verticapy.create_table#

verticapy.create_table(table_name: str, dtype: dict, schema: str | None = None, temporary_table: bool = False, temporary_local_table: bool = True, genSQL: bool = False, raise_error: bool = False) → bool#

Creates a new table using the input columns’ names and data types.

Parameters#

table_name: str: The final table name.
dtype: dict: Dictionary of the user types. Each key represents a column name and each value represents its data type. Example: {“age”: “int”, “name”: “varchar”}
schema: str, optional: Schema name.
temporary_table: bool, optional: If set to True, a temporary table is created.
temporary_local_table: bool, optional: If set to True, a temporary local table is be created. The parameter ‘schema’ must be empty, otherwise this parameter is ignored.
genSQL: bool, optional: If set to True, the SQL code for creating the final table is generated but not executed.
raise_error: bool, optional: If the relation couldn’t be created, raises the entire error.

Returns#

bool: True if the table was successfully created, False otherwise.

Examples#

The create_table function offers multiple options.

Let’s import the function.

from verticapy.sql import create_table

You can generate the SQL needed to create the table.

create_table(
    table_name = "employees",
    schema = "public",
    dtype = {"name": "VARCHAR(60)", "salary": "FLOAT"},
    genSQL = True,
)

Out[2]: 'CREATE TABLE "public"."employees"("name" VARCHAR(60), "salary" FLOAT);'

Or create the table.

create_table(
    table_name = "employees",
    schema = "public",
    dtype = {"name": "VARCHAR(60)", "salary": "FLOAT"},
)

Out[3]: True

The table can be utilized as a vDataFrame.

import verticapy as vp

vp.vDataFrame("public.employees")

	Abc name Varchar(60)	123 salary Float(22)

Rows: 0 | Columns: 2