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()
123SepalLengthCmNumeric(7)123SepalWidthCmNumeric(7)123PetalLengthCmNumeric(7)123PetalWidthCmNumeric(7)AbcSpeciesVarchar(30)1 3.3 4.5 5.6 7.8 Iris-setosa 2 3.3 4.5 5.6 7.8 Iris-setosa 3 3.3 4.5 5.6 7.8 Iris-setosa 4 3.3 4.5 5.6 7.8 Iris-setosa 5 3.3 4.5 5.6 7.8 Iris-setosa 6 3.3 4.5 5.6 7.8 Iris-setosa 7 3.3 4.5 5.6 7.8 Iris-setosa 8 3.3 4.5 5.6 7.8 Iris-setosa 9 3.3 4.5 5.6 7.8 Iris-setosa 10 3.3 4.5 5.6 7.8 Iris-setosa 11 3.3 4.5 5.6 7.8 Iris-setosa 12 3.3 4.5 5.6 7.8 Iris-setosa 13 3.3 4.5 5.6 7.8 Iris-setosa 14 3.3 4.5 5.6 7.8 Iris-setosa 15 3.3 4.5 5.6 7.8 Iris-setosa 16 3.3 4.5 5.6 7.8 Iris-setosa 17 3.3 4.5 5.6 7.8 Iris-setosa 18 3.3 4.5 5.6 7.8 Iris-setosa 19 3.3 4.5 5.6 7.8 Iris-setosa 20 3.3 4.5 5.6 7.8 Iris-setosa 21 3.3 4.5 5.6 7.8 Iris-setosa 22 3.3 4.5 5.6 7.8 Iris-setosa 23 3.3 4.5 5.6 7.8 Iris-setosa 24 3.3 4.5 5.6 7.8 Iris-setosa 25 3.3 4.5 5.6 7.8 Iris-setosa 26 3.3 4.5 5.6 7.8 Iris-setosa 27 3.3 4.5 5.6 7.8 Iris-setosa 28 3.3 4.5 5.6 7.8 Iris-setosa 29 3.3 4.5 5.6 7.8 Iris-setosa 30 3.3 4.5 5.6 7.8 Iris-setosa 31 3.3 4.5 5.6 7.8 Iris-setosa 32 3.3 4.5 5.6 7.8 Iris-setosa 33 3.3 4.5 5.6 7.8 Iris-setosa 34 3.3 4.5 5.6 7.8 Iris-setosa 35 3.3 4.5 5.6 7.8 Iris-setosa 36 3.3 4.5 5.6 7.8 Iris-setosa 37 3.3 4.5 5.6 7.8 Iris-setosa 38 3.3 4.5 5.6 7.8 Iris-setosa 39 3.3 4.5 5.6 7.8 Iris-setosa 40 3.3 4.5 5.6 7.8 Iris-setosa 41 3.3 4.5 5.6 7.8 Iris-setosa 42 3.3 4.5 5.6 7.8 Iris-setosa 43 4.3 3.0 1.1 0.1 Iris-setosa 44 4.3 4.7 9.6 1.8 Iris-virginica 45 4.3 4.7 9.6 1.8 Iris-virginica 46 4.3 4.7 9.6 1.8 Iris-virginica 47 4.3 4.7 9.6 1.8 Iris-virginica 48 4.3 4.7 9.6 1.8 Iris-virginica 49 4.3 4.7 9.6 1.8 Iris-virginica 50 4.3 4.7 9.6 1.8 Iris-virginica 51 4.3 4.7 9.6 1.8 Iris-virginica 52 4.3 4.7 9.6 1.8 Iris-virginica 53 4.3 4.7 9.6 1.8 Iris-virginica 54 4.3 4.7 9.6 1.8 Iris-virginica 55 4.3 4.7 9.6 1.8 Iris-virginica 56 4.3 4.7 9.6 1.8 Iris-virginica 57 4.3 4.7 9.6 1.8 Iris-virginica 58 4.3 4.7 9.6 1.8 Iris-virginica 59 4.3 4.7 9.6 1.8 Iris-virginica 60 4.3 4.7 9.6 1.8 Iris-virginica 61 4.3 4.7 9.6 1.8 Iris-virginica 62 4.3 4.7 9.6 1.8 Iris-virginica 63 4.3 4.7 9.6 1.8 Iris-virginica 64 4.3 4.7 9.6 1.8 Iris-virginica 65 4.3 4.7 9.6 1.8 Iris-virginica 66 4.3 4.7 9.6 1.8 Iris-virginica 67 4.3 4.7 9.6 1.8 Iris-virginica 68 4.3 4.7 9.6 1.8 Iris-virginica 69 4.3 4.7 9.6 1.8 Iris-virginica 70 4.3 4.7 9.6 1.8 Iris-virginica 71 4.3 4.7 9.6 1.8 Iris-virginica 72 4.3 4.7 9.6 1.8 Iris-virginica 73 4.3 4.7 9.6 1.8 Iris-virginica 74 4.3 4.7 9.6 1.8 Iris-virginica 75 4.3 4.7 9.6 1.8 Iris-virginica 76 4.3 4.7 9.6 1.8 Iris-virginica 77 4.3 4.7 9.6 1.8 Iris-virginica 78 4.3 4.7 9.6 1.8 Iris-virginica 79 4.3 4.7 9.6 1.8 Iris-virginica 80 4.3 4.7 9.6 1.8 Iris-virginica 81 4.3 4.7 9.6 1.8 Iris-virginica 82 4.3 4.7 9.6 1.8 Iris-virginica 83 4.3 4.7 9.6 1.8 Iris-virginica 84 4.3 4.7 9.6 1.8 Iris-virginica 85 4.3 4.7 9.6 1.8 Iris-virginica 86 4.4 2.9 1.4 0.2 Iris-setosa 87 4.4 3.0 1.3 0.2 Iris-setosa 88 4.4 3.2 1.3 0.2 Iris-setosa 89 4.5 2.3 1.3 0.3 Iris-setosa 90 4.6 3.1 1.5 0.2 Iris-setosa 91 4.6 3.2 1.4 0.2 Iris-setosa 92 4.6 3.4 1.4 0.3 Iris-setosa 93 4.6 3.6 1.0 0.2 Iris-setosa 94 4.7 3.2 1.3 0.2 Iris-setosa 95 4.7 3.2 1.6 0.2 Iris-setosa 96 4.8 3.0 1.4 0.1 Iris-setosa 97 4.8 3.0 1.4 0.3 Iris-setosa 98 4.8 3.1 1.6 0.2 Iris-setosa 99 4.8 3.4 1.6 0.2 Iris-setosa 100 4.8 3.4 1.9 0.2 Iris-setosa Rows: 1-100 | Columns: 5Note
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 theiris
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
toFalse
for multiple inserts.See also
read_csv()
: Ingests a CSV file using flex tables.read_json()
: Ingests a JSON file using flex tables.