pyshare-lib 0.0.3

PyShare - A Python Sharing library for DataFrames

PyShare: A Python Sharing library for DataFrames

This is a library for easy sharing of Python DataFrame objects. It's powered by DuckDB. 🦆

How to install

pip install pyshare-lib

Example usage

import pandas as pd
from pyshare import Share

share = Share("apples", public=True)

df = pd.DataFrame({"tree_id": ["alice", "bob"], "bud_percentage": [42.1, 39.3]})
df.attrs = {"flavor": "sweet/sharp", "country": "The Netherlands"}
share["elstar"] = df

df = pd.DataFrame({"tree_id": ["charlie", "dora"], "bud_percentage": [93.1, 87.3]})
df.attrs = {"flavor": "tart", "country": "Australia"}
share["granny smith"] = df

share

Output:

Share(name="apples", path="md:_share/apples/2fb46588-de57-4a24-9e85-d8cf7ef78be1")
┌──────────────┬──────────────┬────────────────┬─────────────┬─────────────────┐
│     name     │ column_count │ estimated_size │   flavor    │     country     │
│   varchar    │    int64     │     int64      │   varchar   │     varchar     │
├──────────────┼──────────────┼────────────────┼─────────────┼─────────────────┤
│ elstar       │            2 │              2 │ sweet/sharp │ The Netherlands │
│ granny smith │            2 │              2 │ tart        │ Australia       │
└──────────────┴──────────────┴────────────────┴─────────────┴─────────────────┘

from pyshare import Share

share = Share(name="apples", path="md:_share/apples/2fb46588-de57-4a24-9e85-d8cf7ef78be1")
df = share.get(flavor="sweet/sharp")

df.attrs

Output:

Connecting in read-only mode
{'name': 'elstar', 'flavor': 'sweet/sharp', 'country': 'The Netherlands'}

Configuration

Each share creates a DuckDB database, either on your local machine or on MotherDuck. By default, your shares are saved under ~/.pyshare/data.

To override where local files are stored, set the environment variable PYSHARE_PATH.

To use MotherDuck, export your MotherDuck token to an environment variable MOTHERDUCK_TOKEN.

Fetching and updating data

You can easily find your dataframes by name or any of the attributes you specified:

# any one of these wll give you the elstar table
df = share["elstar"]
df = share.get(name="elstar")
df = share.get(flavor="sweet/sharp")
df = share.get(country="The Netherlands")

# this will get you the granny smith table
df = share["granny smith"]
df = share.get(country="Australia")

# get all matches for a field
for df in share.get_all(tree="large"):
    print(df.attrs)

# get a dataframe of all attributes
share.df()

You can update your dataframe like so, which won't update the attributes unless you specify df.attrs:

df = pd.DataFrame({"tree_id": ["alice", "bob", "eva"], "age": ["young", "old", "ancient"]})
share["elstar"] = df

To overwrite or update the attributes without updating the table, run:

# overwrite
share.attrs["elstar"] = {"parentage": ["Ingrid Marie", "Golden Delicious"]}

# update
share.attrs["elstar"]["parentage"] = ["Ingrid Marie", "Golden Delicious"]
share.attrs["elstar"].update({"parentage": ["Ingrid Marie", "Golden Delicious"]})

Happy sharing!