Data Analysis From Scratch With Python: Step By Step Guide

ax.set_title('How fast do you want to go today?')
plt.show()
It looks complex at first. But what it did was to import the necessary

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libraries, set the data, and describe how it should be shown. Writing it all from
scratch might be difficult. The good news is we can copy the code examples and
modify it according to our purposes and new data.
Aside from horizontal bar charts, matplotlib is also useful for creating and
displaying scatterplots, boxplots, and other visual representations of data: 
"""
Simple demo of a scatter plot.
"""
import numpy as np
import matplotlib.pyplot as plt
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radii
plt.scatter(x, y, s=area, c=colors, alpha=0.5)

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plt.show()
import matplotlib.pyplot as plt
from numpy.random import rand
fig, ax = plt.subplots()
for color in ['red', 'green', 'blue']:
n = 750
x, y = rand(2, n)
scale = 200.0 * rand(n)
ax.scatter(x, y, c=color, s=scale, label=color,
alpha=0.3, edgecolors='none')
ax.legend()
ax.grid(True)

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plt.show()
Source of images and code: 
https://matplotlib.org/2.0.2/gallery.html
These are just to show you the usefulness and possibilities in using matplotlib.
Notice that you can make publication-quality data visualizations. Also notice
that you can modify the example codes to your purpose. There’s no need to

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reinvent the wheel. You can copy the appropriate sections and adapt them to
your data.
Perhaps in the future there will be faster and easier ways to create data
visualizations especially when working with huge datasets. You can even create
animated presentations that can change through time. Whichever is the case, the
goal of data visualization is to explore and communicate data. You can choose
other methods but the goal always remains the same.
In this chapter and the previous ones we’ve discussed general things about
analyzing data. In the succeeding chapters, let’s start discussing advanced topics
that are specific to machine learning and advanced data analysis. The initial goal
is to get you familiar with the most common concepts and terms used in data
science circles. Let’s start with defining what do Supervised Learning and
Unsupervised Learning mean.

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