Seaborn : Stories of Imagination and Visualization
Getting Started with Seaborn: Overview and Installation Steps
As kids, we loved flipping through picture books. The stories and images sparked our imagination and made it easier to grasp the narrative.
Visualization plays a key role in demystifying complex ideas. Even children can grasp basic concepts when shown a visual. That's why visualization is often hailed as the ultimate tool for understanding the flow of everyday life.
Python offers a plethora of libraries for data visualization. Today, we'll dive into the world of Seaborn, an essential tool for turning data into captivating visuals.
What is Seaborn?
Seaborn is an outstanding visualization library for plotting in Python. It provides beautiful plotting features to make statistical plots more attractive. It is built on top matplotlib library and is also closely integrated with the Pandas library.
Why choose Seaborn over Matplotlib?
There are several reasons why Seaborn is more efficient compared to Matplotlib.
Seaborn offers a layer of abstraction, making it simpler to use.
It provides superior aesthetics compared to Matplotlib.
It includes a greater number of graphic features.
It has gained significant popularity in recent times.
Seaborn Roadmap:
Seaborn Roadmap consists of achieving two main goals:
Understanding the types of functions it offers.
Understanding its main classifications.
There are two types of functions:
(a) Figure-level functions
(b) Axes-level functions
These two functions are derived from two objects: figure and axes.
(a) Figure: The container inside which the plots are drawn
(b) Axes: The plots drawn inside the container.
Note :
There can be multiple axes inside a figure, but there can't be multiple figures inside the axes. Each of the figure-level and axes-level functions has its own pros and cons.
It is classified into various types such as:
(a) Relational Plots (Scatterplot, Lineplot)
(b) Distribution Plots (Histogram, kdeplot, Rugplot)
(c) Categorical Plots (Stripplot, Swarmplot, Boxplot, Violinplot, Barplot, Pointplot, Countplot, etc.)
(d) Matrix Plots (Heatmap, Clustermap)
(e) Regression Plots (Regplot)
(f) Multiplot or Multi Grid Plots (Facetgrid, Pairgrid, Jointgrid)
Image source: Kaggle
Seaborn Installation Process
Seaborn is supported on Python 3. Installing Seaborn is very easy. You can install it using Python’s pip manager or the conda package manager.
# install seaborn with pip
pip install seaborn
Open your anaconda prompt to install seaborn using conda package manager.
# install seaborn with conda
conda install seaborn
Once you completed the installation process, you can easily use the seaborn pre-defined datasets and perform visualization operations on it.
Out of various pre-defined datasets of Seaborn, some of them are (Tips, Titanic, Iris, fmri and many more).
Example: Plotting a Line plot:
import seaborn as sns
fmri= sns.load_dataset("fmri")
sns.lineplot(x="timepoint", y="signal", data=fmri)
Output:
Conclusion
Seaborn is a powerful data visualization library in Python. It offers an easy way to create clear and informative statistical graphics. With many visualization tools, Seaborn helps you quickly explore and share insights from complex data sets.
From scatter plots and line plots to heatmaps and facet grids, Seaborn provides various visualizations for different needs. Plus, Seaborn works well with Pandas and NumPy, making it essential for data analysts and scientists.
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