deleted outdated functions, added more clarity to deploy

docs/publishing/sections/10_deploy_to_portfolio.md

@@ -8,7 +8,7 @@ After your Jupyter Notebook, README.md, and `.yml` files are setup properly, it'
 
 **Note:** The build command must be run from the root of the repo at `~/data-analyses`!
 
-1. Navigate back to the repo data-analyses and install the portfolio requirements with
+1. Navigate back to the `~/data-analyses` and install the portfolio requirements with
    `pip install -r portfolio/requirements.txt`
 2. Then run `python portfolio/portfolio.py build my_report` to build your report
    - **Note:** `my_report.yml` will be replaced by the name of your `.yml` file in [data-analyses/portfolio/sites](https://github.com/cal-itp/data-analyses/tree/main/portfolio/sites).
@@ -44,14 +44,16 @@ After your Jupyter Notebook, README.md, and `.yml` files are setup properly, it'
     - Pass-through flag to papermill; if true, papermill will not actually execute cells.
   - `--execute-papermill / --no-execute-papermill`
     - If false, will skip calls to papermill
+    - For example, if only the `README.md` is updated but the notebooks have remained the same, you would run
+      `python portfolio/portfolio.py build my_report --no-execute-papermill --deploy`.
   - `--no-stderr / --no-no-stderr`
     - If true, will clear stderr stream for cell outputs
   - `--continue-on-error / --no-continue-on-error`
     - Default: no-continue-on-error
 
 ## Adding to the Makefile
 
-Another way to write to the Analytics Portfolio is to use the Makefile and run
+Another and more efficient way to write to the Analytics Portfolio is to use the Makefile and run
 `make build_my_report -f Makefile` in data-analyses
 
 Example makefile in [`cal-itp/data-analyses`](https://github.com/cal-itp/data-analyses/blob/main/Makefile):

docs/publishing/sections/5_notebooks_styling.md

@@ -65,7 +65,7 @@ These are a set of principles to adhere to when writing the narrative content in
   - A good rule of thumb is to start with 1 extra decimal place than what is present in the other columns when deriving statistics (averages, percentiles), and decide from there if you want to round up.
     - An average of `$100,000.0` can simply be rounded to `$100,000`.
     - An average of 5.2 mi might be left as is.
-  - National Institutes of Health [Rounding Rules](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483789/table/ARCHDISCHILD2014) (full [article](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483789/#:~:text=Ideally%20data%20should%20be%20rounded,might%20call%20it%20Goldilocks%20rounding.&text=The%20European%20Association%20of%20Science,2%E2%80%933%20effective%20digits%E2%80%9D.))
+  - National Institutes of Health [Rounding Rules](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4483789/#:~:text=Ideally%20data%20should%20be%20rounded,might%20call%20it%20Goldilocks%20rounding.&text=The%20European%20Association%20of%20Science,2%E2%80%933%20effective%20digits%E2%80%9D.)
 
 - Additional references: [American Psychological Association (APA) style](https://apastyle.apa.org/instructional-aids/numbers-statistics-guide.pdf), and [Purdue](https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/apa_numbers_statistics.html)
 
@@ -89,13 +89,4 @@ These are a set of principles to adhere to when writing the narrative content in
                 route_names,
                 on = ["calitp_itp_id", "route_id"]
     )
-
-
-    agency_names = portfolio_utils.add_agency_name()
-
-    # Merge in the operator's name using calitp_itp_id
-    df = pd.merge(df,
-                agency_names,
-                on = "calitp_itp_id"
-    )
     ```