Math functions

On top of the functions listed below, measures can also be combined with mathematical operators.

• The classic +, - and * operators:

  >>> df = pd.DataFrame(
  ...     columns=["City", "A", "B", "C", "D"],
  ...     data=[
  ...         ("Berlin", 15.0, 10.0, 10.1, 1.0),
  ...         ("London", 24.0, 16.0, 20.5, 3.14),
  ...         ("New York", -27.0, 15.0, 30.7, 10.0),
  ...         ("Paris", 0.0, 0.0, 0.0, 0.0),
  ...     ],
  ... )
  >>> table = session.read_pandas(df, keys={"City"}, table_name="Math")
  >>> cube = session.create_cube(table)
  >>> l, m = cube.levels, cube.measures
  >>> m["Sum"] = m["A.SUM"] + m["B.SUM"]
  >>> m["Subtract"] = m["A.SUM"] - m["B.SUM"]
  >>> m["Multiply"] = m["A.SUM"] * m["B.SUM"]
  >>> cube.query(
  ...     m["A.SUM"],
  ...     m["B.SUM"],
  ...     m["Sum"],
  ...     m["Subtract"],
  ...     m["Multiply"],
  ...     levels=[l["City"]],
  ... )
             A.SUM  B.SUM     Sum Subtract Multiply
  City
  Berlin     15.00  10.00   25.00     5.00   150.00
  London     24.00  16.00   40.00     8.00   384.00
  New York  -27.00  15.00  -12.00   -42.00  -405.00
  Paris        .00    .00     .00      .00      .00
  

• The float division / and integer division //:

  >>> m["Float division"] = m["A.SUM"] / m["B.SUM"]
  >>> m["Int division"] = m["A.SUM"] // m["B.SUM"]
  >>> cube.query(
  ...     m["A.SUM"],
  ...     m["B.SUM"],
  ...     m["Float division"],
  ...     m["Int division"],
  ...     levels=[l["City"]],
  ... )
             A.SUM  B.SUM Float division Int division
  City
  Berlin     15.00  10.00           1.50         1.00
  London     24.00  16.00           1.50         1.00
  New York  -27.00  15.00          -1.80        -2.00
  Paris        .00    .00            NaN          NaN
  

• The exponentiation **:

  >>> m["a²"] = m["A.SUM"] ** 2
  >>> cube.query(m["A.SUM"], m["a²"], levels=[l["City"]])
             A.SUM      a²
  City
  Berlin     15.00  225.00
  London     24.00  576.00
  New York  -27.00  729.00
  Paris        .00     .00
  

• The modulo %:

  >>> m["Modulo"] = m["A.SUM"] % m["B.SUM"]
  >>> cube.query(m["A.SUM"], m["B.SUM"], m["Modulo"], levels=[l["City"]])
             A.SUM  B.SUM Modulo
  City
  Berlin     15.00  10.00   5.00
  London     24.00  16.00   8.00
  New York  -27.00  15.00   3.00
  Paris        .00    .00    NaN
  

abs	Return a measure equal to the absolute value of the passed measure.
ceil	Return a measure equal to the smallest integer that is >= to the passed measure.
cos	Return a measure equal to the cosine of the passed measure in radians.
erf	Return the error function of the input measure.
erfc	Return the complementary error function of the input measure.
exp	Return a measure equal to the exponential value of the passed measure.
floor	Return a measure equal to the largest integer <= to the passed measure.
isnan	Return a measure equal to True when the passed measure is NaN and to False otherwise.
log	Return a measure equal to the natural logarithm (base e) of the passed measure.
log10	Return a measure equal to the base 10 logarithm of the passed measure.
max	Return a measure equal to the maximum of the passed arguments.
min	Return a measure equal to the minimum of the passed arguments.
round	Return a measure equal to the closest integer to the passed measure.
sin	Return a measure equal to the sine of the passed measure in radians.
sqrt	Return a measure equal to the square root of the passed measure.
tan	Return a measure equal to the tangent of the passed measure in radians.