Python pandas 模块，get_option() 实例源码

def print_table(table, name=None, fmt=None):
    """
    Pretty print a pandas DataFrame.
    Uses HTML output if running inside Jupyter Notebook, otherwise
    formatted text output.
    Parameters
    ----------
    table : pandas.Series or pandas.DataFrame
        Table to pretty-print.
    name : str, optional
        Table name to display in upper left corner.
    fmt : str, optional
        Formatter to use for displaying table elements.
        E.g. '{0:.2f}%' for displaying 100 as '100.00%'.
        Restores original setting after displaying.
    """

    if isinstance(table, pd.Series):
        table = pd.DataFrame(table)

    if fmt is not None:
        prev_option = pd.get_option('display.float_format')
        pd.set_option('display.float_format', lambda x: fmt.format(x))

    if name is not None:
        table.columns.name = name

    display(table)

    if fmt is not None:
        pd.set_option('display.float_format', prev_option)

def _ensure_decoded(s):
    """ if we have bytes, decode them to unicode """
    if isinstance(s, (np.bytes_, bytes)):
        s = s.decode(pd.get_option('display.encoding'))
    return s

def setUpClass(cls):
        super(TestClipboard, cls).setUpClass()
        cls.data = {}
        cls.data['string'] = mkdf(5, 3, c_idx_type='s', r_idx_type='i',
                                  c_idx_names=[None], r_idx_names=[None])
        cls.data['int'] = mkdf(5, 3, data_gen_f=lambda *args: randint(2),
                               c_idx_type='s', r_idx_type='i',
                               c_idx_names=[None], r_idx_names=[None])
        cls.data['float'] = mkdf(5, 3,
                                 data_gen_f=lambda r, c: float(r) + 0.01,
                                 c_idx_type='s', r_idx_type='i',
                                 c_idx_names=[None], r_idx_names=[None])
        cls.data['mixed'] = DataFrame({'a': np.arange(1.0, 6.0) + 0.01,
                                       'b': np.arange(1, 6),
                                       'c': list('abcde')})

        # Test columns exceeding "max_colwidth" (GH8305)
        _cw = get_option('display.max_colwidth') + 1
        cls.data['colwidth'] = mkdf(5, 3, data_gen_f=lambda *args: 'x' * _cw,
                                    c_idx_type='s', r_idx_type='i',
                                    c_idx_names=[None], r_idx_names=[None])
        # Test GH-5346
        max_rows = get_option('display.max_rows')
        cls.data['longdf'] = mkdf(max_rows + 1, 3,
                                  data_gen_f=lambda *args: randint(2),
                                  c_idx_type='s', r_idx_type='i',
                                  c_idx_names=[None], r_idx_names=[None])
        # Test for non-ascii text: GH9263
        cls.data['nonascii'] = pd.DataFrame({'en': 'in English'.split(),
                                             'es': 'en español'.split()})
        cls.data_types = list(cls.data.keys())

def printSeries(series, label, header='', asStr=False):
    """
    Print a `series` of values, with a give `label`.

    :param series: (convertible to pandas Series) the values
    :param label: (str) a label to print for the data
    :return: none
    """
    import pandas as pd

    if type(series) == pd.DataFrame:
        df = series
        df = df.T
    else:
        df = pd.DataFrame(pd.Series(series))  # DF is more convenient for printing

    df.columns = [label]

    oldPrecision = pd.get_option('precision')
    pd.set_option('precision', 5)
    s = "%s\n%s" % (header, df.T)
    pd.set_option('precision', oldPrecision)

    if asStr:
        return s
    else:
        print(s)

def _predict(args, cell):
  schema, features = _local_predict.get_model_schema_and_features(args['model'])
  headers = [x['name'] for x in schema]
  img_cols = []
  for k, v in six.iteritems(features):
    if v['transform'] in ['image_to_vec']:
      img_cols.append(v['source_column'])

  data = args['data']
  df = _local_predict.get_prediction_results(
      args['model'], data, headers, img_cols=img_cols, cloud=False,
      show_image=not args['no_show_image'])

  def _show_img(img_bytes):
    return '<img src="data:image/png;base64,' + img_bytes + '" />'

  def _truncate_text(text):
    return (text[:37] + '...') if isinstance(text, six.string_types) and len(text) > 40 else text

  # Truncate text explicitly here because we will set display.max_colwidth to -1.
  # This applies to images to but images will be overriden with "_show_img()" later.
  formatters = {x: _truncate_text for x in df.columns if df[x].dtype == np.object}
  if not args['no_show_image'] and img_cols:
    formatters.update({x + '_image': _show_img for x in img_cols})

  # Set display.max_colwidth to -1 so we can display images.
  old_width = pd.get_option('display.max_colwidth')
  pd.set_option('display.max_colwidth', -1)
  try:
    IPython.display.display(IPython.display.HTML(
        df.to_html(formatters=formatters, escape=False, index=False)))
  finally:
    pd.set_option('display.max_colwidth', old_width)