vastorbit.VastColumn.mean_encode¶
- VastColumn.mean_encode(response: str) VastFrame¶
Encodes the VastColumn using the average of the response partitioned by the different VastColumn categories.
- Parameters:
response (str) – Response VastColumn.
- Returns:
self._parent
- Return type:
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 fromvastorbitare 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()
123pclassInteger123survivedIntegerAbcnameVarchar(164)AbcsexVarchar(20)123ageDouble123sibspInteger123parchIntegerAbcticketVarchar(36)123fareDoubleAbccabinVarchar(30)AbcembarkedVarchar(20)AbcboatVarchar(100)123bodyIntegerAbchome.destVarchar(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: 14Note
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 look at the avg of survived partitioned by embarked
data.groupby(["embarked"], ["AVG(survived) AS survived"])
AbcembarkedVarchar(20)123survivedDouble1 Q 0.35772357723577236 2 S 0.33260393873085337 3 [null] 1.0 4 C 0.5555555555555556 Rows: 1-4 | Columns: 2Let’s apply mean encoding which will replace each category of “embarked” VastColumn by the average of the response
data["embarked"].mean_encode(response = "survived") data["embarked"]
123embarkedDouble1 0.35772357723577236 2 0.35772357723577236 3 0.35772357723577236 4 0.35772357723577236 5 0.35772357723577236 6 0.35772357723577236 7 0.35772357723577236 8 0.35772357723577236 9 0.35772357723577236 10 0.35772357723577236 11 0.35772357723577236 12 0.35772357723577236 13 0.35772357723577236 14 0.35772357723577236 15 0.35772357723577236 16 0.35772357723577236 17 0.35772357723577236 18 0.35772357723577236 19 0.35772357723577236 20 0.35772357723577236 21 0.35772357723577236 22 0.35772357723577236 23 0.35772357723577236 24 0.35772357723577236 25 0.35772357723577236 26 0.35772357723577236 27 0.35772357723577236 28 0.35772357723577236 29 0.35772357723577236 30 0.35772357723577236 31 0.35772357723577236 32 0.35772357723577236 33 0.35772357723577236 34 0.35772357723577236 35 0.35772357723577236 36 0.35772357723577236 37 0.35772357723577236 38 0.35772357723577236 39 0.35772357723577236 40 0.35772357723577236 41 0.35772357723577236 42 0.35772357723577236 43 0.35772357723577236 44 0.35772357723577236 45 0.35772357723577236 46 0.35772357723577236 47 0.35772357723577236 48 0.35772357723577236 49 0.35772357723577236 50 0.35772357723577236 51 0.35772357723577236 52 0.35772357723577236 53 0.35772357723577236 54 0.35772357723577236 55 0.35772357723577236 56 0.35772357723577236 57 0.35772357723577236 58 0.35772357723577236 59 0.35772357723577236 60 0.35772357723577236 61 0.35772357723577236 62 0.35772357723577236 63 0.35772357723577236 64 0.35772357723577236 65 0.35772357723577236 66 0.35772357723577236 67 0.35772357723577236 68 0.35772357723577236 69 0.35772357723577236 70 0.35772357723577236 71 0.35772357723577236 72 0.35772357723577236 73 0.35772357723577236 74 0.35772357723577236 75 0.35772357723577236 76 0.35772357723577236 77 0.35772357723577236 78 0.35772357723577236 79 0.35772357723577236 80 0.35772357723577236 81 0.35772357723577236 82 0.35772357723577236 83 0.35772357723577236 84 0.35772357723577236 85 0.35772357723577236 86 0.35772357723577236 87 0.35772357723577236 88 0.35772357723577236 89 0.35772357723577236 90 0.35772357723577236 91 0.35772357723577236 92 0.35772357723577236 93 0.35772357723577236 94 0.35772357723577236 95 0.35772357723577236 96 0.35772357723577236 97 0.35772357723577236 98 0.35772357723577236 99 0.35772357723577236 100 0.35772357723577236 Rows: 1-100 of 1309 | Column: embarked | Type: doubleSee also
VastFrame.decode(): User Defined Encoding.VastFrame.label_encode(): Label Encoding.VastFrame.discretize(): Discretization.VastFrame.one_hot_encode(): One Hot Encoding.