Trying Data Science(5) : Cramming Data Visualizaion - Matplotlib

Hello, everyone!
Starting today, I'm going to talk about data visualization that looks cool. I've only learned a little bit, but can create various graphs through Matplotlib, which is an 2D and 3D graphics library for generating scientific figures.

Merits of Matplotlib?

• Generally easy to get started for simple plots

• Support for custom labels and texts

• High-quality output in many formats

Importing

import matplotlib.pyplot as plt
%matplotlib inline

Creating a Plot

We can create a very simple line plot.

plt.plot(x, y, 'r') # 'r' is the color red
plt.xlabel('X Axis Title Here')
plt.ylabel('Y Axis Title Here')
plt.title('String Title Here')
plt.show()

Creating Multiplots

We can create multiplots by subplot on same canvas.

#plt.subplot(nrows, ncols, plot_number)
plt.subplot(1,2,1)
plt.plot(x, y, 'r--') # More on color options later
plt.subplot(1,2,2)
plt.plot(y, x, 'g*-');

You can see subplots above. A subplot is a plot area divided into several small areas. Subplot allows you to perform visualization tasks by placing various small graphs or diagrams within a single plot.

Matplotlib Object Oriented Method

We will instantiate figure objects and then call methods or attributes from that object.

# Create Figure (empty canvas)
fig = plt.figure()

# Add set of axes to figure
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) 
# left, bottom, width, height (range 0 to 1)

# Plot on that set of axes
axes.plot(x, y, 'b')
axes.set_xlabel('Set X Label') 
axes.set_ylabel('Set y Label')
axes.set_title('Set Title')

We can easily add more than one axis to the figure.

# Creates blank canvas
fig = plt.figure()

axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # main axes
axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes

# Larger Figure Axes 1
axes1.plot(x, y, 'b')
axes1.set_xlabel('X_label_axes2')
axes1.set_ylabel('Y_label_axes2')
axes1.set_title('Axes 2 Title')

# Insert Figure Axes 2
axes2.plot(y, x, 'r')
axes2.set_xlabel('X_label_axes2')
axes2.set_ylabel('Y_label_axes2')
axes2.set_title('Axes 2 Title');

A common issue with matplolib is overlapping subplots or figures. We can use fig.tight_layout() or plt.tight_layout() method, which automatically adjusts the positions of the axes.

fig, axes = plt.subplots(nrows=1, ncols=2)

for ax in axes:
    ax.plot(x, y, 'g')
    ax.set_xlabel('x')
    ax.set_ylabel('y')
    ax.set_title('title')

fig    
plt.tight_layout()

Let's see one more example of subplot.

fig, axes = plt.subplots(nrows=1, ncols=2,figsize=(12,2))

axes[0].plot(x,y,color="blue", lw=5)
axes[0].set_xlabel('x')
axes[0].set_ylabel('y')

axes[1].plot(x,z,color="red", lw=3, ls='--')
axes[1].set_xlabel('x')
axes[1].set_ylabel('z')

You can see 'figsize' above. It is a tuple of the width and height of the figure in inches.

Legends

The legend adds a legend(in Japanese 凡例) to the graph. The legend serves to describe elements such as lines, dots, and colors used in the graph.

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])

ax.plot(x, x2, label="x2")
ax.plot(x, x3, label="x3")
ax.legend()

The legend () is the part that adds a legend to the graph. There are series for x2 and x3, respectively, and we use legends to separate them by labels.

Conclusion

Matplotlib offers a wide range of features, from simple plots to custom labels, text, and high-quality outputs. To create a graph, you can use the default Matplotlib command, and you can create a subplot to display multiple graphs at once. You can also use object-oriented methods to create and graph plots and axis objects. Now let's use Matplotlib to make our data visualizable.

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