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(nthreads=-1)
# 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 color
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
---------- ---------- ---------- ----------
nan nan nan nan
-0.372305 -0.744047 -1.89198 -0.66457
0.18704 0.176037 0.38628 -1.55655
-1.19211 0.579382 1.99508 1.13262
0.144151 1.39129 -1.01831 -0.678329
0.660908 -0.276543 0.366156 0.861158
-0.373436 0.280039 -0.312323 1.59981
0.257874 3.93677 -0.681923 0.335323
0.193658 -1.20955 -1.57454 -0.825441
0.961897 0.194851 0.807101 -1.56672
[11 rows x 4 columns]
# Generate a second random dataset with 10 rows and 1 column. Label the columns, Y and D.
>>> cols2_df = h2o.H2OFrame.from_python(np.random.randn(10,4).tolist(), column_names=list('YZ'))
>>> cols2_df.describe
Y Z
------------ -----------
nan nan
0.00313617 -0.171366
-1.14186 0.932378
0.251192 -0.384113
0.603271 -0.275116
-0.435936 -0.284039
-1.13324 -0.163877
-0.0475909 -2.65027
1.49039 -0.0887757
0.906927 -1.12668
[11 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
---------- ---------- ---------- ---------- ------------ -----------
nan nan nan nan nan nan
-0.372305 -0.744047 -1.89198 -0.66457 0.00313617 -0.171366
0.18704 0.176037 0.38628 -1.55655 -1.14186 0.932378
-1.19211 0.579382 1.99508 1.13262 0.251192 -0.384113
0.144151 1.39129 -1.01831 -0.678329 0.603271 -0.275116
0.660908 -0.276543 0.366156 0.861158 -0.435936 -0.284039
-0.373436 0.280039 -0.312323 1.59981 -1.13324 -0.163877
0.257874 3.93677 -0.681923 0.335323 -0.0475909 -2.65027
0.193658 -1.20955 -1.57454 -0.825441 1.49039 -0.0887757
0.961897 0.194851 0.807101 -1.56672 0.906927 -1.12668