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How to use one_sample_ttest method in pandera

Best Python code snippet using pandera_python

test_ttests.py

Source:test_ttests.py

...129        passed = 0130        n_iter = 500131        for _ in range(n_iter):132            data1 = rng.normal(-5, 2, 100)133            ttest = one_sample_ttest(data1, -5)134            if ttest['P_value'] < .05:135                passed +=1136        self.assertAlmostEqual(passed / n_iter, .05, delta=.01)        137    def test_onesample_left_tailed(self):138        """Testing onesample left tailed."""139        rng = np.random.default_rng(9876138761251)140        passed = 0141        n_iter = 500142        for _ in range(n_iter):143            data1 = rng.normal(15, 1, 100)144            ttest = one_sample_ttest(data1, 15, 'left')145            if ttest['P_value'] < .05:146                passed +=1147        self.assertAlmostEqual(passed / n_iter, .05, delta=.01)        148    def test_one_sample_right_tailed(self):149        """Testing onesample right tailed."""150        rng = np.random.default_rng(615419864354)151        passed = 0152        n_iter = 500153        for _ in range(n_iter):154            data1 = rng.normal(12.2, 1, 100)155            ttest = one_sample_ttest(data1, 12.2, 'right')156            if ttest['P_value'] < .05:157                passed +=1158        self.assertAlmostEqual(passed / n_iter, .05, delta=.01)159class TestMiscTest(unittest.TestCase):160    """Test Fixture for random ttests."""161    def test_fail_tailed_option(self):162        """Testing bad tailed option."""163        with self.assertRaises(ValueError):164            _p_value_and_confidence_intervals(2.3, 100, 'greater')165    def test_confidence_intervals(self):166        """Testing the confidence interval test."""167        # Taken from a T-Test table168        # Two Tailed169        p, ci = _p_value_and_confidence_intervals(2.228, 10, 'two')...

StatsPythonRaw.py

Source:StatsPythonRaw.py

...34    except Exception as inst:35        report_exception(inst)363738def one_sample_ttest(df: pd.DataFrame) -> None:39    """ Perform a one-sample t-test """40    try:41        weight: list = df.iloc[:, 0].tolist()42        results = Stats.OneSampleTTest(25, weight)43        print_results(results, "One-sample t-test.")44    except Exception as inst:45        report_exception(inst)464748def two_sample_ttest(df_x1: pd.DataFrame, df_x2: pd.DataFrame) -> None:49    """ Run more statistical tests """50    try:51        x1: list = df_x1.iloc[:, 0].tolist()52        x2: list = df_x2.iloc[:, 0].tolist()5354        results: dict = Stats.TwoSampleTTest(x1, x2)55        print_results(results, "Two-sample t-test.")56    except Exception as inst:57        report_exception(inst)585960def plot_data(df_x1: pd.DataFrame, df_x2: pd.DataFrame) -> None:61    """ Plot the data """62    x1: list = df_x1.iloc[:, 0].tolist()63    x2: list = df_x2.iloc[:, 0].tolist()64    data: list = [x1, x2]65    66    green_diamond = dict(markerfacecolor='g', marker='D')67    fig1, ax = plt.subplots()68    ax.set_title('US versus Japan Petrol Consumption (mpg)')69    ax.set_xticklabels(['US', 'Japan'])70    ax.boxplot(data, flierprops=green_diamond)71    plt.show()727374if __name__ == "__main__":75    """ Define standard data sets used elsewhere """76    xs: list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]77    ys: list = [1, 3, 2, 5, 7, 8, 8, 9, 10, 12]7879    run_descriptive_statistics(xs)80    run_linear_regression(xs, ys)8182    ttest_summary_data()8384    filename: str = "../Data/weight.txt"8586    # Read in data frame87    df: pd.DataFrame = pd.read_csv(filename, header=None)8889    one_sample_ttest(df)9091    us_df: pd.DataFrame = pd.read_csv("../Data/us-mpg.txt", header=None)92    jp_df: pd.DataFrame = pd.read_csv("../Data/jp-mpg.txt", header=None)9394    plot_data(us_df, jp_df)95
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compare-2maps-within-subjects.py

Source:compare-2maps-within-subjects.py

...5from nilearn.image import math_img, mean_img, threshold_img6from nistats.second_level_model import SecondLevelModel7from nistats.thresholding import map_threshold8from nilearn.plotting import plot_glass_brain9def one_sample_ttest(filenames, name):10    design_matrix = pd.DataFrame([1] * len(filenames), columns=['intercept'])11    second_level_model = SecondLevelModel().fit(filenames, design_matrix=design_matrix)12    z_map = second_level_model.compute_contrast(output_type='z_score')13    nib.save(zmap, name + '.nii')14    thmap, threshold1 = map_threshold(z_map, level=.001, height_control='fpr', cluster_threshold=10)15    display = plot_glass_brain(thmap, display_mode='lzry', threshold=0, colorbar=True)16    display.savefig(name + '.png')17    display.close()18def compute_diffmaps(scans_A, scans_B):19    assert len(scans_A) == len(scans_B)20    list_fnames = []21    for sub, a, b in enumerate(zip(scans_A, scans_B)):22        diff =  math_img('a - b', img1=a, img=b)23        fname = f'diff_{sub:03}'24        nib.save(fname, diff)25        list_fnames.append(fname)26    return fnames27def compare_2conds_within_subjects(scans_A, scans_B, name):28    files = compute_diffmaps(scans_A, scans_B)...

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