vDataFrame[].drop_outliers¶

In [ ]:

vDataFrame[].drop_outliers(threshold: float = 4.0,
                           use_threshold: bool = True, 
                           alpha: float = 0.05)

Drops the vcolumn outliers.

Parameters¶

Name	Type	Optional	Description
threshold	float	✓	Uses the Gaussian distribution to define the outliers. After normalizing the data (Z-Score), if the absolute value of the record is greater than the threshold it will be considered as an outlier.
use_threshold	bool	✓	Uses the threshold instead of the 'alpha' parameter.
alpha	float	✓	Number representing the outliers threshold. Values lesser than quantile(alpha) or greater than quantile(1-alpha) will be dropped.

Returns¶

vDataFrame : self.parent

Example¶

In [58]:

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 [59]:

# Filtering the 'fare' having a Zscore greater than 3.0
titanic["fare"].drop_outliers(threshold = 3.0)

38 element(s) was/were filtered

	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 Varchar(100)	123 sibsp Int
1	151.55000	0	female	[null]	1	2.000	113781		S	C22 C26	[null]	2		1
2	151.55000	0	male	[null]	1	30.000	113781		S	C22 C26	135	2		1
3	151.55000	0	female	[null]	1	25.000	113781		S	C22 C26	[null]	2		1
4	0.00000	0	male	[null]	1	39.000	112050		S	A36	[null]	0		0
5	49.50420	0	male	[null]	1	71.000	PC 17609		C	[null]	22	0		0
6	25.92500	0	male	[null]	1	[null]	PC 17318		S	[null]	[null]	0		0
7	75.24170	0	male	A	1	36.000	13050		C	C6	[null]	0		0
8	26.00000	0	male	[null]	1	25.000	13905		C	[null]	148	0		0
9	35.50000	0	male	[null]	1	45.000	113784		S	T	[null]	0		0
10	26.55000	0	male	[null]	1	42.000	110489		S	D22	[null]	0		0
11	30.50000	0	male	[null]	1	41.000	113054		S	A21	[null]	0		0
12	50.49580	0	male	[null]	1	48.000	PC 17591		C	B10	208	0		0
13	39.60000	0	male	[null]	1	[null]	112379		C	[null]	[null]	0		0
14	26.55000	0	male	[null]	1	45.000	113050		S	B38	[null]	0		0
15	31.00000	0	male	[null]	1	[null]	113798		S	[null]	[null]	0		0
16	5.00000	0	male	[null]	1	33.000	695		S	B51 B53 B55	[null]	0		0
17	47.10000	0	male	[null]	1	28.000	113059		S	[null]	[null]	0		0
18	47.10000	0	male	[null]	1	17.000	113059		S	[null]	[null]	0		0
19	26.00000	0	male	[null]	1	49.000	19924		S	[null]	[null]	0		0
20	78.85000	0	male	[null]	1	36.000	19877		S	C46	172	0		1
21	61.17500	0	male	[null]	1	46.000	W.E.P. 5734		S	E31	[null]	0		1
22	0.00000	0	male	[null]	1	[null]	112051		S	[null]	[null]	0		0
23	136.77920	0	male	[null]	1	27.000	13508		C	C89	[null]	0		1
24	52.00000	0	male	[null]	1	[null]	110465		S	A14	[null]	0		0
25	25.58750	0	male	[null]	1	47.000	5727		S	E58	[null]	0		0
26	83.15830	0	male	[null]	1	37.000	PC 17756		C	E52	[null]	1		1
27	26.55000	0	male	[null]	1	[null]	113791		S	[null]	[null]	0		0
28	71.00000	0	male	[null]	1	70.000	WE/P 5735		S	B22	269	1		1
29	71.28330	0	male	[null]	1	39.000	PC 17599		C	C85	[null]	0		1
30	52.00000	0	male	[null]	1	31.000	F.C. 12750		S	B71	[null]	0		1
31	106.42500	0	male	[null]	1	50.000	PC 17761		C	C86	62	0		1
32	29.70000	0	male	[null]	1	39.000	PC 17580		C	A18	133	0		0
33	31.67920	0	female	[null]	1	36.000	PC 17531		C	A29	[null]	0		0
34	27.75000	0	male	[null]	1	30.000	113051		C	C111	[null]	0		0
35	26.55000	0	male	[null]	1	[null]	113778		S	D34	[null]	0		0
36	0.00000	0	male	[null]	1	[null]	112058		S	B102	[null]	0		0
37	53.10000	0	male	[null]	1	37.000	113803		S	C123	[null]	0		1
38	38.50000	0	male	[null]	1	47.000	111320		S	E63	275	0		0
39	79.20000	0	male	[null]	1	24.000	PC 17593		C	B86	[null]	0		0
40	34.65420	0	male	[null]	1	71.000	PC 17754		C	A5	[null]	0		0
41	153.46250	0	male	[null]	1	38.000	PC 17582		S	C91	147	1		0
42	79.20000	0	male	[null]	1	46.000	PC 17593		C	B82 B84	[null]	0		0
43	42.40000	0	male	[null]	1	[null]	113796		S	[null]	[null]	0		0
44	83.47500	0	male	[null]	1	45.000	36973		S	C83	[null]	0		1
45	0.00000	0	male	[null]	1	40.000	112059		S	B94	110	0		0
46	93.50000	0	male	[null]	1	55.000	12749		S	B69	307	1		1
47	42.50000	0	male	[null]	1	42.000	113038		S	B11	[null]	0		0
48	51.86250	0	male	[null]	1	[null]	17463		S	E46	[null]	0		0
49	50.00000	0	male	[null]	1	55.000	680		S	C39	[null]	0		0
50	52.00000	0	male	[null]	1	42.000	113789		S	[null]	38	0		1
51	30.69580	0	male	14	1	[null]	PC 17600		C	[null]	[null]	0		0
52	28.71250	0	female	[null]	1	50.000	PC 17595		C	C49	[null]	0		0
53	26.00000	0	male	[null]	1	46.000	694		S	[null]	80	0		0
54	26.00000	0	male	[null]	1	50.000	113044		S	E60	[null]	0		0
55	29.70000	0	male	[null]	1	58.000	11771		C	B37	258	0		0
56	51.86250	0	male	[null]	1	41.000	17464		S	D21	[null]	0		1
57	26.55000	0	male	[null]	1	[null]	113028		S	C124	[null]	0		0
58	27.72080	0	male	[null]	1	[null]	PC 17612		C	[null]	[null]	0		0
59	30.00000	0	male	[null]	1	29.000	113501		S	D6	126	0		0
60	45.50000	0	male	[null]	1	30.000	113801		S	[null]	[null]	0		0
61	26.00000	0	male	[null]	1	30.000	110469		S	C106	[null]	0		0
62	53.10000	0	male	[null]	1	19.000	113773		S	D30	[null]	0		1
63	75.24170	0	male	[null]	1	46.000	13050		C	C6	292	0		0
64	51.86250	0	male	[null]	1	54.000	17463		S	E46	175	0		0
65	82.17080	0	male	[null]	1	28.000	PC 17604		C	[null]	[null]	0		1
66	26.55000	0	male	[null]	1	65.000	13509		S	E38	249	0		0
67	90.00000	0	male	[null]	1	44.000	19928		Q	C78	230	0		2
68	30.50000	0	male	[null]	1	55.000	113787		S	C30	[null]	0		0
69	42.40000	0	male	[null]	1	47.000	113796		S	[null]	[null]	0		0
70	29.70000	0	male	[null]	1	37.000	PC 17596		C	C118	[null]	1		0
71	113.27500	0	male	[null]	1	58.000	35273		C	D48	122	2		0
72	26.00000	0	male	[null]	1	64.000	693		S	[null]	263	0		0
73	61.97920	0	male	[null]	1	65.000	113509		C	B30	234	1		0
74	27.72080	0	male	[null]	1	28.500	PC 17562		C	D43	189	0		0
75	0.00000	0	male	[null]	1	[null]	112052		S	[null]	[null]	0		0
76	28.50000	0	male	[null]	1	45.500	113043		S	C124	166	0		0
77	93.50000	0	male	[null]	1	23.000	12749		S	B24	[null]	0		0
78	66.60000	0	male	[null]	1	29.000	113776		S	C2	[null]	0		1
79	108.90000	0	male	[null]	1	18.000	PC 17758		C	C65	[null]	0		1
80	52.00000	0	male	[null]	1	47.000	110465		S	C110	207	0		0
81	0.00000	0	male	[null]	1	38.000	19972		S	[null]	[null]	0		0
82	135.63330	0	male	[null]	1	22.000	PC 17760		C	[null]	232	0		0
83	50.49580	0	male	[null]	1	31.000	PC 17590		S	A24	[null]	0		0
84	50.00000	0	male	[null]	1	[null]	113767		S	A32	[null]	0		0
85	40.12500	0	male	[null]	1	36.000	13049		C	A10	[null]	0		0
86	59.40000	0	male	[null]	1	55.000	PC 17603		C	[null]	[null]	0		1
87	26.55000	0	male	[null]	1	33.000	113790		S	[null]	109	0		0
88	55.90000	0	male	[null]	1	50.000	13507		S	E44	[null]	0		1
89	26.55000	0	male	[null]	1	56.000	113792		S	[null]	[null]	0		0
90	30.69580	0	male	[null]	1	56.000	17764		C	A7	[null]	0		0
91	60.00000	0	male	[null]	1	24.000	13695		S	C31	[null]	0		1
92	26.00000	0	male	[null]	1	[null]	113056		S	A19	[null]	0		0
93	146.52080	0	male	[null]	1	57.000	PC 17569		C	B78	[null]	0		1
94	26.55000	0	male	[null]	1	62.000	113514		S	C87	[null]	0		0
95	27.72080	0	male	[null]	1	[null]	PC 17605		C	[null]	[null]	0		0
96	32.32080	0	male	[null]	1	61.000	36963		S	D50	46	0		0
97	79.65000	0	male	[null]	1	52.000	110413		S	E67	[null]	1		1
98	110.88330	0	male	[null]	1	49.000	17421		C	C68	[null]	1		1
99	27.72080	0	male	[null]	1	40.000	PC 17601		C	[null]	[null]	0		0
100	33.50000	0	male	[null]	1	61.000	111240		S	B19	245	0		0

Out[59]:

Rows: 1-100 of 1196 | Columns: 14

In [60]:

# Filtering the 'age' greater than the last decile 
# or lesser than the first decile
titanic["age"].drop_outliers(use_threshold = False, alpha = 0.1)

413 element(s) was/were filtered

	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 Varchar(100)	123 sibsp Int
1	151.55000	0	male	[null]	1	30.000	113781		S	C22 C26	135	2		1
2	151.55000	0	female	[null]	1	25.000	113781		S	C22 C26	[null]	2		1
3	0.00000	0	male	[null]	1	39.000	112050		S	A36	[null]	0		0
4	75.24170	0	male	A	1	36.000	13050		C	C6	[null]	0		0
5	26.00000	0	male	[null]	1	25.000	13905		C	[null]	148	0		0
6	35.50000	0	male	[null]	1	45.000	113784		S	T	[null]	0		0
7	26.55000	0	male	[null]	1	42.000	110489		S	D22	[null]	0		0
8	30.50000	0	male	[null]	1	41.000	113054		S	A21	[null]	0		0
9	50.49580	0	male	[null]	1	48.000	PC 17591		C	B10	208	0		0
10	26.55000	0	male	[null]	1	45.000	113050		S	B38	[null]	0		0
11	5.00000	0	male	[null]	1	33.000	695		S	B51 B53 B55	[null]	0		0
12	47.10000	0	male	[null]	1	28.000	113059		S	[null]	[null]	0		0
13	47.10000	0	male	[null]	1	17.000	113059		S	[null]	[null]	0		0
14	26.00000	0	male	[null]	1	49.000	19924		S	[null]	[null]	0		0
15	78.85000	0	male	[null]	1	36.000	19877		S	C46	172	0		1
16	61.17500	0	male	[null]	1	46.000	W.E.P. 5734		S	E31	[null]	0		1
17	136.77920	0	male	[null]	1	27.000	13508		C	C89	[null]	0		1
18	25.58750	0	male	[null]	1	47.000	5727		S	E58	[null]	0		0
19	83.15830	0	male	[null]	1	37.000	PC 17756		C	E52	[null]	1		1
20	71.28330	0	male	[null]	1	39.000	PC 17599		C	C85	[null]	0		1
21	52.00000	0	male	[null]	1	31.000	F.C. 12750		S	B71	[null]	0		1
22	106.42500	0	male	[null]	1	50.000	PC 17761		C	C86	62	0		1
23	29.70000	0	male	[null]	1	39.000	PC 17580		C	A18	133	0		0
24	31.67920	0	female	[null]	1	36.000	PC 17531		C	A29	[null]	0		0
25	27.75000	0	male	[null]	1	30.000	113051		C	C111	[null]	0		0
26	53.10000	0	male	[null]	1	37.000	113803		S	C123	[null]	0		1
27	38.50000	0	male	[null]	1	47.000	111320		S	E63	275	0		0
28	79.20000	0	male	[null]	1	24.000	PC 17593		C	B86	[null]	0		0
29	153.46250	0	male	[null]	1	38.000	PC 17582		S	C91	147	1		0
30	79.20000	0	male	[null]	1	46.000	PC 17593		C	B82 B84	[null]	0		0
31	83.47500	0	male	[null]	1	45.000	36973		S	C83	[null]	0		1
32	0.00000	0	male	[null]	1	40.000	112059		S	B94	110	0		0
33	42.50000	0	male	[null]	1	42.000	113038		S	B11	[null]	0		0
34	52.00000	0	male	[null]	1	42.000	113789		S	[null]	38	0		1
35	28.71250	0	female	[null]	1	50.000	PC 17595		C	C49	[null]	0		0
36	26.00000	0	male	[null]	1	46.000	694		S	[null]	80	0		0
37	26.00000	0	male	[null]	1	50.000	113044		S	E60	[null]	0		0
38	51.86250	0	male	[null]	1	41.000	17464		S	D21	[null]	0		1
39	30.00000	0	male	[null]	1	29.000	113501		S	D6	126	0		0
40	45.50000	0	male	[null]	1	30.000	113801		S	[null]	[null]	0		0
41	26.00000	0	male	[null]	1	30.000	110469		S	C106	[null]	0		0
42	53.10000	0	male	[null]	1	19.000	113773		S	D30	[null]	0		1
43	75.24170	0	male	[null]	1	46.000	13050		C	C6	292	0		0
44	82.17080	0	male	[null]	1	28.000	PC 17604		C	[null]	[null]	0		1
45	90.00000	0	male	[null]	1	44.000	19928		Q	C78	230	0		2
46	42.40000	0	male	[null]	1	47.000	113796		S	[null]	[null]	0		0
47	29.70000	0	male	[null]	1	37.000	PC 17596		C	C118	[null]	1		0
48	27.72080	0	male	[null]	1	28.500	PC 17562		C	D43	189	0		0
49	28.50000	0	male	[null]	1	45.500	113043		S	C124	166	0		0
50	93.50000	0	male	[null]	1	23.000	12749		S	B24	[null]	0		0
51	66.60000	0	male	[null]	1	29.000	113776		S	C2	[null]	0		1
52	108.90000	0	male	[null]	1	18.000	PC 17758		C	C65	[null]	0		1
53	52.00000	0	male	[null]	1	47.000	110465		S	C110	207	0		0
54	0.00000	0	male	[null]	1	38.000	19972		S	[null]	[null]	0		0
55	135.63330	0	male	[null]	1	22.000	PC 17760		C	[null]	232	0		0
56	50.49580	0	male	[null]	1	31.000	PC 17590		S	A24	[null]	0		0
57	40.12500	0	male	[null]	1	36.000	13049		C	A10	[null]	0		0
58	26.55000	0	male	[null]	1	33.000	113790		S	[null]	109	0		0
59	55.90000	0	male	[null]	1	50.000	13507		S	E44	[null]	0		1
60	60.00000	0	male	[null]	1	24.000	13695		S	C31	[null]	0		1
61	110.88330	0	male	[null]	1	49.000	17421		C	C68	[null]	1		1
62	27.72080	0	male	[null]	1	40.000	PC 17601		C	[null]	[null]	0		0
63	34.02080	0	male	[null]	1	47.000	36967		S	D46	[null]	0		0
64	77.28750	0	male	[null]	1	21.000	35281		S	D26	169	1		0
65	26.55000	1	male	3	1	48.000	19952		S	E12	[null]	0		0
66	69.30000	1	female	9	1	24.000	PC 17477		C	B35	[null]	0		0
67	76.29170	1	female	8	1	32.000	11813		C	D15	[null]	0		0
68	52.55420	1	male	5	1	37.000	11751		S	D35	[null]	1		1
69	52.55420	1	female	5	1	47.000	11751		S	D35	[null]	1		1
70	30.00000	1	male	5	1	26.000	111369		C	C148	[null]	0		0
71	91.07920	1	male	7	1	25.000	11967		C	B49	[null]	0		1
72	91.07920	1	female	7	1	19.000	11967		C	B49	[null]	0		1
73	135.63330	1	female	8	1	35.000	PC 17760		S	C99	[null]	0		0
74	26.55000	1	male	D	1	28.000	110564		S	C52	[null]	0		0
75	31.00000	1	male	7	1	40.000	112277		C	A31	[null]	0		0
76	164.86670	1	female	8	1	30.000	36928		S	C7	[null]	0		0
77	55.00000	1	female	6	1	22.000	113505		S	E33	[null]	1		0
78	27.72080	1	female	6	1	44.000	PC 17610		C	B4	[null]	0		0
79	134.50000	1	female	3	1	41.000	16966		C	E40	[null]	0		0
80	26.28750	1	male	5	1	42.000	PC 17476		S	E24	[null]	0		0
81	120.00000	1	female	4	1	14.000	113760		S	B96 B98	[null]	2		1
82	120.00000	1	male	C	1	36.000	113760		S	B96 B98	[null]	2		1
83	120.00000	1	female	4	1	36.000	113760		S	B96 B98	[null]	2		1
84	61.17500	1	female	4	1	47.000	W.E.P. 5734		S	E31	[null]	0		1
85	53.10000	1	male	5	1	27.000	113806		S	E8	[null]	0		1
86	53.10000	1	female	5	1	33.000	113806		S	E8	[null]	0		1
87	86.50000	1	female	8	1	30.000	110152		S	B77	[null]	0		0
88	29.70000	1	male	7	1	45.000	PC 17594		C	A9	[null]	0		0
89	136.77920	1	female	4	1	26.000	13508		C	C89	[null]	0		1
90	151.55000	1	female	11	1	22.000	113781		S	[null]	[null]	0		0
91	83.15830	1	female	14	1	39.000	PC 17756		C	E49	[null]	1		1
92	71.00000	1	female	7	1	36.000	WE/P 5735		S	B22	[null]	2		0
93	71.28330	1	female	4	1	38.000	PC 17599		C	C85	[null]	0		1
94	30.50000	1	male	3	1	27.000	113804		S	[null]	[null]	0		0
95	151.55000	1	female	8	1	33.000	113781		S	[null]	[null]	0		0
96	52.00000	1	female	3	1	27.000	F.C. 12750		S	B71	[null]	2		1
97	57.00000	1	male	3	1	31.000	17474		S	B20	[null]	0		1
98	57.00000	1	female	3	1	17.000	17474		S	B20	[null]	0		1
99	106.42500	1	female	2	1	48.000	PC 17761		C	C86	[null]	0		1
100	83.15830	1	female	7	1	23.000	11767		C	C54	[null]	1		0

Out[60]:

Rows: 1-100 of 783 | Columns: 14

vDataFrame[].fill_outliers	Fills the vcolumns outliers.
vDataFrame.outliers	Adds a new vcolumn labeled with 0 and 1 (1 meaning global outlier).

vDataFrame[].drop_outliers¶

Parameters¶

Returns¶

Example¶

See Also¶