Combining Columns from Two Datasets
-----------------------------------
The ``cbind`` function allows you to combine datasets by adding columns from one dataset into another. Note that when using ``cbind``, the two datasets must have the same number of rows. In addition, if the datasets contain common column names, H2O will append the joined column with ``0``.
.. example-code::
.. code-block:: r
library(h2o)
h2o.init()
# Create two simple, two-column R data frames by inputting values,
# ensuring that both have a common column (in this case, "fruit").
left <- data.frame(fruit = c('apple','orange','banana','lemon','strawberry','blueberry'),
color = c('red','orange','yellow','yellow','red','blue'))
right <- data.frame(fruit = c('apple','orange','banana','lemon','strawberry','watermelon'),
citrus = c(FALSE, TRUE, FALSE, TRUE, FALSE, FALSE))
# Create the H2O data frames from the inputted data.
l.hex <- as.h2o(left)
print(l.hex)
fruit color
1 apple red
2 orange orange
3 banana yellow
4 lemon yellow
5 strawberry red
6 blueberry blue
[6 rows x 2 columns]
r.hex <- as.h2o(right)
print(r.hex)
fruit citrus
1 apple FALSE
2 orange TRUE
3 banana FALSE
4 lemon TRUE
5 strawberry FALSE
6 watermelon FALSE
[6 rows x 2 columns]
# Combine the l.hex and r.hex datasets into a single dataset.
# The columns from r.hex will be appended to the right side of the final dataset.
# In addition, because both datasets include a "fruit" column, H2O will append the
# second "fruit" column name with "0". Note that this is different than ``merge``,
# which combines data from two commonly named columns in two datasets.
columns.hex <- h2o.cbind(l.hex, r.hex)
print(columns.hex)
fruit color fruit0 citrus
1 apple red apple FALSE
2 orange orange orange TRUE
3 banana yellow banana FALSE
4 lemon yellow lemon TRUE
5 strawberry red strawberry FALSE
6 blueberry blue watermelon FALSE
[6 rows x 4 columns]
.. code-block:: python
import h2o
h2o.init()
import numpy as np
# Generate a random dataset with 10 rows 4 columns.
# Label the columns A, B, C, and D.
cols1_df = h2o.H2OFrame.from_python(np.random.randn(10,4).tolist(), column_names=list('ABCD'))
cols1_df.describe
A B C D
--------- --------- --------- ----------
0.660737 -1.11679 0.278233 -0.0326621
-0.124613 -0.668794 0.558957 1.11402
0.944408 -1.6397 0.616223 0.137581
0.739501 0.671192 0.715497 -0.361146
1.52177 0.232701 0.196153 0.499426
-1.48407 0.222175 2.45155 -0.470239
0.880962 0.906569 -0.767418 1.38261
0.509212 0.602155 1.41956 1.96045
1.11071 0.779309 1.77455 -0.400746
-0.881062 -0.897391 0.980548 -0.266982
[10 rows x 4 columns]
# Generate a second random dataset with 10 rows and 2 columns.
# Label the columns, Y and Z.
cols2_df = h2o.H2OFrame.from_python(np.random.randn(10,2).tolist(), column_names=list('YZ'))
cols2_df.describe
Y Z
---------- ----------
0.54945 0.0283338
1.27367 -1.46298
0.875547 0.317876
2.12603 0.371443
0.662796 1.0291
-0.267864 0.86477
-1.51065 0.71466
0.0676983 -0.844925
0.311779 0.0397941
0.363517 0.465146
[10 rows x 2 columns]
# Add the columns from the second dataset into the first.
# H2O will append these as the right-most columns.
colsCombine_df = cols1_df.cbind(cols2_df)
colsCombine_df.describe
A B C D Y Z
--------- --------- --------- ---------- ---------- ----------
0.660737 -1.11679 0.278233 -0.0326621 0.54945 0.0283338
-0.124613 -0.668794 0.558957 1.11402 1.27367 -1.46298
0.944408 -1.6397 0.616223 0.137581 0.875547 0.317876
0.739501 0.671192 0.715497 -0.361146 2.12603 0.371443
1.52177 0.232701 0.196153 0.499426 0.662796 1.0291
-1.48407 0.222175 2.45155 -0.470239 -0.267864 0.86477
0.880962 0.906569 -0.767418 1.38261 -1.51065 0.71466
0.509212 0.602155 1.41956 1.96045 0.0676983 -0.844925
1.11071 0.779309 1.77455 -0.400746 0.311779 0.0397941
-0.881062 -0.897391 0.980548 -0.266982 0.363517 0.465146
[10 rows x 6 columns]