docs: def describe_numeric_1d(series, **kwargs):
      """Compute summary statistics of a numerical (`TYPE_NUM`) variable (a Series).
  
      Also create histograms (mini an full) of its distribution.
  
      Parameters
      ----------
      series : Series
          The variable to describe.
  
      Returns
      -------
      Series
          The description of the variable as a Series with index being stats keys.
      """
      # Format a number as a percentage. For example 0.25 will be turned to 25%.
      _percentile_format = "{:.0%}"
      stats = dict()
      stats['type'] = base.TYPE_NUM
      stats['mean'] = series.mean()
      stats['std'] = series.std()
      stats['variance'] = series.var()
      stats['min'] = series.min()
      stats['max'] = series.max()
      stats['range'] = stats['max'] - stats['min']
      # To avoid to compute it several times
      _series_no_na = series.dropna()
      for percentile in np.array([0.05, 0.25, 0.5, 0.75, 0.95]):
          # The dropna() is a workaround for https://github.com/pydata/pandas/issues/13098
          stats[_percentile_format.format(percentile)] = _series_no_na.quantile(percentile)
      stats['iqr'] = stats['75%'] - stats['25%']
      stats['kurtosis'] = series.kurt()
      stats['skewness'] = series.skew()
      stats['sum'] = series.sum()
      stats['mad'] = series.mad()
      stats['cv'] = stats['std'] / stats['mean'] if stats['mean'] else np.NaN
      stats['n_zeros'] = (len(series) - np.count_nonzero(series))
      stats['p_zeros'] = stats['n_zeros'] * 1.0 / len(series)
      # Histograms
      stats['histogram'] = histogram(series, **kwargs)
      stats['mini_histogram'] = mini_histogram(series, **kwargs)
      return pd.Series(stats, name=series.name)