Teradata Package for Python Function Reference | 20.00 - mdiff - 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
- 20.00.00.02
- Published
- September 2024
- Language
- English (United States)
- Last Update
- 2024-10-17
- dita:id
- TeradataPython_FxRef_Enterprise_2000
- Product Category
- Teradata Vantage
- teradataml.dataframe.dataframe.DataFrame.mdiff = mdiff(self, width, sort_columns, drop_columns=False)
- DESCRIPTION:
Computes the moving difference for the current row and the preceding
"width" rows in a partition, by sorting the rows according to
"sort_columns".
Note:
mdiff 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 0 and less than or equal to 4096.
Types: int
sort_columns:
Required Argument.
Specifies the columns to use for sorting.
Note:
"sort_columns" does not support CLOB and BLOB type of
columns.
Types: str (or) ColumnExpression (or) List of strings(str)
or ColumnExpressions
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","sales")
# Create teradataml dataframe.
>>> df = DataFrame.from_table('sales')
>>> print(df)
Feb Jan Mar Apr datetime
accounts
Blue Inc 90.0 50.0 95.0 101.0 04/01/2017
Orange Inc 210.0 NaN NaN 250.0 04/01/2017
Red Inc 200.0 150.0 140.0 NaN 04/01/2017
Yellow Inc 90.0 NaN NaN NaN 04/01/2017
Jones LLC 200.0 150.0 140.0 180.0 04/01/2017
Alpha Co 210.0 200.0 215.0 250.0 04/01/2017
>>>
# Sorts the Data on column accounts in ascending order and
# calculates moving difference on the window of size 2.
>>> df.mdiff(width=2, sort_columns=df.accounts)
Feb Jan Mar Apr datetime mdiff_Feb mdiff_Jan mdiff_Mar mdiff_Apr mdiff_datetime
accounts
Jones LLC 200.0 150.0 140.0 180.0 04/01/2017 -10.0 -50.0 -75.0 -70.0 0.0
Red Inc 200.0 150.0 140.0 NaN 04/01/2017 0.0 0.0 0.0 -180.0 0.0
Yellow Inc 90.0 NaN NaN NaN 04/01/2017 -120.0 NaN NaN -250.0 0.0
Orange Inc 210.0 NaN NaN 250.0 04/01/2017 120.0 -50.0 -95.0 149.0 0.0
Blue Inc 90.0 50.0 95.0 101.0 04/01/2017 NaN NaN NaN NaN NaN
Alpha Co 210.0 200.0 215.0 250.0 04/01/2017 NaN NaN NaN NaN NaN
>>>
# Sorts the Data on column accounts in ascending order and column
# Feb in descending order, then calculates moving difference by
# dropping the input DataFrame columns on the window of size 2.
>>> df.mdiff(width=2, sort_columns=[df.accounts, df.Feb.desc()], drop_columns=True)
mdiff_Feb mdiff_Jan mdiff_Mar mdiff_Apr
0 -10.0 -50.0 -75.0 -70.0
1 0.0 0.0 0.0 -180.0
2 -120.0 NaN NaN -250.0
3 120.0 -50.0 -95.0 149.0
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
>>>