Seaborn Examples¶

Seaborn python package build on top of matplotlib for plotting statistical data. It has great support for the commonly used pandas data structure in handling tabular data. It's more opinionated than matplotlib which makes it easier to get started with and provides higher level API for plotting which will help us create plots with fewer number of lines and configuration that we need in matplotlib for the same graphics.

Visualizing data using tools like seaborn help us explore and understand the data. In this post we'll explore some of the available graphics demonstrate how we can use to plot different data types we might have.

To help with the clarity of this post, I've organized it into six sections. Following this introductory section, we'll see the prerequisite setup and imports. In the setup section I'll discuss the sample datasets that come with seaborn. The third and fourth sections are about seaborn plots for Categorical and Continues Data, respectively. Then I'll discuss how we can plot comparative plots followed by a section on utilities such as styling and saving plots.

Setup¶

Installation¶

First step is t install the seaborn package. For that, we can use pip as

In [1]:
%pip install seaborn

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Note: you may need to restart the kernel to use updated packages.

Imports¶

To get started with seaborn we need to import the following packages. We import pyplot form matplotlib to use the lower level APIs later for styling and customization. While working with Jupyter notebooks we might want to suppers warnings in the output.

In [3]:
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import warnings

Jupyter Configurations¶

Ask Jupyter to display plots within the notebook

In [4]:
%matplotlib inline
%reload_ext autoreload
%autoreload 2

Suppress warnings

In [5]:
import warnings
warnings.filterwarnings('ignore')

Data¶

Plotting only makes sense if we have some data to visualize. We can plot any data stored in pandas data structure using seaborn. This let's us load and process data using pandas and use seaborn for visualization in parallel. For this post, I'll make use of sample datasets shipped with seaborn. To get the full list of datasets available in our installation we can as seaborn itself

In [6]:
print(sns.get_dataset_names())

['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']

Let's load two datasets and examine their content. More information on the datasets can be found here and here

In [7]:
crashes = sns.load_dataset('car_crashes')
titanic = sns.load_dataset('titanic')

Examine the content of the data

In [8]:
crashes.head()
Out[8]:
total speeding alcohol not_distracted no_previous ins_premium ins_losses abbrev
0 18.8 7.332 5.640 18.048 15.040 784.55 145.08 AL
1 18.1 7.421 4.525 16.290 17.014 1053.48 133.93 AK
2 18.6 6.510 5.208 15.624 17.856 899.47 110.35 AZ
3 22.4 4.032 5.824 21.056 21.280 827.34 142.39 AR
4 12.0 4.200 3.360 10.920 10.680 878.41 165.63 CA
In [9]:
titanic.head()
Out[9]:
survived pclass sex age sibsp parch fare embarked class who adult_male deck embark_town alive alone
0 0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
3 1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True

Categorical Data¶

Categorical data is form of qualitative data that can be stored and identified based on distinct labels. Instead of being measured numerically, it is a type of information that can be grouped into categories. For example, the sex column in our titanic dataset has two labels: male and female. Similarly the abbrev column is a list of state name abbreviations in the US which has a total of 50 labels.  In this section, we will see bar and count plots from seaborn as two graphics to visualize categorical data.

In this section, we will see bar and count plots from seaborn as two graphics to visualize categorical data.

Bar Plots¶

Also referred to as bar chart, bar plot is, a bar plot allows us to visualize the comparisons between the discrete labels or categories in our data. Bar chart is a graph that represents the category of data with horizontally or vertically rectangular bars with lengths and heights that is proportional to the values which they represent. One of the axis of the plot represents the specific categories being compared, while the other axis represents the measured values corresponding to those categories.

In [10]:
sns.barplot(data = titanic, x = 'sex', y='fare')
Out[10]:
<Axes: xlabel='sex', ylabel='fare'>
No description has been provided for this image

By Default the data is aggregated by mean of y

In [11]:
sns.barplot(data = titanic, x = 'sex', y='fare', estimator=np.median)
Out[11]:
<Axes: xlabel='sex', ylabel='fare'>
No description has been provided for this image

Count Plots¶

Similar to bar plot but uses count as the estimator

In [12]:
sns.countplot(data = titanic, x = 'sex')
Out[12]:
<Axes: xlabel='sex', ylabel='count'>
No description has been provided for this image

We can display a count of the number rows in each category of values in the alive column

In [13]:
sns.countplot(data = titanic, x = 'alive')
Out[13]:
<Axes: xlabel='alive', ylabel='count'>
No description has been provided for this image

Distribution Plots¶

Distribution plots for continuous data variables.In the past, Seaborn had a distplot method which supported displaying a histogram plot by with kde on top default. distplot is deprecated and it is recommended we use displot or histplot for find grained control. distplot allow as to display a histogram of univariate or bivariate distribution of the data in a dataset.

In [14]:
sns.distplot(a=crashes['alcohol'])
Out[14]:
<Axes: xlabel='alcohol', ylabel='Density'>
No description has been provided for this image

If we don't what the kde plot to be visible we can tell seaborn not to show it

In [15]:
sns.distplot(a=crashes['alcohol'],kde=False)
Out[15]:
<Axes: xlabel='alcohol'>
No description has been provided for this image
In [16]:
sns.displot(data=crashes['alcohol'])
Out[16]:
<seaborn.axisgrid.FacetGrid at 0x1513a3010>
No description has been provided for this image

The equivalent plot can be displayed using the new displot method

In [17]:
# We can specify name of the data column in the dataset if there are more than one
sns.displot(data=crashes, x='alcohol')
Out[17]:
<seaborn.axisgrid.FacetGrid at 0x15144ef50>
No description has been provided for this image

displot combines a histogram with optional components, such as a Kernel Density Estimation (KDE) line or rug plot. We can specify which type we want to plot using the kind key (default is hist)

In [18]:
sns.displot(data=crashes['alcohol'], kind='kde')
Out[18]:
<seaborn.axisgrid.FacetGrid at 0x15165e890>
No description has been provided for this image

We can enable an overlay of other visualization on top of the default. We can do this by passing a boolean value for the parameters hist, ecdf, kde, rug

In [19]:
sns.displot(data=crashes['alcohol'], kde=True, rug=True)
Out[19]:
<seaborn.axisgrid.FacetGrid at 0x1515b2690>
No description has been provided for this image
In [20]:
sns.displot(data=titanic, x='age', hue='sex', kind='kde', multiple='stack')
Out[20]:
<seaborn.axisgrid.FacetGrid at 0x151748250>
No description has been provided for this image

KDE Plot¶

We can display kde plots using the kdeplot function as well

In [21]:
sns.kdeplot(data=titanic, x='fare')
Out[21]:
<Axes: xlabel='fare', ylabel='Density'>
No description has been provided for this image

Categorical Plots¶

Box Plot¶

Compare different variables

In [22]:
sns.boxplot(data = titanic, x = 'alive', y='fare', hue='sex')
Out[22]:
<Axes: xlabel='alive', ylabel='fare'>
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Violin Plot¶

Compare different variables in a different visualization

In [40]:
sns.violinplot(data = titanic, x = 'alive', y='fare', hue='sex')
Out[40]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image
In [41]:
sns.violinplot(data = titanic, x = 'alive', y='fare', hue='sex', split=True)
Out[41]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image
In [45]:
#survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
sns.violinplot(data = titanic, x = 'alive', y='fare')
Out[45]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image

Strip Plot¶

In [42]:
sns.stripplot(data = titanic, x = 'class', y='fare')
Out[42]:
<Axes: xlabel='class', ylabel='fare'>
No description has been provided for this image
In [43]:
sns.stripplot(data = titanic, x = 'class', y='fare', hue='sex')
Out[43]:
<Axes: xlabel='class', ylabel='fare'>
No description has been provided for this image

Swarm Plot¶

In [44]:
sns.swarmplot(data = titanic, x = 'alive', y='fare')
Out[44]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image

Comparing Data¶

In [23]:
sns.displot(data=titanic, x='age', col='survived',  kind='kde')
Out[23]:
<seaborn.axisgrid.FacetGrid at 0x151881a50>
No description has been provided for this image
In [24]:
sns.displot(data=titanic, x='age', col='survived', hue='sex', kind='kde', multiple='stack')
Out[24]:
<seaborn.axisgrid.FacetGrid at 0x1519cbf10>
No description has been provided for this image

Joint Plot¶

Used for comparing two distributions. By default it uses scatter plot

In [25]:
sns.jointplot(data=crashes, x='speeding',y='alcohol')
Out[25]:
<seaborn.axisgrid.JointGrid at 0x151b780d0>
No description has been provided for this image
In [26]:
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='kde')
Out[26]:
<seaborn.axisgrid.JointGrid at 0x151c91350>
No description has been provided for this image
In [27]:
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg')
Out[27]:
<seaborn.axisgrid.JointGrid at 0x151e06dd0>
No description has been provided for this image
In [28]:
sns.jointplot(data=titanic, x='fare',y='age')
Out[28]:
<seaborn.axisgrid.JointGrid at 0x15207a6d0>
No description has been provided for this image

Pair Plot¶

We can display pair plots across the entire dataset for each pair of numeric attributes

In [29]:
sns.pairplot(data=crashes)
Out[29]:
<seaborn.axisgrid.PairGrid at 0x152171d90>
No description has been provided for this image

We can use hue to have color palettes of categorical data

In [30]:
sns.pairplot(data=titanic, hue='sex')
Out[30]:
<seaborn.axisgrid.PairGrid at 0x1534f2390>
No description has been provided for this image
In [32]:
Out[32]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image
In [34]:
Out[34]:
<Axes: xlabel='class', ylabel='fare'>
No description has been provided for this image
In [35]:
sns.stripplot(data = titanic, x = 'class', y='fare', hue='sex',jitter=True)
Out[35]:
<Axes: xlabel='class', ylabel='fare'>
No description has been provided for this image
In [36]:
sns.stripplot(data = titanic, x = 'class', y='fare', hue='sex',jitter=True, dodge=True)
Out[36]:
<Axes: xlabel='class', ylabel='fare'>
No description has been provided for this image
In [38]:
Out[38]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image
In [39]:
sns.swarmplot(data = titanic, x = 'alive', y='fare', color='red')
Out[39]:
<Axes: xlabel='alive', ylabel='fare'>
No description has been provided for this image

Other Feature¶

Resizing¶

We can resize the plot using height, width and aspect parameters

In [36]:
sns.displot(data = crashes, x = 'total', height = 2 , aspect = 1.6)
Out[36]:
<seaborn.axisgrid.FacetGrid at 0x157d10cd0>
No description has been provided for this image

Styling¶

In [37]:
sns.set_style('darkgrid')
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
Out[37]:
<seaborn.axisgrid.JointGrid at 0x1581e9d90>
No description has been provided for this image
In [38]:
sns.set_style('whitegrid')
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
Out[38]:
<seaborn.axisgrid.JointGrid at 0x1583f3610>
No description has been provided for this image
In [39]:
sns.set_style('ticks')
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
Out[39]:
<seaborn.axisgrid.JointGrid at 0x15855a5d0>
No description has been provided for this image

Label Styling¶

In [25]:
sns.set_context('poster')
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
Out[25]:
<seaborn.axisgrid.JointGrid at 0x149f35950>
No description has been provided for this image
In [41]:
sns.set_context('paper')
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
Out[41]:
<seaborn.axisgrid.JointGrid at 0x15879d2d0>
No description has been provided for this image
In [42]:
sns.jointplot(data=crashes, x='speeding',y='alcohol', kind='reg', height = 4 )
sns.despine(left=True, bottom=True) # False turns off the boundary
No description has been provided for this image

Save Plot¶

Since seaboarn is built on top of the matplotlib package, we can use matplotlib's savefig() function to save the generated plot into image file.
Note: The savefig() function should come before the show() function since the later closes and deletes the image from the memory to save space.

In [43]:
sns.displot(crashes['alcohol'])
plt.savefig('picture.png')
plt.show()
No description has been provided for this image