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Slice columns

H2O-3 Secure lazily slices out columns of data and will only materialize a shared copy upon some type of triggering IO. This example shows how to slice columns from a frame of data.

import h2o
h2o.init()

# Import the iris with headers dataset
path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv"
df = h2o.import_file(path=path)

# Slice a column by index. The resulting dataset will include the first (left-most) 
# column of the original dataset. 
c1 = df[:,0]
c1.describe
  sepal_len
-----------
        5.1
        4.9
        4.7
        4.6
        5
        5.4
        4.6
        5
        4.4
        4.9

[150 rows x 1 column]

# Slice 1 column by name. The resulting dataset will include only the sepal_len column
# from the original dataset. 
c1_1 = df[:, "sepal_len"]
c1_1.describe
  sepal_len
-----------
        5.1
        4.9
        4.7
        4.6
        5
        5.4
        4.6
        5
        4.4
        4.9

[150 rows x 1 column[]

# Slice columns by list of indexes. The resulting dataset will include the first three 
# columns from the original dataset. 
cols = df[:, range(3)]
cols.describe
  sepal_len    sepal_wid    petal_len
-----------  -----------  -----------
        5.1          3.5          1.4
        4.9          3            1.4
        4.7          3.2          1.3
        4.6          3.1          1.5
        5            3.6          1.4
        5.4          3.9          1.7
        4.6          3.4          1.4
        5            3.4          1.5
        4.4          2.9          1.4
        4.9          3.1          1.5

[150 rows x 3 columns]

# Slice cols by a list of names.
cols_1 = df[:, ["sepal_wid", "petal_len", "petal_wid"]]
cols_1 
  sepal_wid    petal_len    petal_wid
-----------  -----------  -----------
        3.5          1.4          0.2
        3            1.4          0.2
        3.2          1.3          0.2
        3.1          1.5          0.2
        3.6          1.4          0.2
        3.9          1.7          0.4
        3.4          1.4          0.3
        3.4          1.5          0.2
        2.9          1.4          0.2
        3.1          1.5          0.1

[150 rows x 3 columns]
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