Top 10 Python packages for mastering Data Science

The significance of learning Python for Data Science

Python has turned out to be one of the commonly used programming languages for Machine Learning as well as in Data Science in general. It has become even more prominent after the rise of Deep Learning and use of GPUs. Python boasts of robust and widely used deep learning frameworks like TensorFlow, PyTorch, Theano and many more.

Python again boasts of a large number of useful python libraries. This significantly eases the workflow while developing a deep learning model or even a machine learning project from scratch, even for the data cleaning step.

However, one might feel clueless as to where you start learning Python, specifically the skills necessary for learning Data Science. I have covered how to get started in this previous post.

In this article, I will cover what are the few Python libraries you must try to first try to get accustomed to. This which help you address about 80% of all problems or tasks while developing a complete Data Science project.

Top 10 Python libraries you definitely need to know


This is THE MOST IMPORTANT package for any ML project. Be it PCA, preprocessing and splitting datasets and the entire pipeline. Its suite of ML models makes you train and test with any random models a breeze. Its rich documentation along with User Guide is of tremendous help for everyone.


Required for importing and exporting data into workable formats for coding in Python


For the numerical operations, reshaping placeholders, numpy arrays and also for generating random numbers from known and unknown distributions. Like for initializing the weights of a neural network.


A suite of packages, datasets, and lexicons that is a must for any NLP(Natural Language Processing) tasks. Some important functionalities are : tokenization, Parts of Speech(POS) tagging, n-gram language models, stemming, lemmatizing and stopwords


For data visualization, especially while working with pandas. Useful for making complicated plots and graphs.


For using regular expressions or regex. Proves very useful for preprocessing and cleaning data. Like for removing punctuation marks.


This is a very useful tool when we need to store some output data, but exporting it to formats like csv, txt, json or xml, would take very large amounts of space. Pickle dumps the data in the form of a Python executable, which can be imported into the Python code in the same format.


Working with Theano for implementing Deep Learning models.


Working with Tensorflow for implementing Deep Learning models.


This is very very useful.

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