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Methods defined here:
- __init__(self, data=None, input_column=None, output_columns=None, output_datatypes=None, delimiter=',', column_length=None, regex='(.*)', regex_set=1, exception=False, data_order_column=None)
- DESCRIPTION:
The Unpack function unpacks data from a single packed column into
multiple columns. The packed column is composed of multiple virtual
columns, which become the output columns. To determine the virtual
columns, the function must have either the delimiter that separates
them in the packed column or their lengths.
Note: This function is only available when teradataml is connected
to Vantage 1.1 or later versions.
PARAMETERS:
data:
Required Argument.
Specifies the teradataml DataFrame containing the input attributes.
data_order_column:
Optional Argument.
Specifies Order By columns for data.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
input_column:
Required Argument.
Specifies the name of the input column that contains the packed
data.
Types: str
output_columns:
Required Argument.
Specifies the names to give to the output columns, in the order in
which the corresponding virtual columns appear in "input_column". If you
specify fewer output column names than there are in virtual input
columns, the function ignores the extra virtual input columns. That
is, if the packed data contains x+y virtual columns and the
output_columns argument specifies x output column names, the function
assigns the names to the first x virtual columns and ignores the
remaining y virtual columns.
Types: str OR list of Strings (str)
output_datatypes:
Required Argument.
Specifies the datatypes of the unpacked output columns. Supported
output_datatypes are VARCHAR, int, float, TIME, DATE, and
TIMESTAMP. If output_datatypes specifies only one value and
output_columns specifies multiple columns, then the specified value
applies to every output_column. If output_datatypes specifies
multiple values, then it must specify a value for each output_column.
The nth datatype corresponds to the nth output_column. The function
can output only 16 VARCHAR columns.
Types: str OR list of Strings (str)
delimiter:
Optional Argument.
Specifies the delimiter (a string) that separates the virtual
columns in the packed data. If the virtual columns are separated
by a delimiter, then specify the delimiter with this argument;
otherwise, specify the column_length argument. Do not specify
both this argument and the column_length argument.
Default Value: ","
Types: str
column_length:
Optional Argument.
Specifies the lengths of the virtual columns; therefore, to use
this argument, you must know the length of each virtual column.
If column_length specifies only one value and output_columns specifies
multiple columns, then the specified value applies to every
output_column.
If column_length specifies multiple values, then it must specify
a value for each output_column. The nth datatype corresponds to
the nth output_column. However, the last value in column_length
can be an asterisk (*), which represents a single virtual column
that contains the remaining data.
For example, if the first three virtual columns have the lengths
2, 1, and 3, and all remaining data belongs to the fourth virtual
column, you can specify column_length ("2", "1", "3", *).
If you specify this argument, you must omit the delimiter argument.
Types: str OR list of Strings (str)
regex:
Optional Argument.
Specifies a regular expression that describes a row of packed data,
enabling the function to find the data values.
A row of packed data contains one data value for each virtual column,
but the row might also contain other information (such as the
virtual column name). In the regex, each data value is enclosed
in parentheses.
For example, suppose that the packed data has two virtual columns,
age and sex, and that one row of packed data is: age:34,sex:male.
The regex that describes the row is ".*:(.*)". The ".*:" matches
the virtual column names, age and sex, and the "(.*)" matches the
values, 34 and male.
To represent multiple data groups in regex, use multiple pairs
of parentheses. By default, the last data group in regex represents
the data value (other data groups are assumed to be virtual column
names or unwanted data). If a different data group represents the
data value, specify its group number with the regex_set argument.
Default value matches the whole string (between delimiters,
if any). When applied to the preceding sample row, the default
regex causes the function to return "age:34" and "sex:male" as
data values.
Default Value: "(.*)"
Types: str
regex_set:
Optional Argument.
Specifies the ordinal number of the data group in regex that
represents the data value in a virtual column. By default, the
last data group in regex represents the data value.
For example, suppose that regex is: "([a-zA-Z]*):(.*)". If
group number is "1", then "([a-zA-Z]*)" represents the data value.
If group number is "2", then "(.*)" represents the data value.
Default Value: 1
Types: int
exception:
Optional Argument.
Specifies whether the function ignores rows that contain invalid
data. By default, the function fails if it encounters a row with
invalid data.
Default Value: False
Types: bool
RETURNS:
Instance of Unpack.
Output teradataml DataFrames can be accessed using attribute
references, such as UnpackObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("Unpack",["ville_tempdata","ville_tempdata1"])
# Create teradataml DataFrame objects
ville_tempdata1 = DataFrame.from_table("ville_tempdata1")
ville_tempdata = DataFrame.from_table("ville_tempdata")
# Example1 - Delimiter Separates Virtual Columns.
# The input table, ville_tempdata, is a collection of temperature readings
# for two cities, Nashville and Knoxville, in the state of Tennessee.
# In the column of packed data, the delimiter comma (,) separates the virtual
# columns.
unpack_out1 = Unpack(data=ville_tempdata,
input_column='packed_temp_data',
output_columns=['city','state','temp_f'],
output_datatypes=['varchar','varchar','real'],
delimiter=',',
regex='(.*)',
regex_set=1,
exception=True)
# Print the results
print(unpack_out1.result)
# Example2 - No Delimiter Separates Virtual Columns.
# The input, ville_tempdata1, contains same data as the previous example,
# except that no delimiter separates the virtual columns in the packed data.
# To enable the function to determine the virtual columns, the function call
# specifes the column lengths.
unpack_out2 = Unpack(data=ville_tempdata1,
input_column='packed_temp_data',
output_columns=['city','state','temp_f'],
output_datatypes=['varchar','varchar','real'],
column_length=['9','9','4'],
regex='(.*)',
regex_set=1,
exception=True)
# Print the results
print(unpack_out2.result)
- __repr__(self)
- Returns the string representation for a Unpack class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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