def entity_from_dataframe(self, entity_id, dataframe, index=None, variable_types=None, make_index=False, time_index=None, secondary_time_index=None, already_sorted=False):
    '\n        Load the data for a specified entity from a Pandas DataFrame.\n\n        Args:\n            entity_id (str) : Unique id to associate with this entity.\n\n            dataframe (pandas.DataFrame) : Dataframe containing the data.\n\n            index (str, optional): Name of the variable used to index the entity.\n                If None, take the first column.\n\n            variable_types (dict[str -> Variable/str], optional):\n                Keys are of variable ids and values are variable types or type_strings. Used to to\n                initialize an entity\'s store.\n\n            make_index (bool, optional) : If True, assume index does not\n                exist as a column in dataframe, and create a new column of that name\n                using integers. Otherwise, assume index exists.\n\n            time_index (str, optional): Name of the variable containing\n                time data. Type must be in :class:`variables.DateTime` or be\n                able to be cast to datetime (e.g. str, float, or numeric.)\n\n            secondary_time_index (dict[str -> Variable]): Name of variable\n                containing time data to use a second time index for the entity.\n\n            already_sorted (bool, optional) : If True, assumes that input dataframe\n                is already sorted by time. Defaults to False.\n\n        Notes:\n\n            Will infer variable types from Pandas dtype\n\n        Example:\n            .. ipython:: python\n\n                import featuretools as ft\n                import pandas as pd\n                transactions_df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],\n                                                "session_id": [1, 2, 1, 3, 4, 5],\n                                                "amount": [100.40, 20.63, 33.32, 13.12, 67.22, 1.00],\n                                                "transaction_time": pd.date_range(start="10:00", periods=6, freq="10s"),\n                                                "fraud": [True, False, True, False, True, True]})\n                es = ft.EntitySet("example")\n                es.entity_from_dataframe(entity_id="transactions",\n                                         index="id",\n                                         time_index="transaction_time",\n                                         dataframe=transactions_df)\n\n                es["transactions"]\n                es["transactions"].df\n\n        '
    variable_types = (variable_types or {})
    if ((time_index is not None) and (time_index == index)):
        raise ValueError(('time_index and index cannot be the same value, %s' % time_index))
    if (time_index is None):
        for (variable, variable_type) in variable_types.items():
            if (variable_type == vtypes.DatetimeTimeIndex):
                raise ValueError(('DatetimeTimeIndex variable %s must be set using time_index parameter' % variable))
    if (len(self.entities) > 0):
        if (not isinstance(dataframe, type(self.entities[0].df))):
            raise ValueError('All entity dataframes must be of the same type. Cannot add entity of type {} to an entityset with existing entities of type {}'.format(type(dataframe), type(self.entities[0].df)))
    entity = Entity(entity_id, dataframe, self, variable_types=variable_types, index=index, time_index=time_index, secondary_time_index=secondary_time_index, already_sorted=already_sorted, make_index=make_index)
    self.entity_dict[entity.id] = entity
    self.reset_data_description()
    return self
Load the data for a specified entity from a Pandas DataFrame.

Args:
    entity_id (str) : Unique id to associate with this entity.

    dataframe (pandas.DataFrame) : Dataframe containing the data.

    index (str, optional): Name of the variable used to index the entity.
        If None, take the first column.

    variable_types (dict[str -> Variable/str], optional):
        Keys are of variable ids and values are variable types or type_strings. Used to to
        initialize an entity's store.

    make_index (bool, optional) : If True, assume index does not
        exist as a column in dataframe, and create a new column of that name
        using integers. Otherwise, assume index exists.

    time_index (str, optional): Name of the variable containing
        time data. Type must be in :class:`variables.DateTime` or be
        able to be cast to datetime (e.g. str, float, or numeric.)

    secondary_time_index (dict[str -> Variable]): Name of variable
        containing time data to use a second time index for the entity.

    already_sorted (bool, optional) : If True, assumes that input dataframe
        is already sorted by time. Defaults to False.

Notes:

    Will infer variable types from Pandas dtype

Example:
    .. ipython:: python

        import featuretools as ft
        import pandas as pd
        transactions_df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],
                                        "session_id": [1, 2, 1, 3, 4, 5],
                                        "amount": [100.40, 20.63, 33.32, 13.12, 67.22, 1.00],
                                        "transaction_time": pd.date_range(start="10:00", periods=6, freq="10s"),
                                        "fraud": [True, False, True, False, True, True]})
        es = ft.EntitySet("example")
        es.entity_from_dataframe(entity_id="transactions",
                                 index="id",
                                 time_index="transaction_time",
                                 dataframe=transactions_df)

        es["transactions"]
        es["transactions"].df
def entity_from_dataframe(self, entity_id, dataframe, index=None, variable_types=None, make_index=False, time_index=None, secondary_time_index=None, already_sorted=False):
    '\n        Load the data for a specified entity from a Pandas DataFrame.\n\n        Args:\n            entity_id (str) : Unique id to associate with this entity.\n\n            dataframe (pandas.DataFrame) : Dataframe containing the data.\n\n            index (str, optional): Name of the variable used to index the entity.\n                If None, take the first column.\n\n            variable_types (dict[str -> Variable/str], optional):\n                Keys are of variable ids and values are variable types or type_strings. Used to to\n                initialize an entity\'s store.\n\n            make_index (bool, optional) : If True, assume index does not\n                exist as a column in dataframe, and create a new column of that name\n                using integers. Otherwise, assume index exists.\n\n            time_index (str, optional): Name of the variable containing\n                time data. Type must be in :class:`variables.DateTime` or be\n                able to be cast to datetime (e.g. str, float, or numeric.)\n\n            secondary_time_index (dict[str -> Variable]): Name of variable\n                containing time data to use a second time index for the entity.\n\n            already_sorted (bool, optional) : If True, assumes that input dataframe\n                is already sorted by time. Defaults to False.\n\n        Notes:\n\n            Will infer variable types from Pandas dtype\n\n        Example:\n            .. ipython:: python\n\n                import featuretools as ft\n                import pandas as pd\n                transactions_df = pd.DataFrame({"id": [1, 2, 3, 4, 5, 6],\n                                                "session_id": [1, 2, 1, 3, 4, 5],\n                                                "amount": [100.40, 20.63, 33.32, 13.12, 67.22, 1.00],\n                                                "transaction_time": pd.date_range(start="10:00", periods=6, freq="10s"),\n                                                "fraud": [True, False, True, False, True, True]})\n                es = ft.EntitySet("example")\n                es.entity_from_dataframe(entity_id="transactions",\n                                         index="id",\n                                         time_index="transaction_time",\n                                         dataframe=transactions_df)\n\n                es["transactions"]\n                es["transactions"].df\n\n        '
    variable_types = (variable_types or {})
    if ((time_index is not None) and (time_index == index)):
        raise ValueError(('time_index and index cannot be the same value, %s' % time_index))
    if (time_index is None):
        for (variable, variable_type) in variable_types.items():
            if (variable_type == vtypes.DatetimeTimeIndex):
                raise ValueError(('DatetimeTimeIndex variable %s must be set using time_index parameter' % variable))
    if (len(self.entities) > 0):
        if (not isinstance(dataframe, type(self.entities[0].df))):
            raise ValueError('All entity dataframes must be of the same type. Cannot add entity of type {} to an entityset with existing entities of type {}'.format(type(dataframe), type(self.entities[0].df)))
    entity = Entity(entity_id, dataframe, self, variable_types=variable_types, index=index, time_index=time_index, secondary_time_index=secondary_time_index, already_sorted=already_sorted, make_index=make_index)
    self.entity_dict[entity.id] = entity
    self.reset_data_description()
    return self