- With
atoti.Session.read_pandas():>>> import numpy as np >>> df = pd.DataFrame( ... columns=["NumPy array", "Python list"], ... data=[ ... (np.array([1.0, 2.0, 3.0]), [1, 2, 3]), ... (np.array([4.0, 5.0, 6.0]), [4, 5, 6]), ... (np.array([7.0, 8.0, 9.0]), [7, 8, 9]), ... ], ... ) >>> pandas_table = session.read_pandas(df, table_name="DataFrame with arrays") >>> pandas_table.head() NumPy array Python list 0 [1.0, 2.0, 3.0] [1, 2, 3] 1 [4.0, 5.0, 6.0] [4, 5, 6] 2 [7.0, 8.0, 9.0] [7, 8, 9] - With
atoti.Session.read_csv():>>> pnl_table = session.read_csv( ... TEST_RESOURCES_PATH / "csv" / "pnl.csv", ... array_separator=";", ... keys={"Continent", "Country"}, ... table_name="PnL", ... ) >>> pnl_table.head().sort_index() PnL Continent Country America Mexico [-10.716, 9.593, 1.356, -21.185, 5.989, 9.686,... Asia China [-1.715, 2.425, -4.059, 4.102, -2.331, -2.662,... India [-18.716, 8.583, -41.356, -11.138, 3.949, 5.66... Europe France [-0.465, -0.025, 0.601, 0.423, -0.815, 0.024, ... UK [11.449, -35.5464, -66.641, -48.498, -6.3126, ...
>>> cube = session.create_cube(pnl_table)
>>> l, m = cube.levels, cube.measures
>>> cube.query(m["PnL.SUM"], m["PnL.MEAN"], levels=[l["Continent"]])
PnL.SUM PnL.MEAN
Continent
America doubleVector[10]{-10.716, ...} doubleVector[10]{-10.716, ...}
Asia doubleVector[10]{-20.431, ...} doubleVector[10]{-10.2155, ...}
Europe doubleVector[10]{10.984, ...} doubleVector[10]{5.492, ...}
-
Arithmetic operators:
>>> m["PnL +10"] = m["PnL.SUM"] + 10.0 >>> cube.query(m["PnL +10"]) PnL +10 0 doubleVector[10]{-10.163, ...}>>> m["PnL -10"] = m["PnL.SUM"] - 10.0 >>> cube.query(m["PnL -10"]) PnL -10 0 doubleVector[10]{-30.163, ...}>>> m["PnL x10"] = m["PnL.SUM"] * 10.0 >>> cube.query(m["PnL x10"]) PnL x10 0 doubleVector[10]{-201.63, ...}>>> m["PnL /10"] = m["PnL.SUM"] / 10.0 >>> cube.query(m["PnL /10"]) PnL /10 0 doubleVector[10]{-2.0163, ...} -
Indexing:
This can be used with
>>> m["First element"] = m["PnL.SUM"][0] >>> cube.query(m["First element"], m["PnL.SUM"]) First element PnL.SUM 0 -20.16 doubleVector[10]{-20.163, ...}atoti.Cube.create_parameter_hierarchy_from_members()to “slice” the array:Non-integer hierarchies can also be created:>>> cube.create_parameter_hierarchy_from_members("Index", list(range(0, 10))) >>> m["PnL at index"] = m["PnL.SUM"][l["Index"]] >>> cube.query(m["PnL at index"], levels=[l["Index"]]) PnL at index Index 0 -20.16 1 -14.97 2 -110.10 3 -76.30 4 .48 5 -57.51 6 -.53 7 -15.49 8 -22.97 9 9.26>>> from datetime import date, timedelta >>> dates = [date(2020, 1, 1) + timedelta(days=offset) for offset in range(0, 10)] >>> cube.create_parameter_hierarchy_from_members( ... "Dates", dates, index_measure_name="Date index" ... ) >>> m["PnL at date"] = m["PnL.SUM"][m["Date index"]] >>> cube.query(m["Date index"], m["PnL at date"], levels=[l["Dates"]]) Date index PnL at date Dates 2020-01-01 0 -20.16 2020-01-02 1 -14.97 2020-01-03 2 -110.10 2020-01-04 3 -76.30 2020-01-05 4 .48 2020-01-06 5 -57.51 2020-01-07 6 -.53 2020-01-08 7 -15.49 2020-01-09 8 -22.97 2020-01-10 9 9.26 -
Slicing:
>>> m["First 2 elements"] = m["PnL.SUM"][0:2] >>> cube.query(m["First 2 elements"], m["PnL.SUM"]) First 2 elements PnL.SUM 0 doubleVector[2]{-20.163, ...} doubleVector[10]{-20.163, ...} -
Selecting elements at given indices:
A
Tuple[int, ...]or a measure of typeINT_ARRAYorLONG_ARRAYcan be provided to create another array measure containing the values at the passed indices:>>> m["First and last"] = m["PnL.SUM"][0, -1] >>> cube.query(m["First and last"]) First and last 0 doubleVector[2]{-20.163, ...}
len | Return a measure equal to the number of elements of the passed array measure. |
max | Return a measure equal to the maximum element of the passed array measure. |
mean | Return a measure equal to the mean of all the elements of the passed array measure. |
min | Return a measure equal to the minimum element of the passed array measure. |
n_greatest | Return an array measure containing the n greatest elements of the passed array measure. |
n_greatest_indices | Return an array measure containing the indices of the n greatest elements of the passed array measure. |
n_lowest | Return an array measure containing the n lowest elements of the passed array measure. |
n_lowest_indices | Return an array measure containing the indices of the n lowest elements of the passed array measure. |
negative_values | Return a measure where all the elements > 0 of the passed array measure are replaced by 0. |
nth_greatest | Return a measure equal to the n-th greatest element of the passed array measure. |
nth_lowest | Return a measure equal to the n-th lowest element of the passed array measure. |
positive_values | Return a measure where all the elements < 0 of the passed array measure are replaced by 0. |
prefix_sum | Return a measure equal to the sum of the previous elements in the passed array measure. |
prod | Return a measure equal to the product of all the elements of the passed array measure. |
quantile | Return a measure equal to the requested quantile of the elements of the passed array measure. |
quantile_index | Return a measure equal to the index of requested quantile of the elements of the passed array measure. |
replace | Return a measure where elements equal to a key of the replacements mapping are replaced with the corresponding value. |
sort | Return an array measure with the elements of the passed array measure sorted. |
std | Return a measure equal to the standard deviation of the elements of the passed array measure. |
sum | Return a measure equal to the sum of all the elements of the passed array measure. |
var | Return a measure equal to the variance of the elements of the passed array measure. |