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Python
Python is a high-level and object-oriented programming language. The name python is indeed not derived from the snake, but it comes from the surreal British comedy group montey python. Due to its interpreter and robust open-source library, Python is widely used for web-based development and cloud computing methodologies. Compared to other programming languages like Perl, Java, Smalltalk, c++, and Ruby, Python is considered the choice of programming language due to a reduction in the program's cost and its easily readable syntax. Python is referred to as a "glue" language, while other object-oriented programming languages such as Java, Javascript, Perl are better characterized as a low-level implementation language.
Python provides various libraries that come with different features for visualizing data. All these libraries come with different features and can support various types of graphs.
Plotly
Python's Plotly visualization library helps us to publish interactive plots online. With Python's Plotly library's help, we can create line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, and subplots multiple-axes, polar charts, and bubble charts.
To install it type the below command in the terminal.
pip install plotly
# importing packages
import plotly.express as px
import pandas as pd
Pros:
We can edit plots in a Python environment as well as a Plotly website.
· Stack overflow and GitHub provides a lot of support for interactive graphs/dashboards
· A wide variety of visuals can be created with simple codes.
· We can create codes which are easy-to-use with little experience.
· It is perfect for interactive dashboards and plots.
· Mapbox is a partner of Plotly, and that allows for customized maps with ease.
· Overall graphics are excellent.
Cons:
· It requires an API key and registration rather than just a pip install
· Initially it is confusing to setup and use Plotly without an online account.
· It plots data/layout objects that are unique to Plotly and aren't intuitive the plot layout hasn't worked for some.
· A variety of Plotly tools available but have out dated documentation.
· It may take some time and effort in mastering the advance features and library’s intricacies even though the Plotly API is well documented.
Industry Usage:
Working in the tech industry as a data scientist, we need to use a variety of exploratory data tools to build web-based applications for both technical and functional stakeholders. When compared to data visualization libraries, Plotly is a graphing library designed especially for analysts, data engineers, and data scientists to quickly visualize trends in their data using Python, R, Matlab, or Javascript. This library is used explicitly in the E-comm retail domain to visualize the rapidly changing trends emerging from trained data models.
Matplotlib Library:
In a data science Journey, there would be scenarios and circumstances based on machine learning to use matplotlib. It is considered the default Python library for visualizations. Matplotlib is known for its flexibility in facilitating a 2-D plotting library in Python. We will find the Pyplot interface of Matplotlib very familiar if we have background knowledge of MATLAB programming.
Industry usage of Matplotlib:
It supports all the out of the box popular charts like bar charts, histograms, scatterplots, power spectra, and error charts. Moreover, it is also an extension that we can use to create advanced visualizations like 3-Dimensional plots.
The Libraries we define before we write a code:
# importing packages
import pandas as pd
import matplotlib.pyplot as plt
Matplotlib is used prominently in public domains like transportation and defense. It is undoubtedly an endpoint to the ML process to indicate the predictions.
Pros
· It is user friendly to identify the property of data.
· It is a powerful tool with a wide range of applications.
· We can access large amounts of data in a simpler way through Matplotlib
· It is an intelligent library to plot any format of data in the context of structured and semi-structured data.
· It supports various forms of data representation and is very flexible.
· We can access high quality images.
· Using Matlplotlib is cost friendly as it is an open source.
· It is extensive and customizable.
· It is flexible to run on various platforms.
· We can create advance visualizations.
· It makes data analysis easier.
· It is easy to navigate.
Cons
While Matlplotlib being a versatile and powerful tool, users might encounter some of its disadvantages.
· Matplotlib can plot anything but cumbersome to adjust the plots to have a nice look and feel or plot non-basic plots.
· Unlike other plotting libraries like Seaborn or Plotly, Matlplotlib can be verbose and less intuitive as it might be time consuming while creating customized plots.
· Matplotlib has an extremely low-level interface; hence it will be complicated enough to deal with non-cleansed data.
· Matplotlib has limited 3D plotting capabilities compared to other libraries.
· Although Matplotlib has comprehensive documentation, user sometime find it cumbersome to navigate and error messages sometimes can be hard to debug.
· Matplotlib’s plot has been found to be a lacking with some modern libraries compared to modern plotting features for some users as it has been here for a long time now.
Seaborn
Seaborn is a higher-level abstraction that leverages Matplotlib, offering appealing design styles and color palettes to enhance the visual appeal of plots.
To install seaborn type the below command in the terminal.
pip install seaborn
Seaborn is constructed based on Matplotlib, allowing seamless integration with Matplotlib. Combining both libraries is straightforward: simply call Seaborn's plotting functions as usual and utilize Matplotlib's customization features thereafter.
# importing packages
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
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