Visualization with Seaborn

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.



In [2]:

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



In [4]:

    
df = pd.read_csv('data/eu_trade_sums.csv')



In [108]:

    
df.head(10)



In [6]:

    
yrs = [str(yr) for yr in range(2002,2016)]



In [7]:

    
df = df.set_index('geo')



In [12]:

    
uk_eu = df[(df['trade_type'] == 'Export') & 
        (df['partner'] == 'EU28')].loc[['EU28', 'UK']][yrs].T



In [13]:

    
uk_eu.plot()









    Out[13]:





<matplotlib.axes._subplots.AxesSubplot at 0x10f1679d0>

Scatter Plot



In [14]:

    
sns.lmplot('EU28', 'UK', data=uk_eu, fit_reg=False)









    Out[14]:





<seaborn.axisgrid.FacetGrid at 0x10f89df50>

Density Plot



In [50]:

    
uk_eu_trade = df[(df['trade_type'] == 'Export') &
                 (df['partner'] == 'EU28')].loc['UK'][yrs].T



In [83]:

    
uk_eu_trade.name = "UK-EU"



In [84]:

    
uk_eu_trade = uk_eu_trade.astype(int)



In [85]:

    
uk_eu_trade.plot()









    Out[85]:





<matplotlib.axes._subplots.AxesSubplot at 0x11805e3d0>



In [49]:

    
uk_noneu_trade = df[(df['trade_type'] == 'Export') &
                (df['partner'] == 'EXT_EU28')].loc['UK'][yrs].T



In [86]:

    
uk_noneu_trade.name = "UK-Non-EU"



In [87]:

    
uk_noneu_trade









    Out[87]:





2002    228493
2003    219982
2004    229696
2005    272441
2006    267942
2007    271456
2008    284998
2009    228765
2010    296174
2011    362484
2012    365917
2013    459670
2014    396446
2015    460978
Name: UK-Non-EU, dtype: int64



In [68]:

    
uk_noneu_trade = uk_noneu_trade.astype(int)



In [69]:

    
uk_noneu_trade.plot()









    Out[69]:





<matplotlib.axes._subplots.AxesSubplot at 0x115bc9750>



In [88]:

    
p = sns.kdeplot(uk_eu_trade, uk_noneu_trade)
p.set(xlabel='UK to EU28', ylabel='UK to Non-EU28', title="UK Trade from 2002-2016")









    Out[88]:





[<matplotlib.text.Text at 0x118112b10>,
 <matplotlib.text.Text at 0x11806a7d0>,
 <matplotlib.text.Text at 0x118286410>]



In [71]:

    
sns.distplot(uk_eu_trade)









    Out[71]:





<matplotlib.axes._subplots.AxesSubplot at 0x114a556d0>



In [72]:

    
sns.distplot(uk_noneu_trade)









    Out[72]:





<matplotlib.axes._subplots.AxesSubplot at 0x116079bd0>

Histogram



In [73]:

    
plt.hist(uk_noneu_trade, alpha=.3)
sns.rugplot(uk_noneu_trade);

Boxplot



In [106]:

    
sns.boxplot(pd.concat([uk_eu_trade, uk_noneu_trade], axis=1))









    



/Users/pmui/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/seaborn/categorical.py:2125: UserWarning: The boxplot API has been changed. Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.
  warnings.warn(msg, UserWarning)






    Out[106]:





<matplotlib.axes._subplots.AxesSubplot at 0x11b113590>

Violin Plot



In [105]:

    
sns.violinplot(pd.concat([uk_eu_trade, uk_noneu_trade], axis=1), orient="v")









    



/Users/pmui/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/seaborn/categorical.py:2296: UserWarning: The violinplot API has been changed. Attempting to adjust your arguments for the new API (which might not work). Please update your code. See the version 0.6 release notes for more info.
  warnings.warn(msg, UserWarning)






    Out[105]:





<matplotlib.axes._subplots.AxesSubplot at 0x11898e950>

Heatmap



In [107]:

    
h = sns.heatmap(pd.concat([uk_eu_trade, uk_noneu_trade], axis=1), annot=False)
h.set(title="UK Trade from 2002-2016")









    Out[107]:





[<matplotlib.text.Text at 0x11b560950>]



In [101]:

    
sns.clustermap(pd.concat([uk_eu_trade, uk_noneu_trade], axis=1))









    Out[101]:





<seaborn.matrix.ClusterGrid at 0x1199d7d50>



In [ ]:

	trade_type	partner	2015	2014	2013	2012	2011	2010	2009	2008	2007	2006	2005	2004	2003	2002	2001	2000	1999
geo
AT	Export	EU28	193043.0	187548.0	184865.0	181689.0	181889.0	166299.0	143052.0	181180.0	176327.0	159517.0	147064.0	142481.0	131439.0	126666.0	NaN	NaN	NaN
AT	Export	EXT_EU28	82468.0	80797.0	78903.0	77668.0	73038.0	63858.0	53376.0	65339.0	62447.0	58309.0	53871.0	46923.0	40317.0	39731.0	NaN	NaN	NaN
AT	Import	EU28	215023.0	210492.0	211444.0	212546.0	212734.0	187011.0	160875.0	197137.0	190105.0	175463.0	165908.0	160883.0	144982.0	134624.0	NaN	NaN	NaN
AT	Import	EXT_EU28	65242.0	63511.0	64554.0	65337.0	62293.0	52877.0	44262.0	53467.0	47817.0	43097.0	38657.0	31628.0	30994.0	30983.0	NaN	NaN	NaN
BE	Export	EU28	517836.0	502888.0	495145.0	486102.0	492083.0	449191.0	402557.0	493688.0	478972.0	446775.0	412912.0	380546.0	349210.0	345248.0	NaN	NaN	NaN
BE	Export	EXT_EU28	201294.0	208167.0	210766.0	208074.0	191352.0	165868.0	129415.0	147922.0	149924.0	137399.0	124557.0	112579.0	102730.0	111874.0	NaN	NaN	NaN
BE	Import	EU28	425974.0	444974.0	451816.0	461888.0	454022.0	407918.0	357932.0	443276.0	423886.0	400297.0	369450.0	334570.0	305704.0	305757.0	NaN	NaN	NaN
BE	Import	EXT_EU28	251526.0	239457.0	228369.0	221686.0	216871.0	182226.0	150802.0	190809.0	176710.0	159810.0	142853.0	124579.0	109685.0	113683.0	NaN	NaN	NaN
BG	Export	EU28	29752.0	27529.0	26702.0	24474.0	25378.0	19103.0	15329.0	18490.0	16692.0	14664.0	11340.0	10006.0	8500.0	7559.0	NaN	NaN	NaN
BG	Export	EXT_EU28	16571.0	16558.0	17842.0	17067.0	15152.0	12019.0	8071.0	11918.0	10333.0	8833.0	6973.0	5412.0	4835.0	4569.0	NaN	NaN	NaN