class_docstr: The plotting method: `df.hvplot(...)` creates a plot similarly to the familiar Pandas
  `df.plot` method.
  
  For more detailed options use a specific plotting method, e.g. `df.hvplot.line`.
  
  Reference: https://hvplot.holoviz.org/reference/index.html
  
  Parameters
  ----------
  x : string, optional
      Field name(s) to draw x-positions from. If not specified, the index is
      used.
  y : string or list, optional
      Field name(s) to draw y-positions from. If not specified, all numerical
      fields are used.
  kind : string, optional
      The kind of plot to generate, e.g. 'area', 'bar', 'line', 'scatter' etc. To see the
      available plots run `print(df.hvplot.__all__)`.
  **kwds : optional
      Additional keywords arguments are documented in `hvplot.help('scatter')` or similar
      depending on the kind of plot.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Examples
  --------
  
  .. code-block::
  
      import pandas as pd
      import hvplot.pandas
  
      df = pd.DataFrame(
          {
              "actual": [100, 150, 125, 140, 145, 135, 123],
              "forecast": [90, 160, 125, 150, 141, 141, 120],
              "numerical": [1.1, 1.9, 3.2, 3.8, 4.3, 5.0, 5.5],
              "date": pd.date_range("2022-01-03", "2022-01-09"),
              "string": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
          },
      )
      line = df.hvplot.line(
          x="numerical",
          y=["actual", "forecast"],
          ylabel="value",
          legend="bottom",
          height=500,
          color=["steelblue", "teal"],
          alpha=0.7,
          line_width=5,
      )
      line
  
  You can can add *markers* to a `line` plot by overlaying with a `scatter` plot.
  
  .. code-block::
  
      markers = df.hvplot.scatter(
          x="numerical", y=["actual", "forecast"], color=["#f16a6f", "#1e85f7"], size=50
      )
      line * markers
  
  Please note that you can pass widgets or reactive functions as arguments instead of
  literal values, c.f. https://hvplot.holoviz.org/user_guide/Widgets.html.
class_docstr_tok: ["The","plotting","method",":","`","df.hvplot","(","...",")","`","creates","a","plot","similarly","to","the","familiar","Pandas","`","df.plot","`","method",".","For","more","detailed","options","use","a","specific","plotting","method",",","e.g",".","`","df.hvplot.line","`",".","Reference",":","https",":","\/\/hvplot.holoviz.org\/reference\/index.html","Parameters","--","--","--","--","--","x",":","string",",","optional","Field","name","(","s",")","to","draw","x-positions","from",".","If","not","specified",",","the","index","is","used",".","y",":","string","or","list",",","optional","Field","name","(","s",")","to","draw","y-positions","from",".","If","not","specified",",","all","numerical","fields","are","used",".","kind",":","string",",","optional","The","kind","of","plot","to","generate",",","e.g",".","'area","'",",","'bar","'",",","'line","'",",","'scatter","'","etc",".","To","see","the","available","plots","run","`","print","(","df.hvplot.__all__",")","`",".","*","*","kwds",":","optional","Additional","keywords","arguments","are","documented","in","`","hvplot.help","(","'scatter","'",")","`","or","similar","depending","on","the","kind","of","plot",".","Returns","--","--","--","-","A","Holoviews","object",".","You","can","`","print","`","the","object","to","study","its","composition","and","run","..","code-block",":",":","import","holoviews","as","hv","hv.help","(","the_holoviews_object",")","to","learn","more","about","its","parameters","and","options",".","Examples","--","--","--","--","..","code-block",":",":","import","pandas","as","pd","import","hvplot.pandas","df","=","pd.DataFrame","(","{","``","actual","''",":","[","100",",","150",",","125",",","140",",","145",",","135",",","123","]",",","``","forecast","''",":","[","90",",","160",",","125",",","150",",","141",",","141",",","120","]",",","``","numerical","''",":","[","1.1",",","1.9",",","3.2",",","3.8",",","4.3",",","5.0",",","5.5","]",",","``","date","''",":","pd.date_range","(","``","2022-01-03","''",",","``","2022-01-09","''",")",",","``","string","''",":","[","``","Mon","''",",","``","Tue","''",",","``","Wed","''",",","``","Thu","''",",","``","Fri","''",",","``","Sat","''",",","``","Sun","''","]",",","}",",",")","line","=","df.hvplot.line","(","x=","''","numerical","''",",","y=","[","``","actual","''",",","``","forecast","''","]",",","ylabel=","''","value","''",",","legend=","''","bottom","''",",","height=500",",","color=","[","``","steelblue","''",",","``","teal","''","]",",","alpha=0.7",",","line_width=5",",",")","line","You","can","can","add","*","markers","*","to","a","`","line","`","plot","by","overlaying","with","a","`","scatter","`","plot",".","..","code-block",":",":","markers","=","df.hvplot.scatter","(","x=","''","numerical","''",",","y=","[","``","actual","''",",","``","forecast","''","]",",","color=","[","``","#","f16a6f","''",",","``","#","1e85f7","''","]",",","size=50",")","line","*","markers","Please","note","that","you","can","pass","widgets","or","reactive","functions","as","arguments","instead","of","literal","values",",","c.f",".","https",":","\/\/hvplot.holoviz.org\/user_guide\/Widgets.html","."]
docstr: A `histogram` displays an approximate representation of the distribution of continuous data.
  
  Reference: https://hvplot.holoviz.org/reference/pandas/hist.html
  
  Parameters
  ----------
  y : string or sequence
      Field(s) in the *wide* data to compute the distribution(s) from.
      Please note the fields should contain continuous data. Not categorical.
  by : string or sequence
      Field(s) in the *long* data to group by.
  bins : int, optional
      The number of bins
  bin_range: tuple, optional
      The lower and upper range of the bins. Default is None.
  normed : bool, optional
      If True the distribution will sum to 1. Default is False.
  cumulative: bool, optional
      If True, then a histogram is computed where each bin gives the counts in that bin plus
      all bins for smaller values. The last bin gives the total number of datapoints.
      Default is False.
  alpha : float, optional
      An alpha value between 0.0 and 1.0 to better visualize multiple fields. Default is 1.0.
  kwds : optional
      Additional keywords arguments are documented in `hvplot.help('hist')`.
  
  Returns
  -------
  A Holoviews object. You can `print` the object to study its composition and run
  
  .. code-block::
  
      import holoviews as hv
      hv.help(the_holoviews_object)
  
  to learn more about its parameters and options.
  
  Examples
  --------
  
  Lets display some *wide* data created by rolling two dices
  
  .. code-block::
  
      import hvplot.pandas
      import numpy as np
      import pandas as pd
  
      df = pd.DataFrame(np.random.randint(1, 7, 6000), columns = ['one'])
      df['two'] = df['one'] + np.random.randint(1, 7, 6000)
      df.hvplot.hist(bins=12, alpha=0.5, color=["lightgreen", "pink"])
  
  If you want to show the distribution of the values of a categorical column,
  you can use Pandas' method `value_counts` and `bar` as shown below
  
  .. code-block::
  
      import hvplot.pandas
      import pandas as pd
  
      data = pd.DataFrame({
          "library": ["bokeh", "plotly", "matplotlib", "bokeh", "matplotlib", "matplotlib"]
      })
  
      data["library"].value_counts().hvplot.bar()
  
  References
  ----------
  
  - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/histogram.html
  - HoloViews: https://holoviews.org/reference/elements/bokeh/Histogram.html
  - Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html
  - Plotly: https://plotly.com/python/histograms/
  - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html
  - Seaborn: https://seaborn.pydata.org/generated/seaborn.histplot.html
  - Wiki: https://en.wikipedia.org/wiki/Histogram
docstr_tok: ["A","`","histogram","`","displays","an","approximate","representation","of","the","distribution","of","continuous","data",".","Reference",":","https",":","\/\/hvplot.holoviz.org\/reference\/pandas\/hist.html","Parameters","--","--","--","--","--","y",":","string","or","sequence","Field","(","s",")","in","the","*","wide","*","data","to","compute","the","distribution","(","s",")","from",".","Please","note","the","fields","should","contain","continuous","data",".","Not","categorical",".","by",":","string","or","sequence","Field","(","s",")","in","the","*","long","*","data","to","group","by",".","bins",":","int",",","optional","The","number","of","bins","bin_range",":","tuple",",","optional","The","lower","and","upper","range","of","the","bins",".","Default","is","None",".","normed",":","bool",",","optional","If","True","the","distribution","will","sum","to","1",".","Default","is","False",".","cumulative",":","bool",",","optional","If","True",",","then","a","histogram","is","computed","where","each","bin","gives","the","counts","in","that","bin","plus","all","bins","for","smaller","values",".","The","last","bin","gives","the","total","number","of","datapoints",".","Default","is","False",".","alpha",":","float",",","optional","An","alpha","value","between","0.0","and","1.0","to","better","visualize","multiple","fields",".","Default","is","1.0.","kwds",":","optional","Additional","keywords","arguments","are","documented","in","`","hvplot.help","(","'hist","'",")","`",".","Returns","--","--","--","-","A","Holoviews","object",".","You","can","`","print","`","the","object","to","study","its","composition","and","run","..","code-block",":",":","import","holoviews","as","hv","hv.help","(","the_holoviews_object",")","to","learn","more","about","its","parameters","and","options",".","Examples","--","--","--","--","Lets","display","some","*","wide","*","data","created","by","rolling","two","dices","..","code-block",":",":","import","hvplot.pandas","import","numpy","as","np","import","pandas","as","pd","df","=","pd.DataFrame","(","np.random.randint","(","1",",","7",",","6000",")",",","columns","=","[","'one","'","]",")","df","[","'two","'","]","=","df","[","'one","'","]","+","np.random.randint","(","1",",","7",",","6000",")","df.hvplot.hist","(","bins=12",",","alpha=0.5",",","color=","[","``","lightgreen","''",",","``","pink","''","]",")","If","you","want","to","show","the","distribution","of","the","values","of","a","categorical","column",",","you","can","use","Pandas","'","method","`","value_counts","`","and","`","bar","`","as","shown","below","..","code-block",":",":","import","hvplot.pandas","import","pandas","as","pd","data","=","pd.DataFrame","(","{","``","library","''",":","[","``","bokeh","''",",","``","plotly","''",",","``","matplotlib","''",",","``","bokeh","''",",","``","matplotlib","''",",","``","matplotlib","''","]","}",")","data","[","``","library","''","]",".value_counts","(",")",".hvplot.bar","(",")","References","--","--","--","--","--","-","Bokeh",":","https",":","\/\/docs.bokeh.org\/en\/latest\/docs\/gallery\/histogram.html","-","HoloViews",":","https",":","\/\/holoviews.org\/reference\/elements\/bokeh\/Histogram.html","-","Pandas",":","https",":","\/\/pandas.pydata.org\/pandas-docs\/stable\/reference\/api\/pandas.DataFrame.plot.hist.html","-","Plotly",":","https",":","\/\/plotly.com\/python\/histograms\/","-","Matplotlib",":","https",":","\/\/matplotlib.org\/stable\/api\/_as_gen\/matplotlib.pyplot.hist.html","-","Seaborn",":","https",":","\/\/seaborn.pydata.org\/generated\/seaborn.histplot.html","-","Wiki",":","https",":","\/\/en.wikipedia.org\/wiki\/Histogram"]
code: def hist(self, y=None, by=None, **kwds):
      """
      A `histogram` displays an approximate representation of the distribution of continuous data.
  
      Reference: https://hvplot.holoviz.org/reference/pandas/hist.html
  
      Parameters
      ----------
      y : string or sequence
          Field(s) in the *wide* data to compute the distribution(s) from.
          Please note the fields should contain continuous data. Not categorical.
      by : string or sequence
          Field(s) in the *long* data to group by.
      bins : int, optional
          The number of bins
      bin_range: tuple, optional
          The lower and upper range of the bins. Default is None.
      normed : bool, optional
          If True the distribution will sum to 1. Default is False.
      cumulative: bool, optional
          If True, then a histogram is computed where each bin gives the counts in that bin plus
          all bins for smaller values. The last bin gives the total number of datapoints.
          Default is False.
      alpha : float, optional
          An alpha value between 0.0 and 1.0 to better visualize multiple fields. Default is 1.0.
      kwds : optional
          Additional keywords arguments are documented in `hvplot.help('hist')`.
  
      Returns
      -------
      A Holoviews object. You can `print` the object to study its composition and run
  
      .. code-block::
  
          import holoviews as hv
          hv.help(the_holoviews_object)
  
      to learn more about its parameters and options.
  
      Examples
      --------
  
      Lets display some *wide* data created by rolling two dices
  
      .. code-block::
  
          import hvplot.pandas
          import numpy as np
          import pandas as pd
  
          df = pd.DataFrame(np.random.randint(1, 7, 6000), columns = ['one'])
          df['two'] = df['one'] + np.random.randint(1, 7, 6000)
          df.hvplot.hist(bins=12, alpha=0.5, color=["lightgreen", "pink"])
  
      If you want to show the distribution of the values of a categorical column,
      you can use Pandas' method `value_counts` and `bar` as shown below
  
      .. code-block::
  
          import hvplot.pandas
          import pandas as pd
  
          data = pd.DataFrame({
              "library": ["bokeh", "plotly", "matplotlib", "bokeh", "matplotlib", "matplotlib"]
          })
  
          data["library"].value_counts().hvplot.bar()
  
      References
      ----------
  
      - Bokeh: https://docs.bokeh.org/en/latest/docs/gallery/histogram.html
      - HoloViews: https://holoviews.org/reference/elements/bokeh/Histogram.html
      - Pandas: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html
      - Plotly: https://plotly.com/python/histograms/
      - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html
      - Seaborn: https://seaborn.pydata.org/generated/seaborn.histplot.html
      - Wiki: https://en.wikipedia.org/wiki/Histogram
      """
      return self(kind="hist", x=None, y=y, by=by, **kwds)
  
imports: import itertools
  from collections import defaultdict
  import param
  from ..converter import HoloViewsConverter
  from ..util import is_list_like, process_dynamic_args
label_desc: Use image node_id 14 for calling the hvPlotTabular obj's underlying member method code with example usage: obj.hist(y, by) and returns: self