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Contents

Introduction to Seaborn - Python

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13. Introduction to Seaborn

13.1. Overview

• Look at distributions

• Seaborn is an alternate data visualization package.

• This has been adopted from the Seaborn Documentation.Read more at https://stanford.edu/~mwaskom/software/seaborn/api.html

#This uses the same mechanisms. 
%matplotlib inline

import numpy as np
import pandas as pd
from scipy import stats, integrate
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)

13.2. Distribution Plots

• Histogram with KDE

• Histogram with Rugplot

import seaborn as sns, numpy as np
sns.set(); np.random.seed(0)
x = np.random.randn(100)
x

array([ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.86755799,
       -0.97727788,  0.95008842, -0.15135721, -0.10321885,  0.4105985 ,
        0.14404357,  1.45427351,  0.76103773,  0.12167502,  0.44386323,
        0.33367433,  1.49407907, -0.20515826,  0.3130677 , -0.85409574,
       -2.55298982,  0.6536186 ,  0.8644362 , -0.74216502,  2.26975462,
       -1.45436567,  0.04575852, -0.18718385,  1.53277921,  1.46935877,
        0.15494743,  0.37816252, -0.88778575, -1.98079647, -0.34791215,
        0.15634897,  1.23029068,  1.20237985, -0.38732682, -0.30230275,
       -1.04855297, -1.42001794, -1.70627019,  1.9507754 , -0.50965218,
       -0.4380743 , -1.25279536,  0.77749036, -1.61389785, -0.21274028,
       -0.89546656,  0.3869025 , -0.51080514, -1.18063218, -0.02818223,
        0.42833187,  0.06651722,  0.3024719 , -0.63432209, -0.36274117,
       -0.67246045, -0.35955316, -0.81314628, -1.7262826 ,  0.17742614,
       -0.40178094, -1.63019835,  0.46278226, -0.90729836,  0.0519454 ,
        0.72909056,  0.12898291,  1.13940068, -1.23482582,  0.40234164,
       -0.68481009, -0.87079715, -0.57884966, -0.31155253,  0.05616534,
       -1.16514984,  0.90082649,  0.46566244, -1.53624369,  1.48825219,
        1.89588918,  1.17877957, -0.17992484, -1.07075262,  1.05445173,
       -0.40317695,  1.22244507,  0.20827498,  0.97663904,  0.3563664 ,
        0.70657317,  0.01050002,  1.78587049,  0.12691209,  0.40198936])

13.2.1. Distribution Plot (distplot)

• Any compbination of hist, rug, kde

• Note it also has in it a KDE plot included

• Can manually set the number of bins

• See documentation here

#Histogram
# https://seaborn.pydata.org/generated/seaborn.distplot.html#seaborn.distplot
ax = sns.distplot(x)

#Adjust number of bins for more fine grained view
ax = sns.distplot(x, bins = 20)

#Include rug and kde (no histogram)
sns.distplot(x, hist=False, rug=True);

#Kernel Density
#https://seaborn.pydata.org/generated/seaborn.rugplot.html#seaborn.rugplot
ax = sns.distplot(x, bins=10, kde=True, rug=True)

13.2.2. Box Plots

• Break data into quartiles.

• Can show distribution/ranges of different categories.

Jhguch at en.wikipedia [CC BY-SA 2.5 (https://creativecommons.org/licenses/by-sa/2.5)], from Wikimedia Commons

sns.set_style("whitegrid")
#This is data on tips (a real dataset) and our familiar iris dataset
tips = sns.load_dataset("tips")
iris = sns.load_dataset("iris")
titanic = sns.load_dataset("titanic")
#Tips is a pandas dataframe
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

ax = sns.boxplot(x=tips["total_bill"])

# Notice we can see the few ouliers on right side
ax = sns.distplot(tips["total_bill"],  kde=True, rug=True)

13.3. Relationship Plots

• Pairplots to show all

• Regplot for 2 continuous variables

• Scatterplot for two continuous variables

• Swarmplot or BoxPlot for continuous and categorical

#Notice how this works for continuous, not great for categorical
h = sns.pairplot(tips, hue="time")

g = sns.pairplot(iris, hue="species")

# Show relationship between 2 continuous variables with regression line. 
sns.regplot(x="total_bill", y="tip", data=tips);

# Break down 
sns.boxplot(x="day", y="total_bill", hue="time", data=tips);

#Uses an algorithm to prevent overlap
sns.swarmplot(x="day", y="total_bill", hue= "time",data=tips);

#Uses an algorithm to prevent overlap
sns.violinplot(x="day", y="total_bill", hue= "time",data=tips);

#Stacking Graphs Is Easy
sns.violinplot(x="day", y="total_bill", data=tips, inner=None)
sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5);

13.4. Visualizing Summary Data

• Barplots will show the

#This 
sns.barplot(x="sex", y="tip", data=tips);
tips

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
5	25.29	4.71	Male	No	Sun	Dinner	4
6	8.77	2.00	Male	No	Sun	Dinner	2
7	26.88	3.12	Male	No	Sun	Dinner	4
8	15.04	1.96	Male	No	Sun	Dinner	2
9	14.78	3.23	Male	No	Sun	Dinner	2
10	10.27	1.71	Male	No	Sun	Dinner	2
11	35.26	5.00	Female	No	Sun	Dinner	4
12	15.42	1.57	Male	No	Sun	Dinner	2
13	18.43	3.00	Male	No	Sun	Dinner	4
14	14.83	3.02	Female	No	Sun	Dinner	2
15	21.58	3.92	Male	No	Sun	Dinner	2
16	10.33	1.67	Female	No	Sun	Dinner	3
17	16.29	3.71	Male	No	Sun	Dinner	3
18	16.97	3.50	Female	No	Sun	Dinner	3
19	20.65	3.35	Male	No	Sat	Dinner	3
20	17.92	4.08	Male	No	Sat	Dinner	2
21	20.29	2.75	Female	No	Sat	Dinner	2
22	15.77	2.23	Female	No	Sat	Dinner	2
23	39.42	7.58	Male	No	Sat	Dinner	4
24	19.82	3.18	Male	No	Sat	Dinner	2
25	17.81	2.34	Male	No	Sat	Dinner	4
26	13.37	2.00	Male	No	Sat	Dinner	2
27	12.69	2.00	Male	No	Sat	Dinner	2
28	21.70	4.30	Male	No	Sat	Dinner	2
29	19.65	3.00	Female	No	Sat	Dinner	2
...	...	...	...	...	...	...	...
214	28.17	6.50	Female	Yes	Sat	Dinner	3
215	12.90	1.10	Female	Yes	Sat	Dinner	2
216	28.15	3.00	Male	Yes	Sat	Dinner	5
217	11.59	1.50	Male	Yes	Sat	Dinner	2
218	7.74	1.44	Male	Yes	Sat	Dinner	2
219	30.14	3.09	Female	Yes	Sat	Dinner	4
220	12.16	2.20	Male	Yes	Fri	Lunch	2
221	13.42	3.48	Female	Yes	Fri	Lunch	2
222	8.58	1.92	Male	Yes	Fri	Lunch	1
223	15.98	3.00	Female	No	Fri	Lunch	3
224	13.42	1.58	Male	Yes	Fri	Lunch	2
225	16.27	2.50	Female	Yes	Fri	Lunch	2
226	10.09	2.00	Female	Yes	Fri	Lunch	2
227	20.45	3.00	Male	No	Sat	Dinner	4
228	13.28	2.72	Male	No	Sat	Dinner	2
229	22.12	2.88	Female	Yes	Sat	Dinner	2
230	24.01	2.00	Male	Yes	Sat	Dinner	4
231	15.69	3.00	Male	Yes	Sat	Dinner	3
232	11.61	3.39	Male	No	Sat	Dinner	2
233	10.77	1.47	Male	No	Sat	Dinner	2
234	15.53	3.00	Male	Yes	Sat	Dinner	2
235	10.07	1.25	Male	No	Sat	Dinner	2
236	12.60	1.00	Male	Yes	Sat	Dinner	2
237	32.83	1.17	Male	Yes	Sat	Dinner	2
238	35.83	4.67	Female	No	Sat	Dinner	3
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

tips

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
5	25.29	4.71	Male	No	Sun	Dinner	4
6	8.77	2.00	Male	No	Sun	Dinner	2
7	26.88	3.12	Male	No	Sun	Dinner	4
8	15.04	1.96	Male	No	Sun	Dinner	2
9	14.78	3.23	Male	No	Sun	Dinner	2
10	10.27	1.71	Male	No	Sun	Dinner	2
11	35.26	5.00	Female	No	Sun	Dinner	4
12	15.42	1.57	Male	No	Sun	Dinner	2
13	18.43	3.00	Male	No	Sun	Dinner	4
14	14.83	3.02	Female	No	Sun	Dinner	2
15	21.58	3.92	Male	No	Sun	Dinner	2
16	10.33	1.67	Female	No	Sun	Dinner	3
17	16.29	3.71	Male	No	Sun	Dinner	3
18	16.97	3.50	Female	No	Sun	Dinner	3
19	20.65	3.35	Male	No	Sat	Dinner	3
20	17.92	4.08	Male	No	Sat	Dinner	2
21	20.29	2.75	Female	No	Sat	Dinner	2
22	15.77	2.23	Female	No	Sat	Dinner	2
23	39.42	7.58	Male	No	Sat	Dinner	4
24	19.82	3.18	Male	No	Sat	Dinner	2
25	17.81	2.34	Male	No	Sat	Dinner	4
26	13.37	2.00	Male	No	Sat	Dinner	2
27	12.69	2.00	Male	No	Sat	Dinner	2
28	21.70	4.30	Male	No	Sat	Dinner	2
29	19.65	3.00	Female	No	Sat	Dinner	2
...	...	...	...	...	...	...	...
214	28.17	6.50	Female	Yes	Sat	Dinner	3
215	12.90	1.10	Female	Yes	Sat	Dinner	2
216	28.15	3.00	Male	Yes	Sat	Dinner	5
217	11.59	1.50	Male	Yes	Sat	Dinner	2
218	7.74	1.44	Male	Yes	Sat	Dinner	2
219	30.14	3.09	Female	Yes	Sat	Dinner	4
220	12.16	2.20	Male	Yes	Fri	Lunch	2
221	13.42	3.48	Female	Yes	Fri	Lunch	2
222	8.58	1.92	Male	Yes	Fri	Lunch	1
223	15.98	3.00	Female	No	Fri	Lunch	3
224	13.42	1.58	Male	Yes	Fri	Lunch	2
225	16.27	2.50	Female	Yes	Fri	Lunch	2
226	10.09	2.00	Female	Yes	Fri	Lunch	2
227	20.45	3.00	Male	No	Sat	Dinner	4
228	13.28	2.72	Male	No	Sat	Dinner	2
229	22.12	2.88	Female	Yes	Sat	Dinner	2
230	24.01	2.00	Male	Yes	Sat	Dinner	4
231	15.69	3.00	Male	Yes	Sat	Dinner	3
232	11.61	3.39	Male	No	Sat	Dinner	2
233	10.77	1.47	Male	No	Sat	Dinner	2
234	15.53	3.00	Male	Yes	Sat	Dinner	2
235	10.07	1.25	Male	No	Sat	Dinner	2
236	12.60	1.00	Male	Yes	Sat	Dinner	2
237	32.83	1.17	Male	Yes	Sat	Dinner	2
238	35.83	4.67	Female	No	Sat	Dinner	3
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

#Notice the selection of palette and how we can swap the axis.
sns.barplot(x="tip", y="day", data=tips,  palette="Greens_d");

#Notice the selection of palette and how we can swap the axis.
sns.barplot(x="total_bill", y="day", data=tips,  palette="Reds_d");

#Saturday is the bigger night
sns.countplot(x="day", data=tips);

12. Web Mining 14. String Manipulation and Regular Expressions