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- SimpleImputeFit(data=None, stats_columns=None, literals_columns=None, partition_column=None, stats=None, literals=None, **generic_arguments)
- DESCRIPTION:
SimpleImputeFit() function outputs values to substitute for missing
values in the input data. The output values are input to SimpleImputeTransform()
function, which makes the substitutions.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
stats_columns:
Required Argument.
Specifies the name(s) of the column(s) in "data" for which to calculate
the statistics.
Types: str OR list of Strings (str)
literals_columns:
Optional Argument.
Specifies the name(s) of the column(s) in "data" for which to impute literals.
Types: str OR list of Strings (str)
partition_column:
Optional Argument.
Specifies the name(s) of the column(s) in "data" to partition on.
Types: str OR list of Strings (str)
stats:
Required Argument.
Specifies the stats to compute on input teradataml DataFrame columns.
Permitted Values: MIN, MAX, MEAN, MEDIAN, MODE
Types: str OR list of Strings (str)
literals:
Optional Argument.
Specifies the literal value to impute on input teradataml DataFrame
columns.
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 SimpleImputeFit.
Output teradataml DataFrames can be accessed using attribute
references, such as SimpleImputeFitObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. output
2. output_data
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLE:
# 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"])
# Create teradataml DataFrame.
titanic = DataFrame.from_table("titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function SimpleImputeFit.
from teradataml import SimpleImputeFit
# Example 1: Create stats for "fare" column and impute value "2"
# in "pclass" column.
fit_obj = SimpleImputeFit(data=titanic,
stats_columns="fare",
literals_columns="pclass",
partition_column="sex",
stats="median",
literals="2")
# Print the result DataFrame.
print(fit_obj.output)
print(fit_obj.output_data)
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