readSQL¶

In [ ]:

readSQL(query: str,
        time_on: bool = False,
        limit: int = 100)

Returns the Result of a SQL query as a tablesample object.

Parameters¶

Name	Type	Optional	Description
query	str	❌	SQL Query.
time_on	bool	✓	If set to True, displays the query elapsed time.
limit	int	✓	Number maximum of elements to display.

Returns¶

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

Example¶

In [17]:

from verticapy.utilities import *
readSQL("SELECT * FROM public.titanic")

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc home.dest Varchar(100)
1	1	0		female	2.000	1	2	113781	151.55000	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.000	1	2	113781	151.55000	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.000	1	2	113781	151.55000	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.000	0	0	112050	0.00000	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.000	0	0	PC 17609	49.50420	[null]	C	[null]	22	Montevideo, Uruguay
6	1	0		male	47.000	1	0	PC 17757	227.52500	C62 C64	C	[null]	124	New York, NY
7	1	0		male	[null]	0	0	PC 17318	25.92500	[null]	S	[null]	[null]	New York, NY
8	1	0		male	24.000	0	1	PC 17558	247.52080	B58 B60	C	[null]	[null]	Montreal, PQ
9	1	0		male	36.000	0	0	13050	75.24170	C6	C	A	[null]	Winnipeg, MN
10	1	0		male	25.000	0	0	13905	26.00000	[null]	C	[null]	148	San Francisco, CA
11	1	0		male	45.000	0	0	113784	35.50000	T	S	[null]	[null]	Trenton, NJ
12	1	0		male	42.000	0	0	110489	26.55000	D22	S	[null]	[null]	London / Winnipeg, MB
13	1	0		male	41.000	0	0	113054	30.50000	A21	S	[null]	[null]	Pomeroy, WA
14	1	0		male	48.000	0	0	PC 17591	50.49580	B10	C	[null]	208	Omaha, NE
15	1	0		male	[null]	0	0	112379	39.60000	[null]	C	[null]	[null]	Philadelphia, PA
16	1	0		male	45.000	0	0	113050	26.55000	B38	S	[null]	[null]	Washington, DC
17	1	0		male	[null]	0	0	113798	31.00000	[null]	S	[null]	[null]	[null]
18	1	0		male	33.000	0	0	695	5.00000	B51 B53 B55	S	[null]	[null]	New York, NY
19	1	0		male	28.000	0	0	113059	47.10000	[null]	S	[null]	[null]	Montevideo, Uruguay
20	1	0		male	17.000	0	0	113059	47.10000	[null]	S	[null]	[null]	Montevideo, Uruguay
21	1	0		male	49.000	0	0	19924	26.00000	[null]	S	[null]	[null]	Ascot, Berkshire / Rochester, NY
22	1	0		male	36.000	1	0	19877	78.85000	C46	S	[null]	172	Little Onn Hall, Staffs
23	1	0		male	46.000	1	0	W.E.P. 5734	61.17500	E31	S	[null]	[null]	Amenia, ND
24	1	0		male	[null]	0	0	112051	0.00000	[null]	S	[null]	[null]	Liverpool, England / Belfast
25	1	0		male	27.000	1	0	13508	136.77920	C89	C	[null]	[null]	Los Angeles, CA
26	1	0		male	[null]	0	0	110465	52.00000	A14	S	[null]	[null]	Stoughton, MA
27	1	0		male	47.000	0	0	5727	25.58750	E58	S	[null]	[null]	Victoria, BC
28	1	0		male	37.000	1	1	PC 17756	83.15830	E52	C	[null]	[null]	Lakewood, NJ
29	1	0		male	[null]	0	0	113791	26.55000	[null]	S	[null]	[null]	Roachdale, IN
30	1	0		male	70.000	1	1	WE/P 5735	71.00000	B22	S	[null]	269	Milwaukee, WI
31	1	0		male	39.000	1	0	PC 17599	71.28330	C85	C	[null]	[null]	New York, NY
32	1	0		male	31.000	1	0	F.C. 12750	52.00000	B71	S	[null]	[null]	Montreal, PQ
33	1	0		male	50.000	1	0	PC 17761	106.42500	C86	C	[null]	62	Deephaven, MN / Cedar Rapids, IA
34	1	0		male	39.000	0	0	PC 17580	29.70000	A18	C	[null]	133	Philadelphia, PA
35	1	0		female	36.000	0	0	PC 17531	31.67920	A29	C	[null]	[null]	New York, NY
36	1	0		male	[null]	0	0	PC 17483	221.77920	C95	S	[null]	[null]	[null]
37	1	0		male	30.000	0	0	113051	27.75000	C111	C	[null]	[null]	New York, NY
38	1	0		male	19.000	3	2	19950	263.00000	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
39	1	0		male	64.000	1	4	19950	263.00000	C23 C25 C27	S	[null]	[null]	Winnipeg, MB
40	1	0		male	[null]	0	0	113778	26.55000	D34	S	[null]	[null]	Westcliff-on-Sea, Essex
41	1	0		male	[null]	0	0	112058	0.00000	B102	S	[null]	[null]	[null]
42	1	0		male	37.000	1	0	113803	53.10000	C123	S	[null]	[null]	Scituate, MA
43	1	0		male	47.000	0	0	111320	38.50000	E63	S	[null]	275	St Anne's-on-Sea, Lancashire
44	1	0		male	24.000	0	0	PC 17593	79.20000	B86	C	[null]	[null]	[null]
45	1	0		male	71.000	0	0	PC 17754	34.65420	A5	C	[null]	[null]	New York, NY
46	1	0		male	38.000	0	1	PC 17582	153.46250	C91	S	[null]	147	Winnipeg, MB
47	1	0		male	46.000	0	0	PC 17593	79.20000	B82 B84	C	[null]	[null]	New York, NY
48	1	0		male	[null]	0	0	113796	42.40000	[null]	S	[null]	[null]	[null]
49	1	0		male	45.000	1	0	36973	83.47500	C83	S	[null]	[null]	New York, NY
50	1	0		male	40.000	0	0	112059	0.00000	B94	S	[null]	110	[null]
51	1	0		male	55.000	1	1	12749	93.50000	B69	S	[null]	307	Montreal, PQ
52	1	0		male	42.000	0	0	113038	42.50000	B11	S	[null]	[null]	London / Middlesex
53	1	0		male	[null]	0	0	17463	51.86250	E46	S	[null]	[null]	Brighton, MA
54	1	0		male	55.000	0	0	680	50.00000	C39	S	[null]	[null]	London / Birmingham
55	1	0		male	42.000	1	0	113789	52.00000	[null]	S	[null]	38	New York, NY
56	1	0		male	[null]	0	0	PC 17600	30.69580	[null]	C	14	[null]	New York, NY
57	1	0		female	50.000	0	0	PC 17595	28.71250	C49	C	[null]	[null]	Paris, France New York, NY
58	1	0		male	46.000	0	0	694	26.00000	[null]	S	[null]	80	Bennington, VT
59	1	0		male	50.000	0	0	113044	26.00000	E60	S	[null]	[null]	London
60	1	0		male	32.500	0	0	113503	211.50000	C132	C	[null]	45	[null]
61	1	0		male	58.000	0	0	11771	29.70000	B37	C	[null]	258	Buffalo, NY
62	1	0		male	41.000	1	0	17464	51.86250	D21	S	[null]	[null]	Southington / Noank, CT
63	1	0		male	[null]	0	0	113028	26.55000	C124	S	[null]	[null]	Portland, OR
64	1	0		male	[null]	0	0	PC 17612	27.72080	[null]	C	[null]	[null]	Chicago, IL
65	1	0		male	29.000	0	0	113501	30.00000	D6	S	[null]	126	Springfield, MA
66	1	0		male	30.000	0	0	113801	45.50000	[null]	S	[null]	[null]	London / New York, NY
67	1	0		male	30.000	0	0	110469	26.00000	C106	S	[null]	[null]	Brockton, MA
68	1	0		male	19.000	1	0	113773	53.10000	D30	S	[null]	[null]	New York, NY
69	1	0		male	46.000	0	0	13050	75.24170	C6	C	[null]	292	Vancouver, BC
70	1	0		male	54.000	0	0	17463	51.86250	E46	S	[null]	175	Dorchester, MA
71	1	0		male	28.000	1	0	PC 17604	82.17080	[null]	C	[null]	[null]	New York, NY
72	1	0		male	65.000	0	0	13509	26.55000	E38	S	[null]	249	East Bridgewater, MA
73	1	0		male	44.000	2	0	19928	90.00000	C78	Q	[null]	230	Fond du Lac, WI
74	1	0		male	55.000	0	0	113787	30.50000	C30	S	[null]	[null]	Montreal, PQ
75	1	0		male	47.000	0	0	113796	42.40000	[null]	S	[null]	[null]	Washington, DC
76	1	0		male	37.000	0	1	PC 17596	29.70000	C118	C	[null]	[null]	Brooklyn, NY
77	1	0		male	58.000	0	2	35273	113.27500	D48	C	[null]	122	Lexington, MA
78	1	0		male	64.000	0	0	693	26.00000	[null]	S	[null]	263	Isle of Wight, England
79	1	0		male	65.000	0	1	113509	61.97920	B30	C	[null]	234	Providence, RI
80	1	0		male	28.500	0	0	PC 17562	27.72080	D43	C	[null]	189	?Havana, Cuba
81	1	0		male	[null]	0	0	112052	0.00000	[null]	S	[null]	[null]	Belfast
82	1	0		male	45.500	0	0	113043	28.50000	C124	S	[null]	166	Surbiton Hill, Surrey
83	1	0		male	23.000	0	0	12749	93.50000	B24	S	[null]	[null]	Montreal, PQ
84	1	0		male	29.000	1	0	113776	66.60000	C2	S	[null]	[null]	Isleworth, England
85	1	0		male	18.000	1	0	PC 17758	108.90000	C65	C	[null]	[null]	Madrid, Spain
86	1	0		male	47.000	0	0	110465	52.00000	C110	S	[null]	207	Worcester, MA
87	1	0		male	38.000	0	0	19972	0.00000	[null]	S	[null]	[null]	Rotterdam, Netherlands
88	1	0		male	22.000	0	0	PC 17760	135.63330	[null]	C	[null]	232	[null]
89	1	0		male	[null]	0	0	PC 17757	227.52500	[null]	C	[null]	[null]	[null]
90	1	0		male	31.000	0	0	PC 17590	50.49580	A24	S	[null]	[null]	Trenton, NJ
91	1	0		male	[null]	0	0	113767	50.00000	A32	S	[null]	[null]	Seattle, WA
92	1	0		male	36.000	0	0	13049	40.12500	A10	C	[null]	[null]	Winnipeg, MB
93	1	0		male	55.000	1	0	PC 17603	59.40000	[null]	C	[null]	[null]	New York, NY
94	1	0		male	33.000	0	0	113790	26.55000	[null]	S	[null]	109	London
95	1	0		male	61.000	1	3	PC 17608	262.37500	B57 B59 B63 B66	C	[null]	[null]	Haverford, PA / Cooperstown, NY
96	1	0		male	50.000	1	0	13507	55.90000	E44	S	[null]	[null]	Duluth, MN
97	1	0		male	56.000	0	0	113792	26.55000	[null]	S	[null]	[null]	New York, NY
98	1	0		male	56.000	0	0	17764	30.69580	A7	C	[null]	[null]	St James, Long Island, NY
99	1	0		male	24.000	1	0	13695	60.00000	C31	S	[null]	[null]	Huntington, WV
100	1	0		male	[null]	0	0	113056	26.00000	A19	S	[null]	[null]	Streatham, Surrey

Out[17]:

Rows: 1-100 of 1234 | Columns: 14

In [18]:

# Time ON
readSQL("SELECT pclass, AVG(survived) FROM public.titanic GROUP BY 1", time_on = True)

Execution: 0.01s

	123 pclass Integer	123 AVG Float
1	1	0.612179487179487
2	2	0.416988416988417
3	3	0.227752639517345

Out[18]:

Rows: 3 | Columns: 2

In [19]:

# Changing the limit
readSQL("SELECT * FROM public.titanic", limit = 5)

	123 pclass Integer	123 survived Integer	Abc Varchar(164)	Abc sex Varchar(20)	123 age Numeric(6,3)	123 sibsp Integer	123 parch Integer	Abc ticket Varchar(36)	123 fare Numeric(10,5)	Abc cabin Varchar(30)	Abc embarked Varchar(20)	Abc boat Varchar(100)	123 body Integer	Abc home.dest Varchar(100)
1	1	0		female	2.000	1	2	113781	151.55000	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
2	1	0		male	30.000	1	2	113781	151.55000	C22 C26	S	[null]	135	Montreal, PQ / Chesterville, ON
3	1	0		female	25.000	1	2	113781	151.55000	C22 C26	S	[null]	[null]	Montreal, PQ / Chesterville, ON
4	1	0		male	39.000	0	0	112050	0.00000	A36	S	[null]	[null]	Belfast, NI
5	1	0		male	71.000	0	0	PC 17609	49.50420	[null]	C	[null]	22	Montevideo, Uruguay

Out[19]:

Rows: 1-5 of 1234 | Columns: 14