bokeh.sampledata#

The bokeh.sampledata module exposes datasets that are used in examples and documentation. Some datasets require separate installation. To install those using pip, execute the command:

pip install bokeh_sampledata

anscombe#

The four data series that comprise Anscombe’s Quartet.

License: Public Domain

Francis J. Anscombe (1973), “Graphs in Statistical Analysis”, The American Statistician, 27(1), 17-21, https://doi.org/10.1080/00031305.1973.10478966. The historical scientific data values are public domain.

This module contains one pandas Dataframe: data.

data

Ix Iy IIx IIy IIIx IIIy IVx IVy
0 10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
1 8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
2 13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
3 9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
4 11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47

Examples

• anscombe

antibiotics#

A table of Will Burtin’s historical data regarding antibiotic efficacies.

License: MIT license

Sourced from: https://gist.github.com/borgar/cd32f1d804951034b224

This module contains one pandas Dataframe: data.

data

bacteria penicillin streptomycin neomycin gram start end colors
0 Mycobacterium tuberculosis 800.0 5.0 2.00 negative 1.016398 1.385997 #e69584
1 Salmonella schottmuelleri 10.0 0.8 0.09 negative 0.646798 1.016398 #e69584
2 Proteus vulgaris 3.0 0.1 0.10 negative 0.277199 0.646798 #e69584
3 Klebsiella pneumoniae 850.0 1.2 1.00 negative -0.092400 0.277199 #e69584
4 Brucella abortus 1.0 2.0 0.02 negative -0.461999 -0.092400 #e69584

Examples

• burtin

airport_routes#

US airport locations and observed domestic airline routes for July 2026.

Airport locations are public-domain OurAirports records. Routes are derived from the U.S. Department of Transportation, Bureau of Transportation Statistics, Reporting Carrier On-Time Performance data. Source: https://www.transtats.bts.gov/

This sample selects completed, non-diverted July 2026 flights and retains operating-carrier, origin, and destination combinations observed at least four times. It is a historical sample, not a flight schedule or a complete network. Reporting-carrier coverage excludes some smaller operators. Carrier identifiers are DOT identifiers, rather than the old OpenFlights identifiers.

Airport identifiers are OurAirports identifiers, which differ from the previous OpenFlights values. Join the two tables using their source/destination ID fields, or use IATA codes for external joins. The BTS code PBI is matched to the OurAirports record labeled DJT in this source snapshot. Time-zone, codeshare, and equipment fields are unspecified. Data is public domain with source credit retained. Source hashes and transformation details are in licenses/datasets.json.

This module contains two pandas DataFrames, airports and routes.

airports

Name City Country IATA ICAO Latitude Longitude Altitude Timezone DST TZ Type source
index
3356 Lehigh Valley International Airport Allentown/Bethlehem United States ABE KABE 40.651773 -75.442797 393 NaN NaN NaN medium_airport OurAirports
3357 Abilene Regional Airport Abilene United States ABI KABI 32.411301 -99.681900 1791 NaN NaN NaN medium_airport OurAirports
5363 Ambler Airport Ambler United States ABL PAFM 67.105529 -157.855339 334 NaN NaN NaN medium_airport OurAirports
16091 Albuquerque International Sunport Albuquerque United States ABQ KABQ 35.039976 -106.608925 5355 NaN NaN NaN large_airport OurAirports
3358 Aberdeen Regional Airport Aberdeen United States ABR KABR 45.449100 -98.421799 1302 NaN NaN NaN medium_airport OurAirports

routes

Airline AirlineID Source start Destination end Codeshare Stops Equipment
0 AA 19805 ABQ 16091 DFW 3488 NaN 0 NaN
1 AA 19805 ABQ 16091 ORD 3754 NaN 0 NaN
2 AA 19805 ABQ 16091 PHX 3772 NaN 0 NaN
3 AA 19805 ALB 3371 CLT 3457 NaN 0 NaN
4 AA 19805 ALB 3371 DCA 3483 NaN 0 NaN

Examples

• airports_graph

airports#

US airports with field elevations > 1500 meters.

License: Public Domain

Historical extract from a retired USGS National Map service, obtained on October 15, 2015. Current program information is available from The National Map supporting themes. The current airport layer may differ from this historical extract.

This module contains one pandas Dataframe: data.

data

name elevation x y
0 CHINLE MUNICIPAL AIRPORT 1691 -1.219788e+07 4.315889e+06
1 ELY AIRPORT /YELLAND FIELD/ AIRPORT 1908 -1.278414e+07 4.764692e+06
2 TRUCKEE-TAHOE AIRPORT 1798 -1.337387e+07 4.767619e+06
3 GARFIELD COUNTY REGIONAL AIRPORT 1691 -1.199211e+07 4.797343e+06
4 SANTA FE MUNICIPAL AIRPORT 1935 -1.180982e+07 4.248063e+06

Examples

• airports_map

autompg#

Automobile data from the 1983 ASA Data Exposition.

License: CC0

Original compilation: David Donoho and Ernesto Ramos (1982), PRIMDATA: Data Sets for Use With PRIM-H, CRCARS dataset. The 1983 ASA exposition instructions identify this source.

This sample contains the 392 complete records from the 406-car dataset archived by StatLib, with renamed columns. Both numeric values and car names retain the original order.

This module contains two pandas Dataframes: autompg and autompg_clean. The “clean” version has cleaned up the "mfr" and "origin" fields.

autompg

mpg cyl displ hp weight accel yr origin name
0 18.0 8 307.0 130 3504 12.0 70 1 chevrolet chevelle malibu
1 15.0 8 350.0 165 3693 11.5 70 1 buick skylark 320
2 18.0 8 318.0 150 3436 11.0 70 1 plymouth satellite
3 16.0 8 304.0 150 3433 12.0 70 1 amc rebel sst
4 17.0 8 302.0 140 3449 10.5 70 1 ford torino

autompg_clean

mpg cyl displ hp weight accel yr origin name mfr
0 18.0 8 307.0 130 3504 12.0 70 North America chevrolet chevelle malibu chevrolet
1 15.0 8 350.0 165 3693 11.5 70 North America buick skylark 320 buick
2 18.0 8 318.0 150 3436 11.0 70 North America plymouth satellite plymouth
3 16.0 8 304.0 150 3433 12.0 70 North America amc rebel sst amc
4 17.0 8 302.0 140 3449 10.5 70 North America ford torino ford

Examples

• kde2d
• pandas_groupby_colormapped
• pandas_groupby_nested
• transform_jitter

autompg2#

EPA fuel economy data for 1999 and 2008 vehicle models.

License: Public Domain

Original source: FuelEconomy.gov, provided by the US Department of Energy and Environmental Protection Agency. This sample contains public-domain EPA vehicle fuel economy records. The subset was distributed as ggplot2’s mpg dataset. This is a different dataset from autompg.

This module contains one pandas Dataframe: autompg2.

autompg2

Unnamed: 0 manufacturer model displ year cyl trans drv cty hwy fl class
0 1 Audi A4 1.8 1999 4 auto(l5) front 18 29 p compact
1 2 Audi A4 1.8 1999 4 manual(m5) front 21 29 p compact
2 3 Audi A4 2.0 2008 4 manual(m6) front 20 31 p compact
3 4 Audi A4 2.0 2008 4 auto(av) front 21 30 p compact
4 5 Audi A4 2.8 1999 6 auto(l5) front 16 26 p compact

Examples

• boxplot
• data_table_plot
• whisker

browsers#

Browser market share by version from November 2013.

The statistics are licensed under CC BY-SA 3.0. Credit Statcounter Global Stats with a link when reusing them, as requested in https://gs.statcounter.com/faq. The CSV is a historical extract.

The browser icons are from Font Awesome Free 6.7.2, Copyright 2024 Fonticons, Inc., under CC BY 4.0. Bokeh rasterizes, colors, and pads the SVG outlines to 32-by-32 PNGs. Attribution and source links are embedded in each PNG and recorded in licenses/datasets.json. The upstream notice ships in licenses/Font-Awesome.txt. Brand icons identify their respective browsers and do not imply endorsement.

This module contains one pandas Dataframe: browsers_nov_2013.

browsers_nov_2013

Version Share Browser VersionNumber
0 Chrome 30.0 18.51 Chrome 30.0
1 Chrome 31.0 17.31 Chrome 31.0
2 Firefox 25.0 11.21 Firefox 25.0
3 IE 10.0 11.10 IE 10.0
4 IE 8.0 8.65 IE 8.0

The module also contains a dictionary icons with 32-by-32 PNG browser icons for Chrome, Firefox, Safari, Opera, and IE.

Examples

• donut

commits#

Time series of commits for a GitHub user between 2012 and 2016.

License: Public Domain

This module contains one pandas Dataframe: data.

data

day time
datetime
2017-04-22 15:11:58-05:00 Sat 15:11:58
2017-04-21 14:20:57-05:00 Fri 14:20:57
2017-04-20 14:35:08-05:00 Thu 14:35:08
2017-04-20 10:34:29-05:00 Thu 10:34:29
2017-04-20 09:17:23-05:00 Thu 09:17:23

Examples

• scatter_jitter

cows#

Butterfat percentage in the milk of five cattle breeds.

License: Public Domain

This module contains one pandas Dataframe: data.

data

butterfat age breed
0 3.74 Mature Ayrshire
1 4.01 Mature Ayrshire
2 3.77 Mature Ayrshire
3 3.78 Mature Ayrshire
4 4.10 Mature Ayrshire

Examples

• density

daylight#

Calculate annual sunrise and sunset times for a location.

License: BSD-3-Clause.

These illustrative calculations follow the NOAA Solar Calculator spreadsheet equations (after Meeus, Astronomical Algorithms), a level horizon, and a 90.833-degree solar zenith for the conventional solar-radius and atmospheric-refraction correction. Times are rounded to the nearest minute. They are calculated estimates, not observations or precise almanac predictions.

daylight_for_year

Calculate a DataFrame for a year from 1901 through 2099, using an IANA time zone:

from bokeh_sampledata.daylight import daylight_for_year

daylight = daylight_for_year(
    2026, latitude=52.2297, longitude=21.0122, timezone="Europe/Warsaw",
)

The columns are Date (local calendar date), Sunrise and Sunset (local clock times), and Summer (1 when daylight saving applies at local noon, otherwise 0). A missing sunrise or sunset, such as during polar day or night, is represented by None. Leap years and time-zone transitions follow the calendar and IANA time-zone database. Skipped local dates are omitted. Sunsets after midnight belong to that local calendar date. If a date contains multiple sunrises or sunsets, the first chronological event is retained. Both events may be absent during either polar day or polar night.

daylight_warsaw_2013

The existing example DataFrame is calculated for Warsaw at 52.2297 degrees north, 21.0122 degrees east, in Europe/Warsaw. Its name and column types are retained.

Date Sunrise Sunset Summer
0 2013-01-01 07:45:00 15:34:00 0
1 2013-01-02 07:45:00 15:35:00 0
2 2013-01-03 07:45:00 15:36:00 0
3 2013-01-04 07:45:00 15:38:00 0
4 2013-01-05 07:44:00 15:39:00 0

Examples

• daylight
• span

degrees#

Provide a table of data regarding bachelor’s degrees earned by women.

The data is broken down by field for any given year.

Licence: CC0

Sourced from: https://www.kaggle.com/datasets/sureshsrinivas/bachelorsdegreewomenusa

This module contains one pandas Dataframe: data.

data

Year Agriculture Architecture Art and Performance Biology Business Communications and Journalism Computer Science Education Engineering English Foreign Languages Health Professions Math and Statistics Physical Sciences Psychology Public Administration Social Sciences and History
0 1970 4.229798 11.921005 59.7 29.088363 9.064439 35.3 13.6 74.535328 0.8 65.570923 73.8 77.1 38.0 13.8 44.4 68.4 36.8
1 1971 5.452797 12.003106 59.9 29.394403 9.503187 35.5 13.6 74.149204 1.0 64.556485 73.9 75.5 39.0 14.9 46.2 65.5 36.2
2 1972 7.420710 13.214594 60.4 29.810221 10.558962 36.6 14.9 73.554520 1.2 63.664263 74.6 76.9 40.2 14.8 47.6 62.6 36.1
3 1973 9.653602 14.791613 60.2 31.147915 12.804602 38.4 16.4 73.501814 1.6 62.941502 74.9 77.4 40.9 16.5 50.4 64.3 36.4
4 1974 14.074623 17.444688 61.9 32.996183 16.204850 40.5 18.9 73.336811 2.2 62.413412 75.3 77.9 41.8 18.2 52.6 66.1 37.3

emissions#

CO2 emissions of selected countries in the years from 1950 to 2012. Note that not all countries have values for the whole time range.

License: Public Domain

This module contains one pandas Dataframe: data.

data

country year emissions
0 Afghanistan 1950.0 0.010346
1 Albania 1950.0 0.244444
2 Algeria 1950.0 0.432728
3 Angola 1950.0 0.045087
4 Argentina 1950.0 1.746283

Examples

• slope_graph

forensic_glass#

Correlations in mineral content for forensic glass samples.

License: Public Domain

This module contains one pandas Dataframe: data.

data

RI Na Mg Al Si K Ca Ba Fe type
0 3.01 13.64 4.49 1.10 71.78 0.06 8.75 0.0 0.0 WinF
1 -0.39 13.89 3.60 1.36 72.73 0.48 7.83 0.0 0.0 WinF
2 -1.82 13.53 3.55 1.54 72.99 0.39 7.78 0.0 0.0 WinF
3 -0.34 13.21 3.69 1.29 72.61 0.57 8.22 0.0 0.0 WinF
4 -0.58 13.27 3.62 1.24 73.08 0.55 8.07 0.0 0.0 WinF

Examples

• correlogram

gapminder#

Fertility, life expectancy, population, and region information from Gapminder.

License: CC BY 2.0

Credit Gapminder.org and the original indicator providers where identified by Gapminder. These extracts restrict and align the original countries and years.

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

The data DataFrame has one row per country and year, covering 244 countries from 1964 through 2013. Its columns are country, year, fertility, life_expectancy, population, region, and country_id. Fertility is children per woman, life expectancy is years, and population is persons. Missing source values remain missing. Region and identifier fields come from the historical Gapminder region table.

For example, select a year with data[data["year"] == 2010] or a country with data[data["country"] == "United States"].

data

country year fertility life_expectancy population region country_id
0 Afghanistan 1964 7.671 33.639 10474903.0 South Asia AF
1 Afghanistan 1965 7.671 34.152 10697983.0 South Asia AF
2 Afghanistan 1966 7.671 34.662 10927724.0 South Asia AF
3 Afghanistan 1967 7.671 35.170 11163656.0 South Asia AF
4 Afghanistan 1968 7.671 35.674 11411022.0 South Asia AF

The module also provides the country-by-year DataFrames fertility, life_expectancy, and population, with string year columns, and the country-indexed regions metadata table.

fertility

1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Country
Afghanistan 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.671 7.670 7.670 7.670 7.669 7.669 7.670 7.671 7.673 7.676 7.679 7.681 7.682 7.682 7.682 7.687 7.700 7.725 7.758 7.796 7.832 7.859 7.869 7.854 7.809 7.733 7.623 7.484 7.321 7.136 6.930 6.702 6.456 6.196 5.928 5.659 5.395 5.141 4.900
Albania 5.711 5.594 5.483 5.376 5.268 5.160 5.050 4.933 4.809 4.677 4.538 4.393 4.244 4.094 3.947 3.807 3.678 3.562 3.460 3.372 3.297 3.233 3.177 3.126 3.075 3.023 2.970 2.917 2.867 2.819 2.772 2.723 2.670 2.611 2.543 2.467 2.383 2.291 2.195 2.097 2.004 1.919 1.849 1.796 1.761 1.744 1.741 1.748 1.760 1.771
Algeria 7.653 7.655 7.657 7.658 7.657 7.652 7.641 7.622 7.591 7.548 7.492 7.422 7.339 7.244 7.138 7.021 6.889 6.741 6.576 6.392 6.192 5.976 5.747 5.508 5.263 5.014 4.761 4.503 4.238 3.971 3.705 3.449 3.207 2.987 2.794 2.634 2.514 2.439 2.407 2.412 2.448 2.507 2.580 2.656 2.725 2.781 2.817 2.829 2.820 2.795
American Samoa NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Andorra NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

life_expectancy

1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Country
Afghanistan 33.639 34.152 34.662 35.170 35.674 36.172 36.663 37.143 37.614 38.075 38.529 38.977 39.417 39.855 40.298 40.756 41.242 41.770 42.347 42.977 43.661 44.400 45.192 46.024 46.880 47.744 48.601 49.439 50.247 51.017 51.738 52.400 52.995 53.527 54.009 54.449 54.863 55.271 55.687 56.122 56.583 57.071 57.582 58.102 58.618 59.124 59.612 60.079 60.524 60.947
Albania 65.475 65.863 66.122 66.316 66.500 66.702 66.948 67.251 67.595 67.966 68.356 68.748 69.121 69.459 69.753 70.001 70.218 70.426 70.646 70.886 71.144 71.398 71.615 71.770 71.853 71.870 71.842 71.799 71.779 71.813 71.920 72.117 72.415 72.796 73.235 73.713 74.200 74.664 75.081 75.437 75.725 75.949 76.124 76.278 76.433 76.598 76.780 76.979 77.185 77.392
Algeria 47.953 48.389 48.806 49.205 49.592 49.976 50.366 50.767 51.195 51.670 52.213 52.861 53.656 54.605 55.697 56.907 58.198 59.524 60.826 62.051 63.160 64.120 64.911 65.554 66.072 66.479 66.796 67.049 67.265 67.468 67.674 67.893 68.123 68.350 68.565 68.769 68.963 69.149 69.330 69.508 69.682 69.854 70.020 70.180 70.332 70.477 70.615 70.747 70.874 71.000
American Samoa NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Andorra NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

population

1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Country
Afghanistan 10474903.0 10697983.0 10927724.0 11163656.0 11411022.0 11676990.0 11964906.0 12273101.0 12593688.0 12915499.0 13223928.0 13505544.0 13766792.0 14003408.0 14179656.0 14249493.0 14185729.0 13984092.0 13672870.0 13300056.0 12931791.0 12625292.0 12372113.0 12183387.0 12156685.0 12414686.0 13032161.0 14069854.0 15472076.0 17053213.0 18553819.0 19789880.0 20684982.0 21299350.0 21752257.0 22227543.0 22856302.0 23677385.0 24639841.0 25678639.0 26693486.0 27614718.0 28420974.0 29145841.0 29839994.0 30577756.0 31411743.0 32358260.0 33397058.0 34499915.0
Albania 1817098.0 1869942.0 1922993.0 1976140.0 2029314.0 2082474.0 2135599.0 2188650.0 2241623.0 2294578.0 2347607.0 2400801.0 2454255.0 2508026.0 2562121.0 2616530.0 2671300.0 2725029.0 2777592.0 2831682.0 2891004.0 2957390.0 3033393.0 3116009.0 3194854.0 3255859.0 3289483.0 3291695.0 3266983.0 3224901.0 3179442.0 3141102.0 3112597.0 3091902.0 3079037.0 3072725.0 3071856.0 3077378.0 3089778.0 3106701.0 3124861.0 3141800.0 3156607.0 3169665.0 3181397.0 3192723.0 3204284.0 3215988.0 3227373.0 3238316.0
Algeria 11654905.0 11923002.0 12229853.0 12572629.0 12945462.0 13338918.0 13746185.0 14165889.0 14600659.0 15052371.0 15524137.0 16018195.0 16533323.0 17068212.0 17624756.0 18205468.0 18811199.0 19442423.0 20095648.0 20762767.0 21433070.0 22098298.0 22753511.0 23398470.0 24035237.0 24668100.0 25299182.0 25930560.0 26557969.0 27169903.0 27751086.0 28291591.0 28786855.0 29242917.0 29673694.0 30099010.0 30533827.0 30982214.0 31441848.0 31913462.0 32396048.0 32888449.0 33391954.0 33906605.0 34428028.0 34950168.0 35468208.0 35980193.0 36485828.0 36983924.0
American Samoa 22672.0 23480.0 24283.0 25087.0 25869.0 26608.0 27288.0 27907.0 28470.0 28983.0 29453.0 29897.0 30305.0 30696.0 31139.0 31727.0 32526.0 33557.0 34797.0 36203.0 37706.0 39253.0 40834.0 42446.0 44048.0 45595.0 47052.0 48402.0 49648.0 50801.0 51885.0 52919.0 53901.0 54834.0 55745.0 56667.0 57625.0 58633.0 59687.0 60774.0 61871.0 62962.0 64045.0 65130.0 66217.0 67312.0 68420.0 69543.0 70680.0 71834.0
Andorra 17438.0 18529.0 19640.0 20772.0 21931.0 23127.0 24364.0 25656.0 26997.0 28357.0 29688.0 30967.0 32156.0 33279.0 34432.0 35753.0 37328.0 39226.0 41390.0 43636.0 45702.0 47414.0 48653.0 49504.0 50236.0 51241.0 52773.0 54996.0 57767.0 60670.0 63111.0 64699.0 65227.0 64905.0 64246.0 63985.0 64634.0 66390.0 69043.0 72203.0 75292.0 77888.0 79874.0 81390.0 82577.0 83677.0 84864.0 86165.0 87518.0 88909.0

regions

Group ID
Country
Angola Sub-Saharan Africa AO
Benin Sub-Saharan Africa BJ
Botswana Sub-Saharan Africa BW
Burkina Faso Sub-Saharan Africa BF
Burundi Sub-Saharan Africa BI

glucose#

A CSV timeseries of blood glucose measurements.

License: Public Domain.

Supplied directly to a Bokeh maintainer by a personal acquaintance as public-domain data.

This module contains one pandas Dataframe: data.

data

isig glucose
datetime
2010-03-24 09:51:00 22.59 258
2010-03-24 09:56:00 22.52 260
2010-03-24 10:01:00 22.23 258
2010-03-24 10:06:00 21.56 254
2010-03-24 10:11:00 20.79 246

Examples

• box_annotation
• datetime_tick_context
• glyph_hover
• words_and_plots

haar_cascade#

Provide a pretrained Haar cascade for frontal face detection.

License: Intel License Agreement for Open Source Computer Vision Library (a BSD-style license), embedded in the XML file and reproduced in licenses/LicenseRef-Intel-OpenCV.txt. Copyright (C) 2000, Intel Corporation. The classifier was created by Rainer Lienhart and is distributed unmodified from OpenCV 4.4.0. Source hashes and provenance are recorded in licenses/datasets.json.

This module contains one attribute, frontalface_default_path, a path usable with OpenCV’s CascadeClassifier and detectMultiScale.

iris#

Provide Fisher’s Iris dataset.

License: CC0

Sourced from: https://www.kaggle.com/datasets/arshid/iris-flower-dataset

This module contains one pandas Dataframe: flowers.

Note

This sampledata is maintained for historical compatibility. Please consider alternatives to Iris such as penguins.

flowers

sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa

Examples

• iris

lincoln#

Mean daily temperatures in Lincoln, Nebraska, 2016.

License: Public Domain

This module contains one pandas Dataframe: data.

data

STATION NAME DATE TMAX TMIN TAVG MONTH
0 USW00094996 LINCOLN 11 SW, NE US 2016-01-01 36.0 15.0 25.5 Jan
1 USW00094996 LINCOLN 11 SW, NE US 2016-01-02 39.0 18.0 28.5 Jan
2 USW00094996 LINCOLN 11 SW, NE US 2016-01-03 32.0 15.0 23.5 Jan
3 USW00094996 LINCOLN 11 SW, NE US 2016-01-04 27.0 15.0 21.0 Jan
4 USW00094996 LINCOLN 11 SW, NE US 2016-01-05 40.0 21.0 30.5 Jan

Examples

• sinaplot

les_mis#

Character encounters in Les Misérables, from Donald Knuth’s Stanford GraphBase.

Derived from jean.dat, Copyright 1993 Stanford University. This renamed JSON file is not part of the Stanford GraphBase. Knuth describes its programs and data as public domain. Its accompanying file notice permits redistribution and requires renamed, identified derivatives. See licenses/LicenseRef-Stanford-GraphBase.txt.

The 77 characters and 254 weighted links retain the historical Bokeh graph. Groups are independently computed by deterministic Louvain community detection, rather than copied from Mike Bostock’s visualization. They are illustrative clusters, not additional literary facts.

Source: https://www-cs-faculty.stanford.edu/~knuth/sgb.html

This module contains one dictionary, data, with nodes (name and group) and links (integer source and target node indices, and encounter count value).

Examples

• les_mis

movies_data#

Film information from Wikidata for the movie explorer example.

License: CC0.

The dataset contains 17,472 films released from 1940 through 2014, selected from Wikidata structured data with review-score statements. The snapshot was retrieved on October 18, 2026. Values are provided as supplied by Wikidata.

Film metadata, Rotten Tomatoes and Metacritic scores, review counts, reported box-office receipts, and recorded Academy Award categories are included where available. BoxOffice is the maximum reported US-dollar gross per film, often worldwide rather than domestic. Award counts can be incomplete. Fresh and Rotten are approximate counts derived from the rounded Tomatometer percentage and review count.

The full legacy omdb and tomatoes table schemas are retained for compatibility with published Bokeh examples. Unavailable values, including IMDb ratings and votes, user scores, and editorial review text, are NULL. Missing genre, director, and cast fields are empty strings so existing text filters work. No synthetic metrics are included.

Source, snapshot, and transformation details are recorded in licenses/datasets.json.

This module has an attribute movie_path. This attribute contains the path to a SQLite database with the data.

mtb#

Route data (including altitude) for a bike race in Eastern Europe.

License: BSD-3-Clause, under the original Bokeh contribution terms. Source credit: Mateusz Paprocki, Bokeh PR #1197 (2014). The BSD notice is in the top-level LICENSE.txt.

Sourced from https://bikemaraton.com.pl

This module contains one pandas Dataframe: obiszow_mtb_xcm.

obiszow_mtb_xcm

lon lat alt
0 16.116775 51.578265 118.0
1 16.116741 51.578265 118.0
2 16.116776 51.578253 118.0
3 16.116792 51.578223 119.0
4 16.116584 51.578058 119.0

Examples

• trail

olympics2014#

Provide medal counts by country for the 2014 Olympics.

License: BSD-3-Clause, under the original Bokeh contribution terms. Source credit: Bryan Van de Ven, original Bokeh contribution (2014). The BSD notice is in the top-level LICENSE.txt.

Sourced from public news sources in 2014.

This module contains a single dict: data.

The dictionary has a key "data" that lists sub-dictionaries, one for each country:

{
    'abbr': 'DEU',
    'medals': {'total': 15, 'bronze': 4, 'gold': 8, 'silver': 3},
    'name': 'Germany'
}

penguins#

Provide data from the Palmer Archipelago (Antarctica) penguin dataset.

License: CC0, as stated by the original palmerpenguins project. Data were collected and made available by Kristen Gorman and Palmer Station, Antarctica LTER. The simplified data is distributed via seaborn-data. Palmerpenguins: Allison Horst, Alison Hill and Kristen Gorman (2020), DOI: 10.5281/zenodo.3960218.

Sourced from mwaskom/seaborn-data

This module contains one pandas Dataframe: data.

data

species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex colors
0 Adelie Torgersen 39.1 18.7 181.0 3750.0 MALE red
1 Adelie Torgersen 39.5 17.4 186.0 3800.0 FEMALE red
2 Adelie Torgersen 40.3 18.0 195.0 3250.0 FEMALE red
3 Adelie Torgersen NaN NaN NaN NaN NaN red
4 Adelie Torgersen 36.7 19.3 193.0 3450.0 FEMALE red

Examples

• filter_group
• linked_brushing
• scatter_blend
• sizing_mode
• splom
• transform_markers
• words_and_plots

perceptions#

Provides access to probly.csv and numberly.csv.

License: MIT license. Copyright (c) 2016 Zoni Nation. The original notice ships in licenses/MIT.txt. Survey responses were collected through Reddit’s r/SampleSize community.

Sourced from: zonination/perceptions

This module contains two pandas Dataframes: probly and numberly.

probly

Almost Certainly Highly Likely Very Good Chance Probable Likely Probably We Believe Better Than Even About Even We Doubt Improbable Unlikely Probably Not Little Chance Almost No Chance Highly Unlikely Chances Are Slight
0 95.0 80 85 75 66 75 66 55.0 50 40 20.0 30 15.0 20 5.0 25 25
1 95.0 75 75 51 75 51 51 51.0 50 20 49.0 25 49.0 5 5.0 10 5
2 95.0 85 85 70 75 70 80 60.0 50 30 10.0 25 25.0 20 1.0 5 15
3 95.0 85 85 70 75 70 80 60.0 50 30 10.0 25 25.0 20 1.0 5 15
4 98.0 95 80 70 70 75 65 60.0 50 10 50.0 5 20.0 5 1.0 2 10

numberly

A couple A few Dozens A lot Some Several Many Fractions of Scores of Hundreds of
0 2 3 30 20 4 7 12 0.15 80 250
1 2 3 24 12 6 10 50 0.50 40 200
2 2 5 30 15 5 4 25 0.25 500 500
3 2 5 30 15 5 4 25 0.25 500 500
4 2 3 48 50 3 5 5 0.01 100000 599

Examples

• ridgeplot
• ridgeplot_subcoordinates

periodic_table#

Provide a periodic table data set.

License: Public Domain

This module contains one pandas Dataframe: elements.

elements

atomic number symbol name atomic mass CPK electronic configuration electronegativity atomic radius ion radius van der Waals radius IE-1 EA standard state bonding type melting point boiling point density metal year discovered group period
0 1 H Hydrogen 1.00794 #FFFFFF 1s1 2.20 37.0 NaN 120.0 1312.0 -73.0 gas diatomic 14.0 20.0 0.00009 nonmetal 1766 1 1
1 2 He Helium 4.002602 #D9FFFF 1s2 NaN 32.0 NaN 140.0 2372.0 0.0 gas atomic NaN 4.0 0.00000 noble gas 1868 18 1
2 3 Li Lithium 6.941 #CC80FF [He] 2s1 0.98 134.0 76 (+1) 182.0 520.0 -60.0 solid metallic 454.0 1615.0 0.54000 alkali metal 1817 1 2
3 4 Be Beryllium 9.012182 #C2FF00 [He] 2s2 1.57 90.0 45 (+2) NaN 900.0 0.0 solid metallic 1560.0 2743.0 1.85000 alkaline earth metal 1798 2 2
4 5 B Boron 10.811 #FFB5B5 [He] 2s2 2p1 2.04 82.0 27 (+3) NaN 801.0 -27.0 solid covalent network 2348.0 4273.0 2.46000 metalloid 1807 13 2

Examples

• custom_tooltip
• elements
• periodic
• periodic_shells

population#

Historical and projected population by age, sex, and location.

Source: United Nations, Department of Economic and Social Affairs, Population Division (2024). World Population Prospects 2024, Online Edition. https://population.un.org/wpp/

License: CC BY 3.0 IGO. Copyright July 2024 United Nations. The sample selects the Medium variant, five-year intervals from 1950 through 2100, countries and areas, World, regions, and subregions. Male/female age-group values are reshaped to the legacy columns and converted from thousands to persons. These changes do not imply UN endorsement.

LocID retains the UN location identifier. Source hashes and transformation notes are recorded in licenses/datasets.json.

This module contains one pandas DataFrame, data.

LocID Location Year Sex AgeGrp AgeGrpStart Value
0 4 Afghanistan 1950 Male 0-4 0 644135.0
1 4 Afghanistan 1950 Male 5-9 5 515707.0
2 4 Afghanistan 1950 Male 10-14 10 462602.0
3 4 Afghanistan 1950 Male 15-19 15 415741.0
4 4 Afghanistan 1950 Male 20-24 20 374861.0

sample_geojson#

GeoJSON locations for the 25 former NHS England area teams.

License: Open Government Licence v2.0. Contains public sector information licensed under the Open Government Licence v2.0.

Published by NHS Choices and converted to GeoJSON by Jeni Tennison in JeniT/nhs-choices, revision 7651daedf0 (February 2014). Names, addresses, and contact details are historical. These area teams were subsequently reorganized. This sample retains the original 25 point features.

Examples

• geojson_source

sample_superstore#

Provide the Sample Superstore data set.

License: CC0

Historical Bokeh extract of the Sample Superstore demonstration dataset. Tableau publishes Sample Superstore files at https://public.tableau.com/app/learn/sample-data. The precise source of this historical extract is unrecorded. A similarly named Kaggle upload carries a CC0 label, but byte identity has not been verified.

This module contains one pandas Dataframe: data.

data

Ship Mode Segment Country City State Postal Code Region Category Sub-Category Sales Quantity Discount Profit
0 Second Class Consumer United States Henderson Kentucky 42420 South Furniture Bookcases 261.9600 2 0.00 41.9136
1 Second Class Consumer United States Henderson Kentucky 42420 South Furniture Chairs 731.9400 3 0.00 219.5820
2 Second Class Corporate United States Los Angeles California 90036 West Office Supplies Labels 14.6200 2 0.00 6.8714
3 Standard Class Consumer United States Fort Lauderdale Florida 33311 South Furniture Tables 957.5775 5 0.45 -383.0310
4 Standard Class Consumer United States Fort Lauderdale Florida 33311 South Office Supplies Storage 22.3680 2 0.20 2.5164

Examples

• crosstab
• treemap

sea_surface_temperature#

Time series of historical average sea surface temperatures.

License: free to use and redistribute (see this table for details).

Historical temperature observations from NERACOOS station B01, Western Maine Shelf, originally obtained from dataset B01_sbe37_all. The current station archive is maintained by the University of Maine. Its observations may differ from this historical extract.

This module contains one pandas Dataframe: sea_surface_temperature.

sea_surface_temperature

temperature
time
2016-02-15 00:00:00+00:00 4.929
2016-02-15 00:30:00+00:00 4.887
2016-02-15 01:00:00+00:00 4.821
2016-02-15 01:30:00+00:00 4.837
2016-02-15 02:00:00+00:00 4.830

sprint#

Historical results for Olympic sprints by year.

License: BSD-3-Clause, under the original Bokeh contribution terms. Source credit: Mateusz Paprocki, Bokeh PR #1484 (2014). The BSD notice is in the top-level LICENSE.txt.

The original example reproduces the New York Times 2012 Olympic sprint visualization.

This module contains one pandas Dataframe: sprint.

sprint

Name Country Medal Time Year Abbrev Speed MetersBack MedalFill MedalLine SelectedName
0 Usain Bolt JAM gold 9.63 2012 JAM 10.384216 0.000000 #efcf6d #c8a850
1 Yohan Blake JAM silver 9.75 2012 JAM 10.256410 1.230769 #cccccc #b0b0b1
2 Justin Gatlin USA bronze 9.79 2012 USA 10.214505 1.634321 #c59e8a #98715d
3 Usain Bolt JAM gold 9.69 2008 JAM 10.319917 0.619195 #efcf6d #c8a850
4 Richard Thompson TRI silver 9.89 2008 TRI 10.111223 2.628918 #cccccc #b0b0b1

Examples

• intervals
• sprint

titanic#

Demographic details of the passengers on board of the Titanic.

License: Public Domain

This module contains one pandas Dataframe: data.

data

name class age sex survived
0 Allen, Miss Elisabeth Walton 1st 29.00 female 1
1 Allison, Miss Helen Loraine 1st 2.00 female 0
2 Allison, Mr Hudson Joshua Creighton 1st 30.00 male 0
3 Allison, Mrs Hudson JC (Bessie Waldo Daniels) 1st 25.00 female 0
4 Allison, Master Hudson Trevor 1st 0.92 male 1

Examples

• pyramid

stocks#

Provide historical ticker data for selected stocks.

License: Public Domain.

Sourced from Quandl WIKI Prices, using an archived copy. Quandl declared the data public domain and permitted redistribution in its publisher notice. The values are provided as supplied by the source.

The series end on March 1, 2013. Open, high, low, close, and volume are unadjusted. adj_close contains the source’s split- and dividend-adjusted closing prices, including adjustments for corporate actions after this period. The GOOG dictionary contains Google’s pre-2014 history, filed under GOOGL in the source.

The data pandas DataFrame contains all five series, with columns ticker, date, open, high, low, close, volume, and adj_close. Dates are parsed as pandas timestamps. Rows are ordered by ticker, then date. The DataFrame is constructed on first access.

data

ticker date open high low close volume adj_close
0 AAPL 2000-03-01 118.56 132.06 118.50 130.31 9616100 4.186663
1 AAPL 2000-03-02 127.00 127.94 120.69 122.00 2779100 3.919675
2 AAPL 2000-03-03 124.87 128.23 120.00 128.00 2887200 4.112446
3 AAPL 2000-03-06 126.00 129.13 125.00 125.69 1880000 4.038229
4 AAPL 2000-03-07 126.44 127.44 121.12 122.87 2437600 3.947627

The module also contains five dicts: AAPL, FB, GOOG, IBM, and MSFT.

Each dictionary has the structure:

AAPL['date']       # list of date string
AAPL['open']       # list of float
AAPL['high']       # list of float
AAPL['low']        # list of float
AAPL['close']      # list of float
AAPL['volume']     # list of int
AAPL['adj_close']  # list of float

Examples

• bounds
• candlestick
• datetime_axis
• hover_tooltip_formatting
• interactive_legend
• legend_hide
• legend_mute
• legend_title
• missing_dates
• multi_legend
• polygon_annotation
• range_tool
• stocks
• transform_customjs

unemployment#

Per-county unemployment data for United States in 2009.

License: Public Domain

Sourced from the U.S. Bureau of Labor Statistics. Current program and historical data information: https://www.bls.gov/lau/

The by_fips dictionary maps five-character county FIPS strings, such as "01001", to unemployment rates. It joins directly to the fips field in us_counties.records(). Rates are percentages.

from bokeh_sampledata import unemployment, us_counties

counties = us_counties.records(state="AL")
rates = [unemployment.by_fips.get(county["fips"]) for county in counties]

County boundaries and identifiers can change over time, so some records may have no matching 2009 rate.

The existing data dictionary remains available with the same tuple keys and values.

The dict is indexed by the two-tuples containing (state_id, county_id) and has the unemployment rate (2009) as the value.

{
    (1, 1): 9.7,
    (1, 3): 9.1,
    ...
}

Examples

• choropleth
• texas_hover_map

unemployment1948#

US Unemployment rate data by month and year, from 1948 to 2013.

License: Public Domain

Sourced from the U.S. Bureau of Labor Statistics. Current program and historical data information: https://www.bls.gov/cps/prev_yrs.htm

This module contains one pandas Dataframe: data.

data

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
0 1948 4.0 4.7 4.5 4.0 3.4 3.9 3.9 3.6 3.4 2.9 3.3 3.6 3.8
1 1949 5.0 5.8 5.6 5.4 5.7 6.4 7.0 6.3 5.9 6.1 5.7 6.0 5.9
2 1950 7.6 7.9 7.1 6.0 5.3 5.6 5.3 4.1 4.0 3.3 3.8 3.9 5.3
3 1951 4.4 4.2 3.8 3.2 2.9 3.4 3.3 2.9 3.0 2.8 3.2 2.9 3.3
4 1952 3.7 3.8 3.3 3.0 2.9 3.2 3.3 3.1 2.7 2.4 2.5 2.5 3.0

Examples

• heatmap_unemployment

us_cities#

Locations of US cities with more than 5000 residents.

License: CC BY 3.0

Sourced from https://www.geonames.org/export/ (subset of cities5000.zip)

This module contains one dict: data.

data['lat']  # list of float
data['lon']  # list of float

us_counties#

Geometry records for US counties and county equivalents.

records() returns 3,233 dictionaries in FIPS order, each containing fips (five-digit string), state_fips (two-digit string), county_fips (three-digit string), state (uppercase postal abbreviation), name, detailed_name, and geometry (a Shapely Polygon or MultiPolygon). Shapely is a standard package dependency.

from bokeh_sampledata.us_counties import records

counties = records()
texas = records(state="TX")
geometry = texas[0]["geometry"]

The optional state argument accepts two-letter ASCII postal abbreviations in either case, including DC and territories. Filtering uses an index before geometry decoding. Unknown two-letter codes return an empty list. Names, numeric FIPS codes, whitespace, and other formats raise ValueError. For example, Autauga County has fips="01001", state_fips="01", and county_fips="001".

Coordinates retain the Census NAD83 longitude/latitude degrees without reprojection. Polygon parts and interior holes are preserved. All geometries are valid and nonempty. Each call returns fresh dictionaries and geometries.

Source and license#

Geometry comes from Bokeh’s historical boundary samples. Coordinate comparison identifies simplified Census TIGER/Line geometry matching the official 2009 release at the source rounding precision. The exact acquisition vintage and export intermediary remain unrecorded. These are historical samples.

The Census source metadata permits use and redistribution with acknowledgement to the Census Bureau. Source: U.S. Census Bureau, TIGER/Line Shapefiles. These terms are compatible with distribution alongside BSD-3-Clause software. The packaged licenses/geographic-boundaries.txt contains the source links, comparison evidence, attribution, and remaining provenance uncertainties.

Invalid geometry in 45 counties is repaired using Shapely make_valid, retaining polygonal components and discarding collapsed line/point remnants. The packaged geographic_boundaries.json manifest records their identifiers. Source metadata, including character replacement artifacts, is retained. Sixteen duplicate Puerto Rico FIPS rows use the last source row, matching the legacy dictionary.

Examples

• choropleth
• texas_hover_map

Legacy plotting data#

The historical data attribute is retained for compatibility. New code should use records(). It is loaded and cached on first access, independently of records(). Importing the module performs no boundary I/O.

data maps numeric (state_id, county_id) tuples to dictionaries containing name, detailed_name, state (lowercase postal abbreviation), lats, and lons. Names can repeat within a state. The combination of detailed_name and state is unique. Coordinates are NumPy float64 arrays with NaN separators between polygon exteriors. Interior holes are omitted. Both APIs use the same geometry dataset, so repairs, ring ordering, and closing points can change coordinates from the historical arrays.

from bokeh_sampledata.us_counties import data

autauga = data[(1, 1)]
latitudes = autauga["lats"]
longitudes = autauga["lons"]

us_holidays#

US federal and selected cultural holidays, parsed with icalendar.

License: BSD-3-Clause. Dates are independently calculated from calendar rules. Federal holidays follow the OPM calendar. The legacy (US-OPM) marker denotes observed dates for Monday-Friday federal employees, moving Saturday holidays to Friday and Sunday holidays to Monday.

Nominal holiday years run from 2000 through 2030. New Year’s Day 2000 is observed on December 31, 1999. Selected cultural and Easter-based Christian observances use their calendar dates. This is an illustrative calendar, not an exhaustive employment or religious calendar.

This module contains one list, us_holidays, of (datetime.date, name) pairs.

Examples

• calendars

us_marriages_divorces#

Provide U.S. marriage and divorce statistics between 1867 and 2014

License: Public Domain

Data organized by Randal S. Olson from CDC/NCHS historical statistics. Current program information: https://www.cdc.gov/nchs/nvss/marriage-divorce.htm

This module contains one pandas Dataframe: data.

data

Year Marriages Divorces Population Marriages_per_1000 Divorces_per_1000
0 1867 357000.0 10000.0 36970000 9.7 0.3
1 1868 345000.0 10000.0 37885000 9.1 0.3
2 1869 348000.0 11000.0 38870000 9.0 0.3
3 1870 352000.0 11000.0 39905000 8.8 0.3
4 1871 359000.0 12000.0 41010000 8.8 0.3

Examples

• us_marriages_divorces_hover

us_states#

Geometry records for US states and the District of Columbia.

records() returns 51 dictionaries in state-code order, each containing code (uppercase postal abbreviation), name, region, and geometry (a Shapely Polygon or MultiPolygon). Shapely is a standard package dependency.

from bokeh_sampledata.us_states import records

states = records()
oregon = next(state for state in states if state["code"] == "OR")
geometry = oregon["geometry"]

Coordinates retain the Census NAD83 longitude/latitude degrees without reprojection. Polygon parts and interior holes are preserved. All geometries are valid and nonempty. Each call returns fresh dictionaries and geometries.

Source and license#

Geometry comes from Bokeh’s historical boundary samples. Coordinate comparison identifies simplified Census TIGER/Line geometry matching the official 2009 release at the source rounding precision. The exact acquisition vintage and export intermediary remain unrecorded. These are historical samples.

The Census source metadata permits use and redistribution with acknowledgement to the Census Bureau. Source: U.S. Census Bureau, TIGER/Line Shapefiles. These terms are compatible with distribution alongside BSD-3-Clause software. The packaged licenses/geographic-boundaries.txt contains the source links, comparison evidence, attribution, and remaining provenance uncertainties.

Invalid geometry in Alaska is repaired using Shapely make_valid, retaining polygonal components and discarding collapsed line/point remnants. Source metadata is retained.

Examples

• airports_graph
• choropleth
• eclipse
• filtering

Legacy plotting data#

The historical data attribute is retained for compatibility. New code should use records(). It is loaded and cached on first access, independently of records(). Importing the module performs no boundary I/O.

data maps two-letter state codes to dictionaries containing name, region, lats, and lons. Coordinates are NumPy float64 arrays with NaN separators between polygon exteriors. Interior holes are omitted. Both APIs use the same geometry dataset, so repairs, ring ordering, and closing points can change coordinates from the historical arrays.

from bokeh_sampledata.us_states import data

oregon = data["OR"]
latitudes = oregon["lats"]
longitudes = oregon["lons"]

world_cities#

Names and locations of world cities with at least 5000 inhabitants.

License: CC BY 3.0

Sourced from https://www.geonames.org/export/ (cities5000.zip)

This module contains one pandas Dataframe: data.

data

name lat lng
0 Ordino 42.55623 1.53319
1 les Escaldes 42.50729 1.53414
2 la Massana 42.54499 1.51483
3 Encamp 42.53474 1.58014
4 Canillo 42.56760 1.59756

Examples

• autocompleteinput
• maps_cities