Python is a versatile language that you can use for a variety of data visualization tasks. Two popular libraries for data visualization in Python are matplotlib and seaborn. So, which one should you use? In this blog post, we will compare matplotlib and seaborn and help you decide which library is right for you.
Data Visualization In Python
Data visualization is the process of creating visual representations for data. It allows you to explore and understand your data in a meaningful way by summarizing it with charts, graphs, and maps.
Introduction to Python Libraries
Python libraries are collections of Python packages that enable developers to perform different tasks without having to re-invent the wheel. One or two lines of code will do what would have taken you weeks to write from scratch. And while Python already comes with some preinstalled libraries, there are thousands more available on the web that can help you achieve just about anything.
In Python, there are several popular libraries that you can use for various types of data visualization tasks:
- matplotlib (for basic plots)
- seaborn (for more advanced statistical plots)
- bokeh (to create interactive plots)
Matplotlib is a general-purpose plotting library for Python. It has been around since 2003, and it provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. Some of the features that matplotlib offers are:
- Interactive plotting from within IPython (IPython is an interactive shell)
- Support for various types of graphs such as line plots and scatter plots
- Matlab syntax to make switching between languages easy. This makes it useful if you have to use both Python and Matlab at work.
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Some of the features offered by seaborn are:
- Support for visualizing regression models
- A variety of color palettes to make your plots look more appealing
- Convenient functions to plot categorical variables (variables that have categories, such as gender) using boxplots and violin plots.
Bokeh is a Python interactive visualization library that targets web browsers for representation. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. Some features offered by Bokeh are:
- Hover too sols to inspect data points in plots
- Panning and zooming on data plots (in other words, you can zoom into specific parts of the plot)
- Linked brushing tools to select data points across different plots. This is useful if you want to compare the selected data points with another plot that has different variables on the x-axis and y-axis.
Top 10 Differences between Matplotlib and Seaborn
Now that you are familiar with the basics of both matplotlib and seaborn, let’s dive deeper into how they compare with each other. Here is a list of the top ten differences between matplotlib and seaborn:
- Matplotlib is better for basic plots while seaborn is better for more advanced statistical plots
- Matplotlib has a lower learning curve than seaborn .
- Seaborn provides more attractive default color palettes than matplotlib. However, you can create your own color palettes in matplotlib if you want.
- Matplotlib supports interactive plotting from within IPython while seaborn does not.
- Seaborn has functions to plot categorical variables using boxplots and violin plots while matplotlib does not.
- Seaborn allows you to visualize regression models while matplotlib does not.
- Matplotlib has a more extensive library of functions than seaborn. However, seaborn is growing at a faster rate than matplotlib.
- Seaborn is licensed under the GNU GPL while matplotlib is licensed under the Python Software Foundation License.
- Matplotlib is more widely used than seaborn.
- Seaborn provides better documentation than matplotlib.
Which Data Visualization Library Should You Use?
So, which library should you use? While there are several factors that can help you decide between the two libraries, one crucial factor is whether or not you want to customize your graphs. If customizing your graphs isn’t important to you, then matplotlib is a good choice because it has more features and is easier to use. However, if you want to create graphs that look sophisticated and professional, then seaborn is the better option.
In the end, it’s up to you which library you choose. Try out both libraries and see which one feels more comfortable for you. Happy plotting!