Teradata Package for Python Function Reference | 20.00 - mlinreg - 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.DataFrame.mlinreg = mlinreg(self, width, sort_column, drop_columns=False)
- DESCRIPTION:
Computes the moving linear regression for the current row and the
preceding "width"-1 rows in a partition, by sorting the rows
according to "sort_columns".
Note:
mlinreg does not support below type of columns.
* BLOB
* BYTE
* CHAR
* CLOB
* DATE
* PERIOD_DATE
* PERIOD_TIME
* PERIOD_TIMESTAMP
* TIME
* TIMESTAMP
* VARBYTE
* VARCHAR
PARAMETERS:
width:
Required Argument.
Specifies the width of the partition. "width" must be
greater than 2 and less than or equal to 4096.
Types: int
sort_column:
Required Argument.
Specifies the column to use for sorting.
Note:
"sort_column" does not support CLOB and BLOB type of
columns.
Types: str (or) ColumnExpression
drop_columns:
Optional Argument.
Specifies whether to retain all the input DataFrame columns
in the output or not. When set to False, columns from input
DataFrame are retained, dropped otherwise.
Default Value: False
Types: bool
RAISES:
TeradataMlException, TypeError
RETURNS:
teradataml DataFrame.
EXAMPLES:
# Load the data to run the example.
>>> from teradataml import load_example_data
>>> load_example_data("dataframe","admissions_train")
>>> df = DataFrame('admissions_train')
>>> print(df)
masters gpa stats programming admitted
id
15 yes 4.00 Advanced Advanced 1
7 yes 2.33 Novice Novice 1
22 yes 3.46 Novice Beginner 0
17 no 3.83 Advanced Advanced 1
13 no 4.00 Advanced Novice 1
38 yes 2.65 Advanced Beginner 1
26 yes 3.57 Advanced Advanced 1
5 no 3.44 Novice Novice 0
34 yes 3.85 Advanced Beginner 0
40 yes 3.95 Novice Beginner 0
>>>
# Sorts the Data on column id in descending order and
# calculates moving linear regression on the window of size 3.
>>> df.mlinreg(width=3, sort_column=df.id.desc())
masters gpa stats programming admitted mlinreg_id mlinreg_gpa mlinreg_admitted
id
6 yes 3.50 Beginner Advanced 1 6.0 1.06 1.0
17 no 3.83 Advanced Advanced 1 17.0 5.64 2.0
16 no 3.70 Advanced Advanced 1 16.0 3.85 1.0
28 no 3.93 Advanced Advanced 1 28.0 4.21 0.0
26 yes 3.57 Advanced Advanced 1 26.0 3.99 -1.0
40 yes 3.95 Novice Beginner 0 NaN NaN NaN
39 yes 3.75 Advanced Beginner 0 NaN NaN NaN
38 yes 2.65 Advanced Beginner 1 38.0 3.55 0.0
27 yes 3.96 Advanced Advanced 0 27.0 3.86 2.0
18 yes 3.81 Advanced Advanced 1 18.0 0.06 -1.0
>>>
# Sorts the Data on column id in ascending order and
# calculates moving linear regression by dropping the
# input DataFrame columns on the window of size 3.
>>> df.mlinreg(width=3, sort_column=df.id.asc(), drop_columns=True)
mlinreg_id mlinreg_gpa mlinreg_admitted
0 35.0 4.15 -1.0
1 24.0 3.72 2.0
2 25.0 0.15 1.0
3 13.0 4.17 1.0
4 15.0 2.90 -1.0
5 NaN NaN NaN
6 NaN NaN NaN
7 3.0 3.57 0.0
8 14.0 4.35 1.0
9 23.0 3.05 -1.0
>>>