Slicing Rows
------------

H2O lazily slices out rows of data and will only materialize a shared copy upon IO. This example shows how to slice rows from a frame of data.

.. example-code::
   .. code-block:: r
   
    > library(h2o)
    > h2o.init()
    > path <- "http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv"
    > df <- h2o.importFile(path)

    # Slice 1 row by index. 
    > c1 <- df[15,]
    > print(c1)
      sepal_len sepal_wid petal_len petal_wid       class
    1       5.8         4       1.2       0.2 Iris-setosa

    [1 row x 5 columns] 

    # Slice a range of rows.
    > c1_1 <- df[25:49,]
    > print(c1_1)
      sepal_len sepal_wid petal_len petal_wid       class
    1       4.8       3.4       1.9       0.2 Iris-setosa
    2       5.0       3.0       1.6       0.2 Iris-setosa
    3       5.0       3.4       1.6       0.4 Iris-setosa
    4       5.2       3.5       1.5       0.2 Iris-setosa
    5       5.2       3.4       1.4       0.2 Iris-setosa
    6       4.7       3.2       1.6       0.2 Iris-setosa

    [25 rows x 5 columns] 

    # Slice using a boolean mask. The output dataset will include rows with a sepal length less than 4.6.
    > mask <- df[,"sepal_len"] < 4.6
    > cols <- df[mask,]
    > print(cols)
      sepal_len sepal_wid petal_len petal_wid       class
    1       4.4       2.9       1.4       0.2 Iris-setosa
    2       4.3       3.0       1.1       0.1 Iris-setosa
    3       4.4       3.0       1.3       0.2 Iris-setosa
    4       4.5       2.3       1.3       0.3 Iris-setosa
    5       4.4       3.2       1.3       0.2 Iris-setosa

    [5 rows x 5 columns] 

    # Filter out rows that contain missing values in a column. Note the use of '!' to perform a logical not.
    > mask <- is.na(df[,"sepal_len"])
    > cols <- df[!mask,]
    > print(cols)
      sepal_len sepal_wid petal_len petal_wid       class
    1       5.1       3.5       1.4       0.2 Iris-setosa
    2       4.9       3.0       1.4       0.2 Iris-setosa
    3       4.7       3.2       1.3       0.2 Iris-setosa
    4       4.6       3.1       1.5       0.2 Iris-setosa
    5       5.0       3.6       1.4       0.2 Iris-setosa
    6       5.4       3.9       1.7       0.4 Iris-setosa

    [150 rows x 5 columns] 

   .. code-block:: python

    >>> import h2o
    >>> h2o.init()
    >>> path = "http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv"
    >>> df = h2o.import_file(path=path)

    # Slice 1 row by index.
    >>> c1 = df[15,:]
    >>> c1
      sepal_len    sepal_wid    petal_len    petal_wid  class
    -----------  -----------  -----------  -----------  -----------
    5.7          4.4          1.5          0.4  Iris-setosa

    [1 row x 5 columns]

    # Slice a range of rows.
    >>> c1_1 = df[range(25,50,1),:]
    >>> c1_1
      sepal_len    sepal_wid    petal_len    petal_wid  class
    -----------  -----------  -----------  -----------  -----------
            5            3            1.6          0.2  Iris-setosa
            5            3.4          1.6          0.4  Iris-setosa
            5.2          3.5          1.5          0.2  Iris-setosa
            5.2          3.4          1.4          0.2  Iris-setosa
            4.7          3.2          1.6          0.2  Iris-setosa
            4.8          3.1          1.6          0.2  Iris-setosa
            5.4          3.4          1.5          0.4  Iris-setosa
            5.2          4.1          1.5          0.1  Iris-setosa
            5.5          4.2          1.4          0.2  Iris-setosa
            4.9          3.1          1.5          0.1  Iris-setosa

    [25 rows x 5 columns]

    # Slice using a boolean mask. The output dataset will include rows with a sepal length less than 4.6.  
    >>> mask = df["sepal_len"] < 4.6
    >>> cols = df[mask,:]
    >>> cols.describe
      sepal_len    sepal_wid    petal_len    petal_wid  class
    -----------  -----------  -----------  -----------  -----------
            4.4          2.9          1.4          0.2  Iris-setosa
            4.3          3            1.1          0.1  Iris-setosa
            4.4          3            1.3          0.2  Iris-setosa
            4.5          2.3          1.3          0.3  Iris-setosa
            4.4          3.2          1.3          0.2  Iris-setosa

    [5 rows x 5 columns]

    # Filter out rows that contain missing values in a column. Note the use of '~' to perform a logical not.
    >>> mask = df["sepal_len"].isna()
    >>> cols = df[~mask,:]
    >>> cols.describe
    >>> cols.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]