Merge branch 'main' into cover-art

Author: brian-rose

_config.yml

@@ -15,6 +15,21 @@ execute:
   # Do not abort notebook execution for errors, since they may be intentional.
   allow_errors: True
 
+#######################################################################################
+# Parse and render settings
+parse:
+  myst_enable_extensions: # default extensions to enable in the myst parser. See https://myst-parser.readthedocs.io/en/latest/using/syntax-optional.html
+    - amsmath
+    - colon_fence
+    - deflist
+    - dollarmath
+    - html_admonition
+    - html_image
+    - linkify
+    - replacements
+    - smartquotes
+    - substitution
+
 # Define the name of the latex output file for PDF builds
 latex:
   latex_documents:

_toc.yml

@@ -7,16 +7,16 @@
   chapters:
     - file: preamble/how-to-use
     - file: preamble/how-to-contribute
+      sections:
+        - file: preamble/template
 
 - part: Foundational skills
   chapters:
     - file: foundations/overview
     - file: foundations/why-python
     - file: foundations/getting-started-python
       sections:
-        - file: foundations/basic-python
-          sections:
-            - file: foundations/Hello
+        - file: foundations/quickstart
         - file: foundations/how-to-run-python
           sections:
             - file: foundations/terminal
@@ -43,5 +43,7 @@
         - file: core/cartopy/01_Cartopy_Intro
     - file: core/datetime
     - file: core/pandas
+      sections:
+        - file: core/pandas/pandas_fullNotebook
     - file: core/data-formats
     - file: core/xarray

core/numpy.md

@@ -1,7 +1,7 @@
-# Numpy
+# NumPy
 
 ```{note}
 This content is under construction!
 ```
 
-This section will contain tutorials on array computing with [numpy](https://numpy.org).
+This section will contain tutorials on array computing with [NumPy](https://numpy.org).

core/overview.md

@@ -1,13 +1,15 @@
 # Overview
 
-This section will contain cross-referenced tutorial material for several packages in the Scientific Python ecosystem that are in wide use across the Geosciences.
+This section contains cross-referenced tutorial material for several packages in the Scientific Python ecosystem that are in wide use across the Geosciences.
+
+We suggest that new users start with the [Foundational Skills](../foundations/overview) section in order to get the most out of these tutorials!
 
 ## Packages
 
-- [Numpy](numpy): Core package for array computing, the workhorse of the Scientific Python stack
-- [matplotlib](matplotlib): Basic plotting
-- [cartopy](cartopy): Plotting on map projections
-- [datetime](datetime): Dealing with time and calendar data
-- [pandas](pandas): Working with labeled tabular data
+- [NumPy](numpy): Core package for array computing, the workhorse of the Scientific Python stack
+- [Matplotlib](matplotlib): Basic plotting
+- [Cartopy](cartopy): Plotting on map projections
+- [Datetime](datetime): Dealing with time and calendar data
+- [Pandas](pandas): Working with labeled tabular data
 - [Data formats](data-formats): Working with common geoscience data formats
-- [xarray](xarray): Working with gridded and labeled N-dimensional data
+- [Xarray](xarray): Working with gridded and labeled N-dimensional data

core/pandas.md

@@ -4,4 +4,12 @@
 This content is under construction!
 ```
 
-This section will contain tutorials on using [pandas](https://pandas.pydata.org) for labeled tabular data.
+## This section will contain tutorials on using [pandas](https://pandas.pydata.org) for labeled tabular data.
+
+---
+
+From the [Pandas documentation](https://pandas.pydata.org/) "is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language."
+
+Pandas is a very powerful library for working with tabular data (i.e. anything you might put in a spreadsheet -- a common data type in the geosciences). It allows us to use labels for our data so that we can write expressive and robust code to manipulate the data.
+
+Key features of Pandas are the ability to read in tabular data, slice and dice data, and exploratory analysis tools native to the library.