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- BincodeFit(data=None, fit_data=None, target_columns=None, method_type=None, nbins=None, label_prefix=None, target_colnames=None, minvalue_column=None, maxvalue_column=None, label_column=None, **generic_arguments)
- DESCRIPTION:
The BinCodeFit() function outputs a DataFrame of information to input to
BinCodeTransform() function, which bin-codes the specified input DataFrame.
Bin-coding is typically used to convert numeric data to categorical data by
binning the numeric data into multiple numeric bins (intervals).
The bins can have a fixed-width with auto-generated labels or can have variable
widths and labels.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
fit_data:
Optional Argument.
Specifies the input teradataml DataFrame containing binning parameters for
VARIABLE-WIDTH. It is not needed for EQUAL-WIDTH.
Types: teradataml DataFrame
target_columns:
Required Argument.
Specifies the input teradataml DataFrame columns to generate bins information
and binning parameters on.
Types: str OR list of Strings (str)
method_type:
Required Argument.
Specifies the Method Type which will be used for histogram computation.
Permitted Values: EQUAL-WIDTH, VARIABLE-WIDTH
Types: str
nbins:
Optional Argument.
Specifies the number of bins to be used when "method_type" is
EQUAL-WIDTH. It is not needed for VARIABLE-WIDTH. If one value is provided,
it applies to all target columns, if more than one value is
specified, "nbins" values apply to "target_columns" in the order
specified by the user.
Types: int OR list of ints
label_prefix:
Optional Argument.
Specify the label prefix to be used when MethodType is EQUAL-WIDTH. If
one value is provided, it applies to all target columns. If more than
one value is specified, "label_prefix" values apply to "target_columns"
in the order specified by the user.
Default Value: target column names.
Types: str OR list of strs
target_colnames:
Optional Argument.
Specifies the "fit_data" column which contains column name for
which bins are specified.
Default Value: ColumnName.
Types: str
minvalue_column:
Optional Argument.
Specifies the "fit_data" column which contains Min Value for the
specified bins.
Default Value: MinValue.
Types: str
maxvalue_column:
Optional Argument.
Specifies the "fit_data" column which contains Max Value for the
specified bins.
Default Value: MaxValue.
Types: str
label_column:
Optional Argument.
Specifies the "fit_data" column which contains label for which
bins are specified.
Default Value: Label.
Types: 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 function supports it, else an exception is raised.
RETURNS:
Instance of BincodeFit.
Output teradataml DataFrames can be accessed using attribute
references, such as BincodeFitObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the function in user space.
# 2. User can import the function, if it is available on the Vantage user is connected to.
# 3. To check the list of analytic functions available on the Vantage user connected to,
# use "display_analytic_functions()"
# 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()
# Import function BincodeFit.
from teradataml import BincodeFit
# Example 1: Transform the data using BincodeFit object with Variable-Width.
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'
)
# Print the result.
print(bin_code_1.output)
# Example 2: Transform the data using BincodeFit object with Equal-Width.
bin_code_2 = BincodeFit(data=titanic_data,
target_columns='age',
method_type='Equal-Width',
nbins=2,
label_prefix='label_prefix'
)
# Print the result.
print(bin_code_2.output)
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