vDataFrame.cumsum¶

In [ ]:

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

Adds a new vcolumn to the vDataFrame by computing the cumulative sum 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 [148]:

from verticapy.datasets import load_amazon
amazon = load_amazon()
display(amazon)

	Abc state Varchar(32)	📅 date Date	123 number Int
1	Acre	1998-01-01	0
2	Alagoas	1998-01-01	0
3	Amapa	1998-01-01	0
4	Amazonas	1998-01-01	0
5	Bahia	1998-01-01	0
6	Ceara	1998-01-01	0
7	Distrito Federal	1998-01-01	0
8	Espirito Santo	1998-01-01	0
9	Goias	1998-01-01	0
10	Maranhao	1998-01-01	0
11	Mato Grosso	1998-01-01	0
12	Mato Grosso	1998-01-01	0
13	Minas Gerais	1998-01-01	0
14	Para	1998-01-01	0
15	Paraiba	1998-01-01	0
16	Paraiba	1998-01-01	0
17	Pernambuco	1998-01-01	0
18	Piau	1998-01-01	0
19	Rio	1998-01-01	0
20	Rio	1998-01-01	0
21	Rio	1998-01-01	0
22	Rondonia	1998-01-01	0
23	Roraima	1998-01-01	0
24	Santa Catarina	1998-01-01	0
25	Sao Paulo	1998-01-01	0
26	Sergipe	1998-01-01	0
27	Tocantins	1998-01-01	0
28	Acre	1998-02-01	0
29	Alagoas	1998-02-01	0
30	Amapa	1998-02-01	0
31	Amazonas	1998-02-01	0
32	Bahia	1998-02-01	0
33	Ceara	1998-02-01	0
34	Distrito Federal	1998-02-01	0
35	Espirito Santo	1998-02-01	0
36	Goias	1998-02-01	0
37	Maranhao	1998-02-01	0
38	Mato Grosso	1998-02-01	0
39	Mato Grosso	1998-02-01	0
40	Minas Gerais	1998-02-01	0
41	Para	1998-02-01	0
42	Paraiba	1998-02-01	0
43	Paraiba	1998-02-01	0
44	Pernambuco	1998-02-01	0
45	Piau	1998-02-01	0
46	Rio	1998-02-01	0
47	Rio	1998-02-01	0
48	Rio	1998-02-01	0
49	Rondonia	1998-02-01	0
50	Roraima	1998-02-01	0
51	Santa Catarina	1998-02-01	0
52	Sao Paulo	1998-02-01	0
53	Sergipe	1998-02-01	0
54	Tocantins	1998-02-01	0
55	Acre	1998-03-01	0
56	Alagoas	1998-03-01	0
57	Amapa	1998-03-01	0
58	Amazonas	1998-03-01	0
59	Bahia	1998-03-01	0
60	Ceara	1998-03-01	0
61	Distrito Federal	1998-03-01	0
62	Espirito Santo	1998-03-01	0
63	Goias	1998-03-01	0
64	Maranhao	1998-03-01	0
65	Mato Grosso	1998-03-01	0
66	Mato Grosso	1998-03-01	0
67	Minas Gerais	1998-03-01	0
68	Para	1998-03-01	0
69	Paraiba	1998-03-01	0
70	Paraiba	1998-03-01	0
71	Pernambuco	1998-03-01	0
72	Piau	1998-03-01	0
73	Rio	1998-03-01	0
74	Rio	1998-03-01	0
75	Rio	1998-03-01	0
76	Rondonia	1998-03-01	0
77	Roraima	1998-03-01	0
78	Santa Catarina	1998-03-01	0
79	Sao Paulo	1998-03-01	0
80	Sergipe	1998-03-01	0
81	Tocantins	1998-03-01	0
82	Acre	1998-04-01	0
83	Alagoas	1998-04-01	0
84	Amapa	1998-04-01	0
85	Amazonas	1998-04-01	0
86	Bahia	1998-04-01	0
87	Ceara	1998-04-01	0
88	Distrito Federal	1998-04-01	0
89	Espirito Santo	1998-04-01	0
90	Goias	1998-04-01	0
91	Maranhao	1998-04-01	0
92	Mato Grosso	1998-04-01	0
93	Mato Grosso	1998-04-01	0
94	Minas Gerais	1998-04-01	0
95	Para	1998-04-01	0
96	Paraiba	1998-04-01	0
97	Paraiba	1998-04-01	0
98	Pernambuco	1998-04-01	0
99	Piau	1998-04-01	0
100	Rio	1998-04-01	0

Rows: 1-100 of 6454 | Columns: 3

In [149]:

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

	Abc state Varchar(32)	📅 date Date	123 number Int	123 cumsum_number Integer
1	Acre	1998-01-01	0	0
2	Acre	1998-02-01	0	0
3	Acre	1998-03-01	0	0
4	Acre	1998-04-01	0	0
5	Acre	1998-05-01	0	0
6	Acre	1998-06-01	3	3
7	Acre	1998-07-01	37	40
8	Acre	1998-08-01	130	170
9	Acre	1998-09-01	509	679
10	Acre	1998-10-01	44	723
11	Acre	1998-11-01	0	723
12	Acre	1998-12-01	7	730
13	Acre	1999-01-01	0	730
14	Acre	1999-02-01	0	730
15	Acre	1999-03-01	0	730
16	Acre	1999-04-01	0	730
17	Acre	1999-05-01	0	730
18	Acre	1999-06-01	0	730
19	Acre	1999-07-01	1	731
20	Acre	1999-08-01	63	794
21	Acre	1999-09-01	209	1003
22	Acre	1999-10-01	60	1063
23	Acre	1999-11-01	0	1063
24	Acre	1999-12-01	0	1063
25	Acre	2000-01-01	0	1063
26	Acre	2000-02-01	0	1063
27	Acre	2000-03-01	11	1074
28	Acre	2000-04-01	1	1075
29	Acre	2000-05-01	1	1076
30	Acre	2000-06-01	1	1077
31	Acre	2000-07-01	1	1078
32	Acre	2000-08-01	136	1214
33	Acre	2000-09-01	265	1479
34	Acre	2000-10-01	18	1497
35	Acre	2000-11-01	0	1497
36	Acre	2000-12-01	0	1497
37	Acre	2001-01-01	0	1497
38	Acre	2001-02-01	0	1497
39	Acre	2001-03-01	0	1497
40	Acre	2001-04-01	0	1497
41	Acre	2001-05-01	0	1497
42	Acre	2001-06-01	1	1498
43	Acre	2001-07-01	3	1501
44	Acre	2001-08-01	396	1897
45	Acre	2001-09-01	290	2187
46	Acre	2001-10-01	137	2324
47	Acre	2001-11-01	1	2325
48	Acre	2001-12-01	0	2325
49	Acre	2002-01-01	0	2325
50	Acre	2002-02-01	1	2326
51	Acre	2002-03-01	0	2326
52	Acre	2002-04-01	0	2326
53	Acre	2002-05-01	0	2326
54	Acre	2002-06-01	0	2326
55	Acre	2002-07-01	39	2365
56	Acre	2002-08-01	728	3093
57	Acre	2002-09-01	1949	5042
58	Acre	2002-10-01	687	5729
59	Acre	2002-11-01	86	5815
60	Acre	2002-12-01	1	5816
61	Acre	2003-01-01	10	5826
62	Acre	2003-02-01	0	5826
63	Acre	2003-03-01	0	5826
64	Acre	2003-04-01	1	5827
65	Acre	2003-05-01	6	5833
66	Acre	2003-06-01	0	5833
67	Acre	2003-07-01	168	6001
68	Acre	2003-08-01	1976	7977
69	Acre	2003-09-01	3942	11919
70	Acre	2003-10-01	740	12659
71	Acre	2003-11-01	15	12674
72	Acre	2003-12-01	1	12675
73	Acre	2004-01-01	0	12675
74	Acre	2004-02-01	3	12678
75	Acre	2004-03-01	0	12678
76	Acre	2004-04-01	2	12680
77	Acre	2004-05-01	7	12687
78	Acre	2004-06-01	5	12692
79	Acre	2004-07-01	42	12734
80	Acre	2004-08-01	645	13379
81	Acre	2004-09-01	1978	15357
82	Acre	2004-10-01	461	15818
83	Acre	2004-11-01	10	15828
84	Acre	2004-12-01	7	15835
85	Acre	2005-01-01	12	15847
86	Acre	2005-02-01	0	15847
87	Acre	2005-03-01	3	15850
88	Acre	2005-04-01	1	15851
89	Acre	2005-05-01	2	15853
90	Acre	2005-06-01	27	15880
91	Acre	2005-07-01	368	16248
92	Acre	2005-08-01	4198	20446
93	Acre	2005-09-01	4253	24699
94	Acre	2005-10-01	547	25246
95	Acre	2005-11-01	14	25260
96	Acre	2005-12-01	2	25262
97	Acre	2006-01-01	4	25266
98	Acre	2006-02-01	0	25266
99	Acre	2006-03-01	0	25266
100	Acre	2006-04-01	0	25266

Out[149]:

Rows: 1-100 of 6454 | Columns: 4

In [25]:

amazon["cumsum_number"].plot(ts = "date", by = "state")

vDataFrame.cumsum¶

Parameters¶

Returns¶

Example¶

See Also¶