Python continues to take leading positions in solving data science tasks and challenges. Last year we made a blog post overviewing the Python’s libraries that proved to be the most helpful at that moment. This year, we expanded our list with new libraries and gave a fresh look to the ones we already talked about, focusing on the updates that have been made during the year.
Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it’s difficult to distinguish one particular leader at the moment. They cover the following topics:
- Core Libraries & Statistics
- Visualization
- Machine Learning
- Deep Learning
- Distributed Deep Learning
- Natural Language Processing
- Data Scraping
Read the full article with comments about each library and access the full picture, here.
