def check_columns()

in src/smallmatter/typecheck/_pdcheck.py [0:0]

• 26 lines of code
• 2 McCabe index (conditional complexity)

def check_columns(df, max_item_to_show=10):
    """Column dtype are computed from non-NaN values to prevent int64 columns becomes float64."""
    column = []
    dtyp = []
    uniq_cnt = []
    data_cnt = []
    nan_cnt = []
    sample_value = []
    d = {
        "column": column,
        "dtype": dtyp,
        "uniq_cnt": uniq_cnt,
        "data_cnt": data_cnt,
        "nan_cnt": nan_cnt,
        "sample_value": sample_value,
    }

    for i in df.columns:
        col = df[i]
        uniques = col.unique()
        cnt = len(col)

        column.append(i)
        dtyp.append(col.dropna().dtype)
        uniq_cnt.append(len(uniques))
        nan_cnt.append(cnt - col.count())
        data_cnt.append(cnt)

        # Convert to string, otherwise jupyter notebook display without padding spaces
        # sample_value.append(str(uniques[:max_item_to_show].tolist()))
        sample_value.append(json.dumps(uniques[:max_item_to_show].tolist()))

    return pd.DataFrame(d, columns=["column", "dtype", "uniq_cnt", "data_cnt", "nan_cnt", "sample_value"])