pw.debug

Methods and classes for debugging Pathway computation.

Typical use:

import pathway as pw
t1 = pw.debug.table_from_markdown('''
pet
Dog
Cat
''')
t2 = t1.select(animal=t1.pet, desc="fluffy")
pw.debug.compute_and_print(t2, include_id=False)

pw.debug.compute_and_print(*tables, include_id=True, short_pointers=True, n_rows=None, **kwargs)

sourceA function running the computations and printing the table.

Parameters
- tables (Table) – tables to be computed and printed
- include_id – whether to show ids of rows
- short_pointers – whether to shorten printed ids
- n_rows (int | None) – number of rows to print, if None whole table will be printed

pw.debug.compute_and_print_update_stream(*tables, include_id=True, short_pointers=True, n_rows=None, **kwargs)

sourceA function running the computations and printing the update stream of the table.

Parameters
- tables (Table) – tables for which the update stream is to be computed and printed
- include_id – whether to show ids of rows
- short_pointers – whether to shorten printed ids
- n_rows (int | None) – number of rows to print, if None whole update stream will be printed

pw.debug.table_from_markdown(table_def, id_from=None, unsafe_trusted_ids=False, schema=None, *, split_on_whitespace=True)

sourceA function for creating a table from its definition in markdown. If it contains a special column __time__, rows will be split into batches with timestamps from the column. A special column __diff__ can be used to set an event type - with 1 treated as inserting the row and -1 as removing it.

pw.debug.table_from_pandas(df, id_from=None, unsafe_trusted_ids=False, schema=None, )

sourceA function for creating a table from a pandas DataFrame. If it contains a special column __time__, rows will be split into batches with timestamps from the column. A special column __diff__ can be used to set an event type - with 1 treated as inserting the row and -1 as removing it.