😎💡📊In search of the hidden: little-known Python libraries for data analysts
PyCaret - An automated machine learning library that simplifies the transition from data preparation to modeling. PyCaret includes features for automatic model comparison, data preprocessing, and integration with MLflow for easy experimentation.
Vaex - A library for lazy loading and efficient processing of very large data. Great for analyzing large datasets with limited computing resources. aex allows you to efficiently work with datasets containing billions of rows, minimizing memory usage and optimizing performance.
Streamlit - A tool for quickly creating interactive web applications for data analytics. Streamlit can be used to develop applications that demonstrate machine learning results, such as image classification or time series forecasting.
Dask - Designed for parallel computing and working with large datasets. Ideal for scaling analytical operations and processing large volumes of data. Dask provides compatibility with tools like Pandas and Numpy and allows you to perform complex calculations on clusters.
Dash by Plotly - Framework for creating analytical web applications. Ideal for creating interactive dashboards and complex data visualizations. Dash allows you to create rich web applications for data analysis, such as visualizing company financial performance or market data trends.