How to use the pingouin.remove_na function in pingouin

With the correction for uniform marginals

    >>> r, pval = circ_corrcc(x, y, correction_uniform=True)
    >>> print(r, pval)
    0.547 0.28585306869206784
    """
    from scipy.stats import norm
    x = np.asarray(x)
    y = np.asarray(y)

    # Check size
    if x.size != y.size:
        raise ValueError('x and y must have the same length.')

    # Remove NA
    x, y = remove_na(x, y, paired=True)
    n = x.size

    # Compute correlation coefficient
    x_sin = np.sin(x - circmean(x))
    y_sin = np.sin(y - circmean(y))

    if not correction_uniform:
        # Similar to np.corrcoef(x_sin, y_sin)[0][1]
        r = np.sum(x_sin * y_sin) / np.sqrt(np.sum(x_sin**2) *
                                            np.sum(y_sin**2))
    else:
        r_minus = np.abs(np.sum(np.exp((x - y) * 1j)))
        r_plus = np.abs(np.sum(np.exp((x + y) * 1j)))
        denom = 2 * np.sqrt(np.sum(x_sin ** 2) * np.sum(y_sin ** 2))
        r = (r_minus - r_plus) / denom

from pingouin import (power_ttest, power_ttest2n, compute_effsize)

    # Check tails
    possible_tails = ['two-sided', 'one-sided', 'greater', 'less']
    assert tail in possible_tails, 'Invalid tail argument.'

    x = np.asarray(x)
    y = np.asarray(y)

    if x.size != y.size and paired:
        warnings.warn("x and y have unequal sizes. Switching to "
                      "paired == False. Check your data.")
        paired = False

    # Remove rows with missing values
    x, y = remove_na(x, y, paired=paired)
    nx, ny = x.size, y.size

    if ny == 1:
        # Case one sample T-test
        tval, pval = ttest_1samp(x, y)
        dof = nx - 1
        se = np.sqrt(x.var(ddof=1) / nx)
    if ny > 1 and paired is True:
        # Case paired two samples T-test
        # Do not compute if two arrays are identical (avoid SciPy warning)
        if np.array_equal(x, y):
            warnings.warn("x and y are equals. Cannot compute T or p-value.")
            tval, pval = np.nan, np.nan
        else:
            tval, pval = ttest_rel(x, y)
        dof = nx - 1

>>> x = [0.785, 1.570, 3.141, 0.839, 5.934]
    >>> y = [1.593, 1.291, -0.248, -2.892, 0.102]
    >>> r, pval = circ_corrcl(x, y)
    >>> print(r, pval)
    0.109 0.9708899750629237
    """
    from scipy.stats import pearsonr, chi2
    x = np.asarray(x)
    y = np.asarray(y)

    # Check size
    if x.size != y.size:
        raise ValueError('x and y must have the same length.')

    # Remove NA
    x, y = remove_na(x, y, paired=True)
    n = x.size

    # Compute correlation coefficent for sin and cos independently
    rxs = pearsonr(y, np.sin(x))[0]
    rxc = pearsonr(y, np.cos(x))[0]
    rcs = pearsonr(np.sin(x), np.cos(x))[0]

    # Compute angular-linear correlation (equ. 27.47)
    r = np.sqrt((rxc**2 + rxs**2 - 2 * rxc * rxs * rcs) / (1 - rcs**2))

    # Compute p-value
    pval = chi2.sf(n * r**2, 2)
    pval = pval / 2 if tail == 'one-sided' else pval
    return np.round(r, 3), pval

>>> np.median(np.array(x) - np.array(y))
    -1.5

    The median is negative, so Pingouin will test for the alternative
    hypothesis that the median of the differences is negative (= less than 0).

    >>> pg.wilcoxon(x, y, tail='one-sided')  # Equivalent to tail = 'less'
              W-val  tail     p-val    RBC   CLES
    Wilcoxon   20.5  less  0.142883 -0.379  0.583
    """
    x = np.asarray(x)
    y = np.asarray(y)

    # Remove NA
    x, y = remove_na(x, y, paired=True)

    # Check tails
    possible_tails = ['two-sided', 'one-sided', 'greater', 'less']
    assert tail in possible_tails, 'Invalid tail argument.'
    if tail == 'one-sided':
        # Detect the direction of the test based on the median
        tail = 'less' if np.median(x - y) < 0 else 'greater'

    # Compute test
    wval, pval = scipy.stats.wilcoxon(x, y, zero_method='wilcox',
                                      correction=True, alternative=tail)

    # Effect size 1: common language effect size (McGraw and Wong 1992)
    diff = x[:, None] - y
    cles = max((diff < 0).sum(), (diff > 0).sum()) / diff.size

MWU   97.0  less  0.00278  0.515  0.758

    Or simply leave it to Pingouin, using the `'one-sided'` argument, in which
    case Pingouin will compare the medians of ``x`` and ``y`` and select the
    most appropriate tail based on that:

    >>> # Since np.median(x) < np.median(y), this is equivalent to tail='less'
    >>> pg.mwu(x, y, tail='one-sided')
         U-val  tail    p-val    RBC   CLES
    MWU   97.0  less  0.00278  0.515  0.758
    """
    x = np.asarray(x)
    y = np.asarray(y)

    # Remove NA
    x, y = remove_na(x, y, paired=False)

    # Check tails
    possible_tails = ['two-sided', 'one-sided', 'greater', 'less']
    assert tail in possible_tails, 'Invalid tail argument.'
    if tail == 'one-sided':
        # Detect the direction of the test based on the median
        tail = 'less' if np.median(x) < np.median(y) else 'greater'

    uval, pval = scipy.stats.mannwhitneyu(x, y, use_continuity=True,
                                          alternative=tail)

    # Effect size 1: common language effect size (McGraw and Wong 1992)
    diff = x[:, None] - y
    cles = max((diff < 0).sum(), (diff > 0).sum()) / diff.size

    # Effect size 2: rank biserial correlation (Wendt 1972)