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Combine rows from two datasets

You can use the rbind function to combine two similar datasets into a single large dataset. This can be used, for example, to create a larger dataset by combining data from a validation dataset with its training or testing dataset.

note

When using rbind, the two datasets must have the same set of columns.

import h2o
import numpy as np
h2o.init()

# Generate a random dataset with 100 rows 4 columns. 
# Label the columns A, B, C, and D.
df1 = h2o.H2OFrame.from_python(np.random.randn(100,4).tolist(), column_names=list('ABCD'))
df1.describe
        A           B          C           D
---------  ----------  ---------  ----------
 0.412228  -0.991376   -1.44374   -0.276455
 0.348039  -0.193704   -0.370882   0.162211
 0.125303  -1.24546    -0.916738   1.08088
 0.293062   0.516151    0.739798  -0.430679
-0.363344   0.0558051  -1.43888    1.13882
-1.17492   -0.332647   -1.18689    0.533313
 0.154774   1.46559     0.373058  -0.915895
 0.555835  -0.0891554  -1.19151    0.623667
-1.13092    0.843549   -0.532341  -0.0739869
 0.752855  -0.168504   -0.750161  -2.46084

[100 rows x 4 columns]

# Generate a second random dataset with 100 rows and 4 columns. 
# Again, label the columns, A, B, C, and D.
df2 = h2o.H2OFrame.from_python(np.random.randn(100,4).tolist(), column_names=list('ABCD'))
df2.describe
          A          B          C          D
-----------  ---------  ---------  ---------
 0.00118227  -0.835817   1.06634    1.81794
-0.542678    -0.494483   0.109813   0.714271
-0.365611    -0.679095   0.891982  -1.93362
-0.0533568    0.86035   -2.28902   -1.287
-0.572775     1.30954    0.27412   -0.287373
 0.310976    -0.594283  -0.566955   0.221888
 1.34778     -1.02348    0.243686   0.319585
 0.383136    -0.113979  -0.901779  -0.383478
-0.968212    -0.606603  -0.828677   0.699539
 0.491119    -0.629774  -0.632143   0.2898

[100 rows x 4 columns]

# Bind the rows from the second dataset into the first dataset.
df1.rbind(df2)
        A           B          C           D
---------  ----------  ---------  ----------
 0.412228  -0.991376   -1.44374   -0.276455
 0.348039  -0.193704   -0.370882   0.162211
 0.125303  -1.24546    -0.916738   1.08088
 0.293062   0.516151    0.739798  -0.430679
-0.363344   0.0558051  -1.43888    1.13882
-1.17492   -0.332647   -1.18689    0.533313
 0.154774   1.46559     0.373058  -0.915895
 0.555835  -0.0891554  -1.19151    0.623667
-1.13092    0.843549   -0.532341  -0.0739869
 0.752855  -0.168504   -0.750161  -2.46084

[200 rows x 4 columns]
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