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.

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

# Slice 1 row by index.
> c1 <- df[15,]

# Slice a range of rows.
> c1_1 <- df[25:49,]

# 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,]

# 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,]

>>> import h2o
>>> h2o.init()
>>> path = "data/iris/iris_wheader.csv"
>>> df = h2o.import_file(path=path)

# Slice 1 row by index.
>>> c1 = df[15,:]

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

# 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

# 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,:]