§
    ²[6gô  ã                   ó^   — d Z dd„Zdd„Zd„ Zd„ Zd„ Zd„ Zd	„ Zdd
„Zdd„Z	dd„Z
dd„Zd„ ZdS )zE
Built-in datasets for demonstration, educational and test purposes.
FNc                 óZ  — t          d¦  «        }|r||d         |k             }| r9|d                              t          ¦  «        dz                        d¦  «        |d<   |s|                     ddgd¬¦  «        }|r/|                     t          d	d
dddddddd¬¦
  «
        dd¬¦  «         |S )a  
    Each row represents a country on a given year.

    https://www.gapminder.org/data/

    Returns:
        A `pandas.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
    Ú	gapminderÚyearz-01-01údatetime64[ns]Úcentroid_latÚcentroid_loné   )ÚaxisÚCountryÚ	ContinentÚYearzLife ExpectancyzGDP per CapitaÚ
PopulationzISO Alpha Country CodezISO Numeric Country CodezCentroid LatitudezCentroid Longitude)
ÚcountryÚ	continentr   ÚlifeExpÚ	gdpPercapÚpopÚ	iso_alphaÚiso_numr   r   ÚcolumnsT©Úmapperr	   Úinplace)Ú_get_datasetÚastypeÚstrÚdropÚrenameÚdict)Ú	datetimesÚ	centroidsr   Úpretty_namesÚdfs        úS/var/www/surfInsights/venv3-11/lib/python3.11/site-packages/plotly/data/__init__.pyr   r      sè   € õ 
�kÑ	"Ô	"€BØð $Ø��6”
˜dÒ"Ô#ˆØð RØ˜”j×'Ò'­Ñ,Ô,¨xÑ7×?Ò?Ð@PÑQÔQˆˆ6‰
Øð ?Ø�WŠW�n nÐ5¸AˆWÑ>Ô>ˆØð 
Ø
�	Š	ÝØ!Ø%ØØ)Ø*Ø Ø2Ø2Ø0Ø1ðñ ô ð Øð 	ñ 	
ô 	
ð 	
ð  €Ió    c                 ó€   — t          d¦  «        }| r,|                     t          ddddddd¬	¦  «        d
d¬¦  «         |S )a  
    Each row represents a restaurant bill.

    https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html

    Returns:
        A `pandas.DataFrame` with 244 rows and the following columns:
        `['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`.Útipsz
Total BillÚTipzPayer GenderzSmokers at TablezDay of WeekÚMealz
Party Size)Ú
total_billÚtipÚsexÚsmokerÚdayÚtimeÚsizer   Tr   )r   r   r   )r!   r"   s     r#   r&   r&   /   sj   € õ 
�fÑ	Ô	€BØð 
Ø
�	Š	ÝØ'ØØ"Ø)Ø!ØØ!ðñ ô ð Øð 	ñ 	
ô 	
ð 	
ð €Ir$   c                  ó    — t          d¦  «        S )a  
    Each row represents a flower.

    https://en.wikipedia.org/wiki/Iris_flower_data_set

    Returns:
        A `pandas.DataFrame` with 150 rows and the following columns:
        `['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`.Úiris©r   © r$   r#   r1   r1   K   s   € õ ˜ÑÔÐr$   c                  ó    — t          d¦  «        S )zä
    Each row represents a level of wind intensity in a cardinal direction, and its frequency.

    Returns:
        A `pandas.DataFrame` with 128 rows and the following columns:
        `['direction', 'strength', 'frequency']`.Úwindr2   r3   r$   r#   r5   r5   W   s   € õ ˜ÑÔÐr$   c                  ó    — t          d¦  «        S )a"  
    Each row represents voting results for an electoral district in the 2013 Montreal
    mayoral election.

    Returns:
        A `pandas.DataFrame` with 58 rows and the following columns:
        `['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`.Úelectionr2   r3   r$   r#   r7   r7   a   ó   € õ ˜
Ñ#Ô#Ð#r$   c                  ó’  — ddl } ddl}ddl}|j                             |j                             |j                             t          ¦  «        ¦  «        ddd¦  «        }|                      |d¦  «        5 }|                     | 	                    ¦   «          
                    d¦  «        ¦  «        }ddd¦  «         n# 1 swxY w Y   |S )a0  
    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.é    NÚpackage_dataÚdatasetszelection.geojson.gzÚrzutf-8)ÚgzipÚjsonÚosÚpathÚjoinÚdirnameÚ__file__ÚGzipFileÚloadsÚreadÚdecode)r>   r?   r@   rA   ÚfÚresults         r#   Úelection_geojsonrK   l   sî   € ð €K€K€KØ€K€K€KØ€I€I€IàŒ7�<Š<Ø
Œ�Š˜œŸš­Ñ1Ô1Ñ2Ô2ØØØñ	ô €Dð 
�Š�t˜SÑ	!Ô	!ð 6 QØ—’˜AŸFšF™HœHŸOšO¨GÑ4Ô4Ñ5Ô5ˆð6ð 6ð 6ñ 6ô 6ð 6ð 6ð 6ð 6ð 6ð 6øøøð 6ð 6ð 6ð 6à€Ms   Á5;B<Â<C ÃC c                  ó    — t          d¦  «        S )a!  
    Each row represents the availability of car-sharing services near the centroid of a zone
    in Montreal over a month-long period.

    Returns:
        A `pandas.DataFrame` with 249 rows and the following columns:
        `['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`.Úcarsharer2   r3   r$   r#   rM   rM   ƒ   r8   r$   c                 óª   — t          d¦  «        }|r|d                              d¦  «        |d<   | r!|                     d¦  «        }d|j        _        |S )a´  
    Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019.

    Returns:
        A `pandas.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
        If `datetimes` is True, the 'date' column will be a datetime column
        is named 'company'ÚstocksÚdater   Úcompany)r   r   Ú	set_indexr   Úname)Úindexedr   r"   s      r#   rO   rO   Ž   s\   € õ 
�hÑ	Ô	€BØð 9Ø˜”Z×&Ò&Ð'7Ñ8Ô8ˆˆ6‰
Øð $Ø�\Š\˜&Ñ!Ô!ˆØ#ˆŒ
ŒØ€Ir$   c                 ó@   — t          d¦  «        }| rd|j        _        |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.


    Returns:
        A `pandas.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" Ú
experimentÚparticipant)r   ÚindexrS   ©rT   r"   s     r#   rV   rV   ¡   s'   € õ 
�lÑ	#Ô	#€BØð &Ø%ˆŒŒØ€Ir$   c                 ój   — t          d¦  «        }| r!|                     d¦  «        }d|j        _        |S )au  
    This dataset represents the medal table for Olympic Short Track Speed Skating for the
    top three nations as of 2020.

    Returns:
        A `pandas.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'ÚmedalsÚnationÚmedal)r   rR   r   rS   rY   s     r#   Úmedals_wider^   ±   s8   € õ 
�hÑ	Ô	€BØð "Ø�\Š\˜(Ñ#Ô#ˆØ!ˆŒ
ŒØ€Ir$   c                 ó€   — t          d¦  «                             dgdd¬¦  «        }| r|                     d¦  «        }|S )a>  
    This dataset represents the medal table for Olympic Short Track Speed Skating for the
    top three nations as of 2020.

    Returns:
        A `pandas.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\   Úcountr]   )Úid_varsÚ
value_nameÚvar_name)r   ÚmeltrR   rY   s     r#   Úmedals_longre   Â   sO   € õ 
�hÑ	Ô	×	$Ò	$Ø�
 w¸ð 
%ñ 
ô 
€Bð ð $Ø�\Š\˜(Ñ#Ô#ˆØ€Ir$   c           	      óâ   — dd l }dd l}|                     |j                             |j                             |j                             t          ¦  «        ¦  «        dd| dz   ¦  «        ¦  «        S )Nr:   r;   r<   z.csv.gz)Úpandasr@   Úread_csvrA   rB   rC   rD   )Údrg   r@   s      r#   r   r   Ó   si   € Ø€M€M€MØ€I€I€Ià�?Š?Ø
Œ�ŠØŒG�OŠO˜BœGŸOšO­HÑ5Ô5Ñ6Ô6ØØØ�	‰Mñ		
ô 	
ñô ð r$   )FFNF)F)FF)Ú__doc__r   r&   r1   r5   r7   rK   rM   rO   rV   r^   re   r   r3   r$   r#   ú<module>rk      sì   ððð ð
&ð &ð &ð &ðRð ð ð ð8	 ð 	 ð 	 ð ð  ð  ð$ð $ð $ðð ð ð.$ð $ð $ðð ð ð ð&ð ð ð ð ð ð ð ð"ð ð ð ð"ð ð ð ð r$   