5 Hidden Gem Python Libraries for Data Science

Data science has evolved so much that it is almost necessary to rely on the Python ecosystem to improve workload efficiency. That’s why so many Python libraries have been developed to accommodate data science tasks.

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However, many great libraries may flounder in obscurity while overshadowed by popular libraries such as Pandas, Scikit-learn, Seaborn, and others. In truth, many hidden gems work better in certain situations than the popular libraries.

This article will explore 5 such hidden gem Python libraries for data science that can help your work.

1. Cleanlab

 
Data science is all about data. If you have bad-quality data, your analysis and model will be bad. There is even a phrase, “Garbage In, Garbage Out.” That is why we need to manage our data quality well. Cleanlab is a library that can help you improve data quality.

Cleanlab automatically cleans our datasets and identifies issues with the target label. The library works well for finding problems within a dataset and handling errors to improve the model’s performance. If you have a quality data problem, do not hesitate to check out the Cleanlab library.

2. H3 Uber

 
Geographical data can make for among the the most exciting data science projects, yet it is one of the most difficult to process. Consistently maintaining the data to achieve precise spatial data is challenging because the segmentation is usually irregular and changes over time.

Uber’s open-source H3 library can help facilitate the use of geographical data. The H3 library uses the hexagonal grid system, dividing the data into hexagonal cells and structures to acquire consistent location data. The data could be used for any accurate location-based analysis and improve the geographical applications.

3. IceCream

 
No, it’s not the dessert. The Python library IceCream is a sweet treat that can enrich your data science work by improving the debugging process. In general, much of the programming activity is happening in the background, and we do not see what happens, including the data structure and processing.

IceCream turns the simple print function into a debugging machine that can produce better information. The library could do many things, such as produce output that prints both the function or variable names with their values while highlighting the output syntax. Printing the data structures is also pretty, taking the mess and confusion out of the equation. Additionally, it can inspect the overall execution of your program.

4. Fairlearn

 
Data science projects are useful to businesses, but we must also remember that many datasets we use are related to humans in numerous ways. The model system we establish needs to be as unbiased as possible and eliminate any possibility that could discriminate against certain social groups. It might not be your first instinct to perform any bias assessment during model creation, but it should always be there. That’s where Fairlearn could help you.

Fairlearn is a Python library that helps mitigate unfair issues in our machine learning systems. The library consists of fairness metrics and algorithms. The fairness metrics evaluate which groups were negatively impacted by the model and how fair it is overall. At the same time, the algorithm provides mitigation techniques to minimize bias and unfairness.

5. Scikit-posthocs

 
Data science involves a lot of statistical analysis, especially comparing datasets and groups. People might think that data science is all about machine learning modeling, but simple statistics can solve many projects. One common analysis is hypothesis testing between groups, such as ANOVA.

Post-hoc analysis is done after the initial analysis of ANOVA or similar analysis if significance is found during the analysis. Scikit-posthocs is a Python library that facilitates post hoc analysis in our workflows. It provides all the tools that you can use to perform parametric and non-parametric tests with an API similar to the Scikit-learn. Try using this library if you want to validate your test results.

Conclusion

 
In this article, we have explored 5 different Python libraries for data science that you might not have previously been familiar with. Try using these hidden gems, and you could add to your arsenal of analysis libraries.
 
 

Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.