xpressinsight.data.PolarsDataFrame
Purpose
Creates an annotation for a
PolarsDataFrame entity to be fetched from another scenario and stored in a Polars DataFrame.
Synopsis
xpressinsight.data.PolarsDataFrame(columns: List[Column], *, index_names: List[str] = None, index_types: List[Type[BasicType]] = None) -> Type[polars.DataFrame]
Arguments
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columns
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The columns which make up this data frame.
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index_names
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The names of the columns to use for Insight 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. |
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index_types
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The expected data-types for each of the Insight index(es) of the data being read. If given, you must specify a type for each index of this series and :fct-ref:`insight.get_scenario_data will verify the index values being fetched is of these types.
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Example
Example declaring a data frame
MixedTable which has three columns.
>>> MixedTable: xi.data.PolarsDataFrame(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
PolarsDataFrame is part of the package
xpressinsight.data.
2. This function returns an
Annotated type containing the
PolarsDataFrame entity object; for example, if
xpressinsight has been imported as
xi, then
xi.data.PolarsDataFrame(index_names=['idx'], columns=[xi.data.Column("c1", dtype=xi.integer)]) is equivalent to
Annotated[polars.DataFrame, xi.PolarsDataFrame(index=['idx'], columns=[xi.Column("c1", dtype=xi.integer)])].
3. Polars-type entities will only be available if
polars has been installed in your Python environment.
Related topics
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