vDataFrame[].describe¶

In [ ]:

vDataFrame[].describe(method: str = "auto", 
                      max_cardinality: int = 6,
                      numcol: str = "")

Aggregates the vcolumn using multiple statistical aggregations: min, max, median, unique... depending on the input method.

Parameters¶

Name	Type	Optional	Description
method	str	✓	The describe method. auto : Sets the method to 'numerical' if the vcolumn is numerical, 'categorical' otherwise. categorical : Uses only categorical aggregations during the computation. cat_stats : Computes statistics of a numerical column for each vcolumn category. In this case, the parameter 'numcol' must be defined. numerical : Uses popular numerical aggregations during the computation.
max_cardinality	int	✓	Cardinality threshold to use to determine if the vcolumn will be considered as categorical.
numcol	str	✓	Numerical vcolumn to use when the parameter method is set to 'cat_stats'.

Returns¶

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

Example¶

In [39]:

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 [40]:

market["Price"].describe(method = "numerical")

	value
name	"Price"
dtype	float
unique	308.0
count	314
mean	2.07751098603612
std	1.51037749575861
min	0.3166387792
25%	1.072761874325
50%	1.566898080825
75%	2.60376599205
max	10.1637125484

Out[40]:

In [41]:

# Only the count of the 3 most occurent categories will be displayed
market["Form"].describe(method = "categorical",
                        max_cardinality = 3)

	value
name	"Form"
dtype	varchar(32)
unique	37.0
count	314.0
Fresh	90
Others	90
Frozen	57
Canned	47

Out[41]:

In [42]:

# Statistics of 'Price' partitioned by 'Form' 
market["Form"].describe(method = "cat_stats", 
                        numcol = "Price")

	123 count Integer	123 percent Numeric(36,18)	123 mean Float	123 std Float	123 min Float	123 10% Float	123 25% Float	123 50% Float	123 75% Float	123 90% Float	123 max Float
Beefsteak,	1	0.318471337579617834	3.15921213872	nan	3.15921213872	3.15921213872	3.15921213872	3.15921213872	3.15921213872	3.15921213872	3.15921213872
Boiled	2	0.636942675159235669	3.830154965845	0.0050356546423244	3.8265942203	3.827306369409	3.8283745930725	3.830154965845	3.8319353386175	3.833003562281	3.83371571139
Canned	47	14.968152866242038217	1.1649022634076	0.595638890885002	0.7843472032	0.8428270506668	0.902851046936	0.965253514158	1.12269898745	1.38030526322	3.38561047493
Canned,	2	0.636942675159235669	4.63647686809	0.635894274349174	4.18683171458	4.276760745282	4.411654291335	4.63647686809	4.861299444845	4.996192990898	5.0861220216
Canned, packed in syrup or water	2	0.636942675159235669	3.622330820065	0.146494766407389	3.51874337733	3.539460865877	3.5705370986975	3.622330820065	3.6741245414325	3.705200774253	3.7259182628
Cooked whole	2	0.636942675159235669	0.7576457039395	0.0227769157205302	0.741539992379	0.7447611346911	0.74959284815925	0.7576457039395	0.76569855971975	0.7705302731879	0.7737514155
Dried	30	9.554140127388535032	3.711283795031	2.60822802642759	1.085109083	1.40199224367	1.5408802667725	1.975096291315	5.5080500887675	7.371265288069	10.1637125484
Dried (Prunes)	2	0.636942675159235669	4.38513566562	0.493264261155465	4.03634516164	4.106103262436	4.21074041363	4.38513566562	4.55953091761	4.664168068804	4.7339261696
Florets	4	1.273885350318471338	2.83238728437	0.435863589578331	2.3624557989	2.424260489439	2.5169675252475	2.848222451715	3.1636422108375	3.227845945425	3.27064843515
Fresh	90	28.662420382165605096	2.0580829673886	1.3097843628429	0.3166387792	0.6502812872536	1.1824457705325	1.794282245305	2.6144100355475	3.21868987291	6.97581079886
Fresh green cabbage	2	0.636942675159235669	0.601539811679	0.0315813933837907	0.579208394258	0.5836746777422	0.5903741029685	0.601539811679	0.6127055203895	0.6194049456158	0.6238712291
Fresh red cabbage	2	0.636942675159235669	1.040287378205	0.0228570141526365	1.0241250285	1.027357498441	1.0322062033525	1.040287378205	1.0483685530575	1.053217257969	1.05644972791
Fresh, consumed with peel	2	0.636942675159235669	1.27575847412	0.0285287803154144	1.2555855801	1.259620158904	1.26567202711	1.27575847412	1.28584492113	1.291896789336	1.29593136814
Fresh, peeled	2	0.636942675159235669	1.27575847412	0.0285287803154144	1.2555855801	1.259620158904	1.26567202711	1.27575847412	1.28584492113	1.291896789336	1.29593136814
Frozen	57	18.152866242038216561	2.19253958499914	1.4220758283626	0.5104657455	0.6838963431082	1.47304331307	1.71622542952	2.6115889333	3.99499239038	6.22141832776
Frozen french fries	2	0.636942675159235669	1.19722809107	0.0289791581345278	1.17673673184	1.180835003686	1.186982411455	1.19722809107	1.207473770685	1.213621178454	1.2177194503
Full Heads	2	0.636942675159235669	1.660066592085	0.248946451608926	1.484034868	1.519241212817	1.5720507300425	1.660066592085	1.7480824541275	1.800891971353	1.83609831617
Grape and cherry,	2	0.636942675159235669	3.386044798935	0.137298351321261	3.28896020367	3.308377122723	3.3375025013025	3.386044798935	3.4345870965675	3.463712475147	3.4831293942
Heads	4	1.273885350318471338	1.550249242225	0.296939263295378	1.22881085623	1.285259222461	1.3699317718075	1.526534491735	1.7068519621525	1.834211062381	1.9191171292
Hearts	2	0.636942675159235669	2.653314584035	0.140980565011309	2.5536262705	2.573563933207	2.6034704272675	2.653314584035	2.7031587408025	2.733065234863	2.75300289757
Juice, ready to drink	5	1.592356687898089172	1.880649400672	1.02261447851835	0.8494909232	0.97187426266	1.15544927185	1.4452477587	2.94278510731	2.983278408304	3.0102739423
Large round,	1	0.318471337579617834	2.0066199543	nan	2.0066199543	2.0066199543	2.0066199543	2.0066199543	2.0066199543	2.0066199543	2.0066199543
Packed in juice	10	3.184713375796178344	1.592047339472	0.276362085872738	1.14342311575	1.262846143585	1.42523277745	1.568821415055	1.8328037811875	1.888501493631	1.96460660811
Packed in syrup or water	8	2.547770700636942675	1.41017352542375	0.188283136462026	1.12844843614	1.206155196971	1.2554976509675	1.432312637015	1.57530559675	1.599425325235	1.63007957295
Packed in syrup, syrup discarded	2	0.636942675159235669	1.70366667044	0.214428912851844	1.55204253208	1.582367359752	1.62785460126	1.70366667044	1.77947873962	1.824965981128	1.8552908088
Raisins	2	0.636942675159235669	3.538290033495	0.0528567963134104	3.50091463439	3.508389714211	3.5196023339425	3.538290033495	3.5569777330475	3.568190352779	3.5756654326
Raw	2	0.636942675159235669	3.830154965845	0.0050356546423244	3.8265942203	3.827306369409	3.8283745930725	3.830154965845	3.8319353386175	3.833003562281	3.83371571139
Raw baby	2	0.636942675159235669	1.44238764128	0.00717063902494663	1.4373172338	1.438331315296	1.43985243754	1.44238764128	1.44492284502	1.446443967264	1.44745804876
Raw whole	2	0.636942675159235669	0.7576457039395	0.0227769157205302	0.741539992379	0.7447611346911	0.74959284815925	0.7576457039395	0.76569855971975	0.7705302731879	0.7737514155
Ready to drink	9	2.866242038216560510	0.829661001553667	0.103203179629046	0.6311325278	0.708056676044	0.796288571647	0.8455769036	0.909642229515	0.9251872040504	0.9717956757
Roma and plum,	1	0.318471337579617834	1.2921776573	nan	1.2921776573	1.2921776573	1.2921776573	1.2921776573	1.2921776573	1.2921776573	1.2921776573
Roma,	1	0.318471337579617834	1.24341736034	nan	1.24341736034	1.24341736034	1.24341736034	1.24341736034	1.24341736034	1.24341736034	1.24341736034
Sauerkraut	2	0.636942675159235669	1.208865437055	0.0788702025476506	1.153095782	1.164249713011	1.1809806095275	1.208865437055	1.2367502645825	1.253481161099	1.26463509211
Sliced	2	0.636942675159235669	3.81600451483	0.00789112051889268	3.81042465	3.811540622966	3.813214582415	3.81600451483	3.818794447245	3.820468406694	3.82158437966
Sticks	2	0.636942675159235669	2.1695111646	0.0501455588369294	2.1340528999	2.14114455284	2.15178203225	2.1695111646	2.18724029695	2.19787777636	2.2049694293
Trimmed bunches	2	0.636942675159235669	1.10324987289	0.0147135928643669	1.0928457916	1.094926607858	1.098047832245	1.10324987289	1.108451913535	1.111573137922	1.11365395418
Whole	2	0.636942675159235669	3.480119338735	0.0963560092729786	3.41198535117	3.425612148683	3.4460523449525	3.480119338735	3.5141863325175	3.534626528787	3.5482533263

Out[42]:

vDataFrame[].describe¶

Parameters¶

Returns¶

Example¶

See Also¶