vDataFrame.pacf¶

In [ ]:

vDataFrame.pacf(column: str,
                ts: str,
                by: list = [],
                p=12,
                unit: str = "rows",
                confidence: bool = True,
                alpha: float = 0.95,
                show: bool = True,
                ax = None,
                **style_kwds,)

Computes the partial autocorrelations of the input vcolumn.

Parameters¶

Name	Type	Optional	Description
ts	str	❌	A time series vcolumn (date or numerical type) used to order the data.
column	str	❌	Input vcolumn to use to compute the Auto Correlation Plot.
by	list	✓	vcolumns used in the partition.
p	int / list	✓	Either the maximum number of lag to consider during the computation (positive int) or a list of the different lags to include in the computation (list of positive integers).
unit	str	✓	Unit to use to compute the lags. rows : Natural lags. else : Any time unit, for example you can write 'hour' to compute the hours lags or 'day' to compute the days lags.
confidence	bool	✓	If set to True, the confidence band width is drawn.
alpha	float	✓	Significance Level. Probability to accept H0. Only used to compute the confidence band width.
show	bool	✓	If True, the plot will be drawn using Matplotlib.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [16]:

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

	📅 date Date	Abc state Varchar(32)	123 number Int
1	1998-01-01	ACRE	0
2	1998-01-01	ALAGOAS	0
3	1998-01-01	AMAPÁ	0
4	1998-01-01	AMAZONAS	0
5	1998-01-01	BAHIA	0
6	1998-01-01	CEARÁ	0
7	1998-01-01	DISTRITO FEDERAL	0
8	1998-01-01	ESPÍRITO SANTO	0
9	1998-01-01	GOIÁS	0
10	1998-01-01	MARANHÃO	0
11	1998-01-01	MATO GROSSO	0
12	1998-01-01	MATO GROSSO DO SUL	0
13	1998-01-01	MINAS GERAIS	0
14	1998-01-01	PARANÁ	0
15	1998-01-01	PARAÍBA	0
16	1998-01-01	PARÁ	0
17	1998-01-01	PERNAMBUCO	0
18	1998-01-01	PIAUÍ	0
19	1998-01-01	RIO DE JANEIRO	0
20	1998-01-01	RIO GRANDE DO NORTE	0
21	1998-01-01	RIO GRANDE DO SUL	0
22	1998-01-01	RONDÔNIA	0
23	1998-01-01	RORAIMA	0
24	1998-01-01	SANTA CATARINA	0
25	1998-01-01	SERGIPE	0
26	1998-01-01	SÃO PAULO	0
27	1998-01-01	TOCANTINS	0
28	1998-02-01	ACRE	0
29	1998-02-01	ALAGOAS	0
30	1998-02-01	AMAPÁ	0
31	1998-02-01	AMAZONAS	0
32	1998-02-01	BAHIA	0
33	1998-02-01	CEARÁ	0
34	1998-02-01	DISTRITO FEDERAL	0
35	1998-02-01	ESPÍRITO SANTO	0
36	1998-02-01	GOIÁS	0
37	1998-02-01	MARANHÃO	0
38	1998-02-01	MATO GROSSO	0
39	1998-02-01	MATO GROSSO DO SUL	0
40	1998-02-01	MINAS GERAIS	0
41	1998-02-01	PARANÁ	0
42	1998-02-01	PARAÍBA	0
43	1998-02-01	PARÁ	0
44	1998-02-01	PERNAMBUCO	0
45	1998-02-01	PIAUÍ	0
46	1998-02-01	RIO DE JANEIRO	0
47	1998-02-01	RIO GRANDE DO NORTE	0
48	1998-02-01	RIO GRANDE DO SUL	0
49	1998-02-01	RONDÔNIA	0
50	1998-02-01	RORAIMA	0
51	1998-02-01	SANTA CATARINA	0
52	1998-02-01	SERGIPE	0
53	1998-02-01	SÃO PAULO	0
54	1998-02-01	TOCANTINS	0
55	1998-03-01	ACRE	0
56	1998-03-01	ALAGOAS	0
57	1998-03-01	AMAPÁ	0
58	1998-03-01	AMAZONAS	0
59	1998-03-01	BAHIA	0
60	1998-03-01	CEARÁ	0
61	1998-03-01	DISTRITO FEDERAL	0
62	1998-03-01	ESPÍRITO SANTO	0
63	1998-03-01	GOIÁS	0
64	1998-03-01	MARANHÃO	0
65	1998-03-01	MATO GROSSO	0
66	1998-03-01	MATO GROSSO DO SUL	0
67	1998-03-01	MINAS GERAIS	0
68	1998-03-01	PARANÁ	0
69	1998-03-01	PARAÍBA	0
70	1998-03-01	PARÁ	0
71	1998-03-01	PERNAMBUCO	0
72	1998-03-01	PIAUÍ	0
73	1998-03-01	RIO DE JANEIRO	0
74	1998-03-01	RIO GRANDE DO NORTE	0
75	1998-03-01	RIO GRANDE DO SUL	0
76	1998-03-01	RONDÔNIA	0
77	1998-03-01	RORAIMA	0
78	1998-03-01	SANTA CATARINA	0
79	1998-03-01	SERGIPE	0
80	1998-03-01	SÃO PAULO	0
81	1998-03-01	TOCANTINS	0
82	1998-04-01	ACRE	0
83	1998-04-01	ALAGOAS	0
84	1998-04-01	AMAPÁ	0
85	1998-04-01	AMAZONAS	0
86	1998-04-01	BAHIA	0
87	1998-04-01	CEARÁ	0
88	1998-04-01	DISTRITO FEDERAL	0
89	1998-04-01	ESPÍRITO SANTO	0
90	1998-04-01	GOIÁS	0
91	1998-04-01	MARANHÃO	0
92	1998-04-01	MATO GROSSO	0
93	1998-04-01	MATO GROSSO DO SUL	0
94	1998-04-01	MINAS GERAIS	0
95	1998-04-01	PARANÁ	0
96	1998-04-01	PARAÍBA	0
97	1998-04-01	PARÁ	0
98	1998-04-01	PERNAMBUCO	0
99	1998-04-01	PIAUÍ	0
100	1998-04-01	RIO DE JANEIRO	0

Rows: 1-100 | Columns: 3

In [17]:

# Partial Autocorrelation Plot for each 'month' lag
# p = 48: it will compute 48 'months' lags
amazon.pacf(ts = "date", 
            column = "number", 
            p = 48,
            by = ["state"],
            unit = "month")

Out[17]:

	value	confidence
0	1.0	0.024396841824873748
1	0.751943401147962	0.03561577920085401
2	-0.330630246649617	0.03740123196527105
3	0.0660786405110271	0.03747358016816702
4	-0.0877542122241304	0.037598666691328594
5	0.00247419131637423	0.03760167863147811
6	0.00390472357284618	0.03760483583269254
7	0.0323548559720412	0.03762433446337734
8	0.113200013841434	0.03782966176322886
9	0.165558835644855	0.03826198873324272
10	0.26340262573523	0.0393309932207076
11	0.436834958860234	0.042127366586962825
12	0.399089864152307	0.04432768673622492
13	-0.230326259952651	0.0450391818983646
14	0.00885801439977179	0.0450437176851836
15	-0.0341915645824884	0.045062695353479516
16	0.0175435372519646	0.04507026978640173
17	-0.0169507658560746	0.04507757460115683
18	0.0105929097398807	0.045082562077172615
19	0.00181146555173769	0.04508610830721674
20	0.0137968171260079	0.04509213239577946
21	0.0725612619917386	0.045165303260065535
22	0.121653760111067	0.045364079292018286
23	0.255300259073092	0.0462178115199157
24	0.204544591701252	0.046759055131954184
25	-0.0941090934907759	0.046875721085035425
26	-0.0861819773986812	0.04697395461828774
27	0.0451267398590262	0.04700351160774375
28	-0.0239463442582067	0.04701446058021534
29	-0.010132812014009	0.047019424755553374
30	0.00806500756577723	0.04702391143385374
31	-0.0168096791101548	0.04703116529904154
32	0.0119351444009647	0.04703663844162875
33	0.0168362005075437	0.047043905932522966
34	0.0343143959389318	0.047062542602361104
35	0.143973534714595	0.047329037779824085
36	0.171521831586786	0.047703300609601124
37	0.0361931614851663	0.047723452049141746
38	-0.0747154298234191	0.047797150118625314
39	0.0193376766575298	0.047805559762077544
40	0.0155549311234021	0.047812317214445565
41	-0.0529035670914843	0.04785109354666675
42	0.0197355128650475	0.04785970063627545
43	-0.027224539529036	0.04787271092612343
44	-0.010862192526151	0.04787792187907807
45	-0.0388928924170341	0.047900588720196084
46	-0.0317292560269995	0.04791692297627169
47	-0.0556229532245688	0.04795935694502657
48	-0.0533621903690616	0.04799868849319452

Rows: 1-49 | Columns: 3

In [18]:

# Partial Autocorrelation Plot using only the selected lags
amazon.pacf(ts = "date", 
            column = "number", 
            by = ["state"],
            p = [1, 3, 6, 7],
            unit = "year")

Out[18]:

	value	confidence
1	0.616179405550688	0.024396841824873748
3	0.0011960416348521	0.024398767003202322
6	0.147611842714051	0.024926658917010142
7	0.0182394810621248	0.024936536450197444

Rows: 1-4 | Columns: 3

vDataFrame.acf	Computes the Correlations between a vcolumn and its lags.
vDataFrame.asfreq	Interpolates and computes a regular time interval vDataFrame.
vDataFrame.corr	Computes the Correlation Matrix of a vDataFrame.
vDataFrame.cov	Computes the Covariance Matrix of the vDataFrame.

vDataFrame.pacf¶

Parameters¶

Returns¶

Example¶

See Also¶