-Ph3dZddlZddlmZddlmcmZhdZhdZ ddZ dd Z dd Z dd Z dd Zd ZddZddZddZddZddZdZdS)zE Built-in datasets for demonstration, educational and test purposes. N) import_module>cudfmodinpandaspolarspyarrow>rrrFrcrtjtd|d}|r+|tjd|k}|r|tjtjdtjtj dgj d}|s| d d }|r,| td d d ddddddd }|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 continentrlifeExp gdpPercappop iso_alphaiso_numrr)nw from_native _get_datasetfiltercol with_columns concat_strcastStringlitstr to_datetimedroprenamedict to_native) datetimes centroidsr pretty_namesr dfs T/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/plotly/data/__init__.pyr r s8P [k:::t   B / YYrvf~~- . . __ M$$RY[[1126(3C3CD  ++Z+00    5 WW^^ 4 4 YY !%)* 2201       <<>>c tjtd|d}|r)|t ddddd 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)rrr r+r,r-)r0r r1s r2r5r5Wsx*  VEEERV W W WB  YY '")!!       <<>>r3c$td|S)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 r r s r2r@r@|s" K 8 8 88r3c$td|S)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 rAr s r2rCrCs K 8 8 88r3c$td|S)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 rAr s r2rErE  < < <|tjdj }|r5| 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_SUPPORTNotImplementedErrorrrr r#r"r%r&r(r)r- set_indexcolumnsname)indexedr.r msgr1s r2r^r^s2';&AAAG+GGG!#&&& X;777D   l26&>>&&ry{{3344? __RVF^^/;;== > > \\^^ % %f - -#  <<>>r3c|r|tvrd|d}t|tjt d|d}|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" r\r] experimentr Tr participant)rarbrrr r-indexrerfr rgr1s r2riri s,';&AAAG+GGG!#&&& \{;;;   B \\^^%  <<>>r3c"|r|tvrd|d}t|tjt d|d}|r5|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' r\r]medalsr Tr nationmedal) rarbrrr r-rcrdrerls r2 medals_widerq1s.';&AAAG+GGG!#&&& X;777D   B \\^^ % %h / /!  <<>>r3c|r|tvrd|d}t|tjt d|ddgdd }|rtj|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. r\r]rnr Tr rocountrp)rk value_name variable_name)rarbrrr unpivotmaybe_set_indexr-rls r2 medals_longrxVs*';&AAAG+GGG!#&&& X;777D    gj.  H - - <<>>r3cXtjtjtjtdd|dz}|t vrd|dt }t | |dkrd}n |dkrd }n|}t|}n&#t$rd |d |d }t|wxYw | |S#t$r4}d |d|}t| |j d}~wwxYw)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 rHrIz.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) rMrNrOrPrQAVAILABLE_BACKENDSrbrModuleNotFoundErrorread_csv Exceptionwith_traceback __traceback__)dr filepathrgmodule_to_loadbackendes r2r r {sm*w|| 1122 I H,,, 'k ' '$ ' ' "#&&& ' ) # #*NN G # #+NN(N// '''N[NN NNN!#&&&'=))) ===999a99nn++AO<<<=s$ #B//#CC++ D)5/D$$D))FFNFr)Fr)r)FFr)__doc__rM importlibrnarwhals.stable.v1stablev1rrzrar r5r@rCrErXrZr^rirqrxr r3r2rsj ######EEE999  FFFFR""""J9999(9999$====&2====&))))X!!!!H""""J""""J3=3=3=3=3=r3