| | |
- Statistics(data, columns=None, exclude_columns=None, extended_options='none', group_columns=None, statistical_method='population', stats_options=None, filter=None, gen_sql_only=False)
- DESCRIPTION:
Statistics analysis provides several common and not so common statistical measures
for numeric data columns. Extended options include additional analyses and measures
such as Values, Modes, Quantiles, and Ranks. Use statistical measures to understand
the characteristics and properties of each numeric column, and to look for outlying
values and anomalies.
Statistics analysis can be performed on columns of numeric or date data type. For
columns of type DATE, statistics other than count, minimum, maximum, and mean are
calculated by first converting to the number of days since 1900.
PARAMETERS:
data:
Required Argument.
Specifies the input data to perform Statistics analysis.
Types: teradataml DataFrame
columns:
Required Argument.
Specifies the name(s) of the column(s) to analyze. Occasionally, it can also
accept permitted strings to specify all columns, or all numeric columns, or
all numeric and date columns.
Permitted Values:
* Name(s) of the column(s) in "data".
* Pre-defined strings:
* 'all' - all columns
* 'allnumeric' - all numeric columns
* 'allnumericanddate' - all numeric and date columns
Types: str OR list of Strings (str)
exclude_columns:
Optional Argument.
Specifies the name(s) of the column(s) to exclude from the analysis, if a
column specifier such as 'all', 'allnumeric', 'allnumericanddate' is used
in the "columns" argument.
Types: str OR list of Strings (str)
extended_options:
Optional Argument.
Specifies the extended options for calculating statistics.
Permitted Values: 'all', 'none', 'modes', 'quantiles', 'values', 'rank'
Default Value: 'none'
Types: str OR list of Strings (str)
group_columns:
Optional Argument.
Specifies the name(s) of column(s) to perform separate analysis for each group.
Types: str OR list of Strings (str)
statistical_method:
Optional Argument.
Specifies the statistical method.
Permitted Values: 'sample', 'population'
Default Value: 'population'
Types: str
stats_options:
Optional Argument.
Specifies the basic statistics to be calculated.
Permitted Values:
* all
* count (cnt)
* minimum (min)
* maximum (max)
* mean
* standarddeviation (std)
* skewness (skew)
* kurtosis (kurt)
* standarderror (ste)
* coefficientofvariance (cv)
* variance (var)
* sum
* uncorrectedsumofsquares (uss)
* correctedsumofsquares (css)
Default Value: ['cnt', 'min', 'max', 'mean', 'std']
Types: str OR list of Strings (str)
filter:
Optional Argument.
Specifies the clause to filter rows selected for analysis within Statistics.
For example,
filter = "cust_id > 0"
Types: str
gen_sql_only:
Optional Argument.
Specifies whether to generate only SQL for the function.
When set to True, function SQL is generated, not executed, which can be accessed
using show_query() method, otherwise SQL is just executed but not returned.
Default Value: False
Types: bool
RETURNS:
An instance of Statistics.
Output teradataml DataFrames can be accessed using attribute references, such as
StatisticsObj.<attribute_name>.
Output teradataml DataFrame attribute name is: result.
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. To execute Vantage Analytic Library functions,
# a. import "valib" object from teradataml.
# b. set 'configure.val_install_location' to the database name where Vantage
# analytic library functions are installed.
# 2. Datasets used in these examples can be loaded using Vantage Analytic Library
# installer.
# Import valib object from teradataml to execute this function.
from teradataml import valib
# Set the 'configure.val_install_location' variable.
from teradataml import configure
configure.val_install_location = "SYSLIB"
# Create required teradataml DataFrame.
df = DataFrame("customer")
print(df)
# Example 1: Perform Statistics analysis using default values on 'income' column.
obj = valib.Statistics(data=df, columns="income")
# Print the results.
print(obj.result)
# Example 2: Perform Statistics analysis on 'income' column with values grouped
# by 'gender' and only for rows with income greater than 0.
obj = valib.Statistics(data=df,
columns="income",
group_columns="gender",
filter="income > 0")
# Print the results.
print(obj.result)
# Example 3: Perform Statistics analysis requesting all statistical measures
# and extended options.
obj = valib.Statistics(data=df,
columns="income",
stats_options="all",
extended_options="all")
# Print the results.
print(obj.result)
# Example 4: Perform Statistics analysis requesting specific statistical measures
# and extended options and return sample statistics.
obj = valib.Statistics(data=df,
columns="income",
stats_options=["cnt", "max", "min", "'mean",
"css", "uss", "kurt", "skew"],
extended_options=["modes", "rank"],
statistical_method="sample")
# Print the results.
print(obj.result)
# Example 5: Generate only SQL for the function, but do not execute the same.
obj = valib.Statistics(data=df,
columns="income",
group_columns="gender",
filter="income > 0",
gen_sql_only=True)
# Print the generated SQL.
print(obj.show_query("sql"))
# Print both generated SQL and stored procedure call.
print(obj.show_query("both"))
# Print the stored procedure call.
print(obj.show_query())
print(obj.show_query("sp"))
|