human
Complete the below
def _filter_data(lhs, rhs):

    ''''Data filtering routine for dynamic VAR
    lhs : DataFrame
    original data
    rhs : DataFrame
    lagged variables
    Returns'
    '''
 ... What is a the Python program you requested?
assistant
#Here is the Python program you requested.
    def _has_all_columns(df):
        return (np.isfinite(df.values).sum(1) == len(df.columns))
    rhs_valid = _has_all_columns(rhs)
    if (not rhs_valid.all()):
        pre_filtered_rhs = rhs[rhs_valid]
    else:
        pre_filtered_rhs = rhs
    index = lhs.index.union(rhs.index)
    if ((not index.equals(rhs.index)) or (not index.equals(lhs.index))):
        rhs = rhs.reindex(index)
        lhs = lhs.reindex(index)
        rhs_valid = _has_all_columns(rhs)
    lhs_valid = _has_all_columns(lhs)
    valid = (rhs_valid & lhs_valid)
    if (not valid.all()):
        filt_index = rhs.index[valid]
        filtered_rhs = rhs.reindex(filt_index)
        filtered_lhs = lhs.reindex(filt_index)
    else:
        (filtered_rhs, filtered_lhs) = (rhs, lhs)
    return (filtered_lhs, filtered_rhs, pre_filtered_rhs, index, valid)