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- CategoricalSummary(data=None, target_columns=None, **generic_arguments)
- DESCRIPTION:
The CategoricalSummary() function displays the distinct values and their counts for
each specified input DataFrame column.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
target_columns:
Required Argument.
Specifies the name(s) of the column(s) in "data" for which
categorical summary needs to be determined.
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 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.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table
or not. When set to True, results are stored in a 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
SQL Engine function supports, else an exception is raised.
RETURNS:
Instance of CategoricalSummary.
Output teradataml DataFrames can be accessed using attribute
references, such as CategoricalSummaryObj.<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")
# Create teradataml DataFrame object.
titanic_data = DataFrame.from_table("titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Example: Find the distinct values and counts for column 'sex'.
obj = CategoricalSummary(data=titanic_data,
target_columns="sex"
)
# Print the result DataFrame.
print(obj.result)
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