Easy Python

Introduction Before officially stepping into geoscience applications, we still need to learn Pandas and Matplotlib. They are used for DataFrame (or you can think of it as a table) processing…

Introduction We started our Python journey with NumPy. Although scientific computation might be our primary purpose for using Python, it still relies on the basic Python ecosystem for support. Therefore,…

Introduction Previously, we covered the basic concepts, creation, computation, and statistics of NumPy arrays, as well as more advanced applications like indexing, slicing, and concatenation. We also suggested some potential…

Introduction Last time we discussed two forms of reconstructing earth science data: one is indexing (including advanced indexing – slicing), which extracts the parts we need from a complete sequence;…

Introduction When we need to compute values at specific positions or regions within an array, how should we proceed? Translated to earth science: when we have global gridded data, we…

Introduction All our earth science data processing is, in essence, a game of numbers. Therefore, the specific usage methods of NumPy are inherently identical to the geophysical methods we employ….

Introduction NumPy is the foundation and most important component of the Python scientific computing ecosystem (SC, meaning Scientific Calculation, which is also the acronym in this series title). It is…