Awesome Data Analysis
Section: Useful Python Tools for Data Analysis · Geographic data operations with pandas.
Entry
Appears in 7 awesome lists
Python tools for geographic data (GeoSeries/GeoDataFrame) built on pandas.
Section: Useful Python Tools for Data Analysis · Geographic data operations with pandas.
Section: Python · Python tools for geographic data.
Section: Python · Python tools for geographic data
Section: Data Science and Analytics · Python tools for geographic data
Section: Spatial Analysis · Python tools for geographic data.
Section: Vector Map · A project to add support for geographic data to pandas objects.
Section: Geolocation · Python tools for geographic data (GeoSeries/GeoDataFrame) built on pandas.
| Python | - Parallel computing with task scheduling in Python with a Pandas like API
| Rust, Python | - Polars is a blazingly fast DataFrames library implemented in Rust using Apache Arrow Columnar Format as memory model.
(label: good first issue) Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support…
| Python | - Modin: Speed up your Pandas workflows by changing a single line of code
An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
Python library with multiple transformers to engineer and select features for machine learning models. scikit-learn compatible with fit() and transform() methods for encoding, imputation, variable transformation, and feature selection. BSD-3-Clause licensed.