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- BincodeTransform(data=None, object=None, accumulate=None, **generic_arguments)
- DESCRIPTION:
The BincodeTransform() function is used to convert the continuous numeric data to
categorical data. The BincodeTransform() function takes the output of BincodeFit()
function to apply the transform on input DataFrame.
BincodeTransform() function takes two DataFrames as input:
1. Input dataframe which contains numeric data to be converted to categorical
data.
2. Output of BincodeFit() function which contains the binning data.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
object:
Required Argument.
Specifies the teradataml DataFrame containing the binning parameters
generated by BincodeFit() function or the instance of BincodeFit.
Types: teradataml DataFrame or BincodeFit
accumulate:
Optional Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to copy to the
output. By default, the function copies no input teradataml
DataFrame columns to the output.
Types: str OR list of Strings (str)
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in table or
not. When set to True, results are persisted in table; otherwise,
results are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in volatile table
or not. When set to True, results are stored in volatile table,
otherwise not.
Default Value: False
Types: boolean
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying
SQLE Engine function supports, else an exception is raised.
RETURNS:
Instance of BincodeTransform.
Output teradataml DataFrames can be accessed using attribute
references, such as BincodeTransformObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
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.
# Load the example data.
load_example_data("teradataml", ["titanic", "bin_fit_ip"])
# Create teradataml DataFrame objects.
titanic_data = DataFrame.from_table("titanic")
bin_fit_ip = DataFrame.from_table("bin_fit_ip")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1: Transform the data using BincodeFit object with Variable-Width.
# Create BincodeFit object.
bin_code_1 = BincodeFit(data=titanic_data,
fit_data=bin_fit_ip,
fit_data_order_column = ['minVal', 'maxVal'],
target_columns='age',
minvalue_column='minVal',
maxvalue_column='maxVal',
label_column='label',
method_type='Variable-Width',
label_prefix='label_prefix'
)
# Run BincodeTransform.
obj = BincodeTransform(data=titanic_data,
object=bin_code_1.output,
object_order_column="TD_MinValue_BINFIT",
accumulate=['passenger', 'ticket']
)
# Print the result DataFrame.
print(obj.result)
# Example 2: Transform the data using BincodeFit object with Equal-Width.
# Create BincodeFit object.
bin_code_2 = BincodeFit(data=titanic_data,
target_columns='age',
method_type='Equal-Width',
nbins=2,
label_prefix='label_prefix'
)
# Run BincodeTransform.
obj = BincodeTransform(data=titanic_data,
object=bin_code_2.output,
accumulate=['passenger', 'ticket']
)
# Print the result DataFrame.
print(obj.result)
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