Metadata-Version: 2.1
Name: pins
Version: 0.7.0
Summary: Publish data sets, models, and other python objects, making it easy to share them across projects and with your colleagues.
Home-page: https://github.com/machow/pins-python
Author: Michael Chow
Author-email: mc_al_github@fastmail.com
License: MIT
Keywords: data,tidyverse
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: aws
Provides-Extra: azure
Provides-Extra: gcs
Provides-Extra: doc
Provides-Extra: test
License-File: LICENSE

# pins-python

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/machow/pins-python/HEAD)

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

```shell
python -m pip install pins
```

## Usage

See the [documentation](https://rstudio.github.io/pins-python) 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!


```python
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:


```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='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()`:


```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
drive or in DropBox, or if you use [RStudio
Connect](https://www.rstudio.com/products/connect/) you can use
`board_rsconnect()`:

```python
# 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:

```python
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](CONTRIBUTING.md)
