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7 Python Libraries For Data Science That Will Wow You

These are the unexplored gems of data science

Photo by Headshatter from Pexels
Photo by Headshatter from Pexels

In the 21st-century Data Science has attracted a lot of attention and has been recognised as one of the most exciting fields to work on.

With the immense growth of data science and its applications, a number of Libraries, frameworks and toolkits have also been developed which along with the traditional data science libraries like Numpy, Pandas Matplotlib, Scikit-learn can make programmers’ lives easier.

In today’s article, we are going to take a look at 7 such libraries which are not widely used but can definitely help you improve your workflow.

1. Pandas_ml

Pandas_ml is a Python library that is made with the integration of Pandas, scikit-learn and XGBoost. That means we can do preprocessing of data (Pandas), implement machine learning algorithms (scikit-learn) and do gradient boosting (XGBoost) at the same place using just one library.

We can also use this library for data visualisation purposes.

Installation

pip install pandas_ml

You can learn more about this library here.

2. Imbalanced-learn

An imbalanced dataset i.e. when the classes in a data set are not represented equally can be a problem while doing classification.

This Python library helps to address this problem by resampling the classes and making the data set somehow balanced so that we can get more accurate results. The dependencies that are required to use imbalanced-learn are SciPy, NumPy, Scikit-learn and joblib.

Installation

pip install -U imbalanced-learn

You can learn more about this library here.

3. Pyflux

Pyflux is a Python library that is used for working with problems related to time series analysis. This library comes with a great number of trusted time series models like ARIMA, GARCH and VAR which makes working with time series problems a lot easier.

Installation

pip install pyflux

You can learn more about this library here.

4. Statsmodels

The main focus of this Python library is the implementation of different purely statistical models. It is built on top of libraries like NumPy and Scipy and uses Pandas for data processing.

Using this library you can implement advanced statistical models much faster and efficiently than Numpy or Scipy. It uses pasty which gives it an R like interface.

Installation

pip install statsmodels

You can learn more about this library here.

5. Ipyvolume

Ipyvolume is a Python library for 3D visualization of data in the Jupyter notebooks with minimum effort. Although it is currently under development and not much stable, in the future it can become an excellent library for visualization.

Installation

pip install ipyvolume

You can learn more about this library here.

6. Surprise

The library is roughly the abbreviation of a simple Python recommendation system engine built on scikit-learn. As the name suggests it was made for building simple recommender systems in python.

This library has a large array of ready-to-use prediction algorithms and also comes with built-in datasets which can be really helpful for designing recommendation systems.

Also, it comes with tools to analyse and compare the performance and to tune the hyperparameters.

Installation

pip install scikit-surprise

You can learn more about this library here.

7. Dabl

Dabl is the short form of Data analysis baseline library. This library can be used to automate repetitive tasks like data preprocessing, data cleaning and feature engineering etc.

Here, we can find some pre-built machine learning models like classification all regression which makes it really easier for beginners to execute them without working much about the code.

Although this library is still in the development phase and is not advised by the developers to use in production level code, this time it is definitely worth giving a try.

Installation

pip install dabl

You can learn more about this library here.


Conclusion

That’s all for this article. We have discussed 7 amazing python libraries for data science.

Most of the libraries mentioned here are in the early stage of their development and aren’t always 100% efficient. But depending on your field of work knowing a few of these libraries can make your workflow easier.

Most of these libraries are open source so they are free to use and you can get good support from the community if you are stuck while learning them.


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