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# pandas.DataFrame.plot.hexbin

DataFrame.plot.hexbin(x, y, C=None, reduce_C_function=None, gridsize=None, **kwargs)

  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 (`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.

    **kwargs

      Additional keyword arguments are documented in `DataFrame.plot()`.

  Returns:
    matplotlib.Axes

      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.

      >>> 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.

      >>> 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",
      ... )