Slicing RowsΒΆ
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)
> h2o.init(nthreads=-1)
> df <- h2o.importFile(path)
> path <- "data/iris/iris_wheader.csv"
# 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,:]