Content:

4.2.1 Stripplot
4.2.2 Swarmplot
4.2.3 Boxplot
4.2.4 Violinplot
4.2.5 Countplot
4.2.6 Barplot
4.2.7 Pointplot

Import Libraries



In [1]:

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

Import data



In [2]:

    
tips = sns.load_dataset('tips')
tips.head()









    Out[2]:







  
    
      
      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



In [3]:

    
# Iris data in wide-form
iris = sns.load_dataset('iris')
iris.head()









    Out[3]:







  
    
      
      sepal_length
      sepal_width
      petal_length
      petal_width
      species
    
  
  
    
      0
      5.1
      3.5
      1.4
      0.2
      setosa
    
    
      1
      4.9
      3.0
      1.4
      0.2
      setosa
    
    
      2
      4.7
      3.2
      1.3
      0.2
      setosa
    
    
      3
      4.6
      3.1
      1.5
      0.2
      setosa
    
    
      4
      5.0
      3.6
      1.4
      0.2
      setosa



In [4]:

    
# Melt iris data to long-form
irislong = pd.melt(iris,id_vars='species', var_name='measurement')
irislong.head()









    Out[4]:







  
    
      
      species
      measurement
      value
    
  
  
    
      0
      setosa
      sepal_length
      5.1
    
    
      1
      setosa
      sepal_length
      4.9
    
    
      2
      setosa
      sepal_length
      4.7
    
    
      3
      setosa
      sepal_length
      4.6
    
    
      4
      setosa
      sepal_length
      5.0

4.2.1 Stripplot

Stripplot is also known as dotplot in Minitab/Excel. It shows all the data points for each category.

Data can be in long-form or wide-form.



In [5]:

    
# Plotting using wide-form iris data, each numeric column will be plotted
sns.stripplot(data=iris);



In [6]:

    
# Plottin using long-form iris data, indicate x, y values
sns.stripplot(x='measurement',y='value',data=irislong);



In [7]:

    
# Long-form of data is preferred to take advantage of hue parameter, which allows visualisation of a second categorical variable
sns.stripplot(x='measurement',y='value',data=irislong,hue='species',jitter=0.05);

4.2.2 Swarmplot

Swarmplot is similar to stripplot, but the points are adjusted automatically so that they don't overlap.



In [8]:

    
sns.swarmplot(x='measurement',y='value',hue='species',data=irislong);



In [9]:

    
# Another example using Tips data set
sns.swarmplot(x="day", y="total_bill", data=tips);



In [10]:

    
sns.swarmplot(x="day", y="total_bill", hue='smoker', data=tips);

4.2.3 Boxplot

Boxplot can be applied to long-form or wide-form of data. But long-form is preferred to take advantage of hue parameter.



In [11]:

    
sns.boxplot(x='measurement',y='value',data=irislong);



In [12]:

    
sns.boxplot(x='measurement',y='value',hue='species',data=irislong);

Combining boxplot with swarmplot



In [13]:

    
sns.set_style("whitegrid")
sns.boxplot(x='measurement',y='value',data=irislong);
sns.swarmplot(x='measurement',y='value',data=irislong,color='0.4',alpha=0.75);

4.2.4 Violinplot

Violinplot draws a combination of boxplot and kde plot.



In [14]:

    
sns.violinplot(x='measurement',y='value',data=irislong);



In [15]:

    
sns.violinplot(x='measurement',y='value',hue='species',data=irislong);



In [16]:

    
sns.violinplot(x='day',y='tip',data=tips);



In [17]:

    
# Split the violin into half for a second categorical (binary) variable.
ax = sns.violinplot(x='day',y='tip',data=tips,hue='sex',split=True) # Use dodge instead of split for new version 8.0
ax.legend(bbox_to_anchor=(1,0.5));



In [18]:

    
# Customising the inner of violinplot
# Default: Box 
# Other choices: point, quartile, stick
sns.violinplot(x='day',y='tip',data=tips,inner='point');

Combining violinplot with swarmplot.



In [53]:

    
sns.violinplot(x='day',y='tip',data=tips,inner=None)
sns.swarmplot(x='day',y='tip',data=tips,color='k',alpha=0.5);

4.2.5 Countplot

The traditional bar chart is called Countplot in Seaborn.



In [19]:

    
# Count the frequency of each categorical variable
sns.countplot(x='day',data=tips);



In [12]:

    
# Display the count for each bar
ax = sns.countplot(x='day',data=tips);
for p in ax.patches:
    h = p.get_height()
    ax.text(p.get_x()+p.get_width()/2, h+2, '{:1.0f}'.format(h), ha="center") 
ax.set_title('Number of customers per day');

4.2.6 Barplot

In Seaborn, the Barplot shows the point estimate and confidence intervals as bars. It will include an error bar by default.

Do not confuse Seaborn Barplot with traditional bar chart in matplotlib or Excel.



In [15]:

    
# Average of a numerical variable, with CI (default 95%)
ax = sns.barplot(x='day',y='tip',data=tips)
ax.set_title('Average tip per day');



In [35]:

    
ax = sns.barplot(x='day',y='tip',data=tips,hue='time',capsize=0.1,ci=90)
ax.set_title('Average tip per day')
# Display legend outside the axes
ax.legend(bbox_to_anchor=(1,0.5),title='Time');

4.2.7 Pointplot

Pointplot also shows the point estimate and CI like Barplot, but without the bar.



In [8]:

    
sns.pointplot(x='day',y='tip',data=tips);



In [10]:

    
sns.pointplot(x='day',y='tip',hue='time',data=tips,dodge=True);



In [ ]:

	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

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

	species	measurement	value
0	setosa	sepal_length	5.1
1	setosa	sepal_length	4.9
2	setosa	sepal_length	4.7
3	setosa	sepal_length	4.6
4	setosa	sepal_length	5.0