💡📊Selection of libraries for data analysis
Lux is an add-on to the popular Pandas data analysis package. It allows you to quickly create visual representations of data sets and apply basic statistical analysis with a minimum amount of code.
Pandas-profiling - helps generate a profiling report. This report gives a detailed overview of the variables in your dataset. It provides insight into statistics for individual characteristics of the data, such as the distribution, as well as the mean, minimum and maximum values. The same report provides insight into correlations and interactions between variables.
Sweet-Viz - provides fast visualization and analysis of data. Sweet-Viz's main selling point is its extensive HTML dashboard with useful views and data summaries, which is generated by executing just one line of code.
D-Tale is a Python library that provides an interactive and user-friendly interface for visualizing and analyzing Pandas data structures. It uses Flask as the backend and React as the frontend, making it easy to view and explore Pandas data frames, Series objects, MultiIndex, DatetimeIndex and RangeIndex. It integrates easily with Jupyter, Python terminals and ipython.
AutoViz is a Python library that provides automatic data visualization capabilities, allowing users to visualize data sets of any size with just one line of code. The program automatically generates reports in various formats, including HTML and Bokeh, and allows users to interact with the generated HTML reports.
KLib is a Python library that provides automatic exploratory data analysis (EDA) and data profiling capabilities. It offers various features and visualizations to quickly explore and analyze data sets.
SpeedML is a Python library that aims to speed up the development process of a machine learning pipeline. It integrates commonly used ML packages such as Pandas, NumPy, Scikit-learn, XGBoost and Matplotlib. SpeedML also provides functionality for automated EDA