There are hundreds of Python libraries aimed to make lives easier for data scientists. Some good and some bad, some large libraries covering many areas and some that only do a couple things very well. Here is a list of 5 Python libraries that every data scientist is required to have installed in their environment.
Let’s jump right in!
Pandas is crucial for any data manipulation you are doing. Pandas encompasses a wide range of different data import and export methods, in addition to methods for indexing and manipulating data.
Pandas is mostly known for its DataFrame data type. The data frame is similar to what you will find in other statistical software. It is very effective for reshaping, splitting, aggregating, merging, and selecting data. It also utilizes Matplotlib (as we will discuss in a little bit) to plot a data frame quickly and simply. Most of the data wrangling in your project will employ Pandas.
Want to learn more about Pandas? Check out this blog post about the Basics of Pandas
NumPy adds support for large, multi-dimensional arrays and matrices in addition to a large collection of mathematical functions built to interact with and operate the arrays/matrices. It is designed to handle numerical data, and it does the job very well and is in most instances faster than Python’s standard methods.
Matplotlib is a great library for creating two-dimensional graphs and diagrams. It makes it simple to throw together a histogram of a pandas data frame, or plot out scatterplots, bar charts, and much more. You can import styles, or easily change most any attribute of the figure. Again, this library is a great plotting library if you are in need of quick figures and it integrates very nicely with Pandas dataframes.
SciPy stands for Scientific Python, and includes functions for advanced calculations. This library is used by all math intensive professions, including data science and engineering.
Scikit-learn is the go-to library for standard machine learning and data mining tasks. It includes algorithms for clustering, regressions, model selection, classification, and more. With more than 1,000 contributors, the library is constantly updated and new features are added constantly, and models are often improved over time.
Now that you have installed all these libraries, get out there and start exploring. These libraries are super helpful and will help you accomplish more in less time. No need to reinvent the wheel!
Thank you for taking the time to read this post. If you liked it, go ahead and subscribe to my newsletter, or leave me a comment in the comment section below. Thanks!