Today, Python is arguably the best computer language for Data Science. If you are looking for Python libraries for data science, then you are at the right place. Here we have listed a few best python libraries used in data science.
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and other knowledge to organise unstructured data into structured.
Best Python Libraries for Data Science
Data Science projects depend heavily on Python nowadays. So, if you want to pursue a career in data science, you should know about the Python libraries used in data science. Here is the list:
NumPy is first in this list, which is also essential for Data Science projects. The tool is easy to learn and equally crucial for every project related to Data.
The good thing about NumPy is that it is free to use and has plenty of online communities where you can take help. What makes the tool so lovable is that it can quickly solve complex mathematical implementations.
Pandas is also on this list mainly because you can perform custom operations related to Data Science. You can easily organize data using Pandas.
Not only that, you can use this tool as it is flexible to other Python tools, as well. If you are interested in using the apply method, Pandas is the ideal choice that you can rely on.
Matplotlib is, first of all, a two-dimensional plotting library that you can use in most Data Science projects. You can do cross-platform projects using Matplotlib.
A tool is also a form of Jupyter Notebook. So, you can create web server apps with it as well. It also comes with a number of GUI toolkits such as GTK+, Tkinter, Qt, and even wxPython.
SciPy is a powerful tool by which you can handle mathematical problems, which comes with many modules. With SciPy, you can manage numerical routines with ease.
That’s not all, it also supports signal processing too. Apart from that, you can also use the NumPy arrays for simple data structures. SciPy is a tool that is ideal for Data Science projects mainly because of its power to manage data.
Scikit-Learn is yet another tool that we have also mentioned before. However, we must even know that it is also one of the best Python tools for the Data Science project as well. You can extract features that are related to images and text.
At the same time, we can also reuse several contexts using Scikit-Learn. The tool is also usable in a wide range of algorithms, such as clustering, factor analysis, and many more.
Natural Language Toolkit (NLTK) is a Python tool which is renowned for the part-of-speech tagger, which it has in-built. The n-gram and collocations feature makes the tool lovable across the world.
Apart from that, NLTK comes with a Named-entity recognition feature as well. Lexical analysis is yet another feature that you would admire. Overall, all the features suggest that you can use this tool for most Data Science projects.
That’s it; these are the best python libraries for data science that you can use. Yes, that are other useful Python tools for data science. Here we have only listed the most popular ones. You can also check our article on the top 10 programming languages for data science.
I hope this article was helpful to you. If you liked the article, share it with your friends. If you have some suggestions, do not hesitate to leave them in the comments section below. We will add that to the article.
Leave a Reply