Model.transform¶

In [ ]:

Model.transform(vdf = None,
                X: list = [])

Applies the Model on a vDataFrame.

Parameters¶

Name	Type	Optional	Description
vdf	vDataFrame	✓	input vDataFrame.
X	list	✓	List of the input vcolumns.

Returns¶

vDataFrame : object result of the model transformation.

Example¶

In [4]:

from verticapy.learn.preprocessing import Normalizer
model = Normalizer("norm_iris")
model.fit("public.iris", 
          ["PetalLengthCm", "PetalWidthCm", "SepalWidthCm"])
model.transform()

	123 PetalLengthCm Float	123 PetalWidthCm Float	123 SepalWidthCm Float
1	-1.50682152428195	-1.43962681337367	-0.12454037930146
2	-1.33679402028825	-1.30859281943796	-0.3551707113412
3	-1.39346985495282	-1.30859281943796	-0.12454037930146
4	-1.39346985495282	-1.30859281943796	0.33672028477802
5	-1.39346985495282	-1.17755882550225	-1.73895270357964
6	-1.28011818562368	-1.30859281943796	0.10608995273828
7	-1.33679402028825	-1.30859281943796	0.33672028477802
8	-1.33679402028825	-1.17755882550225	0.797980948857498
9	-1.56349735894652	-1.30859281943796	1.25924161293698
10	-1.39346985495282	-1.30859281943796	0.33672028477802
11	-1.22344235095911	-1.30859281943796	0.33672028477802
12	-1.33679402028825	-1.43962681337367	-0.12454037930146
13	-1.33679402028825	-1.17755882550225	-0.12454037930146
14	-1.22344235095911	-1.30859281943796	0.10608995273828
15	-1.22344235095911	-1.30859281943796	0.797980948857498
16	-1.05341484696541	-1.30859281943796	0.797980948857498
17	-0.25995316166148	-0.260320867952277	-1.5083223715399
18	0.420156854313322	0.656917089597693	-1.27769203950016
19	-1.33679402028825	-1.30859281943796	-0.12454037930146
20	-1.28011818562368	-1.43962681337367	0.10608995273828
21	-1.28011818562368	-1.43962681337367	0.10608995273828
22	-1.28011818562368	-1.43962681337367	0.10608995273828
23	-0.146601492332346	-0.260320867952277	-2.43084369969886
24	-0.25995316166148	-0.260320867952277	-1.73895270357964
25	-1.22344235095911	-1.30859281943796	-0.12454037930146
26	-1.45014568961738	-1.30859281943796	0.33672028477802
27	-1.33679402028825	-1.30859281943796	0.567350616817758
28	-1.28011818562368	-1.30859281943796	0.797980948857498
29	-1.22344235095911	-1.04652483156654	0.797980948857498
30	-1.39346985495282	-1.17755882550225	1.02861128089724
31	-1.22344235095911	-0.784456843695118	1.02861128089724
32	-1.33679402028825	-1.30859281943796	1.25924161293698
33	-0.42998066565518	-0.129286874016567	-1.27769203950016
34	-1.16676651629455	-0.915490837630828	0.567350616817758
35	-1.28011818562368	-1.30859281943796	0.797980948857498
36	-1.33679402028825	-1.30859281943796	1.02861128089724
37	-1.33679402028825	-1.17755882550225	1.02861128089724
38	-1.28011818562368	-1.04652483156654	1.48987194497672
39	-1.28011818562368	-1.17755882550225	1.72050227701646
40	-1.22344235095911	-1.30859281943796	1.72050227701646
41	-1.05341484696541	-1.04652483156654	1.72050227701646
42	0.0801018463259213	0.263815107790563	-0.816431375420679
43	-1.33679402028825	-1.30859281943796	0.797980948857498
44	-1.28011818562368	-1.30859281943796	1.02861128089724
45	-1.28011818562368	-1.43962681337367	2.41239327313568
46	-1.28011818562368	-1.30859281943796	1.48987194497672
47	0.420156854313322	0.394849101726273	-0.12454037930146
48	-1.28011818562368	-1.04652483156654	0.797980948857498
49	-1.16676651629455	-1.30859281943796	0.797980948857498
50	-1.28011818562368	-1.30859281943796	1.48987194497672
51	-1.39346985495282	-1.04652483156654	1.9511326090562
52	-1.16676651629455	-1.04652483156654	1.9511326090562
53	0.136777680990488	0.132781113854853	-1.73895270357964
54	-0.0332498230032121	-0.260320867952277	-1.5083223715399
55	0.0234260116613545	-0.129286874016567	-1.5083223715399
56	0.136777680990488	0.132781113854853	-1.27769203950016
57	0.363481019648756	0.00174711991914261	-1.04706170746042
58	-1.39346985495282	-1.30859281943796	1.02861128089724
59	-1.33679402028825	-1.30859281943796	2.64302360517542
60	0.0801018463259213	-0.129286874016567	-1.27769203950016
61	0.250129350319622	0.132781113854853	-0.816431375420679
62	0.646860192971589	1.05001907140482	-0.58580104338094
63	-0.089925657667779	0.132781113854853	-0.3551707113412
64	0.193453515655055	0.132781113854853	-0.12454037930146
65	0.420156854313322	0.394849101726273	-0.12454037930146
66	0.703536027636156	1.05001907140482	-1.27769203950016
67	-0.146601492332346	-0.260320867952277	-1.04706170746042
68	0.193453515655055	0.132781113854853	-0.58580104338094
69	0.420156854313322	0.132781113854853	-0.58580104338094
70	0.250129350319622	0.132781113854853	-0.3551707113412
71	0.250129350319622	0.00174711991914261	-0.12454037930146
72	-1.16676651629455	-1.17755882550225	1.72050227701646
73	-1.28011818562368	-1.04652483156654	3.1042842692549
74	0.136777680990488	0.00174711991914261	-1.04706170746042
75	0.0801018463259213	0.00174711991914261	-0.816431375420679
76	0.193453515655055	-0.260320867952277	-0.816431375420679
77	0.760211862300723	0.918985077469113	-0.816431375420679
78	0.760211862300723	0.918985077469113	-0.816431375420679
79	0.760211862300723	1.57415504714766	-0.58580104338094
80	-1.45014568961738	-1.30859281943796	2.18176294109594
81	0.250129350319622	0.394849101726273	-0.12454037930146
82	0.760211862300723	0.787951083533403	-0.12454037930146
83	0.590184358307022	0.787951083533403	0.33672028477802
84	0.136777680990488	-0.260320867952277	-1.96958303561938
85	0.703536027636156	0.394849101726273	-1.96958303561938
86	0.760211862300723	0.525883095661983	-0.816431375420679
87	0.420156854313322	0.394849101726273	-0.3551707113412
88	0.590184358307022	0.787951083533403	-0.12454037930146
89	0.420156854313322	0.525883095661983	0.797980948857498
90	1.04359103562356	0.263815107790563	-1.04706170746042
91	0.136777680990488	0.132781113854853	-0.58580104338094
92	0.533508523642456	0.00174711991914261	-0.58580104338094
93	0.533508523642456	0.263815107790563	-0.3551707113412
94	0.476832688977889	0.263815107790563	-0.12454037930146
95	0.646860192971589	0.787951083533403	-0.12454037930146
96	0.420156854313322	0.394849101726273	-1.96958303561938
97	0.590184358307022	0.787951083533403	-0.58580104338094
98	0.306805184984188	0.132781113854853	-0.3551707113412
99	0.930239366294423	1.44312105321195	0.797980948857498
100	0.363481019648756	0.132781113854853	-1.73895270357964

Out[4]:

Rows: 1-100 of 150 | Columns: 3