I/O Options

Whether you are using a generator or a strategy, you will want to define inputs and outputs for your data. These are standard across datafuzz as they are defined in the DataSet class. The current supported data types for a dataset are:

• lists
• numpy 2D arrays
• pandas dataframes

datafuzz will utilize pandas dataframes internally to represent and modify the records if you have pandas installed. If you want to avoid this, you may pass pandas=False in your initialization of your DataSet object.

Input options

You can read in several additional data formats, which will be used to create a DataSet object. These are normally defined in the Parser object, or are passed in the DataSet object itself. Options are as follows:

files:

defined by specifying file://$PATH_AND_FILENAME. Currently, only CSV and JSON files are supported.

sql queries:

defined by passing 'sql' as input. You must then also pass optional arguments for your parser (db_uri and query)

Output options

Output can be generated from every DataSet object by calling the to_output method, which will return either the output string or the output object. It will return a string for non-Python objects (such as sql tables and files) and an object for all native objects.

For output, you can define the following options:

files:

defined by specifying file://$PATH_AND_FILENAME. Currently, only CSV and JSON files are supported.

sql table:

defined by passing 'sql' as output. You must then also pass optional arguments for your output (db_uri and table)

pandas dataframe:

defined by passing 'pandas'

numpy 2D array:

defined by passing 'numpy'

list:

defined by passing 'list'

If you are interested in an example of using datafuzz as a stream, please see the streaming example in the example directory.