Model.transform¶

In [ ]:

Model.transform()

Creates a vDataFrame of the model.

Returns¶

vDataFrame : object result of the model transformation.

Example¶

In [26]:

from verticapy.learn.preprocessing import CountVectorizer
model = CountVectorizer("cv_titanic")
model.fit("public.titanic", 
          ["name", "embarked", "cabin"])
model.transform()

	Abc token Varchar(128)	123 df Numeric(36,18)	123 cnt Int	123 rnk Int
1	[null]	0.415936952714535902	950	1
2	mr	0.057793345008756567	132	2
3	mrs	0.032837127845884413	75	3
4	miss	0.023642732049036778	54	4
5	william	0.007880910683012259	18	5
6	john	0.007443082311733800	17	6
7	master	0.003940455341506130	9	7
8	edward	0.003502626970227671	8	8
9	walter	0.003064798598949212	7	9
10	thomas	0.003064798598949212	7	9
11	mary	0.003064798598949212	7	9
12	henry	0.003064798598949212	7	9
13	george	0.003064798598949212	7	9
14	charles	0.003064798598949212	7	9
15	frederick	0.002626970227670753	6	15
16	fortune	0.002626970227670753	6	15
17	elizabeth	0.002626970227670753	6	15
18	c25	0.002626970227670753	6	15
19	c2	0.002626970227670753	6	15
20	richard	0.002189141856392294	5	20
21	james	0.002189141856392294	5	20
22	helen	0.002189141856392294	5	20
23	edith	0.002189141856392294	5	20
24	arthur	0.002189141856392294	5	20
25	y	0.001751313485113835	4	25
26	taylor	0.001751313485113835	4	25
27	smith	0.001751313485113835	4	25
28	ryerson	0.001751313485113835	4	25
29	robert	0.001751313485113835	4	25
30	margaret	0.001751313485113835	4	25
31	graham	0.001751313485113835	4	25
32	dr	0.001751313485113835	4	25
33	carter	0.001751313485113835	4	25
34	c26	0.001751313485113835	4	25
35	becker	0.001751313485113835	4	25
36	b98	0.001751313485113835	4	25
37	b6	0.001751313485113835	4	25
38	b59	0.001751313485113835	4	25
39	b5	0.001751313485113835	4	25
40	allison	0.001751313485113835	4	25
41	widener	0.001313485113835377	3	41
42	white	0.001313485113835377	3	41
43	thayer	0.001313485113835377	3	41
44	taussig	0.001313485113835377	3	41
45	spencer	0.001313485113835377	3	41
46	spedden	0.001313485113835377	3	41
47	sandstrom	0.001313485113835377	3	41
48	peter	0.001313485113835377	3	41
49	newell	0.001313485113835377	3	41
50	minahan	0.001313485113835377	3	41
51	martha	0.001313485113835377	3	41
52	marie	0.001313485113835377	3	41
53	lamsonsc101	0.001313485113835377	3	41
54	hudson	0.001313485113835377	3	41
55	herbert	0.001313485113835377	3	41
56	helene	0.001313485113835377	3	41
57	hays	0.001313485113835377	3	41
58	emil	0.001313485113835377	3	41
59	e	0.001313485113835377	3	41
60	douglas	0.001313485113835377	3	41
61	dodge	0.001313485113835377	3	41
62	crosby	0.001313485113835377	3	41
63	compton	0.001313485113835377	3	41
64	col	0.001313485113835377	3	41
65	boriecb57	0.001313485113835377	3	41
66	b60	0.001313485113835377	3	41
67	b53	0.001313485113835377	3	41
68	alice	0.001313485113835377	3	41
69	albert	0.001313485113835377	3	41
70	washingtonsa34	0.000875656742556918	2	70
71	washington	0.000875656742556918	2	70
72	warren	0.000875656742556918	2	70
73	victor	0.000875656742556918	2	70
74	tyrell	0.000875656742556918	2	70
75	thornton	0.000875656742556918	2	70
76	strom	0.000875656742556918	2	70
77	straus	0.000875656742556918	2	70
78	stengel	0.000875656742556918	2	70
79	sophia	0.000875656742556918	2	70
80	snyder	0.000875656742556918	2	70
81	silvey	0.000875656742556918	2	70
82	samuel	0.000875656742556918	2	70
83	ruth	0.000875656742556918	2	70
84	polksb96	0.000875656742556918	2	70
85	penasco	0.000875656742556918	2	70
86	pears	0.000875656742556918	2	70
87	paul	0.000875656742556918	2	70
88	ostby	0.000875656742556918	2	70
89	norman	0.000875656742556918	2	70
90	nelson	0.000875656742556918	2	70
91	navratil	0.000875656742556918	2	70
92	moor	0.000875656742556918	2	70
93	may	0.000875656742556918	2	70
94	marvin	0.000875656742556918	2	70
95	maria	0.000875656742556918	2	70
96	major	0.000875656742556918	2	70
97	lucile	0.000875656742556918	2	70
98	lucien	0.000875656742556918	2	70
99	lines	0.000875656742556918	2	70
100	lily	0.000875656742556918	2	70

Out[26]:

Rows: 1-100 of 750 | Columns: 4