Data Analysis From Scratch With Python: Step By Step Guide

Importing & Using Matplotlib
According 
to 
their 
homepage 
(
https://matplotlib.org/2.0.2/index.html
):
“Matplotlib is a Python 2D plotting library which produces publication quality
figures in a variety of hardcopy formats and interactive environments across
platforms. Matplotlib can be used in Python scripts, the Python and IPython
shell, the jupyter notebook, web application servers, and four graphical user
interface toolkits.”
In other words, you can easily generate plots, histograms, bar charts, scatterplots,
and many more using Python and a few lines of code. Instead of spending so
much time figuring things out, you can focus on generating plots for faster
analysis and data exploration.
That sounds a mouthful. But always remember it’s still about exploring and
communicating data. Let’s look at an example to make this clear. First, here’s a
simple 
horizontal 
bar 
chart
(
https://matplotlib.org/2.0.2/examples/lines_bars_and_markers/barh_demo.html
):

---

To create that, you only need this block of code: 
import matplotlib.pyplot as
plt
plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
plt.rcdefaults()
fig, ax = plt.subplots()
# Example data
people = ('Tom', 'Dick', 'Harry', 'Slim', 'Jim')
y_pos = np.arange(len(people))
performance = 3 + 10 * np.random.rand(len(people))
error = np.random.rand(len(people))
ax.barh(y_pos, performance, xerr=error, align='center',
color='green', ecolor='black')
ax.set_yticks(y_pos)
ax.set_yticklabels(people)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Performance')