def test_load_retail_diff():
nrows = 10
df = load_retail(nrows=nrows, return_dataframe=True)
assert df.shape[0] == nrows
nrows_second = 11
df = load_retail(nrows=nrows_second, return_dataframe=True)
assert df.shape[0] == nrows_second
assert 'order_product_id' in df.columns
assert df['order_product_id'].is_unique
def test_load_retail_diff():
nrows = 10
df = load_retail(nrows=nrows, init_woodwork=False)
assert df.ww.schema is None
assert df.shape[0] == nrows
nrows_second = 11
df = load_retail(nrows=nrows_second, init_woodwork=False)
assert df.ww.schema is None
assert df.shape[0] == nrows_second
assert 'order_product_id' in df.columns
assert df['order_product_id'].is_unique
def test_load_retail():
df = load_retail(nrows=10, init_woodwork=True)
assert isinstance(df.ww.schema, TableSchema)
expected_logical_types = {
'order_product_id': Categorical,
'order_id': Categorical,
'product_id': Categorical,
'description': NaturalLanguage,
'quantity': Integer,
'order_date': Datetime,
'unit_price': Double,
'customer_name': Categorical,
'country': Categorical,
'total': Double,
'cancelled': Boolean,
}
for col_name, column in df.ww.columns.items():
assert column.logical_type == expected_logical_types[col_name]
assert df.ww.index == 'order_product_id'
assert df.ww.time_index == 'order_date'
assert df.ww.semantic_tags['order_product_id'] == {'index'}
assert df.ww.semantic_tags['order_date'] == {'time_index'}
def test_load_retail_datatable():
dt = load_retail(nrows=10, return_dataframe=False)
assert isinstance(dt, DataTable)
expected_logical_types = {
'order_product_id': Categorical,
'order_id': Categorical,
'product_id': Categorical,
'description': NaturalLanguage,
'quantity': Integer,
'order_date': Datetime,
'unit_price': Double,
'customer_name': Categorical,
'country': Categorical,
'total': Double,
'cancelled': Boolean,
}
for column in dt.columns.values():
assert column.logical_type == expected_logical_types[column.name]
assert dt.index == 'order_product_id'
assert dt.time_index == 'order_date'
assert dt.columns['order_product_id'].semantic_tags == {'index'}
assert dt.columns['order_date'].semantic_tags == {'time_index'}
def test_load_retail():
df = load_retail(nrows=10, init_woodwork=True)
assert isinstance(df.ww.schema, TableSchema)
expected_logical_types = {
"order_product_id": Categorical,
"order_id": Categorical,
"product_id": Categorical,
"description": NaturalLanguage,
"quantity": Integer,
"order_date": Datetime,
"unit_price": Double,
"customer_name": Categorical,
"country": Categorical,
"total": Double,
"cancelled": Boolean,
}
for col_name, column in df.ww.columns.items():
assert isinstance(column.logical_type,
expected_logical_types[col_name])
assert df.ww.index == "order_product_id"
assert df.ww.time_index == "order_date"
assert df.ww.semantic_tags["order_product_id"] == {"index"}
assert df.ww.semantic_tags["order_date"] == {"time_index"}