Wolfram Knowledgebase | Things to Try

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.
Instant Access to Real-World Data. Compute with and visualize a growing collection of carefully curated data across hundreds of essential domains, seamlessly integrated with the entire Wolfram Language.

Compute with Geographic Data

Create a map with geographic data (this may take longer to evaluate):
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In[]:=
GeoRegionValuePlot
Europe
COUNTRIES
->"GreenhouseGasEmissions",MissingStyle->Blue
Out[]=
Retrieve a collection of data for each country:
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In[]:=
rawdata=EntityValueEntityClass["Country","Countries"],
poverty fraction
,
GDP
nominal|per capita
,
population
,
continent
;
Delete missing data and group by continent:
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In[]:=
cleandata=GroupBy[DeleteMissing[rawdata,1,1],Last,MapApply[Function[{pov,gdp,pop},{pov,QuantityMagnitude[gdp],pop}]]];
Plot the cleaned data:
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In[]:=
BubbleChartcleandata,


Compute with Physical Quantities

Retrieve wavelengths for a class of spectral lines:
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In[]:=
wavelengths=EntityValue
helium I
4000
Å
to
7500
Å
SPECTRAL LINES
,
observed wavelength
;
Plot these lines in the visible spectrum:
Run
In[]:=
Graphics​​Map[​​{ColorData["VisibleSpectrum"][#],Line[{{#,0},{#,1}}]}&,​​QuantityMagnitude[wavelengths,"Nanometers"]​​],
​​
Retrieve thermodynamic data for substances:
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In[]:=
substances={"Water","Methanol","Octane"};​​temps=Quantity[Range[1,130],"DegreesCelsius"];​​densitydata=Table[​​{temps,ThermodynamicData[substance,"Density",{"Temperature"->temps}]}//Transpose,​​{substance,substances}];
Visualize the retrieved data:
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In[]:=
ListLinePlot[densitydata,AxesLabel->Automatic,PlotLegends->substances]
Compare the sharp density decreases in the plot with known boiling points:
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In[]:=
EntityValue[Interpreter["Chemical"][substances],"BoilingPoint","Association"]

Compute with Biological and Ecological Data

Plot the location of anatomical structures in 3D:
Run
In[]:=
AnatomyPlot3D
heart
ANATOMICAL STRUCTURE
,
lung
ANATOMICAL STRUCTURE

Compare anatomical entities:
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In[]:=
organs=
heart
ANATOMICAL STRUCTURE
,
liver
ANATOMICAL STRUCTURE
,
brain
ANATOMICAL STRUCTURE
;​​properties={"Mass","CellCount"};​​ListPlot[EntityValue[organs,properties,"Association"],AxesLabel->properties]
Define biosequences and explore their structural properties:
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In[]:=
insulin=
BioSequence[
]
;​​MoleculePlot[Molecule[insulin]]
Define a class of entities for data retrieval:
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In[]:=
endangeredMammal=EntityClass"TaxonomicSpecies",
phyletic groups
->
mammal
SPECIES PHYLETIC GROUP
,
conservation status
->
Endangered
SPECIES CONSERVATION STATUS
;​​
Visualize the number of endangered mammals present by country:
Run
In[]:=
GeoRegionValuePlot[EntityValue[endangeredMammal,"ObservationCountries"]//Flatten//Tally]
Out[]=

Analyze Financial Data

Retrieve financial data and present it in a standard format:
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In[]:=
TradingChart["GE",{"Volume","SimpleMovingAverage","BollingerBands"}]
Interpret financial entities from text:
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In[]:=
stocks=Interpreter["Financial"][{"GOOGL","MSFT","AAPL"}]
Compare properties of entities over time:
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In[]:=
property=
cumulative fractional change
;​​DateListPlot​​EntityValuestocks,Datedproperty,Interval
Wed 1 Jan 2020
,Yesterday,PlotLegendsCommonName[stocks]

Compute with Socio-Cultural Data

Visualize the genre preferences of an artist:
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In[]:=
genres=EntityValueEntityClass"Artwork","Artist"->
Rembrandt
PERSON
,"ArtGenre"//Flatten;​​PieChart[Counts[genres],ChartLabels->Automatic]
Retrieve construction start dates from an implicitly defined class of castles:
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In[]:=
castleAndYear=EntityValueEntityClass"Castle",
country
->
United Kingdom
COUNTRY
,"ConstructionStartDate","NonMissingEntityAssociation";
Look for trends in time:
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In[]:=
DateHistogram[castleAndYear,{{900},{1300},"Decade"}]
Organize the data from a particularly active period:
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In[]:=
castlesOfInterest=SelectcastleAndYear,
Year: 1060
≤#≤
Year: 1110
&;​​castlesByDecade=GroupBy[castlesOfInterest//Normal,DateObject[#[[2]],"Decade"]&,Map[First]]//KeySort