Data Visualization

• Graphs and plots are efficient way to communicate data.
• matplotlib is a powerful most used Python package for 2D-graphics.

IPython

enhanced interactive Python shell that has lots of interesting features including named inputs and outputs, access to shell commands, improved debugging and much more

pylab

provides a convenient interface to the matplotlib object-oriented plotting library

Simple plot

• Example:

  import numpy as np

  X = np.linspace(-np.pi, np.pi, 256, endpoint=True) C, S = np.cos(X), np.sin(X)

  Changing colors and line widths … plt.figure(figsize=(10,6), dpi=80) plt.plot(X, C, color=”blue”, linewidth=2.5, linestyle=”-“) plt.plot(X, S, color=”red”, linewidth=2.5, linestyle=”-“) … Setting limits … plt.xlim(X.min()1.1, X.max()1.1) plt.ylim(C.min()1.1, C.max()1.1) … Setting tick labels … plt.xticks( [-np.pi, -np.pi/2, 0, np.pi/2, np.pi]) plt.yticks([-1, 0, +1]) … … plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r’$-\pi$’, r’$-\pi/2$’, r’$0$’, r’$+\pi/2$’, r’$+\pi$’])

  plt.yticks([-1, 0, +1], [r’$-1$’, r’$0$’, r’$+1$’]) … Moving spines … ax = plt.gca() ax.spines[‘right’].set_color(‘none’) ax.spines[‘top’].set_color(‘none’) ax.xaxis.set_ticks_position(‘bottom’) ax.spines[‘bottom’].set_position((‘data’,0)) ax.yaxis.set_ticks_position(‘left’) ax.spines[‘left’].set_position((‘data’,0)) .. Adding a legend … plt.plot(X, C, color=”blue”, linewidth=2.5, linestyle=”-“, label=”cosine”) plt.plot(X, S, color=”red”, linewidth=2.5, linestyle=”-“, label=”sine”)

  plt.legend(loc=’upper left’, frameon=False) …

Notes

• Animation can be done using matplotlib, especially after launching the new version being.
• Types of Plots:
  • Regular Plot (Plot lines/markers)
  • Scatter Plot
  • Bar Plot
  • Contour Plot
  • IMSHOW (Image showing)
  • Quiver Plot
  • Pie Chart
  • Grid
  • Multi-Plot
  • Polar-Axis
  • 3D Plots
  • Text