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Data Visualization

This topic matters as it relates to learning about Data Visualization and how to implement them to analyze data in Python.

Matplotlib Tutorial

matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats.

  • IPython An enhanced interactive Python shell that has lots of interesting features including named inputs and outputs, access to shell commands, improved debugging and much more. It allows interactive matplotlib sessions that have Matlab/Mathematica-like functionality.

  • pyplot Provides a convenient interface to the matplotlib object-oriented plotting library. It is modeled closely after Matlab(TM). Therefore, the majority of plotting commands in pyplot have Matlab(TM) analogs with similar arguments. Important commands are explained with interactive examples.

To get data for the sine and cosine functions:

import numpy as np

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

Simple plot

  • 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 ticks 
    plt.xticks( [-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
plt.yticks([-1, 0, +1])
  • Setting tick labels ```python 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
```python
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))

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Things I want to know more about

  • I would like to know more about how to implement data visualization efficiently to analyze data in Python.

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