vDataFrame.outliers¶

In [ ]:

vDataFrame.outliers(columns: list = [],
                    name: str = "distribution_outliers",
                    threshold: float = 3.0,
                    robust: bool = False)

Adds a new vcolumn labeled with 0 and 1. 1 means that the record is a global outlier.

Parameters¶

Name	Type	Optional	Description
columns	list	✓	List of the vcolumns names. If empty, all the numerical vcolumns will be used.
name	str	✓	Name of the new vcolumn.
threshold	float	✓	Threshold equals to the critical score.
robust	bool	✓	If set to True, the score used will be the Robust Z-Score instead of the Z-Score.

Returns¶

vDataFrame : self

Example¶

In [44]:

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 [55]:

titanic.scatter(columns = ["age", "fare"])
titanic.outliers(columns = ["age", "fare"],
                 name = "outliers",
                 threshold = 2.5)

In [56]:

titanic.scatter(columns = ["age", "fare"], 
                catcol = "outliers")

vDataFrame.outliers¶

Parameters¶

Returns¶

Example¶

See Also¶