Data Science
Top 12 Python Libraries For Data Science In 2021
These libraries are a great starting point to launch your data science journey.
Whether you’re a beginner or a seasoned veteran looking to keep up with the trends, these 12 Python libraries are tools you definitely need in your data science toolkit for 2021. Keep in mind that this list isn’t exhaustive, so please add your favorite libraries in the comments below if I’ve left them out!
Data Mining
Note: When scraping data from the web, please check the terms and guidelines of your data sources before scraping. It’s important to follow all licensing and copyright rules that the data source may have.
1. Scrapy
Scrapy is one of the most popular tools for Python developers who are looking to scrape structured data from the web. Scrapy is great for building web crawlers that can gather structured data from web pages of any format, making this an excellent tool for gathering data.
2. BeautifulSoup
Another great library for gathering and organizing web data, BeautifulSoup makes scraping websites easy. BeautifulSoup is great for web pages that use special characters as you can easily pass different encoding formats to its functions when gathering web data.
3. Requests
Call me old fashioned, but there’s nothing like the requests library when it comes to gathering web-based data, especially from APIs. Requests makes it easy to interact with APIs and other HTML sources in simple, one-line solutions.
Data Processing
4. Pandas
Pandas is an open-source library that is one of the most widely-used data science libraries, and with over 2300 contributors on their GitHub repo, this library isn’t going away any time soon. Pandas makes data processing and wrangling easy, ingesting data from tons of different sources like CSV, SQL, or JSON, giving you great manipulation features like handling missing data, imputing missing data files, and manipulating columns, and even providing some basic but very useful visualizations.
5. Numpy
If you’re looking to do any advanced mathematics to any of your datasets, then you need to be importing the Numpy library. Used heavily in deep learning and machine learning, this is an absolute must for any computationally heavy algorithms and analyses. Numpy’s multi-dimensional arrays also make complex problems infinitely easier than standard lists.
6. Scipy
Scipy is derived from Numpy and can also do tons of different mathematically complex calculations. This library will make your life particularly easier if you’re looking to do multi-dimensional image processing, differential equations, or linear algebra.
Machine Learning
7. Keras
Now into the deep stuff. Keras has emerged as the go-to library for deep learning, specifically when it comes to neural networks. It’s built on top of TensorFlow but is built to be much more user-friendly, enabling users to do lightweight and quick experiments with their deep learning APIs.
8. TensorFlow
TensorFlow is so named because of its use of multi-dimensional arrays, which it calls tensors. There’s a reason that all of the big tech companies are using TensorFlow for their neural network algorithms: this library can do practically anything. Great use cases include sentiment analysis, voice recognition, video detection, time series analysis, and facial recognition, among other things. TensorFlow was developed by Google, so this isn’t going away any time soon.
9. PyTorch
Whereas TensorFlow uses static graphs, PyTorch can define and manipulate graphs on the go, making it a bit more flexible. Despite the more Pythonic approach of PyTorch over TensorFlow, the latter is simply more popular, so it’s easier to find resources on it. If you’re looking for something flexible and a little easier to pick up than TensorFlow, this library (which was developed by Facebook) is a great resource.
Visualization
10. Bokeh
Some of the most stunning visualizations I’ve seen created by Python code were developed using the Bokeh library. Bokeh provides interactive visualization options that can easily be displayed in other Python web tools like Flask, making this a great option for sharing visualizations to wide audiences.
11. Seaborn
The best features for Seaborn (at least when it comes to data science) are the correlation graphs that make it super easy to visually spot correlation across all of the dimensions of your dataset. Seaborn is built on top of MatPlotLib, so it’s easily accessible and a great tool for quickly visualizing your data.
12. Plotly
Plotly is another great tool for creating advanced interactive visualizations that are great for doing exploratory analysis and for displaying results. There’s really nothing that Plotly can’t do, but certain kinds of visualizations are a bit more user friendly in this library rather than other alternatives. Ultimately it just comes down to a matter of taste and familiarity when it comes to choosing the best visualization library for your project.
This list is by no means exhaustive, but this is a great place to start for anyone on their data science journey. What are your favorite Python libraries for data science? Let me know what you’re working on in the comments below!
P.S. Do you ever get tired of waiting for Pandas to upload your data to your database? You can load them 10x faster by utilizing native SQL commands.
