parameters: (self, x, y, C=None, reduce_C_function=None, gridsize=None,
                 **kwds)
return_statement: return self(kind='hexbin', x=x, y=y, C=C, **kwds)
docstring: Generate a hexagonal binning plot.
  
          Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
          (the default), this is a histogram of the number of occurrences
          of the observations at ``(x[i], y[i])``.
  
          If `C` is specified, specifies values at given coordinates
          ``(x[i], y[i])``. These values are accumulated for each hexagonal
          bin and then reduced according to `reduce_C_function`,
          having as default the NumPy's mean function (:meth:`numpy.mean`).
          (If `C` is specified, it must also be a 1-D sequence
          of the same length as `x` and `y`, or a column label.)
  
          Parameters
          ----------
          x : int or str
              The column label or position for x points.
          y : int or str
              The column label or position for y points.
          C : int or str, optional
              The column label or position for the value of `(x, y)` point.
          reduce_C_function : callable, default `np.mean`
              Function of one argument that reduces all the values in a bin to
              a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
          gridsize : int or tuple of (int, int), default 100
              The number of hexagons in the x-direction.
              The corresponding number of hexagons in the y-direction is
              chosen in a way that the hexagons are approximately regular.
              Alternatively, gridsize can be a tuple with two elements
              specifying the number of hexagons in the x-direction and the
              y-direction.
          **kwds
              Additional keyword arguments are documented in
              :meth:`pandas.DataFrame.plot`.
  
          Returns
          -------
          matplotlib.AxesSubplot
              The matplotlib ``Axes`` on which the hexbin is plotted.
  
          See Also
          --------
          DataFrame.plot : Make plots of a DataFrame.
          matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,
              the matplotlib function that is used under the hood.
  
          Examples
          --------
          The following examples are generated with random data from
          a normal distribution.
  
          .. plot::
              :context: close-figs
  
              >>> n = 10000
              >>> df = pd.DataFrame({'x': np.random.randn(n),
              ...                    'y': np.random.randn(n)})
              >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
  
          The next example uses `C` and `np.sum` as `reduce_C_function`.
          Note that `'observations'` values ranges from 1 to 5 but the result
          plot shows values up to more than 25. This is because of the
          `reduce_C_function`.
  
          .. plot::
              :context: close-figs
  
              >>> n = 500
              >>> df = pd.DataFrame({
              ...     'coord_x': np.random.uniform(-3, 3, size=n),
              ...     'coord_y': np.random.uniform(30, 50, size=n),
              ...     'observations': np.random.randint(1,5, size=n)
              ...     })
              >>> ax = df.plot.hexbin(x='coord_x',
              ...                     y='coord_y',
              ...                     C='observations',
              ...                     reduce_C_function=np.sum,
              ...                     gridsize=10,
              ...                     cmap="viridis")
docstring_summary: Generate a hexagonal binning plot.
docstring_tokens: Generate a hexagonal binning plot .
function: def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
                 **kwds):
          """
          Generate a hexagonal binning plot.
  
          Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
          (the default), this is a histogram of the number of occurrences
          of the observations at ``(x[i], y[i])``.
  
          If `C` is specified, specifies values at given coordinates
          ``(x[i], y[i])``. These values are accumulated for each hexagonal
          bin and then reduced according to `reduce_C_function`,
          having as default the NumPy's mean function (:meth:`numpy.mean`).
          (If `C` is specified, it must also be a 1-D sequence
          of the same length as `x` and `y`, or a column label.)
  
          Parameters
          ----------
          x : int or str
              The column label or position for x points.
          y : int or str
              The column label or position for y points.
          C : int or str, optional
              The column label or position for the value of `(x, y)` point.
          reduce_C_function : callable, default `np.mean`
              Function of one argument that reduces all the values in a bin to
              a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
          gridsize : int or tuple of (int, int), default 100
              The number of hexagons in the x-direction.
              The corresponding number of hexagons in the y-direction is
              chosen in a way that the hexagons are approximately regular.
              Alternatively, gridsize can be a tuple with two elements
              specifying the number of hexagons in the x-direction and the
              y-direction.
          **kwds
              Additional keyword arguments are documented in
              :meth:`pandas.DataFrame.plot`.
  
          Returns
          -------
          matplotlib.AxesSubplot
              The matplotlib ``Axes`` on which the hexbin is plotted.
  
          See Also
          --------
          DataFrame.plot : Make plots of a DataFrame.
          matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,
              the matplotlib function that is used under the hood.
  
          Examples
          --------
          The following examples are generated with random data from
          a normal distribution.
  
          .. plot::
              :context: close-figs
  
              >>> n = 10000
              >>> df = pd.DataFrame({'x': np.random.randn(n),
              ...                    'y': np.random.randn(n)})
              >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
  
          The next example uses `C` and `np.sum` as `reduce_C_function`.
          Note that `'observations'` values ranges from 1 to 5 but the result
          plot shows values up to more than 25. This is because of the
          `reduce_C_function`.
  
          .. plot::
              :context: close-figs
  
              >>> n = 500
              >>> df = pd.DataFrame({
              ...     'coord_x': np.random.uniform(-3, 3, size=n),
              ...     'coord_y': np.random.uniform(30, 50, size=n),
              ...     'observations': np.random.randint(1,5, size=n)
              ...     })
              >>> ax = df.plot.hexbin(x='coord_x',
              ...                     y='coord_y',
              ...                     C='observations',
              ...                     reduce_C_function=np.sum,
              ...                     gridsize=10,
              ...                     cmap="viridis")
          """
          if reduce_C_function is not None:
              kwds['reduce_C_function'] = reduce_C_function
          if gridsize is not None:
              kwds['gridsize'] = gridsize
          return self(kind='hexbin', x=x, y=y, C=C, **kwds)
function_tokens: def hexbin ( self , x , y , C = None , reduce_C_function = None , gridsize = None , * * kwds ) : if reduce_C_function is not None : kwds [ 'reduce_C_function' ] = reduce_C_function if gridsize is not None : kwds [ 'gridsize' ] = gridsize return self ( kind = 'hexbin' , x = x , y = y , C = C , * * kwds )