vDataFrame.cumprod¶

In [ ]:

vDataFrame.cumprod(column: str, 
                   by: list = [], 
                   order_by = [],
                   name: str = "")

Adds a new vcolumn to the vDataFrame by computing the cumulative product of the input vcolumn.

Parameters¶

Name	Type	Optional	Description
column	str	❌	Input vcolumn.
by	list	✓	vcolumns used in the partition.
order_by	dict / list	✓	List of the vcolumns to use to sort the data using asc order or dictionary of all the sorting methods. For example, to sort by "column1" ASC and "column2" DESC, write {"column1": "asc", "column2": "desc"}
name	str	✓	Name of the new vcolumn. If empty, a default name will be generated.

Returns¶

vDataFrame : self

Example¶

In [146]:

from verticapy.datasets import load_amazon
amazon = load_amazon().last(ts = "date", offset = "6 months")
display(amazon)

6292 element(s) was/were filtered

	Abc state Varchar(32)	📅 date Date	123 number Int
1	Paraiba	2017-06-01	0
2	Sergipe	2017-06-01	0
3	Alagoas	2017-07-01	0
4	Sergipe	2017-07-01	0
5	Sergipe	2017-08-01	0
6	Sergipe	2017-09-01	0
7	Sergipe	2017-10-01	0
8	Distrito Federal	2017-11-01	0
9	Alagoas	2017-08-01	1
10	Sergipe	2017-11-01	1
11	Alagoas	2017-06-01	2
12	Amapa	2017-06-01	2
13	Paraiba	2017-07-01	2
14	Pernambuco	2017-07-01	2
15	Rio	2017-06-01	3
16	Amapa	2017-07-01	3
17	Espirito Santo	2017-07-01	3
18	Roraima	2017-07-01	3
19	Paraiba	2017-08-01	3
20	Espirito Santo	2017-11-01	3
21	Roraima	2017-06-01	4
22	Rio	2017-07-01	4
23	Alagoas	2017-09-01	4
24	Pernambuco	2017-06-01	5
25	Alagoas	2017-11-01	10
26	Alagoas	2017-10-01	14
27	Pernambuco	2017-08-01	20
28	Rio	2017-08-01	21
29	Ceara	2017-06-01	23
30	Distrito Federal	2017-06-01	23
31	Ceara	2017-07-01	26
32	Espirito Santo	2017-08-01	30
33	Paraiba	2017-09-01	30
34	Amapa	2017-08-01	33
35	Roraima	2017-08-01	35
36	Espirito Santo	2017-06-01	37
37	Sao Paulo	2017-11-01	37
38	Roraima	2017-09-01	43
39	Rio	2017-11-01	43
40	Distrito Federal	2017-07-01	44
41	Acre	2017-06-01	45
42	Rio	2017-09-01	48
43	Santa Catarina	2017-10-01	51
44	Espirito Santo	2017-10-01	56
45	Espirito Santo	2017-09-01	58
46	Distrito Federal	2017-10-01	66
47	Pernambuco	2017-11-01	66
48	Rio	2017-06-01	68
49	Pernambuco	2017-09-01	69
50	Santa Catarina	2017-06-01	70
51	Rio	2017-07-01	70
52	Rio	2017-10-01	85
53	Rio	2017-11-01	89
54	Amapa	2017-09-01	95
55	Acre	2017-11-01	98
56	Rio	2017-10-01	101
57	Paraiba	2017-11-01	101
58	Rio	2017-06-01	103
59	Goias	2017-11-01	117
60	Amazonas	2017-06-01	119
61	Ceara	2017-08-01	121
62	Distrito Federal	2017-09-01	122
63	Paraiba	2017-10-01	123
64	Mato Grosso	2017-11-01	133
65	Paraiba	2017-10-01	134
66	Minas Gerais	2017-11-01	136
67	Rio	2017-11-01	137
68	Distrito Federal	2017-08-01	147
69	Santa Catarina	2017-11-01	152
70	Roraima	2017-10-01	156
71	Rio	2017-08-01	164
72	Paraiba	2017-06-01	169
73	Bahia	2017-11-01	174
74	Mato Grosso	2017-06-01	175
75	Sao Paulo	2017-10-01	179
76	Rondonia	2017-06-01	192
77	Piau	2017-06-01	193
78	Sao Paulo	2017-06-01	217
79	Rio	2017-10-01	238
80	Pernambuco	2017-10-01	256
81	Paraiba	2017-11-01	292
82	Bahia	2017-07-01	300
83	Roraima	2017-11-01	327
84	Bahia	2017-06-01	329
85	Rio	2017-09-01	343
86	Minas Gerais	2017-06-01	360
87	Rondonia	2017-11-01	372
88	Tocantins	2017-11-01	434
89	Goias	2017-06-01	442
90	Piau	2017-11-01	449
91	Acre	2017-07-01	457
92	Amapa	2017-10-01	468
93	Ceara	2017-09-01	505
94	Amazonas	2017-11-01	552
95	Goias	2017-07-01	577
96	Santa Catarina	2017-07-01	591
97	Minas Gerais	2017-07-01	610
98	Santa Catarina	2017-09-01	626
99	Piau	2017-07-01	643
100	Para	2017-06-01	679

Rows: 1-100 of 162 | Columns: 3

In [147]:

amazon.cumprod(column = "number",
               by = ["state"],
               order_by = ["date"],
               name = "cumprod_number")

	Abc state Varchar(32)	📅 date Date	123 number Int	123 cumprod_number Float
1	Acre	2017-06-01	45	45.0
2	Acre	2017-07-01	457	20565.0
3	Acre	2017-08-01	1493	30703545.0
4	Acre	2017-09-01	3429	105282455805.0
5	Acre	2017-10-01	1508	158765943353940.0
6	Acre	2017-11-01	98	1.55590624486861e+16
7	Amazonas	2017-06-01	119	119.0
8	Amazonas	2017-07-01	1975	235025.0
9	Amazonas	2017-08-01	6316	1484417900.0
10	Amazonas	2017-09-01	4033	5986657390699.99
11	Amazonas	2017-10-01	1581	9464905334696700.0
12	Amazonas	2017-11-01	552	5.22462774475256e+18
13	Bahia	2017-06-01	329	329.0
14	Bahia	2017-07-01	300	98700.0000000002
15	Bahia	2017-08-01	1018	100476600.0
16	Bahia	2017-09-01	1791	179953590600.0
17	Bahia	2017-10-01	4005	720714130353002.0
18	Bahia	2017-11-01	174	1.25404258681422e+17
19	Goias	2017-06-01	442	442.0
20	Goias	2017-07-01	577	255034.0
21	Goias	2017-08-01	1492	380510727.999999
22	Goias	2017-09-01	3238	1232093737264.0
23	Goias	2017-10-01	2401	2958257063170860.0
24	Goias	2017-11-01	117	3.46116076390991e+17
25	Maranhao	2017-06-01	885	885.0
26	Maranhao	2017-07-01	2521	2231085.0
27	Maranhao	2017-08-01	3875	8645454375.00002
28	Maranhao	2017-09-01	14825	128168861109375.0
29	Maranhao	2017-10-01	6011	7.70423024128453e+17
30	Maranhao	2017-11-01	2300	1.77197295549544e+21
31	Piau	2017-06-01	193	193.0
32	Piau	2017-07-01	643	124099.0
33	Piau	2017-08-01	1572	195083628.0
34	Piau	2017-09-01	3422	667576175016.001
35	Piau	2017-10-01	3004	2005398829748070.0
36	Piau	2017-11-01	449	9.00424074556878e+17
37	Roraima	2017-06-01	4	4.0
38	Roraima	2017-07-01	3	12.0
39	Roraima	2017-08-01	35	420.0
40	Roraima	2017-09-01	43	18060.0
41	Roraima	2017-10-01	156	2817360.0
42	Roraima	2017-11-01	327	921276720.0
43	Sergipe	2017-06-01	0	0.0
44	Sergipe	2017-07-01	0	0.0
45	Sergipe	2017-08-01	0	0.0
46	Sergipe	2017-09-01	0	0.0
47	Sergipe	2017-10-01	0	0.0
48	Sergipe	2017-11-01	1	0.0
49	Tocantins	2017-06-01	1320	1320.0
50	Tocantins	2017-07-01	2397	3164040.0
51	Tocantins	2017-08-01	3536	11188045440.0
52	Tocantins	2017-09-01	10737	120126043889280.0
53	Tocantins	2017-10-01	2969	3.56654224307274e+17
54	Tocantins	2017-11-01	434	1.54787933349357e+20
55	Alagoas	2017-06-01	2	2.0
56	Alagoas	2017-07-01	0	0.0
57	Alagoas	2017-08-01	1	0.0
58	Alagoas	2017-09-01	4	0.0
59	Alagoas	2017-10-01	14	0.0
60	Alagoas	2017-11-01	10	0.0
61	Amapa	2017-06-01	2	2.0
62	Amapa	2017-07-01	3	6.0
63	Amapa	2017-08-01	33	198.0
64	Amapa	2017-09-01	95	18810.0
65	Amapa	2017-10-01	468	8803080.00000001
66	Amapa	2017-11-01	858	7553042640.0
67	Ceara	2017-06-01	23	23.0
68	Ceara	2017-07-01	26	598.0
69	Ceara	2017-08-01	121	72358.0
70	Ceara	2017-09-01	505	36540790.0
71	Ceara	2017-10-01	949	34677209710.0001
72	Ceara	2017-11-01	812	28157894284520.0
73	Distrito Federal	2017-06-01	23	23.0
74	Distrito Federal	2017-07-01	44	1012.0
75	Distrito Federal	2017-08-01	147	148764.0
76	Distrito Federal	2017-09-01	122	18149208.0
77	Distrito Federal	2017-10-01	66	1197847728.0
78	Distrito Federal	2017-11-01	0	0.0
79	Espirito Santo	2017-06-01	37	37.0
80	Espirito Santo	2017-07-01	3	111.0
81	Espirito Santo	2017-08-01	30	3330.0
82	Espirito Santo	2017-09-01	58	193140.0
83	Espirito Santo	2017-10-01	56	10815840.0
84	Espirito Santo	2017-11-01	3	32447520.0
85	Mato Grosso	2017-06-01	175	175.0
86	Mato Grosso	2017-06-01	2041	357175.0
87	Mato Grosso	2017-07-01	1050	375033750.0
88	Mato Grosso	2017-07-01	2790	1046344162500.0
89	Mato Grosso	2017-08-01	1488	1556960113800000.0
90	Mato Grosso	2017-08-01	6229	9.69830454886018e+18
91	Mato Grosso	2017-09-01	23945	2.32225902422457e+23
92	Mato Grosso	2017-09-01	2984	6.92962092828614e+26
93	Mato Grosso	2017-10-01	4800	3.32621804557735e+30
94	Mato Grosso	2017-10-01	685	2.27845936122046e+33
95	Mato Grosso	2017-11-01	133	3.03035095042324e+35
96	Mato Grosso	2017-11-01	813	2.46367532269408e+38
97	Minas Gerais	2017-06-01	360	360.0
98	Minas Gerais	2017-07-01	610	219600.0
99	Minas Gerais	2017-08-01	2142	470383199.999999
100	Minas Gerais	2017-09-01	4697	2209389890400.0

Out[147]:

Rows: 1-100 of 162 | Columns: 4

vDataFrame.cumprod¶

Parameters¶

Returns¶

Example¶

See Also¶