
55	106.42500	1	female	1	1	30.0	PC 17761	Miss.	C	[null]	[null]	0		0
56	211.33750	1	female	1	1	39.0	24160	Miss.	S	[null]	[null]	0		0
57	52.55420	1	female	1	1	45.0	11753	Mrs.	S	D19	[null]	0		1
58	51.86250	1	female	1	1	37.2635658914729	17464	Mrs.	S	D21	[null]	0		1
59	80.00000	1	female	1	1	38.0	113572	Miss.	[null]	B28	[null]	0		0
60	90.00000	1	female	1	1	35.0	19943	Mrs.	S	C93	[null]	0		1
61	52.00000	1	female	1	1	35.0	113789	Mrs.	S	[null]	[null]	0		1
62	77.95830	1	female	1	1	51.0	13502	Mrs.	S	D11	[null]	0		1
63	57.97920	1	female	1	1	44.0	111361	Mrs.	C	B18	[null]	1		0
64	57.97920	1	female	1	1	16.0	111361	Miss.	C	B18	[null]	1		0
65	93.50000	1	female	1	1	52.0	12749	Mrs.	S	B69	[null]	1		1
66	83.15830	1	female	1	1	24.0	11767	Miss.	C	C54	[null]	0		0
67	83.47500	1	female	1	1	35.0	36973	Mrs.	S	C83	[null]	0		1
68	76.72920	1	female	1	1	49.0	PC 17572	Mrs.	C	D33	[null]	0		1
69	55.44170	1	female	1	1	25.0	11765	Mrs.	C	E50	[null]	0		1
70	63.35830	1	female	1	1	45.0	PC 17759	Mrs.	C	D10 D12	[null]	1		0
71	153.46250	1	female	1	1	58.0	PC 17582	Mrs.	S	C125	[null]	1		0
72	30.00000	1	female	1	1	19.0	112053	Miss.	S	B42	[null]	0		0
73	89.10420	1	female	1	1	37.2635658914729	17453	Mrs.	C	C92	[null]	0		1
74	59.40000	1	female	1	1	45.0	112378	Mrs.	C	[null]	[null]	1		0
75	59.40000	1	female	1	1	22.0	112378	Miss.	C	[null]	[null]	1		0
76	211.50000	1	female	1	1	35.0	113503	Miss.	C	C130	[null]	0		0
77	53.10000	1	female	1	1	35.0	113803	Mrs.	S	C123	[null]	0		1
78	79.20000	1	female	1	1	48.0	13567	Mrs.	C	B41	[null]	1		1
79	49.50000	1	female	1	1	22.0	13568	Miss.	C	B39	[null]	2		0
80	133.65000	1	female	1	1	37.2635658914729	PC 17611	Mrs.	S	[null]	[null]	0		1
81	56.92920	1	female	1	1	30.0	PC 17485	Miss.	C	E36	[null]	0		0
82	263.00000	1	female	1	1	60.0	19950	Mrs.	S	C23 C25 C27	[null]	4		1
83	263.00000	1	female	1	1	23.0	19950	Miss.	S	C23 C25 C27	[null]	2		3
84	263.00000	1	female	1	1	28.0	19950	Miss.	S	C23 C25 C27	[null]	2		3
85	263.00000	1	female	1	1	24.0	19950	Miss.	S	C23 C25 C27	[null]	2		3
86	110.88330	1	female	1	1	37.2635658914729	17421	Miss.	C	[null]	[null]	0		0
87	31.68330	1	female	1	1	37.2635658914729	PC 17598	Mrs.	S	[null]	[null]	0		0
88	78.26670	1	female	1	1	54.0	36947	Miss.	C	D20	[null]	0		1
89	227.52500	1	female	1	1	38.0	PC 17757	Miss.	C	C45	[null]	0		0
90	83.15830	1	female	1	1	23.0	11767	Mrs.	C	C54	[null]	1		0
91	106.42500	1	female	1	1	48.0	PC 17761	Mrs.	C	C86	[null]	0		1
92	247.52080	1	female	1	1	27.0	PC 17558	Mrs.	C	B58 B60	[null]	1		1
93	81.85830	1	female	1	1	54.0	33638	Mrs.	S	A34	[null]	1		1
94	57.00000	1	female	1	1	17.0	17474	Mrs.	S	B20	[null]	0		1
95	52.00000	1	female	1	1	27.0	F.C. 12750	Mrs.	S	B71	[null]	2		1
96	151.55000	1	female	1	1	33.0	113781	Miss.	S	[null]	[null]	0		0
97	71.28330	1	female	1	1	38.0	PC 17599	Mrs.	C	C85	[null]	0		1
98	26.55000	1	female	1	1	64.0	112901	Mrs.	S	B26	[null]	1		1
99	71.00000	1	female	1	1	36.0	WE/P 5735	Miss.	S	B22	[null]	2		0
100	25.70000	1	female	1	1	55.0	11770	Mrs.	S	C101	[null]	0		2

Out[111]:

Rows: 1-100 of 1234 | Columns: 14

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

vDataFrame.apply¶

Parameters¶

Returns¶

Example¶

See Also¶