How to use the dataflows.join function in dataflows

def test_join_row_number_format_string():
    from dataflows import load, set_type, join
    flow = Flow(
        load('data/population.csv'),
        load('data/cities_comment.csv'),
        join(
            source_name='population',
            source_key='city with population in row {#}',
            target_name='cities_comment',
            target_key='{comment}',
            fields={'population': {'name': 'population'}}
        ),
    )
    data = flow.results()[0]
    assert data == [[
        {'city': 'paris', 'population': 2, 'comment': 'city with population in row 2'},
        {'city': 'london', 'population': 8, 'comment': 'city with population in row 1'},
        {'city': 'rome', 'population': 3, 'comment': 'city with population in row 3'},
    ]]

def test_join_row_number_readme_example():
    from dataflows import load, set_type, join
    flow = Flow(
        load('data/values.csv'),
        load('data/names.csv'),
        join(
            source_name='values',
            source_key=['#'],
            target_name='names',
            target_key=['#'],
            fields={'values': {'name': 'values'}}
        ),
    )
    data = flow.results()[0]
    assert data == [[
        {'id': 1, 'names': 'name1', 'values': 'value1'},
        {'id': 2, 'names': 'name2', 'values': 'value2'},
    ]]

def test_example_9():
    from dataflows import Flow, load, dump_to_path, join, concatenate, filter_rows

    f = Flow(
        # Emmy award nominees and winners
        load('data/emmy.csv', name='emmies'),
        filter_rows(equals=[dict(winner=1)]),
        concatenate(dict(
                emmy_nominee=['nominee'],
            ), 
            dict(name='emmies_filtered'),
            resources='emmies'),
        # Academy award nominees and winners
        load('data/academy.csv', encoding='utf8', name='oscars'),
        join('emmies_filtered', ['emmy_nominee'],  # Source resource
             'oscars', ['Name'],                   # Target resource
             full=False   # Don't add new fields, remove unmatched rows
        ),
        filter_rows(equals=[dict(Winner='1')]),
        dump_to_path('out/double_winners')
    )
    _ = f.process()

]

    houses = [
        {'house': 'House of Lannister'},
        {'house': 'House of Greyjoy'},
        {'house': 'House of Stark'},
        {'house': 'House of Targaryen'},
        {'house': 'House of Martell'},
        {'house': 'House of Tyrell'},
    ]

    res, _, _ = Flow(
        characters,
        set_type('age', type='number'),
        houses,
        join(
            'res_1',
            'House of {house}',
            'res_2',
            '{house}',
            dict(
                max_age={
                    'name': 'age',
                    'aggregate': 'max'
                },
                avg_age={
                    'name': 'age',
                    'aggregate': 'avg'
                },
                representative={
                    'name': 'first_name',
                    'aggregate': 'last'

def test_join_full_outer():
    from dataflows import load, set_type, join
    flow = Flow(
        load('data/population.csv'),
        load('data/cities.csv'),
        join(
            source_name='population',
            source_key=['id'],
            target_name='cities',
            target_key=['id'],
            fields={'population': {'name': 'population'}},
            mode='full-outer',
        ),
    )
    data = flow.results()[0]
    assert data == [[
        {'id': 1, 'city': 'london', 'population': 8},
        {'id': 2, 'city': 'paris', 'population': 2},
        {'id': 3, 'city': 'rome', 'population': None},
        {'id': 4, 'city': None, 'population': 3},
    ]]

def test_join_preserve_source_fields_order():
    from dataflows import load, join
    flow = Flow(
        load('data/cities_metadata.csv'),
        load('data/cities.csv'),
        join(
            source_name='cities_metadata',
            source_key='{id}',
            target_name='cities',
            target_key='{id}',
            fields={'key1': {'name': 'key1'}, 'key2': {'name': 'key2'}}
        ),
    )
    data, package, stats = flow.results()
    assert package.descriptor['resources'][0]['schema']['fields'] == [
        {'name': 'id', 'type': 'integer', 'format': 'default'},
        {'name': 'city', 'type': 'string', 'format': 'default'},
        {'name': 'key2', 'type': 'string', 'format': 'default'},
        {'name': 'key1', 'type': 'string', 'format': 'default'}
    ]
    assert data == [[
        {'id': 1, 'city': 'london', 'key1': 'val1', 'key2': 'val2'},

def test_join_row_number():
    from dataflows import load, set_type, join
    flow = Flow(
        load('data/population.csv'),
        load('data/cities.csv'),
        join(
            source_name='population',
            source_key=['#'],
            target_name='cities',
            target_key=['#'],
            fields={'population': {'name': 'population'}}
        ),
    )
    data = flow.results()[0]
    assert data == [[
        {'id': 1, 'city': 'london', 'population': 8},
        {'id': 2, 'city': 'paris', 'population': 2},
        {'id': 3, 'city': 'rome', 'population': 3},
    ]]

def flow(parameters):
    source = parameters['source']
    target = parameters['target']
    return Flow(
        load_lazy_json(source['name']),
        join(
            source['name'],
            source['key'],
            target['name'],
            target['key'],
            parameters['fields'],
            parameters.get('full', True),
            source.get('delete', False)
        ),
        update_resource(
            target['name'],
            **{
                PROP_STREAMING: True
            }