A picture is worth a thousand words. Studies have established that the human brain processes information in charts and graphs more easily than in Excel sheets or reports. A key stakeholder of a company or a product might not be interested in the tables or numbers as much as the graphical representation. So, data visualization is of top priority when it comes to data analysis. So, let’s discuss how Python data visualization can be leveraged for data analysis in this blog today
Python is a scientific programming language used extensively for data science, data analysis, and visualizing the data. While we can also visualize the data and perform EDA (exploratory data analysis) with Tableau or Power BI, we can create powerful data visualizations using Python as well. There is an ample number of libraries available for creating static and interactive data visualizations such as Seaborn, Matplotlib, etc.
Let’s see these libraries for Python visualization.
Matplotlib is a great data visualization library to create 2-D interactive graphs. It helps in plotting high-grade, publication-quality graphs that we can zoom, pan, and update. Furthermore, it allows for customization of the visual style and layout of the graphs. It was first introduced in 2003. It is one of the most widely used libraries for creating impressive plots in Python. Also, it can be used to embed graphs into a variety of GUIs such as Tkinter, QT, wxPython, etc. You can plot almost any kind of graph using Matplotlib such as bar graphs, histograms, line graphs, pie charts, scatterplots, box and whisker plots, error graphs, stem plots, power spectra, and many more.
Seaborn is a Python data visualization library built on top of Matplotlib. Seaborn consists of various dataset-related functions that can be applied to data frames for plotting required graphs. We can perform various aggregations or other data manipulations before plotting the graphs for rendering informative output. Seaborn Python is responsible for creating some of the most beautiful and interactive data representations. One can create bar graphs, scatterplots, histograms, line and pie graphs, among others using Seaborn.
GGplot is a Python visualization library that is based on the ggplot2 library of the R programming language. We can create graphical representations using APIs using GGplots. Furthermore, we can create various layers of visualizations using GGplots in a single visualization. The GGplots allow the users to simply map the variables to different aesthetics in the graph and handle the rest of the plotting by itself while the user can focus on reading and interpreting the graphs.
Altair is again a Python data visualization library for creating beautiful graphs. It is based on the Vega and Vega-lite which are declarative formats for creating, saving, and sharing visualization designs. When the Altair is installed, it comes with some dependencies like Python 3.6, entrypoints, NumPy, Pandas, jsonschema, and Toolz which need to be pre-installed before Altair. However, not to worry, as they all get installed with the Altair package. Altair visualizations can be created by executing the code using any of the above libraries.
Bokeh is a data visualization library developed based on the GGplot. It can be used to create detailed graphics allowing maximum interactivity between the data values. You can create various interactive plots on web applications, HTML pages, or JSON objects. Bokeh offers 3-levels of intensity in creating visualizations. While the first and the second level offers to create basic to medium-level controls on creating graphs, the third level provides complete autonomy in creating high-level graphics and is ideal for data analysts.
Pygal is more similar to Plotly or Bokeh libraries in terms of embedding the visualizations as a part of web pages or HTML documents. However, what additionally Pygal offers is the SVGs, the Scalable Vector Graphics. The SVG ensures that the quality of the visualization is not impacted despite zooming in to any scale. However, Pygal is suggested for smaller datasets as bigger datasets can distort the rendering with too many data points.
Geopltolib is one of the excellent libraries for visualizations and one of the most important ones as it helps in creating maps using geographical data. It helps in creating dot-density plots, choropleths, symbol maps, among others. However, the catch is it needs to have NumPy and Pyglet included before installing Geoplotlib.
The Python developers can use any of the above-mentioned popular data visualization libraries based on the need. Plotly, Bokeh, or Pygal can be leveraged when the graphs need to be embedded in the web applications, for map-based visualization Geoplotlib can be used. For 3-D plots, Plotly can be used. Hence, the data analysts can choose the ideal libraries for creating stunning visualizations.