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Python libraries to be used for visualization
In this article, we will show you various Python libraries for visualization and their features.
In today's day and age, viewing the results of our analysis and inferring results is often more convenient than going through textual data or CSV files to understand the results.
As a result, data visualization is an easy way to find answers to complex questions. It also allows users to express their results more effectively than tables do.
Data Visualization in Python
Python provides several plotting libraries, including Matplotlib, Seaborn, and many other data visualization packages, each with unique features for creating informative, customized, and appealing plots to present data in the most simple and effective way possible.
Matplotlib is a Python plotting library for creating static, dynamic, and interactive visualizations. Its computational mathematics extension is NumPy.
Despite being over a decade old, it remains the most popular plotting library in the Python world. It was designed to resemble MATLAB, a licensed programming language that was developed in the 1980s. Since matplotlib was the first Python data visualization library, many other libraries have been built on top of it or are intended to work in tandem with it during research.
Some libraries, such as pandas and Seaborn, act as matplotlib "wrappers." They simplify the use of a variety of matplotlib methods by reducing the amount of code required.
Although matplotlib is great for visualizing details, it isn't very practical for quickly and easily creating publication-quality charts.
Plotly supports scatter plots, histograms, line charts, bar charts, pie charts, error bars, box plots, multiple axes, sparklines, dendrograms, 3-D charts, and other chart types.
Contour plots, which are uncommon in other data visualization libraries, are also available in Plotly. Plotly is also available for use without an internet connection.
Seaborn is a Python library that allows you to create statistical graphics. It has advanced software for producing visually appealing and informative statistical graphics. Matplotlib is used primarily for education and research by data scientists, while Seaborn is used for publications and real-world demonstrations. Seaborn is now the de facto Python Data Visualization library.
Seaborn makes use of matplotlib's power to create beautiful charts with just a few lines of code.
The main difference is that its default designs and color palettes are designed to be more visually appealing and traditional.
Matplotlib is used to create graphs.
Its dataset-oriented plotting mechanisms work with data frames and vectors that contain entire datasets, internally performing concept mapping and statistical aggregation to produce insightful plots.
Seaborn is a fully accessible Python library that we can install in our Python environment by using the pip install function.
It intends to make visualization a critical component of data exploration and comprehension.
ggplot is a versatile library for plotting graphs in Python that was originally implemented in R. It is a Domain-Specific language used to create domain-specific visualisations, primarily for data analysis.
Ggplot allows the graph to be plotted in a straightforward manner with only two lines of code. The same code written with matplotlib, on the other hand, is very complex and involves many lines of code. As a result, ggplot makes graph coding easier. It is an extremely valuable Python library.
To use all of the features of ggplot, you must use pandas.
Unlike matplotlib, ggplot allows you to overlay elements to create a full plot. For instance, you could start with axes and then add points, a line, a trendline, and so on.
Despite the fact that "The Grammar of Graphics" has been lauded as a "intuitive" plotting tool, experienced matplotlib users may find it difficult to adjust to this new paradigm.
According to the author, ggplot is not intended for highly personalized graphics. It foregoes complexity in favour of a simpler plotting process.
Altair is a Python statistical data visualization library. It is based on Vega and Vega-Lite, which are declarative languages for creating, saving, and sharing interactive data visualization designs. Altair can be used to quickly create beautiful data visualizations of plots such as bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, and so on. Altair requires Python 3.6, entry points, jsonschema, NumPy, Pandas, and Toolz, all of which are installed automatically with the Altair installation commands. To obtain the data visualizations in Altair, open Jupyter Notebook or JupyterLab and execute any of the code. Altair's source code is currently available on GitHub.
Altair can generate beautiful data visualizations of plots such as bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, and more with a little coding.
Altair has requirements such as Python 3.6, entry points, jsonschema, NumPy, Pandas, and Toolz, which are all automatically activated by the Altair setup commands.
The Grammar of Graphics is the foundation of Bokeh, a library similar to ggplot. However, it is a Python library that has not been imported from R. Produces interactive web-ready plots that can be output in a variety of formats, including HTML documents and JSON objects.
Bokeh has long been regarded as one of the most popular libraries for real-time streaming and data processing.
Bokeh is available to users in three levels: High Level, Middle Level, and Low Level. High-level users can easily and quickly create histograms and bar charts. The matplotlib framework can be used by intermediate users to create dots for scatter plots.
The ability to generate interactive, web-ready plots that can be conveniently exported as JSON objects, HTML files, or interactive web services is its main advantage.
Bokeh also provides broadcasting and real-time statistics.
Bokeh provides three layouts with varying degrees of power to accommodate different user styles.
The highest level allows for simple map creation. It can generate popular graphs such as bar plots, box plots, and histograms.
Pygal is a Python data visualization library designed specifically for making sexy charts! (As stated on their website!) While Pygal is similar to Plotly or Bokeh in that it generates data visualization charts that can be embedded in web pages and accessed via a web browser, the main difference is that it can generate charts in the form of SVGs, or Scalable Vector Graphics. These SVGs ensure that you can see your charts clearly without losing any quality, even when they are scaled. However, SVGs are only useful for smaller datasets because too many data points make rendering difficult and the charts can become sluggish.
The Pygal library on operating systems can be used to create simple graphs.
This library can be used in combination with popular Python web interfaces like flask and Django to generate dynamic and interactive graphs on a web page.
Pygal can create graphs such as line, bar, histogram, XY, pie, radar, box, Dot, and so on.
The charts and graphs can also be exported in a variety of formats, including SVG, PNG, Etree, and others. Pygal is best suited for small web applications involving quick and simple graphs.
Geoplotlib is a Python toolbox for visualizing geographical data that is open source. It enables the creation of hardware-accelerated interactive visualizations in pure Python and includes implementations of dot maps, kernel density estimation, spatial graphs, Voronoi tesselation, shapefiles, and a variety of other common spatial visualizations.
Geoplotlib can generate a wide range of maps, including equivalent area maps, heat maps, and point density maps.
Most data visualization libraries don't provide much support for creating maps or working with geographic data, which is why geoplotlib is such an important Python library.
It facilitates the creation of geographical maps in particular, with a variety of map types available such as dot-density maps, choropleths, and symbol maps.
In this article, we learned about the most popular Python libraries for visualization and their features.
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