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Methods defined here:
- __init__(self, data=None, data_partition_column='1', data_order_column=None)
- DESCRIPTION:
The CCMPrepare function prepares an input teradataml DataFrame for
the CCM function by adding a partition column, ccm id, and
partitioning the data. Using the CCMPrepare function is optional.
However, partitioning the data, instead of having all sequences on
one vworker, may increase the speed of the CCM function for large
data sets consisting of multiple sequences.
PARAMETERS:
data:
Required Argument.
Specifies the teradataml DataFrame containing the input data.
data_partition_column:
Optional Argument.
Specifies the Partition By columns for "data".
Values to this argument can be provided as list, if multiple
columns are used for partition.
Default Value: 1
Types: str OR list of Strings (str)
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)
RETURNS:
Instance of CCMPrepare.
Output teradataml DataFrames can be accessed using attribute
references, such as CCMPrepareObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("ccmprepare", "ccmprepare_input")
# Create teradataml DataFrame objects. The ccmprepare_input table,
# ccmprepare_input, is a collection of nine time series consisting
# of 10 values for each of three variables (expenditure, income,
# and investment)
ccmprepare_input = DataFrame.from_table("ccmprepare_input")
# Example - Prepare the given input for CCM.
ccmprepare_out = CCMPrepare(data=ccmprepare_input,
data_partition_column='id'
)
# Print the result teradataml DataFrame
print(ccmprepare_out)
- __repr__(self)
- Returns the string representation for a CCMPrepare class instance.
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