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.

.. tabs::
   .. code-tab:: r 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-tab:: python

        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 1 row by index
        c1 = df[15,:]
        c1.describe

        # Slice a range of rows
        c1_1 = df[range(25,50,1),:]
        c1_1.describe

        # 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

        # 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
         sepal_len   sepal_wid   petal_len    petal_wid  clas
        ----------  ----------  ----------  -----------  -----------
               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 3 columns]