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- Resample(data=None, data_filter_expr=None, timecode_start_value=None, timecode_duration=None, sequence_start_value=None, sequence_duration=None, interpolate=None, weight=None, spline_params_method='NOT_A_KNOT', spline_params_yp1=0.0, spline_params_ypn=0.0, **generic_arguments)
- DESCRIPTION:
The Resample() function transforms an irregular time series into a
regular time series. It can also be used to alter the sampling interval
for a time series.
PARAMETERS:
data:
Required Argument.
Specifies the irregular time series that is to be altered.
Types: TDSeries
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
timecode_start_value:
Optional Argument.
Specifies the first sampling index to interpolate.
Note:
Provide either arguments "timecode_start_value" and
"timecode_duration", or arguments "sequence_start_value"
and "sequence_duration".
Types: str
timecode_duration:
Optional Argument.
Specifies the sampling interval associated with the result series.
Note:
Provide either arguments "timecode_start_value" and
"timecode_duration", or arguments "sequence_start_value"
and "sequence_duration".
Permitted Values:
* CAL_YEARS
* CAL_MONTHS
* CAL_DAYS
* WEEKS
* DAYS
* HOURS
* MINUTES
* SECONDS
* MILLISECONDS
* MICROSECONDS
Types: str
sequence_start_value:
Optional Argument.
Specifies the first sampling index to interpolate.
Note:
Provide either arguments "timecode_start_value" and
"timecode_duration", or arguments "sequence_start_value"
and "sequence_duration".
Types: float
sequence_duration:
Optional Argument.
Specifies the sampling interval associated with the result series.
Note:
Provide either arguments "timecode_start_value" and
"timecode_duration", or arguments "sequence_start_value"
and "sequence_duration".
Types: float
interpolate:
Required Argument.
Specifies the interpolation strategies.
Permitted Values:
* LINEAR
* LAG
* LEAD
* WEIGHTED
* SPLINE
Types: str
weight:
Optional Argument.
Specifies the interpolated weighted value.
Note:
* Applicable only when "interpolate" set to 'WEIGHTED'.
Types: float
spline_params_method:
Optional Argument.
Specifies the type of spline method to use.
Note:
* Applicable only when "interpolate" set to 'SPLINE'.
Permitted Values:
* CLAMPED
* NATURAL
* NOT_A_KNOT
Default Value: NOT_A_KNOT
Types: str
spline_params_yp1:
Optional Argument.
Specifies the value of the first derivative for the left boundary
condition.
Notes:
* Used only when "interpolate" set to 'SPLINE'.
* Used only when "spline_params_method" set to 'CLAMPED'.
Default Value: 0.0
Types: float
spline_params_ypn:
Optional Argument.
Specifies the value of the first derivative for the right boundary
condition.
Notes:
* Used only when "interpolate" set to 'SPLINE'.
* Used only when "spline_params_method" set to 'CLAMPED'.
Default Value: 0.0
Types: float
**generic_arguments:
Specifies the generic keyword arguments of UAF functions.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Note that, when UAF function is executed, an
analytic result table (ART) is created.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile ART or not. When set to
True, results are stored in a volatile ART,
otherwise not.
Default Value: False
Types: bool
output_table_name:
Optional Argument.
Specifies the name of the table to store results.
If not specified, a unique table name is internally
generated.
Types: str
output_db_name:
Optional Argument.
Specifies the name of the database to create output
table into. If not specified, table is created into
database specified by the user at the time of context
creation or configuration parameter. Argument is ignored,
if "output_table_name" is not specified.
Types: str
RETURNS:
Instance of Resample.
Output teradataml DataFrames can be accessed using attribute
references, such as Resample_obj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
# Load the example data.
load_example_data("uaf", ["production_data"])
# Create teradataml DataFrame object.
production_data = DataFrame.from_table("production_data")
# Example 1 : Execute function to transform irregular time series into
# regular time series when row index style is "SEQUENCE".
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=production_data,
id="product_id",
row_index_style="SEQUENCE",
row_index="TD_SEQNO",
payload_field="beer_sales",
payload_content="REAL")
# Execute Resample for TDSeries.
uaf_out1 = Resample(data=data_series_df,
interpolate='LINEAR',
sequence_start_value=0.0,
sequence_duration=1.0)
# Print the result DataFrame.
print(uaf_out1.result)
# Example 2 : Execute function to transform irregular time series into
# regular time series when row index style is "TIMECODE".
# Create teradataml TDSeries object.
data_series_df = TDSeries(data=production_data,
id="product_id",
row_index_style="TIMECODE",
row_index="TD_TIMECODE",
payload_field="beer_sales",
payload_content="REAL")
# Execute Resample for TDSeries.
uaf_out2 = Resample(data=data_series_df,
interpolate='LINEAR',
timecode_start_value="TIMESTAMP '2021-01-01 00:00:00'",
timecode_duration="MINUTES(30)")
# Print the result DataFrame.
print(uaf_out2.result)
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