Use the get_values() function to retrieve all values (only) present in a teradataml DataFrame.
The values are retrieved as per a numpy.ndarray representation of the teradataml DataFrame. This format is equivalent to the get_values() representation of a Pandas DataFrame.
An optional integer valued argument num_rows allows the user to specify the number of rows to retrieve values for from the teradataml DataFrame.
- Row and column indexing starts from 0, so the first column = index 0, second column = index 1, and so on.
- When a Pandas DataFrame is saved to the database and then retrieved back as a teradataml DataFrame, the get_values() method on a Pandas DataFrame, and the corresponding teradataml DataFrames have the following type differences:
- teradataml DataFrame get_values() retrieves 'bool' type Pandas DataFrame values ('True' or 'False') as BYTEINTS ('1' or '0');
- teradataml DataFrame get_values() retrieves 'Timedelta' type Pandas DataFrame values as equivalent values in seconds.
Example Prerequisite
>>> df = DataFrame("admissions_train")
>>> df masters gpa stats programming admitted id 5 no 3.44 novice novice 0 7 yes 2.33 novice novice 1 22 yes 3.46 novice beginner 0 17 no 3.83 advanced advanced 1 13 no 4.00 advanced novice 1 19 yes 1.98 advanced advanced 0 36 no 3.00 advanced novice 0 15 yes 4.00 advanced advanced 1 34 yes 3.85 advanced beginner 0 40 yes 3.95 novice beginner 0
Example 1: Retrieves values present in a teradataml DataFrame
>>> vals = df.get_values()
>>> vals array([['yes', 4.0, 'advanced', 'advanced', 1], ['yes', 3.45, 'advanced', 'advanced', 0], ['yes', 3.5, 'advanced', 'beginner', 1], ['yes', 4.0, 'novice', 'beginner', 0], . . . ['no', 3.68, 'novice', 'beginner', 1], ['yes', 3.5, 'beginner', 'advanced', 1], ['yes', 3.79, 'advanced', 'novice', 0], ['no', 3.0, 'advanced', 'novice', 0], ['yes', 1.98, 'advanced', 'advanced', 0]], dtype=object)
Example 2: Retrieve values for a given number of rows from a teradataml DataFrame
>>> vals = df1.get_values(num_rows = 3) >>> vals array([['yes', 4.0, 'advanced', 'advanced', 1], ['yes', 3.45, 'advanced', 'advanced', 0], ['yes', 3.5, 'advanced', 'beginner', 1]], dtype=object)
Example 3: Access specific values from the entire set received
# Retrieve all values from an entire row (for example, the first row): >>> vals[0] array(['yes', 4.0, 'advanced', 'advanced', 1], dtype=object)
# Specify a range to retrieve values from a subset of rows (For example, first 3 rows): >>> vals[0:3] array([['yes', 4.0, 'advanced', 'advanced', 1], ['yes', 3.45, 'advanced', 'advanced', 0], ['yes', 3.5, 'advanced', 'beginner', 1]], dtype=object)
# Retrieve all values from an entire column (For example, the first column): >>> vals[:, 0] array(['yes', 'yes', 'yes', 'yes', 'yes', 'no', 'yes', 'yes', 'yes', 'yes', 'no', 'no', 'yes', 'yes', 'no', 'yes', 'no', 'yes', 'no', 'no', 'no', 'no', 'no', 'no', 'yes', 'yes', 'no', 'no', 'yes', 'yes', 'yes', 'no', 'no', 'yes', 'no', 'no', 'yes', 'yes', 'no','yes'], dtype=object)
# Retrieve a single value from a given row and column (For example, 3rd row, and 2nd column): >>> vals[2,1] 3.5