Merge pull request #189 from rstudio/docs-posit-connect

Docs posit connect

README.Rmd

@@ -12,11 +12,13 @@ pd.set_option("display.notebook_repr_html", False)
 The pins package publishes data, models, and other Python objects, making it
 easy to share them across projects and with your colleagues. You can pin
 objects to a variety of pin *boards*, including folders (to share on a
-networked drive or with services like DropBox), RStudio Connect, and Amazon
-S3.
+networked drive or with services like DropBox), Posit Connect, Amazon
+S3, and Google Cloud Storage.
 Pins can be automatically versioned, making it straightforward to track changes,
 re-run analyses on historical data, and undo mistakes.
 
+See the [documentation](https://rstudio.github.io/pins-python) for getting started.
+
 ## Installation
 
 ```shell
@@ -50,7 +52,7 @@ board.pin_write(mtcars.head(), "mtcars", type="csv")
 
 Above, we saved the data as a CSV, but depending on
 what you’re saving and who else you want to read it, you might use the
-`type` argument to instead save it as a `joblib` or `arrow` file (NOTE: arrow is not yet supported).
+`type` argument to instead save it as a `joblib`, `parquet`, or `json` file.
 
 You can later retrieve the pinned data with `.pin_read()`:
 
@@ -61,7 +63,7 @@ board.pin_read("mtcars")
 A board on your computer is good place to start, but the real power of
 pins comes when you use a board that’s shared with multiple people. To
 get started, you can use `board_folder()` with a directory on a shared
-drive or in DropBox, or if you use [RStudio
+drive or in DropBox, or if you use [Posit
 Connect](https://www.rstudio.com/products/connect/) you can use
 `board_rsconnect()`:
 
@@ -81,12 +83,12 @@ board = board_rsconnect()
 board.pin_read("hadley/sales-summary")
 ```
 
-You can easily control who gets to access the data using the RStudio
+You can easily control who gets to access the data using the Posit
 Connect permissions pane.
 
 The pins package also includes boards that allow you to share data on
-services like Amazon’s S3 (`board_s3()`), with plans to support other backends--
-such as Azure's blob storage.
+services like Amazon’s S3 (`board_s3()`), Google Cloud Storage (`board_gcs()`),
+and Azure blob storage (`board_azure()`).
 
 ## Development
 

README.md

@@ -5,11 +5,13 @@
 The pins package publishes data, models, and other Python objects, making it
 easy to share them across projects and with your colleagues. You can pin
 objects to a variety of pin *boards*, including folders (to share on a
-networked drive or with services like DropBox), RStudio Connect, Amazon S3,
-and Google Cloud Storage.
+networked drive or with services like DropBox), Posit Connect, Amazon
+S3, and Google Cloud Storage.
 Pins can be automatically versioned, making it straightforward to track changes,
 re-run analyses on historical data, and undo mistakes.
 
+See the [documentation](https://rstudio.github.io/pins-python) for getting started.
+
 ## Installation
 
 ```shell
@@ -43,19 +45,21 @@ arguments: an object, a name, and a pin type:
 board.pin_write(mtcars.head(), "mtcars", type="csv")
 ```
 
-    Writing to pin 'mtcars'
+    Writing pin:
+    Name: 'mtcars'
+    Version: 20230321T151326Z-120a5
 
 
 
 
 
-    Meta(title='mtcars: a pinned 5 x 11 DataFrame', description=None, created='20220526T165625Z', pin_hash='120a54f7e0818041', file='mtcars.csv', file_size=249, type='csv', api_version=1, version=Version(created=datetime.datetime(2022, 5, 26, 16, 56, 25, 738735), hash='120a54f7e0818041'), name='mtcars', user={})
+    Meta(title='mtcars: a pinned 5 x 11 DataFrame', description=None, created='20230321T151326Z', pin_hash='120a54f7e0818041', file='mtcars.csv', file_size=249, type='csv', api_version=1, version=Version(created=datetime.datetime(2023, 3, 21, 15, 13, 26, 362951), hash='120a54f7e0818041'), tags=None, name='mtcars', user={}, local={})
 
 
 
 Above, we saved the data as a CSV, but depending on
 what you’re saving and who else you want to read it, you might use the
-`type` argument to instead save it as a `joblib` or `arrow` file.
+`type` argument to instead save it as a `joblib`, `parquet`, or `json` file.
 
 You can later retrieve the pinned data with `.pin_read()`:
 
@@ -79,7 +83,7 @@ board.pin_read("mtcars")
 A board on your computer is good place to start, but the real power of
 pins comes when you use a board that’s shared with multiple people. To
 get started, you can use `board_folder()` with a directory on a shared
-drive or in DropBox, or if you use [RStudio
+drive or in DropBox, or if you use [Posit
 Connect](https://www.rstudio.com/products/connect/) you can use
 `board_rsconnect()`:
 
@@ -99,12 +103,12 @@ board = board_rsconnect()
 board.pin_read("hadley/sales-summary")
 ```
 
-You can easily control who gets to access the data using the RStudio
+You can easily control who gets to access the data using the Posit
 Connect permissions pane.
 
 The pins package also includes boards that allow you to share data on
-services like Amazon’s S3 (`board_s3()`), and Google Cloud Storage (`board_gcs`), with plans to support other backends--
-such as Azure's blob storage.
+services like Amazon’s S3 (`board_s3()`), Google Cloud Storage (`board_gcs()`),
+and Azure blob storage (`board_azure()`).
 
 ## Development
 

pins/constructors.py

@@ -29,12 +29,12 @@ def board_deparse(board: BaseBoard):
     Examples
     --------
 
-    The example below deparses a board connected to RStudio Connect.
+    The example below deparses a board connected to Posit Connect.
 
     >>> board_deparse(board_connect(server_url="http://example.com", api_key="xxx"))
     "board_connect(server_url='http://example.com')"
 
-    Note that the deparsing an RStudio Connect board does not keep the api_key,
+    Note that the deparsing a Posit Connect board does not keep the api_key,
     which is sensitive information. In this case, you can set the CONNECT_API_KEY
     environment variable to connect.
 
@@ -92,7 +92,7 @@ def board(
     Parameters
     ----------
     protocol:
-        File system protocol. E.g. file, s3, github, rsc (for RStudio Connect).
+        File system protocol. E.g. file, s3, github, rsc (for Posit Connect).
         See fsspec.filesystem for more information.
     path:
         A base path the board should use. For example, the directory the board lives in,
@@ -356,12 +356,12 @@ def board_url(path: str, pin_paths: dict, cache=DEFAULT, allow_pickle_read=None)
 def board_connect(
     server_url=None, versioned=True, api_key=None, cache=DEFAULT, allow_pickle_read=None
 ):
-    """Create a board to read and write pins from an RStudio Connect instance.
+    """Create a board to read and write pins from an Posit Connect instance.
 
     Parameters
     ----------
     server_url:
-        Url to the RStudio Connect server.
+        Url to the Posit Connect server.
     api_key:
         API key for server. If not specified, pins will attempt to read it from
         CONNECT_API_KEY environment variable.

pins/rsconnect/html/index.html

@@ -49,7 +49,7 @@ <h3>{{pin_name}}</h3>
     <section>
     <h3>Python Code</h3>
 
-    <pre id="pin-python" class="pin-code"><code class="python">from pins import board_rsconnect
+    <pre id="pin-python" class="pin-code"><code class="python">from pins import board_connect
 board = {{board_deparse}}
 board.pin_read("{{pin_name}}")</code></pre>
 

pins/tests/_snapshots/test_board_pin_write_rsc_index_html/index.html

@@ -58,7 +58,7 @@ <h3>derek/test_rsc_pin</h3>
     <section>
     <h3>Python Code</h3>
 
-    <pre id="pin-python" class="pin-code"><code class="python">from pins import board_rsconnect
+    <pre id="pin-python" class="pin-code"><code class="python">from pins import board_connect
 board = board_connect(server_url='http://localhost:3939')
 board.pin_read("derek/test_rsc_pin")</code></pre>