vastorbit.VastFrame.one_hot_encode

VastFrame.one_hot_encode(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, max_cardinality: int = 12, prefix_sep: str = '_', drop_first: bool = True, use_numbers_as_suffix: bool = False) → VastFrame

Encodes the VastColumns using the One Hot Encoding algorithm.

Parameters:

• columns (SQLColumns, optional) – List of the VastColumns used to train the One Hot Encoding model. If empty, only the VastColumns with a cardinality less than ‘max_cardinality’ are used.

• max_cardinality (int, optional) – Cardinality threshold used to determine whether the VastColumn is taken into account during the encoding This parameter is used only if the parameter ‘columns’ is empty.

• prefix_sep (str, optional) – Prefix delimitor of the dummies names.

• drop_first (bool, optional) – Drops the first dummy to avoid the creation of correlated features.

• use_numbers_as_suffix (bool, optional) – Uses numbers as suffix instead of the VastColumns categories.

Returns:

self

Return type:

VastFrame

Examples

We import vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use the Titanic dataset.

import vastorbit.datasets as vod

data = vod.load_titanic()

	123 pclass Integer	123 survived Integer	Abc name Varchar(164)	Abc sex Varchar(20)	123 age Double	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Double	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc home.dest Varchar(100)
1	3	1	McCormack, Mr. Thomas Joseph	male	[null]	0	0	367228	7.75	[null]	Q	[null]	[null]	[null]
2	3	1	McCoy, Miss. Agnes	female	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
3	3	1	McCoy, Miss. Alicia	female	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
4	3	1	McCoy, Mr. Bernard	male	[null]	2	0	367226	23.25	[null]	Q	16	[null]	[null]
5	3	1	McDermott, Miss. Brigdet Delia	female	[null]	0	0	330932	7.7875	[null]	Q	13	[null]	[null]
6	3	0	McEvoy, Mr. Michael	male	[null]	0	0	36568	15.5	[null]	Q	[null]	[null]	[null]
7	3	1	McGovern, Miss. Mary	female	[null]	0	0	330931	7.8792	[null]	Q	13	[null]	[null]
8	3	1	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	[null]	Q	[null]	[null]	[null]
9	3	0	McGowan, Miss. Katherine	female	35.0	0	0	9232	7.75	[null]	Q	[null]	[null]	[null]
10	3	0	McMahon, Mr. Martin	male	[null]	0	0	370372	7.75	[null]	Q	[null]	[null]	[null]
11	3	0	McNamee, Mr. Neal	male	24.0	1	0	376566	16.1	[null]	S	[null]	[null]	[null]
12	3	0	McNamee, Mrs. Neal (Eileen O'Leary)	female	19.0	1	0	376566	16.1	[null]	S	[null]	53	[null]
13	3	0	McNeill, Miss. Bridget	female	[null]	0	0	370368	7.75	[null]	Q	[null]	[null]	[null]
14	3	0	Meanwell, Miss. (Marion Ogden)	female	[null]	0	0	SOTON/O.Q. 392087	8.05	[null]	S	[null]	[null]	[null]
15	3	0	Meek, Mrs. Thomas (Annie Louise Rowley)	female	[null]	0	0	343095	8.05	[null]	S	[null]	[null]	[null]
16	3	0	Meo, Mr. Alfonzo	male	55.5	0	0	A.5. 11206	8.05	[null]	S	[null]	201	[null]
17	3	0	Mernagh, Mr. Robert	male	[null]	0	0	368703	7.75	[null]	Q	[null]	[null]	[null]
18	3	1	Midtsjo, Mr. Karl Albert	male	21.0	0	0	345501	7.775	[null]	S	15	[null]	[null]
19	3	0	Miles, Mr. Frank	male	[null]	0	0	359306	8.05	[null]	S	[null]	[null]	[null]
20	3	0	Mineff, Mr. Ivan	male	24.0	0	0	349233	7.8958	[null]	S	[null]	[null]	[null]
21	3	0	Minkoff, Mr. Lazar	male	21.0	0	0	349211	7.8958	[null]	S	[null]	[null]	[null]
22	3	0	Mionoff, Mr. Stoytcho	male	28.0	0	0	349207	7.8958	[null]	S	[null]	[null]	[null]
23	3	0	Mitkoff, Mr. Mito	male	[null]	0	0	349221	7.8958	[null]	S	[null]	[null]	[null]
24	3	1	Mockler, Miss. Helen Mary "Ellie"	female	[null]	0	0	330980	7.8792	[null]	Q	16	[null]	[null]
25	3	0	Moen, Mr. Sigurd Hansen	male	25.0	0	0	348123	7.65	F G73	S	[null]	309	[null]
26	3	1	Moor, Master. Meier	male	6.0	0	1	392096	12.475	E121	S	14	[null]	[null]
27	3	1	Moor, Mrs. (Beila)	female	27.0	0	1	392096	12.475	E121	S	14	[null]	[null]
28	3	0	Moore, Mr. Leonard Charles	male	[null]	0	0	A4. 54510	8.05	[null]	S	[null]	[null]	[null]
29	3	1	Moran, Miss. Bertha	female	[null]	1	0	371110	24.15	[null]	Q	16	[null]	[null]
30	3	0	Moran, Mr. Daniel J	male	[null]	1	0	371110	24.15	[null]	Q	[null]	[null]	[null]
31	3	0	Moran, Mr. James	male	[null]	0	0	330877	8.4583	[null]	Q	[null]	[null]	[null]
32	3	0	Morley, Mr. William	male	34.0	0	0	364506	8.05	[null]	S	[null]	[null]	[null]
33	3	0	Morrow, Mr. Thomas Rowan	male	[null]	0	0	372622	7.75	[null]	Q	[null]	[null]	[null]
34	3	1	Moss, Mr. Albert Johan	male	[null]	0	0	312991	7.775	[null]	S	B	[null]	[null]
35	3	1	Moubarek, Master. Gerios	male	[null]	1	1	2661	15.2458	[null]	C	C	[null]	[null]
36	3	1	Moubarek, Master. Halim Gonios ("William George")	male	[null]	1	1	2661	15.2458	[null]	C	C	[null]	[null]
37	3	1	Moubarek, Mrs. George (Omine "Amenia" Alexander)	female	[null]	0	2	2661	15.2458	[null]	C	C	[null]	[null]
38	3	1	Moussa, Mrs. (Mantoura Boulos)	female	[null]	0	0	2626	7.2292	[null]	C	[null]	[null]	[null]
39	3	0	Moutal, Mr. Rahamin Haim	male	[null]	0	0	374746	8.05	[null]	S	[null]	[null]	[null]
40	3	1	Mullens, Miss. Katherine "Katie"	female	[null]	0	0	35852	7.7333	[null]	Q	16	[null]	[null]
41	3	1	Mulvihill, Miss. Bertha E	female	24.0	0	0	382653	7.75	[null]	Q	15	[null]	[null]
42	3	0	Murdlin, Mr. Joseph	male	[null]	0	0	A./5. 3235	8.05	[null]	S	[null]	[null]	[null]
43	3	1	Murphy, Miss. Katherine "Kate"	female	[null]	1	0	367230	15.5	[null]	Q	16	[null]	[null]
44	3	1	Murphy, Miss. Margaret Jane	female	[null]	1	0	367230	15.5	[null]	Q	16	[null]	[null]
45	3	1	Murphy, Miss. Nora	female	[null]	0	0	36568	15.5	[null]	Q	16	[null]	[null]
46	3	0	Myhrman, Mr. Pehr Fabian Oliver Malkolm	male	18.0	0	0	347078	7.75	[null]	S	[null]	[null]	[null]
47	3	0	Naidenoff, Mr. Penko	male	22.0	0	0	349206	7.8958	[null]	S	[null]	[null]	[null]
48	3	1	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.225	[null]	C	C	[null]	[null]
49	3	1	Nakid, Miss. Maria ("Mary")	female	1.0	0	2	2653	15.7417	[null]	C	C	[null]	[null]
50	3	1	Nakid, Mr. Sahid	male	20.0	1	1	2653	15.7417	[null]	C	C	[null]	[null]
51	3	1	Nakid, Mrs. Said (Waika "Mary" Mowad)	female	19.0	1	1	2653	15.7417	[null]	C	C	[null]	[null]
52	3	0	Nancarrow, Mr. William Henry	male	33.0	0	0	A./5. 3338	8.05	[null]	S	[null]	[null]	[null]
53	3	0	Nankoff, Mr. Minko	male	[null]	0	0	349218	7.8958	[null]	S	[null]	[null]	[null]
54	3	0	Nasr, Mr. Mustafa	male	[null]	0	0	2652	7.2292	[null]	C	[null]	[null]	[null]
55	3	0	Naughton, Miss. Hannah	female	[null]	0	0	365237	7.75	[null]	Q	[null]	[null]	[null]
56	3	0	Nenkoff, Mr. Christo	male	[null]	0	0	349234	7.8958	[null]	S	[null]	[null]	[null]
57	3	1	Nicola-Yarred, Master. Elias	male	12.0	1	0	2651	11.2417	[null]	C	C	[null]	[null]
58	3	1	Nicola-Yarred, Miss. Jamila	female	14.0	1	0	2651	11.2417	[null]	C	C	[null]	[null]
59	3	0	Nieminen, Miss. Manta Josefina	female	29.0	0	0	3101297	7.925	[null]	S	[null]	[null]	[null]
60	3	0	Niklasson, Mr. Samuel	male	28.0	0	0	363611	8.05	[null]	S	[null]	[null]	[null]
61	3	1	Nilsson, Miss. Berta Olivia	female	18.0	0	0	347066	7.775	[null]	S	D	[null]	[null]
62	3	1	Nilsson, Miss. Helmina Josefina	female	26.0	0	0	347470	7.8542	[null]	S	13	[null]	[null]
63	3	0	Nilsson, Mr. August Ferdinand	male	21.0	0	0	350410	7.8542	[null]	S	[null]	[null]	[null]
64	3	0	Nirva, Mr. Iisakki Antino Aijo	male	41.0	0	0	SOTON/O2 3101272	7.125	[null]	S	[null]	[null]	Finland Sudbury, ON
65	3	1	Niskanen, Mr. Juha	male	39.0	0	0	STON/O 2. 3101289	7.925	[null]	S	9	[null]	[null]
66	3	0	Nosworthy, Mr. Richard Cater	male	21.0	0	0	A/4. 39886	7.8	[null]	S	[null]	[null]	[null]
67	3	0	Novel, Mr. Mansouer	male	28.5	0	0	2697	7.2292	[null]	C	[null]	181	[null]
68	3	1	Nysten, Miss. Anna Sofia	female	22.0	0	0	347081	7.75	[null]	S	13	[null]	[null]
69	3	0	Nysveen, Mr. Johan Hansen	male	61.0	0	0	345364	6.2375	[null]	S	[null]	[null]	[null]
70	3	0	O'Brien, Mr. Thomas	male	[null]	1	0	370365	15.5	[null]	Q	[null]	[null]	[null]
71	3	0	O'Brien, Mr. Timothy	male	[null]	0	0	330979	7.8292	[null]	Q	[null]	[null]	[null]
72	3	1	O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey)	female	[null]	1	0	370365	15.5	[null]	Q	[null]	[null]	[null]
73	3	0	O'Connell, Mr. Patrick D	male	[null]	0	0	334912	7.7333	[null]	Q	[null]	[null]	[null]
74	3	0	O'Connor, Mr. Maurice	male	[null]	0	0	371060	7.75	[null]	Q	[null]	[null]	[null]
75	3	0	O'Connor, Mr. Patrick	male	[null]	0	0	366713	7.75	[null]	Q	[null]	[null]	[null]
76	3	0	Odahl, Mr. Nils Martin	male	23.0	0	0	7267	9.225	[null]	S	[null]	[null]	[null]
77	3	0	O'Donoghue, Ms. Bridget	female	[null]	0	0	364856	7.75	[null]	Q	[null]	[null]	[null]
78	3	1	O'Driscoll, Miss. Bridget	female	[null]	0	0	14311	7.75	[null]	Q	D	[null]	[null]
79	3	1	O'Dwyer, Miss. Ellen "Nellie"	female	[null]	0	0	330959	7.8792	[null]	Q	[null]	[null]	[null]
80	3	1	Ohman, Miss. Velin	female	22.0	0	0	347085	7.775	[null]	S	C	[null]	[null]
81	3	1	O'Keefe, Mr. Patrick	male	[null]	0	0	368402	7.75	[null]	Q	B	[null]	[null]
82	3	1	O'Leary, Miss. Hanora "Norah"	female	[null]	0	0	330919	7.8292	[null]	Q	13	[null]	[null]
83	3	1	Olsen, Master. Artur Karl	male	9.0	0	1	C 17368	3.1708	[null]	S	13	[null]	[null]
84	3	0	Olsen, Mr. Henry Margido	male	28.0	0	0	C 4001	22.525	[null]	S	[null]	173	[null]
85	3	0	Olsen, Mr. Karl Siegwart Andreas	male	42.0	0	1	4579	8.4042	[null]	S	[null]	[null]	[null]
86	3	0	Olsen, Mr. Ole Martin	male	[null]	0	0	Fa 265302	7.3125	[null]	S	[null]	[null]	[null]
87	3	0	Olsson, Miss. Elina	female	31.0	0	0	350407	7.8542	[null]	S	[null]	[null]	[null]
88	3	0	Olsson, Mr. Nils Johan Goransson	male	28.0	0	0	347464	7.8542	[null]	S	[null]	[null]	[null]
89	3	1	Olsson, Mr. Oscar Wilhelm	male	32.0	0	0	347079	7.775	[null]	S	A	[null]	[null]
90	3	0	Olsvigen, Mr. Thor Anderson	male	20.0	0	0	6563	9.225	[null]	S	[null]	89	Oslo, Norway Cameron, WI
91	3	0	Oreskovic, Miss. Jelka	female	23.0	0	0	315085	8.6625	[null]	S	[null]	[null]	[null]
92	3	0	Oreskovic, Miss. Marija	female	20.0	0	0	315096	8.6625	[null]	S	[null]	[null]	[null]
93	3	0	Oreskovic, Mr. Luka	male	20.0	0	0	315094	8.6625	[null]	S	[null]	[null]	[null]
94	3	0	Osen, Mr. Olaf Elon	male	16.0	0	0	7534	9.2167	[null]	S	[null]	[null]	[null]
95	3	1	Osman, Mrs. Mara	female	31.0	0	0	349244	8.6833	[null]	S	[null]	[null]	[null]
96	3	0	O'Sullivan, Miss. Bridget Mary	female	[null]	0	0	330909	7.6292	[null]	Q	[null]	[null]	[null]
97	3	0	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.075	[null]	S	[null]	4	[null]
98	3	0	Palsson, Master. Paul Folke	male	6.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]
99	3	0	Palsson, Miss. Stina Viola	female	3.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]
100	3	0	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.075	[null]	S	[null]	[null]	[null]

Rows: 1-100 | Columns: 14

Note

vastorbit offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the vastorbit environment.

Let’s select few categorical features

data = data.select(["pclass", "sex", "survived", "embarked"])
data

	123 pclass Integer	Abc sex Varchar(20)	123 survived Integer	Abc embarked Varchar(20)
1	3	male	1	Q
2	3	female	1	Q
3	3	female	1	Q
4	3	male	1	Q
5	3	female	1	Q
6	3	male	0	Q
7	3	female	1	Q
8	3	female	1	Q
9	3	female	0	Q
10	3	male	0	Q
11	3	male	0	S
12	3	female	0	S
13	3	female	0	Q
14	3	female	0	S
15	3	female	0	S
16	3	male	0	S
17	3	male	0	Q
18	3	male	1	S
19	3	male	0	S
20	3	male	0	S
21	3	male	0	S
22	3	male	0	S
23	3	male	0	S
24	3	female	1	Q
25	3	male	0	S
26	3	male	1	S
27	3	female	1	S
28	3	male	0	S
29	3	female	1	Q
30	3	male	0	Q
31	3	male	0	Q
32	3	male	0	S
33	3	male	0	Q
34	3	male	1	S
35	3	male	1	C
36	3	male	1	C
37	3	female	1	C
38	3	female	1	C
39	3	male	0	S
40	3	female	1	Q
41	3	female	1	Q
42	3	male	0	S
43	3	female	1	Q
44	3	female	1	Q
45	3	female	1	Q
46	3	male	0	S
47	3	male	0	S
48	3	female	1	C
49	3	female	1	C
50	3	male	1	C
51	3	female	1	C
52	3	male	0	S
53	3	male	0	S
54	3	male	0	C
55	3	female	0	Q
56	3	male	0	S
57	3	male	1	C
58	3	female	1	C
59	3	female	0	S
60	3	male	0	S
61	3	female	1	S
62	3	female	1	S
63	3	male	0	S
64	3	male	0	S
65	3	male	1	S
66	3	male	0	S
67	3	male	0	C
68	3	female	1	S
69	3	male	0	S
70	3	male	0	Q
71	3	male	0	Q
72	3	female	1	Q
73	3	male	0	Q
74	3	male	0	Q
75	3	male	0	Q
76	3	male	0	S
77	3	female	0	Q
78	3	female	1	Q
79	3	female	1	Q
80	3	female	1	S
81	3	male	1	Q
82	3	female	1	Q
83	3	male	1	S
84	3	male	0	S
85	3	male	0	S
86	3	male	0	S
87	3	female	0	S
88	3	male	0	S
89	3	male	1	S
90	3	male	0	S
91	3	female	0	S
92	3	female	0	S
93	3	male	0	S
94	3	male	0	S
95	3	female	1	S
96	3	female	0	Q
97	3	male	0	S
98	3	male	0	S
99	3	female	0	S
100	3	female	0	S

Rows: 1-100 | Columns: 4

Let’s apply encoding on all the VastColumns of the datasets

data.one_hot_encode()

	123 pclass Integer	Abc sex Varchar(20)	123 survived Integer	Abc embarked Varchar(20)	123 pclass_1 Integer	123 pclass_2 Integer	123 sex_female Integer	123 embarked_C Integer	123 embarked_Q Integer
1	3	male	1	Q	0	0	0	0	1
2	3	female	1	Q	0	0	1	0	1
3	3	female	1	Q	0	0	1	0	1
4	3	male	1	Q	0	0	0	0	1
5	3	female	1	Q	0	0	1	0	1
6	3	male	0	Q	0	0	0	0	1
7	3	female	1	Q	0	0	1	0	1
8	3	female	1	Q	0	0	1	0	1
9	3	female	0	Q	0	0	1	0	1
10	3	male	0	Q	0	0	0	0	1
11	3	male	0	S	0	0	0	0	0
12	3	female	0	S	0	0	1	0	0
13	3	female	0	Q	0	0	1	0	1
14	3	female	0	S	0	0	1	0	0
15	3	female	0	S	0	0	1	0	0
16	3	male	0	S	0	0	0	0	0
17	3	male	0	Q	0	0	0	0	1
18	3	male	1	S	0	0	0	0	0
19	3	male	0	S	0	0	0	0	0
20	3	male	0	S	0	0	0	0	0
21	3	male	0	S	0	0	0	0	0
22	3	male	0	S	0	0	0	0	0
23	3	male	0	S	0	0	0	0	0
24	3	female	1	Q	0	0	1	0	1
25	3	male	0	S	0	0	0	0	0
26	3	male	1	S	0	0	0	0	0
27	3	female	1	S	0	0	1	0	0
28	3	male	0	S	0	0	0	0	0
29	3	female	1	Q	0	0	1	0	1
30	3	male	0	Q	0	0	0	0	1
31	3	male	0	Q	0	0	0	0	1
32	3	male	0	S	0	0	0	0	0
33	3	male	0	Q	0	0	0	0	1
34	3	male	1	S	0	0	0	0	0
35	3	male	1	C	0	0	0	1	0
36	3	male	1	C	0	0	0	1	0
37	3	female	1	C	0	0	1	1	0
38	3	female	1	C	0	0	1	1	0
39	3	male	0	S	0	0	0	0	0
40	3	female	1	Q	0	0	1	0	1
41	3	female	1	Q	0	0	1	0	1
42	3	male	0	S	0	0	0	0	0
43	3	female	1	Q	0	0	1	0	1
44	3	female	1	Q	0	0	1	0	1
45	3	female	1	Q	0	0	1	0	1
46	3	male	0	S	0	0	0	0	0
47	3	male	0	S	0	0	0	0	0
48	3	female	1	C	0	0	1	1	0
49	3	female	1	C	0	0	1	1	0
50	3	male	1	C	0	0	0	1	0
51	3	female	1	C	0	0	1	1	0
52	3	male	0	S	0	0	0	0	0
53	3	male	0	S	0	0	0	0	0
54	3	male	0	C	0	0	0	1	0
55	3	female	0	Q	0	0	1	0	1
56	3	male	0	S	0	0	0	0	0
57	3	male	1	C	0	0	0	1	0
58	3	female	1	C	0	0	1	1	0
59	3	female	0	S	0	0	1	0	0
60	3	male	0	S	0	0	0	0	0
61	3	female	1	S	0	0	1	0	0
62	3	female	1	S	0	0	1	0	0
63	3	male	0	S	0	0	0	0	0
64	3	male	0	S	0	0	0	0	0
65	3	male	1	S	0	0	0	0	0
66	3	male	0	S	0	0	0	0	0
67	3	male	0	C	0	0	0	1	0
68	3	female	1	S	0	0	1	0	0
69	3	male	0	S	0	0	0	0	0
70	3	male	0	Q	0	0	0	0	1
71	3	male	0	Q	0	0	0	0	1
72	3	female	1	Q	0	0	1	0	1
73	3	male	0	Q	0	0	0	0	1
74	3	male	0	Q	0	0	0	0	1
75	3	male	0	Q	0	0	0	0	1
76	3	male	0	S	0	0	0	0	0
77	3	female	0	Q	0	0	1	0	1
78	3	female	1	Q	0	0	1	0	1
79	3	female	1	Q	0	0	1	0	1
80	3	female	1	S	0	0	1	0	0
81	3	male	1	Q	0	0	0	0	1
82	3	female	1	Q	0	0	1	0	1
83	3	male	1	S	0	0	0	0	0
84	3	male	0	S	0	0	0	0	0
85	3	male	0	S	0	0	0	0	0
86	3	male	0	S	0	0	0	0	0
87	3	female	0	S	0	0	1	0	0
88	3	male	0	S	0	0	0	0	0
89	3	male	1	S	0	0	0	0	0
90	3	male	0	S	0	0	0	0	0
91	3	female	0	S	0	0	1	0	0
92	3	female	0	S	0	0	1	0	0
93	3	male	0	S	0	0	0	0	0
94	3	male	0	S	0	0	0	0	0
95	3	female	1	S	0	0	1	0	0
96	3	female	0	Q	0	0	1	0	1
97	3	male	0	S	0	0	0	0	0
98	3	male	0	S	0	0	0	0	0
99	3	female	0	S	0	0	1	0	0
100	3	female	0	S	0	0	1	0	0

Rows: 1-100 of 1309 | Columns: 9

Let’s apply encoding on two specific VastColumns viz. “pclass” and “embarked”

data = data.select(["pclass", "sex", "survived", "embarked"])
data.one_hot_encode(columns = ["pclass", "embarked"])

	123 pclass Integer	Abc sex Varchar(20)	123 survived Integer	Abc embarked Varchar(20)	123 pclass_1 Integer	123 pclass_2 Integer	123 embarked_C Integer	123 embarked_Q Integer
1	3	male	1	Q	0	0	0	1
2	3	female	1	Q	0	0	0	1
3	3	female	1	Q	0	0	0	1
4	3	male	1	Q	0	0	0	1
5	3	female	1	Q	0	0	0	1
6	3	male	0	Q	0	0	0	1
7	3	female	1	Q	0	0	0	1
8	3	female	1	Q	0	0	0	1
9	3	female	0	Q	0	0	0	1
10	3	male	0	Q	0	0	0	1
11	3	male	0	S	0	0	0	0
12	3	female	0	S	0	0	0	0
13	3	female	0	Q	0	0	0	1
14	3	female	0	S	0	0	0	0
15	3	female	0	S	0	0	0	0
16	3	male	0	S	0	0	0	0
17	3	male	0	Q	0	0	0	1
18	3	male	1	S	0	0	0	0
19	3	male	0	S	0	0	0	0
20	3	male	0	S	0	0	0	0
21	3	male	0	S	0	0	0	0
22	3	male	0	S	0	0	0	0
23	3	male	0	S	0	0	0	0
24	3	female	1	Q	0	0	0	1
25	3	male	0	S	0	0	0	0
26	3	male	1	S	0	0	0	0
27	3	female	1	S	0	0	0	0
28	3	male	0	S	0	0	0	0
29	3	female	1	Q	0	0	0	1
30	3	male	0	Q	0	0	0	1
31	3	male	0	Q	0	0	0	1
32	3	male	0	S	0	0	0	0
33	3	male	0	Q	0	0	0	1
34	3	male	1	S	0	0	0	0
35	3	male	1	C	0	0	1	0
36	3	male	1	C	0	0	1	0
37	3	female	1	C	0	0	1	0
38	3	female	1	C	0	0	1	0
39	3	male	0	S	0	0	0	0
40	3	female	1	Q	0	0	0	1
41	3	female	1	Q	0	0	0	1
42	3	male	0	S	0	0	0	0
43	3	female	1	Q	0	0	0	1
44	3	female	1	Q	0	0	0	1
45	3	female	1	Q	0	0	0	1
46	3	male	0	S	0	0	0	0
47	3	male	0	S	0	0	0	0
48	3	female	1	C	0	0	1	0
49	3	female	1	C	0	0	1	0
50	3	male	1	C	0	0	1	0
51	3	female	1	C	0	0	1	0
52	3	male	0	S	0	0	0	0
53	3	male	0	S	0	0	0	0
54	3	male	0	C	0	0	1	0
55	3	female	0	Q	0	0	0	1
56	3	male	0	S	0	0	0	0
57	3	male	1	C	0	0	1	0
58	3	female	1	C	0	0	1	0
59	3	female	0	S	0	0	0	0
60	3	male	0	S	0	0	0	0
61	3	female	1	S	0	0	0	0
62	3	female	1	S	0	0	0	0
63	3	male	0	S	0	0	0	0
64	3	male	0	S	0	0	0	0
65	3	male	1	S	0	0	0	0
66	3	male	0	S	0	0	0	0
67	3	male	0	C	0	0	1	0
68	3	female	1	S	0	0	0	0
69	3	male	0	S	0	0	0	0
70	3	male	0	Q	0	0	0	1
71	3	male	0	Q	0	0	0	1
72	3	female	1	Q	0	0	0	1
73	3	male	0	Q	0	0	0	1
74	3	male	0	Q	0	0	0	1
75	3	male	0	Q	0	0	0	1
76	3	male	0	S	0	0	0	0
77	3	female	0	Q	0	0	0	1
78	3	female	1	Q	0	0	0	1
79	3	female	1	Q	0	0	0	1
80	3	female	1	S	0	0	0	0
81	3	male	1	Q	0	0	0	1
82	3	female	1	Q	0	0	0	1
83	3	male	1	S	0	0	0	0
84	3	male	0	S	0	0	0	0
85	3	male	0	S	0	0	0	0
86	3	male	0	S	0	0	0	0
87	3	female	0	S	0	0	0	0
88	3	male	0	S	0	0	0	0
89	3	male	1	S	0	0	0	0
90	3	male	0	S	0	0	0	0
91	3	female	0	S	0	0	0	0
92	3	female	0	S	0	0	0	0
93	3	male	0	S	0	0	0	0
94	3	male	0	S	0	0	0	0
95	3	female	1	S	0	0	0	0
96	3	female	0	Q	0	0	0	1
97	3	male	0	S	0	0	0	0
98	3	male	0	S	0	0	0	0
99	3	female	0	S	0	0	0	0
100	3	female	0	S	0	0	0	0

Rows: 1-100 of 1309 | Columns: 8

Let’s apply encoding on all features having cardinality less than 3

data = data.select(["pclass", "sex", "survived", "embarked"])
data.one_hot_encode(
    max_cardinality = 3,
    drop_first = False,
)

	123 pclass Integer	Abc sex Varchar(20)	123 survived Integer	Abc embarked Varchar(20)	123 sex_female Integer	123 sex_male Integer
1	3	male	1	Q	0	1
2	3	female	1	Q	1	0
3	3	female	1	Q	1	0
4	3	male	1	Q	0	1
5	3	female	1	Q	1	0
6	3	male	0	Q	0	1
7	3	female	1	Q	1	0
8	3	female	1	Q	1	0
9	3	female	0	Q	1	0
10	3	male	0	Q	0	1
11	3	male	0	S	0	1
12	3	female	0	S	1	0
13	3	female	0	Q	1	0
14	3	female	0	S	1	0
15	3	female	0	S	1	0
16	3	male	0	S	0	1
17	3	male	0	Q	0	1
18	3	male	1	S	0	1
19	3	male	0	S	0	1
20	3	male	0	S	0	1
21	3	male	0	S	0	1
22	3	male	0	S	0	1
23	3	male	0	S	0	1
24	3	female	1	Q	1	0
25	3	male	0	S	0	1
26	3	male	1	S	0	1
27	3	female	1	S	1	0
28	3	male	0	S	0	1
29	3	female	1	Q	1	0
30	3	male	0	Q	0	1
31	3	male	0	Q	0	1
32	3	male	0	S	0	1
33	3	male	0	Q	0	1
34	3	male	1	S	0	1
35	3	male	1	C	0	1
36	3	male	1	C	0	1
37	3	female	1	C	1	0
38	3	female	1	C	1	0
39	3	male	0	S	0	1
40	3	female	1	Q	1	0
41	3	female	1	Q	1	0
42	3	male	0	S	0	1
43	3	female	1	Q	1	0
44	3	female	1	Q	1	0
45	3	female	1	Q	1	0
46	3	male	0	S	0	1
47	3	male	0	S	0	1
48	3	female	1	C	1	0
49	3	female	1	C	1	0
50	3	male	1	C	0	1
51	3	female	1	C	1	0
52	3	male	0	S	0	1
53	3	male	0	S	0	1
54	3	male	0	C	0	1
55	3	female	0	Q	1	0
56	3	male	0	S	0	1
57	3	male	1	C	0	1
58	3	female	1	C	1	0
59	3	female	0	S	1	0
60	3	male	0	S	0	1
61	3	female	1	S	1	0
62	3	female	1	S	1	0
63	3	male	0	S	0	1
64	3	male	0	S	0	1
65	3	male	1	S	0	1
66	3	male	0	S	0	1
67	3	male	0	C	0	1
68	3	female	1	S	1	0
69	3	male	0	S	0	1
70	3	male	0	Q	0	1
71	3	male	0	Q	0	1
72	3	female	1	Q	1	0
73	3	male	0	Q	0	1
74	3	male	0	Q	0	1
75	3	male	0	Q	0	1
76	3	male	0	S	0	1
77	3	female	0	Q	1	0
78	3	female	1	Q	1	0
79	3	female	1	Q	1	0
80	3	female	1	S	1	0
81	3	male	1	Q	0	1
82	3	female	1	Q	1	0
83	3	male	1	S	0	1
84	3	male	0	S	0	1
85	3	male	0	S	0	1
86	3	male	0	S	0	1
87	3	female	0	S	1	0
88	3	male	0	S	0	1
89	3	male	1	S	0	1
90	3	male	0	S	0	1
91	3	female	0	S	1	0
92	3	female	0	S	1	0
93	3	male	0	S	0	1
94	3	male	0	S	0	1
95	3	female	1	S	1	0
96	3	female	0	Q	1	0
97	3	male	0	S	0	1
98	3	male	0	S	0	1
99	3	female	0	S	1	0
100	3	female	0	S	1	0

Rows: 1-100 of 1309 | Columns: 6

See also

VastFrame.decode() : User Defined Encoding.

VastFrame.label_encode() : Label Encoding.

VastFrame.mean_encode() : Mean Encoding.

VastFrame.discretize() : Discretization.