class_docstr: Extended descriptive statistics for data
  
  Parameters
  ----------
  data : array_like
      Data to describe. Must be convertible to a pandas DataFrame.
  stats : Sequence[str], optional
      Statistics to include. If not provided the full set of statistics is
      computed. This list may evolve across versions to reflect best
      practices. Supported options are:
      "nobs", "missing", "mean", "std_err", "ci", "ci", "std", "iqr",
      "iqr_normal", "mad", "mad_normal", "coef_var", "range", "max",
      "min", "skew", "kurtosis", "jarque_bera", "mode", "freq",
      "median", "percentiles", "distinct", "top", and "freq". See Notes for
      details.
  numeric : bool, default True
      Whether to include numeric columns in the descriptive statistics.
  categorical : bool, default True
      Whether to include categorical columns in the descriptive statistics.
  alpha : float, default 0.05
      A number between 0 and 1 representing the size used to compute the
      confidence interval, which has coverage 1 - alpha.
  use_t : bool, default False
      Use the Student's t distribution to construct confidence intervals.
  percentiles : sequence[float]
      A distinct sequence of floating point values all between 0 and 100.
      The default percentiles are 1, 5, 10, 25, 50, 75, 90, 95, 99.
  ntop : int, default 5
      The number of top categorical labels to report. Default is
  
  Attributes
  ----------
  numeric_statistics
      The list of supported statistics for numeric data
  categorical_statistics
      The list of supported statistics for categorical data
  default_statistics
      The default list of statistics
  
  See Also
  --------
  pandas.DataFrame.describe
      Basic descriptive statistics
  describe
      A simplified version that returns a DataFrame
  
  Notes
  -----
  The selectable statistics include:
  
  * "nobs" - Number of observations
  * "missing" - Number of missing observations
  * "mean" - Mean
  * "std_err" - Standard Error of the mean assuming no correlation
  * "ci" - Confidence interval with coverage (1 - alpha) using the normal or
    t. This option creates two entries in any tables: lower_ci and upper_ci.
  * "std" - Standard Deviation
  * "iqr" - Interquartile range
  * "iqr_normal" - Interquartile range relative to a Normal
  * "mad" - Mean absolute deviation
  * "mad_normal" - Mean absolute deviation relative to a Normal
  * "coef_var" - Coefficient of variation
  * "range" - Range between the maximum and the minimum
  * "max" - The maximum
  * "min" - The minimum
  * "skew" - The skewness defined as the standardized 3rd central moment
  * "kurtosis" - The kurtosis defined as the standardized 4th central moment
  * "jarque_bera" - The Jarque-Bera test statistic for normality based on
    the skewness and kurtosis. This option creates two entries, jarque_bera
    and jarque_beta_pval.
  * "mode" - The mode of the data. This option creates two entries in all tables,
    mode and mode_freq which is the empirical frequency of the modal value.
  * "median" - The median of the data.
  * "percentiles" - The percentiles. Values included depend on the input value of
    ``percentiles``.
  * "distinct" - The number of distinct categories in a categorical.
  * "top" - The mode common categories. Labeled top_n for n in 1, 2, ..., ``ntop``.
  * "freq" - The frequency of the common categories. Labeled freq_n for n in 1,
    2, ..., ``ntop``.
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code: def __str__(self) -> str:
      return str(self.summary().as_text())
  
imports: from statsmodels.compat.pandas import PD_LT_2, Appender, is_numeric_dtype
  from statsmodels.compat.scipy import SP_LT_19
  from typing import Union
  from collections.abc import Sequence
  import numpy
  import pandas
  from scipy import stats
  from statsmodels.iolib.table import SimpleTable
  from statsmodels.stats.stattools import jarque_bera
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.docstring import Docstring, Parameter
  from statsmodels.tools.validation import array_like, bool_like, float_like, int_like
label_desc: Use image node_id 7 for calling the Description obj's underlying member method code with example usage: obj.__str__() and returns: str