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verticapy.insert_into

verticapy.insert_into(table_name: str, data: list, schema: str | None = None, column_names: list | None = None, copy: bool = True, genSQL: bool = False) int | str

Inserts the dataset into an existing Vertica table.

Parameters

table_name: str

Name of the table to insert into.

data: list

The data to ingest.

schema: str, optional

Schema name.

column_names: list, optional

Name of the column(s) to insert into.

copy: bool, optional

If set to True, the batch insert is converted to a COPY statement with prepared statements. Otherwise, the INSERTs are performed sequentially.

genSQL: bool, optional

If set to True, the SQL code that would be used to insert the data is generated, but not executed.

Returns

int

The number of rows ingested.

Examples

For this example, we will use the Iris dataset.

import verticapy.datasets as vpd

data = vpd.load_iris()
123
SepalLengthCm
Numeric(7)
123
SepalWidthCm
Numeric(7)
123
PetalLengthCm
Numeric(7)
123
PetalWidthCm
Numeric(7)
Abc
Species
Varchar(30)
13.34.55.67.8Iris-setosa
23.34.55.67.8Iris-setosa
33.34.55.67.8Iris-setosa
43.34.55.67.8Iris-setosa
53.34.55.67.8Iris-setosa
63.34.55.67.8Iris-setosa
73.34.55.67.8Iris-setosa
83.34.55.67.8Iris-setosa
93.34.55.67.8Iris-setosa
103.34.55.67.8Iris-setosa
113.34.55.67.8Iris-setosa
123.34.55.67.8Iris-setosa
133.34.55.67.8Iris-setosa
143.34.55.67.8Iris-setosa
153.34.55.67.8Iris-setosa
163.34.55.67.8Iris-setosa
173.34.55.67.8Iris-setosa
183.34.55.67.8Iris-setosa
193.34.55.67.8Iris-setosa
203.34.55.67.8Iris-setosa
213.34.55.67.8Iris-setosa
223.34.55.67.8Iris-setosa
233.34.55.67.8Iris-setosa
243.34.55.67.8Iris-setosa
253.34.55.67.8Iris-setosa
263.34.55.67.8Iris-setosa
274.33.01.10.1Iris-setosa
284.34.79.61.8Iris-virginica
294.34.79.61.8Iris-virginica
304.34.79.61.8Iris-virginica
314.34.79.61.8Iris-virginica
324.34.79.61.8Iris-virginica
334.34.79.61.8Iris-virginica
344.34.79.61.8Iris-virginica
354.34.79.61.8Iris-virginica
364.34.79.61.8Iris-virginica
374.34.79.61.8Iris-virginica
384.34.79.61.8Iris-virginica
394.34.79.61.8Iris-virginica
404.34.79.61.8Iris-virginica
414.34.79.61.8Iris-virginica
424.34.79.61.8Iris-virginica
434.34.79.61.8Iris-virginica
444.34.79.61.8Iris-virginica
454.34.79.61.8Iris-virginica
464.34.79.61.8Iris-virginica
474.34.79.61.8Iris-virginica
484.34.79.61.8Iris-virginica
494.34.79.61.8Iris-virginica
504.34.79.61.8Iris-virginica
514.34.79.61.8Iris-virginica
524.34.79.61.8Iris-virginica
534.34.79.61.8Iris-virginica
544.42.91.40.2Iris-setosa
554.43.01.30.2Iris-setosa
564.43.21.30.2Iris-setosa
574.52.31.30.3Iris-setosa
584.63.11.50.2Iris-setosa
594.63.21.40.2Iris-setosa
604.63.41.40.3Iris-setosa
614.63.61.00.2Iris-setosa
624.73.21.30.2Iris-setosa
634.73.21.60.2Iris-setosa
644.83.01.40.1Iris-setosa
654.83.01.40.3Iris-setosa
664.83.11.60.2Iris-setosa
674.83.41.60.2Iris-setosa
684.83.41.90.2Iris-setosa
694.92.43.31.0Iris-versicolor
704.92.54.51.7Iris-virginica
714.93.01.40.2Iris-setosa
724.93.11.50.1Iris-setosa
734.93.11.50.1Iris-setosa
744.93.11.50.1Iris-setosa
755.02.03.51.0Iris-versicolor
765.02.33.31.0Iris-versicolor
775.03.01.60.2Iris-setosa
785.03.21.20.2Iris-setosa
795.03.31.40.2Iris-setosa
805.03.41.50.2Iris-setosa
815.03.41.60.4Iris-setosa
825.03.51.30.3Iris-setosa
835.03.51.60.6Iris-setosa
845.03.61.40.2Iris-setosa
855.12.53.01.1Iris-versicolor
865.13.31.70.5Iris-setosa
875.13.41.50.2Iris-setosa
885.13.51.40.2Iris-setosa
895.13.51.40.3Iris-setosa
905.13.71.50.4Iris-setosa
915.13.81.50.3Iris-setosa
925.13.81.60.2Iris-setosa
935.13.81.90.4Iris-setosa
945.22.73.91.4Iris-versicolor
955.23.41.40.2Iris-setosa
965.23.51.50.2Iris-setosa
975.24.11.50.1Iris-setosa
985.33.71.50.2Iris-setosa
995.43.04.51.5Iris-versicolor
1005.43.41.50.4Iris-setosa
Rows: 1-100 | Columns: 5

Note

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.

We import the insert_into function and insert different element to the iris table.

from verticapy.sql import insert_into

You can insert all the elements at once with a single COPY statement by using the following command.

insert_into(
    table_name = "iris",
    schema = "public",
    data = [
        [3.3, 4.5, 5.6, 7.8, "Iris-setosa"],
        [4.3, 4.7, 9.6, 1.8, "Iris-virginica"],
    ],
)

Out[2]: 2

If you want to use multiple inserts to avoid a general failure and insert what you can, use the following approach.

insert_into(
    table_name = "iris",
    schema = "public",
    data = [
        [3.3, 4.5, 5.6, 7.8, "Iris-setosa"],
        [4.3, 4.7, 9.6, 1.8, "Iris-virginica"],
    ],
    copy = False,
)

Out[3]: 2

If you want to examine the generated SQL without executing it, use the following command.

insert_into(
    table_name = "iris",
    schema = "public",
    data = [
        [3.3, 4.5, 5.6, 7.8, "Iris-setosa"],
        [4.3, 4.7, 9.6, 1.8, "Iris-virginica"],
    ],
    genSQL = True,
)

Out[4]: 
['INSERT INTO "public"."iris" ("SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm", "Species") VALUES (\'3.3\',\'4.5\',\'5.6\',\'7.8\',\'Iris-setosa\')',
 'INSERT INTO "public"."iris" ("SepalLengthCm", "SepalWidthCm", "PetalLengthCm", "PetalWidthCm", "Species") VALUES (\'4.3\',\'4.7\',\'9.6\',\'1.8\',\'Iris-virginica\')']

Note

Set copy to False for multiple inserts.

See also

read_csv() : Ingests a CSV file using flex tables.
read_json() : Ingests a JSON file using flex tables.