In [1]:

%%classpath add mvn
tech.tablesaw tablesaw-beakerx 0.36.0
com.jimmoores quandl-tablesaw 2.0.0

In [4]:

import tech.tablesaw.aggregate.AggregateFunctions.*
import tech.tablesaw.api.*
import tech.tablesaw.columns.*

Out[4]:

null

Comments¶

Tablesaw's usability is still far away from Python pandas or Spark DataFrame (even though it is the best implementation for JVM currently). I suggest you stick to Spark DataFrame or Python pandas at this time.
You can display Tablesaw tables with BeakerX table display widget by running the following command.
```
 tech.tablesaw.beakerx.TablesawDisplayer.register()
```

Column¶

In [6]:

val numbers = arrayOf(1, 2, 3, 4)
val col = DoubleColumn.create("nc", numbers)
println(col.print())

Column: nc
1
2
3
4

Out[6]:

null

In [7]:

col.get(2)

Out[7]:

3.0

In [10]:

col.multiply(4)

Out[10]:

Double column: nc * 4.0

In [11]:

println(col.multiply(4).print())

Column: nc * 4.0
4
8
12
16

Out[11]:

null

In [13]:

col.isLessThan(3.0)

Out[13]:

Selection of size: 2

In [16]:

val c = col.isLessThan(3.0)
c

Out[16]:

Selection of size: 2

In [18]:

println(col.where(c).print())

Column: nc
1
2

Out[18]:

null

In [20]:

col.where(col.isLessThan(3.0).and(col.isPositive()))

Out[20]:

Double column: nc

In [21]:

println(col.where(col.isLessThan(3.0).and(col.isPositive())).print())

Column: nc
1
2

Out[21]:

null

In [22]:

col.where(Selection.with(0, 2))

error: unresolved reference: Selection
col.where(Selection.with(0, 2))
          ^

In [ ]:

col.where(Selection.withRange(1, 3))

Table/DataFrame¶

In [6]:

val flights = Table.read().csv("../../home/media/data/flights14.csv")

Out[6]:

null

In [5]:

flights.structure()

In [6]:

flights.columnNames()

Out[6]:

[year, month, day, dep_time, dep_delay, arr_time, arr_delay, cancelled, carrier, tailnum, flight, origin, dest, air_time, distance, hour, min]

In [7]:

flights.shape()

Out[7]:

39 rows X 17 cols

In [9]:

flights.first(10)

In [10]:

flights.summary()

Out[10]:

Table summary for: flights14.csv
     Column: year     
 Measure   |  Value  |
----------------------
        n  |     39  |
      sum  |  78546  |
     Mean  |   2014  |
      Min  |   2014  |
      Max  |   2014  |
    Range  |      0  |
 Variance  |      0  |
 Std. Dev  |      0  |
    Column: month     
 Measure   |  Value  |
----------------------
        n  |     39  |
      sum  |     39  |
     Mean  |      1  |
      Min  |      1  |
      Max  |      1  |
    Range  |      0  |
 Variance  |      0  |
 Std. Dev  |      0  |
     Column: day      
 Measure   |  Value  |
----------------------
        n  |     39  |
      sum  |     39  |
     Mean  |      1  |
      Min  |      1  |
      Max  |      1  |
    Range  |      0  |
 Variance  |      0  |
 Std. Dev  |      0  |
         Column: dep_time          
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |               54119  |
     Mean  |  1387.6666666666665  |
      Min  |                 553  |
      Max  |                2133  |
    Range  |                1580  |
 Variance  |  197312.91228070174  |
 Std. Dev  |   444.1991808645101  |
         Column: dep_delay         
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |                 609  |
     Mean  |  15.615384615384615  |
      Min  |                 -10  |
      Max  |                 191  |
    Range  |                 201  |
 Variance  |  1612.7165991902837  |
 Std. Dev  |  40.158642895275776  |
         Column: arr_time         
 Measure   |        Value        |
----------------------------------
        n  |                 39  |
      sum  |              62670  |
     Mean  |  1606.923076923077  |
      Min  |                 37  |
      Max  |               2342  |
    Range  |               2305  |
 Variance  |  276612.8623481781  |
 Std. Dev  |  525.9399797963434  |
         Column: arr_delay         
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |                 671  |
     Mean  |    17.2051282051282  |
      Min  |                 -27  |
      Max  |                 185  |
    Range  |                 212  |
 Variance  |  2045.4831309041833  |
 Std. Dev  |   45.22701770959681  |
  Column: cancelled   
 Measure   |  Value  |
----------------------
        n  |     39  |
      sum  |      0  |
     Mean  |      0  |
      Min  |      0  |
      Max  |      0  |
    Range  |      0  |
 Variance  |      0  |
 Std. Dev  |      0  |
   Column: carrier    
 Category  |  Count  |
----------------------
       AA  |     39  |
   Column: tailnum    
 Category  |  Count  |
----------------------
   N3KDAA  |      1  |
   N3KRAA  |      1  |
   N3BSAA  |      1  |
   N3HFAA  |      1  |
   N3BJAA  |      1  |
   N5FJAA  |      1  |
   N328AA  |      1  |
   N3ESAA  |      1  |
   N5CEAA  |      1  |
   N3JWAA  |      1  |
      ...  |    ...  |
   N336AA  |      1  |
   N3BCAA  |      1  |
   N3JMAA  |      1  |
   N3CWAA  |      1  |
   N323AA  |      1  |
   N3EHAA  |      1  |
   N319AA  |      1  |
   N338AA  |      1  |
   N327AA  |      1  |
   N335AA  |      1  |
          Column: flight          
 Measure   |        Value        |
----------------------------------
        n  |                 39  |
      sum  |              13176  |
     Mean  |  337.8461538461538  |
      Min  |                  1  |
      Max  |               1171  |
    Range  |               1170  |
 Variance  |  89952.60728744938  |
 Std. Dev  |  299.9210017445417  |
    Column: origin    
 Category  |  Count  |
----------------------
      JFK  |     18  |
      EWR  |      4  |
      LGA  |     17  |
     Column: dest     
 Category  |  Count  |
----------------------
      STT  |      1  |
      BOS  |      2  |
      DFW  |      3  |
      AUS  |      1  |
      IAH  |      1  |
      SJU  |      1  |
      ORD  |     14  |
      SFO  |      1  |
      SEA  |      1  |
      LAX  |      8  |
      MIA  |      5  |
      PBI  |      1  |
         Column: air_time          
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |                8026  |
     Mean  |  205.79487179487182  |
      Min  |                  35  |
      Max  |                 365  |
    Range  |                 330  |
 Variance  |   9050.535762483129  |
 Std. Dev  |    95.1343038156223  |
         Column: distance          
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |               52212  |
     Mean  |  1338.7692307692312  |
      Min  |                 187  |
      Max  |                2586  |
    Range  |                2399  |
 Variance  |   562719.0242914981  |
 Std. Dev  |   750.1460019832793  |
           Column: hour            
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |                 528  |
     Mean  |  13.538461538461533  |
      Min  |                   5  |
      Max  |                  21  |
    Range  |                  16  |
 Variance  |  19.991902834008094  |
 Std. Dev  |  4.4712305726732655  |
            Column: min            
 Measure   |        Value         |
-----------------------------------
        n  |                  39  |
      sum  |                1319  |
     Mean  |  33.820512820512825  |
      Min  |                   2  |
      Max  |                  57  |
    Range  |                  55  |
 Variance  |  274.04588394062074  |
 Std. Dev  |  16.554331274340885  |

In [8]:

val values = doubleArrayOf(1.0, 2.0, 3.0, 7.0, 9.44242, 11.0)
values

Out[8]:

[1.0, 2.0, 3.0, 7.0, 9.44242, 11.0]

In [9]:

val column = DoubleColumn.create("my_numbers", values)

Out[9]:

null

In [11]:

DoubleColumn.create("col", 0.until(10).map{
    i -> i.toDouble()
})

In [5]:

import tech.tablesaw.api.Table
import tech.tablesaw.api.DoubleColumn

val table = Table.create("Table1")

for (i in 0.until(10)) {
    val column = DoubleColumn.create("column_" + i, 0.until(10).map{
        j -> (j + 10 * i).toDouble()
    })
    table.addColumns(column)
}
table.print()

Out[5]:

                                                             Table1                                                              
 column_0  |  column_1  |  column_2  |  column_3  |  column_4  |  column_5  |  column_6  |  column_7  |  column_8  |  column_9  |
---------------------------------------------------------------------------------------------------------------------------------
        0  |        10  |        20  |        30  |        40  |        50  |        60  |        70  |        80  |        90  |
        1  |        11  |        21  |        31  |        41  |        51  |        61  |        71  |        81  |        91  |
        2  |        12  |        22  |        32  |        42  |        52  |        62  |        72  |        82  |        92  |
        3  |        13  |        23  |        33  |        43  |        53  |        63  |        73  |        83  |        93  |
        4  |        14  |        24  |        34  |        44  |        54  |        64  |        74  |        84  |        94  |
        5  |        15  |        25  |        35  |        45  |        55  |        65  |        75  |        85  |        95  |
        6  |        16  |        26  |        36  |        46  |        56  |        66  |        76  |        86  |        96  |
        7  |        17  |        27  |        37  |        47  |        57  |        67  |        77  |        87  |        97  |
        8  |        18  |        28  |        38  |        48  |        58  |        68  |        78  |        88  |        98  |
        9  |        19  |        29  |        39  |        49  |        59  |        69  |        79  |        89  |        99  |