iode.Variables.__pow__
- Variables.__pow__(other)[source]
Compute the expression \(self^{other}\) ( self ** other ).
- Parameters:
- other: int, float, numpy ndarray, pandas Series, pandas DataFrame, larray Array or iode Variables
If other is an int or a float, compute ‘value ** other’ for all values of the current (subset of) Variables object. 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.
- Returns:
- Variables
Warning
Using a numpy ndarray 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
>>> # compute 'value ** other' for all values of the current >>> # (subset of) Variables object. >>> new_vars_subset = vars_subset ** 2 >>> new_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 688.59 909.57 1201.45 66.60 172.42 ACAG 956.91 1622.96 1862.61 256.93 1751.09 AOUC 1.05 1.06 1.06 1.09 1.10 AOUC_ 0.93 0.95 0.96 0.98 0.99 AQC 1.13 1.23 1.33 1.34 1.35
>>> # compute 'V[name, period] ** W[name, period]' for each name and period >>> # for all names and periods >>> other = vars_subset.copy() >>> other = 2.0 >>> new_vars_subset = vars_subset ** other >>> new_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 688.59 909.57 1201.45 66.60 172.42 ACAG 956.91 1622.96 1862.61 256.93 1751.09 AOUC 1.05 1.06 1.06 1.09 1.10 AOUC_ 0.93 0.95 0.96 0.98 0.99 AQC 1.13 1.23 1.33 1.34 1.35
>>> # compute 'iode_var[period] ** series[period]' for each period >>> series = pd.Series([1.0, 2.0, 0.5, 1./4., 2.0], index=vars_subset.periods_as_str) >>> series 1991Y1 1.00 1992Y1 2.00 1993Y1 0.50 1994Y1 0.25 1995Y1 2.00 dtype: float64 >>> updated_ACAF = vars_subset["ACAF"] ** series >>> updated_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 26.24 909.57 5.89 1.69 172.42
>>> # compute 'single_period_subset[name] ** series[name]' for each name >>> series = pd.Series([1.0, 2.0, 0.5, 1./4., 2.0], index=vars_subset.names) >>> series ACAF 1.00 ACAG 2.00 AOUC 0.50 AOUC_ 0.25 AQC 2.00 dtype: float64 >>> vars_subset_1995Y1 = 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 -13.13 ACAG 1751.09 AOUC 1.02 AOUC_ 1.00 AQC 1.35
>>> # compute 'iode_var[name, period] ** df[name, period]' for each name and period >>> data = np.array([[1.0, 2.0, 0.5, 1./4., 2.0], ... [2.0, -1.0, 2.0, -1.0, 2.0], ... [1./4., 2.0, 1.0, 2.0, 0.5], ... [0.5, 1./4., 2.0, 1.0, 2.0], ... [2.0, 0.5, 1./4., 2.0, 1.0]]) >>> df = pd.DataFrame(data, index=vars_subset.names, columns=vars_subset.periods_as_str) >>> df 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 1.00 2.00 0.50 0.25 2.0 ACAG 2.00 -1.00 2.00 -1.00 2.0 AOUC 0.25 2.00 1.00 2.00 0.5 AOUC_ 0.50 0.25 2.00 1.00 2.0 AQC 2.00 0.50 0.25 2.00 1.0 >>> new_vars_subset = vars_subset ** df >>> new_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 26.24 909.57 5.89 1.69 172.42 ACAG 956.91 -0.02 1862.61 -0.06 1751.09 AOUC 1.01 1.06 1.03 1.09 1.02 AOUC_ 0.98 0.99 0.96 0.99 0.99 AQC 1.13 1.05 1.04 1.34 1.16
>>> # compute 'iode_var[name, period] ** array[name, period]' for each name and period >>> 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 1.0 2.0 0.5 0.25 2.0 ACAG 2.0 -1.0 2.0 -1.0 2.0 AOUC 0.25 2.0 1.0 2.0 0.5 AOUC_ 0.5 0.25 2.0 1.0 2.0 AQC 2.0 0.5 0.25 2.0 1.0 >>> new_vars_subset = vars_subset ** array >>> new_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 26.24 909.57 5.89 1.69 172.42 ACAG 956.91 -0.02 1862.61 -0.06 1751.09 AOUC 1.01 1.06 1.03 1.09 1.02 AOUC_ 0.98 0.99 0.96 0.99 0.99 AQC 1.13 1.05 1.04 1.34 1.16
>>> # WARNING: using 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). >>> # compute 'iode_var[period] ** data[t]' for each period >>> data = np.array([1.0, 2.0, 0.5, 1./4., 2.0]) >>> updated_ACAF = vars_subset["ACAF"] ** data >>> updated_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 26.24 909.57 5.89 1.69 172.42 >>> # compute 'single_period_subset[name] ** data[i]' for each name >>> vars_subset_1995Y1 = 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 -13.13 ACAG 1751.09 AOUC 1.02 AOUC_ 1.00 AQC 1.35 >>> # compute 'iode_var[name, period] ** data[i, t]' for each name and period >>> data = np.array([[1.0, 2.0, 0.5, 1./4., 2.0], ... [2.0, -1.0, 2.0, -1.0, 2.0], ... [1./4., 2.0, 1.0, 2.0, 0.5], ... [0.5, 1./4., 2.0, 1.0, 2.0], ... [2.0, 0.5, 1./4., 2.0, 1.0]]) >>> new_vars_subset = vars_subset ** data >>> new_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 26.24 909.57 5.89 1.69 172.42 ACAG 956.91 -0.02 1862.61 -0.06 1751.09 AOUC 1.01 1.06 1.03 1.09 1.02 AOUC_ 0.98 0.99 0.96 0.99 0.99 AQC 1.13 1.05 1.04 1.34 1.16