Skip to main content

construct_from_dataframe()

This operation creates a collection with the specified dataframe.

Request Syntax​

construct_from_dataframe(
    name: str, 
    primary_field: str,
    dataframe: pandas.DataFrame
)

PARAMETERS:

  • name (string) -

    [REQUIRED]

    The name of the collection to create.

  • primary_field (string) -

    [REQUIRED]

    The name of the primary field. It should be one of the column labels in the following dataframe.

  • dataframe (pandas.DataFrame)

    [REQUIRED]

    The dataframe containing the data to be inserted into the collection.

    You can form a data frame in any way, as demonstrated in the Example section on this page.

    dataframe = pd.DataFrame({
        "id": [5,6,7,8,9],
        "vector": [
            [0.1,0.2,-0.3,-0.4,0.5],
            [0.3,-0.1,-0.2,-0.6,0.7],
            [-0.6,-0.3,0.2,0.8,0.7],
            [0.6,0.2,-0.3,-0.8,0.5],
            [0.3,0.1,-0.2,-0.6,-0.7],
        ]
    })

RETURN TYPE:

tuple (Collection, MutationResults)

RETURNS:

A tuple containing the collection and a MutationResult object returned by the insert() operation.

A MutationResult object contains the following fields:

  • insert_count (int)

    The count of inserted entities.

  • delete_count (int)

    The count of deleted entities.

  • upsert_count (int)

    The count of upserted entities.

  • succ_count (int)

    The count of successful executions during this operation.

  • succ_index (list)

    A list of index numbers starting from 0, each indicating a successful operation.

  • err_count (int)

    The count of failed executions during this operation.

  • err_index (list)

    A list of index numbers starting from 0, each indicating a failed operation.

  • primary_keys (list)

    A list of primary keys for the inserted entities.

  • timestamp (int)

    The timestamp at which this operation is completed.

EXCEPTIONS:

  • SchemaNotReadyException

    This exception will be raised when the specified primary field is not valid.

Examples​

import pandas as pd
from pymilvus import Collection

collection, results = Collection.construct_from_dataframe(
    name="test_collection",
    primary_field="id",
    dataframe=pd.DataFrame({
        "id": [0,1,2,3,4],
        "vector": [
            [0.1,0.2,-0.3,-0.4,0.5],
            [0.3,-0.1,-0.2,-0.6,0.7],
            [-0.6,-0.3,0.2,0.8,0.7],
            [0.6,0.2,-0.3,-0.8,0.5],
            [0.3,0.1,-0.2,-0.6,-0.7],
        ]
    }),
)

The following operations are related to construct_from_dataframe():