Data Visualization with Seaborn Line Plot - GeeksforGeeks

12 Nov, 2020
Prerequisite:
Seaborn
Matplotlib 
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.
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

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:
Dataset used- Bestsellers (The plot shows data related to bestseller novels of amazon.)
Python3
# import modulesimport seaborn as snimport matplotlib.pyplot as pltimport pandas as pd  # import datadata = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")  # selcting required rows and columnsdata = data.iloc[2:10, :]  # plotting a single line graphsn.lineplot(x="Year", y="User Rating", data=data)  # displaying the plotplt.show()
Output:
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:
Dataset used- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python
import seaborn as snimport matplotlib.pyplot as pltimport 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:
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:
Dataset used- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python3
# import modulesimport seaborn as snimport matplotlib.pyplot as pltimport pandas as pd  # import datadata = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv")  # select required datadata = data.iloc[2:10, :]  # plot data with different color schemesn.lineplot(x="kepid", y="koi_period", data=data, hue="koi_score")  # display plotplt.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:
Dataset used- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python3
import seaborn as snimport matplotlib.pyplot as pltimport pandas as pd  data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv")  data = data.iloc[2:10, :]  # using just stylesn.lineplot(x="kepid", y="koi_period", data=data, style="koi_score")  plt.show()    # using style and huesn.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:
Dataset used- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python3
import seaborn as snimport matplotlib.pyplot as pltimport pandas as pd  data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\cumulative.csv")  data = data.iloc[2:10, :]  # using just stylesn.lineplot(x="kepid", y="koi_period", data=data, size="koi_score")  plt.show()    # using style, size and huesn.lineplot(x="kepid", y="koi_period", data=data,            size="koi_score", hue="koi_score", style="koi_score")  plt.show()
Output:
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:
Dataset used- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python3
import seaborn as snimport matplotlib.pyplot as pltimport 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:
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- cumulative (Data shows an exoplanet space research dataset compiled by nasa.)
Python3
import seaborn as snimport matplotlib.pyplot as pltimport 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:
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