Data Visualization with Seaborn Line Plot

Last Updated : 21 Mar, 2024

Prerequisite:

Presenting data graphically to emit some information is known as data visualization. It basically is an image to help a person interpret what the data represents and study it and its nature in detail. Dealing with large scale data row-wise is an extremely tedious task, hence data visualization serves as an ideal alternative.

Seaborn is a Python library which is based on matplotlib and is used for data visualization. It provides a medium to present data in a statistical graph format as an informative and attractive medium to impart some information.

Installation

Like any another python library, seaborn can be easily installed using pip:

pip install seaborn

This library is a part of Anaconda distribution and usually works just by import if your IDE is supported by Anaconda, but it can be installed too by the following command:

conda install seaborn

Single Line Plot

A single line plot presents data on x-y axis using a line joining datapoints. To obtain a graph Seaborn comes with an inbuilt function to draw a line plot called lineplot().

Syntax: lineplot(x,y,data)

where,

x– data variable for x-axis

y- data variable for y-axis

data- data to be plotted

Example:

Python3

# import modules 
import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
# import data 
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv") 
  
# selecting required rows and columns 
data = data.iloc[2:10, :] 
  
# plotting a single line graph 
sn.lineplot(x="Year", y="User Rating", data=data) 
  
# displaying the plot 
plt.show() 

Output:

Setting different styles

A line plot can also be displayed in different background style using set() function available under seaborn module itself.

Syntax:

set()

Attributes:

Context: plotting context parameters
Style: defines style
Palette: sets a color palette
Font
Font_scale: sets font size
Color_codes: If set True then palette is activated, short hand notations for colors can be remapped from the palette.
rc: parameter to over-ride the above parameters

Example:

Python

import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
data = data.iloc[2:10, :] 
  
sn.lineplot(x="kepid", y="koi_duration", data=data, hue="koi_score") 
sn.set(style="darkgrid") 
  
plt.show() 

Output:

Multiple Line Plot

Functionalities at times dictate data to be compared against one another and for such cases a multiple plot can be drawn. A multiple line plot helps differentiate between data so that it can be studied and understood with respect to some other data. Each lineplot basically follows the concept of a single line plot but differs on the way it is presented on the screen. Lineplot of each data can be made different by changing its color, line style, size or all listed, and a scale can be used to read it.

To differentiate on the basis of color

lineplot(x,y,data,hue)

where, hue decides on basis of which variable the data is supposed to be displayed

Example:

Python3

# import modules 
import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
# import data 
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
# select required data 
data = data.iloc[2:10, :] 
  
# plot data with different color scheme 
sn.lineplot(x="kepid", y="koi_period", data=data, hue="koi_score") 
  
# display plot 
plt.show() 

To differentiate on the basis of line style

lineplot(x,y,data,style)

where, style decides on basis of which variable the data is supposed to be displayed

Example:

Python3

import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
data = data.iloc[2:10, :] 
  
# using just style 
sn.lineplot(x="kepid", y="koi_period", data=data, style="koi_score") 
  
plt.show() 
  
  
# using style and hue 
sn.lineplot(x="kepid", y="koi_period", data=data, 
            hue="koi_score", style="koi_score") 
  
plt.show() 

Output:

To differentiate on the basis of size

lineplot(x,y,data,size)

where, size decides on basis of which variable the data is supposed to be displayed

Example:

Python3

import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
data = data.iloc[2:10, :] 
  
# using just style 
sn.lineplot(x="kepid", y="koi_period", data=data, size="koi_score") 
  
plt.show() 
  
  
# using style, size and hue 
sn.lineplot(x="kepid", y="koi_period", data=data, 
            size="koi_score", hue="koi_score", style="koi_score") 
  
plt.show() 

Output:

Error Bars in Line Plot

Error bars are used to show error rates in a line plot which can be used to study intervals in a plot. For this, err_style attribute can be employed. This takes only two attributes, either band or bars.

Syntax:

lineplot(x,y,data,err_style)

Example:

Python3

import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
data = data.iloc[2:10, :] 
  
sn.lineplot(x="kepid", y="koi_duration", data=data, err_style="band") 
  
plt.show() 

Output:

Color Palette along the Line Plot

The color scheme depicted by lines can be changed using a palette attribute along with hue. Different colors supported using palette can be chosen from- SEABORN COLOR PALETTE

Syntax:

lineplot(x,y,data,hue,palette)

Example:

Dataset used- (Data shows an exoplanet space research dataset compiled by nasa.)

Python3

import seaborn as sn 
import matplotlib.pyplot as plt 
import pandas as pd 
  
data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv") 
  
data = data.iloc[2:10,:] 
  
sn.lineplot(x = "kepid", y = "koi_period",data=data, hue="koi_score", palette="pastel") 
  
plt.show() 

Output:

Creating A Time Series Plot With Seaborn And Pandas

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Article Tags :

Data Visualization with Seaborn Line Plot

Installation

Single Line Plot

Python3

Setting different styles

Python

Multiple Line Plot

Python3

Python3

Python3

Error Bars in Line Plot

Python3

Color Palette along the Line Plot

Python3

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