import numpy as np
import matplotlib.pyplot as plt
figsize = (10, 10)
Introduction
Visualization is one of the most important things of data analysis. Besides just producing ___readable___ plots, we should make an effort to improve the overall attractiveness of the plots. matplotlib is a powerful package for ___Python___ users. Let's start with an example.
Anatomy of a Figure
Before we go deeper, let's take a look at the structure of a figure in matplotlib:
Line plot
First we generate sample data
np.random.seed(1234)
X_arr = np.arange(10)
Y_arr = 3 * X_arr + 2 + np.random.random(size=X_arr.size) # linear with some noise
print(X_arr)
print(Y_arr)
To plot a simple scatter plot, we can use plt.scatter() function
plt.figure(figsize=figsize)
plt.plot(X_arr, Y_arr)
plt.title("My First Plot")
plt.show()
plt.figure(figsize=figsize)
# Use `+` as marker; color set as `g` (green); size proportion to Y values
plt.scatter(X_arr, Y_arr, marker="+", c="g")
# How about adding a line to it? Let's use `plt.plot()`
# set line style to dashed; color as `r` (red)
plt.plot(X_arr, Y_arr, "--r")
# set x/y axis limits: first two are xlow and xhigh; last two are ylow and yhigh
plt.axis([0, 10, 0, 35])
# set x/y labels
plt.xlabel("My X Axis")
plt.ylabel("My Y Axis")
# set title
plt.legend(["line", "datapoints"])
plt.title("My Second Plot")
plt.show()
# `plt.subplots()` returns a figure object (which is the whole thing as shown above)
# and `axes` that control specific plots in the figure.
# Here our "subplots" layout is by default 1 row and 1 col and therefore 1 plot
fig, ax = plt.subplots(figsize=figsize)
# plot should be done on the `axis`: ax
ax.plot(X_arr, Y_arr)
ax.set_title("plotting with plt.subplots() is pretty much the same")
plt.show()
Applying what we did earlier:
fig, ax = plt.subplots(figsize=figsize)
# What we just did, applying to `ax`
ax.scatter(X_arr, Y_arr, marker="+", c="g", s=Y_arr * 10)
ax.plot(X_arr, Y_arr, linestyle="dashed", color="k")
ax.axis([0, 10, 0, 35])
ax.set_xlabel("My X Axis")
ax.set_ylabel("My Y Axis")
ax.set_title("My First Plot")
plt.show()
This is especially useful when handling multiple plots in one figure.
# Now the returned `ax_arr` would be np array with a shape a 2x3
fig, ax_arr = plt.subplots(2, 3, figsize=figsize)
ax_arr[0, 0].plot(X_arr, Y_arr)
ax_arr[0, 1].scatter(X_arr, Y_arr)
fig.suptitle("my subplots")
plt.show()
mu, sigma = 15, 1
gaussian_arr = np.random.normal(mu, sigma, size=10000)
np.mean(gaussian_arr), np.std(gaussian_arr, ddof=1)
fig, ax = plt.subplots(figsize=figsize)
# `hist()` will return something but we usually do not need.
freq_arr, bin_arr, _ = ax.hist(gaussian_arr)
ax.set_title("Histogram")
plt.show()
We can actually customize and make it prettier
fig, ax = plt.subplots(figsize=figsize)
# Facecolor set to green; transparency (`alpha`) level: 30%
freq_arr, bin_arr, _ = ax.hist(gaussian_arr, facecolor="g", alpha=0.3)
# Add grid
ax.grid()
ax.set_title("Histogram- some more features")
plt.show()
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=figsize)
ax = fig.gca(projection="3d") # get current axis
theta = np.linspace(-4 * np.pi, 4 * np.pi, 100)
z = np.linspace(-2, 2, 100)
r = z ** 2 + 1
x = r * np.sin(theta)
y = r * np.cos(theta)
ax.plot(x, y, z, label="parametric curve")
ax.legend()
plt.title("3d plot")
plt.show()
Note on IDE plotting
In regular IDE plotting, after each plot one should put
plt.show()
to show the pop-up plot window in which one can interact with the plots (zoom, rotate, etc.), and this will stop the run of your program until closed.
A way to overcome this is running:
plt.show(block=False)
but then when the script completes, all figures are closed...
My preferred solution is running with block=False to all figures except the last one.