verticapy.vDataFrame.to_db#
- vDataFrame.to_db(name: str, usecols: str | list[str] | None = None, relation_type: Literal['view', 'temporary', 'table', 'local', 'insert'] = 'view', inplace: bool = False, db_filter: str | list[str] | StringSQL | list[StringSQL] = '', nb_split: int = 0, order_by: None | str | list[str] | dict = None, segmented_by: str | list[str] | None = None) vDataFrame #
Saves the
vDataFrame
current relation to the Vertica database.Parameters#
- name: str
Name of the relation. To save the relation in a specific schema, you can write
'"my_schema"."my_relation"'
. Use double quotes ‘”’ to avoid errors due to special characters.- usecols: SQLColumns, optional
vDataColumn
to select from the finalvDataFrame
relation. If empty, allvDataColumn
are selected.- relation_type: str, optional
Type of the relation.
- view:
View.
- table:
Table.
- temporary:
Temporary Table.
- local:
Local Temporary Table.
- insert:
Inserts into an existing table.
- inplace: bool, optional
If set to
True
, thevDataFrame
is replaced with the new relation.- db_filter: SQLExpression, optional
Filter used before creating the relation in the DB. It can be a
list
of conditions or an expression. This parameter is useful for creating train and test sets on TS.- nb_split: int, optional
If this parameter is greater than 0, it adds a new column
'_verticapy_split_'
to the final relation. This column contains values in[0;nb_split - 1]
where each category represents1 / nb_split
of the entire distribution.- order_by: SQLColumns | dict, optional
List of the
vDataColumn
used to sort the data, using asc order or adictionary
of all sorting methods. For example, to sort by “column1” ASC and “column2” DESC, write:{"column1": "asc", "column2": "desc"}
- segmented_by: SQLColumns, optional
This parameter is only used when
relation_type
is ‘table’ or ‘temporary’. Otherwise, it is ignored. List of the vDataColumns used to segment the data; All the columns used will be passed to the HASH function.
Returns#
- vDataFrame
self
Examples#
We import
verticapy
:import verticapy as vp
Hint
By assigning an alias to
verticapy
, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions fromverticapy
are used as intended without interfering with functions from other libraries.For this example, we will use the Titanic dataset.
import verticapy.datasets as vpd data = vpd.load_titanic()
123pclassInteger123survivedIntegerAbcVarchar(164)AbcsexVarchar(20)123ageNumeric(8)123sibspInteger123parchIntegerAbcticketVarchar(36)123fareNumeric(12)AbccabinVarchar(30)AbcembarkedVarchar(20)AbcboatVarchar(100)123bodyIntegerAbchome.destVarchar(100)1 1 0 female 2.0 1 2 113781 151.55 C22 C26 S [null] [null] Montreal, PQ / Chesterville, ON 2 1 0 male 30.0 1 2 113781 151.55 C22 C26 S [null] 135 Montreal, PQ / Chesterville, ON 3 1 0 female 25.0 1 2 113781 151.55 C22 C26 S [null] [null] Montreal, PQ / Chesterville, ON 4 1 0 male 39.0 0 0 112050 0.0 A36 S [null] [null] Belfast, NI 5 1 0 male 71.0 0 0 PC 17609 49.5042 [null] C [null] 22 Montevideo, Uruguay 6 1 0 male 47.0 1 0 PC 17757 227.525 C62 C64 C [null] 124 New York, NY 7 1 0 male [null] 0 0 PC 17318 25.925 [null] S [null] [null] New York, NY 8 1 0 male 24.0 0 1 PC 17558 247.5208 B58 B60 C [null] [null] Montreal, PQ 9 1 0 male 36.0 0 0 13050 75.2417 C6 C A [null] Winnipeg, MN 10 1 0 male 25.0 0 0 13905 26.0 [null] C [null] 148 San Francisco, CA 11 1 0 male 45.0 0 0 113784 35.5 T S [null] [null] Trenton, NJ 12 1 0 male 42.0 0 0 110489 26.55 D22 S [null] [null] London / Winnipeg, MB 13 1 0 male 41.0 0 0 113054 30.5 A21 S [null] [null] Pomeroy, WA 14 1 0 male 48.0 0 0 PC 17591 50.4958 B10 C [null] 208 Omaha, NE 15 1 0 male [null] 0 0 112379 39.6 [null] C [null] [null] Philadelphia, PA 16 1 0 male 45.0 0 0 113050 26.55 B38 S [null] [null] Washington, DC 17 1 0 male [null] 0 0 113798 31.0 [null] S [null] [null] [null] 18 1 0 male 33.0 0 0 695 5.0 B51 B53 B55 S [null] [null] New York, NY 19 1 0 male 28.0 0 0 113059 47.1 [null] S [null] [null] Montevideo, Uruguay 20 1 0 male 17.0 0 0 113059 47.1 [null] S [null] [null] Montevideo, Uruguay 21 1 0 male 49.0 0 0 19924 26.0 [null] S [null] [null] Ascot, Berkshire / Rochester, NY 22 1 0 male 36.0 1 0 19877 78.85 C46 S [null] 172 Little Onn Hall, Staffs 23 1 0 male 46.0 1 0 W.E.P. 5734 61.175 E31 S [null] [null] Amenia, ND 24 1 0 male [null] 0 0 112051 0.0 [null] S [null] [null] Liverpool, England / Belfast 25 1 0 male 27.0 1 0 13508 136.7792 C89 C [null] [null] Los Angeles, CA 26 1 0 male [null] 0 0 110465 52.0 A14 S [null] [null] Stoughton, MA 27 1 0 male 47.0 0 0 5727 25.5875 E58 S [null] [null] Victoria, BC 28 1 0 male 37.0 1 1 PC 17756 83.1583 E52 C [null] [null] Lakewood, NJ 29 1 0 male [null] 0 0 113791 26.55 [null] S [null] [null] Roachdale, IN 30 1 0 male 70.0 1 1 WE/P 5735 71.0 B22 S [null] 269 Milwaukee, WI 31 1 0 male 39.0 1 0 PC 17599 71.2833 C85 C [null] [null] New York, NY 32 1 0 male 31.0 1 0 F.C. 12750 52.0 B71 S [null] [null] Montreal, PQ 33 1 0 male 50.0 1 0 PC 17761 106.425 C86 C [null] 62 Deephaven, MN / Cedar Rapids, IA 34 1 0 male 39.0 0 0 PC 17580 29.7 A18 C [null] 133 Philadelphia, PA 35 1 0 female 36.0 0 0 PC 17531 31.6792 A29 C [null] [null] New York, NY 36 1 0 male [null] 0 0 PC 17483 221.7792 C95 S [null] [null] [null] 37 1 0 male 30.0 0 0 113051 27.75 C111 C [null] [null] New York, NY 38 1 0 male 19.0 3 2 19950 263.0 C23 C25 C27 S [null] [null] Winnipeg, MB 39 1 0 male 64.0 1 4 19950 263.0 C23 C25 C27 S [null] [null] Winnipeg, MB 40 1 0 male [null] 0 0 113778 26.55 D34 S [null] [null] Westcliff-on-Sea, Essex 41 1 0 male [null] 0 0 112058 0.0 B102 S [null] [null] [null] 42 1 0 male 37.0 1 0 113803 53.1 C123 S [null] [null] Scituate, MA 43 1 0 male 47.0 0 0 111320 38.5 E63 S [null] 275 St Anne's-on-Sea, Lancashire 44 1 0 male 24.0 0 0 PC 17593 79.2 B86 C [null] [null] [null] 45 1 0 male 71.0 0 0 PC 17754 34.6542 A5 C [null] [null] New York, NY 46 1 0 male 38.0 0 1 PC 17582 153.4625 C91 S [null] 147 Winnipeg, MB 47 1 0 male 46.0 0 0 PC 17593 79.2 B82 B84 C [null] [null] New York, NY 48 1 0 male [null] 0 0 113796 42.4 [null] S [null] [null] [null] 49 1 0 male 45.0 1 0 36973 83.475 C83 S [null] [null] New York, NY 50 1 0 male 40.0 0 0 112059 0.0 B94 S [null] 110 [null] 51 1 0 male 55.0 1 1 12749 93.5 B69 S [null] 307 Montreal, PQ 52 1 0 male 42.0 0 0 113038 42.5 B11 S [null] [null] London / Middlesex 53 1 0 male [null] 0 0 17463 51.8625 E46 S [null] [null] Brighton, MA 54 1 0 male 55.0 0 0 680 50.0 C39 S [null] [null] London / Birmingham 55 1 0 male 42.0 1 0 113789 52.0 [null] S [null] 38 New York, NY 56 1 0 male [null] 0 0 PC 17600 30.6958 [null] C 14 [null] New York, NY 57 1 0 female 50.0 0 0 PC 17595 28.7125 C49 C [null] [null] Paris, France New York, NY 58 1 0 male 46.0 0 0 694 26.0 [null] S [null] 80 Bennington, VT 59 1 0 male 50.0 0 0 113044 26.0 E60 S [null] [null] London 60 1 0 male 32.5 0 0 113503 211.5 C132 C [null] 45 [null] 61 1 0 male 58.0 0 0 11771 29.7 B37 C [null] 258 Buffalo, NY 62 1 0 male 41.0 1 0 17464 51.8625 D21 S [null] [null] Southington / Noank, CT 63 1 0 male [null] 0 0 113028 26.55 C124 S [null] [null] Portland, OR 64 1 0 male [null] 0 0 PC 17612 27.7208 [null] C [null] [null] Chicago, IL 65 1 0 male 29.0 0 0 113501 30.0 D6 S [null] 126 Springfield, MA 66 1 0 male 30.0 0 0 113801 45.5 [null] S [null] [null] London / New York, NY 67 1 0 male 30.0 0 0 110469 26.0 C106 S [null] [null] Brockton, MA 68 1 0 male 19.0 1 0 113773 53.1 D30 S [null] [null] New York, NY 69 1 0 male 46.0 0 0 13050 75.2417 C6 C [null] 292 Vancouver, BC 70 1 0 male 54.0 0 0 17463 51.8625 E46 S [null] 175 Dorchester, MA 71 1 0 male 28.0 1 0 PC 17604 82.1708 [null] C [null] [null] New York, NY 72 1 0 male 65.0 0 0 13509 26.55 E38 S [null] 249 East Bridgewater, MA 73 1 0 male 44.0 2 0 19928 90.0 C78 Q [null] 230 Fond du Lac, WI 74 1 0 male 55.0 0 0 113787 30.5 C30 S [null] [null] Montreal, PQ 75 1 0 male 47.0 0 0 113796 42.4 [null] S [null] [null] Washington, DC 76 1 0 male 37.0 0 1 PC 17596 29.7 C118 C [null] [null] Brooklyn, NY 77 1 0 male 58.0 0 2 35273 113.275 D48 C [null] 122 Lexington, MA 78 1 0 male 64.0 0 0 693 26.0 [null] S [null] 263 Isle of Wight, England 79 1 0 male 65.0 0 1 113509 61.9792 B30 C [null] 234 Providence, RI 80 1 0 male 28.5 0 0 PC 17562 27.7208 D43 C [null] 189 ?Havana, Cuba 81 1 0 male [null] 0 0 112052 0.0 [null] S [null] [null] Belfast 82 1 0 male 45.5 0 0 113043 28.5 C124 S [null] 166 Surbiton Hill, Surrey 83 1 0 male 23.0 0 0 12749 93.5 B24 S [null] [null] Montreal, PQ 84 1 0 male 29.0 1 0 113776 66.6 C2 S [null] [null] Isleworth, England 85 1 0 male 18.0 1 0 PC 17758 108.9 C65 C [null] [null] Madrid, Spain 86 1 0 male 47.0 0 0 110465 52.0 C110 S [null] 207 Worcester, MA 87 1 0 male 38.0 0 0 19972 0.0 [null] S [null] [null] Rotterdam, Netherlands 88 1 0 male 22.0 0 0 PC 17760 135.6333 [null] C [null] 232 [null] 89 1 0 male [null] 0 0 PC 17757 227.525 [null] C [null] [null] [null] 90 1 0 male 31.0 0 0 PC 17590 50.4958 A24 S [null] [null] Trenton, NJ 91 1 0 male [null] 0 0 113767 50.0 A32 S [null] [null] Seattle, WA 92 1 0 male 36.0 0 0 13049 40.125 A10 C [null] [null] Winnipeg, MB 93 1 0 male 55.0 1 0 PC 17603 59.4 [null] C [null] [null] New York, NY 94 1 0 male 33.0 0 0 113790 26.55 [null] S [null] 109 London 95 1 0 male 61.0 1 3 PC 17608 262.375 B57 B59 B63 B66 C [null] [null] Haverford, PA / Cooperstown, NY 96 1 0 male 50.0 1 0 13507 55.9 E44 S [null] [null] Duluth, MN 97 1 0 male 56.0 0 0 113792 26.55 [null] S [null] [null] New York, NY 98 1 0 male 56.0 0 0 17764 30.6958 A7 C [null] [null] St James, Long Island, NY 99 1 0 male 24.0 1 0 13695 60.0 C31 S [null] [null] Huntington, WV 100 1 0 male [null] 0 0 113056 26.0 A19 S [null] [null] Streatham, Surrey Rows: 1-100 | Columns: 14Note
VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.
Let’s do some transformations.
data.get_dummies() data.normalize()
123pclassFloat100%... 123survivedFloat100%123embarked_QBool100%1 -1.5245848565393982 ... -0.7573073711539632 0 2 -1.5245848565393982 ... -0.7573073711539632 0 3 -1.5245848565393982 ... -0.7573073711539632 0 4 -1.5245848565393982 ... -0.7573073711539632 0 5 -1.5245848565393982 ... -0.7573073711539632 0 6 -1.5245848565393982 ... -0.7573073711539632 0 7 -1.5245848565393982 ... -0.7573073711539632 0 8 -1.5245848565393982 ... -0.7573073711539632 0 9 -1.5245848565393982 ... -0.7573073711539632 0 10 -1.5245848565393982 ... -0.7573073711539632 0 11 -1.5245848565393982 ... -0.7573073711539632 0 12 -1.5245848565393982 ... -0.7573073711539632 0 13 -1.5245848565393982 ... -0.7573073711539632 0 14 -1.5245848565393982 ... -0.7573073711539632 0 15 -1.5245848565393982 ... -0.7573073711539632 0 16 -1.5245848565393982 ... -0.7573073711539632 0 17 -1.5245848565393982 ... -0.7573073711539632 0 18 -1.5245848565393982 ... -0.7573073711539632 0 19 -1.5245848565393982 ... -0.7573073711539632 0 20 -1.5245848565393982 ... -0.7573073711539632 0 Let’s save the result in the Database.
data.to_db( name = '"public"."data_normalized"', usecols = ["fare", "sex", "survived"], relation_type = "table", ) vp.vDataFrame('"public"."data_normalized"')
123fareNumeric(46,28)99%... AbcsexVarchar(20)100%123survivedNumeric(63,30)100%1 [null] ... male -0.7573073711539632 2 -0.6451344183800711 ... male -0.7573073711539632 3 -0.6451344183800711 ... male -0.7573073711539632 4 -0.6451344183800711 ... male -0.7573073711539632 5 -0.6451344183800711 ... male -0.7573073711539632 6 -0.6451344183800711 ... male -0.7573073711539632 7 -0.6451344183800711 ... male -0.7573073711539632 8 -0.6451344183800711 ... male -0.7573073711539632 9 -0.6451344183800711 ... male -0.7573073711539632 10 -0.6451344183800711 ... male -0.7573073711539632 11 -0.6451344183800711 ... male -0.7573073711539632 12 -0.6451344183800711 ... male -0.7573073711539632 13 -0.6451344183800711 ... male -0.7573073711539632 14 -0.6451344183800711 ... male -0.7573073711539632 15 -0.6451344183800711 ... male 1.3193977310771285 16 -0.6451344183800711 ... male 1.3193977310771285 17 -0.5849058045380454 ... male 1.3193977310771285 18 -0.568917907391952 ... male -0.7573073711539632 19 -0.5501605729120098 ... male -0.7573073711539632 20 -0.5266545461586647 ... male -0.7573073711539632 Let’s add a split column in the final relation.
data.to_db( name = '"public"."data_norm_split"', usecols = ["fare", "sex", "survived"], relation_type = "table", nb_split = 3, ) vp.vDataFrame('"public"."data_norm_split"')
123fareNumeric(46,28)99%... AbcsexVarchar(20)100%123_verticapy_split_Float100%1 [null] ... male 2.0 2 -0.6451344183800711 ... male 0.0 3 -0.6451344183800711 ... male 0.0 4 -0.6451344183800711 ... male 1.0 5 -0.6451344183800711 ... male 1.0 6 -0.6451344183800711 ... male 1.0 7 -0.6451344183800711 ... male 1.0 8 -0.6451344183800711 ... male 2.0 9 -0.6451344183800711 ... male 2.0 10 -0.6451344183800711 ... male 2.0 11 -0.6451344183800711 ... male 2.0 12 -0.6451344183800711 ... male 2.0 13 -0.6451344183800711 ... male 2.0 14 -0.6451344183800711 ... male 2.0 15 -0.6451344183800711 ... male 1.0 16 -0.6451344183800711 ... male 2.0 17 -0.5849058045380454 ... male 1.0 18 -0.568917907391952 ... male 2.0 19 -0.5501605729120098 ... male 1.0 20 -0.5266545461586647 ... male 0.0 Let’s use conditions to filter data.
data.to_db( name = '"public"."data_norm_filter"', usecols = ["fare", "sex", "survived"], relation_type = "table", db_filter = "sex = 'female'", ) vp.vDataFrame('"public"."data_norm_filter"')
123fareNumeric(46,28)100%... AbcsexVarchar(20)100%123survivedNumeric(63,30)100%1 -0.5121710347247853 ... female 1.3193977310771285 2 -0.5078972116787226 ... female 1.3193977310771285 3 -0.5078174336485294 ... female 1.3193977310771285 4 -0.5078174336485294 ... female 1.3193977310771285 5 -0.5078174336485294 ... female 1.3193977310771285 6 -0.5078174336485294 ... female 1.3193977310771285 7 -0.5074223424513823 ... female 1.3193977310771285 8 -0.506789816640565 ... female -0.7573073711539632 9 -0.5027534282081724 ... female 1.3193977310771285 10 -0.5017239117232986 ... female -0.7573073711539632 11 -0.5017239117232986 ... female 1.3193977310771285 12 -0.5002195260110845 ... female -0.7573073711539632 13 -0.5002195260110845 ... female -0.7573073711539632 14 -0.49982443481393735 ... female 1.3193977310771285 15 -0.49982443481393735 ... female 1.3193977310771285 16 -0.49839982713191644 ... female 1.3193977310771285 17 -0.49824217054843944 ... female -0.7573073711539632 18 -0.49824217054843944 ... female 1.3193977310771285 19 -0.4981623925182463 ... female 1.3193977310771285 20 -0.49792495790457614 ... female -0.7573073711539632 Note
The
vDataFrame.
to_db()
method enables you to save thevDataFrame
into various types of relations, including views, temporary tables, temporary local tables, and regular tables. It also allows for inserting elements into an existing table, as well as ordering and segmenting the data using theorder_by
andsegmented_by
parameters.See also