atoti.measures.Measures#
- final class atoti.measures.Measures#
-
The built-in measure contributors.COUNT counts how many rows from the cube’s fact table contributed to each aggregate of a query:
Example
>>> df = pd.DataFrame( ... columns=["ID", "Continent", "Country", "City", "Color"], ... data=[ ... (1, "Asia", "Japan", "Tokyo", "red"), ... (2, "Asia", "Japan", "Kyoto", "red"), ... (3, "Asia", "Singapore", "Singapore", "white"), ... (4, "Europe", "Spain", "Madrid", "green"), ... (5, "Europe", "Spain", "Barcelona", "blue"), ... ], ... ) >>> table = session.read_pandas(df, keys=["ID"], table_name="Cities") >>> cube = session.create_cube(table, mode="manual") >>> h, l, m = cube.hierarchies, cube.levels, cube.measures >>> h["ID"] = [table["ID"]] >>> cube.query(m["contributors.COUNT"]) contributors.COUNT 0 5 >>> cube.query(m["contributors.COUNT"], levels=[l["ID"]], include_totals=True) contributors.COUNT ID Total 5 1 1 2 1 3 1 4 1 5 1
The caption of this measure can be changed with
I18nConfig
.A measure can evaluate to the current member of an expressed level:
Example
>>> h["Color"] = [table["Color"]] >>> m["Color"] = l["Color"] >>> cube.query( ... m["Color"], ... m["contributors.COUNT"], ... levels=[l["Color"]], ... include_totals=True, ... ) Color contributors.COUNT Color Total 5 blue blue 1 green green 1 red red 2 white white 1
Or, for a multilevel hierarchy:
Example
>>> h["Geography"] = [table["Continent"], table["Country"], table["City"]] >>> m["Geography"] = h["Geography"] >>> cube.query( ... m["Geography"], ... m["contributors.COUNT"], ... levels=[l["City"]], ... include_totals=True, ... ) Geography contributors.COUNT Continent Country City Total 5 Asia Asia 3 Japan Japan 2 Kyoto Kyoto 1 Tokyo Tokyo 1 Singapore Singapore 1 Singapore Singapore 1 Europe Europe 2 Spain Spain 2 Barcelona Barcelona 1 Madrid Madrid 1
A measure can be compared to other objects, such as a constant, a
Level
, or another measure. If some condition inputs evaluate toNone
, the resulting measure will evaluate toFalse
:Example
>>> df = pd.DataFrame( ... columns=["Product", "Quantity", "Threshold"], ... data=[ ... ("bag", 5, 1), ... ("car", 1, 5), ... ("laptop", 4, None), ... ("phone", None, 2), ... ("watch", 3, 3), ... ], ... ) >>> table = session.read_pandas(df, keys=["Product"], table_name="Products") >>> cube = session.create_cube(table) >>> l, m = cube.levels, cube.measures >>> m["Condition"] = m["Quantity.SUM"] > m["Threshold.SUM"] >>> cube.query( ... m["Quantity.SUM"], ... m["Threshold.SUM"], ... m["Condition"], ... levels=[l["Product"]], ... include_totals=True, ... ) Quantity.SUM Threshold.SUM Condition Product Total 13.00 11.00 True bag 5.00 1.00 True car 1.00 5.00 False laptop 4.00 False phone 2.00 False watch 3.00 3.00 False
Measures can be defined and redefined without restarting the cube but, at the moment, deleting a measure will restart the cube so it will be slower:
Example
>>> m["test"] = 13 # no cube restart >>> m["test"] = 42 # no cube restart >>> del m["test"] # cube restart
See also
atoti.agg
,atoti.array
,atoti.function
,atoti.math
, andatoti.string
for other ways to define measures.Measure
to configure existing measures.