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Slice rows

H2O-3 Secure 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]
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