Data visualization is one of the core elements of a data science project. It can often be the difference between a successful and a mundane project.With so many libraries, techniques and methods at your disposal , it becomes overwhelming in deciding when to use what.
Exploratory data analysis is about getting to know and understand your data before you make any assumptions about it. Libraries like Matplotlib and Pandas helps in obtaining confidence in data. Interactive applications will elevate your project and encourage user engagement.With libraries like Bokeh and plot.ly, it’s becoming easier to make interactive figures, and having a way to present data science results in a compelling manner is crucial.
Listed below are eight essential python data visualization libraries that can come in handy as per the project requirements.
It is fast and easy to implement and contains a software library that is used within Python for powerful data analysis and manipulating data visualization.
The main feature of Pandas is data-frame that supplies built in options for plotting visualization in two dimension tabular style. Pandas works great with other libraries to create a wide variety of plots. Pandas data structure can have different written values as well as labels and their axes.
The simple plot looks acceptable and easy to read. However, it is impossible to customize the graph into more detailed visualization just by using Pandas.
The library provides main visualization algorithms, including scatter plots, line plots, histograms, bar plots, box plots, and more. It is worth noting that the library has fairly extensive documentation, that makes it comfortable enough to work with even for beginners in the sphere of data processing and visualization.Settings include the ability to set arbitrary colors, shapes, line type or marker, line thickness, transparency level, font size and type, and so on. These packages are fantastic for getting a first look at your data but lack features when it comes to presentation. Matplotlib is a low-level library that allows for incredible levels of customization, but there are many other tools that make great presentation-worthy graphics much easier.
Despite the wide popularity of the Matplotlib library, it has one drawback, which can become critical for some users: the low-level API. Therefore, in order to create truly complex infographics, you may need to write a lot of generic code. Fortunately, this problem is successfully leveled by the Seaborn library, which is a kind of high-level wrapper over Matplotlib. With its help, users are able to create colorful specific visualizations: heat maps, time series, violin charts, and much more. It is used to create more attractive and informative statistical graphics. While Seaborn is a different package, it can also be used to develop the attractiveness of Matplotlib graphics. While Matplotlib is great, we always want to do better. Being highly customizable, Seaborn allows users wide opportunities to add unique and fancy looks to their charts in easily with no time costs.
Seaborn produces beautiful visualizations but libraries like bokeh can provide interaction techniques such as brushing, filtering, zoom, and hover. It also supports streaming, and real-time data. Its unique selling proposition is its ability to create interactive, web-ready plots, which can easily output as JSON objects, HTML documents, or interactive web applications.
Bokeh has three interfaces with varying degrees of control to accommodate different types of users. The topmost level is for creating charts quickly. It includes methods for creating common charts such as bar plots, box plots, and histograms. The middle level allows the user to control the basic building blocks of each chart (for example, the dots in a scatter plot) and has the same specificity as Matplotlib. The bottom level is geared toward developers and software engineers. It has no pre-set defaults and requires the user to define every element of the chart.
Bokeh has an easy-to-use interface that makes very professional graphs and dashboards.
Plotly is an interactive online visualization tool that is being used for data analytics, scientific graphs and other visualizations. It contains a great API including one for Python. The library generates an amazing and beautiful highly interactive plots with tool-tips and varieties of other tool options such as zooming effect, panning, selecting, auto-scale, moving, resetting and so on. It is easily modified by clicking on different parts and parameters of the graph without code knowledge.
It is positioned primarily as an online platform, on which the users can create and publish their own visualizations. However, the library can also be used offline without uploading the visualization to the plotly server.
You’ll need to follow the docs to get your API key set up. Once you do, it all seems to work pretty seamlessly. The one caveat is that everything you are doing is posted on the web so make sure you are ok with it. There is an option to keep plots private so you do have control over that aspect.
A matplotlib-like interface to generate the HTML and java-script to render all the data you’d like on top of Google Maps. Several plotting methods make creating exploratory map views effortless.
It is a declarative statistical visualization library for Python, based on Vega and Vega-Lite. Declarative means you only need to mention the links between data columns to the encoding channels, such as x-axis, y-axis, color, etc. and the rest of the plotting details are handled automatically. Building on this declarative plotting idea, a surprising range of simple sophisticated plots and visualizations can be created using a relatively concise grammar.With Altair, more time can be spent on understanding the data and its meaning.One of the unique features of Altair, inherited from Vega-Lite, is a declarative grammar of not just visualization, but interaction.
The basic steps to create an Altair chart are:
- create a chart object with a pandas DataFrame (in tidy format)
- choose the appropriate marking (mark_bar in this example)
- encode the x and y values with the appropriate columns in the DataFrame
Geoplotlib is a toolbox used for plotting geographical data and map creation. It can be used to create a variety of map-types, like choropleths, heatmaps, and dot density maps. Pyglet (an object-oriented programming interface) is required to be installed to use Geoplotlib.
Geoplotlib reduces the complexity of designing visualizations by providing a set of in-built tools for the most common tasks such as density visualization, spatial graphs, and shape files.
END NOTE -
- Pandas is handy for simple plots but you need to be willing to learn matplotlib to customize.
- Seaborn can support some more complex visualization approaches but still requires matplotlib knowledge to tweak. The color schemes are a nice bonus.
- Bokeh is a robust tool if you want to set up your own visualization server but may be overkill for the simple scenarios.
- Altair produces beautiful and effective visualizations with a minimal amount of code.
- Plotly generates the most interactive graphs. You can save them offline and create very rich web-based visualizations.
- Gmplot is plotting data on Google Maps, the easy way. Since most Python data visualization libraries don’t offer maps, it’s good to have a library dedicated to them.