bokeh.sampledata¶

The sampledata module can be used to download data sets used in Bokeh examples.

The simplest way to download the data is to use the execute the command line program:

bokeh sampledata

Alternatively, the download function described below may be called programmatically.

>>> import bokeh.sampledata
>>> bokeh.sampledata.download()

By default, data is downloaded and stored to a directory $HOME/.bokeh/data. This directory will be created if it does not already exist.

Bokeh also looks for a YAML configuration file at $HOME/.bokeh/config. The YAML key sampledata_dir can be set to the absolute path of a directory where the data should be stored. For example, add the following line to the config file:

sampledata_dir: /tmp/bokeh_data

This will cause the sample data to be stored in /tmp/bokeh_data.

download(progress: bool = True) → None[source]¶: Download larger data sets for various Bokeh examples.

anscombe¶

The four data series that comprise Anscombe’s Quartet.

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

antibiotics¶

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

This module contains one pandas Dataframe: data.

data

	bacteria	penicillin	streptomycin	neomycin	gram
0	Mycobacterium tuberculosis	800.0	5.0	2.00	negative
1	Salmonella schottmuelleri	10.0	0.8	0.09	negative
2	Proteus vulgaris	3.0	0.1	0.10	negative
3	Klebsiella pneumoniae	850.0	1.2	1.00	negative
4	Brucella abortus	1.0	2.0	0.02	negative

airport_routes¶

Airport routes data from OpenFlights.org.

Sourced from https://openflights.org/data.html on September 07, 2017.

This module contains two pandas Dataframes: airports and routes.

airports

	AirportID	Name	City	Country	IATA	ICAO	Latitude	Longitude	Altitude	Timezone	DST	TZ	Type	source
0	3411	Barter Island LRRS Airport	Barter Island	United States	BTI	PABA	70.134003	-143.582001	2	-9	A	America/Anchorage	airport	OurAirports
1	3413	Cape Lisburne LRRS Airport	Cape Lisburne	United States	LUR	PALU	68.875099	-166.110001	16	-9	A	America/Anchorage	airport	OurAirports
2	3414	Point Lay LRRS Airport	Point Lay	United States	PIZ	PPIZ	69.732903	-163.005005	22	-9	A	America/Anchorage	airport	OurAirports
3	3415	Hilo International Airport	Hilo	United States	ITO	PHTO	19.721399	-155.048004	38	-10	N	Pacific/Honolulu	airport	OurAirports
4	3416	Orlando Executive Airport	Orlando	United States	ORL	KORL	28.545500	-81.332901	113	-5	A	America/New_York	airport	OurAirports

routes

	Airline	AirlineID	Source	SourceID	Destination	DestinationID	Codeshare	Equipment
0	2O	146	ADQ	3531	KLN	7162	NaN	BNI
1	2O	146	KLN	7162	KYK	7161	NaN	BNI
2	3E	10739	BRL	5726	ORD	3830	NaN	CNC
3	3E	10739	BRL	5726	STL	3678	NaN	CNC
4	3E	10739	DEC	4042	ORD	3830	NaN	CNC

airport¶

US airports with field elevations > 1500 meters.

Sourced from http://services.nationalmap.gov on October 15, 2015.

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

autompg¶

A version of the Auto MPG data set.

Derived from https://archive.ics.uci.edu/ml/datasets/auto+mpg

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

autompg2¶

A version of the Auto MPG data set.

Derived from https://archive.ics.uci.edu/ml/datasets/auto+mpg

This module contains one pandas Dataframe: autompg.

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

browsers¶

Browser market share by version from November 2013.

Data sourced from http://gs.statcounter.com/#browser_version-ww-monthly-201311-201311-bar

Icon images sourced from https://github.com/alrra/browser-logos

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 base64-encoded PNGs of the logos for Chrome, Firefox, Safari, Opera, and IE.

commits¶

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

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

daylight¶

Provide 2013 Warsaw daylight hours.

Sourced from http://www.sunrisesunset.com

This module contains one pandas Dataframe: daylight_warsaw_2013.

daylight_warsaw_2013

	Date	Sunrise	Sunset
0	2013-01-01	07:45:00	15:34:00
1	2013-01-02	07:45:00	15:35:00
2	2013-01-03	07:45:00	15:36:00
3	2013-01-04	07:45:00	15:37:00
4	2013-01-05	07:44:00	15:38:00

degrees¶

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

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

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

gapminder¶

Four of the datasets from Gapminder.

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

Licensed under CC-BY.

This module contains four pandas Dataframes: fertility, life_expectancy, population, and regions.

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.

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

haar_cascade¶

Provide a Haar cascade file for face recognition.

This module contains an attribute frontalface_default_path . Use this attribute to obtain the path to a Haar cascade file for frontal face recognition that can be used by OpenCV.

iris¶

Provide Fisher’s Iris 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

les_mis¶

Provide JSON data for co-occurrence of characters in Les Miserables.

Derived from http://ftp.cs.stanford.edu/pub/sgb/jean.dat

This module contains one dictionary: data.

data

{
    'nodes': [
        {'name': 'Myriel', 'group': 1},
        ...
        {'name': 'Mme.Hucheloup', 'group': 8}
    ],
    'links': [
        {'source': 1, 'target': 0, 'value': 1},
        ...
        {'source': 76, 'target': 58, 'value': 1}
    ]
}

movies_data¶

A small subset of data from the Open Movie Database.

Data is licensed CC BY-NC 4.0.

This modules 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.

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

olympics2014¶

Provide medal counts by country for the 2014 Olympics.

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.

Derived from https://github.com/mwaskom/seaborn-data/blob/master/penguins.csv

Data distributed under the CC-0 license.

This module contains one pandas Dataframe: data.

data

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

perceptions¶

Provides access to probly.csv and numberly.csv.

Sourced from: https://github.com/zonination/perceptions

Data distributed under the MIT license.

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

periodic_table¶

Provide a periodic table data set.

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

population¶

Historical and projected population data by age, gender, and country.

Sourced from: https://population.un.org/wpp/Download/Standard/Population/

Data is licenced CC BY 3.0 IGO.

This module contains one pandas Dataframe: data.

data

	LocID	Location	Year	Sex	AgeGrp	AgeGrpStart	Value
0	4	Afghanistan	1950	Male	0-4	0	662064.0
1	4	Afghanistan	1950	Male	5-9	5	508166.0
2	4	Afghanistan	1950	Male	10-14	10	444396.0
3	4	Afghanistan	1950	Male	15-19	15	390480.0
4	4	Afghanistan	1950	Male	20-24	20	337318.0

sample_geojson¶

Provide geojson data for the UK NHS England area teams.

Sourced from https://github.com/JeniT/nhs-choices with data licensed under the Open Government Licence.

A snapshot of data available from NHS Choices on November 14th, 2015.

sea_surface_temperature¶

Time series of historical average sea surface temperatures.

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.

This module contains one pandas Dataframe: sprint.

sprint

	Name	Country	Medal	Time	Year
0	Usain Bolt	JAM	GOLD	9.63	2012
1	Yohan Blake	JAM	SILVER	9.75	2012
2	Justin Gatlin	USA	BRONZE	9.79	2012
3	Usain Bolt	JAM	GOLD	9.69	2008
4	Richard Thompson	TRI	SILVER	9.89	2008

stocks¶

Provide historical ticker data for selected stocks.

This module 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

unemployment¶

Per-county unemployment data for Unites States in 2009.

This module contains one dict: data.

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,
    ...
}

unemployment1948¶

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

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

us_cities¶

Locations of US cities with more than 5000 residents.

This module contains one dict: data.

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

us_counties¶

This modules exposes geometry data for Unites States.

This module contains one dict: data.

The data is indexed by two-tuples of `` (state_id, county_id)`` that have the following dictionaries as values:

In [25]: data[(1,1)]
Out[25]:
{
    'name': 'Autauga',
    'detailed name': 'Autauga County, Alabama',
    'state': 'al',
    'lats': [32.4757, ..., 32.48112],
    'lons': [-86.41182, ..., -86.41187]
}

Entries for 'name' can have duplicates for certain states (e.g. Virginia). The combination of 'detailed name' and 'state' will always be unique.

us_holidays¶

Calendar file of US Holidays from Mozilla provided by icalendar.

Sourced from: https://www.mozilla.org/en-US/projects/calendar/holidays/

This module contains one list: us_holidays.

us_holidays

[
    (datetime.date(1966, 12, 26), 'Kwanzaa'),
    (datetime.date(2000, 1, 1), "New Year's Day"),
    ...
    (datetime.date(2020, 12, 25), 'Christmas Day (US-OPM)')
]

us_marriages_divorces¶

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

Data from the CDC’s National Center for Health Statistics (NHCS) database (http://www.cdc.gov/nchs/).

Data organized by Randal S. Olson (http://www.randalolson.com)

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

us_states¶

Geometry data for US States.

This module contains one dict: data.

The data is indexed by the two letter state code (e.g., ‘CA’, ‘TX’) and has the following structure:

In [4]: data["OR"]
Out[4]:
{
    'name': 'Oregon',
    'region': 'Northwest',
    'lats': [46.29443, ..., 46.26068],
    'lons': [-124.03622, ..., -124.15935]
}

world_cities¶

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

Derived from cities5000.zip file downloaded from http://www.geonames.org/export/

Licensed under CC-BY.

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

bokeh.resources

bokeh.server