Data Processing and ETL with Python.
Parallel Pandas for data that doesn’t fit in the memory. Build with Pandas on Ray. (Not for Windows.)
Speed up calculations. (not yet tested)
Similar to scikit-learn, but closer to R syntax. I like the reports and presentation of results, which are really useful ootb.
Module for Webcrawling & NLP.
Timeseries transformations & classification (not yet tested)
Automated feature extraction from time-series (not yet tested)
Fast time series classification (not yet tested)
Forecasting and anomaly detection on time series (not yet tested)
AutoML & optimization
SciKit Learn based auto ML library. (not yet tested)
Hyperparameter scanning and optimization for Keras.
Visualizing the impact of hyper parameters on the model performance. (not yet tested)
Visual ML Model debugging. Allows to detect which subset of data a model is inaccurately predicting and explains the potential cause of poor model performance. (not yet tested)
Decision tree visualization and model interpretation.
Create interactive dashboards and charts. Similar to Shiny for R.
Interactive charting in Jupyter Notebook.
Visualization in IPython Notebooks.
Visulization in IPython Notebook and more. Using Vega.
Framework for writign REST-APIs, an alternative Flask which is more prominent. (not yet tested)
Headless browser for scraping dynamic websites with js etc.
Scrape data from webpages. Take a look at this short intro or this more elaborated tutorial
Simpler high level API for the headless browser module Selenium. (not yet tested)
Alternative to Flask. (not yet tested)
Easy command line interaction. Provides easy & comprehensive use of CLI parameters.
Profile Python programs to identify performance problems and issues in resource usage.
Progressbar for CLI and Jupyter Notebooks.
Powerful debugging tool. (not yet tested)
Visualize results during Tensorflow Training. (not yet tested)