In the previous two sessions, we introduced Pandas’ data structures and common calculation methods. Time series analysis is a crucial data processing approach in the field of geoscience. By understanding…

In the previous session, we introduced Pandas’ basic data structures and indexing methods. Building on the understanding of how Pandas organizes data and how we select the data we need…

After gaining a basic understanding of the previous content, we will now explore the Pandas section with greater freedom and ease to implement more functionalities. Pandas is a Python software…

Introduction In the previous session, we discussed using control flow statements in Python to achieve batch processing and automation. By repeating calculations with loop statements and differentiating execution paths under…

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…