Title :¶

Exercise: Visualization Improvisation

Description :¶

For this exercise we would like you to get creative and experiment!

You have the freedom to plot anything that you'd like from this data. You're expected to produce two plots, both of which should adhere to the principles learned in lecture (e.g., make it clear to understand/digest, effective, simple, not misleading, etc).

Please feel inspired to challenge yourself by making a type of plot you've never made before -- perhaps never even seen before! Give a brief explanation of the reason and usefulness of the plot.

Your data is the Boston housing prices dataset. We will load it directly from sklearn

Resource: for tons of great coding examples, visit the matplotlib website.

CS109A Introduction to Data Science

Lecture 14, Exercise: Visualization¶

Harvard University
Fall 2020
Instructors: Pavlos Protopapas, Kevin Rader, and Chris Tanner

In [6]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston

In [7]:

# load the boston housing dataset
boston = load_boston()
boston_pd = pd.DataFrame(boston.data)
boston_pd.columns = boston.feature_names
boston_pd.describe()

Out[7]:

	CRIM	ZN	INDUS	CHAS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
count	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000	506.000000
mean	3.613524	11.363636	11.136779	0.069170	0.554695	6.284634	68.574901	3.795043	9.549407	408.237154	18.455534	356.674032	12.653063
std	8.601545	23.322453	6.860353	0.253994	0.115878	0.702617	28.148861	2.105710	8.707259	168.537116	2.164946	91.294864	7.141062
min	0.006320	0.000000	0.460000	0.000000	0.385000	3.561000	2.900000	1.129600	1.000000	187.000000	12.600000	0.320000	1.730000
25%	0.082045	0.000000	5.190000	0.000000	0.449000	5.885500	45.025000	2.100175	4.000000	279.000000	17.400000	375.377500	6.950000
50%	0.256510	0.000000	9.690000	0.000000	0.538000	6.208500	77.500000	3.207450	5.000000	330.000000	19.050000	391.440000	11.360000
75%	3.677083	12.500000	18.100000	0.000000	0.624000	6.623500	94.075000	5.188425	24.000000	666.000000	20.200000	396.225000	16.955000
max	88.976200	100.000000	27.740000	1.000000	0.871000	8.780000	100.000000	12.126500	24.000000	711.000000	22.000000	396.900000	37.970000

In [8]:

# our canonical example
plt.figure(figsize=(5, 4))
plt.hist(boston.target)
plt.title('Boston Housing Prices')
plt.xlabel('Price ($1000)')
plt.ylabel('# of Houses')
plt.show()

In [4]:

# YOUR FIRST PLOT

In [5]:

# YOUR SECOND PLOT