Data Visualization with Seaborn Line Plot

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.

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:

Dataset used- Bestsellers (The plot shows data related to bestseller novels of amazon.)

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:

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

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:

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

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.)

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.)

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:

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

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

Output: