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Accessing Data in a Dataset

To access the data inside of a dataset, each dataset has a few functions that can be called on to access different parts of the dataset. These are listed in the table below.

print cities.getColumnAsList(0)

print cities.getColumnCount()

print cities.getColumnIndex("Timezone")

print cities.getColumnName(1)

print cities.getColumnNames()

print cities.getColumnType(3)

<type 'java.lang.Integer'>

print cities.getColumnTypes()

print cities.getRowCount()

print cities.getValueAt(1, 2)

print cities.getValueAt(2, "Population")

Looping Through a Dataset

Oftentimes you need to loop through the items in a dataset similar to how you would loop through a list of items. You can use the functions above to do this.

# We use the same cities dataset from above. Using the range function, we can come up with a range of values that represents the number of columns.
for row in range(cities.getRowCount()):
	for col in range(cities.getColumnCount()):
		print cities.getValueAt(row, col) # Will print out every item in our cities dataset, starting on the first row and moving left to right.

Accessing Data in a PyDataset

PyDatasets are special in that they can be handled similarly to other Python sequences. Any dataset object can be converted to a PyDataset using the function system.dataset.toPyDataSet. All of the functions listed above can be used on a PyDataset, but the data can also be accessed much easier, similar to how you would a list.

# First convert the cities dataset to a PyDataset.
pyData = system.dataset.toPyDataSet(cities)

# The data can then be accessed using two brackets at the end with row and column indexes. This will print "PST"
print pyData[1][2]

Looping Through a PyDataset

Looping through a PyDataset is also a bit easier to do, working similar to other sequences. The first for loop will pull out each row, which acts like a list and can be used in a second for loop to extract the values.

# Convert to a PyDataset
pyData = system.dataset.toPyDataSet(cities)

# The for loop pulls out the whole row, so typically the variable row is used.
for row in pyData:
	# Now that we have a single row, we can loop through the columns just like a list.
	for value in row:
		print value

Additionally, a single column of data can be extracted by looping through the PyDataset.

# Convert to a PyDataset
pyData = system.dataset.toPyDataSet(cities)

# Use a for loop to extract out a single row at a time
for row in pyData:
	# Use either the column index or the column name to extract a single value from that row.
	city = row[0]
	population = row["Population"]
	print city, population

PyRow

A PyRow is a row in a PyDataset. It works similarly to a Python list.

The examples and outputs are based on the results in the table below. In addition, "print" commands are used, but should be replaced by appropriate logging methods (such as system.util.getLogger) depending on the scope of the script.

for row in pyDataset:
    try:
	    print row.index("Apple")
	except:
		print "No apples in this row"

0
No apples in this row
0

for row in pyDataset:
	print row.count("Apple")

1
0
2

Repeating Elements

You can also have repeating elements in a row:

for row in PyDataset
	print row * 2

[u'Apple', u'Orange', u'Apple', u'Orange']
[u'Banana', u'Orange', u'Banana', u'Orange']
[u'Apple', u'Apple', u'Apple', u'Apple']

Altering a Dataset

Technically, you cannot alter a dataset. Datasets are immutable, meaning they cannot change. You can, however, create new datasets. To change a dataset, you really create a new one and then replace the old one with the new one. There are system functions that are available that can alter or manipulate datasets in other ways. Any of the functions in the system.dataset section can be used on datasets, the most common ones have been listed below:

• system.dataset.addRow

• system.dataset.deleteRow

• system.dataset.setValue

• system.dataset.updateRow

The important thing to realize about all of these datasets is that, again, they do not actually alter the input dataset. They return a new dataset. You need to actually use that returned dataset to do anything useful.

For example, the following code is an example of the setValue function, and would change the population value for Los Angeles.

# Create a new dataset with the new value.
newData = system.dataset.setValue(cities, 1, "Population", 5000000)

# The cities dataset remains unchanged, and we can see this by looping through both datasets.for row in range(cities.getRowCount()):
for row in range(cities.getRowCount()):
	for col in range(cities.getColumnCount()):
		print cities.getValueAt(row, col)

for row in range(newData.getRowCount()):
	for col in range(newData.getColumnCount()):
		print newData.getValueAt(row, col)