Companion notebook to Lecture 3 of Harvard CS109: Data Science. Prepared by Chris Beaumont.
Inspiration taken from http://nbviewer.ipython.org/5357268
Also see these same examples with Matplotlib defaults
Addendum Since this notebook was prepared in 2013, several of these stylistic improvements (along with many others) have been incorporated into the prettyplotlib package. Seaborn likewise includes some automatic style improvements over matplotlib's defaults, and Matplotlib 1.4 will include a database of improved styles.
In [1]:
#brewer2mpl makes it easier to use color tables from colorbrewer2.org in matplotlib
!pip install brewer2mpl
In [2]:
%matplotlib inline
from urllib import urlopen
import brewer2mpl
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
In [3]:
# Set up some better defaults for matplotlib
from matplotlib import rcParams
#colorbrewer2 Dark2 qualitative color table
dark2_colors = brewer2mpl.get_map('Dark2', 'Qualitative', 7).mpl_colors
rcParams['figure.figsize'] = (10, 6)
rcParams['figure.dpi'] = 150
rcParams['axes.color_cycle'] = dark2_colors
rcParams['lines.linewidth'] = 2
rcParams['axes.facecolor'] = 'white'
rcParams['font.size'] = 14
rcParams['patch.edgecolor'] = 'white'
rcParams['patch.facecolor'] = dark2_colors[0]
rcParams['font.family'] = 'StixGeneral'
def remove_border(axes=None, top=False, right=False, left=True, bottom=True):
"""
Minimize chartjunk by stripping out unnecesasry plot borders and axis ticks
The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn
"""
ax = axes or plt.gca()
ax.spines['top'].set_visible(top)
ax.spines['right'].set_visible(right)
ax.spines['left'].set_visible(left)
ax.spines['bottom'].set_visible(bottom)
#turn off all ticks
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticks_position('none')
#now re-enable visibles
if top:
ax.xaxis.tick_top()
if bottom:
ax.xaxis.tick_bottom()
if left:
ax.yaxis.tick_left()
if right:
ax.yaxis.tick_right()
In [4]:
file = urlopen('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/ggplot2/diamonds.csv')
diamonds = pd.read_csv(file)
file = urlopen('http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/titanic.csv')
titanic = pd.read_csv(file)
In [5]:
change = [23.2, 22.7, 19.7, 13.9, 13.1, 12.8, 12.7,
12.6, 12.0, 11.5, 10.8, 10.4, 10.4, 9.8, 9.2,
9.2, 8.8, 7.7, 6.9, 6.9, 6.4, 5.6, 5.3, 5.3, 5.2, 4.9,
4.8, 4.6, 3.6, 3.1, 0.7, -.3, -.7, -1.2, -1.5, -1.7,
-1.7, -1.8, -2, -2.3, -2.4, -3.6, -3.7,
-4.9, -6.5, -6.6, -11.6, -14.8, -17.6, -23.1]
city = ['Philadelphia', 'Tucson', 'Kansas City, MO',
'El Paso', 'Portland, Ore.', 'New York', 'Dallas',
'Columbus', 'Mesa', 'Austin', 'Atlanta', 'Fort Worth',
'Miami', 'Houston', 'Chicago', 'Oakland', 'Virginia Beach',
'Baltimore', 'Denver', 'Detroit', 'San Antonio', 'Phoenix',
'Oklahoma City', 'Indianapolis', 'Milwaukee', 'Sacramento',
'Washington, D.C.', 'Colorado Springs', 'Honolulu', 'Nashville',
'Jacksonville', 'Louisville', 'Seattle',
'Memphis', 'Fresno', 'Boston', 'Mineappolis',
'San Jose', 'Tulsa', 'Charlotte', 'San Diego', 'Los Angeles',
'Long Beach', 'Cleveland', 'San Francisco', 'Albuquerque',
'Arlington, TX', 'Omaha', 'Wichita', 'Las Vegas']
grad = pd.DataFrame({'change' : change, 'city': city})
In [6]:
plt.figure(figsize=(3, 8))
change = grad.change[grad.change > 0]
city = grad.city[grad.change > 0]
pos = np.arange(len(change))
plt.title('1995-2005 Change in HS graduation rate')
plt.barh(pos, change)
#add the numbers to the side of each bar
for p, c, ch in zip(pos, city, change):
plt.annotate(str(ch), xy=(ch + 1, p + .5), va='center')
#cutomize ticks
ticks = plt.yticks(pos + .5, city)
xt = plt.xticks()[0]
plt.xticks(xt, [' '] * len(xt))
#minimize chartjunk
remove_border(left=False, bottom=False)
plt.grid(axis = 'x', color ='white', linestyle='-')
#set plot limits
plt.ylim(pos.max() + 1, pos.min() - 1)
plt.xlim(0, 30)
Out[6]:
In [7]:
change = grad.change[grad.change < 0].values
city = grad.city[grad.change < 0].values
pos = np.arange(len(change))
red = (0.78, 0.22, 0.18) # RGB triplet
plt.figure(figsize=(3, 6), dpi=200)
plt.barh(pos, change, color=red)
plt.yticks(pos + .5, city)
#add the numbers to the side of each bar
for p, c, ch in zip(pos, city, change):
plt.annotate(str(ch), xy=(ch - 1, p + .5), va='center', ha='right')
#cutomize ticks
plt.gca().yaxis.tick_right()
ticks = plt.yticks(pos + .5, city)
xt = plt.xticks()[0]
plt.xticks(xt, [' '] * len(xt))
#Remove chartjunk
remove_border(left=False, bottom=False)
plt.grid(axis = 'x', color ='white', linestyle='-')
plt.ylim(pos.max() + 1, pos.min()- .5)
plt.xlim(-30, 0)
plt.title('1995-2005 Change in HS graduation rate')
Out[7]:
In [8]:
years = np.arange(2004, 2009)
heights = np.random.random(years.shape) * 7000 + 3000
box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors
plt.bar(years - .4, heights, color=box_colors)
plt.grid(axis='y', color='white', linestyle='-', lw=1)
plt.yticks([2000, 4000, 6000, 8000])
fmt = plt.ScalarFormatter(useOffset=False)
plt.gca().xaxis.set_major_formatter(fmt)
plt.xlim(2003.5, 2008.5)
remove_border(left=False)
for x, y in zip(years, heights):
plt.annotate("%i" % y, (x, y + 200), ha='center')
In [9]:
plt.figure(tight_layout=True, figsize=(6, 4))
plt.subplot(121)
plt.scatter(diamonds.carat, diamonds.price, color='k')
plt.ylim(0, diamonds.price.max())
plt.xlim(0, 5)
plt.xlabel("Carat")
plt.ylabel("Price")
remove_border()
plt.subplot(122)
plt.scatter(diamonds.carat, diamonds.price, color='k', alpha=.01)
plt.ylim(0, diamonds.price.max())
plt.xlim(0, 5)
plt.xlabel("Carat")
plt.ylabel("Price")
remove_border()
In [10]:
# the raw data
x = diamonds.carat[diamonds.carat < 2]
y = diamonds.price[diamonds.carat < 2]
plt.plot(x, y, 'o', mec='none', alpha=.05)
#fit and overplot a 2nd order polynomial
params = np.polyfit(x, y, 2)
xp = np.linspace(x.min(), 2, 20)
yp = np.polyval(params, xp)
plt.plot(xp, yp, 'k')
#overplot an error band
sig = np.std(y - np.polyval(params, x))
plt.fill_between(xp, yp - sig, yp + sig,
color='k', alpha=0.2)
plt.xlabel("Carat")
plt.ylabel("Price")
plt.xlim(0, 2)
remove_border()
In [11]:
t = titanic.groupby(['pclass']).size()
print t
plt.subplot(aspect=True)
plt.pie(t, labels=t.index.values, colors = dark2_colors[0:3], autopct='%i%%')
plt.title("Passenger Class on the Titanic")
Out[11]:
In [12]:
tclass = titanic.groupby(['pclass', 'survived']).size().unstack()
print tclass
red, blue = '#B2182B', '#2166AC'
plt.subplot(121)
plt.bar([0, 1, 2], tclass[0], color=red, label='Died')
plt.bar([0, 1, 2], tclass[1], bottom=tclass[0], color=blue, label='Survived')
plt.xticks([0.5, 1.5, 2.5], ['1st Class', '2nd Class', '3rd Class'], rotation='horizontal')
plt.ylabel("Number")
plt.xlabel("")
plt.legend(loc='upper left')
remove_border()
#normalize each row by transposing, normalizing each column, and un-transposing
tclass = (1. * tclass.T / tclass.T.sum()).T
plt.subplot(122)
plt.bar([0, 1, 2], tclass[0], color=red, label='Died')
plt.bar([0, 1, 2], tclass[1], bottom=tclass[0], color=blue, label='Survived')
plt.xticks([0.5, 1.5, 2.5], ['1st Class', '2nd Class', '3rd Class'], rotation='horizontal')
plt.ylabel("Fraction")
plt.xlabel("")
remove_border()
plt.show()
In [13]:
plt.hist(diamonds.depth, bins=np.linspace(50, 70, 200))
plt.xlabel("Depth")
remove_border()
plt.xlim(55, 70)
plt.show()
plt.hist(diamonds.depth, bins=np.linspace(50, 70, 40))
plt.xlabel("Depth")
remove_border()
plt.xlim(55, 70)
plt.show()
In [14]:
#KernelDensity objects estimate the (log of the) density of points
#see http://scikit-learn.org/stable/modules/density.html
from sklearn.neighbors.kde import KernelDensity
age = titanic.age.dropna().values # drop missing values, turn to normal numpy array
age = age.reshape(-1, 1) # scikit-learn expects data matrices of shape [ndata, ndim]
kde = KernelDensity(bandwidth=2).fit(age)
x = np.linspace(age.min(), age.max(), 100).reshape(-1, 1)
density = np.exp(kde.score_samples(x))
plt.plot(x, density)
plt.plot(age, age * 0, 'ok', alpha=.03)
plt.ylim(-.001, .035)
plt.xlabel("Age")
plt.ylabel("Density")
remove_border()
In [15]:
male_age = titanic.age[titanic.sex == 'male']
female_age = titanic.age[titanic.sex == 'female']
plt.boxplot([male_age, female_age])
plt.ylabel("Titanic Passanger Age")
plt.xticks([1, 2], ["Male", "Female"])
plt.ylim(0, 85)
remove_border()
In [16]:
from sklearn.datasets import make_blobs
from matplotlib.colors import LogNorm
X, _ = make_blobs(n_samples=20000, centers=3, random_state=42, cluster_std=2)
plt.scatter(X[:, 0], X[:, 1], 2, color='k')
plt.title("Points")
plt.xlim(-15, 15)
plt.ylim(-15, 15)
plt.gca().set_position([.125, .125, .62, .775])
plt.show()
plt.hist2d(X[:, 0], X[:, 1], bins=40, cmap='Greens', norm=LogNorm())
ax = plt.gca()
plt.title("Heatmap")
plt.colorbar()
plt.xlim(-15, 15)
plt.ylim(-15, 15)
plt.show()