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…
MongoDB is a non-relational database written in C++. It is an open-source database system based on distributed file storage. Its content is stored in a JSON-like object format. Its field…
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…
Today, a colleague handed me a .pyd file and asked me to run some data analysis. I was completely stumped… I wonder how many of our readers know how to…
LaTeX is a typesetting system based on ΤΕΧ, exceptionally good at displaying complex mathematical formulas. latexify is a Python library that generates LaTeX mathematical expressions. This project can easily generate descriptions of…
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…