How to Create a Chart in Python

Displaying data in the form of charts or graphs in Python requires the use of specialized, external code libraries, and the open-source community offers a programmer a number of good ones to choose from. This demonstration uses the matplotlib library to show how to make a simple chart in Python. Matplotlib has both a broad and deep feature set and is well documented; this makes it a good place to start. If time allows, interested programmers should research other available libraries before committing to any one of them.

Things You'll Need

• Python scripting environment
• Text or code editor
• Matplotlib 2D plotting library

Instructions

Creating a Chart Using Python

1. Import the matplotlib pyplot module into the code file:

   import matplotlib.pyplot as plt

2. Define variables used for bounding the chart axes and creating data to be plotted:

   max_x, max_y, min_x, min_y = 11.0, 11.0*11.0, 0.0, 0.0

3. Declare arrays to separately hold x and y values to be plotted:

   x_arr = []
   y_arr = []

4. Fill the arrays with data. Typically you will draw this data from external sources such as files or database queries. Here the code creates sample data, implementing the formula y(x) = x*x:

   for i in range(min_x,max_x):
   x_arr.append(float(i))
   y_arr.append(float(i*i))

5. Create a FigureCanvas object using the imported matplotlib pyplot object:

   fig = plt.figure()

6. Add the graph’s axes to the FigureCanvas by calling the function "add_axes" and passing it an array of values in the form of: left, bottom, width, height. These values define where the graph is placed on the canvas. Values can range from 0.0 to 1.0:

   ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])

7. Format the graph, adding labels and defining the minimum and maximum values for each axis:

   ax.set_xlabel('x data')
   ax.set_ylabel('y data')
   ax.set_xlim(min_x,max_x)
   ax.set_ylim(min_y,max_y)

8. Plot the graph by passing in the two arrays containing the x and y values retrieved from the CSV file. Customize the line plot by passing in optional values such as line color (color) or line width (lw):

   ax.plot(x_arr,y_arr, color='red', lw=2)

9. Call the pyplot module’s show method to display the chart in a window. This functionality and the style of window will depend on the operating system environment and the local installation of matplotlib:

   plt.show()

10. Store the image by calling savefig to create a bitmap file on disk:

    fig.savefig('test.png')

Tips & Warnings

• To create files that the Python interpreter can read, you must use an ascii text or code editor that creates text-only files.

• You can store graph images in many different image formats including: png, pdf, ps and svg.

• Some aspects of the matplotlib library installation and functionality vary on different computer platforms. Read the documentation carefully.

• The library is capable of displaying numerical information in a vast number of ways and can be finely customized.

References

• "Learning Python, 3rd Edition"; Mark Lutz; 2007
• Introductory matplotlib tutorial

Resources

• Matplotlib: Matplotlib Python library main page
• Python: Comprehensive list of Python plotting libraries