AnalyticsDojo

Introduction to Seaborn - Python

rpi.analyticsdojo.com

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)

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])

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)

png

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

png

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

png

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

png

Box Plots

  • Break data into quartiles.
  • Can show distribution/ranges of different categories.

Boxplot vs PDF

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"])

png

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

png

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")

png

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

png

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

png

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

png

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

png

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

png

#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);

png

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

png

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");

png

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

png

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

png