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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)
14.63.61.00.2Iris-setosa
24.73.21.30.2Iris-setosa
34.73.21.60.2Iris-setosa
44.83.01.40.1Iris-setosa
54.83.11.60.2Iris-setosa
64.83.41.90.2Iris-setosa
74.93.01.40.2Iris-setosa
84.93.11.50.1Iris-setosa
94.93.11.50.1Iris-setosa
104.93.11.50.1Iris-setosa
115.02.33.31.0Iris-versicolor
125.03.41.50.2Iris-setosa
135.13.51.40.2Iris-setosa
145.43.04.51.5Iris-versicolor
155.43.41.50.4Iris-setosa
165.43.91.30.4Iris-setosa
175.52.43.71.0Iris-versicolor
185.52.43.81.1Iris-versicolor
195.62.74.21.3Iris-versicolor
205.73.04.21.2Iris-versicolor
215.74.41.50.4Iris-setosa
225.82.85.12.4Iris-virginica
235.93.24.81.8Iris-versicolor
246.13.04.61.4Iris-versicolor
256.13.04.91.8Iris-virginica
266.32.54.91.5Iris-versicolor
276.33.34.71.6Iris-versicolor
286.33.36.02.5Iris-virginica
296.42.94.31.3Iris-versicolor
306.53.05.51.8Iris-virginica
316.53.05.82.2Iris-virginica
326.73.05.01.7Iris-versicolor
336.82.84.81.4Iris-versicolor
346.83.25.92.3Iris-virginica
357.03.24.71.4Iris-versicolor
367.13.05.92.1Iris-virginica
377.73.86.72.2Iris-virginica
384.42.91.40.2Iris-setosa
394.52.31.30.3Iris-setosa
404.83.41.60.2Iris-setosa
415.02.03.51.0Iris-versicolor
425.13.31.70.5Iris-setosa
435.13.41.50.2Iris-setosa
445.22.73.91.4Iris-versicolor
455.23.51.50.2Iris-setosa
465.24.11.50.1Iris-setosa
475.43.91.70.4Iris-setosa
485.53.51.30.2Iris-setosa
495.63.04.11.3Iris-versicolor
505.82.73.91.2Iris-versicolor
515.82.75.11.9Iris-virginica
525.82.75.11.9Iris-virginica
535.93.04.21.5Iris-versicolor
545.93.05.11.8Iris-virginica
556.02.75.11.6Iris-versicolor
566.02.94.51.5Iris-versicolor
576.12.84.71.2Iris-versicolor
586.22.84.81.8Iris-virginica
596.22.94.31.3Iris-versicolor
606.32.34.41.3Iris-versicolor
616.32.74.91.8Iris-virginica
626.43.25.32.3Iris-virginica
636.52.84.61.5Iris-versicolor
646.53.05.22.0Iris-virginica
656.53.25.12.0Iris-virginica
666.62.94.61.3Iris-versicolor
676.63.04.41.4Iris-versicolor
686.73.14.41.4Iris-versicolor
696.73.14.71.5Iris-versicolor
706.93.14.91.5Iris-versicolor
716.93.15.42.1Iris-virginica
726.93.25.72.3Iris-virginica
737.23.05.81.6Iris-virginica
747.23.26.01.8Iris-virginica
757.32.96.31.8Iris-virginica
767.72.66.92.3Iris-virginica
773.34.55.67.8Iris-setosa
783.34.55.67.8Iris-setosa
793.34.55.67.8Iris-setosa
803.34.55.67.8Iris-setosa
813.34.55.67.8Iris-setosa
823.34.55.67.8Iris-setosa
833.34.55.67.8Iris-setosa
843.34.55.67.8Iris-setosa
853.34.55.67.8Iris-setosa
863.34.55.67.8Iris-setosa
873.34.55.67.8Iris-setosa
883.34.55.67.8Iris-setosa
893.34.55.67.8Iris-setosa
903.34.55.67.8Iris-setosa
913.34.55.67.8Iris-setosa
923.34.55.67.8Iris-setosa
933.34.55.67.8Iris-setosa
943.34.55.67.8Iris-setosa
953.34.55.67.8Iris-setosa
963.34.55.67.8Iris-setosa
973.34.55.67.8Iris-setosa
983.34.55.67.8Iris-setosa
993.34.55.67.8Iris-setosa
1003.34.55.67.8Iris-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.