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:
Data Visualization with Seaborn Line Plot
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
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