vDataFrame.filter¶

In [ ]:

vDataFrame.filter(conditions: (list, str) = [], *args, **kwds,)

Filters the vDataFrame using the input expressions. The current vDataFrame will be modified.

Parameters¶

Name	Type	Optional	Description
conditions	list	✓	List of expressions. For example to keep only the records where the vcolumn 'column' is greater than 5 and lesser than 10 you can write ['"column" > 5', '"column" < 10'].

Returns¶

vDataFrame : self

Example¶

In [5]:

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 home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.0	1	2	113781	151.55	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.0	0	0	112050	0.0	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.0	0	0	PC 17609	49.5042	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.0	1	0	PC 17757	227.525	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.925	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.0	0	1	PC 17558	247.5208	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.0	0	0	13050	75.2417	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.0	0	0	13905	26.0	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.0	0	0	113784	35.5	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.0	0	0	110489	26.55	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.0	0	0	113054	30.5	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.0	0	0	PC 17591	50.4958	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.6	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.0	0	0	113050	26.55	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.0	[null]	S	[null]	[null]	[null]
18	1	0		male	33.0	0	0	695	5.0	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.0	0	0	113059	47.1	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.0	0	0	19924	26.0	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.0	1	0	19877	78.85	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.0	1	0	W.E.P. 5734	61.175	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.0	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.0	1	0	13508	136.7792	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.0	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.0	0	0	5727	25.5875	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.0	1	1	PC 17756	83.1583	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.0	1	1	WE/P 5735	71.0	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.0	1	0	PC 17599	71.2833	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.0	1	0	F.C. 12750	52.0	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.0	1	0	PC 17761	106.425	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.0	0	0	PC 17580	29.7	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.7792	C95	S	[null]	[null]	[null]
37	1	0		male	30.0	0	0	113051	27.75	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.0	3	2	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.0	1	4	19950	263.0	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.0	B102	S	[null]	[null]	[null]
42	1	0		male	37.0	1	0	113803	53.1	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.0	0	0	111320	38.5	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.0	0	0	PC 17593	79.2	B86	C	[null]	[null]	[null]
45	1	0		male	71.0	0	0	PC 17754	34.6542	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.0	0	1	PC 17582	153.4625	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.0	0	0	PC 17593	79.2	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.4	[null]	S	[null]	[null]	[null]
49	1	0		male	45.0	1	0	36973	83.475	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.0	0	0	112059	0.0	B94	S	[null]	110	[null]
51	1	0		male	55.0	1	1	12749	93.5	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.0	0	0	113038	42.5	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.8625	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.0	0	0	680	50.0	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.0	1	0	113789	52.0	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.6958	[null]	C	14	[null]	New York, NY
57	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.0	0	0	694	26.0	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.0	0	0	113044	26.0	E60	S	[null]	[null]	London
60	1	0		male	32.5	0	0	113503	211.5	C132	C	[null]	45	[null]
61	1	0		male	58.0	0	0	11771	29.7	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.0	1	0	17464	51.8625	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.7208	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.0	0	0	113501	30.0	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.0	0	0	113801	45.5	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.0	0	0	110469	26.0	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.0	1	0	113773	53.1	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.0	0	0	13050	75.2417	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.0	0	0	17463	51.8625	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.0	1	0	PC 17604	82.1708	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.0	0	0	13509	26.55	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.0	2	0	19928	90.0	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.0	0	0	113787	30.5	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.0	0	0	113796	42.4	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.0	0	1	PC 17596	29.7	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.0	0	2	35273	113.275	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.0	0	0	693	26.0	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.0	0	1	113509	61.9792	B30	C	[null]	234	Providence, RI
80	1	0		male	28.5	0	0	PC 17562	27.7208	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.0	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.5	0	0	113043	28.5	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.0	0	0	12749	93.5	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.0	1	0	113776	66.6	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.0	1	0	PC 17758	108.9	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.0	0	0	110465	52.0	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.0	0	0	19972	0.0	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.0	0	0	PC 17760	135.6333	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.525	[null]	C	[null]	[null]	[null]
90	1	0		male	31.0	0	0	PC 17590	50.4958	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.0	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.0	0	0	13049	40.125	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.0	1	0	PC 17603	59.4	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.0	0	0	113790	26.55	[null]	S	[null]	109	London
95	1	0		male	61.0	1	3	PC 17608	262.375	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.0	1	0	13507	55.9	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.0	0	0	113792	26.55	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.0	0	0	17764	30.6958	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.0	1	0	13695	60.0	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.0	A19	S	[null]	[null]	Streatham, Surrey

Rows: 1-100 | Columns: 14

In [6]:

# filtering using an expression
titanic.filter("sex = 'female' AND pclass = 1")

1094 elements were filtered.

Out[6]:

	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		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]
2	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]
3	1	0		female	36.0	0	0	PC 17531	31.6792	A29	C	[null]	[null]
4	1	0		female	50.0	0	0	PC 17595	28.7125	C49	C	[null]	[null]
5	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]
6	1	1		female	29.0	0	0	24160	211.3375	B5	S	2	[null]
7	1	1		female	63.0	1	0	13502	77.9583	D7	S	10	[null]
8	1	1		female	53.0	2	0	11769	51.4792	C101	S	D	[null]
9	1	1		female	18.0	1	0	PC 17757	227.525	C62 C64	C	4	[null]
10	1	1		female	24.0	0	0	PC 17477	69.3	B35	C	9	[null]
11	1	1		female	50.0	0	1	PC 17558	247.5208	B58 B60	C	6	[null]
12	1	1		female	32.0	0	0	11813	76.2917	D15	C	8	[null]
13	1	1		female	47.0	1	1	11751	52.5542	D35	S	5	[null]
14	1	1		female	42.0	0	0	PC 17757	227.525	[null]	C	4	[null]
15	1	1		female	29.0	0	0	PC 17483	221.7792	C97	S	8	[null]
16	1	1		female	19.0	1	0	11967	91.0792	B49	C	7	[null]
17	1	1		female	35.0	0	0	PC 17760	135.6333	C99	S	8	[null]
18	1	1		female	30.0	0	0	36928	164.8667	C7	S	8	[null]
19	1	1		female	58.0	0	0	113783	26.55	C103	S	8	[null]
20	1	1		female	45.0	0	0	PC 17608	262.375	[null]	C	4	[null]
21	1	1		female	22.0	0	1	113505	55.0	E33	S	6	[null]
22	1	1		female	44.0	0	0	PC 17610	27.7208	B4	C	6	[null]
23	1	1		female	59.0	2	0	11769	51.4792	C101	S	D	[null]
24	1	1		female	60.0	0	0	11813	76.2917	D15	C	8	[null]
25	1	1		female	41.0	0	0	16966	134.5	E40	C	3	[null]
26	1	1		female	53.0	0	0	PC 17606	27.4458	[null]	C	6	[null]
27	1	1		female	58.0	0	1	PC 17755	512.3292	B51 B53 B55	C	3	[null]
28	1	1		female	14.0	1	2	113760	120.0	B96 B98	S	4	[null]
29	1	1		female	36.0	1	2	113760	120.0	B96 B98	S	4	[null]
30	1	1		female	[null]	0	0	17770	27.7208	[null]	C	5	[null]
31	1	1		female	76.0	1	0	19877	78.85	C46	S	6	[null]
32	1	1		female	47.0	1	0	W.E.P. 5734	61.175	E31	S	4	[null]
33	1	1		female	33.0	1	0	113806	53.1	E8	S	5	[null]
34	1	1		female	36.0	0	0	PC 17608	262.375	B61	C	4	[null]
35	1	1		female	30.0	0	0	110152	86.5	B77	S	8	[null]
36	1	1		female	[null]	0	1	113505	55.0	E33	S	6	[null]
37	1	1		female	26.0	1	0	13508	136.7792	C89	C	4	[null]
38	1	1		female	22.0	0	0	113781	151.55	[null]	S	11	[null]
39	1	1		female	39.0	1	1	PC 17756	83.1583	E49	C	14	[null]
40	1	1		female	64.0	0	2	PC 17756	83.1583	E45	C	14	[null]
41	1	1		female	55.0	2	0	11770	25.7	C101	S	2	[null]
42	1	1		female	36.0	0	2	WE/P 5735	71.0	B22	S	7	[null]
43	1	1		female	64.0	1	1	112901	26.55	B26	S	7	[null]
44	1	1		female	38.0	1	0	PC 17599	71.2833	C85	C	4	[null]
45	1	1		female	33.0	0	0	113781	151.55	[null]	S	8	[null]
46	1	1		female	27.0	1	2	F.C. 12750	52.0	B71	S	3	[null]
47	1	1		female	17.0	1	0	17474	57.0	B20	S	3	[null]
48	1	1		female	54.0	1	1	33638	81.8583	A34	S	5	[null]
49	1	1		female	27.0	1	1	PC 17558	247.5208	B58 B60	C	6	[null]
50	1	1		female	48.0	1	0	PC 17761	106.425	C86	C	2	[null]
51	1	1		female	23.0	0	1	11767	83.1583	C54	C	7	[null]
52	1	1		female	38.0	0	0	PC 17757	227.525	C45	C	4	[null]
53	1	1		female	54.0	1	0	36947	78.2667	D20	C	4	[null]
54	1	1		female	[null]	0	0	PC 17598	31.6833	[null]	S	7	[null]
55	1	1		female	[null]	0	0	17421	110.8833	[null]	C	4	[null]
56	1	1		female	24.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
57	1	1		female	28.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
58	1	1		female	23.0	3	2	19950	263.0	C23 C25 C27	S	10	[null]
59	1	1		female	60.0	1	4	19950	263.0	C23 C25 C27	S	10	[null]
60	1	1		female	30.0	0	0	PC 17485	56.9292	E36	C	1	[null]
61	1	1		female	[null]	1	0	PC 17611	133.65	[null]	S	5	[null]
62	1	1		female	22.0	0	2	13568	49.5	B39	C	5	[null]
63	1	1		female	48.0	1	1	13567	79.2	B41	C	5	[null]
64	1	1		female	35.0	1	0	113803	53.1	C123	S	D	[null]
65	1	1		female	35.0	0	0	113503	211.5	C130	C	4	[null]
66	1	1		female	22.0	0	1	112378	59.4	[null]	C	7	[null]
67	1	1		female	45.0	0	1	112378	59.4	[null]	C	7	[null]
68	1	1		female	[null]	1	0	17453	89.1042	C92	C	5	[null]
69	1	1		female	19.0	0	0	112053	30.0	B42	S	3	[null]
70	1	1		female	58.0	0	1	PC 17582	153.4625	C125	S	3	[null]
71	1	1		female	45.0	0	1	PC 17759	63.3583	D10 D12	C	7	[null]
72	1	1		female	25.0	1	0	11765	55.4417	E50	C	5	[null]
73	1	1		female	49.0	1	0	PC 17572	76.7292	D33	C	3	[null]
74	1	1		female	35.0	1	0	36973	83.475	C83	S	D	[null]
75	1	1		female	24.0	0	0	11767	83.1583	C54	C	7	[null]
76	1	1		female	52.0	1	1	12749	93.5	B69	S	3	[null]
77	1	1		female	16.0	0	1	111361	57.9792	B18	C	4	[null]
78	1	1		female	44.0	0	1	111361	57.9792	B18	C	4	[null]
79	1	1		female	51.0	1	0	13502	77.9583	D11	S	10	[null]
80	1	1		female	35.0	1	0	113789	52.0	[null]	S	8	[null]
81	1	1		female	35.0	1	0	19943	90.0	C93	S	D	[null]
82	1	1		female	38.0	0	0	113572	80.0	B28	[null]	6	[null]
83	1	1		female	[null]	1	0	17464	51.8625	D21	S	8	[null]
84	1	1		female	45.0	1	0	11753	52.5542	D19	S	5	[null]
85	1	1		female	39.0	0	0	24160	211.3375	[null]	S	2	[null]
86	1	1		female	30.0	0	0	PC 17761	106.425	[null]	C	2	[null]
87	1	1		female	49.0	0	0	17465	25.9292	D17	S	8	[null]
88	1	1		female	55.0	0	0	112377	27.7208	[null]	C	6	[null]
89	1	1		female	16.0	0	1	PC 17592	39.4	D28	S	9	[null]
90	1	1		female	51.0	0	1	PC 17592	39.4	D28	S	9	[null]
91	1	1		female	21.0	0	0	13502	77.9583	D9	S	10	[null]
92	1	1		female	58.0	0	0	PC 17569	146.5208	B80	C	[null]	[null]
93	1	1		female	15.0	0	1	24160	211.3375	B5	S	2	[null]
94	1	1		female	16.0	0	0	110152	86.5	B79	S	8	[null]
95	1	1		female	18.0	1	0	113773	53.1	D30	S	10	[null]
96	1	1		female	[null]	1	0	PC 17604	82.1708	[null]	C	6	[null]
97	1	1		female	33.0	1	0	19928	90.0	C78	Q	14	[null]
98	1	1		female	37.0	1	0	19928	90.0	C78	Q	14	[null]
99	1	1		female	31.0	1	0	35273	113.275	D36	C	6	[null]
100	1	1		female	23.0	1	0	35273	113.275	D36	C	6	[null]

Rows: 1-100 of 140 | Columns: 14

In [3]:

# filtering using multiple conditions
titanic.filter(["fare > 50", "survived = 0"])

137 elements were filtered

Out[3]:

	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 home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
3	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]	New York, NY

Rows: 3 | Columns: 14

In [7]:

titanic.filter("fare > 50", "survived = 0")

137 elements were filtered

Out[7]:

	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 home.dest Varchar(100)
1	1	0		female	2.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		female	25.0	1	2	113781	151.55	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
3	1	0		female	63.0	1	0	PC 17483	221.7792	C55 C57	S	[null]	[null]	New York, NY

Rows: 3 | Columns: 14

vDataFrame.search	Searches the elements which matches with the input conditions.
vDataFrame.at_time	Filters the data at the input time.
vDataFrame.between_time	Filters the data between two time ranges.
vDataFrame.first	Filters the data by only keeping the first records.
vDataFrame.last	Filters the data by only keeping the last records.

vDataFrame.filter¶

Parameters¶

Returns¶

Example¶

See Also¶