text: """
  3D Scatter plots
  ----------------
  
  The :meth:`pygmt.Figure.plot3d` method can be used to plot symbols in 3D.
  In the example below, we show how the
  `Iris flower dataset <https://en.wikipedia.org/wiki/Iris_flower_data_set>`__
  can be visualized using a perspective 3D plot. The ``region``
  parameter has to include the :math:`x`, :math:`y`, :math:`z` axis limits in the
  form of (xmin, xmax, ymin, ymax, zmin, zmax), which can be done automatically
  using :meth:`pygmt.info`. To plot the z-axis frame, set ``frame`` as a
  minimum to something like ``frame=["WsNeZ", "zaf"]``. Use ``perspective`` to
  control the azimuth and elevation angle of the view, and ``zscale`` to adjust
  the vertical exaggeration factor.
  """
  
  import pandas as pd
  import pygmt
  
  # Load sample iris data, and convert 'species' column to categorical dtype
  df = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")
  df.species = df.species.astype(dtype="category")
  
  # Use pygmt.info to get region bounds (xmin, xmax, ymin, ymax, zmin, zmax)
  # The below example will return a numpy array like [0.0, 3.0, 4.0, 8.0, 1.0, 7.0]
  region = pygmt.info(
      table=df[["petal_width", "sepal_length", "petal_length"]],  # x, y, z columns
      per_column=True,  # report the min/max values per column as a numpy array
      # round the min/max values of the first three columns to the nearest multiple
      # of 1, 2 and 0.5, respectively
      spacing=(1, 2, 0.5),
  )
  
  # Make a 3D scatter plot, coloring each of the 3 species differently
  fig = pygmt.Figure()
  
  # Define a colormap to be used for three categories, define the range of the
  # new discrete CPT using series=(lowest_value, highest_value, interval),
  # use color_model="+c" to write the discrete color palette "cubhelix" in
  # categorical format
  pygmt.makecpt(cmap="cubhelix", color_model="+c", series=(0, 3, 1))
  
  fig.plot3d(
      # Use petal width, sepal length and petal length as x, y and z data input,
      # respectively
      x=df.petal_width,
      y=df.sepal_length,
      z=df.petal_length,
      # Vary each symbol size according to another feature (sepal width, scaled by 0.1)
      sizes=0.1 * df.sepal_width,
      # Use 3D cubes ("u") as symbols, with size in centimeter units ("c")
      style="uc",
      # Points colored by categorical number code
      color=df.species.cat.codes.astype(int),
      # Use colormap created by makecpt
      cmap=True,
      # Set map dimensions (xmin, xmax, ymin, ymax, zmin, zmax)
      region=region,
      # Set frame parameters
      frame=[
          "WsNeZ3",  # z axis label positioned on 3rd corner
          'xafg+l"Petal Width"',
          'yafg+l"Sepal Length"',
          'zafg+l"Petal Length"',
      ],
      # Set perspective to azimuth NorthWest (315°), at elevation 25°
      perspective=[315, 25],
      # Vertical exaggeration factor
      zscale=1.5,
  )
  fig.show()
  