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Python libraries that are hidden gems in data science

Python has exploded in the data science community in recent years. This is because it has such a robust ecosystem of libraries and tools for data scientists to use. Python has become increasingly popular as a development and research tool in the data science community. It is one of the most popular programming languages for implementing machine learning and deep learning programs.

Python's active community and open-source packages like Pandas, Tensorflow, and Keras have led it to become The Language for Data Science. Currently, there are over 137,000 Python libraries available to programmers all over the world.

Data science is all about finding hidden patterns in data. You can use various techniques to sift through data to find relationships and meaning. Python makes data analysis easy with its various libraries that allow you to dive deep into mathematical algorithms. Many different Python libraries and tools can be used for data science.

While these libraries can help you simplify your analysis, it is challenging to learn everything about them. Most beginners miss out on Python's lesser-known libraries, methods, and functions that can make their lives easier and their codes more efficient. However, by exploring all of Python's features, you can set yourself apart from other programmers.

This blog is about some of these lesser-known gems in Python data science libraries that are hidden away and really should be more popular. These hidden gems include:

Mito

The Mito Python library allows us to analyze data in seconds. Mito simplifies the data framework and does not require knowledge of all Pandas' methods and functions. It also generates code, allowing us to see which methods and functions are used.

Missingno

One can use data visualizations to manage missing values more effectively with Missingo. There are four plots in the Missingno library for visualizing data completeness: bar plots, matrix plots, heatmaps, and dendrograms. There are advantages to each method for identifying omitted data. You can locate missing values, their extent, and whether they are correlated with one another. If analyzed closely, missing values may reveal a hidden story that is often overlooked.

Bamboolib

Data analysis and visualization are the most critical but tedious processes. In Jupyter Notebook and JupyterLab, Bamboolib provides developers with a GUI for Pandas DataFrames, allowing them to integrate Python seamlessly. A hidden gem library for analyzing, imagining, and managing information, it is a brilliant and highly supportive tool. As it doesn't require any coding knowledge, it can be used by individuals who don't come from a programming background.

PPScore

PPScore, developed by Bamboolib developers, is a library for predicting power in datasets. PPScore is a correlation matrix. In a particular dataset, the PPScore can identify linear and non-linear relationships among columns in an asymmetric way. In PPScore, 0 represents no predictive power, and 1 illustrates perfect predictive power. This can be used instead of the correlation (matrix).

AutoViz

Data analysis and exploratory tasks can be performed using this tool. Even large datasets can be visualized with the library, which can handle even the most complicated tasks. Data visualization can be retrieved with a single code. The library automatically helps visualize JSON, CSV, and txt files.

Pillow

Pillow library extends the Python interpreter's image processing capabilities by providing various image formats, representations, and methods for image processing. Pillow has many capabilities, such as image transformation, rotation, resizing, statistics, etc. Designed for fast access to data stored in pixels, it supports a wide range of file formats.

Dabl

In Data Analysis Baseline Library (Dabl), the boilerplate task is reduced, and the components are automated. The Scikit-Learn library inspired it. Several features of the Dabl library make it easy to analyze, process, and model data in Python. You can automate several steps of your Data Science pipeline with Dabl. In Data Science, data preprocessing, data cleaning, and feature engineering constitute 80% of the work and can be automated with Dabl.

As the data science industry grows, these libraries will give you a competitive edge. Explore these Python hidden gems and stay on the lookout for more. Data is the new oil, and models are the new refineries in the new oil age. Using data science, one can use any data to extract meaningful information. A career in data science or analytics can be a significant step forward for your career. 

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