vDataFrame[].mean_encode¶

In [ ]:

vDataFrame[].mean_encode(response: str)

Encodes the vcolumn using the average of the response partitioned by the different vcolumn categories.

Parameters¶

Name	Type	Optional	Description
response	str	❌	Response vcolumn.

Returns¶

vDataFrame : self.parent

Example¶

In [29]:

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 [30]:

# Let's look at the avg of survived partitioned by embarked
titanic.groupby(["embarked"], ["AVG(survived) AS survived"])

	Abc embarked Varchar(20)	123 survived Float
1	C	0.537549407114625
2	Q	0.273584905660377
3	S	0.324169530355097
4	[null]	1.0

Out[30]:

Rows: 4 | Columns: 2

In [31]:

# Mean Encoding will replace each category by the average of the response 
titanic["embarked"].mean_encode(response = "survived")

The mean encoding was successfully done.

	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)	123 embarked Int	Abc cabin Varchar(30)	123 body Int	123 parch Int	Abc home.dest Varchar(100)	123 sibsp Int
1	80.00000	1	female	6	1	38.000	113572		1.0	B28	[null]	0	[null]	0
2	80.00000	1	female	6	1	62.000	113572		1.0	B28	[null]	0	Cincinatti, OH	0
3	14.45420	1	female	[null]	3	15.000	2659		0.537549407114625	[null]	[null]	0	[null]	1
4	7.22920	1	female	C	3	38.000	2688		0.537549407114625	[null]	[null]	0	[null]	0
5	7.22500	1	male	13 15	3	22.000	2658		0.537549407114625	[null]	[null]	0	[null]	0
6	15.24580	1	female	C	3	29.000	2650		0.537549407114625	[null]	[null]	2	[null]	0
7	15.24580	1	female	C	3	9.000	2650		0.537549407114625	[null]	[null]	1	[null]	1
8	15.24580	1	male	C	3	7.000	2650		0.537549407114625	[null]	[null]	1	[null]	1
9	8.51670	1	male	16	3	0.420	2625		0.537549407114625	[null]	[null]	1	[null]	0
10	22.35830	1	female	D	3	[null]	2668		0.537549407114625	[null]	[null]	2	[null]	0
11	22.35830	1	female	D	3	[null]	2668		0.537549407114625	F E69	[null]	1	[null]	1
12	22.35830	1	male	C	3	[null]	2668		0.537549407114625	[null]	[null]	1	[null]	1
13	11.24170	1	female	C	3	14.000	2651		0.537549407114625	[null]	[null]	0	[null]	1
14	11.24170	1	male	C	3	12.000	2651		0.537549407114625	[null]	[null]	0	[null]	1
15	15.74170	1	male	C	3	20.000	2653		0.537549407114625	[null]	[null]	1	[null]	1
16	7.22920	1	female	[null]	3	[null]	2626		0.537549407114625	[null]	[null]	0	[null]	0
17	15.24580	1	male	C	3	[null]	2661		0.537549407114625	[null]	[null]	1	[null]	1
18	7.22500	1	female	C	3	[null]	2649		0.537549407114625	[null]	[null]	0	[null]	0
19	7.22920	1	male	15	3	[null]	2677		0.537549407114625	[null]	[null]	0	[null]	0
20	7.22920	1	male	10	3	25.000	2654		0.537549407114625	F E57	[null]	0	[null]	0
21	13.41670	1	male	15	3	39.000	349256		0.537549407114625	[null]	[null]	1	[null]	0
22	13.41670	1	female	15	3	4.000	349256		0.537549407114625	[null]	[null]	1	[null]	0
23	7.89580	1	male	15	3	29.000	349240		0.537549407114625	[null]	[null]	0	[null]	0
24	7.22920	1	male	15	3	20.000	2663		0.537549407114625	[null]	[null]	0	[null]	0
25	19.25830	1	female	C	3	24.000	2666		0.537549407114625	[null]	[null]	3	Syria New York, NY	0
26	19.25830	1	female	C	3	5.000	2666		0.537549407114625	[null]	[null]	1	Syria New York, NY	2
27	19.25830	1	female	C	3	0.750	2666		0.537549407114625	[null]	[null]	1	Syria New York, NY	2
28	19.25830	1	female	C	3	0.750	2666		0.537549407114625	[null]	[null]	1	Syria New York, NY	2
29	7.22920	1	female	C	3	13.000	2687		0.537549407114625	[null]	[null]	0	Syria Youngstown, OH	0
30	18.78750	1	male	15	3	26.000	2699		0.537549407114625	[null]	[null]	0	Syria Fredericksburg, VA	0
31	7.22920	1	female	C	3	18.000	2657		0.537549407114625	[null]	[null]	0	Greensburg, PA	0
32	7.22500	0	male	[null]	3	27.000	2670		0.537549407114625	[null]	[null]	0	[null]	0
33	7.22500	0	male	[null]	3	26.500	2656		0.537549407114625	[null]	304	0	[null]	0
34	14.45420	0	female	[null]	3	[null]	2665		0.537549407114625	[null]	[null]	0	[null]	1
35	14.45420	0	female	[null]	3	14.500	2665		0.537549407114625	[null]	328	0	[null]	1
36	14.45830	0	male	[null]	3	[null]	2627		0.537549407114625	[null]	[null]	0	[null]	0
37	7.22500	0	male	[null]	3	[null]	2647		0.537549407114625	[null]	[null]	0	[null]	0
38	7.22500	0	male	[null]	3	45.500	2628		0.537549407114625	[null]	312	0	[null]	0
39	14.45420	0	male	C	3	27.000	2659		0.537549407114625	[null]	[null]	0	[null]	1
40	7.22920	0	male	[null]	3	[null]	2641		0.537549407114625	[null]	[null]	0	[null]	0
41	7.22920	0	male	[null]	3	[null]	2673		0.537549407114625	[null]	[null]	0	[null]	0
42	7.22500	0	male	[null]	3	[null]	2684		0.537549407114625	[null]	[null]	0	[null]	0
43	6.43750	0	male	[null]	3	[null]	2681		0.537549407114625	[null]	[null]	0	[null]	0
44	6.43750	0	male	[null]	3	[null]	2621		0.537549407114625	[null]	[null]	0	[null]	1
45	8.66250	0	male	[null]	3	33.000	349239		0.537549407114625	[null]	[null]	0	[null]	0
46	7.22920	0	male	[null]	3	22.000	2669		0.537549407114625	[null]	[null]	0	[null]	0
47	21.67920	0	male	[null]	3	[null]	2662		0.537549407114625	[null]	[null]	0	[null]	2
48	21.67920	0	male	[null]	3	[null]	2662		0.537549407114625	[null]	[null]	0	[null]	2
49	21.67920	0	male	[null]	3	[null]	2662		0.537549407114625	[null]	[null]	0	[null]	2
50	7.22500	0	male	[null]	3	[null]	2676		0.537549407114625	[null]	[null]	0	[null]	0
51	7.22500	0	male	[null]	3	25.000	2672		0.537549407114625	[null]	[null]	0	[null]	0
52	7.22920	0	male	[null]	3	[null]	2671		0.537549407114625	[null]	[null]	0	[null]	0
53	7.22920	0	male	[null]	3	[null]	2629		0.537549407114625	[null]	[null]	0	[null]	0
54	8.71250	0	male	[null]	3	[null]	3411		0.537549407114625	[null]	[null]	0	[null]	0
55	7.22920	0	male	[null]	3	28.500	2697		0.537549407114625	[null]	181	0	[null]	0
56	7.22920	0	male	[null]	3	[null]	2652		0.537549407114625	[null]	[null]	0	[null]	0
57	7.89580	0	male	[null]	3	[null]	349255		0.537549407114625	[null]	[null]	0	[null]	0
58	7.89580	0	male	[null]	3	35.000	349213		0.537549407114625	[null]	[null]	0	[null]	0
59	7.22920	0	male	[null]	3	[null]	2655		0.537549407114625	F E46	[null]	0	[null]	0
60	6.43750	0	male	[null]	3	34.500	2683		0.537549407114625	[null]	196	0	[null]	0
61	7.22500	0	male	[null]	3	[null]	2624		0.537549407114625	[null]	[null]	0	[null]	0
62	7.89580	0	male	[null]	3	[null]	349253		0.537549407114625	[null]	[null]	0	[null]	0
63	14.45420	0	male	[null]	3	[null]	2660		0.537549407114625	[null]	[null]	0	[null]	1
64	7.22920	0	male	[null]	3	[null]	2700		0.537549407114625	[null]	[null]	0	[null]	0
65	7.22920	0	male	[null]	3	30.000	2685		0.537549407114625	[null]	[null]	0	[null]	0
66	18.78750	0	male	[null]	3	11.000	2699		0.537549407114625	[null]	[null]	0	[null]	0
67	7.22920	0	male	[null]	3	23.500	2693		0.537549407114625	[null]	188	0	[null]	0
68	7.89580	0	male	[null]	3	[null]	349254		0.537549407114625	[null]	[null]	0	[null]	0
69	7.22500	0	male	[null]	3	[null]	2631		0.537549407114625	[null]	[null]	0	[null]	0
70	7.22920	0	male	[null]	3	15.000	2695		0.537549407114625	[null]	[null]	1	Syria	1
71	7.22920	0	male	[null]	3	17.000	2690		0.537549407114625	[null]	[null]	1	[null]	1
72	7.22920	0	male	[null]	3	39.000	2675		0.537549407114625	[null]	[null]	2	Syria Ottawa, ON	0
73	7.22500	0	male	[null]	3	[null]	2674		0.537549407114625	[null]	[null]	0	[null]	0
74	7.89580	0	male	[null]	3	33.000	349241		0.537549407114625	[null]	51	0	Austria Niagara Falls, NY	0
75	7.22920	0	male	[null]	3	[null]	2686		0.537549407114625	[null]	[null]	0	[null]	0
76	7.22500	0	male	[null]	3	22.500	2698		0.537549407114625	[null]	9	0	[null]	0
77	14.45420	0	male	[null]	3	18.000	2680		0.537549407114625	[null]	[null]	0	Greece	1
78	14.45420	0	male	[null]	3	26.000	2680		0.537549407114625	[null]	[null]	0	Greece	1
79	14.45830	0	female	[null]	3	[null]	2689		0.537549407114625	[null]	[null]	0	Ottawa, ON	1
80	14.45830	0	male	[null]	3	[null]	2689		0.537549407114625	[null]	[null]	0	Ottawa, ON	1
81	15.24580	0	female	[null]	3	[null]	2678		0.537549407114625	[null]	[null]	2	Syria Kent, ON	0
82	7.22500	0	male	[null]	3	[null]	2664		0.537549407114625	[null]	[null]	0	Syria	0
83	15.24580	0	female	[null]	3	9.000	2678		0.537549407114625	[null]	[null]	1	Syria Kent, ON	1
84	15.24580	0	male	[null]	3	6.000	2678		0.537549407114625	[null]	[null]	1	Syria Kent, ON	1
85	4.01250	0	male	[null]	3	20.000	2648		0.537549407114625	[null]	[null]	0	Syria	0
86	7.22920	0	male	[null]	3	[null]	2622		0.537549407114625	[null]	[null]	0	[null]	0
87	14.45420	0	female	[null]	3	45.000	2691		0.537549407114625	[null]	[null]	1	Syria Ottawa, ON	0
88	14.45420	0	female	[null]	3	18.000	2691		0.537549407114625	[null]	[null]	1	Syria Ottawa, ON	0
89	7.22500	0	male	[null]	3	40.000	2623		0.537549407114625	[null]	[null]	0	[null]	0
90	7.22500	0	male	[null]	3	20.000	2679		0.537549407114625	[null]	[null]	0	[null]	0
91	7.22500	0	male	[null]	3	30.000	2694		0.537549407114625	[null]	[null]	0	Ottawa, ON	0
92	14.45830	0	female	[null]	3	17.000	2627		0.537549407114625	[null]	[null]	0	[null]	0
93	7.22500	0	male	[null]	3	21.000	2692		0.537549407114625	[null]	[null]	0	Ottawa, ON	0
94	27.72080	1	female	12	2	24.000	SC/PARIS 2167		0.537549407114625	[null]	[null]	0	Lucca, Italy / California	1
95	12.73750	1	male	14	2	30.000	C.A. 34644		0.537549407114625	[null]	[null]	0	Milford, NH	0
96	13.85830	1	male	9	2	29.000	SC/PARIS 2147		0.537549407114625	[null]	[null]	0	Spain / Havana, Cuba	0
97	13.86250	1	male	9	2	[null]	SC/PARIS 2146		0.537549407114625	[null]	[null]	0	Spain / Havana, Cuba	0
98	30.07080	1	female	[null]	2	14.000	237736		0.537549407114625	[null]	[null]	0	New York, NY	1
99	37.00420	1	female	10	2	24.000	S.C./PARIS 2079		0.537549407114625	[null]	[null]	1	Paris / Montreal, PQ	1
100	37.00420	1	male	10	2	1.000	S.C./PARIS 2079		0.537549407114625	[null]	[null]	2	Paris / Montreal, PQ	0

Out[31]:

Rows: 1-100 of 1234 | Columns: 14

vDataFrame[].decode	Encodes the vcolumn using a User Defined Encoding.
vDataFrame[].discretize	Discretizes the vcolumn.
vDataFrame[].label_encode	Encodes the vcolumn using the Label Encoding.
vDataFrame[].get_dummies	Encodes the vcolumn using the One Hot Encoding.

vDataFrame[].mean_encode¶

Parameters¶

Returns¶

Example¶

See Also¶