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 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 --------- --------- --------- ---------- 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 1 column. Label the columns, Y and D. >>> 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]