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vega_datasets

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A Python package for online & offline access to vega datasets

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최종 버전 다운로드 (.zip)

- - build.yml
  - lint.yml
- download_datasets.py
- generate_datasets_json.py
- - airports.csv
  - anscombe.json
  - barley.json
  - burtin.json
  - cars.json
  - crimea.json
  - driving.json
  - iowa-electricity.csv
  - iris.json
  - la-riots.csv
  - ohlc.json
  - seattle-temps.csv
  - seattle-weather.csv
  - sf-temps.csv
  - stocks.csv
  - us-employment.csv
  - wheat.json
- - __init__.py
  - test_core.py
  - test_download.py
  - test_errors.py
  - test_local_datasets.py
  - test_metadata.py
- __init__.py
- core.py
- dataset_info.json
- datasets.json
- local_datasets.json
- utils.py
.gitignore
.travis.yml
CHANGES.md
LICENSE
Makefile
MANIFEST.in
mypy.ini
pyproject.toml
README.md
RELEASING.md
requirements.txt
requirements_dev.txt
setup.cfg
setup.py

// repository documentation

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## Notice: `vega_datasets` Repository Archived

The **`vega_datasets`** repository has been archived. All datasets previously available through `vega_datasets` are now **integrated directly into Altair**, providing a simpler and more reliable experience.

For details on this change and recommended migration strategies, see [Altair PR #3848](https://github.com/vega/altair/pull/3848).

### Migration Path

Migrating from the Python `vega_datasets` package to the new built-in `altair.datasets` module requires only a single import change:

```python
# old (deprecated)
from vega_datasets import data

# new (recommended)
from altair.datasets import data
```

The new `altair.datasets` module provides the same familiar datasets with improved portability and no external dependency.

---

# vega_datasets

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A Python package for offline access to [vega datasets](https://github.com/vega/vega-datasets).

This package has several goals:

- Provide straightforward access in Python to the datasets made available at [vega-datasets](https://github.com/vega/vega-datasets).
- return the results in the form of a Pandas dataframe.
- wherever dataset size and/or license constraints make it possible, bundle the dataset with the package so that datasets can be loaded in the absence of a web connection.

Currently the package bundles a half-dozen datasets, and falls back to using HTTP requests for the others.

## Installation
``vega_datasets`` is compatible with Python 3.5 or newer. Install with:
```
$ pip install vega_datasets
```

## Usage

The main object in this library is ``data``:

```python
>>> from vega_datasets import data
```

It contains attributes that access all available datasets, locally if
available. For example, here is the well-known iris dataset:

```python
>>> df = data.iris()
>>> df.head()
   petalLength  petalWidth  sepalLength  sepalWidth species
0          1.4         0.2          5.1         3.5  setosa
1          1.4         0.2          4.9         3.0  setosa
2          1.3         0.2          4.7         3.2  setosa
3          1.5         0.2          4.6         3.1  setosa
4          1.4         0.2          5.0         3.6  setosa
```

If you're curious about the source data, you can access the URL for any of the available datasets:

```python
>>> data.iris.url
'https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/iris.json'
```

For datasets bundled with the package, you can also find their location on disk:

```python
>>> data.iris.filepath
'/lib/python3.6/site-packages/vega_datasets/data/iris.json'
```

## Available Datasets

To list all the available datsets, use ``list_datasets``:

```python
>>> data.list_datasets()
['7zip', 'airports', 'anscombe', 'barley', 'birdstrikes', 'budget', 'budgets', 'burtin', 'cars', 'climate', 'co2-concentration', 'countries', 'crimea', 'disasters', 'driving', 'earthquakes', 'ffox', 'flare', 'flare-dependencies', 'flights-10k', 'flights-200k', 'flights-20k', 'flights-2k', 'flights-3m', 'flights-5k', 'flights-airport', 'gapminder', 'gapminder-health-income', 'gimp', 'github', 'graticule', 'income', 'iris', 'jobs', 'londonBoroughs', 'londonCentroids', 'londonTubeLines', 'lookup_groups', 'lookup_people', 'miserables', 'monarchs', 'movies', 'normal-2d', 'obesity', 'points', 'population', 'population_engineers_hurricanes', 'seattle-temps', 'seattle-weather', 'sf-temps', 'sp500', 'stocks', 'udistrict', 'unemployment', 'unemployment-across-industries', 'us-10m', 'us-employment', 'us-state-capitals', 'weather', 'weball26', 'wheat', 'world-110m', 'zipcodes']
```

To list local datasets (i.e. those that are bundled with the package and can be used without a web connection), use the ``local_data`` object instead:

```python
>>> from vega_datasets import local_data
>>> local_data.list_datasets()

['airports', 'anscombe', 'barley', 'burtin', 'cars', 'crimea', 'driving', 'iowa-electricity', 'iris', 'seattle-temps', 'seattle-weather', 'sf-temps', 'stocks', 'us-employment', "wheat"]
```

We plan to add more local datasets in the future, subject to size and licensing constraints. See the [local datasets issue](https://github.com/altair-viz/vega_datasets/issues/1) if you would like to help with this.

## Dataset Information

If you want more information about any dataset, you can use the ``description`` property:

```python
>>> data.iris.description
'This classic dataset contains lengths and widths of petals and sepals for 150 iris flowers, drawn from three species. It was introduced by R.A. Fisher in 1936 [1]_.'
```

This information is also part of the ``data.iris`` doc string.
Descriptions are not yet included for all the datasets in the package; we hope to add more information on this in the future.