ȯei)pUddlmZddlZddlmZmZmZddlmZddl m Z ddl m Z ejrRddlZddlmZmZddlZddlZddlZddlmZdd l mZej4d k\r dd lmZmZndd lmZmZdd l mZdd lm Z m!Z!ddgZ"Gddeee fZ#eGdde#ee fZ$de%d<dZ&y)) annotationsN)Genericfinaloverload)IntoDataFrameT)_reader) IntoFrameT)AnyLiteral) DatasetCache)Reader) ) LiteralStringSelf)_Backend)Dataset ExtensionLoaderloadceZdZUdZded<ee d ddZee ddZee ddZe d ddZeddZ d dd Z d dd Z e dd Z dd Z y )raW Load example datasets *remotely* from `vega-datasets`_, with caching. A new ``Loader`` must be initialized by specifying a backend:: from altair.datasets import Loader load = Loader.from_backend("polars") load Loader[polars] .. _vega-datasets: https://github.com/vega/vega-datasets "Reader[IntoDataFrameT, IntoFrameT]rcyNcls backend_names Y/var/www/html/glpi_dashboard/venv/lib/python3.12/site-packages/altair/datasets/_loader.py from_backendzLoader.from_backend3.1cyrrrs rr zLoader.from_backend9r!r"cyrrrs rr zLoader.from_backend?s&)r"cJ|jtj|S)ad Initialize a new loader, with the specified backend. Parameters ---------- backend_name DataFrame package/config used to return data. * *polars*: Using `polars defaults`_ * *pandas*: Using `pandas defaults`_. * *pandas[pyarrow]*: Using ``dtype_backend="pyarrow"`` * *pyarrow*: (*Experimental*) .. warning:: Most datasets use a `JSON format not supported`_ by ``pyarrow`` Examples -------- Using ``polars``:: from altair.datasets import Loader load = Loader.from_backend("polars") cars = load("cars") type(cars) polars.dataframe.frame.DataFrame Using ``pandas``:: load = Loader.from_backend("pandas") cars = load("cars") type(cars) pandas.core.frame.DataFrame Using ``pandas``, backed by ``pyarrow`` dtypes:: load = Loader.from_backend("pandas[pyarrow]") co2 = load("co2") type(co2) pandas.core.frame.DataFrame co2.dtypes Date datetime64[ns] CO2 double[pyarrow] adjusted CO2 double[pyarrow] dtype: object .. _polars defaults: https://docs.pola.rs/api/python/stable/reference/io.html .. _pandas defaults: https://pandas.pydata.org/docs/reference/io.html .. _JSON format not supported: https://arrow.apache.org/docs/python/json.html#reading-json-files ) from_readerr _from_backendrs rr zLoader.from_backendEszw44\BCCr"c6|j|}||_|Sr)__new__r)rreaderobjs rr&zLoader.from_readerskk#  r"Nc >|jj||fi|S)u7 Get a remote dataset and load as tabular data. Parameters ---------- name Name of the dataset/`Path.stem`_. suffix File extension/`Path.suffix`_. .. note:: Only needed if ``name`` is available in multiple formats. **kwds Arguments passed to the underlying read function. Examples -------- Using ``polars``:: from altair.datasets import Loader load = Loader.from_backend("polars") source = load("iowa_electricity") source.columns ['year', 'source', 'net_generation'] source.head(5) shape: (5, 3) ┌────────────┬──────────────┬────────────────┐ │ year ┆ source ┆ net_generation │ │ --- ┆ --- ┆ --- │ │ date ┆ str ┆ i64 │ ╞════════════╪══════════════╪════════════════╡ │ 2001-01-01 ┆ Fossil Fuels ┆ 35361 │ │ 2002-01-01 ┆ Fossil Fuels ┆ 35991 │ │ 2003-01-01 ┆ Fossil Fuels ┆ 36234 │ │ 2004-01-01 ┆ Fossil Fuels ┆ 36205 │ │ 2005-01-01 ┆ Fossil Fuels ┆ 36883 │ └────────────┴──────────────┴────────────────┘ Using ``pandas``:: load = Loader.from_backend("pandas") source = load("iowa_electricity") source.columns Index(['year', 'source', 'net_generation'], dtype='object') source.head(5) year source net_generation 0 2001-01-01 Fossil Fuels 35361 1 2002-01-01 Fossil Fuels 35991 2 2003-01-01 Fossil Fuels 36234 3 2004-01-01 Fossil Fuels 36205 4 2005-01-01 Fossil Fuels 36883 Using ``pyarrow``:: load = Loader.from_backend("pyarrow") source = load("iowa_electricity") source.column_names ['year', 'source', 'net_generation'] source.slice(0, 5) pyarrow.Table year: date32[day] source: string net_generation: int64 ---- year: [[2001-01-01,2002-01-01,2003-01-01,2004-01-01,2005-01-01]] source: [["Fossil Fuels","Fossil Fuels","Fossil Fuels","Fossil Fuels","Fossil Fuels"]] net_generation: [[35361,35991,36234,36205,36883]] .. _Path.stem: https://docs.python.org/3/library/pathlib.html#pathlib.PurePath.stem .. _Path.suffix: https://docs.python.org/3/library/pathlib.html#pathlib.PurePath.suffix )rdataset)selfnamesuffixkwdss r__call__zLoader.__call__s#n$t||##D&9D99r"c:|jj||S)a Return the address of a remote dataset. Parameters ---------- name Name of the dataset/`Path.stem`_. suffix File extension/`Path.suffix`_. .. note:: Only needed if ``name`` is available in multiple formats. .. _Path.stem: https://docs.python.org/3/library/pathlib.html#pathlib.PurePath.stem .. _Path.suffix: https://docs.python.org/3/library/pathlib.html#pathlib.PurePath.suffix Examples -------- The returned url will always point to an accessible dataset:: import altair as alt from altair.datasets import Loader load = Loader.from_backend("polars") load.url("cars") "https://cdn.jsdelivr.net/npm/vega-datasets@v2.11.0/data/cars.json" We can pass the result directly to a chart:: url = load.url("cars") alt.Chart(url).mark_point().encode(x="Horsepower:Q", y="Miles_per_Gallon:Q") )rurl)r.r/r0s rr4z Loader.urlsP||f--r"c.|jjS)ab Caching of remote dataset requests. Configure cache path:: self.cache.path = "..." 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