Statistical Graphics with seaborn¶
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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
Data set¶
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%load_ext rpy2.ipython
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%R data(C02)
co2 = %R CO2
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co2.head()
Out[4]:
| Plant | Type | Treatment | conc | uptake | |
|---|---|---|---|---|---|
| 1 | 1 | Quebec | nonchilled | 95.0 | 16.0 |
| 2 | 1 | Quebec | nonchilled | 175.0 | 30.4 |
| 3 | 1 | Quebec | nonchilled | 250.0 | 34.8 |
| 4 | 1 | Quebec | nonchilled | 350.0 | 37.2 |
| 5 | 1 | Quebec | nonchilled | 500.0 | 35.3 |
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co2.shape
Out[5]:
(84, 5)
Look and feel¶
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g = sns.boxplot(x='Plant', y='uptake', data=co2)
Changing the style¶
Styles available
- dark
- white
- darkgrid
- whitegrid
- ticks
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sns.set_style('darkgrid')
g = sns.boxplot(x='Plant', y='uptake', data=co2)
Changing context¶
Contexts available
- notebook
- paper
- talk
- poster
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sns.set_context('poster')
g = sns.boxplot(x='Plant', y='uptake', data=co2)
Changing font scale¶
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sns.set_context('notebook', font_scale=1.5)
g = sns.boxplot(x='Plant', y='uptake', data=co2)
Changing Colors¶
- Seaborn tutorial
- See more examples
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pal = sns.choose_colorbrewer_palette('qualitative')
Widget Javascript not detected. It may not be installed or enabled properly.
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g = sns.boxplot(x='Plant', y='uptake', data=co2, palette=pal)
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pal = sns.choose_dark_palette()
Widget Javascript not detected. It may not be installed or enabled properly.
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g = sns.boxplot(x='Plant', y='uptake', data=co2, palette=pal)
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pal = sns.choose_light_palette()
Widget Javascript not detected. It may not be installed or enabled properly.
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g = sns.boxplot(x='Plant', y='uptake', data=co2, palette=pal)
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pal = sns.choose_diverging_palette()
Widget Javascript not detected. It may not be installed or enabled properly.
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g = sns.boxplot(x='Plant', y='uptake', data=co2, palette=pal)
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pal = sns.choose_colorbrewer_palette('divergent')
Widget Javascript not detected. It may not be installed or enabled properly.
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g = sns.boxplot(x='Plant', y='uptake', hue='Treatment',
data=co2, palette=pal)
Types of plots¶
Showing distributions¶
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g = sns.distplot(co2.uptake, rug=True)
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g = sns.kdeplot(co2.uptake)
Note: conc is actually a categorical factor that I am abusing as a
continuous value for plotting.
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g = sns.kdeplot(co2.conc, co2.uptake)
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g = sns.jointplot(x='conc', y='uptake', data=co2, kind='kde')
Showing relationships¶
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g = sns.regplot(x='conc', y='uptake', data=co2)
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g = sns.regplot(x='conc', y='uptake',
lowess=True,
data=co2)
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sns.lmplot(x='conc', y='uptake', data=co2,
row='Treatment', col='Type')
pass
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sns.lmplot(x='conc', y='uptake', data=co2,
order=2,
row='Treatment', col='Type')
pass
Showing categories¶
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g = sns.swarmplot(x='Treatment', y='uptake', data=co2)
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sns.set_style('white')
g = sns.violinplot(x='Treatment', y='uptake', hue='Type',
split=True, data=co2)
sns.despine(offset=10, trim=True)
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g = sns.factorplot(x='Treatment', y='uptake', row='conc', col='Type',
margin_titles=True,
data=co2, kind='bar')
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