vDataFrame[].quantile¶

In [ ]:

vDataFrame[].quantile(x: float, 
                      approx: bool = True)

Aggregates the vcolumn using an input 'quantile'.

Parameters¶

Name	Type	Optional	Description
x	float	❌	Number representing the quantile. It must be a float between 0 and 1. For example 0.25 will return Q1.
approx	bool	✓	If set to True, the approximate quantile is returned. By setting this parameter to False, the function's performance can drastically decrease.

Returns¶

float : quantile (or approximate quantile).

Example¶

In [6]:

from verticapy.datasets import load_market
market = load_market()
display(market)

	Abc Form Varchar(32)	Abc Name Varchar(32)	123 Price Float
1	Beefsteak,	Tomatoes	3.15921213872
2	Boiled	Spinach	3.8265942203
3	Boiled	Spinach	3.83371571139
4	Canned	Artichoke	3.3174627313
5	Canned	Artichoke	3.38561047493
6	Canned	Asparagus	2.5802971683
7	Canned	Asparagus	2.7200086787
8	Canned	Beets	0.9427311158
9	Canned	Beets	1.01727546324
10	Canned	Black beans	0.945681045
11	Canned	Black beans	0.980579717755
12	Canned	Blackeye peas	0.910440813422
13	Canned	Blackeye peas	0.9310398825
14	Canned	Carrots	0.9231914223
15	Canned	Carrots	1.06049032785
16	Canned	Collard greens	0.8534190293
17	Canned	Collard greens	0.902593822572
18	Canned	Corn	0.8537281609
19	Canned	Corn	0.903905336945
20	Canned	Great northern beans	0.8733322508
21	Canned	Great northern beans	0.923915876218
22	Canned	Green beans	0.824040727
23	Canned	Green beans	0.826939082717
24	Canned	Green peas	0.9870138533
25	Canned	Green peas	1.01306918204
26	Canned	Kale	1.0686461381
27	Canned	Lima beans	1.3282126206
28	Canned	Lima beans	1.3835692655
29	Canned	Mixed Vegetables	1.10743317098
30	Canned	Mixed Vegetables	1.1153572418
31	Canned	Mixed Vegetables	1.1300407331
32	Canned	Mixed Vegetables	1.32442847693
33	Canned	Mustard greens	0.8227393425
34	Canned	Mustard greens	0.976791582056
35	Canned	Navy beans	0.9462506636
36	Canned	Navy beans	0.970423315186
37	Canned	Pinto beans	0.8013951325
38	Canned	Pinto beans	0.866189704098
39	Canned	Potatoes	0.9031082713
40	Canned	Potatoes	0.965992955381
41	Canned	Pumpkin	1.35228040942
42	Canned	Pumpkin	1.3781292617
43	Canned	Red Kidney Beans	0.861913772
44	Canned	Red Kidney Beans	0.901679904473
45	Canned	Spinach	1.1345923224
46	Canned	Spinach	1.1471519614
47	Canned	Tomatoes	0.912876896
48	Canned	Tomatoes	0.924836358886
49	Canned	Turnip greens	0.7843472032
50	Canned	Turnip greens	0.965253514158
51	Canned,	Olives	4.18683171458
52	Canned,	Olives	5.0861220216
53	Canned, packed in syrup or water	Cherries	3.51874337733
54	Canned, packed in syrup or water	Cherries	3.7259182628
55	Cooked whole	Carrots	0.741539992379
56	Cooked whole	Carrots	0.7737514155
57	Dried	Apricots	7.3309645323
58	Dried	Apricots	7.73397208999
59	Dried	Black beans	1.4038762924
60	Dried	Black beans	1.48978420263
61	Dried	Blackeye peas	1.58990085218
62	Dried	Blackeye peas	1.613022434
63	Dried	Cranberries	4.6894038279
64	Dried	Cranberries	4.78674137766
65	Dried	Dates	4.79135143479
66	Dried	Dates	5.5117046698
67	Dried	Figs	5.7483176391
68	Dried	Figs	6.1253708776
69	Dried	Great northern beans	1.53402874163
70	Dried	Great northern beans	1.5891818482
71	Dried	Lentils	1.3850358051
72	Dried	Lentils	1.5614348422
73	Dried	Lima beans	1.73065564313
74	Dried	Lima beans	2.2195369395
75	Dried	Mangoes	8.50464930168
76	Dried	Mangoes	10.1637125484
77	Dried	Navy beans	1.4787507743
78	Dried	Navy beans	1.50577973442
79	Dried	Papaya	4.56591253937
80	Dried	Papaya	5.2722599372
81	Dried	Pineapple	5.49708634567
82	Dried	Pineapple	5.8687608122
83	Dried	Pinto beans	1.085109083
84	Dried	Pinto beans	1.20380752717
85	Dried	Red Kidney Beans	1.67188888931
86	Dried	Red Kidney Beans	1.6865123081
87	Dried (Prunes)	Plums	4.03634516164
88	Dried (Prunes)	Plums	4.7339261696
89	Florets	Broccoli	2.3624557989
90	Florets	Broccoli	2.56847143403
91	Florets	Cauliflower	3.1279734694
92	Florets	Cauliflower	3.27064843515
93	Fresh	Acorn squash	1.1193087167
94	Fresh	Acorn squash	1.1722478842
95	Fresh	Apples	1.56751539145
96	Fresh	Apples	1.6155336441
97	Fresh	Apricots	3.0400719671
98	Fresh	Apricots	3.087137817
99	Fresh	Artichoke	2.21305047929
100	Fresh	Artichoke	2.3637333814

Rows: 1-100 | Columns: 3

In [7]:

# Approx quantile
market["Price"].quantile(x = 0.5, approx=False)

Out[7]:

1.566898080825

In [8]:

# Exact quantile
market["Price"].quantile(x = 0.5, approx=False)

Out[8]:

1.566898080825

vDataFrame[].quantile¶

Parameters¶

Returns¶

Example¶

See Also¶