How to use the vortexasdk.api.search_result.Result function in vortexasdk

from typing import List

import pandas as pd

from vortexasdk.api import Product
from vortexasdk.api.entity_flattening import flatten_dictionary
from vortexasdk.api.search_result import Result
from vortexasdk.logger import get_logger
from vortexasdk.result_conversions import create_dataframe, create_list

logger = get_logger(__name__)


class ProductResult(Result):
    """Container class that holds the result obtained from calling the `Product` endpoint."""

    def to_list(self) -> List[Product]:
        """Represent products as a list."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), Product)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent products as a `pd.DataFrame`.

        # Arguments
            columns: The product features we want in the dataframe. Enter `columns='all'` to include all features.
            Defaults to `columns = ['id', 'name', 'layer.0', 'parent.0.name']`.

from typing import List

import pandas as pd

from vortexasdk.api.search_result import Result
from vortexasdk.api.timeseries_item import TimeSeriesItem
from vortexasdk.logger import get_logger
from vortexasdk.result_conversions import create_dataframe, create_list

logger = get_logger(__name__)


class TimeSeriesResult(Result):
    """Container class that holds the result obtained from calling a time series endpoint."""

    def to_list(self) -> List[TimeSeriesItem]:
        """Represents time series as a list."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), TimeSeriesItem)

    def to_df(self, columns=None) -> pd.DataFrame:
        """Represents the timeseries as a dataframe.

        Returns a `pd.DataFrame`, of time series items with columns:
         key: The time series key
         value: The value of the time series for a given key
         count: The number of records contributing to this time series record.

        # Example:

from typing import List

import pandas as pd

from vortexasdk.api import VesselMovement
from vortexasdk.api.entity_flattening import (
    convert_vessel_movement_to_flat_dict,
)
from vortexasdk.api.search_result import Result
from vortexasdk.result_conversions import create_dataframe, create_list
from vortexasdk.logger import get_logger

logger = get_logger(__name__)


class VesselMovementsResult(Result):
    """
    Container class holdings search results returns from the vessel movements endpoint.

    This class has two methods, `to_list()`, and `to_df()`, allowing search results to be represented as a list of `VesselMovement`s,
     or as a `pd.DataFrame` , respectively.
    """

    def to_list(self) -> List[VesselMovement]:
        """Represent vessel movements as a list of `VesselMovementEntity`s."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), VesselMovement)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent vessel movements as a `pd.DataFrame`.

from typing import List

import pandas as pd

from vortexasdk.logger import get_logger
from vortexasdk.api import Vessel
from vortexasdk.api.search_result import Result
from vortexasdk.result_conversions import create_dataframe, create_list

logger = get_logger(__name__)


class VesselsResult(Result):
    """Container class that holds the result obtained from calling the `Vessels` endpoint."""

    def to_list(self) -> List[Vessel]:
        """Represent vessels as a list."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), Vessel)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent vessels as a `pd.DataFrame`.

        # Arguments
            columns: The vessel features we want in the dataframe. Enter `columns='all'` to include all features.
            Defaults to `columns = ['id', 'name', 'imo', 'vessel_class']`.

from typing import List

import pandas as pd

from vortexasdk.api import Attribute
from vortexasdk.api.search_result import Result
from vortexasdk.result_conversions import create_dataframe, create_list
from vortexasdk.logger import get_logger

logger = get_logger(__name__)


class AttributeResult(Result):
    """Container class that holds the result obtained from calling the `Attributes` endpoint."""

    def to_list(self) -> List[Attribute]:
        """Represent attributes as a list."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), Attribute)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent attributes as a `pd.DataFrame`.

        # Arguments
            columns: The attributes features we want in the dataframe. Enter `columns='all'` to include all features.
            Defaults to `columns = ['id', 'name', 'type']`.

from typing import List

import pandas as pd

from vortexasdk.api import Corporation
from vortexasdk.api.search_result import Result
from vortexasdk.logger import get_logger
from vortexasdk.result_conversions import create_dataframe, create_list

logger = get_logger(__name__)


class CorporationsResult(Result):
    """Container class that holds the result obtained from calling the `Vessels` endpoint."""

    def to_list(self) -> List[Corporation]:
        """Represent vessels as a list."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), Corporation)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent corporations as a `pd.DataFrame`.

        # Arguments
            columns: The corporation features we want in the dataframe. Enter `columns='all'` to include all features.
            Defaults to `columns = ['id', 'name', 'corporate_entity_type']`.

from typing import List

import pandas as pd

from vortexasdk.api import Geography
from vortexasdk.api.search_result import Result


class GeographyResult(Result):
    """Container class that holds the result obtained from calling the `Geography` endpoint."""

    def __init__(self, records):
        super().__init__(records=records, return_type=Geography)

    def to_list(self) -> List[Geography]:
        """Represent geographies as a list."""
        return super().to_list()

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent geographies as a `pd.DataFrame`.

        # Arguments
            columns: The geography features we want in the dataframe. Enter `columns='all'` to include all features.
            Defaults to `columns = ['id', 'name', 'layer']`.

from typing import List

import pandas as pd

from vortexasdk.api import CargoMovement
from vortexasdk.api.entity_flattening import (
    convert_cargo_movement_to_flat_dict,
)
from vortexasdk.api.search_result import Result
from vortexasdk.result_conversions import create_dataframe, create_list
from vortexasdk.logger import get_logger

logger = get_logger(__name__)


class CargoMovementsResult(Result):
    """
    Container class holdings search results returns from the cargo movements endpoint.

    This class has two methods, `to_list()`, and `to_df()`, allowing search results to be represented as a list of `CargoMovements`,
     or as a `pd.DataFrame` , respectively.
    """

    def to_list(self) -> List[CargoMovement]:
        """Represent cargo movements as a list of `CargoMovementEntity`s."""
        # noinspection PyTypeChecker
        return create_list(super().to_list(), CargoMovement)

    def to_df(self, columns=None) -> pd.DataFrame:
        """
        Represent cargo movements as a `pd.DataFrame`.