- joinery [joi-nuh-ree]
- 1. In woodworking, the craft of joining together pieces of wood to produce more complex items.
- 2. In Java, a data analysis library for joining together pieces of data to produce insight.
Remember FizzBuzz (of course you do!), well imagine you have just
solved the puzzle (well done!) and you have written the results to
a comma-delimited file for further analysis. Now you want to know
how many times are the strings Fizz, Buzz, and FizzBuzz
printed out.
You could answer this question any number of ways, for example you
could modify the original program, or reach for Python/pandas, or even
(for the sadistic among us, you know who you are) type out a one-liner
at the command prompt (probably including cut, sort, and uniq).
Well, now you have one more option. This option is especially good if you are 1) using Java already and 2) may need to integrate your solution with other Java applications in the future.
You can answer this question with joinery.
df.groupBy("value")
.count()
.sortBy("-number")
.head(3)Printing out the resulting data frame gives us the following table.
value number
0 Fizz 27
1 Buzz 14
2 FizzBuzz 6
See FizzBuzz.java for the complete code.
Get the executable jar and try it for yourself.
$ java -jar joinery-dataframe-1.4-jar-with-dependencies.jar shell
# Rhino 1.7 release 2 2009 03 22
# Java HotSpot(TM) 64-Bit Server VM, Oracle Corporation, 1.8.0_31
> df = new DataFrame()
[empty data frame]
> df.add("value")
[empty data frame]
> [10, 20, 30].forEach(function(val) {
df.append([val])
})
> df
value
0 10
1 20
2 30
>A maven repository for joinery is hosted on JCenter. For instructions on setting up your maven profile to use JCenter, visit https://bintray.com/bintray/jcenter.
<dependency>
<groupId>joinery</groupId>
<artifactId>joinery-dataframe</artifactId>
<version>1.4</version>
</dependency>JCenter also allows for direct download using the button below.
joinery includes some tools to make working with data frames easier.
These tools are available by running joinery.DataFrame as an application.
$ java joinery.DataFrame
usage: joinery.DataFrame [compare|plot|show|shell] [csv-file ...]Show displays the tabular data of a data frame in a gui window.
$ java joinery.DataFrame show data.csv
Plot displays the numeric data of a data frame as a chart.
$ java joinery.DataFrame plot data.csv
Launches an interactive JavaScript shell for working with data frames.
$ java joinery.DataFrame shell
# Rhino 1.7 release 2 2009 03 22
# Java HotSpot(TM) 64-Bit Server VM, Oracle Corporation, 1.8.0_31
> df = DataFrame.readCsv('https://www.quandl.com/api/v1/datasets/GOOG/NASDAQ_AAPL.csv')
Date Open High Low Close Volume
0 2015-02-20 128.62000000 129.50000000 128.05000000 129.50000000 48948419.00000000
1 2015-02-19 128.48000000 129.03000000 128.33000000 128.45000000 37362381.00000000
2 2015-02-18 127.62000000 128.78000000 127.45000000 128.72000000 44891737.00000000
3 2015-02-17 127.49000000 128.88000000 126.92000000 127.83000000 63152405.00000000
4 2015-02-13 127.28000000 127.28000000 125.65000000 127.08000000 54272219.00000000
5 2015-02-12 126.06000000 127.48000000 125.57000000 126.46000000 74474466.00000000
6 2015-02-11 122.77000000 124.92000000 122.50000000 124.88000000 73561797.00000000
7 2015-02-10 120.17000000 122.15000000 120.16000000 122.02000000 62008506.00000000
8 2015-02-09 118.55000000 119.84000000 118.43000000 119.72000000 38889797.00000000
... 8629 rows skipped ...
8638 1980-12-12 0.00000000 4.12000000 4.11000000 4.11000000 14657300.00000000
> df.types()
[class java.util.Date, class java.lang.Double, class java.lang.Double, class java.lang.Double, class java.lang.Double, class java.lang.Double]
> df.sortBy('Date')
Date Open High Low Close Volume
8638 1980-12-12 0.00000000 4.12000000 4.11000000 4.11000000 14657300.00000000
8637 1980-12-15 0.00000000 3.91000000 3.89000000 3.89000000 5496400.00000000
8636 1980-12-16 0.00000000 3.62000000 3.61000000 3.61000000 3304000.00000000
8635 1980-12-17 0.00000000 3.71000000 3.70000000 3.70000000 2701300.00000000
8634 1980-12-18 0.00000000 3.82000000 3.80000000 3.80000000 2295300.00000000
8633 1980-12-19 0.00000000 4.05000000 4.04000000 4.04000000 1519700.00000000
8632 1980-12-22 0.00000000 4.25000000 4.23000000 4.23000000 1167600.00000000
8631 1980-12-23 0.00000000 4.43000000 4.41000000 4.41000000 1467200.00000000
8630 1980-12-24 0.00000000 4.66000000 4.64000000 4.64000000 1500100.00000000
... 8629 rows skipped ...
0 2015-02-20 128.62000000 129.50000000 128.05000000 129.50000000 48948419.00000000
> .reindex('Date')
Open High Low Close Volume
1980-12-12 0.00000000 4.12000000 4.11000000 4.11000000 14657300.00000000
1980-12-15 0.00000000 3.91000000 3.89000000 3.89000000 5496400.00000000
1980-12-16 0.00000000 3.62000000 3.61000000 3.61000000 3304000.00000000
1980-12-17 0.00000000 3.71000000 3.70000000 3.70000000 2701300.00000000
1980-12-18 0.00000000 3.82000000 3.80000000 3.80000000 2295300.00000000
1980-12-19 0.00000000 4.05000000 4.04000000 4.04000000 1519700.00000000
1980-12-22 0.00000000 4.25000000 4.23000000 4.23000000 1167600.00000000
1980-12-23 0.00000000 4.43000000 4.41000000 4.41000000 1467200.00000000
1980-12-24 0.00000000 4.66000000 4.64000000 4.64000000 1500100.00000000
... 8629 rows skipped ...
2015-02-20 128.62000000 129.50000000 128.05000000 129.50000000 48948419.00000000
> .retain('Close')
Close
1980-12-12 4.11000000
1980-12-15 3.89000000
1980-12-16 3.61000000
1980-12-17 3.70000000
1980-12-18 3.80000000
1980-12-19 4.04000000
1980-12-22 4.23000000
1980-12-23 4.41000000
1980-12-24 4.64000000
... 8629 rows skipped ...
2015-02-20 129.50000000
> .plot()The complete api documentation for the DataFrame class is available
at http://cardillo.github.io/joinery
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