parameters: (self, maxsize=40, **kwargs)
return_statement: return plot
docstring: Draw a box plot with size indicating content using
          :func:`matplotlib.pyplot.scatter`.
          
          The data will be normalized, with the largest box using a marker of
          size maxsize (in points).
docstring_summary: Draw a box plot with size indicating content using
          :func:`matplotlib.pyplot.scatter`.
          
          The data will be normalized, with the largest box using a marker of
          size maxsize (in points).
docstring_tokens: Draw a box plot with size indicating content using : func : matplotlib . pyplot . scatter . The data will be normalized with the largest box using a marker of size maxsize ( in points ) .
function: def box(self, maxsize=40, **kwargs):
          """
          Draw a box plot with size indicating content using
          :func:`matplotlib.pyplot.scatter`.
          
          The data will be normalized, with the largest box using a marker of
          size maxsize (in points).
          """
          x = np.hstack([self.x for i in range(self.nbinsy)])
          y = np.hstack([[yval for i in range(self.nbinsx)] for yval in self.y])
          maxvalue = np.max(self.content)
          if maxvalue == 0:
              maxvalue = 1
          sizes = np.array(self.content).flatten() / maxvalue * maxsize
          plot = plt.scatter(x, y, sizes, marker='s', **kwargs)
          return plot
function_tokens: def box ( self , maxsize = 40 , * * kwargs ) : x = np . hstack ( [ self . x for i in range ( self . nbinsy ) ] ) y = np . hstack ( [ [ yval for i in range ( self . nbinsx ) ] for yval in self . y ] ) maxvalue = np . max ( self . content ) if maxvalue == 0 : maxvalue = 1 sizes = np . array ( self . content ) . flatten ( ) / maxvalue * maxsize plot = plt . scatter ( x , y , sizes , marker = 's' , * * kwargs ) return plot