stefmolinHands-On-Data-Analysis-with-Pandas-2nd-edition
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Materials for following along with Hands-On Data Analysis with Pandas – Second Edition
The main features of stefmolin/hands-on-data-analysis-with-pandas-2nd-edition are: Data Manipulation Libraries.
Open-source alternatives to stefmolin/hands-on-data-analysis-with-pandas-2nd-edition include: arrow-py/arrow — Arrow is a Python date and time library that provides a simplified interface for creating and manipulating timestamps… cupy/cupy — CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and… dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… dateutil/dateutil — Useful extensions to the standard Python datetime features. fugue-project/fugue — | Tutorials | API Documentation | Chat with us on slack! | | ----------------------------------------------------------… ajcr/100-pandas-puzzles — Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power.
Arrow is a Python date and time library that provides a simplified interface for creating and manipulating timestamps by wrapping the Python standard library. It serves as a tool for managing date objects, handling timezone offsets, and performing relative date calculations. The library is distinguished by its ability to humanize timestamps into natural language relative descriptions across multiple locales and parse human-readable time phrases back into precise date objects. It also features a specialized parser for converting ISO 8601 strings and custom formatted text into date objects. Br
CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio
Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
Inspired by 100 Numpy exerises, here are 100* short puzzles for testing your knowledge of pandas' power.