In [1]:

import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

%load_ext version_information
%load_ext rpy2.ipython

Customizing Plots

In [2]:

url = 'http://bit.ly/2b72LNj'
df = pd.read_csv(url)

In [3]:

df.head()

Out[3]:

	model	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
0	Mazda RX4	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	Mazda RX4 Wag	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	Datsun 710	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	Hornet 4 Drive	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	Hornet Sportabout	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2

Customizing matplotllib graphics

In [4]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))
pass

Adding labels

In [5]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))
plt.title('MPG versus Weight')
plt.xlabel('Weight')
plt.ylabel('MPG')
pass

Changing Axes Limits

In [6]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))
plt.xlim([1, 6])
plt.ylim([0, 40])

pass

Changing coordinate systems

In [7]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))
plt.yscale("log")
plt.yticks(np.linspace(10, 100, 10))
pass

Changing attributes of visual eleemnets

In [8]:

plt.plot('wt', 'mpg', color = 'orange', linestyle = 'dashed',
         marker = 's', mec = 'blue', mew = 1, mfc = 'red',
         data = df.sort_values('wt'))
pass

Adding annotations

In [9]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))
plt.text(4.0, 30.0, 'Interesting!', fontsize = 15, color = 'red')
plt.text(2.4, 32, 'A', bbox = dict(facecolor='yellow', alpha =0.5))
plt.arrow(4.3, 29, -0.80, -3, head_length = 0.9, head_width = 0.3, fc='r', ec='r', lw =0.75)
pass

Adding legends

In [10]:

plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'), label = 'Fuel Consumption')
plt.plot('wt', 'hp', '-o', data = df.sort_values('wt'), label = 'Horsepower')
plt.legend(loc = 'upper left', fontsize = 13)
pass

Using styles

In [11]:

plt.style.available

Out[11]:

['classic',
 'seaborn-whitegrid',
 'bmh',
 'seaborn-paper',
 'ggplot',
 'seaborn-pastel',
 'seaborn-notebook',
 'seaborn-ticks',
 'seaborn-dark-palette',
 'seaborn-white',
 'seaborn-darkgrid',
 'dark_background',
 'grayscale',
 'fivethirtyeight',
 'seaborn-colorblind',
 'seaborn-talk',
 'seaborn-poster',
 'seaborn-deep',
 'seaborn-bright',
 'seaborn-muted',
 'seaborn-dark',
 'presentation']

In [12]:

with plt.style.context('seaborn-white'):
    plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))

In [13]:

with plt.style.context('seaborn-dark-palette'):
    plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))

In [14]:

with plt.style.context('bmh'):
    plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))

In [15]:

with plt.xkcd():
    plt.plot('wt', 'mpg', '-o', data = df.sort_values('wt'))

Customizing seaborn graphics

Since seaborn is built on top of matplotlib, customization options for matplotlib will also work with seaborn. However, seabornn plotting functions often give much more scope for customization.

In [16]:

ax = sns.swarmplot('gear', 'mpg', hue = 'wt',
                   size = 15, palette = "BuGn_r",
                   data = df)
ax.legend_.remove()
pass

Layout for Multiple Plots

Data aware grids

In [17]:

g = sns.FacetGrid(df, row="am", col="cyl", margin_titles=True)
g.map(sns.boxplot, 'carb', 'mpg')
pass

In [18]:

g = sns.FacetGrid(df, row="am", col="cyl", margin_titles=True,
                  sharex = False, sharey = False)
g.map(sns.distplot, 'mpg', rug = True, color = "orange",)
pass

/Users/cliburn/anaconda2/envs/p3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

Pairwise Plots

In [19]:

df1 = df[['mpg', 'hp', 'drat', 'wt', 'qsec']]

In [20]:

g = sns.PairGrid(df1)
g.map_upper(plt.scatter)
g.map_lower(sns.kdeplot)
g.map_diag(sns.kdeplot, lw=3, legend=False)
pass

/Users/cliburn/anaconda2/envs/p3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

Several seaborn plots use these grids under the hood

In [21]:

sns.lmplot(x = 'wt', y = 'mpg', col = 'am', data = df)
pass

Laying Out Multiple Different Types of Plots

In [22]:

plt.figure(figsize=(9,9))
ax1 = plt.subplot2grid((3,3), (0,0), colspan=3)
ax2 = plt.subplot2grid((3,3), (1,0), colspan=2)
ax3 = plt.subplot2grid((3,3), (1, 2), rowspan=2)
ax4 = plt.subplot2grid((3,3), (2, 0))
ax5 = plt.subplot2grid((3,3), (2, 1))

sns.regplot('wt', 'mpg', data = df, ax = ax1)
sns.violinplot('gear', 'hp', data = df, ax = ax2)
sns.swarmplot('wt', 'gear', data = df, orient = "h",
              size = 10, alpha = 0.8, split = True, palette = 'pastel', ax = ax3)
sns.kdeplot(df.wt, df.hp, ax = ax4)
sns.barplot('cyl', 'hp', data = df,
            palette = sns.light_palette('orange'),  ax = ax5)
plt.tight_layout()

Exercises

1a. Load the iris data set found in data/iris.csv into a DataFrame named iris.

• How many rows and columns are there in iris?
• Display the first 6 rows.

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1b. Make a regression plot of Sepal.Length (x-coordinate) by Sepal.Width (y-coordinate) using the lmplot function. Add a title “Bad regression”.

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1c. Create a new figure where you fit separate linear regressions of Sepal.Length (x-coordinate) by Sepal.Width for each species on the same plot.

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1d. Create a new figure where you have separate linear regression plots of Sepal.Length (x-coordinate) by Sepal.Width for each species. This figure should have 1 row and 3 columns.

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1e Create a new figure with 2 roww and 2 columns, where each figure shows a swarmplot comparing one of the 4 flower features (Sepal.Length, Sepal.Width, Petal.Length, Petal.Width) across Species.

Challenge: Can you do this in two lines of code?

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1f. Repeat the exercise in 1e, but change the titles of the subplots to “One” and “Two” for the first row and “Three”, “Four” for the second row.

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Version Information

In [23]:

%load_ext version_information
%version_information

The version_information extension is already loaded. To reload it, use:
  %reload_ext version_information

Out[23]:

Software	Version
Python	3.5.2 64bit [GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]
IPython	5.0.0
OS	Darwin 15.6.0 x86_64 i386 64bit
Tue Aug 16 09:06:03 2016 EDT

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