def test_product():
ctx = Prosto("My Prosto")
t1 = ctx.populate(
table_name="Table 1", attributes=["A"],
func="lambda **m: pd.DataFrame({'A': [1.0, 2.0, 3.0]})", tables=[]
)
t2 = ctx.populate(
table_name="Table 2", attributes=["B"],
func="lambda **m: pd.DataFrame({'B': ['x', 'y', 'z']})", tables=[]
)
product = ctx.product(
table_name="Product", attributes=["t1", "t2"],
tables=["Table 1", "Table 2"]
)
t1.evaluate()
t2.evaluate()
product.evaluate()
assert len(product.get_df().columns) == 2
assert len(product.get_df()) == 9
assert product.get_df().columns.to_list() == ["t1", "t2"]
def test_product_inheritance():
"""
We add an addition calculate column to the product table which uses a column of a base table.
The system has to automatically insert a new operation by resolving this missing column.
"""
ctx = Prosto("My Prosto")
t1 = ctx.populate(
table_name="Table 1", attributes=["A"],
func="lambda **m: pd.DataFrame({'A': [1.0, 2.0, 3.0]})", tables=[]
)
t2 = ctx.populate(
table_name="Table 2", attributes=["B"],
func="lambda **m: pd.DataFrame({'B': ['x', 'y', 'z']})", tables=[]
)
product = ctx.product(
table_name="Product", attributes=["t1", "t2"],
tables=["Table 1", "Table 2"]
)
# In this calculate column, we use a column of the product table which actually exists only in a base table
clm = ctx.calculate(
name="My column", table=product.id,
func="lambda x: x + 1.0", columns=["A"], model=None
)
ctx.run()
# We get two columns in addition to two attributes: one merge (augmented) and one calculate column
assert len(product.get_df().columns) == 4
clm_data = product.get_series('My column')
assert clm_data.to_list() == [2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0]