class_docstr: DataFrame plotting accessor and method
  
  Examples
  --------
  >>> df.plot_bokeh.line()
  >>> df.plot_bokeh.scatter('x', 'y')
  >>> df.plot_bokeh.hexbin()
  
  These plotting methods can also be accessed by calling the accessor as a
  method with the ``kind`` argument:
  ``df.plot_bokeh(kind='line')`` is equivalent to ``df.plot_bokeh.line()``
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docstr: Draw one histogram of the DataFrame's columns.
  
  A histogram is a representation of the distribution of data.
  This function groups the values of all given Series in the DataFrame
  into bins and draws all bins in one figure.
  This is useful when the DataFrame's Series are in a similar scale.
  
  Parameters
  ----------
  ...
  bins : int, default 10
      Number of histogram bins to be used.
  **kwds
      Additional keyword arguments are documented in
      :meth:`pandas.DataFrame.plot_bokeh`.
  
  Returns
  -------
  Bokeh.plotting.figure
  
  See Also
  --------
  DataFrame.hist : Draw histograms per DataFrame's Series.
  Series.hist : Draw a histogram with Series' data.
  
  Examples
  --------
  When we draw a dice 6000 times, we expect to get each value around 1000
  times. But when we draw two dices and sum the result, the distribution
  is going to be quite different. A histogram illustrates those
  distributions.
  
  .. plot::
      :context: close-figs
  
      >>> df = pd.DataFrame(
      ...     np.random.randint(1, 7, 6000),
      ...     columns = ['one'])
      >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
      >>> ax = df.plot_bokeh.hist(bins=12, alpha=0.5)
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code: def hist(self, **kwds):
      """
      Draw one histogram of the DataFrame's columns.
  
      A histogram is a representation of the distribution of data.
      This function groups the values of all given Series in the DataFrame
      into bins and draws all bins in one figure.
      This is useful when the DataFrame's Series are in a similar scale.
  
      Parameters
      ----------
      ...
      bins : int, default 10
          Number of histogram bins to be used.
      **kwds
          Additional keyword arguments are documented in
          :meth:`pandas.DataFrame.plot_bokeh`.
  
      Returns
      -------
      Bokeh.plotting.figure
  
      See Also
      --------
      DataFrame.hist : Draw histograms per DataFrame's Series.
      Series.hist : Draw a histogram with Series' data.
  
      Examples
      --------
      When we draw a dice 6000 times, we expect to get each value around 1000
      times. But when we draw two dices and sum the result, the distribution
      is going to be quite different. A histogram illustrates those
      distributions.
  
      .. plot::
          :context: close-figs
  
          >>> df = pd.DataFrame(
          ...     np.random.randint(1, 7, 6000),
          ...     columns = ['one'])
          >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
          >>> ax = df.plot_bokeh.hist(bins=12, alpha=0.5)
      """
      return self(kind="hist", **kwds)
  
imports: import datetime
  import numbers
  import warnings
  from copy import deepcopy
  from typing import Iterable, List, Optional, Union
  import numpy
  import pandas
  from bokeh.layouts import column
  from bokeh.models import ColorBar, ColumnDataSource, DatetimeTickFormatter, FuncTickFormatter, HoverTool, LinearColorMapper, RangeTool
  from bokeh.models.ranges import Range1d
  from bokeh.palettes import Inferno256, all_palettes
  from bokeh.plotting import figure
  from bokeh.transform import cumsum, dodge
  from pandas.core.base import PandasObject
  from pandas.errors import ParserError
  from .base import embedded_html, set_fontsizes_of_figure, show
  from .geoplot import geoplot
  from .utils import _extract_additional_columns
label_desc: Use image node_id 9 for calling the FramePlotMethods obj's underlying member method code with example usage: obj.hist() and returns: self