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Python, one of the most commonly used language in Data Analysis. It is known for its versatility, readability and simplicity. Python has many different libraries. To perform Data Analysis and Visualization, some of the common libraries are
Pandas - Data manipulation and analysis
Numpy - Numerical computing with powerful array objects
Matplotlib - Plotting and visualization
Seaborn - Statistical data visualization built on top of Matplotlib
Plotly -Interactive plots and dashboards
Bokeh - Interactive and web-ready plots
Among these the two libraries, Matplotlib and Seaborn are used to create visualization. Let's compare some of the features between these two. Before presenting our data visually, we need to ensure our data is clean and formatted appropriately.
Matplotlib:
Matplotlib - powerful library, provides high control over every aspect of a plot
Highly customizable - colors, annotations or labels, axis limits, and the layout of plots
One of the strengths of Matplotlib is its flexibility.
It is great for creating basic plots such as line plots, scatter plots, and bar charts
Some of the highlights of Matplotlib are:
Standalone scripts and interactive use: The Figure will not be shown until we call plt.show()
Flexible Customization: Matplotlib offers extensive customization options, Users can customize plot elements such as colors, markers, linestyles, fonts, labels, annotations, axes, and legends to meet their specific requirements. This flexibility enables the creation of highly customized and publication-quality visualizations.
Wide Range of Plot Types: line plots, scatter plots, bar plots, histograms, pie charts, box plots, violin plots, heatmaps, contour plots, 3D plots, multi-panel plots, subplots, animations, and interactive plots with ease.
Seaborn:
Seaborn is built on top of Matplotlib and provides a higher-level interface for creating statistical graphics.
Default style - built-in themes and color palettes
Integration with Pandas - Seaborn is designed to work seamlessly with Pandas DataFrames, making it easy to visualize data directly from a dataset
Built-in functionality - Advanced visualization functions can save time and effort when creating complex visualizations heatmaps, violin plots, and box plots
Some of the highlights of seaborn are:
A high-level API for statistical graphics - The clean data can be visualized in n number of ways and there is no fixed best way to do it. However it is made easier in seaborn.
Statistical estimation - performs the statistical estimation automatically
Distributional representations - displot() used to show different ways of distributions
Plots for categorical data - catplot() used to visualize categorical datas in different ways
Multivariate views on complex datasets - jointplot(), combines multiple kind of plots to give summary about the dataset
Lower-level tools for building figures - by combining axes-level plotting functions with objects that manage the layout of the figure, linking the structure of a dataset to a grid of axes.
Opinionated defaults and flexible customization - creates complete graphics in a single function call. It automatically adds axis labels and legends
When should you use Matplotlib Vs. Seaborn?
Matplotlib - complete control over every aspect & to create complex visualization
Seaborn - working with dataframes and want to quickly create statistical graphics with minimal effort
Both Seaborn and Matplotlib have their strengths and weaknesses
Choosing between them ultimately depends on your specific needs and preferences
To show the comparison between the Matplotlib and seaborn, I took the example dataset from kaggle
I imported the dataset as below
To install Matplot Vs Seaborn:
Functionality | Matplotlib | Seaborn |
Installation | pip install matplotlib | pip install seaborn |
Importing Matplotlib | import matplotlib.pyplot as plt | import seaborn as sns import matplotlib.pyplot as plt |
Histogram: Matplot Vs Seaborn:
I created a histogram for the reason for visit using Matplot and Seaborn
Chart/Graph name | Matplotlib | Seaborn |
Histogram | plt.hist(EDVisits['ReasonForVisit'], bins=10) plt.xticks(rotation=45) plt.xlabel('Reason') plt.ylabel('Patient count') plt.title('Histogram of Hospital visit Reason') plt.show() | sns.histplot(EDVisits['ReasonForVisit'], bins=10) plt.title('Histogram of Hospital visit Reason') plt.xticks(rotation=45) plt.show(); |
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Though I explicitly mentioned x and y labels in Matplotlib, still I need to mention the borders etc., whereas in seaborn most of the formats are applied by default and the graph looks visually good.
Pie Chart in Matplotlib Vs Seaborn:
The pie chart remains same for both the Matplotlib and the Seaborn. I counted the patients under each "Reason for visit' category and assigned it into a new dataframe by resetting their index.
plt.pie(count['count'],labels=count['ReasonForVisit'])
plt.title('Pie Chart - Reason for Hospital Visit')
plt.axis('equal')
plt.show()
Constructing Pair Plots with Matplotlib Vs Seaborn:
Seaborn: Provides a straightforward and efficient way to create pair plots with minimal code. Customizations are easy to apply.
Matplotlib: Requires more manual effort to create pair plots. Offers extensive customization options, but at the cost of more complex and verbose code.
Seaborn is the preferred choice for creating pair plots due to its simplicity and ease of use. However, if you need highly customized plots or have specific requirements that Seaborn cannot fulfill, Matplotlib offers the necessary flexibility.
Saving plots in Matplotlib Vs Seaborn:
Both Matplotlib and Seaborn allow you to save plots to files. The main difference lies in the plotting functions and the ease of creating statistical plots with Seaborn, but the process of saving those plots remains consistent across both libraries.
# Save as PNG
plt.savefig('plot.png')
# Save as PDF
plt.savefig('plot.pdf')
Conclusion:
Matplotlib and Seaborn are complementary tools in the Python data visualization.
Matplotlib provides a low-level, flexible foundation for creating a wide variety of visualizations. Some of the plots to create in matplotlib are straight forward, whereas few are little more complex.While Seaborn builds on this foundation to offer more concise syntax and additional functionality specifically used toward statistical visualization. So if you are a beginner, it is wise to go with the seaborn. Understanding the similarities and differences between these libraries can help us to choose the right tool for your specific data visualization needs.