vDataFrame.cummin¶

In [ ]:

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

Adds a new vcolumn to the vDataFrame by computing the cumulative minimum 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 [11]:

from verticapy.datasets import load_amazon
amazon = load_amazon()
amazon = amazon.search("date BETWEEN '2005-05-01' AND '2006-02-01'")
display(amazon)

	📅 date Date	Abc state Varchar(32)	123 number Int
1	2005-05-01	ACRE	2
2	2005-05-01	ALAGOAS	0
3	2005-05-01	AMAPÁ	0
4	2005-05-01	AMAZONAS	8
5	2005-05-01	BAHIA	120
6	2005-05-01	CEARÁ	1
7	2005-05-01	DISTRITO FEDERAL	2
8	2005-05-01	ESPÍRITO SANTO	5
9	2005-05-01	GOIÁS	164
10	2005-05-01	MARANHÃO	74
11	2005-05-01	MATO GROSSO	328
12	2005-05-01	MATO GROSSO DO SUL	1641
13	2005-05-01	MINAS GERAIS	134
14	2005-05-01	PARANÁ	125
15	2005-05-01	PARAÍBA	0
16	2005-05-01	PARÁ	37
17	2005-05-01	PERNAMBUCO	1
18	2005-05-01	PIAUÍ	22
19	2005-05-01	RIO DE JANEIRO	11
20	2005-05-01	RIO GRANDE DO NORTE	3
21	2005-05-01	RIO GRANDE DO SUL	24
22	2005-05-01	RONDÔNIA	38
23	2005-05-01	RORAIMA	1
24	2005-05-01	SANTA CATARINA	21
25	2005-05-01	SERGIPE	0
26	2005-05-01	SÃO PAULO	212
27	2005-05-01	TOCANTINS	236
28	2005-06-01	ACRE	27
29	2005-06-01	ALAGOAS	0
30	2005-06-01	AMAPÁ	2
31	2005-06-01	AMAZONAS	78
32	2005-06-01	BAHIA	211
33	2005-06-01	CEARÁ	6
34	2005-06-01	DISTRITO FEDERAL	3
35	2005-06-01	ESPÍRITO SANTO	8
36	2005-06-01	GOIÁS	189
37	2005-06-01	MARANHÃO	529
38	2005-06-01	MATO GROSSO	439
39	2005-06-01	MATO GROSSO DO SUL	2294
40	2005-06-01	MINAS GERAIS	246
41	2005-06-01	PARANÁ	114
42	2005-06-01	PARAÍBA	0
43	2005-06-01	PARÁ	304
44	2005-06-01	PERNAMBUCO	3
45	2005-06-01	PIAUÍ	171
46	2005-06-01	RIO DE JANEIRO	16
47	2005-06-01	RIO GRANDE DO NORTE	0
48	2005-06-01	RIO GRANDE DO SUL	50
49	2005-06-01	RONDÔNIA	153
50	2005-06-01	RORAIMA	5
51	2005-06-01	SANTA CATARINA	58
52	2005-06-01	SERGIPE	0
53	2005-06-01	SÃO PAULO	236
54	2005-06-01	TOCANTINS	669
55	2005-07-01	ACRE	368
56	2005-07-01	ALAGOAS	0
57	2005-07-01	AMAPÁ	0
58	2005-07-01	AMAZONAS	676
59	2005-07-01	BAHIA	269
60	2005-07-01	CEARÁ	14
61	2005-07-01	DISTRITO FEDERAL	24
62	2005-07-01	ESPÍRITO SANTO	9
63	2005-07-01	GOIÁS	363
64	2005-07-01	MARANHÃO	1152
65	2005-07-01	MATO GROSSO	859
66	2005-07-01	MATO GROSSO DO SUL	4172
67	2005-07-01	MINAS GERAIS	467
68	2005-07-01	PARANÁ	155
69	2005-07-01	PARAÍBA	0
70	2005-07-01	PARÁ	4364
71	2005-07-01	PERNAMBUCO	1
72	2005-07-01	PIAUÍ	445
73	2005-07-01	RIO DE JANEIRO	20
74	2005-07-01	RIO GRANDE DO NORTE	2
75	2005-07-01	RIO GRANDE DO SUL	47
76	2005-07-01	RONDÔNIA	798
77	2005-07-01	RORAIMA	2
78	2005-07-01	SANTA CATARINA	90
79	2005-07-01	SERGIPE	0
80	2005-07-01	SÃO PAULO	284
81	2005-07-01	TOCANTINS	1082
82	2005-08-01	ACRE	4198
83	2005-08-01	ALAGOAS	2
84	2005-08-01	AMAPÁ	4
85	2005-08-01	AMAZONAS	2316
86	2005-08-01	BAHIA	948
87	2005-08-01	CEARÁ	51
88	2005-08-01	DISTRITO FEDERAL	20
89	2005-08-01	ESPÍRITO SANTO	12
90	2005-08-01	GOIÁS	639
91	2005-08-01	MARANHÃO	2239
92	2005-08-01	MATO GROSSO	4484
93	2005-08-01	MATO GROSSO DO SUL	11570
94	2005-08-01	MINAS GERAIS	1029
95	2005-08-01	PARANÁ	800
96	2005-08-01	PARAÍBA	0
97	2005-08-01	PARÁ	13055
98	2005-08-01	PERNAMBUCO	10
99	2005-08-01	PIAUÍ	943
100	2005-08-01	RIO DE JANEIRO	98

Rows: 1-100 | Columns: 3

In [12]:

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

Out[12]:

	📅 date Date	Abc state Varchar(32)	123 number Int	123 cummin_number Integer
1	2005-05-01	ACRE	2	2
2	2005-06-01	ACRE	27	2
3	2005-07-01	ACRE	368	2
4	2005-08-01	ACRE	4198	2
5	2005-09-01	ACRE	4253	2
6	2005-10-01	ACRE	547	2
7	2005-11-01	ACRE	14	2
8	2005-12-01	ACRE	2	2
9	2006-01-01	ACRE	4	2
10	2006-02-01	ACRE	0	0
11	2005-05-01	ALAGOAS	0	0
12	2005-06-01	ALAGOAS	0	0
13	2005-07-01	ALAGOAS	0	0
14	2005-08-01	ALAGOAS	2	0
15	2005-09-01	ALAGOAS	7	0
16	2005-10-01	ALAGOAS	17	0
17	2005-11-01	ALAGOAS	25	0
18	2005-12-01	ALAGOAS	16	0
19	2006-01-01	ALAGOAS	29	0
20	2006-02-01	ALAGOAS	14	0
21	2005-05-01	AMAPÁ	0	0
22	2005-06-01	AMAPÁ	2	0
23	2005-07-01	AMAPÁ	0	0
24	2005-08-01	AMAPÁ	4	0
25	2005-09-01	AMAPÁ	97	0
26	2005-10-01	AMAPÁ	335	0
27	2005-11-01	AMAPÁ	685	0
28	2005-12-01	AMAPÁ	38	0
29	2006-01-01	AMAPÁ	6	0
30	2006-02-01	AMAPÁ	2	0
31	2005-05-01	AMAZONAS	8	8
32	2005-06-01	AMAZONAS	78	8
33	2005-07-01	AMAZONAS	676	8
34	2005-08-01	AMAZONAS	2316	8
35	2005-09-01	AMAZONAS	2812	8
36	2005-10-01	AMAZONAS	545	8
37	2005-11-01	AMAZONAS	261	8
38	2005-12-01	AMAZONAS	17	8
39	2006-01-01	AMAZONAS	20	8
40	2006-02-01	AMAZONAS	10	8
41	2005-05-01	BAHIA	120	120
42	2005-06-01	BAHIA	211	120
43	2005-07-01	BAHIA	269	120
44	2005-08-01	BAHIA	948	120
45	2005-09-01	BAHIA	4066	120
46	2005-10-01	BAHIA	8405	120
47	2005-11-01	BAHIA	1595	120
48	2005-12-01	BAHIA	116	116
49	2006-01-01	BAHIA	225	116
50	2006-02-01	BAHIA	277	116
51	2005-05-01	CEARÁ	1	1
52	2005-06-01	CEARÁ	6	1
53	2005-07-01	CEARÁ	14	1
54	2005-08-01	CEARÁ	51	1
55	2005-09-01	CEARÁ	185	1
56	2005-10-01	CEARÁ	1378	1
57	2005-11-01	CEARÁ	2483	1
58	2005-12-01	CEARÁ	1360	1
59	2006-01-01	CEARÁ	211	1
60	2006-02-01	CEARÁ	23	1
61	2005-05-01	DISTRITO FEDERAL	2	2
62	2005-06-01	DISTRITO FEDERAL	3	2
63	2005-07-01	DISTRITO FEDERAL	24	2
64	2005-08-01	DISTRITO FEDERAL	20	2
65	2005-09-01	DISTRITO FEDERAL	23	2
66	2005-10-01	DISTRITO FEDERAL	18	2
67	2005-11-01	DISTRITO FEDERAL	2	2
68	2005-12-01	DISTRITO FEDERAL	0	0
69	2006-01-01	DISTRITO FEDERAL	0	0
70	2006-02-01	DISTRITO FEDERAL	0	0
71	2005-05-01	ESPÍRITO SANTO	5	5
72	2005-06-01	ESPÍRITO SANTO	8	5
73	2005-07-01	ESPÍRITO SANTO	9	5
74	2005-08-01	ESPÍRITO SANTO	12	5
75	2005-09-01	ESPÍRITO SANTO	8	5
76	2005-10-01	ESPÍRITO SANTO	62	5
77	2005-11-01	ESPÍRITO SANTO	0	0
78	2005-12-01	ESPÍRITO SANTO	5	0
79	2006-01-01	ESPÍRITO SANTO	13	0
80	2006-02-01	ESPÍRITO SANTO	45	0
81	2005-05-01	GOIÁS	164	164
82	2005-06-01	GOIÁS	189	164
83	2005-07-01	GOIÁS	363	164
84	2005-08-01	GOIÁS	639	164
85	2005-09-01	GOIÁS	1341	164
86	2005-10-01	GOIÁS	1717	164
87	2005-11-01	GOIÁS	144	144
88	2005-12-01	GOIÁS	5	5
89	2006-01-01	GOIÁS	85	5
90	2006-02-01	GOIÁS	21	5
91	2005-05-01	MARANHÃO	74	74
92	2005-06-01	MARANHÃO	529	74
93	2005-07-01	MARANHÃO	1152	74
94	2005-08-01	MARANHÃO	2239	74
95	2005-09-01	MARANHÃO	6292	74
96	2005-10-01	MARANHÃO	6472	74
97	2005-11-01	MARANHÃO	4769	74
98	2005-12-01	MARANHÃO	1660	74
99	2006-01-01	MARANHÃO	221	74
100	2006-02-01	MARANHÃO	22	22

Rows: 1-100 | Columns: 4

In [13]:

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

vDataFrame.cummin¶

Parameters¶

Returns¶

Example¶

See Also¶