set_option

In [ ]:
set_option(option: str, 
           value: Union[bool, int, str] = None)

Sets options for VerticaPy.

Parameters

Name Type Optional Description
option
list
Option to use.
  • cache [bool] : If set to True, the vDataFrame saves computed aggregations in memory.
  • colors [list] : List of colors used to draw graphics.
  • color_style [str] : Style used to color the graphics, one of the following: "rgb", "sunset", "retro", "shimbg", "swamp", "med", "orchid", "magenta", "orange", "vintage", "vivid", "berries", "refreshing", "summer", "tropical", "india", "default".
  • max_columns [int] : Maximum number of columns to display. If the specified value is invalid, nothing is changed.
  • max_rows [int] : Maximum number of rows to display. If the specified value is invalid, nothing is changed.
  • mode [str] : How to display VerticaPy outputs.
    • full : Regular display mode.
    • light : Minimalist display mode.
  • overwrite_model [bool] : If set to True and you try to train a model with an existing name, the existing model is automatically overwritten.
  • percent_bar [bool] : If set to True, the percent of non-missing values is displayed.
  • print_info [bool] : If set to True, information is be printed each time the vDataFrame is modified.
  • random_state [int] : Integer used to seed the random number generation in VerticaPy.
  • sql_on [bool] : If set to True, displays all the SQL queries.
  • temp_schema [str] : Specifies the temporary schema that certain methods/functions use to create intermediate objects, if needed.
  • time_on [bool] : If set to True, displays the elapsed time for all SQL queries.
  • tqdm [bool] : If set to True, a loading bar is displayed when using iterative functions.
value
object
The new option value.

Example

In [21]:
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 [2]:
# Sets the maximum number of columns display
from verticapy import *
set_option("max_columns", 3)
titanic
Out[2]:
123
pclass
Int
...
123
body
Int
Abc
home.dest
Varchar(100)
11...[null]Montreal, PQ / Chesterville, ON
21...135Montreal, PQ / Chesterville, ON
31...[null]Montreal, PQ / Chesterville, ON
41...[null]Belfast, NI
51...22Montevideo, Uruguay
61...124New York, NY
71...[null]New York, NY
81...[null]Montreal, PQ
91...[null]Winnipeg, MN
101...148San Francisco, CA
111...[null]Trenton, NJ
121...[null]London / Winnipeg, MB
131...[null]Pomeroy, WA
141...208Omaha, NE
151...[null]Philadelphia, PA
161...[null]Washington, DC
171...[null][null]
181...[null]New York, NY
191...[null]Montevideo, Uruguay
201...[null]Montevideo, Uruguay
211...[null]Ascot, Berkshire / Rochester, NY
221...172Little Onn Hall, Staffs
231...[null]Amenia, ND
241...[null]Liverpool, England / Belfast
251...[null]Los Angeles, CA
261...[null]Stoughton, MA
271...[null]Victoria, BC
281...[null]Lakewood, NJ
291...[null]Roachdale, IN
301...269Milwaukee, WI
311...[null]New York, NY
321...[null]Montreal, PQ
331...62Deephaven, MN / Cedar Rapids, IA
341...133Philadelphia, PA
351...[null]New York, NY
361...[null][null]
371...[null]New York, NY
381...[null]Winnipeg, MB
391...[null]Winnipeg, MB
401...[null]Westcliff-on-Sea, Essex
411...[null][null]
421...[null]Scituate, MA
431...275St Anne's-on-Sea, Lancashire
441...[null][null]
451...[null]New York, NY
461...147Winnipeg, MB
471...[null]New York, NY
481...[null][null]
491...[null]New York, NY
501...110[null]
511...307Montreal, PQ
521...[null]London / Middlesex
531...[null]Brighton, MA
541...[null]London / Birmingham
551...38New York, NY
561...[null]New York, NY
571...[null]Paris, France New York, NY
581...80Bennington, VT
591...[null]London
601...45[null]
611...258Buffalo, NY
621...[null]Southington / Noank, CT
631...[null]Portland, OR
641...[null]Chicago, IL
651...126Springfield, MA
661...[null]London / New York, NY
671...[null]Brockton, MA
681...[null]New York, NY
691...292Vancouver, BC
701...175Dorchester, MA
711...[null]New York, NY
721...249East Bridgewater, MA
731...230Fond du Lac, WI
741...[null]Montreal, PQ
751...[null]Washington, DC
761...[null]Brooklyn, NY
771...122Lexington, MA
781...263Isle of Wight, England
791...234Providence, RI
801...189?Havana, Cuba
811...[null]Belfast
821...166Surbiton Hill, Surrey
831...[null]Montreal, PQ
841...[null]Isleworth, England
851...[null]Madrid, Spain
861...207Worcester, MA
871...[null]Rotterdam, Netherlands
881...232[null]
891...[null][null]
901...[null]Trenton, NJ
911...[null]Seattle, WA
921...[null]Winnipeg, MB
931...[null]New York, NY
941...109London
951...[null]Haverford, PA / Cooperstown, NY
961...[null]Duluth, MN
971...[null]New York, NY
981...[null]St James, Long Island, NY
991...[null]Huntington, WV
1001...[null]Streatham, Surrey
Rows: 1-100 | Columns: 14
In [3]:
# Sets the maximum number of rows to display
from verticapy import *
set_option("max_rows", 5)
titanic
Out[3]:
123
pclass
Int
...
123
body
Int
Abc
home.dest
Varchar(100)
11...[null]Montreal, PQ / Chesterville, ON
21...135Montreal, PQ / Chesterville, ON
31...[null]Montreal, PQ / Chesterville, ON
41...[null]Belfast, NI
51...22Montevideo, Uruguay
Rows: 1-5 | Columns: 14
In [8]:
# Light Mode
set_option("mode", "light")
titanic
Out[8]:
pclass
...
body
home.dest
11...[null]Montreal, PQ / Chesterville, ON
21...135Montreal, PQ / Chesterville, ON
31...[null]Montreal, PQ / Chesterville, ON
41...[null]Belfast, NI
51...22Montevideo, Uruguay
Rows: 1-5 | Columns: 14
In [9]:
# Full Mode
set_option("mode", "full")
titanic
Out[9]:
123
pclass
Int
...
123
body
Int
Abc
home.dest
Varchar(100)
11...[null]Montreal, PQ / Chesterville, ON
21...135Montreal, PQ / Chesterville, ON
31...[null]Montreal, PQ / Chesterville, ON
41...[null]Belfast, NI
51...22Montevideo, Uruguay
Rows: 1-5 | Columns: 14
In [10]:
# Displaying the loading bar
set_option("percent_bar", True)
titanic
Out[10]:
123
pclass
Int
100%
...
123
body
Int
9%
Abc
home.dest
Varchar(100)
57%
11...[null]Montreal, PQ / Chesterville, ON
21...135Montreal, PQ / Chesterville, ON
31...[null]Montreal, PQ / Chesterville, ON
41...[null]Belfast, NI
51...22Montevideo, Uruguay
Rows: 1-5 of 1234 | Columns: 14
In [18]:
# Displaying the query and execution time
set_option("sql_on", True)
set_option("time_on", True)
titanic.corr()

Computes the pearson Corr Matrix.

  SELECT
    CORR_MATRIX("pclass", "survived", "age", "sibsp", "parch", "fare", "body") OVER ()  
  FROM
"public"."titanic"
Execution: 0.016s
Out[18]:
...
"fare"
"body"
"pclass"...-0.561687581153705-0.0472355333131433
"survived"...0.264150360783869[null]
"age"...0.1785751641174640.0581765649177871
"sibsp"...0.152727345121592-0.116358134692659
"parch"...0.2218024035824470.0727168903614719
"fare"...1.0-0.0372842548942878
"body"...-0.03728425489428781.0
Rows: 1-7 | Columns: 8
In [8]:
# Setting a seed for the random state
set_option("random_state", 2)
In [12]:
# The random state is seeded
titanic.sample(0.1).shape()
Out[12]:
(0, 14)
In [13]:
titanic.sample(0.1).shape()
Out[13]:
(0, 14)
In [28]:
# Setting graphic colors
set_option("colors", ["blue", "red"])
titanic.hist(["pclass", "survived"])
Out[28]:
<AxesSubplot:xlabel='"pclass"', ylabel='Density'>
In [22]:
# Changing graphics color
set_option("color_style", "retro")
titanic.hist(["pclass", "survived"])
Out[22]:
<AxesSubplot:xlabel='"pclass"', ylabel='Density'>