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dataframe operations

Reason this release was yanked:

cli error

Project description

FrameX

GitHub PyPI

Framex is a light-weight, dataset obtaining library for fast prototyping, tutorial creation, and experimenting.

Built on top of Polars.

Installation

To get started, install the library with:

pip install framex

Usage

CLI

Get a single dataset:

fx get iris

or get multiple datasets:

fx get iris mpg titanic

which will download dataset(s) to the current directory.

For more parameters

fx get --help

To get the name of the available datasets on the remote server.

fx list

this will list all available datasets on the remote server.

Python

import framex as fx

Loading datasets

iris = fx.load("iris")

which returns a polars DataFrame
Therefore, you can use all the polars functions and methods on the returned DataFrame.

iris.head()
shape: (5, 5)
┌──────────────┬─────────────┬──────────────┬─────────────┬─────────┐
│ sepal_length ┆ sepal_width ┆ petal_length ┆ petal_width ┆ species │
│ ---          ┆ ---         ┆ ---          ┆ ---         ┆ ---     │
│ f32          ┆ f32         ┆ f32          ┆ f32         ┆ str     │
╞══════════════╪═════════════╪══════════════╪═════════════╪═════════╡
│ 5.1          ┆ 3.5         ┆ 1.4          ┆ 0.2         ┆ setosa  │
│ 4.9          ┆ 3.0         ┆ 1.4          ┆ 0.2         ┆ setosa  │
│ 4.7          ┆ 3.2         ┆ 1.3          ┆ 0.2         ┆ setosa  │
│ 4.6          ┆ 3.1         ┆ 1.5          ┆ 0.2         ┆ setosa  │
│ 5.0          ┆ 3.6         ┆ 1.4          ┆ 0.2         ┆ setosa  │
└──────────────┴─────────────┴──────────────┴─────────────┴─────────┘
iris = fx.load("iris", lazy=True)

which returns a polars LazyFrame

Both these operations create local copies of the datasets by default cache=True.

Available datasets

To see the list of available datasets, run:

fx.available()
{'remote': ['iris', 'mpg', 'netflix', 'starbucks', 'titanic'], 'local': ['titanic']}

which returns a dictionary of both locally and remotely available datasets.

To see only local or remote datasets, run:

fx.available("local")
fx.available("remote")
{'local': ['titanic']}
{'remote': ['iris', 'mpg', 'netflix', 'starbucks', 'titanic']}

Project details


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