Teradata Package for Python Function Reference | 20.00 - TDMatrix - Teradata Package for Python - Look here for syntax, methods and examples for the functions included in the Teradata Package for Python.
Teradata® Package for Python Function Reference - 20.00
- Deployment
- VantageCloud
- VantageCore
- Edition
- Enterprise
- IntelliFlex
- VMware
- Product
- Teradata Package for Python
- Release Number
- 20.00.00.03
- Published
- December 2024
- ft:locale
- en-US
- ft:lastEdition
- 2024-12-19
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- lifecycle
- latest
- Product Category
- Teradata Vantage
- teradataml.dataframe.dataframe.TDMatrix.__init__ = __init__(self, data, id, row_index, column_index, row_index_style='TIMECODE', column_index_style='TIMECODE', id_sequence=None, payload_field=None, payload_content=None, layer=None)
- DESCRIPTION:
Create a TDMatrix object from a teradataml DataFrame representing
a MATRIX in time series
which is used as input to Unbounded Array Framework,
time series functions.
A matrix is a two-dimensional array that has rows and columns.
A matrix is identified by its matrix id, i.e., "id" argument,
and is indexed by "row_index" and "column_index" arguments.
A matrix can be a one of the following types:
* Row-major matrix: Each row is a wavelet that is grouped by
its matrix id and "row_index", and ordered by its "column_index".
* Column-major matrix: Each column is a wavelet that is grouped
by its matrix id and "column_index", and ordered by its "row_index".
PARAMETERS:
data:
Required Argument.
Specifies the teradataml Dataframe.
Types: teradataml DataFrame
id:
Required Argument.
Specifies the name of the column in "data" containing the
identifier values.
Types: str or list of str
row_index:
Required Argument.
Specifies the name of the column in "data" containing the
row indexing values.
Types: str
column_index:
Required Argument.
Specifies the name of the column in "data" containing
the column.
indexing values.
Types: str
row_index_style:
Optional Argument.
Specifies the style of row indexing.
Default Value: "TIMECODE"
Permitted Values: "TIMECODE", "SEQUENCE"
Types: str
column_index_style:
Optional Argument.
Specifies the style of column indexing.
Default Value: "TIMECODE"
Permitted Values: "TIMECODE", "SEQUENCE"
Types: str
id_sequence:
Optional Argument.
Specifies a sequence of series to plot.
Types: str or list of str
payload_field:
Optional Argument.
Specifies the names of the fields for payload.
Types: str or list of str
payload_content:
Optional Argument.
Specifies the payload content type.
Permitted Values: "REAL", "COMPLEX", "AMPL_PHASE",
"AMPL_PHASE_RADIANS", "AMPL_PHASE_DEGREES",
"MULTIVAR_REAL", "MULTIVAR_COMPLEX",
"MULTIVAR_ANYTYPE", "MULTIVAR_AMPL_PHASE",
"MULTIVAR_AMPL_PHASE_RADIANS ",
"MULTIVAR_AMPL_PHASE_DEGREES"
Types: str
layer:
Optional Argument.
Specifies the layer name of the ART table, if dataframe is created on ART table.
Types: str
RAISES:
None
RETURNS:
None
EXAMPLES:
# Create a TDMatrix object.
>>> from teradataml import create_context, load_example_data, DataFrame, TDMatrix
>>> con = create_context(host = host, user=user, password=passw)
>>> load_example_data("dataframe", "admissions_train")
# Create a DataFrame to be passed as input to TDMatrix.
>>> data = DataFrame("admissions_train")
# Create a TDMatrix object to be passed as input to UAF functions.
>>> res = TDMatrix(data=data, id='admitted', row_index='id',
column_index = 'admitted', row_index_style="TIMECODE",
payload_field='payload_field',payload_content='REAL')
>>> res
masters gpa stats programming admitted
id
15 yes 4.00 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
40 yes 3.95 Novice Beginner 0
7 yes 2.33 Novice Novice 1
22 yes 3.46 Novice Beginner 0
26 yes 3.57 Advanced Advanced 1
17 no 3.83 Advanced Advanced 1