Encoding

Encoding features is a very important part of the data science life cycle. In data science, generality is important and having too many categories can compromise that and lead to incorrect results. In addition, some algorithmic optimizations are linear and prefer categorized information, and some can’t process non-numerical features.

There are many encoding techniques:

• User-Defined Encoding: The most flexible encoding. The user can choose how to encode the different categories.

• Label Encoding: Each category is converted to an integer using a bijection to [0;n-1] where n is the feature number of unique values.

• One-hot Encoding: This technique creates dummies (values in {0,1}) of each category. The categories are then separated into n features.

• Mean Encoding: This technique uses the frequencies of each category for a specific response column.

• Discretization: This technique uses various mathematical technique to encode continuous features into categories.

To demonstrate encoding data in vastorbit, we’ll use the well-known titanic dataset.

from vastorbit.datasets import load_titanic

titanic = load_titanic()
titanic.head(100)

	123 pclass Integer 100%	123 survived Integer 100%	Abc name Varchar(164) 100%	Abc sex Varchar(20) 100%	123 age Double 79%	123 sibsp Integer 100%	123 parch Integer 100%	Abc ticket Varchar(36) 100%	123 fare Double 99%	Abc cabin Varchar(30) 22%	Abc embarked Varchar(20) 99%	Abc boat Varchar(100) 37%	123 body Integer 9%	Abc home.dest Varchar(100) 56%
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]

Let’s look at the age of the passengers.

titanic["age"].hist()

By using the discretize() method, we can discretize the data using equal-width binning.

titanic["age"].discretize(method = "same_width", h = 10)
titanic["age"].bar(max_cardinality = 10)

We can also discretize the data using frequency bins.

titanic = load_titanic()
titanic["age"].discretize(method = "same_freq", nbins = 5)
titanic["age"].bar(max_cardinality = 5)

We can view the available techniques in the discretize() method with the help() method.

help(titanic["age"].discretize)

To encode a categorical feature, we can use label encoding. For example, the column sex has two categories (male and female) that we can represent with 0 and 1, respectively.

titanic["sex"].label_encode()
titanic["sex"].head(100)

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

When a feature has few categories, the most suitable choice is the one-hot encoding. Label encoding converts a categorical feature to numerical without retaining its mathematical relationships. Let’s use a one-hot encoding on the embarked column.

titanic["embarked"].one_hot_encode()
titanic.select(["embarked", "embarked_C", "embarked_Q"])

	Abc embarked Varchar(20) 99%	123 embarked_C Integer 100%	123 embarked_Q Integer 100%
1	Q	0	1
2	Q	0	1
3	Q	0	1
4	Q	0	1
5	Q	0	1
6	Q	0	1
7	Q	0	1
8	Q	0	1
9	Q	0	1
10	Q	0	1
11	S	0	0
12	S	0	0
13	Q	0	1
14	S	0	0
15	S	0	0
16	S	0	0
17	Q	0	1
18	S	0	0
19	S	0	0
20	S	0	0

One-hot encoding can be expensive if the column in question has a large number of categories. In that case, we should use mean encoding. Mean encoding replaces each category of a variable with its corresponding average over a partition by a response column. This makes it an efficient way to encode the data, but be careful of over-fitting.

Let’s use a mean encoding on the home.dest variable.

titanic["home.dest"].mean_encode("survived")
titanic.head(100)

	123 pclass Integer 100%	123 survived Integer 100%	Abc name Varchar(164) 100%	123 sex Integer 100%	Abc age Varchar 79%	123 sibsp Integer 100%	123 parch Integer 100%	Abc ticket Varchar(36) 100%	123 fare Double 99%	Abc cabin Varchar(30) 22%	Abc embarked Varchar(20) 99%	Abc boat Varchar(100) 37%	123 body Integer 9%	123 home.dest Int 100%	123 embarked_C Bool 100%	123 embarked_Q Bool 100%
1	3	1	de Mulder, Mr. Theodore	1	[25.0;31.0]	0	0	345774	9.5	[null]	S	11	[null]	1.0	0	0
2	3	0	Ford, Mr. Arthur	1	[null]	0	0	A/5 1478	8.05	[null]	S	[null]	[null]	0.0	0	0
3	2	0	Reeves, Mr. David	1	[31.0;42.0]	0	0	C.A. 17248	10.5	[null]	S	[null]	[null]	0.0	0	0
4	3	1	Glynn, Miss. Mary Agatha	0	[null]	0	0	335677	7.75	[null]	Q	13	[null]	1.0	0	1
5	3	0	Carr, Miss. Jeannie	0	[31.0;42.0]	0	0	368364	7.75	[null]	Q	[null]	[null]	0.0	0	1
6	2	0	Chapman, Mr. John Henry	1	[31.0;42.0]	1	0	SC/AH 29037	26.0	[null]	S	[null]	17	0.0	0	0
7	2	0	Chapman, Mrs. John Henry (Sara Elizabeth Lawry)	0	[25.0;31.0]	1	0	SC/AH 29037	26.0	[null]	S	[null]	[null]	0.0	0	0
8	2	0	Botsford, Mr. William Hull	1	[25.0;31.0]	0	0	237670	13.0	[null]	S	[null]	[null]	0.0	0	0
9	3	1	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	0	[0.17;19.0]	0	0	2657	7.2292	[null]	C	C	[null]	1.0	1	0
10	2	1	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	0	[42.0;80.0]	1	1	F.C.C. 13529	26.25	[null]	S	14	[null]	0.6666666666666666	0	0
11	2	1	Hart, Miss. Eva Miriam	0	[0.17;19.0]	0	2	F.C.C. 13529	26.25	[null]	S	14	[null]	0.6666666666666666	0	0
12	2	0	Hart, Mr. Benjamin	1	[42.0;80.0]	1	1	F.C.C. 13529	26.25	[null]	S	[null]	[null]	0.6666666666666666	0	0
13	1	1	Pears, Mrs. Thomas (Edith Wearne)	0	[19.0;25.0]	1	0	113776	66.6	C2	S	8	[null]	0.5	0	0
14	1	0	Pears, Mr. Thomas Clinton	1	[25.0;31.0]	1	0	113776	66.6	C2	S	[null]	[null]	0.5	0	0
15	3	1	Cohen, Mr. Gurshon "Gus"	1	[0.17;19.0]	0	0	A/5 3540	8.05	[null]	S	12	[null]	1.0	0	0
16	2	0	del Carlo, Mr. Sebastiano	1	[25.0;31.0]	1	0	SC/PARIS 2167	27.7208	[null]	C	[null]	295	0.5	1	0
17	2	1	del Carlo, Mrs. Sebastiano (Argenia Genovesi)	0	[19.0;25.0]	1	0	SC/PARIS 2167	27.7208	[null]	C	12	[null]	0.5	1	0
18	1	0	Crosby, Capt. Edward Gifford	1	[42.0;80.0]	1	1	WE/P 5735	71.0	B22	S	[null]	269	0.6666666666666666	0	0
19	1	1	Crosby, Miss. Harriet R	0	[31.0;42.0]	0	2	WE/P 5735	71.0	B22	S	7	[null]	0.6666666666666666	0	0
20	1	1	Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead)	0	[42.0;80.0]	1	1	112901	26.55	B26	S	7	[null]	0.6666666666666666	0	0
21	1	0	Rood, Mr. Hugh Roscoe	1	[null]	0	0	113767	50.0	A32	S	[null]	[null]	0.0	0	0
22	2	1	Herman, Mrs. Samuel (Jane Laver)	0	[42.0;80.0]	1	2	220845	65.0	[null]	S	9	[null]	0.6	0	0
23	2	0	Sweet, Mr. George Frederick	1	[0.17;19.0]	0	0	220845	65.0	[null]	S	[null]	[null]	0.6	0	0
24	2	0	Herman, Mr. Samuel	1	[42.0;80.0]	1	2	220845	65.0	[null]	S	[null]	[null]	0.6	0	0
25	2	1	Herman, Miss. Kate	0	[19.0;25.0]	1	2	220845	65.0	[null]	S	9	[null]	0.6	0	0
26	2	1	Herman, Miss. Alice	0	[19.0;25.0]	1	2	220845	65.0	[null]	S	9	[null]	0.6	0	0
27	3	1	Baclini, Miss. Eugenie	0	[0.17;19.0]	2	1	2666	19.2583	[null]	C	C	[null]	1.0	1	0
28	3	1	Baclini, Miss. Helene Barbara	0	[0.17;19.0]	2	1	2666	19.2583	[null]	C	C	[null]	1.0	1	0
29	3	1	Baclini, Miss. Marie Catherine	0	[0.17;19.0]	2	1	2666	19.2583	[null]	C	C	[null]	1.0	1	0
30	3	1	Baclini, Mrs. Solomon (Latifa Qurban)	0	[19.0;25.0]	0	3	2666	19.2583	[null]	C	C	[null]	1.0	1	0
31	3	0	Barbara, Miss. Saiide	0	[0.17;19.0]	0	1	2691	14.4542	[null]	C	[null]	[null]	0.0	1	0
32	3	0	Barbara, Mrs. (Catherine David)	0	[42.0;80.0]	0	1	2691	14.4542	[null]	C	[null]	[null]	0.0	1	0
33	3	0	Elias, Mr. Joseph	1	[31.0;42.0]	0	2	2675	7.2292	[null]	C	[null]	[null]	0.0	1	0
34	2	0	Giles, Mr. Ralph	1	[19.0;25.0]	0	0	248726	13.5	[null]	S	[null]	297	0.0	0	0
35	1	1	Brown, Mrs. John Murray (Caroline Lane Lamson)	0	[42.0;80.0]	2	0	11769	51.4792	C101	S	D	[null]	1.0	0	0
36	2	1	Ball, Mrs. (Ada E Hall)	0	[31.0;42.0]	0	0	28551	13.0	D	S	10	[null]	1.0	0	0
37	1	1	Bowen, Miss. Grace Scott	0	[42.0;80.0]	0	0	PC 17608	262.375	[null]	C	4	[null]	1.0	1	0
38	3	0	Ekstrom, Mr. Johan	1	[42.0;80.0]	0	0	347061	6.975	[null]	S	[null]	[null]	0.0	0	0
39	2	1	Webber, Miss. Susan	0	[31.0;42.0]	0	0	27267	13.0	E101	S	12	[null]	1.0	0	0
40	1	0	Minahan, Dr. William Edward	1	[42.0;80.0]	2	0	19928	90.0	C78	Q	[null]	230	0.5	0	1
41	1	1	Minahan, Mrs. William Edward (Lillian E Thorpe)	0	[31.0;42.0]	1	0	19928	90.0	C78	Q	14	[null]	0.5	0	1
42	1	1	Williams, Mr. Richard Norris II	1	[19.0;25.0]	0	1	PC 17597	61.3792	[null]	C	A	[null]	0.5	1	0
43	1	0	Williams, Mr. Charles Duane	1	[42.0;80.0]	0	1	PC 17597	61.3792	[null]	C	[null]	[null]	0.5	1	0
44	3	0	Elsbury, Mr. William James	1	[42.0;80.0]	0	0	A/5 3902	7.25	[null]	S	[null]	[null]	0.0	0	0
45	3	1	Finoli, Mr. Luigi	1	[null]	0	0	SOTON/O.Q. 3101308	7.05	[null]	S	15	[null]	1.0	0	0
46	3	0	Eklund, Mr. Hans Linus	1	[0.17;19.0]	0	0	347074	7.775	[null]	S	[null]	[null]	0.0	0	0
47	3	1	Bradley, Miss. Bridget Delia	0	[19.0;25.0]	0	0	334914	7.725	[null]	Q	13	[null]	1.0	0	1
48	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	0	[42.0;80.0]	0	1	11767	83.1583	C50	C	7	[null]	1.0	1	0
49	1	1	Earnshaw, Mrs. Boulton (Olive Potter)	0	[19.0;25.0]	0	1	11767	83.1583	C54	C	7	[null]	1.0	1	0
50	3	1	Asplund, Mr. Johan Charles	1	[19.0;25.0]	0	0	350054	7.7958	[null]	S	13	[null]	1.0	0	0
51	2	0	Harbeck, Mr. William H	1	[42.0;80.0]	0	0	248746	13.0	[null]	S	[null]	35	0.0	0	0
52	3	1	Goldsmith, Master. Frank John William "Frankie"	1	[0.17;19.0]	0	2	363291	20.525	[null]	S	C D	[null]	0.6666666666666666	0	0
53	3	0	Goldsmith, Mr. Frank John	1	[31.0;42.0]	1	1	363291	20.525	[null]	S	[null]	[null]	0.6666666666666666	0	0
54	3	1	Goldsmith, Mrs. Frank John (Emily Alice Brown)	0	[25.0;31.0]	1	1	363291	20.525	[null]	S	C D	[null]	0.6666666666666666	0	0
55	3	0	Aronsson, Mr. Ernst Axel Algot	1	[19.0;25.0]	0	0	349911	7.775	[null]	S	[null]	[null]	0.0	0	0
56	2	1	Angle, Mrs. William A (Florence "Mary" Agnes Hughes)	0	[31.0;42.0]	1	0	226875	26.0	[null]	S	11	[null]	0.5	0	0
57	2	0	Angle, Mr. William A	1	[31.0;42.0]	1	0	226875	26.0	[null]	S	[null]	[null]	0.5	0	0
58	1	1	Tucker, Mr. Gilbert Milligan Jr	1	[25.0;31.0]	0	0	2543	28.5375	C53	C	7	[null]	1.0	1	0
59	3	0	Waelens, Mr. Achille	1	[19.0;25.0]	0	0	345767	9.0	[null]	S	[null]	[null]	0.0	0	0
60	3	0	Adams, Mr. John	1	[25.0;31.0]	0	0	341826	8.05	[null]	S	[null]	103	0.0	0	0
61	3	0	Brocklebank, Mr. William Alfred	1	[31.0;42.0]	0	0	364512	8.05	[null]	S	[null]	[null]	0.0	0	0
62	3	0	Denkoff, Mr. Mitto	1	[null]	0	0	349225	7.8958	[null]	S	[null]	[null]	0.0	0	0
63	2	0	Myles, Mr. Thomas Francis	1	[42.0;80.0]	0	0	240276	9.6875	[null]	Q	[null]	[null]	0.0	0	1
64	2	1	Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford)	0	[31.0;42.0]	1	1	29750	39.0	[null]	S	14	[null]	0.6666666666666666	0	0
65	2	1	Brown, Miss. Edith Eileen	0	[0.17;19.0]	0	2	29750	39.0	[null]	S	14	[null]	0.6666666666666666	0	0
66	2	0	Brown, Mr. Thomas William Solomon	1	[42.0;80.0]	1	1	29750	39.0	[null]	S	[null]	[null]	0.6666666666666666	0	0
67	2	1	Renouf, Mrs. Peter Henry (Lillian Jefferys)	0	[25.0;31.0]	3	0	31027	21.0	[null]	S	[null]	[null]	0.5	0	0
68	2	0	Renouf, Mr. Peter Henry	1	[31.0;42.0]	1	0	31027	21.0	[null]	S	12	[null]	0.5	0	0
69	1	1	Barkworth, Mr. Algernon Henry Wilson	1	[42.0;80.0]	0	0	27042	30.0	A23	S	B	[null]	1.0	0	0
70	2	0	Downton, Mr. William James	1	[42.0;80.0]	0	0	28403	26.0	[null]	S	[null]	[null]	0.0	0	0
71	3	0	Canavan, Mr. Patrick	1	[19.0;25.0]	0	0	364858	7.75	[null]	Q	[null]	[null]	0.0	0	1
72	2	1	Brown, Miss. Amelia "Mildred"	0	[19.0;25.0]	0	0	248733	13.0	F33	S	11	[null]	1.0	0	0
73	1	0	Loring, Mr. Joseph Holland	1	[25.0;31.0]	0	0	113801	45.5	[null]	S	[null]	[null]	0.5	0	0
74	2	1	Kelly, Mrs. Florence "Fannie"	0	[42.0;80.0]	0	0	223596	13.5	[null]	S	9	[null]	0.5	0	0
75	3	1	Badman, Miss. Emily Louisa	0	[0.17;19.0]	0	0	A/4 31416	8.05	[null]	S	C	[null]	1.0	0	0
76	1	1	Sloper, Mr. William Thompson	1	[25.0;31.0]	0	0	113788	35.5	A6	S	7	[null]	1.0	0	0
77	1	1	Shutes, Miss. Elizabeth W	0	[31.0;42.0]	0	0	PC 17582	153.4625	C125	S	3	[null]	1.0	0	0
78	3	1	Abelseth, Miss. Karen Marie	0	[0.17;19.0]	0	0	348125	7.65	[null]	S	16	[null]	1.0	0	0
79	3	0	Assaf, Mr. Gerios	1	[19.0;25.0]	0	0	2692	7.225	[null]	C	[null]	[null]	0.2	1	0
80	3	0	Attalah, Mr. Sleiman	1	[25.0;31.0]	0	0	2694	7.225	[null]	C	[null]	[null]	0.2	1	0
81	3	1	Assaf Khalil, Mrs. Mariana ("Miriam")	0	[42.0;80.0]	0	0	2696	7.225	[null]	C	C	[null]	0.2	1	0
82	3	0	Caram, Mrs. Joseph (Maria Elias)	0	[null]	1	0	2689	14.4583	[null]	C	[null]	[null]	0.2	1	0
83	3	0	Caram, Mr. Joseph	1	[null]	1	0	2689	14.4583	[null]	C	[null]	[null]	0.2	1	0
84	1	1	Ostby, Miss. Helene Ragnhild	0	[19.0;25.0]	0	1	113509	61.9792	B36	C	5	[null]	0.5	1	0
85	1	0	Ostby, Mr. Engelhart Cornelius	1	[42.0;80.0]	0	1	113509	61.9792	B30	C	[null]	234	0.5	1	0
86	2	1	Abelson, Mrs. Samuel (Hannah Wizosky)	0	[25.0;31.0]	1	0	P/PP 3381	24.0	[null]	C	10	[null]	0.5	1	0
87	2	0	Abelson, Mr. Samuel	1	[25.0;31.0]	1	0	P/PP 3381	24.0	[null]	C	[null]	[null]	0.5	1	0
88	2	1	Reynaldo, Ms. Encarnacion	0	[25.0;31.0]	0	0	230434	13.0	[null]	S	9	[null]	1.0	0	0
89	3	0	Andersson, Miss. Ellis Anna Maria	0	[0.17;19.0]	4	2	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
90	3	0	Andersson, Miss. Ingeborg Constanzia	0	[0.17;19.0]	4	2	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
91	3	0	Andersson, Miss. Sigrid Elisabeth	0	[0.17;19.0]	4	2	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
92	3	0	Andersson, Mr. Anders Johan	1	[31.0;42.0]	1	5	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
93	3	0	Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren)	0	[31.0;42.0]	1	5	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
94	3	0	Andersson, Miss. Ebba Iris Alfrida	0	[0.17;19.0]	4	2	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
95	3	0	Andersson, Master. Sigvard Harald Elias	1	[0.17;19.0]	4	2	347082	31.275	[null]	S	[null]	[null]	0.0	0	0
96	1	0	Roebling, Mr. Washington Augustus II	1	[25.0;31.0]	0	0	PC 17590	50.4958	A24	S	[null]	[null]	0.0	0	0
97	1	0	Blackwell, Mr. Stephen Weart	1	[42.0;80.0]	0	0	113784	35.5	T	S	[null]	[null]	0.0	0	0
98	1	1	Spedden, Mr. Frederic Oakley	1	[42.0;80.0]	1	1	16966	134.5	E34	C	3	[null]	1.0	1	0
99	1	1	Spedden, Master. Robert Douglas	1	[0.17;19.0]	0	2	16966	134.5	E34	C	3	[null]	1.0	1	0
100	1	1	Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)	0	[31.0;42.0]	1	1	16966	134.5	E34	C	3	[null]	1.0	1	0

vastorbit offers many encoding techniques. For example, the case_when() and decode() methods allow the user to use a customized encoding on a column. The discretize() method allows you to reduce the number of categories in a column. It’s important to get familiar with all the techniques available so you can make informed decisions about which to use for a given dataset.