o Mh3@sdZddlZddlmZddlmmZhdZhdZ     d dd Z d!d d Z d"d d Z d"ddZ d"ddZddZd"ddZd#ddZd!ddZd!ddZd!ddZddZdS)$zE Built-in datasets for demonstration, educational and test purposes. N) import_module>cudfmodinpandaspolarspyarrow>rrrFrcCstjtd|ddd}|r|td|k}|r3|ttdtt dgj j dd}|s;| d d }|rN| td d d dddddddd }|S)a  Each row represents a country on a given year. https://www.gapminder.org/data/ Parameters ---------- datetimes: bool Whether or not 'year' column will converted to datetime type centroids: bool If True, ['centroid_lat', 'centroid_lon'] columns are added year: int | None If provided, the dataset will be filtered for that year pretty_names: bool If True, prettifies the column names return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 1704 rows and the following columns: `['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap', 'iso_alpha', 'iso_num']`. If `datetimes` is True, the 'year' column will be a datetime column If `centroids` is True, two new columns are added: ['centroid_lat', 'centroid_lon'] If `year` is an integer, the dataset will be filtered for that year gapminder return_typeT eager_onlyyearz-01-01z%Y-%m-%d)format centroid_lat centroid_lonCountry ContinentYearzLife ExpectancyzGDP per Capita PopulationzISO Alpha Country CodezISO Numeric Country CodezCentroid LatitudezCentroid Longitude) country continentr lifeExp gdpPercappop iso_alphaiso_numrr)nw from_native _get_datasetfiltercol with_columns concat_strcastStringlitstr to_datetimedroprenamedict to_native) datetimes centroidsr pretty_namesr dfr0U/var/www/html/Persson_Maskin/env/lib/python3.10/site-packages/plotly/data/__init__.pyrs>(  rc Cs>tjtd|ddd}|r|tddddd d d d }|S) a  Each row represents a restaurant bill. https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html Parameters ---------- pretty_names: bool If True, prettifies the column names return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 244 rows and the following columns: `['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`. tipsr Tr z Total BillTipz Payer GenderzSmokers at Tablez Day of WeekMealz Party Size) total_billtipsexsmokerdaytimesize)rrrr)r*r+)r.r r/r0r0r1r2Ws r2cC td|dS)a Each row represents a flower. https://en.wikipedia.org/wiki/Iris_flower_data_set Parameters ---------- return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 150 rows and the following columns: `['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`. irisr rr r0r0r1r=|s r=cCr<)a Each row represents a level of wind intensity in a cardinal direction, and its frequency. Parameters ---------- return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 128 rows and the following columns: `['direction', 'strength', 'frequency']`. windr r>r r0r0r1r?s r?cCr<)a Each row represents voting results for an electoral district in the 2013 Montreal mayoral election. Parameters ---------- return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 58 rows and the following columns: `['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`. electionr r>r r0r0r1r@ r@cCsddl}ddl}ddl}|j|j|jtddd}||d}|| d}Wd|S1s9wY|S)a@ Each feature represents an electoral district in the 2013 Montreal mayoral election. Returns ------- A GeoJSON-formatted `dict` with 58 polygon or multi-polygon features whose `id` is an electoral district numerical ID and whose `district` property is the ID and district name. rN package_datadatasetszelection.geojson.gzrzutf-8) gzipjsonospathjoindirname__file__GzipFileloadsreaddecode)rErFrGrHfresultr0r0r1election_geojsons  rRcCr<)a Each row represents the availability of car-sharing services near the centroid of a zone in Montreal over a month-long period. Parameters ---------- return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe` with 249 rows and the following columns: `['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`. carsharer r>r r0r0r1rSrArScCs|r|tvrd|d}t|tjtd|dddtdt}|r3|tdj }|rB| d}d|j _|S| S) ai Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019. Parameters ---------- indexed: bool Whether or not the 'date' column is used as the index and the column index is named 'company'. Applicable only if `return_type='pandas'` datetimes: bool Whether or not the 'date' column will be of datetime type return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 100 rows and the following columns: `['date', 'GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT']`. If `indexed` is True, the 'date' column is used as the index and the column index is named 'company' If `datetimes` is True, the 'date' column will be a datetime column Backend ' ' does not support setting indexstocksr Tr datecompany)BACKENDS_WITH_INDEX_SUPPORTNotImplementedErrorrrrr!r r#r$r&r'r+ set_indexcolumnsname)indexedr,r msgr/r0r0r1rVs   rVcCsV|r|tvrd|d}t|tjtd|ddd}|r'|}d|j_|S|S)a Each row in this wide dataset represents the results of 100 simulated participants on three hypothetical experiments, along with their gender and control/treatment group. Parameters ---------- indexed: bool If True, then the index is named "participant". Applicable only if `return_type='pandas'` return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 100 rows and the following columns: `['experiment_1', 'experiment_2', 'experiment_3', 'gender', 'group']`. If `indexed` is True, the data frame index is named "participant" rTrU experimentr Tr participant)rYrZrrrr+indexr]r^r r_r/r0r0r1r` s   r`cCs\|r|tvrd|d}t|tjtd|ddd}|r*|d}d|j_|S|S) a This dataset represents the medal table for Olympic Short Track Speed Skating for the top three nations as of 2020. Parameters ---------- indexed: bool Whether or not the 'nation' column is used as the index and the column index is named 'medal'. Applicable only if `return_type='pandas'` return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 3 rows and the following columns: `['nation', 'gold', 'silver', 'bronze']`. If `indexed` is True, the 'nation' column is used as the index and the column index is named 'medal' rTrUmedalsr Tr nationmedal) rYrZrrrr+r[r\r]rcr0r0r1 medals_wide1s   rgcCs\|r|tvrd|d}t|tjtd|dddjdgdd d }|r*t|d}|S) an This dataset represents the medal table for Olympic Short Track Speed Skating for the top three nations as of 2020. Parameters ---------- indexed: bool Whether or not the 'nation' column is used as the index. Applicable only if `return_type='pandas'` return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type Dataframe with 9 rows and the following columns: `['nation', 'medal', 'count']`. If `indexed` is True, the 'nation' column is used as the index. rTrUrdr Tr recountrf)rb value_name variable_name)rYrZrrrunpivotmaybe_set_indexr+rcr0r0r1 medals_longVs    rmc Cstjtjtjtdd|d}|tvr#d|dt}t|z|dkr+d}n |dkr2d }n|}t|}WntyMd |d |d }t|wz| |WSt yp}zd |d|}t | |j d}~ww)aI Loads the dataset using the specified backend. Notice that the available backends are 'pandas', 'polars', 'pyarrow' and they all have a `read_csv` function (pyarrow has it via pyarrow.csv). Therefore we can dynamically load the library using `importlib.import_module` and then call `backend.read_csv(filepath)`. Parameters ---------- d: str Name of the dataset to load. return_type: {'pandas', 'polars', 'pyarrow', 'modin', 'cudf'} Type of the resulting dataframe Returns ------- Dataframe of `return_type` type rBrCz.csv.gzzUnsupported return_type. Found z, expected one of rz pyarrow.csvrz modin.pandasz return_type=z, but z is not installedzUnable to read 'z' dataset due to: N) rGrHrIrJrKAVAILABLE_BACKENDSrZrModuleNotFoundErrorread_csv Exceptionwith_traceback __traceback__)dr filepathr_module_to_loadbackender0r0r1r{s<   r)FFNFr)Fr)r)FFr)__doc__rG importlibrnarwhals.stable.v1stablev1rrnrYrr2r=r?r@rRrSrVr`rgrmrr0r0r0r1s.  I %     , $ % %