vDataFrame.select

In [ ]:
vDataFrame.select(columns: list)

Returns a copy of the vDataFrame with only the selected vcolumns.

Parameters

Name Type Optional Description
columns
list
List of the vcolumns to select. It can also be a list of customized expressions.

Returns

vDataFrame : object with only the selected columns.

Example

In [13]:
from verticapy.datasets import load_titanic
titanic = load_titanic()
display(titanic)
123
pclass
Int
123
survived
Int
Abc
Varchar(164)
Abc
sex
Varchar(20)
123
age
Numeric(6,3)
123
sibsp
Int
123
parch
Int
Abc
ticket
Varchar(36)
123
fare
Numeric(10,5)
Abc
cabin
Varchar(30)
Abc
embarked
Varchar(20)
Abc
boat
Varchar(100)
123
body
Int
Abc
home.dest
Varchar(100)
110female2.012113781151.55C22 C26S[null][null]Montreal, PQ / Chesterville, ON
210male30.012113781151.55C22 C26S[null]135Montreal, PQ / Chesterville, ON
310female25.012113781151.55C22 C26S[null][null]Montreal, PQ / Chesterville, ON
410male39.0001120500.0A36S[null][null]Belfast, NI
510male71.000PC 1760949.5042[null]C[null]22Montevideo, Uruguay
610male47.010PC 17757227.525C62 C64C[null]124New York, NY
710male[null]00PC 1731825.925[null]S[null][null]New York, NY
810male24.001PC 17558247.5208B58 B60C[null][null]Montreal, PQ
910male36.0001305075.2417C6CA[null]Winnipeg, MN
1010male25.0001390526.0[null]C[null]148San Francisco, CA
1110male45.00011378435.5TS[null][null]Trenton, NJ
1210male42.00011048926.55D22S[null][null]London / Winnipeg, MB
1310male41.00011305430.5A21S[null][null]Pomeroy, WA
1410male48.000PC 1759150.4958B10C[null]208Omaha, NE
1510male[null]0011237939.6[null]C[null][null]Philadelphia, PA
1610male45.00011305026.55B38S[null][null]Washington, DC
1710male[null]0011379831.0[null]S[null][null][null]
1810male33.0006955.0B51 B53 B55S[null][null]New York, NY
1910male28.00011305947.1[null]S[null][null]Montevideo, Uruguay
2010male17.00011305947.1[null]S[null][null]Montevideo, Uruguay
2110male49.0001992426.0[null]S[null][null]Ascot, Berkshire / Rochester, NY
2210male36.0101987778.85C46S[null]172Little Onn Hall, Staffs
2310male46.010W.E.P. 573461.175E31S[null][null]Amenia, ND
2410male[null]001120510.0[null]S[null][null]Liverpool, England / Belfast
2510male27.01013508136.7792C89C[null][null]Los Angeles, CA
2610male[null]0011046552.0A14S[null][null]Stoughton, MA
2710male47.000572725.5875E58S[null][null]Victoria, BC
2810male37.011PC 1775683.1583E52C[null][null]Lakewood, NJ
2910male[null]0011379126.55[null]S[null][null]Roachdale, IN
3010male70.011WE/P 573571.0B22S[null]269Milwaukee, WI
3110male39.010PC 1759971.2833C85C[null][null]New York, NY
3210male31.010F.C. 1275052.0B71S[null][null]Montreal, PQ
3310male50.010PC 17761106.425C86C[null]62Deephaven, MN / Cedar Rapids, IA
3410male39.000PC 1758029.7A18C[null]133Philadelphia, PA
3510female36.000PC 1753131.6792A29C[null][null]New York, NY
3610male[null]00PC 17483221.7792C95S[null][null][null]
3710male30.00011305127.75C111C[null][null]New York, NY
3810male19.03219950263.0C23 C25 C27S[null][null]Winnipeg, MB
3910male64.01419950263.0C23 C25 C27S[null][null]Winnipeg, MB
4010male[null]0011377826.55D34S[null][null]Westcliff-on-Sea, Essex
4110male[null]001120580.0B102S[null][null][null]
4210male37.01011380353.1C123S[null][null]Scituate, MA
4310male47.00011132038.5E63S[null]275St Anne's-on-Sea, Lancashire
4410male24.000PC 1759379.2B86C[null][null][null]
4510male71.000PC 1775434.6542A5C[null][null]New York, NY
4610male38.001PC 17582153.4625C91S[null]147Winnipeg, MB
4710male46.000PC 1759379.2B82 B84C[null][null]New York, NY
4810male[null]0011379642.4[null]S[null][null][null]
4910male45.0103697383.475C83S[null][null]New York, NY
5010male40.0001120590.0B94S[null]110[null]
5110male55.0111274993.5B69S[null]307Montreal, PQ
5210male42.00011303842.5B11S[null][null]London / Middlesex
5310male[null]001746351.8625E46S[null][null]Brighton, MA
5410male55.00068050.0C39S[null][null]London / Birmingham
5510male42.01011378952.0[null]S[null]38New York, NY
5610male[null]00PC 1760030.6958[null]C14[null]New York, NY
5710female50.000PC 1759528.7125C49C[null][null]Paris, France New York, NY
5810male46.00069426.0[null]S[null]80Bennington, VT
5910male50.00011304426.0E60S[null][null]London
6010male32.500113503211.5C132C[null]45[null]
6110male58.0001177129.7B37C[null]258Buffalo, NY
6210male41.0101746451.8625D21S[null][null]Southington / Noank, CT
6310male[null]0011302826.55C124S[null][null]Portland, OR
6410male[null]00PC 1761227.7208[null]C[null][null]Chicago, IL
6510male29.00011350130.0D6S[null]126Springfield, MA
6610male30.00011380145.5[null]S[null][null]London / New York, NY
6710male30.00011046926.0C106S[null][null]Brockton, MA
6810male19.01011377353.1D30S[null][null]New York, NY
6910male46.0001305075.2417C6C[null]292Vancouver, BC
7010male54.0001746351.8625E46S[null]175Dorchester, MA
7110male28.010PC 1760482.1708[null]C[null][null]New York, NY
7210male65.0001350926.55E38S[null]249East Bridgewater, MA
7310male44.0201992890.0C78Q[null]230Fond du Lac, WI
7410male55.00011378730.5C30S[null][null]Montreal, PQ
7510male47.00011379642.4[null]S[null][null]Washington, DC
7610male37.001PC 1759629.7C118C[null][null]Brooklyn, NY
7710male58.00235273113.275D48C[null]122Lexington, MA
7810male64.00069326.0[null]S[null]263Isle of Wight, England
7910male65.00111350961.9792B30C[null]234Providence, RI
8010male28.500PC 1756227.7208D43C[null]189?Havana, Cuba
8110male[null]001120520.0[null]S[null][null]Belfast
8210male45.50011304328.5C124S[null]166Surbiton Hill, Surrey
8310male23.0001274993.5B24S[null][null]Montreal, PQ
8410male29.01011377666.6C2S[null][null]Isleworth, England
8510male18.010PC 17758108.9C65C[null][null]Madrid, Spain
8610male47.00011046552.0C110S[null]207Worcester, MA
8710male38.000199720.0[null]S[null][null]Rotterdam, Netherlands
8810male22.000PC 17760135.6333[null]C[null]232[null]
8910male[null]00PC 17757227.525[null]C[null][null][null]
9010male31.000PC 1759050.4958A24S[null][null]Trenton, NJ
9110male[null]0011376750.0A32S[null][null]Seattle, WA
9210male36.0001304940.125A10C[null][null]Winnipeg, MB
9310male55.010PC 1760359.4[null]C[null][null]New York, NY
9410male33.00011379026.55[null]S[null]109London
9510male61.013PC 17608262.375B57 B59 B63 B66C[null][null]Haverford, PA / Cooperstown, NY
9610male50.0101350755.9E44S[null][null]Duluth, MN
9710male56.00011379226.55[null]S[null][null]New York, NY
9810male56.0001776430.6958A7C[null][null]St James, Long Island, NY
9910male24.0101369560.0C31S[null][null]Huntington, WV
10010male[null]0011305626.0A19S[null][null]Streatham, Surrey
Rows: 1-100 | Columns: 14
In [14]:
titanic.select(["name", "survived"])
Out[14]:
Abc
Varchar(164)
123
survived
Int
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
160
170
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
360
370
380
390
400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640
650
660
670
680
690
700
710
720
730
740
750
760
770
780
790
800
810
820
830
840
850
860
870
880
890
900
910
920
930
940
950
960
970
980
990
1000
Rows: 1-100 | Columns: 2
In [15]:
# Customized expressions selection
titanic.select(["name", 
                "(CASE WHEN sex = 'male' THEN 1 ELSE 0 END) AS gender"])
Out[15]:
Abc
Varchar(164)
123
gender
Int
10
21
30
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
331
341
350
361
371
381
391
401
411
421
431
441
451
461
471
481
491
501
511
521
531
541
551
561
570
581
591
601
611
621
631
641
651
661
671
681
691
701
711
721
731
741
751
761
771
781
791
801
811
821
831
841
851
861
871
881
891
901
911
921
931
941
951
961
971
981
991
1001
Rows: 1-100 | Columns: 2

See Also

vDataFrame.search Searches the elements which matches with the input conditions.