Slicing Columns¶

H2O 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.

r
python

> library(h2o)
> h2o.init(nthreads=-1)
> path <- "data/iris/iris_wheader.csv"
> df <- h2o.importFile(path)

# slice 1 column by index
> c1 <- df[,1]

# slice 1 column by name
> c1_1 <- df[, "sepal_len"]

# slice cols by vector of indexes
> cols <- df[, 1:4]

# slice cols by vector of names
> cols_1 <- df[, c("sepal_len", "sepal_wid", "petal_len", "petal_wid")]

>>> import h2o
>>> h2o.init()

# Import the iris with headers dataset
>>> path = "data/iris/iris_wheader.csv"
>>> df = h2o.import_file(path=path)
>>> df.describe
  sepal_len    sepal_wid    petal_len    petal_wid  class
-----------  -----------  -----------  -----------  -----------
        5.1          3.5          1.4          0.2  Iris-setosa
        4.9          3            1.4          0.2  Iris-setosa
        4.7          3.2          1.3          0.2  Iris-setosa
        4.6          3.1          1.5          0.2  Iris-setosa
        5            3.6          1.4          0.2  Iris-setosa
        5.4          3.9          1.7          0.4  Iris-setosa
        4.6          3.4          1.4          0.3  Iris-setosa
        5            3.4          1.5          0.2  Iris-setosa
        4.4          2.9          1.4          0.2  Iris-setosa
        4.9          3.1          1.5          0.1  Iris-setosa

[150 rows x 5 columns]

# Slice a column by index. The resulting dataset will include the first (left-most) colum 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]