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.

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)