Combining Rows from Two DatasetsΒΆ
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 that when using rbind, the two datasets must have the same set of columns.
> library(h2o)
> h2o.init(nthreads=-1)
# Import exsiting training and testing datasets
> ecg1Path = "../../../smalldata/anomaly/ecg_discord_train.csv"
> ecg1.hex = h2o.importFile(path=ecg1Path, destination_frame="ecg1.hex")
> ecg2Path = "../../../smalldata/anomaly/ecg_discord_test.csv"
> ecg2.hex = h2o.importFile(path=ecg2Path, destination_frame="ecg2.hex")
# Combine the two datasets into a single, larger dataset
> ecgCombine.hex <- h2o.rbind(ecg1.hex, ecg2.hex)
>>> 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
----------- ---------- ----------- -----------
nan nan nan nan
-0.148045 0.516651 -0.218871 -2.11336
0.818191 -1.07749 -0.303827 0.0234708
-0.894042 -1.83727 1.69621 -0.306524
-1.90056 0.528147 -0.745829 0.325673
-1.14653 0.146565 -1.12463 -1.39162
0.81608 0.21313 -0.122169 1.47247
0.419028 1.14975 0.913349 0.975779
0.419134 -1.63199 0.633799 0.482761
0.0366856 -1.09199 -0.0831492 2.17306
[101 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
----------- ----------- ---------- ----------
nan nan nan nan
0.626459 -1.80634 -1.08245 1.29828
1.31526 -0.223264 0.172243 -0.76666
1.70095 -0.666482 -0.486086 -1.16518
-0.241271 -1.08439 1.75451 1.37618
-0.151067 -0.830386 0.7113 -0.979204
-2.18042 -1.85949 -0.466211 0.707786
-0.0657297 -0.0092001 1.3721 -0.570298
1.59816 -0.149408 -0.874023 -0.883033
-0.367047 -0.586965 -0.98553 -1.33043
[101 rows x 4 columns]
# Bind the rows from the second dataset into the first dataset.
>>> df1.rbind(df2)
>>> df1.describe
A B C D
----------- ---------- ----------- -----------
nan nan nan nan
-0.148045 0.516651 -0.218871 -2.11336
0.818191 -1.07749 -0.303827 0.0234708
-0.894042 -1.83727 1.69621 -0.306524
-1.90056 0.528147 -0.745829 0.325673
-1.14653 0.146565 -1.12463 -1.39162
0.81608 0.21313 -0.122169 1.47247
0.419028 1.14975 0.913349 0.975779
0.419134 -1.63199 0.633799 0.482761
0.0366856 -1.09199 -0.0831492 2.17306
[202 rows x 4 columns]