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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def minimum_valid_values_in_any_group(df, levels=None, n=1, invalid=np.nan):
 """ Filter ``DataFrame`` by at least n valid values in at least one group. Taking a Pandas ``DataFrame`` with a ``MultiIndex`` column index, filters rows to remove rows where there are less than `n` valid values per group. Groups are defined by the `levels` parameter indexing into the column index. For example, a ``MultiIndex`` with top and second level Group (A,B,C) and Replicate (1,2,3) using ``levels=[0,1]`` would filter on `n` valid values per replicate. Alternatively, ``levels=[0]`` would filter on `n` valid values at the Group level only, e.g. A, B or C. By default valid values are determined by `np.nan`. However, alternatives can be supplied via `invalid`. :param df: Pandas ``DataFrame`` :param levels: ``list`` of ``int`` specifying levels of column ``MultiIndex`` to group by :param n: ``int`` minimum number of valid values threshold :param invalid: matching invalid value :return: filtered Pandas ``DataFrame`` """

    df = df.copy()
    
    if levels is None:
        if 'Group' in df.columns.names:
            levels = [df.columns.names.index('Group')]

    # Filter by at least 7 (values in class:timepoint) at least in at least one group
    if invalid is np.nan:
        dfx = ~np.isnan(df)
    else:
        dfx = df != invalid
    
    dfc = dfx.astype(int).sum(axis=1, level=levels)
    
    dfm = dfc.max(axis=1) >= n
    
    mask = dfm.values
    
    return df.iloc[mask, :]