Installing Seaborn

Seaborn is another data visuaization library, like matplotlib but a little less insane. Install it by using pip3.

pip3 install seaborn

Using Seaborn

First let’s read in our data.

%matplotlib inline
import pandas as pd

df = pd.read_csv("../countries.csv")
df.head(2)
country continent gdp_per_capita life_expectancy population
0 Afghanistan Asia 663 54.863 22856302
1 Albania Europe 4195 74.200 3071856

Histograms

Seaborn doesn’t plug into pandas, so you can’t just do .plot. You need to import seaborn separately, then feed it the dataframe and the data.

I think everything has weird names when you’re using seaborn, but that’s just me.

sns.distplot([45,32,12,57,86,423,23])
<matplotlib.axes._subplots.AxesSubplot at 0x10a066be0>

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df.life_expectancy
0      54.863
1      74.200
2      68.963
3      45.234
4      73.544
5      73.822
6      71.494
7      79.930
8      78.330
9      66.851
10     72.370
11     74.497
12     65.309
13     73.118
14     68.990
15     77.910
16     70.425
17     55.261
18     60.307
19     62.994
20     74.882
21     50.567
22     70.261
23     75.927
24     71.590
25     50.589
26     48.251
27     62.030
28     51.960
29     79.410
        ...  
158    67.851
159    48.689
160    79.840
161    79.990
162    73.316
163    76.020
164    63.570
165    50.010
166    70.921
167    59.468
168    53.585
169    70.810
170    68.533
171    73.159
172    70.010
173    63.925
174    48.126
175    67.790
176    74.163
177    78.040
178    76.800
179    74.777
180    66.999
181    67.438
182    72.432
183    73.777
184    70.929
185    60.404
186    41.802
187    43.976
Name: life_expectancy, Length: 188, dtype: float64

import seaborn as sns

sns.distplot(df.life_expectancy)
<matplotlib.axes._subplots.AxesSubplot at 0x10a1e5e10>

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More bins!

sns.distplot(df.life_expectancy, bins=60)
<matplotlib.axes._subplots.AxesSubplot at 0x10a2b8e48>

png

Wait, we want a count on the left-hand side, not a percentage

sns.distplot(df.life_expectancy, bins=30, kde=False)
<matplotlib.axes._subplots.AxesSubplot at 0x10a585748>

png

sns.distplot(df.life_expectancy, bins=30, kde=False, vertical=True)
<matplotlib.axes._subplots.AxesSubplot at 0x10a6b9630>

png

Scatter plot

sns.jointplot(x="gdp_per_capita", y="life_expectancy", data=df)
<seaborn.axisgrid.JointGrid at 0x10a9a0358>

png

No wait, let’s get rid of all that weird stuff

Even though the documentation says that’s how you do a scatter plot, I don’t think you can get rid of all of that weird stuff. So, time to use another plot.

sns.regplot(x="gdp_per_capita", y="life_expectancy", data=df);

png

No wait, I don’t want that regression line

sns.regplot(x="gdp_per_capita", y="life_expectancy", data=df, fit_reg=False);

png

No wait, I want to use a method with a different name that for some reason

does the same time

sns.lmplot(x="gdp_per_capita", y="life_expectancy", data=df, fit_reg=False);

png

But also COLOR!

sns.lmplot(x="gdp_per_capita", y="life_expectancy", hue="continent", data=df, fit_reg=False);

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Small multiples

sns.lmplot(x="gdp_per_capita", y="life_expectancy", col="continent", data=df, fit_reg=False);

png

But those are TOO SMALL

sns.lmplot(x="gdp_per_capita", 
           y="life_expectancy", 
           col="continent", 
           col_wrap=3, 
           data=df, 
           fit_reg=False,
           hue="continent")
<seaborn.axisgrid.FacetGrid at 0x10ce23940>

png

Bar graphs

sns.barplot(x="country", y="gdp_per_capita", data=df.head())
<matplotlib.axes._subplots.AxesSubplot at 0x10c808e80>

png

Sideways!

Beware, we have to switch x and y.

sns.barplot(x="gdp_per_capita", y="country", data=df.head(), orient='h')
<matplotlib.axes._subplots.AxesSubplot at 0x10d242b00>

png

Saving to pdf

Same as ever!

import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams['pdf.fonttype'] = 42

sns.barplot(x="gdp_per_capita", y="country", data=df.head(), orient='h')
plt.tight_layout()
plt.savefig("seaborn.pdf")

png