pins 0.7.0

Publish data sets, models, and other python objects, making it easy to share them across projects and with your colleagues.

pins-python

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. Pins can be automatically versioned, making it straightforward to track changes, re-run analyses on historical data, and undo mistakes.

Installation

python -m pip install pins

Usage

See the documentation for getting started.

To use the pins package, you must first create a pin board. A good place to start is board_folder(), which stores pins in a directory you specify. Here I’ll use a special version of board_folder() called board_temp() which creates a temporary board that’s automatically deleted when your Python script or notebook session ends. This is great for examples, but obviously you shouldn't use it for real work!

import pins
from pins.data import mtcars

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:

board.pin_write(mtcars.head(), "mtcars", type="csv")

Writing to pin 'mtcars'





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={})

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.

You can later retrieve the pinned data with .pin_read():

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 drive or in DropBox, or if you use RStudio Connect you can use board_rsconnect():

# Note that this uses one approach to connecting,
# the environment variables CONNECT_SERVER and CONNECT_API_KEY

board = pins.board_rsconnect()
board.pin_write(tidy_sales_data, "hadley/sales-summary", type="csv")

Then, someone else (or an automated report) can read and use your pin:

board = board_rsconnect()
board.pin_read("hadley/sales-summary")

You can easily control who gets to access the data using the RStudio 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.

Development

See CONTRIBUTING.md