When we talk about data analytics in Python, most people immediately think of Pandas or visualization tools. But behind the scenes, one powerful library quietly does the heavy lifting-NumPy. If you’re ...

This is a collection of exercises that have been collected in the numpy mailing list, on stack overflow and in the numpy documentation. The goal of this collection is to offer a quick reference for ...

Neural activity data can be associated with behavioral and physiological variables by analyzing their changes in the temporal domain. However, such relationships are often difficult to quantify and ...

The authors ask whether a simple whole-head spectral power analysis of human magnetoencephalography data recorded at rest in a large cohort of adults shows robust effects of age, and their results ...

Python as a language is relatively slow for heavy data processing because native Python loops run in the Python interpreter, which adds significant overhead. However, Python’s data ...

store NumPy arrays in TIFF (Tagged Image File Format) files, and read image and metadata from TIFF-like files used in bioimaging. Image and metadata can be read from TIFF, BigTIFF, OME-TIFF, GeoTIFF, ...

NumPy is known for being fast, but could it go even faster? Here’s how to use Cython to accelerate array iterations in NumPy. NumPy gives Python users a wickedly fast library for working with data in ...

Money may not grow on trees, but it does grow in GitHub repos. Open source projects produce the most valuable and sophisticated software on the planet, free for the taking, dramatically lowering the ...

Microsoft released a U.S.-wide vector building dataset in 2018. Although the vector building layers provide relatively accurate geometries, their use in large-extent geospatial analysis comes at a ...