Remove all under-construction banners (#646)

* Remove all under-construction banners

* Wording and formatting fixes

* Update core/datetime.md

Co-authored-by: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com>

* Update core/data-formats.md

Co-authored-by: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com>

---------

Co-authored-by: Katelyn FitzGerald <7872563+kafitzgerald@users.noreply.github.com>

Author: brian-rose

core/data-formats.md

@@ -1,7 +1,3 @@
 # Data Formats
 
-```{note}
-This content is under construction!
-```
-
-There are many data file formats used commonly in the geosciences, such as NetCDF and GRIB. This section contains tutorials on how to interact with these files in Python.
+Geoscientific data are commonly stored in self-describing files that are readable on multiple computing platforms and programming languages (including Python). This section contains a tutorial on the widely-used netCDF data format.

core/datetime.md

@@ -1,14 +1,10 @@
 # Datetime
 
-```{note}
-This content is under construction!
-```
+This section contains a basic tutorial on dealing with dates and times in scientific Python using a module from the Python standard library known as [datetime](https://docs.python.org/3/library/datetime.html).
 
-This section contains tutorials on dealing with times and calendars in scientific Python. The first and most basic of these tutorials covers the standard Python library known as [datetime](https://docs.python.org/3/library/datetime.html).
-
-When this chapter is fully built out, it will include a comprehensive guide to different time libraries, where to use them, and when they might be useful. This set of time libraries includes these libraries, among others:
+Other time libraries used in the geosciences include:
 
 - [Numpy `datetime64`](https://numpy.org/doc/stable/reference/arrays.datetime.html) (for efficient vectorized date and time operations)
 - [cftime library](https://unidata.github.io/cftime/) (for dealing with dates and times in non-standard calendars)
 
-These tutorials will be cross-referenced with other tutorials on time-related topics, such as dealing with timeseries data in [Pandas](pandas.md) and [Xarray](xarray.md).
+The Python packages that are commonly used for timeseries data in the geosciences such as [Pandas](pandas.md) and [Xarray](xarray.md) leverage these libraries.

core/pandas.md

@@ -1,14 +1,11 @@
 # Pandas
 
-```{note}
-This content is under construction!
-```
-
-This section will contain tutorials on using [pandas](https://pandas.pydata.org) for labeled tabular data.
+This section contains tutorials on using [pandas](https://pandas.pydata.org) for labeled tabular data.
 
 ---
 
-From the [official documentation](https://pandas.pydata.org/), Pandas {cite:p}`reback2020pandas` "is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language."
+From the [official documentation](https://pandas.pydata.org/), Pandas {cite:p}`reback2020pandas` 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 (e.g., spreadsheets, comma-separated-value files, or database printouts; all of these are quite common for geoscientific data). It allows us to use labels for our data; this, in turn, allows us to write expressive and robust code to manipulate the data.