How to Create a Chart in Python

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How to Create a Chart in Python
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
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Instructions

  1. 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.

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References

Resources

  • Photo Credit Hemera Technologies/AbleStock.com/Getty Images

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