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]