vDataFrame[].median¶

In [ ]:

vDataFrame[].median()

Aggregates the vcolumn using 'median'.

Returns¶

float : median

Example¶

In [32]:

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

	123 Price Float	Abc Form Varchar(32)	Abc Name Varchar(32)
1	1.1193087167	Fresh	Acorn squash
2	1.1722478842	Fresh	Acorn squash
3	1.56751539145	Fresh	Apples
4	1.6155336441	Fresh	Apples
5	0.5104657455	Frozen	Apples
6	0.537867915537	Frozen	Apples
7	0.6311325278	Ready to drink	Apples
8	0.727287713105	Ready to drink	Apples
9	7.3309645323	Dried	Apricots
10	7.73397208999	Dried	Apricots
11	3.0400719671	Fresh	Apricots
12	3.087137817	Fresh	Apricots
13	1.4742760156	Packed in juice	Apricots
14	1.64748926094	Packed in juice	Apricots
15	1.55204253208	Packed in syrup, syrup discarded	Apricots
16	1.8552908088	Packed in syrup, syrup discarded	Apricots
17	3.3174627313	Canned	Artichoke
18	3.38561047493	Canned	Artichoke
19	2.21305047929	Fresh	Artichoke
20	2.3637333814	Fresh	Artichoke
21	5.7683252728	Frozen	Artichoke
22	6.22141832776	Frozen	Artichoke
23	2.5802971683	Canned	Asparagus
24	2.7200086787	Canned	Asparagus
25	3.0756105724	Fresh	Asparagus
26	3.21349409304	Fresh	Asparagus
27	5.85731991397	Frozen	Asparagus
28	6.0657860935	Frozen	Asparagus
29	2.225544076	Fresh	Avocado
30	2.23587438158	Fresh	Avocado
31	0.5494172928	Fresh	Bananas
32	0.566983414531	Fresh	Bananas
33	0.9427311158	Canned	Beets
34	1.01727546324	Canned	Beets
35	3.41021478336	Frozen	Berries, mixed
36	3.63694066	Frozen	Berries, mixed
37	0.945681045	Canned	Black beans
38	0.980579717755	Canned	Black beans
39	1.4038762924	Dried	Black beans
40	1.48978420263	Dried	Black beans
41	5.661671336	Fresh	Blackberries
42	5.77470825035	Fresh	Blackberries
43	3.38850085426	Frozen	Blackberries
44	3.6920108696	Frozen	Blackberries
45	0.910440813422	Canned	Blackeye peas
46	0.9310398825	Canned	Blackeye peas
47	1.58990085218	Dried	Blackeye peas
48	1.613022434	Dried	Blackeye peas
49	4.3911083709	Fresh	Blueberries
50	4.73462168973	Fresh	Blueberries
51	3.471925452	Frozen	Blueberries
52	3.64325041591	Frozen	Blueberries
53	2.3624557989	Florets	Broccoli
54	2.56847143403	Florets	Broccoli
55	1.8227837671	Frozen	Broccoli
56	1.86970036313	Frozen	Broccoli
57	1.63609690647	Heads	Broccoli
58	1.9191171292	Heads	Broccoli
59	2.76355337151	Fresh	Brussels sprouts
60	2.9620929796	Fresh	Brussels sprouts
61	2.0397358015	Frozen	Brussels sprouts
62	2.13825366618	Frozen	Brussels sprouts
63	1.24473670387	Fresh	Butternut squash
64	1.2920110231	Fresh	Butternut squash
65	0.579208394258	Fresh green cabbage	Cabbage
66	0.6238712291	Fresh green cabbage	Cabbage
67	1.0241250285	Fresh red cabbage	Cabbage
68	1.05644972791	Fresh red cabbage	Cabbage
69	1.153095782	Sauerkraut	Cabbage
70	1.26463509211	Sauerkraut	Cabbage
71	0.520793672	Fresh	Cantaloupe
72	0.535873776106	Fresh	Cantaloupe
73	0.9231914223	Canned	Carrots
74	1.06049032785	Canned	Carrots
75	0.741539992379	Cooked whole	Carrots
76	0.7737514155	Cooked whole	Carrots
77	1.3965860252	Frozen	Carrots
78	1.46051272856	Frozen	Carrots
79	1.4373172338	Raw baby	Carrots
80	1.44745804876	Raw baby	Carrots
81	0.741539992379	Raw whole	Carrots
82	0.7737514155	Raw whole	Carrots
83	3.1279734694	Florets	Cauliflower
84	3.27064843515	Florets	Cauliflower
85	1.6814349408	Frozen	Cauliflower
86	1.716444729	Frozen	Cauliflower
87	1.22881085623	Heads	Cauliflower
88	1.416972077	Heads	Cauliflower
89	2.1340528999	Sticks	Celery
90	2.2049694293	Sticks	Celery
91	1.0928457916	Trimmed bunches	Celery
92	1.11365395418	Trimmed bunches	Celery
93	3.51874337733	Canned, packed in syrup or water	Cherries
94	3.7259182628	Canned, packed in syrup or water	Cherries
95	3.2130681107	Fresh	Cherries
96	3.59298975549	Fresh	Cherries
97	0.8534190293	Canned	Collard greens
98	0.902593822572	Canned	Collard greens
99	2.6294683753	Fresh	Collard greens
100	2.63083790515	Fresh	Collard greens

Rows: 1-100 of 314 | Columns: 3

In [33]:

market["price"].median()

Out[33]:

1.566898080825

vDataFrame[].median¶

Returns¶

Example¶

See Also¶