This post is attempting to enlighten you about the most useful and popular Python libraries used by data scientists. And why only Python, because it has been the leading programming language for solving real-time data science problems.

These libraries have been tested to give excellent results in various areas like Machine Learning (ML), Deep Learning, Artificial Intelligence (AI), and Data Science challenges. Hence, you can confidently induct any of these without putting too much time and effort in R&D.

In every data science project, programmers, even architects, use to spend considerable time researching the Python libraries that can be the best fit. And we believe this post might give them the right heads up, cut short the time spent, and let them deliver projects much faster.

Python Libraries You Must be Using for Data Science

Please note that while working on data science projects, you have several tasks at hand. Hence, you can and should divide them into different categories. Therefore, it becomes smooth and more efficient for you to distribute and manage progress.

Therefore, we’ve also fine-tuned this post and divide the set of Python libraries into these task categories. So, let’s begin with the first thing you should be doing:

Python Libraries Used for Data Collection

Lack of data is the most common challenge that a programmer usually face. Even if s/he has got access to the right set of data sources, they are not able to extract the appropriate amount of data from there.

That’s why you must learn different strategies to collect data. And it is even itself has become the core skills towards becoming a sound machine learning engineer.

So, we’ve here brought three most essential and time-tested Python libraries for scraping and collecting data.

Selenium Python

Selenium is a web test automation framework, was initially created for Software testers. It provides Web Driver APIs for browsers to interact with user actions and return responses.

It is one of the coolest tools for web automation testing. However, it is quite rich in functionality, and one can easily use its APIs to create web crawlers. We have provided in-depth tutorials to learn to use Selenium Python.

Please go through the linked tutorials and design an excellent online data collection tool.

Scrapy

Scrapy is another Python framework that you can use for scraping data from multiple websites. With this, you get a variety of tools to efficiently parse data from websites, process on-demand, and store in a user-defined format.

It is simple, fast, and open-source written in Python. You can enable selectors (such as XPath, CSS) to extract data from the web page.

Beautiful Soup

This Python library implements excellent functionality to scrap websites and collect data from web pages. Also, it is perfectly legal and authentic to do so as the information is already publically available.

Moreover, if you attempt to download data manually, then it becomes hectic and time-intensive. None the less, Beautiful Soup is available for you to do this cleanly.

Beautiful Soup has a builtin HTML and an XML parser that crawls websites, parses data, and stores in parse trees. This entire process, from crawling to data collection, is known as Web Scraping.

It is super easy to install all the above three Python libraries by using the Python package manager (pip).

Best Libraries for Data Cleaning and Rinsing

After completing the data collection, the next step is to filter out the anomalies by performing cleaning and rising. It is the mandatory step to follow before you can use this data for building/training your model.

We’ve inducted the following four libraries for this purpose. Since the data can be both structured and non-structured, so you may need to use a combination to prepare an ideal data set.

Spacy

Spacy (or spaCy) is an open-source library package for Natural Language Processing (NLP) in Python. Cython is used to develop it and also added a unique ability to extract data using natural language understanding.

It provides a standardized API set that is easy to use and fast as compared to other competitive libraries.

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