iode.Variables.__itruediv__
- Variables.__itruediv__(other)[source]
divide the current (subset of) Variables object by other.
- Parameters:
- other: int, float, numpy ndarray, pandas Series, pandas DataFrame, larray Array or iode Variables
If other is an int or a float, divide all values of the current (subset of) Variables object by the scalar. If other is a numpy ndarray, the shape of the ndarray must be compatible with the current (subset of) Variables object. Specifically, the number of rows must be equal to the number of variables and the number of columns must be equal to the number of periods. If other is a pandas Series, it must represent either a single variable or a single period. If other is a pandas DataFrame, it must represent the same variables names and periods as the current (subset of) Variables object. Specifically, the index of the DataFrame must be equal to the variables names and the columns of the DataFrame must be equal to the periods. If other is an larray Array, its last axis must be equal to the periods and be named ‘time’. If the Array has more than two axes, the first n-1 axes are combined to form the variables names. The first (combined) axis must be equal to the variables names. If other is an iode Variables object, it must share the same sample and represent the same set of variables names as self.
Warning
Dividing a numpy ndarray to a Variables object is not recommended as there is no compatibility check between for the names and periods. The result is not guaranteed to be the one you expected. This possibility is provided for speed reasons (when the database or the subset is large).
Examples
>>> import numpy as np >>> import pandas as pd >>> import larray as la >>> from iode import SAMPLE_DATA_DIR >>> from iode import variables, NA, Sample >>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var") Loading .../fun.var 394 objects loaded >>> vars_subset = variables["A*", "1991Y1:1995Y1"] >>> vars_subset.names ['ACAF', 'ACAG', 'AOUC', 'AOUC_', 'AQC'] >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 26.24 30.16 34.66 8.16 -13.13 ACAG -30.93 -40.29 -43.16 -16.03 -41.85 AOUC 1.02 1.03 1.03 1.05 1.05 AOUC_ 0.96 0.97 0.98 0.99 1.00 AQC 1.06 1.11 1.15 1.16 1.16
>>> # divide all values of a subset of a Variables object by a scalar >>> vars_subset /= 2.0 >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 13.12 15.08 17.33 4.08 -6.57 ACAG -15.47 -20.14 -21.58 -8.01 -20.92 AOUC 0.51 0.52 0.52 0.52 0.52 AOUC_ 0.48 0.49 0.49 0.49 0.50 AQC 0.53 0.56 0.58 0.58 0.58
>>> # divide (a subset of) a Variables object by another >>> vars_subset /= vars_subset >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 1.00 1.00 1.00 1.00 1.00 ACAG 1.00 1.00 1.00 1.00 1.00 AOUC 1.00 1.00 1.00 1.00 1.00 AOUC_ 1.00 1.00 1.00 1.00 1.00 AQC 1.00 1.00 1.00 1.00 1.00
>>> # divide a a single variable by a pandas Series >>> series = pd.Series([1.0, 0.5, 0.25, 0.2, 0.1], index=vars_subset.periods_as_str) >>> series 1991Y1 1.00 1992Y1 0.50 1993Y1 0.25 1994Y1 0.20 1995Y1 0.10 dtype: float64 >>> vars_subset["ACAF"] /= series >>> vars_subset["ACAF"] Workspace: Variables nb variables: 1 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 1.00 2.00 4.00 5.00 10.00
>>> # divide a pandas Series to the subset corresponding to a single period >>> series = pd.Series([1.0, 0.5, 0.25, 0.2, 0.1], index=vars_subset.names) >>> series ACAF 1.00 ACAG 0.50 AOUC 0.25 AOUC_ 0.20 AQC 0.10 dtype: float64 >>> vars_subset[:, "1995Y1"] /= series >>> vars_subset[:, "1995Y1"] Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1995Y1:1995Y1 mode: LEVEL name 1995Y1 ACAF 10.00 ACAG 2.00 AOUC 4.00 AOUC_ 5.00 AQC 10.00
>>> # divide a (subset of a) Variables object by a pandas DataFrame >>> data = np.array([[0.1, 0.2, 0.25, 0.5, 1.0], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1]]) >>> df = pd.DataFrame(data, index=vars_subset.names, columns=vars_subset.periods_as_str) >>> df 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 0.1 0.2 0.25 0.5 1.0 ACAG 1.0 0.5 0.25 0.2 0.1 AOUC 1.0 0.5 0.25 0.2 0.1 AOUC_ 1.0 0.5 0.25 0.2 0.1 AQC 1.0 0.5 0.25 0.2 0.1 >>> vars_subset /= df >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 10.00 10.00 16.00 10.00 10.00 ACAG 1.00 2.00 4.00 5.00 20.00 AOUC 1.00 2.00 4.00 5.00 40.00 AOUC_ 1.00 2.00 4.00 5.00 50.00 AQC 1.00 2.00 4.00 5.00 100.00
>>> # divide (a subset of) a Variables object by an larray Array >>> axis_names = la.Axis(name="names", labels=vars_subset.names) >>> axis_time = la.Axis(name="time", labels=vars_subset.periods_as_str) >>> array = la.Array(data, axes=(axis_names, axis_time)) >>> array names\time 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 0.1 0.2 0.25 0.5 1.0 ACAG 1.0 0.5 0.25 0.2 0.1 AOUC 1.0 0.5 0.25 0.2 0.1 AOUC_ 1.0 0.5 0.25 0.2 0.1 AQC 1.0 0.5 0.25 0.2 0.1 >>> vars_subset /= array >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 100.00 50.00 64.00 20.00 10.00 ACAG 1.00 4.00 16.00 25.00 200.00 AOUC 1.00 4.00 16.00 25.00 400.00 AOUC_ 1.00 4.00 16.00 25.00 500.00 AQC 1.00 4.00 16.00 25.00 1000.00
>>> # WARNING: dividing a numpy ndarray to a (subset of a) Variables object is not recommended >>> # as there is no compatibility check between for the names and periods. >>> # The result is not guaranteed to be the one you expected. >>> # This possibility is provided for speed reasons >>> # (when dealing with large subsets/databases). >>> # divide a single variable by a numpy 1D ndarray >>> data = np.array([1.0, 0.5, 1.0, 0.5, 1.0]) >>> vars_subset["ACAF"] /= data >>> vars_subset["ACAF"] Workspace: Variables nb variables: 1 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 100.00 100.00 64.00 40.00 10.00 >>> # divide the subset corresponding to a single period by a numpy 1D ndarray >>> vars_subset[:, "1995Y1"] /= data >>> vars_subset[:, "1995Y1"] Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1995Y1:1995Y1 mode: LEVEL name 1995Y1 ACAF 10.00 ACAG 400.00 AOUC 400.00 AOUC_ 1000.00 AQC 1000.00 >>> # divide a (subset of a) Variables object by a numpy 2D ndarray >>> data = np.array([[0.1, 0.2, 0.25, 0.5, 1.0], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1], ... [1.0, 0.5, 0.25, 0.2, 0.1]]) >>> vars_subset /= data >>> vars_subset Workspace: Variables nb variables: 5 filename: ...fun.var description: Modèle fun - Simulation 1 sample: 1991Y1:1995Y1 mode: LEVEL name 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 1000.00 500.00 256.00 80.00 10.00 ACAG 1.00 8.00 64.00 125.00 4000.00 AOUC 1.00 8.00 64.00 125.00 4000.00 AOUC_ 1.00 8.00 64.00 125.00 10000.00 AQC 1.00 8.00 64.00 125.00 10000.00