mxi(dZddlmZddlZddlmZejrddlm Z ddl Z ddl Z ddlZddlmZddlmZGdd ZGd d ZeZy) z Data object interface for Altair datasets. This module provides a `data` object that allows accessing datasets as attributes and calling them with backend options, similar to the vega_datasets interface. ) annotationsN)Loader) LiteralString)_Backend)DatasetcveZdZdZdddZdd ddZeddZejddZeddZ dddd Z dd Z e j dd Ze j dd Ze j dd Ze jdd ddZdd ddZy)DatasetAccessora Accessor for individual datasets that can be called with backend options. This object provides access to a specific dataset with support for different backends and autocompletion. Call this object to load the dataset: dataset_accessor(engine="polars", **kwds) Parameters for __call__: engine : {"polars", "pandas", "pandas[pyarrow]", "pyarrow"}, optional The backend to use for loading the dataset. **kwds : Any Additional arguments passed to the loader. Examples -------- >>> from altair.datasets import data >>> >>> # Load with default backend >>> cars_df = data.cars() >>> >>> # Load with specific backend >>> cars_polars = data.cars(engine="polars") >>> cars_pandas = data.cars(engine="pandas") >>> # Note: pandas[pyarrow] backend requires pyarrow package >>> >>> # Get URL >>> url = data.cars.url >>> >>> # Use explicit load method >>> cars_df = data.cars.load(engine="polars") c ddl}||_||_||j|j|_d|d|d|d|d|d }||_y)Nrz Load the 'a,' dataset. Parameters ---------- engine : {"polars", "pandas", "pandas[pyarrow]", "pyarrow"}, optional The backend to use for loading the dataset. **kwds : Any Additional arguments passed to the loader. Returns ------- DataFrame or Table The loaded dataset. Examples -------- >>> data.z)() # Load with default backend >>> data.z9(engine="polars") # Load with specific backend >>> data.z!.url # Get dataset URL >>> data.z/.load(engine="polars") # Explicit load method )inspect_name_backend signature _call_impl __signature____doc__)selfnamebackendr docstrings L/home/htdocs/ttos/venv/lib/python3.12/site-packages/altair/datasets/_data.py__init__zDatasetAccessor.__init__<ss" ")  $..t?"4&)    ' ,! N)enginec n|rtj|n |j}||jfi|SN)r from_backend_loaderr )rrkwdsloads rrzDatasetAccessor._call_impl\s0 /5v""6*$,,DJJ'$''rct|dr |jStj|j|_|jS)N _prev_loader)hasattrr!rrr rs rrzDatasetAccessor._loaderes; 4 ($$ $"// >   rc||_yr)r!)rvalues rrzDatasetAccessor._loaderls !rcL|jj|jS)aZ Get the URL for this dataset. Returns ------- str The URL of the dataset. Examples -------- >>> from altair.datasets import data >>> cars_url = data.cars.url >>> print(cars_url) https://cdn.jsdelivr.net/npm/vega-datasets@v3.2.1/data/cars.json )rurlr r#s rr'zDatasetAccessor.urlps"|| ++rc *|jdd|i|S)a Load the dataset with the specified engine. This method provides the same functionality as calling the accessor directly, but with more explicit parameter autocompletion in some IDEs. Parameters ---------- engine : {"polars", "pandas", "pandas[pyarrow]", "pyarrow"}, optional The backend to use for loading the dataset. **kwds : Any Additional arguments passed to the loader. Returns ------- DataFrame or Table The loaded dataset. Examples -------- >>> from altair.datasets import data >>> cars_df = data.cars.load(engine="polars") >>> movies_df = data.movies.load(engine="pandas") rrrrrs rrzDatasetAccessor.loads2t5f555rc<d|jd|jdS)NzDatasetAccessor('z', default_engine='z'))r r r#s r__repr__zDatasetAccessor.__repr__s!"4::,.A$--PRSSrc yrr)r+s r__call__zDatasetAccessor.__call__ rc yrr)r+s rr/zDatasetAccessor.__call__r0rc yrr)r+s rr/zDatasetAccessor.__call__s rc yrr)r+s rr/zDatasetAccessor.__call__s rc *|jdd|i|S)a Load the dataset with the specified engine. Parameters ---------- engine : {{"polars", "pandas", "pandas[pyarrow]", "pyarrow"}}, optional The backend to use for loading the dataset. **kwds Additional arguments passed to the loader. Returns ------- The loaded dataset as a DataFrame/Table from the specified engine. Examples -------- >>> from altair.datasets import data >>> >>> # Load with default engine >>> df = data.cars() >>> >>> # Load with specific engine >>> df = data.cars(engine="polars") rr)r*r+s rr/zDatasetAccessor.__call__s<t5f555rpandas)rrrrreturnNone)rz_Backend | Nonert.Anyr7r9)r7Loader[t.Any, t.Any])r%r:r7r8r7str)rzt.Literal['polars']rr9r7z pl.DataFrame)rz&t.Literal['pandas', 'pandas[pyarrow]']rr9r7z pd.DataFrame)rzt.Literal['pyarrow']rr9r7zpa.Table)__name__ __module__ __qualname__rrrpropertyrsetterr'rr-toverloadr/r)rrr r s D!F#'( ( (  (!!  ^^"",,$1566TZZ$   ZZ7   ZZ%   ZZ#'    #'6 6 6  6rr c^eZdZdZd d dZd dZd fd ZddZddZd dZ ddZ dd Z xZ S) DataObjectaq Main data object that provides access to all datasets as attributes. This is the primary interface for loading Altair datasets. It provides a simple, intuitive way to access datasets with autocompletion support. Examples -------- >>> from altair.datasets import data >>> >>> # Access datasets as attributes with autocompletion >>> cars_df = data.cars() >>> movies_df = data.movies(engine="pandas") >>> >>> # Get URLs >>> cars_url = data.cars.url >>> movies_url = data.movies.url >>> >>> # Set default engine for all datasets >>> data.set_default_engine("polars") >>> penguins_df = data.penguins() # Uses polars engine >>> >>> # List available datasets >>> available_datasets = data.list_datasets() >>> print(f"Available datasets: {len(available_datasets)}") Available datasets: 72 c.||_i|_d|_yr)r _accessors_dataset_names)rrs rrzDataObject.__init__s") :<DHrc|jB ddlm}|}t|jj |_|jS|jS#t $rg|_Y|jSwxYw)z6Get the list of available dataset names from metadata.r)CsvCache)rHaltair.datasets._cacherJlistmappingkeys Exception)rrJcaches r_get_dataset_nameszDataObject._get_dataset_namessu    & ); &*5==+=+=+?&@#"""t""" )&(#""" )s5AA98A9c\tt| }|j}||zS)z7Return list of available attributes for autocompletion.)rLsuper__dir__rQ)rstandard_attrs dataset_names __class__s rrTzDataObject.__dir__s-ego/0//1  --rc|j}||vr|dd}d|d|}t|t||j|j|<|j|S)N z Dataset 'z!' not found. Available datasets: )rQAttributeErrorr r rG)rrrVavailable_datasets error_msgs r __getattr__zDataObject.__getattr__sr//1 } $!.s!3 D6!BCUBVW !+ + /dmm Dt$$rcF||_|jjy)a Set the default engine for all datasets. Parameters ---------- engine : {"polars", "pandas", "pandas[pyarrow]", "pyarrow"} The backend to use as default for all datasets. Examples -------- >>> from altair.datasets import data >>> data.set_default_engine("polars") >>> # Now all datasets will use polars by default >>> cars_df = data.cars() # Uses polars >>> movies_df = data.movies() # Uses polars N)r rGclear)rrs rset_default_enginezDataObject.set_default_engine#s"  rc"|jS)a Get a list of all available dataset names. Returns ------- list[str] List of available dataset names. Examples -------- >>> from altair.datasets import data >>> datasets = data.list_datasets() >>> print(f"Available datasets: {len(datasets)}") Available datasets: 72 >>> print(datasets[:5]) # First 5 datasets ['airports', 'annual_precip', 'anscombe', 'barley', 'birdstrikes'] )rQr#s r list_datasetszDataObject.list_datasets8s$&&((rc|jS)a Get the current default engine. Returns ------- str The current default engine. Examples -------- >>> from altair.datasets import data >>> data.set_default_engine("pandas") >>> print(data.get_default_engine()) pandas >>> data.set_default_engine("polars") >>> print(data.get_default_engine()) polars )r r#s rget_default_enginezDataObject.get_default_engineLs&}}rcZt|j}d|jd|dS)Nz!AltairDataObject(default_engine='z ', datasets=))lenrQr )r dataset_counts rr-zDataObject.__repr__as/D3356 24==/m_\]^^rr5)rrr7r8)r7zlist[Dataset | LiteralString])r7z list[str])rrr7r )rrr7r8)r7rr;) r=r>r?rrrQrTr]r`rbrdr- __classcell__)rWs@rrErEs28I #. % *)(*_rrE)r __future__rtypingrBaltair.datasets._loaderr TYPE_CHECKINGtyping_extensionsrr6pdpolarsplpyarrowpaaltair.datasets._readerraltair.datasets._typingrr rEdatar)rrrwsS#*??/0/F6F6RA_A_H|r