How to use the dtale.utils.classify_type function in dtale

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def test_classify_type():
    assert utils.classify_type('string') == 'S'
    assert utils.classify_type('boolean') == 'B'
    assert utils.classify_type('float64') == 'F'
    assert utils.classify_type('integer64') == 'I'
    assert utils.classify_type('timestamp') == 'D'
    assert utils.classify_type('timedelta') == 'TD'
    assert utils.classify_type('foo') == 'S'

def _format_dtype(col_index, col):
        dtype = dtypes[col]
        dtype_data = dict(name=col, dtype=dtype, index=col_index)
        if classify_type(dtype) == 'F' and not data[col].isnull().all():  # floats
            dtype_data['min'] = mins[col]
            dtype_data['max'] = maxs[col]
        return dtype_data

:param data_id: integer string identifier for a D-Tale process's data
    :type data_id: str
    :param column: required dash separated string "START-END" stating a range of row indexes to be returned
                   to the screen
    :return: JSON {
        describe: object representing output from :meth:`pandas:pandas.Series.describe`,
        unique_data: array of unique values when data has <= 100 unique values
        success: True/False
    }

    """
    try:
        data = DATA[data_id]
        additional_aggs = None
        dtype = next((dtype_info['dtype'] for dtype_info in DTYPES[data_id] if dtype_info['name'] == column), None)
        if classify_type(dtype) in ['I', 'F']:
            additional_aggs = ['sum', 'median', 'mode', 'var', 'sem', 'skew', 'kurt']
        desc = load_describe(data[column], additional_aggs=additional_aggs)
        return_data = dict(describe=desc, success=True)
        uniq_vals = data[column].unique()
        if 'unique' not in return_data['describe']:
            return_data['describe']['unique'] = json_int(len(uniq_vals), as_string=True)
        if len(uniq_vals) <= 100:
            uniq_f = find_dtype_formatter(get_dtypes(data)[column])
            return_data['uniques'] = dict(
                data=[uniq_f(u, nan_display='N/A') for u in uniq_vals],
                top=False
            )
        else:  # get top 100 most common values
            uniq_vals = data[column].value_counts().sort_values(ascending=False).head(100).index.values
            uniq_f = find_dtype_formatter(get_dtypes(data)[column])
            return_data['uniques'] = dict(