Python3.12: Two tests are failing with AssertionError

[penguinpee]

Python 3.12 was unleashed recently in Fedora. Building pingouin against Python 3.12 results in two tests failing:

=================================== FAILURES ===================================
______________________ TestCorrelation.test_partial_corr _______________________
self = <pingouin.tests.test_correlation.TestCorrelation testMethod=test_partial_corr>
    def test_partial_corr(self):
        """Test function partial_corr.
    
        Compare with the R package ppcor (which is also used by JASP).
        """
        df = read_dataset("partial_corr")
        #######################################################################
        # PARTIAL CORRELATION
        #######################################################################
        # With one covariate
>       pc = partial_corr(data=df, x="x", y="y", covar="cv1")
df         =            x         y       cv1       cv2       cv3
0   5.230740  7.141488  1.825430 -1.085631 -0.255619
1   6.013931...681  1.754886
28  3.697089  6.129922  3.138867 -0.140069  1.495644
29  5.466289  7.747689  1.053789 -0.861755  1.069393
self       = <pingouin.tests.test_correlation.TestCorrelation testMethod=test_partial_corr>
pingouin/tests/test_correlation.py:137: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data =            x         y       cv1       cv2       cv3
0   5.230740  7.141488  1.825430 -1.085631 -0.255619
1   6.013931...681  1.754886
28  3.697089  6.129922  3.138867 -0.140069  1.495644
29  5.466289  7.747689  1.053789 -0.861755  1.069393
x = 'x', y = 'y', covar = 'cv1', x_covar = None, y_covar = None
alternative = 'two-sided', method = 'pearson'
    @pf.register_dataframe_method
    def partial_corr(
        data=None,
        x=None,
        y=None,
        covar=None,
        x_covar=None,
        y_covar=None,
        alternative="two-sided",
        method="pearson",
    ):
        """Partial and semi-partial correlation.
    
        Parameters
        ----------
        data : :py:class:`pandas.DataFrame`
            Pandas Dataframe. Note that this function can also directly be used
            as a :py:class:`pandas.DataFrame` method, in which case this argument
            is no longer needed.
        x, y : string
            x and y. Must be names of columns in ``data``.
        covar : string or list
            Covariate(s). Must be a names of columns in ``data``. Use a list if
            there are two or more covariates.
        x_covar : string or list
            Covariate(s) for the ``x`` variable. This is used to compute
            semi-partial correlation (i.e. the effect of ``x_covar`` is removed
            from ``x`` but not from ``y``). Only one of ``covar``,  ``x_covar`` and
            ``y_covar`` can be specified.
        y_covar : string or list
            Covariate(s) for the ``y`` variable. This is used to compute
            semi-partial correlation (i.e. the effect of ``y_covar`` is removed
            from ``y`` but not from ``x``). Only one of ``covar``,  ``x_covar`` and
            ``y_covar`` can be specified.
        alternative : string
            Defines the alternative hypothesis, or tail of the partial correlation. Must be one of
            "two-sided" (default), "greater" or "less". Both "greater" and "less" return a one-sided
            p-value. "greater" tests against the alternative hypothesis that the partial correlation is
            positive (greater than zero), "less" tests against the hypothesis that the partial
            correlation is negative.
        method : string
            Correlation type:
    
            * ``'pearson'``: Pearson :math:`r` product-moment correlation
            * ``'spearman'``: Spearman :math:`\\rho` rank-order correlation
    
        Returns
        -------
        stats : :py:class:`pandas.DataFrame`
    
            * ``'n'``: Sample size (after removal of missing values)
            * ``'r'``: Partial correlation coefficient
            * ``'CI95'``: 95% parametric confidence intervals around :math:`r`
            * ``'p-val'``: p-value
    
        See also
        --------
        corr, pcorr, pairwise_corr, rm_corr
    
        Notes
        -----
        Partial correlation [1]_ measures the degree of association between ``x``
        and ``y``, after removing the effect of one or more controlling variables
        (``covar``, or :math:`Z`). Practically, this is achieved by calculating the
        correlation coefficient between the residuals of two linear regressions:
    
        .. math:: x \\sim Z, y \\sim Z
    
        Like the correlation coefficient, the partial correlation
        coefficient takes on a value in the range from –1 to 1, where 1 indicates a
        perfect positive association.
    
        The semipartial correlation is similar to the partial correlation,
        with the exception that the set of controlling variables is only
        removed for either ``x`` or ``y``, but not both.
    
        Pingouin uses the method described in [2]_ to calculate the (semi)partial
        correlation coefficients and associated p-values. This method is based on
        the inverse covariance matrix and is significantly faster than the
        traditional regression-based method. Results have been tested against the
        `ppcor <https://cran.r-project.org/web/packages/ppcor/index.html>`_
        R package.
    
        .. important:: Rows with missing values are automatically removed from
            data.
    
        References
        ----------
        .. [1] https://en.wikipedia.org/wiki/Partial_correlation
    
        .. [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681537/
    
        Examples
        --------
        1. Partial correlation with one covariate
    
        >>> import pingouin as pg
        >>> df = pg.read_dataset('partial_corr')
        >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3)
                  n      r         CI95%  p-val
        pearson  30  0.568  [0.25, 0.77]  0.001
    
        2. Spearman partial correlation with several covariates
    
        >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.521  [0.18, 0.75]  0.005
    
        3. Same but one-sided test
    
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 alternative="greater", method='spearman').round(3)
                   n      r        CI95%  p-val
        spearman  30  0.521  [0.24, 1.0]  0.003
    
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 alternative="less", method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.521  [-1.0, 0.72]  0.997
    
        4. As a pandas method
    
        >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.578  [0.27, 0.78]  0.001
    
        5. Partial correlation matrix (returns only the correlation coefficients)
    
        >>> df.pcorr().round(3)
                 x      y    cv1    cv2    cv3
        x    1.000  0.493 -0.095  0.130 -0.385
        y    0.493  1.000 -0.007  0.104 -0.002
        cv1 -0.095 -0.007  1.000 -0.241 -0.470
        cv2  0.130  0.104 -0.241  1.000 -0.118
        cv3 -0.385 -0.002 -0.470 -0.118  1.000
    
        6. Semi-partial correlation on x
    
        >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3)
                  n      r        CI95%  p-val
        pearson  30  0.463  [0.1, 0.72]  0.015
        """
        from pingouin.utils import _flatten_list
    
        # Safety check
        assert alternative in [
            "two-sided",
            "greater",
            "less",
        ], "Alternative must be one of 'two-sided' (default), 'greater' or 'less'."
        assert method in [
            "pearson",
            "spearman",
        ], 'only "pearson" and "spearman" are supported for partial correlation.'
        assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame."
        assert data.shape[0] > 2, "Data must have at least 3 samples."
        if covar is not None and (x_covar is not None or y_covar is not None):
            raise ValueError("Cannot specify both covar and {x,y}_covar.")
        if x_covar is not None and y_covar is not None:
            raise ValueError("Cannot specify both x_covar and y_covar.")
        assert x != covar, "x and covar must be independent"
        assert y != covar, "y and covar must be independent"
        assert x != y, "x and y must be independent"
        if isinstance(covar, list):
            assert x not in covar, "x and covar must be independent"
            assert y not in covar, "y and covar must be independent"
        # Check that columns exist
        col = _flatten_list([x, y, covar, x_covar, y_covar])
>       assert all([c in data for c in col]), "columns are not in dataframe."
E       AssertionError: columns are not in dataframe.
_flatten_list = <function _flatten_list at 0x7f69387c63e0>
alternative = 'two-sided'
col        = ['x', 'y', 'cv1', None, None]
covar      = 'cv1'
data       =            x         y       cv1       cv2       cv3
0   5.230740  7.141488  1.825430 -1.085631 -0.255619
1   6.013931...681  1.754886
28  3.697089  6.129922  3.138867 -0.140069  1.495644
29  5.466289  7.747689  1.053789 -0.861755  1.069393
method     = 'pearson'
x          = 'x'
x_covar    = None
y          = 'y'
y_covar    = None
pingouin/correlation.py:843: AssertionError
_______________________ TestPairwise.test_pairwise_corr ________________________
self = <pingouin.tests.test_pairwise.TestPairwise testMethod=test_pairwise_corr>
    def test_pairwise_corr(self):
        """Test function pairwise_corr"""
        # Load JASP Big 5 DataSets (remove subject column)
        data = read_dataset("pairwise_corr").iloc[:, 1:]
        stats = pairwise_corr(data=data, method="pearson", alternative="two-sided")
        jasp_rval = [-0.350, -0.01, -0.134, -0.368, 0.267, 0.055, 0.065, 0.159, -0.013, 0.159]
        assert np.allclose(stats["r"].round(3).to_numpy(), jasp_rval)
        assert stats["n"].to_numpy()[0] == 500
        # Correct for multiple comparisons
        pairwise_corr(data=data, method="spearman", alternative="greater", padjust="bonf")
        # Check with a subset of columns
        pairwise_corr(data=data, columns=["Neuroticism", "Extraversion"])
        with pytest.raises(ValueError):
            pairwise_corr(data=data, columns="wrong")
        # Check with non-numeric columns
        data["test"] = "test"
        pairwise_corr(data=data, method="pearson")
        # Check different variation of product / combination
        n = data.shape[0]
        data["Age"] = np.random.randint(18, 65, n)
        data["IQ"] = np.random.normal(105, 1, n)
        data["One"] = 1
        data["Gender"] = np.repeat(["M", "F"], int(n / 2))
        pairwise_corr(data, columns=["Neuroticism", "Gender"], method="shepherd")
        pairwise_corr(data, columns=["Neuroticism", "Extraversion", "Gender"])
        pairwise_corr(data, columns=["Neuroticism"])
        pairwise_corr(data, columns="Neuroticism", method="skipped")
        pairwise_corr(data, columns=[["Neuroticism"]], method="spearman")
        pairwise_corr(data, columns=[["Neuroticism"], None], method="percbend")
        pairwise_corr(data, columns=[["Neuroticism", "Gender"], ["Age"]])
        pairwise_corr(data, columns=[["Neuroticism"], ["Age", "IQ"]])
        pairwise_corr(data, columns=[["Age", "IQ"], []])
        pairwise_corr(data, columns=["Age", "Gender", "IQ", "Wrong"])
        pairwise_corr(data, columns=["Age", "Gender", "Wrong"])
        # Test with no good combinations
        with pytest.raises(ValueError):
            pairwise_corr(data, columns=["Gender", "Gender"])
        # Test when one column has only one unique value
        pairwise_corr(data=data, columns=["Age", "One", "Gender"])
        stats = pairwise_corr(data, columns=["Neuroticism", "IQ", "One"])
        assert stats.shape[0] == 1
        # Test with covariate
>       pairwise_corr(data, covar="Age")
data       =      Neuroticism  Extraversion  Openness  ...          IQ  One Gender
0        2.47917       4.20833   3.93750  ...  1...  105.904904    1      F
499      2.54167       3.56250   3.14583  ...  105.719773    1      F
[500 rows x 10 columns]
jasp_rval  = [-0.35, -0.01, -0.134, -0.368, 0.267, 0.055, ...]
n          = 500
self       = <pingouin.tests.test_pairwise.TestPairwise testMethod=test_pairwise_corr>
stats      =              X   Y   method  ...     p-unc   BF10     power
0  Neuroticism  IQ  pearson  ...  0.670885  0.061  0.070912
[1 rows x 10 columns]
pingouin/tests/test_pairwise.py:681: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
pingouin/pairwise.py:1458: in pairwise_corr
    cor_st = partial_corr(
        X          = array(['Neuroticism', 'Neuroticism', 'Neuroticism', 'Neuroticism',
       'Neuroticism', 'Neuroticism', 'Extraversion'...eness',
       'Agreeableness', 'Agreeableness', 'Conscientiousness',
       'Conscientiousness', 'Age'], dtype='<U17')
        Y          = array(['Extraversion', 'Openness', 'Agreeableness', 'Conscientiousness',
       'Age', 'IQ', 'Openness', 'Agreeablenes...ableness', 'Conscientiousness', 'Age', 'IQ',
       'Conscientiousness', 'Age', 'IQ', 'Age', 'IQ', 'IQ'], dtype='<U17')
        alternative = 'two-sided'
        col1       = 'Neuroticism'
        col2       = 'Extraversion'
        columns    = None
        combs      = array([['Neuroticism', 'Extraversion'],
       ['Neuroticism', 'Openness'],
       ['Neuroticism', 'Agreeableness'],
 ...', 'IQ'],
       ['Conscientiousness', 'Age'],
       ['Conscientiousness', 'IQ'],
       ['Age', 'IQ']], dtype='<U17')
        corr       = <function corr at 0x7f69378bfc40>
        covar      = ['Age']
        data       =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
0        2.47917       4.20833   3.9...8  105.904904
499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
[500 rows x 7 columns]
        i          = 0
        keys       = ['Neuroticism', 'Extraversion', 'Openness', 'Agreeableness', 'Conscientiousness', 'Age', ...]
        method     = 'pearson'
        multi_index = False
        nan_policy = 'pairwise'
        old_options = {'round': None, 'round.column.BF10': <function _format_bf at 0x7f693a3de660>, 'round.column.CI95%': 2}
        padjust    = 'none'
        partial_corr = <function partial_corr at 0x7f69378bfce0>
        stats      =                     X                  Y   method  ... p-val BF10 power
0         Neuroticism       Extraversion  pear...n  ...   NaN  NaN   NaN
14  Conscientiousness                 IQ  pearson  ...   NaN  NaN   NaN
[15 rows x 11 columns]
        traverse   = <function pairwise_corr.<locals>.traverse at 0x7f692d9ee3e0>
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
data =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
0        2.47917       4.20833   3.9...8  105.904904
499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
[500 rows x 7 columns]
x = 'Neuroticism', y = 'Extraversion', covar = ['Age'], x_covar = None
y_covar = None, alternative = 'two-sided', method = 'pearson'
    @pf.register_dataframe_method
    def partial_corr(
        data=None,
        x=None,
        y=None,
        covar=None,
        x_covar=None,
        y_covar=None,
        alternative="two-sided",
        method="pearson",
    ):
        """Partial and semi-partial correlation.
    
        Parameters
        ----------
        data : :py:class:`pandas.DataFrame`
            Pandas Dataframe. Note that this function can also directly be used
            as a :py:class:`pandas.DataFrame` method, in which case this argument
            is no longer needed.
        x, y : string
            x and y. Must be names of columns in ``data``.
        covar : string or list
            Covariate(s). Must be a names of columns in ``data``. Use a list if
            there are two or more covariates.
        x_covar : string or list
            Covariate(s) for the ``x`` variable. This is used to compute
            semi-partial correlation (i.e. the effect of ``x_covar`` is removed
            from ``x`` but not from ``y``). Only one of ``covar``,  ``x_covar`` and
            ``y_covar`` can be specified.
        y_covar : string or list
            Covariate(s) for the ``y`` variable. This is used to compute
            semi-partial correlation (i.e. the effect of ``y_covar`` is removed
            from ``y`` but not from ``x``). Only one of ``covar``,  ``x_covar`` and
            ``y_covar`` can be specified.
        alternative : string
            Defines the alternative hypothesis, or tail of the partial correlation. Must be one of
            "two-sided" (default), "greater" or "less". Both "greater" and "less" return a one-sided
            p-value. "greater" tests against the alternative hypothesis that the partial correlation is
            positive (greater than zero), "less" tests against the hypothesis that the partial
            correlation is negative.
        method : string
            Correlation type:
    
            * ``'pearson'``: Pearson :math:`r` product-moment correlation
            * ``'spearman'``: Spearman :math:`\\rho` rank-order correlation
    
        Returns
        -------
        stats : :py:class:`pandas.DataFrame`
    
            * ``'n'``: Sample size (after removal of missing values)
            * ``'r'``: Partial correlation coefficient
            * ``'CI95'``: 95% parametric confidence intervals around :math:`r`
            * ``'p-val'``: p-value
    
        See also
        --------
        corr, pcorr, pairwise_corr, rm_corr
    
        Notes
        -----
        Partial correlation [1]_ measures the degree of association between ``x``
        and ``y``, after removing the effect of one or more controlling variables
        (``covar``, or :math:`Z`). Practically, this is achieved by calculating the
        correlation coefficient between the residuals of two linear regressions:
    
        .. math:: x \\sim Z, y \\sim Z
    
        Like the correlation coefficient, the partial correlation
        coefficient takes on a value in the range from –1 to 1, where 1 indicates a
        perfect positive association.
    
        The semipartial correlation is similar to the partial correlation,
        with the exception that the set of controlling variables is only
        removed for either ``x`` or ``y``, but not both.
    
        Pingouin uses the method described in [2]_ to calculate the (semi)partial
        correlation coefficients and associated p-values. This method is based on
        the inverse covariance matrix and is significantly faster than the
        traditional regression-based method. Results have been tested against the
        `ppcor <https://cran.r-project.org/web/packages/ppcor/index.html>`_
        R package.
    
        .. important:: Rows with missing values are automatically removed from
            data.
    
        References
        ----------
        .. [1] https://en.wikipedia.org/wiki/Partial_correlation
    
        .. [2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681537/
    
        Examples
        --------
        1. Partial correlation with one covariate
    
        >>> import pingouin as pg
        >>> df = pg.read_dataset('partial_corr')
        >>> pg.partial_corr(data=df, x='x', y='y', covar='cv1').round(3)
                  n      r         CI95%  p-val
        pearson  30  0.568  [0.25, 0.77]  0.001
    
        2. Spearman partial correlation with several covariates
    
        >>> # Partial correlation of x and y controlling for cv1, cv2 and cv3
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.521  [0.18, 0.75]  0.005
    
        3. Same but one-sided test
    
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 alternative="greater", method='spearman').round(3)
                   n      r        CI95%  p-val
        spearman  30  0.521  [0.24, 1.0]  0.003
    
        >>> pg.partial_corr(data=df, x='x', y='y', covar=['cv1', 'cv2', 'cv3'],
        ...                 alternative="less", method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.521  [-1.0, 0.72]  0.997
    
        4. As a pandas method
    
        >>> df.partial_corr(x='x', y='y', covar=['cv1'], method='spearman').round(3)
                   n      r         CI95%  p-val
        spearman  30  0.578  [0.27, 0.78]  0.001
    
        5. Partial correlation matrix (returns only the correlation coefficients)
    
        >>> df.pcorr().round(3)
                 x      y    cv1    cv2    cv3
        x    1.000  0.493 -0.095  0.130 -0.385
        y    0.493  1.000 -0.007  0.104 -0.002
        cv1 -0.095 -0.007  1.000 -0.241 -0.470
        cv2  0.130  0.104 -0.241  1.000 -0.118
        cv3 -0.385 -0.002 -0.470 -0.118  1.000
    
        6. Semi-partial correlation on x
    
        >>> pg.partial_corr(data=df, x='x', y='y', x_covar=['cv1', 'cv2', 'cv3']).round(3)
                  n      r        CI95%  p-val
        pearson  30  0.463  [0.1, 0.72]  0.015
        """
        from pingouin.utils import _flatten_list
    
        # Safety check
        assert alternative in [
            "two-sided",
            "greater",
            "less",
        ], "Alternative must be one of 'two-sided' (default), 'greater' or 'less'."
        assert method in [
            "pearson",
            "spearman",
        ], 'only "pearson" and "spearman" are supported for partial correlation.'
        assert isinstance(data, pd.DataFrame), "data must be a pandas DataFrame."
        assert data.shape[0] > 2, "Data must have at least 3 samples."
        if covar is not None and (x_covar is not None or y_covar is not None):
            raise ValueError("Cannot specify both covar and {x,y}_covar.")
        if x_covar is not None and y_covar is not None:
            raise ValueError("Cannot specify both x_covar and y_covar.")
        assert x != covar, "x and covar must be independent"
        assert y != covar, "y and covar must be independent"
        assert x != y, "x and y must be independent"
        if isinstance(covar, list):
            assert x not in covar, "x and covar must be independent"
            assert y not in covar, "y and covar must be independent"
        # Check that columns exist
        col = _flatten_list([x, y, covar, x_covar, y_covar])
>       assert all([c in data for c in col]), "columns are not in dataframe."
E       AssertionError: columns are not in dataframe.
_flatten_list = <function _flatten_list at 0x7f69387c63e0>
alternative = 'two-sided'
col        = ['Neuroticism', 'Extraversion', 'Age', None, None]
covar      = ['Age']
data       =      Neuroticism  Extraversion  Openness  ...  Conscientiousness  Age          IQ
0        2.47917       4.20833   3.9...8  105.904904
499      2.54167       3.56250   3.14583  ...            2.89583   32  105.719773
[500 rows x 7 columns]
method     = 'pearson'
x          = 'Neuroticism'
x_covar    = None
y          = 'Extraversion'
y_covar    = None
pingouin/correlation.py:843: AssertionError

pingouin/requirements-test.txt

Line 6 in 7923141

numpy<=1.23

Fedora currently has numpy 1.24 across all branches. But since the package builds fine with all tests passing using numpy 1.24 and Python 3.11, I suspect some issues (removed deprecations?) in relation to Python 3.12.

[raphaelvallat]

Thanks for reporting. It appears that the _flatten_list function is no longer properly removing the None values:

pingouin/pingouin/utils.py

Line 301 in 7923141

x = list(filter(None.__ne__, x))

Can you please try running the following?

from pingouin.utils import _flatten_list
x = ['X1', ['M1', 'M2'], 'Y1', None]
_flatten_list(x)

My output:

['X1', 'M1', 'M2', 'Y1']

[penguinpee]

You are correct. Running this in the build environment results in:

['X1', 'M1', 'M2', 'Y1', None]

[raphaelvallat]

Thanks @penguinpee — can you please run the following code in your test env?

import collections.abc

def _flatten_list(x, include_tuple=False):
    """Flatten an arbitrarily nested list into a new list.
    """
    # If x is not iterable, return x
    if not isinstance(x, collections.abc.Iterable):
        return x
    # Initialize empty output variable
    result = []
    # Loop over items in x
    for el in x:
        # Check if element is iterable
        el_is_iter = isinstance(el, collections.abc.Iterable)
        if el_is_iter:
            if not isinstance(el, (str, tuple)):
                result.extend(_flatten_list(el))
            else:
                if isinstance(el, tuple) and include_tuple:
                    result.extend(_flatten_list(el))
                else:
                    result.append(el)
        else:
            result.append(el)
    # Remove None from output
    result = [r for r in result if r is not None]
    return result


x = ['X1', ['M1', 'M2', None], 'Y1', None]
_flatten_list(x)  # Expected output: ['X1', 'M1', 'M2', 'Y1']

[penguinpee]

Yes, that prints the expected output:

Out[1]: ['X1', 'M1', 'M2', 'Y1']

[raphaelvallat]

Thanks! Created a PR: #370