| | |
Methods defined here:
- __init__(self, data=None, unpivot=None, input_types=False, attribute_column='attribute', value_column='value_col', accumulate=None, data_sequence_column=None, data_order_column=None)
- DESCRIPTION:
The Unpivot function pivots data that is stored in columns into rows.
It is the reverse of the Pivot function.
PARAMETERS:
data:
Required Argument.
Specifies the teradataml DataFrame containing the data to be pivoted.
data_order_column:
Optional Argument.
Specifies Order By columns for data.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
unpivot:
Required Argument.
Specifies the names of the unpivot columns — the input columns to
unpivot (convert to rows).
Types: str OR list of Strings (str)
input_types:
Optional Argument.
Specifies whether the unpivoted value column, in the output teradataml DataFrame,
has the same data type as its corresponding unpivot column (if
possible). For each unpivoted column, the function outputs the values
in a single VARCHAR column. If you specify "true", the function
outputs each unpivoted value column in a separate column. If the
unpivot column has a 'real' data type, the unpivoted value column has
the data type 'float'; if the unpivot column has an 'integer' data type,
the unpivoted value column has the data type 'int'; if the unpivot
column has any other data type, the unpivoted value column has the
data type 'VARCHAR'.
Default Value: False
Types: bool
attribute_column:
Optional Argument.
Specifies the name of the unpivoted attribute column in the output
teradataml DataFrame.
Default Value: "attribute"
Types: str
value_column:
Optional Argument.
Specifies the name of the unpivoted value column in the output
teradataml DataFrame.
Default Value: "value_col"
Types: str
accumulate:
Required Argument.
Specifies the names of input columns, other than unpivot columns,
to copy to the output teradataml DataFrame. You must specify
these columns in the same order that they appear in the input
teradataml DataFrame. No accumulate_column can be an unpivot column.
Types: str OR list of Strings (str)
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of Unpivot.
Output teradataml DataFrames can be accessed using attribute
references, such as UnpivotObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data('Unpivot', "unpivot_input")
# Create teradataml DataFrame objects.
unpivot_input = DataFrame.from_table("unpivot_input")
# Example 1 -
unpivot_out1 = Unpivot(data=unpivot_input,
unpivot = ["temp","pressure","dewpoint"],
input_types = False,
attribute_column = "attribute",
value_column = "value_col",
accumulate = ["sn","city","week"])
# Print the result
print(unpivot_out1.result)
# Example 2 -
unpivot_out2 = Unpivot(data=unpivot_input,
unpivot = ["temp","pressure","dewpoint"],
input_types = True,
attribute_column = "climate_attributes",
value_column = "attributevalue",
accumulate = ["sn","city","week"])
# Print the result
print(unpivot_out2.result)
# Example 3 -
unpivot_out3 = Unpivot(data=unpivot_input,
unpivot = ["temp","pressure","dewpoint"],
input_types = False,
accumulate = ["sn","city","week"])
# Print the result
print(unpivot_out3.result)
- __repr__(self)
- Returns the string representation for a Unpivot class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
|