def generate_null_snvs(df, snvs, num_null_sets=5):
    """
    Generate a set of null SNVs based on an input list of SNVs and categorical
    annotations. 
    
    Parameters
    ----------
    df : pandas.DataFrame
        Pandas dataframe where each column is a categorization of SNPs. 
        The index should be SNPs of the form chrom:pos.
        
    snvs : list
        List of input SNVs in the format chrom:pos. Entries that aren't in
        the index of df will be dropped.
        
    num_null_sets : int
        Number of sets of null SNVs to generate.
        
    Returns
    -------
    null_sets : pandas.Dataframe
        Pandas dataframe with input SNVs as first column and null SNVs as
        following columns.

    """
    import numpy as np
    import random
    random.seed(20151007)
    input_snvs = list(set(df.index) & set(snvs))
    sig = df.ix[input_snvs]
    not_sig = df.ix[set(df.index) - set(snvs)]
    sig['group'] = sig.apply(lambda x: '::'.join(x), axis=1)
    not_sig['group'] = not_sig.apply(lambda x: '::'.join(x), axis=1)
    null_sets = []
    vc = sig.group.value_counts()
    bins = {c:sorted(list(df[c].value_counts().index)) for c in df.columns}
    ordered_inputs = []
    for i in vc.index:
        ordered_inputs += list(sig[sig.group == i].index)
        tdf = not_sig[not_sig.group == i]
        count = vc[i]
        for n in xrange(num_null_sets):
            if tdf.shape[0] == 0:
                groups = [i]
                while tdf.shape[0] == 0:
                    # If there are no potential null SNVs in this group, we'll
                    # expand the group randomly.
                    g = groups[-1]
                    # Choose random bin.
                    cols = list(not_sig.columns)
                    cols.remove('group')
                    b = random.choice(cols)
                    # Get possibilities for that bin.
                    t = bins[b]
                    # Get last set of bin values and the value for the bin we
                    # want to change.
                    d = dict(zip(not_sig.columns, g.split('::')))
                    cat = d[b]
                    # Randomly walk away from bin value.
                    ind = t.index(cat)
                    if ind == 0:
                        ind += 1
                    elif ind == len(t) - 1:
                        ind -= 1
                    else:
                        ind += random.choice([-1, 1])
                    d[b] = t[ind]
                    groups.append('::'.join(pd.Series(d)[not_sig.columns].astype(str)))
                    tdf = not_sig[not_sig.group.apply(lambda x: x in groups)]
            if count <= tdf.shape[0]:
                ind = random.sample(tdf.index, count)
            else:
                ind = list(np.random.choice(tdf.index, size=count, replace=True))
            if i == vc.index[0]:
                null_sets.append(ind)
            else:
                null_sets[n] += ind
    null_sets = pd.DataFrame(null_sets).T
    null_sets.columns = ['null_{}'.format(x) for x in null_sets.columns]
    cs = list(null_sets.columns)
    null_sets['input'] = ordered_inputs
    null_sets = null_sets[['input'] + cs]
    return null_sets