Seaborn

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

Install Seaborn

In [1]:

pip install seaborn

Get Data Repository:

When you install seaborn , it comes with datasets for you to practice on. You can go through Data Repository.

Import All Libraries

In [2]:

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

Get Dataset names:

In [3]:

sns.get_dataset_names()

Out[3]:

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

Load Dataset

In [4]:

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

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

Out[4]:

	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

How to create your first graph

Load Dataset

We use load_dataset method to load datasets from data repository.

In [5]:

sns.load_dataset('tips')

Out[5]:

	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
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

Creating graph with the help of seaborn is as easy as matplotlib.Suppose we want to create a scatter plot of relationship between Totalbill and tips. All you need to do is to use scatterplot method.

Note :We use scatterplot() method to create scatter plot instead of scatter like we used to do in matplotlib.

In [8]:

sns.scatterplot(x='total_bill',y='tip',data=df)
plt.show()

hue:

hue : (optional) This parameter take column name for colour encoding.

Suppose we also want to know smokers along with total_bill and tip. We can show this third information with the help of hue.

In [7]:

sns.scatterplot(x='total_bill',y='tip',hue='smoker',data=df)
plt.show()

You can also change order of hue by using hue_order property.

In [9]:

sns.scatterplot(x='total_bill',y='tip',hue='smoker',hue_order=['No','Yes'],data=df)
plt.show()

Palette:

What if you want to change color encoding for smokers and non-smokers.

For this you can use palette property.

In [11]:

c = {'No':'Green','Yes':'Red'}
sns.scatterplot(x='total_bill',y='tip',hue='smoker',hue_order=['No','Yes'],palette = c,data=df)
plt.show()

smoker is a categorical value , but you can also use any numerical value in hue. Let's say i want to color encode sizes.

In [12]:

# sns.scatterplot(x='total_bill',y='tip',hue='size',data=df)
# plt.show()

relplot:

relplot is used for many purposes , but the main purpose behind using replot is sub-plotting. Suppose we want to suplot our graph based on smoker and non-smoker.

We can use row,col to achieve this result.

Let's see this with an example.

In [14]:

sns.relplot(x='total_bill',y='tip',data=df,col='smoker')
plt.show()

What if i used row property.

In [15]:

# sns.relplot(x='total_bill',y='tip',data=df,row='smoker')
# plt.show()

Graph would have appeared like this.

We can do same with size by choosing col as size.

In [17]:

# %matplotlib notebook
# sns.relplot(x='total_bill',y='tip',hue='size',data=df,col='size')
# plt.show()

Now as you can see it has one column and all the graphs are in a single column. We can use col_wrap to restrict number of graphs in a single column.

In [21]:

# %matplotlib inline
# sns.relplot(x='total_bill',y='tip',hue='size',data=df,col='size',col_wrap=3)
# plt.show()

Applying Row and Column property in a single plot

what if i want to see data of smokers in column and data of time in rows, we can use col and row property in a single plot.

In [22]:

sns.relplot(x='total_bill',y='tip',data=df,row='time',col='smoker')
plt.show()

using categorical column

what if one of my axis is categorical column ?

In [23]:

# sns.relplot(x='total_bill',y='time',data=df)
# plt.show()

size

relplot offers a property size that changes the size of values. Let's see this with an example.

In [25]:

sns.relplot(x='total_bill',y='tip',data=df,size='size')
plt.show()

A Useful Example

Seaborn offers another dataset named flights. Let's see this data first.

In [13]:

import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('flights')
df

Out[13]:

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121
...	...	...	...
139	1960	Aug	606
140	1960	Sep	508
141	1960	Oct	461
142	1960	Nov	390
143	1960	Dec	432

144 rows × 3 columns

If we want to know how many passengers were on board on a particular month and year.

In [7]:

sns.relplot(x='passengers',y='month',hue = 'year',data=df)

Categorical column and relplot

Whenever there is a categorical column and a numerical column , we donot use relplot. Let me show you why?

In [15]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('tips')
sns.relplot(x='time',y='tip',data=df)

This obviously is not a clear graph. So whenever we deal with categorical column we use catplot(categorical plot).

Catplot

Categorical plot is always a best option if one value is Categorical and another is numerical.

In [17]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('tips')
sns.catplot(x='time',y='tip',data=df)

A Useful example

Suppose we want to know total bill dayswise , we can use catplot as day is a categorical plot while total bill is numerical.

In [23]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='total_bill',y='day',data=df)
plt.show()

You can change graph to bar by using kind = 'bar' property.

In [25]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='total_bill',y='day',data=df,kind='bar')
plt.show()

You can change graph to bar by using kind = 'point' property.

In [27]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='total_bill',y='day',data=df,kind='point')
plt.show()

You can change graph to bar by using kind = 'violin' property.

In [29]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='total_bill',y='day',data=df,kind='violin')
plt.grid()
plt.show()

From this violin chart it is very easy to figure out pattern of bills that were given on any day. Like on thursday most of the total bill were between 10 to 20 dollars.

You can change graph to bar by using kind = 'boxen' property.

In [31]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='day',y='total_bill',data=df,kind='boxen')
plt.grid()
plt.show()

Boxen chart also works like violin chart. The only difference is Boxen chart assigns darkest colour to the area where most of the data lies.

As you can see a horizontal line , this horizontal line is median of the data.

dots that you see in boxen chart are outliers

You can change graph to bar by using kind = 'box' property.

In [32]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('tips')
sns.catplot(x='day',y='total_bill',data=df,kind='box')
plt.grid()
plt.show()

Countplot

seaborn.countplot() method is used to Show the counts of observations in each categorical bin.

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('tips')
sns.countplot(y='smoker',data=df)

A Useful Example

Suppose we want to count Type 1 of pokemon in our pokemon dataset. We can use countplot here.

In [22]:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

df = pd.read_csv('Pokemon.csv')
sns.countplot(y='Type 1',data=df)

Introduction to pairplot:

Plot pairwise relationships in a dataset.

By default, this function will create a grid of Axes such that each numeric variable in data will by shared across the y-axes across a single row and the x-axes across a single column.

In [35]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('tips')
sns.pairplot(data = df)
plt.show()

You can also add hue property to see gender of customers.

In [36]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('tips')
sns.pairplot(data = df,hue='sex')
plt.show()

A Useful Example :

In [38]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('iris')
df

Out[38]:

	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
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	virginica
146	6.3	2.5	5.0	1.9	virginica
147	6.5	3.0	5.2	2.0	virginica
148	6.2	3.4	5.4	2.3	virginica
149	5.9	3.0	5.1	1.8	virginica

150 rows × 5 columns

Check Range of Sepal length for species:

Now as we have discussed before in the note, whenever we are dealing with ranges , Histogram is always a better option. So let us create a histogram for species and their sepal length.

In [40]:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('iris')
f = sns.FacetGrid(data = df,col='species')
f.map(plt.hist,'sepal_length')
plt.show()