What are some Underrated Python Libraries?


In this article, we will learn some underrated Python libraries. The following is a list of some of the underrated libraries in Python −

  • Emmett

  • Jam.py

  • Missingo

  • Emot

  • Shogun

  • Blaze

  • Bamboolib

  • Swifter

  • Caffe

  • Myia

  • Featuretools

  • Altair

  • AutoViz

Emmett

The Emmett web framework is the first package that is severely underrated and underestimated. The Emmett web framework is flexible and may be used for many various applications in web development.

Another advantage is that the Emmett web framework is relatively simple to utilize. It uses Flask−like syntax, making it relatively simple to learn if you are already familiar with Flask.

Jam.py

Jam.py is the next underrated Python package on this list. Jam.py is another web framework that excels at one task. The program is used to create data−driven dashboards, and it is extremely good at doing so. One particularly cool feature of this package is that you don't even need to know how to program to utilize it. The package will start a web server session. This session can then be joined via a web browser, and you can use the interactive Integrated Development Environment while ignoring your code for the most part.

This is a really cool approach to dashboards, in my opinion. Even expert programmers will appreciate not having to write any code. This makes Jam.py a really cool and unique solution for Python users and non−programmers as well.

Missingo

Missingo can assist in the management of missing values by using data visualizations more efficiently. Missingo includes four types of charts based on matplotlib for a better comprehension of the missing data. These are made out of bar charts. A heatmap, matrix, and dendrogram are all available.

Emot

Emojis are now commonly utilized by everyone. It can be difficult for developers that deal with natural language processing to perform tasks that involve emojis. Emot is a library that allows developers to do away with the emojis from the text data. This library works great with both Python 2 and Python 3.

Shogun

Shogun is the next package on this list that focuses on machine learning. Shogun is a machine−learning library that was originally built in C++ and subsequently converted to Python via an API. Although Shogun does not stick to the standard conventions of the Python programming language established by Sklearn for machine learning, the package still contains enough code to support its models well. The software is actually quite capable and simple to use.

Blaze

When it comes to Blaze, the Blaze module is just the tip of the iceberg. Blaze is a collection of tools that are commonly used in Python for performant computation and machine learning. A module that makes it simple to speed up your Python algorithms, such as Dask, is always welcome. There are numerous tools in the Blaze ecosystem that are really useful.

Returning to the Blaze package, this package is used to consistently query various types of data storage. The blaze may be used to easily move data between formats and to make SQL, Hadoop/Spark, and local data all operate with consistent calls.

Bamboolib

Data analysis and visualization is the most crucial but time−consuming and tough process.

Bamboolib is a Graphical User Interface(GUI) for pandas DataFrames that allows developers to work with Python in Jupyter Notebook or JupyterLab. It is well−known as a smart and extremely helpful library for analyzing, imagining, and managing information. It can be utilized by people who do not have a programming background because it does not require any coding skills.

Swifter

Swifter is a library with a single simple function−it makes apply() operations much faster. This is accomplished using a considerably more efficient apply() method designed exclusively for Pandas Series objects.

Caffe

Caffe is a deep learning framework designed with expression, speed, and modularity in mind. The package is fantastic, and the framework is extremely versatile and efficient due to its modular nature. The components are all modular and work together to form a network. There is also a strong emphasis on speed, so this package is definitely worth checking out for some of the fantastic and speedy models included.

Myia

We wanted to include this package because We think it's really amazing. Myia is a programming language designed for high performance. It is accessible via Python and focuses on being faster than Python. The idea is to have Myia run in the background while Python is written in the foreground.

Featuretools

Another extremely important software is Featuretools. Feature engineering can be difficult, especially when you are unsure of which features to engineer from. Featuretools, on the other hand, tries to solve that exact problem by automating feature selection. In some ways, automating machine learning is amusing. Regardless of the comedy, We highly recommend checking out this package because there are many applications where it can save a significant amount of time.

Altair

Of all the modules on this list, We would strongly recommend downloading Altair. Altair is a statistical plotting automation package. On the surface, this appears to be a rather weird notion. To be honest, We were doubtful about the functionalities of this software. Altair, on the other hand, produced some very amazing visualizations of our data that were incredibly insightful. It was a surprising experience to observe an artificial intelligence do the selection and visualization for us.

AutoViz

One of the most underrated libraries for doing exploratory data analysis tasks. The library is useful for data visualization activities and can handle huge datasets. To retrieve data visualization, a single code can be utilized. Simply enter the JSON, CSV, or txt file and the library will assist with visualization.

Conclusion

This list demonstrates how fortunate we are to have Python's wonderful, ever−expanding ecosystem of tools. We even end up with automated solutions for certain areas of the job because there are so many amazing solutions for each task a Data Scientist may have to undertake. With these wonderful packages under its belt, Python's magnum opus is now complete.

Updated on: 26-Dec-2022

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