**Adding Data Visualization to Python for Producing Graphs**

Pages: 1, 2, 3, **4**

### Plot Routines

As with the basic DISLIN library, pxDislin contains high-level plot generating functions. Similar to the first example, creating a 2D plot in pxDislin is as simple as

```
>>> x=arange(100.0)
>>> y = sin(x/3)+cos(x/5)
>>> plot = dScatter(x,y)
>>> plot.add_axis(dAxis(0,100,-4,4))
>>> plot.show().
```

After which you should have

To retrieve the plot data, type `plot.xl`

(`xl`

is the info attribute holding the dependent variable). To see the list of info variables, type `object._info.`

Following is an example showing the info parameters of the just generated scatter plot.

```
>>> plot._info
['axis', 'legend', 'title', 'xl', 'yl']
```

You can create 3D plots with pxDislin as well. The following recreates a previous example. Note that the `d3DSurface`

object takes a function to be evaluated over a range as the first argument. The second and third arguments specify the start, stop and range of the x and y variables respectively.

```
>>> def f(x,y):
... dtr = 3.1415 / 180.0
... return(sin(x*3*dtr)*sin(y*2*dtr))
...
>>> plot = d3DSurface(f,(0,180.0,1),(0,180.0,1))
>>> plot.surface(clr_top=20)
>>> plot.surface(clr_bottom=230)
>>> plot.show()
```

### End Game

With only a brief introduction to the DISLIN package, it is hard to see its full potential. In the coming articles there will more examples of its use and capabilities. I encourage you to read up on both DISLIN and pxDislin to discover for yourself the additional capabilities. Besides, who can resist making 3D color plots of interesting functions? A catalog of some basic plots and associated functions can be found here. Have fun!

### Looking Ahead

With the introductions behind us, next month we will take a graphical look into the world of linear algebra. By using the tools of geometry we can gain some insight into the meaning of vectors and matrices.

*
Eric Hagemann
specializes in fast algorithms for crunching numbers on all varieties of computers from embedded to mainframe.
*

*Read more Numerically Python columns*.

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