mann_whitney_u_test_fl()

Applies to: ✅ Microsoft Fabric ✅ Azure Data Explorer

The function mann_whitney_u_test_fl() is a UDF (user-defined function) that performs the Mann-Whitney U Test.

Prerequisites

• The Python plugin must be enabled on the database. This is required for the inline Python used in the function.

Syntax

T | mann_whitney_u_test_fl(data1, data2, test_statistic,p_value [, use_continuity ])

Learn more about syntax conventions.

Parameters

Name	Type	Required	Description
data1	string	✔️	The name of the column containing the first set of data to be used for the test.
data2	string	✔️	The name of the column containing the second set of data to be used for the test.
test_statistic	string	✔️	The name of the column to store test statistic value for the results.
p_value	string	✔️	The name of the column to store p-value for the results.
use_continuity	bool		Determines if a continuity correction (1/2) is applied. Default is true.

Function definition

You can define the function by either embedding its code as a query-defined function, or creating it as a stored function in your database, as follows:

Query-defined

Define the function using the following let statement. No permissions are required.

Important

A let statement can't run on its own. It must be followed by a tabular expression statement. To run a working example of mann_whitney_u_test_fl(), see Example.

let mann_whitney_u_test_fl = (tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string, use_continuity:bool=true)
{
    let kwargs = bag_pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value, 'use_continuity', use_continuity);
    let code = ```if 1:
        from scipy import stats
        data1 = kargs["data1"]
        data2 = kargs["data2"]
        test_statistic = kargs["test_statistic"]
        p_value = kargs["p_value"]
        use_continuity = kargs["use_continuity"]
        def func(row):
            statistics = stats.mannwhitneyu(row[data1], row[data2], use_continuity=use_continuity)
            return statistics[0], statistics[1]
        result = df
        result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")
        ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
};
// Write your query to use the function here.

Stored

Define the stored function once using the following .create function. Database User permissions are required.

Important

You must run this code to create the function before you can use the function as shown in the Example.

.create-or-alter function with (folder = "Packages\\Stats", docstring = "Mann-Whitney U Test")
mann_whitney_u_test_fl(tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string, use_continuity:bool=true)
{
    let kwargs = bag_pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value, 'use_continuity', use_continuity);
    let code = ```if 1:
        from scipy import stats
        data1 = kargs["data1"]
        data2 = kargs["data2"]
        test_statistic = kargs["test_statistic"]
        p_value = kargs["p_value"]
        use_continuity = kargs["use_continuity"]
        def func(row):
            statistics = stats.mannwhitneyu(row[data1], row[data2], use_continuity=use_continuity)
            return statistics[0], statistics[1]
        result = df
        result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")
        ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
}

Example

The following example uses the invoke operator to run the function.

Query-defined

To use a query-defined function, invoke it after the embedded function definition.

let mann_whitney_u_test_fl = (tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string, use_continuity:bool=true)
{
    let kwargs = bag_pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value, 'use_continuity', use_continuity);
    let code = ```if 1:
        from scipy import stats
        data1 = kargs["data1"]
        data2 = kargs["data2"]
        test_statistic = kargs["test_statistic"]
        p_value = kargs["p_value"]
        use_continuity = kargs["use_continuity"]
        def func(row):
            statistics = stats.mannwhitneyu(row[data1], row[data2], use_continuity=use_continuity)
            return statistics[0], statistics[1]
        result = df
        result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")
        ```;
    tbl
    | evaluate python(typeof(*), code, kwargs)
};
datatable(id:string, sample1:dynamic, sample2:dynamic) [
'Test #1', dynamic([23.64, 20.57, 20.42]), dynamic([27.1, 22.12, 33.56]),
'Test #2', dynamic([20.85, 21.89, 23.41]), dynamic([35.09, 30.02, 26.52]),
'Test #3', dynamic([20.13, 20.5, 21.7, 22.02]), dynamic([32.2, 32.79, 33.9, 34.22])
]
| extend test_stat= 0.0, p_val = 0.0
| invoke mann_whitney_u_test_fl('sample1', 'sample2', 'test_stat', 'p_val')

Stored

Important

For this example to run successfully, you must first run the Function definition code to store the function.

datatable(id:string, sample1:dynamic, sample2:dynamic) [
'Test #1', dynamic([23.64, 20.57, 20.42]), dynamic([27.1, 22.12, 33.56]),
'Test #2', dynamic([20.85, 21.89, 23.41]), dynamic([35.09, 30.02, 26.52]),
'Test #3', dynamic([20.13, 20.5, 21.7, 22.02]), dynamic([32.2, 32.79, 33.9, 34.22])
]
| extend test_stat= 0.0, p_val = 0.0
| invoke mann_whitney_u_test_fl('sample1', 'sample2', 'test_stat', 'p_val')

Output

id	sample1	sample2	test_stat	p_val
Test #1	[23.64, 20.57, 20.42]	[27.1, 22.12, 33.56]	1	0.095215131912761986
Test #2	[20.85, 21.89, 23.41]	[35.09, 30.02, 26.52]	0	0.04042779918502612
Test #3	[20.13, 20.5, 21.7, 22.02]	[32.2, 32.79, 33.9, 34.22]	0	0.015191410988288745