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- QQNorm(data=None, target_columns=None, rank_columns=None, output_columns=None, accumulate=None, **generic_arguments)
- DESCRIPTION:
Function determines if values in input data columns follow normal distribution or not.
It returns the quantiles of the column values and corresponding theoretical quantile
values from a normal distribution. If the column values are normally distributed, then
the quantiles of column values and normal quantile values appear in a straight line
when plotted on a 2D graph.
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" to generate standard normal quantiles.
Types: str OR list of Strings (str)
rank_columns:
Required Argument.
Specifies the name(s) of the column(s) in "data" containing rank values for "target_columns".
Types: str OR list of Strings (str)
output_columns:
Optional Argument.
Specifies the name(s) of the output column(s) to be generated that contain the theoretical
quantiles of the target column(s). If not specified, name(s) will be generated as
"<column name in target_columns>_theoretical_quantiles".
Types: str OR list of strs
accumulate:
Optional Argument.
Specifies the names of input teradataml DataFrame columns to copy 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 QQNorm.
Output teradataml DataFrames can be accessed using attribute
references, such as QQNormObj.<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 sample rank_df DataFrame.
import pandas as pd
rank_df = pd.DataFrame({"age": [16, 33, 58, 71, 80],
"fare": [86.7, 5, 29.7, 34.6542, 30],
"rank_age": [1, 2, 3, 4, 5],
"rank_fare": [5, 1, 2, 4, 3]})
from teradataml import copy_to_sql
copy_to_sql(rank_df, "rank_table")
# Create teradataml DataFrame object.
rank_df = DataFrame.from_table("rank_table")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function QQNorm.
from teradataml import QQNorm
# Example 1: Get theoretical quantile values for 'age' and 'fare'.
obj = QQNorm(data=rank_df,
target_columns=["age", "fare"],
rank_columns=["rank_age", "rank_fare"])
# Print the result DataFrame.
print(obj.result)
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