docs: regenerate README

Author: machow

README.md

@@ -27,6 +27,7 @@ specify. Here I’ll use a special version of `board_folder()` called
 deleted when your R session ends. This is great for examples, but
 obviously you shouldn't use it for real work!
 
+
 ```python
 import pins
 from pins.data import mtcars
@@ -37,20 +38,44 @@ board = pins.board_temp()
 You can “pin” (save) data to a board with the `.pin_write()` method. It requires three
 arguments: an object, a name, and a pin type:
 
+
 ```python
 board.pin_write(mtcars.head(), "mtcars", type="csv")
 ```
 
+    Writing to pin 'mtcars'
+
+
+
+
+
+    Meta(title='mtcars: a pinned 5 x 11 DataFrame', description=None, created='20220513T174015Z', pin_hash='120a54f7e0818041', file='mtcars.csv', file_size=249, type='csv', api_version=1, version=Version(created=datetime.datetime(2022, 5, 13, 17, 40, 15, 184665), hash='120a54f7e0818041'), name='mtcars', user={})
+
+
+
 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).
 
 You can later retrieve the pinned data with `.pin_read()`:
 
+
 ```python
 board.pin_read("mtcars")
 ```
 
+
+
+
+        mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  carb
+    0  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4     4
+    1  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4     4
+    2  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4     1
+    3  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3     1
+    4  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3     2
+
+
+
 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