Slice rows¶
H2O-3 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.
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]
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]