two_sample_t_test_fl()

The function two_sample_t_test_fl() performs the Two-Sample T-Test.

Note

• two_sample_t_test_fl() is a UDF (user-defined function). For more information, see usage.
• This function contains inline Python and requires enabling the python() plugin on the cluster.
• If the assumption is that the two datasets to be compared have different variances, we suggest using the native welch_test().

Syntax

T | invoke two_sample_t_test_fl(data1, data2, test_statistic,p_value, equal_var)

Arguments

• data1: The name of the column containing the first set of data to be used for the test.
• data2: The name of the column containing the second set of data to be used for the test.
• test_statistic: The name of the column to store test statistic value for the results.
• p_value: The name of the column to store p-value for the results.
• equal_var: If true (default), performs a standard independent 2 sample test that assumes equal population variances. If false, performs Welch’s t-test, which does not assume equal population variance. As mentioned above, consider using the native welch_test().

Usage

two_sample_t_test_fl() is a user-defined tabular function, to be applied using the invoke operator. You can either embed its code in your query, or install it in your database. There are two usage options: ad hoc and persistent usage. See the below tabs for examples.

Ad hoc

For ad hoc usage, embed its code using the let statement. No permission is required.

let two_sample_t_test_fl = (tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string, equal_var:bool=true)
{
    let kwargs = pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value, 'equal_var', equal_var);
    let code =
        'from scipy import stats\n'
        'import pandas\n'
        '\n'
        'data1 = kargs["data1"]\n'
        'data2 = kargs["data2"]\n'
        'test_statistic = kargs["test_statistic"]\n'
        'p_value = kargs["p_value"]\n'
        'equal_var = kargs["equal_var"]\n'
        '\n'
        'def func(row):\n'
        '    statistics = stats.ttest_ind(row[data1], row[data2], equal_var=equal_var)\n'
        '    return statistics[0], statistics[1]\n'
        'result = df\n'
        'result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")\n'
    ;
    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 two_sample_t_test_fl('sample1', 'sample2', 'test_stat', 'p_val')

Persistent

For persistent usage, use .create function. Creating a function requires database user permission.

One-time installation

.create-or-alter function with (folder = "Packages\\Stats", docstring = "Two-Sample t-Test")
two_sample_t_test_fl(tbl:(*), data1:string, data2:string, test_statistic:string, p_value:string, equal_var:bool=true)
{
    let kwargs = pack('data1', data1, 'data2', data2, 'test_statistic', test_statistic, 'p_value', p_value, 'equal_var', equal_var);
    let code =
        'from scipy import stats\n'
        'import pandas\n'
        '\n'
        'data1 = kargs["data1"]\n'
        'data2 = kargs["data2"]\n'
        'test_statistic = kargs["test_statistic"]\n'
        'p_value = kargs["p_value"]\n'
        'equal_var = kargs["equal_var"]\n'
        '\n'
        'def func(row):\n'
        '    statistics = stats.ttest_ind(row[data1], row[data2], equal_var=equal_var)\n'
        '    return statistics[0], statistics[1]\n'
        'result = df\n'
        'result[[test_statistic, p_value]]  = df.apply(func, axis=1, result_type = "expand")\n'
    ;
    tbl
    | evaluate python(typeof(*), code, kwargs)
}

Usage

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 two_sample_t_test_fl('sample1', 'sample2', 'test_stat', 'p_val')

id             sample1                    sample2                test_stat          p_val
Test #1, [23.64, 20.57, 20.42], [27.1, 22.12, 33.56], -1.7415675457565645, 0.15655096653487446
Test #2, [20.85, 21.89, 23.41], [35.09, 30.02, 26.52], -3.2711673491022579, 0.030755331219276136
Test #3, [20.13, 20.5, 21.7, 22.02], [32.2, 32.79, 33.9, 34.22], -18.5515946201742, 1.5823717131966134E-06