Initializing help system before first use

# xpressinsight.data.DataFrame

Purpose
Creates an annotation for a DataFrame entity to be fetched from another scenario and stored in a Pandas DataFrame.
Synopsis
`xpressinsight.data.DataFrame(columns: List[data.Column], *, index_names: List[str] = None, index_types: List[Type[BasicType]] = None) -> Type[pandas.DataFrame]`
Arguments

columns | The columns which make up this data frame.
index_names | The names of the columns to use for the index(es) of the data being read. If not given, names derived from the index entities in the other scenario will be used. If given, the names must be unique and there must be one for each index column.
index_types | The expected data-types for each of the index(es) of the data being read. If given, you must specify a type for each index of this series and AppInterface.get_scenario_data will verify the index values being fetched is of these types.

Example
Example declaring a data frame MixedTable which has three columns.

    >>> MixedTable: xi.data.DataFrame(index='Years', columns=[
    ...     xi.data.Column("IntCol", dtype=xi.integer, default=-1),
    ...     xi.data.Column("StrCol", dtype=xi.string),
    ...     xi.data.Column("ResultCol", dtype=xi.real)
    ... ])

Further information
1. The function DataFrame is part of the package xpressinsight.data.
2. This function returns an Annotated type containing the DataFrame entity object; for example, if xpressinsight has been imported as xi, then xi.data.DataFrame(index_names=['idx'], columns=[xi.data.Column("c1", dtype=xi.integer)]) is equivalent to Annotated[pandas.DataFrame, xi.DataFrame(index=['idx'], columns=[xi.Column("c1", dtype=xi.integer)])].
Related topics
BasicType,
Column,
data.Column
Parent Topic Scenario Data Entity Definitions

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