"""``PlotlyDataset`` generates a plot from a pandas DataFrame and saves it to a JSON
file using an underlying filesystem (e.g.: local, S3, GCS). It loads the JSON into a
plotly figure.
"""
import warnings
from copy import deepcopy
from typing import Any, Dict
import pandas as pd
import plotly.express as px
from kedro.io.core import Version
from plotly import graph_objects as go
from kedro_datasets import KedroDeprecationWarning
from kedro_datasets.plotly.json_dataset import JSONDataset
[docs]class PlotlyDataset(JSONDataset):
"""``PlotlyDataset`` generates a plot from a pandas DataFrame and saves it to a JSON
file using an underlying filesystem (e.g.: local, S3, GCS). It loads the JSON into a
plotly figure.
``PlotlyDataset`` is a convenience wrapper for ``plotly.JSONDataset``. It generates
the JSON file directly from a pandas DataFrame through ``plotly_args``.
Example usage for the
`YAML API <https://kedro.readthedocs.io/en/stable/data/\
data_catalog_yaml_examples.html>`_:
.. code-block:: yaml
bar_plot:
type: plotly.PlotlyDataset
filepath: data/08_reporting/bar_plot.json
plotly_args:
type: bar
fig:
x: features
y: importance
orientation: h
layout:
xaxis_title: x
yaxis_title: y
title: Title
Example usage for the
`Python API <https://kedro.readthedocs.io/en/stable/data/\
advanced_data_catalog_usage.html>`_:
::
>>> from kedro_datasets.plotly import PlotlyDataset
>>> import plotly.express as px
>>> import pandas as pd
>>>
>>> df_data = pd.DataFrame([[0, 1], [1, 0]], columns=('x1', 'x2'))
>>>
>>> dataset = PlotlyDataset(
... filepath='scatter_plot.json',
... plotly_args={
... 'type': 'scatter',
... 'fig': {'x': 'x1', 'y': 'x2'},
... }
... )
>>> dataset.save(df_data)
>>> reloaded = dataset.load()
>>> assert px.scatter(df_data, x='x1', y='x2') == reloaded
"""
[docs] def __init__( # noqa: PLR0913
self,
filepath: str,
plotly_args: Dict[str, Any],
load_args: Dict[str, Any] = None,
save_args: Dict[str, Any] = None,
version: Version = None,
credentials: Dict[str, Any] = None,
fs_args: Dict[str, Any] = None,
metadata: Dict[str, Any] = None,
) -> None:
"""Creates a new instance of ``PlotlyDataset`` pointing to a concrete JSON file
on a specific filesystem.
Args:
filepath: Filepath in POSIX format to a JSON file prefixed with a protocol like `s3://`.
If prefix is not provided `file` protocol (local filesystem) will be used.
The prefix should be any protocol supported by ``fsspec``.
Note: `http(s)` doesn't support versioning.
plotly_args: Plotly configuration for generating a plotly figure from the
dataframe. Keys are `type` (plotly express function, e.g. bar,
line, scatter), `fig` (kwargs passed to the plotting function), theme
(defaults to `plotly`), `layout`.
load_args: Plotly options for loading JSON files.
Here you can find all available arguments:
https://plotly.com/python-api-reference/generated/plotly.io.from_json.html#plotly.io.from_json
All defaults are preserved.
save_args: Plotly options for saving JSON files.
Here you can find all available arguments:
https://plotly.com/python-api-reference/generated/plotly.io.write_json.html
All defaults are preserved.
version: If specified, should be an instance of
``kedro.io.core.Version``. If its ``load`` attribute is
None, the latest version will be loaded. If its ``save``
attribute is None, save version will be autogenerated.
credentials: Credentials required to get access to the underlying filesystem.
E.g. for ``GCSFileSystem`` it should look like `{'token': None}`.
fs_args: Extra arguments to pass into underlying filesystem class constructor
(e.g. `{"project": "my-project"}` for ``GCSFileSystem``), as well as
to pass to the filesystem's `open` method through nested keys
`open_args_load` and `open_args_save`.
Here you can find all available arguments for `open`:
https://filesystem-spec.readthedocs.io/en/latest/api.html#fsspec.spec.AbstractFileSystem.open
All defaults are preserved, except `mode`, which is set to `w` when saving.
metadata: Any arbitrary metadata.
This is ignored by Kedro, but may be consumed by users or external plugins.
"""
super().__init__(filepath, load_args, save_args, version, credentials, fs_args)
self._plotly_args = plotly_args
_fs_args = deepcopy(fs_args) or {}
_fs_open_args_load = _fs_args.pop("open_args_load", {})
_fs_open_args_save = _fs_args.pop("open_args_save", {})
_fs_open_args_save.setdefault("mode", "w")
self._fs_open_args_load = _fs_open_args_load
self._fs_open_args_save = _fs_open_args_save
self.metadata = metadata
def _describe(self) -> Dict[str, Any]:
return {**super()._describe(), "plotly_args": self._plotly_args}
def _save(self, data: pd.DataFrame) -> None:
fig = self._plot_dataframe(data)
super()._save(fig)
def _plot_dataframe(self, data: pd.DataFrame) -> go.Figure:
plot_type = self._plotly_args.get("type")
fig_params = self._plotly_args.get("fig", {})
fig = getattr(px, plot_type)(data, **fig_params) # type: ignore
fig.update_layout(template=self._plotly_args.get("theme", "plotly"))
fig.update_layout(self._plotly_args.get("layout", {}))
return fig
_DEPRECATED_CLASSES = {
"PlotlyDataSet": PlotlyDataset,
}
def __getattr__(name):
if name in _DEPRECATED_CLASSES:
alias = _DEPRECATED_CLASSES[name]
warnings.warn(
f"{repr(name)} has been renamed to {repr(alias.__name__)}, "
f"and the alias will be removed in Kedro-Datasets 2.0.0",
KedroDeprecationWarning,
stacklevel=2,
)
return alias
raise AttributeError(f"module {repr(__name__)} has no attribute {repr(name)}")