Introduction In the previous session, we discussed Python’s most basic variables and some data types. They form the fundamental cells of our program design. So, how can we chain together…

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…

Python has been around for so long, yet it still lacks a de facto standard for project management and build tools. As a result, Python project structures and build methods…

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…

Exploratory Data Analysis (EDA) is a crucial part of data science model development and dataset investigation. When presented with a new dataset, a significant amount of time is often spent…