iode.Variables.__iadd__
- Variables.__iadd__(other)[source]
Add other to the current (subset of) Variables object.
- Parameters:
- other: int, float, numpy ndarray, pandas Series, pandas DataFrame, larray Array or iode Variables
If other is an int or a float, add the scalar to 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, add the two Variables objects. self and other must share the same sample and represent the same set of variables names.
Warning
Adding 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
>>> # add a scalar to all values of the current subset of a Variables object >>> 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 28.24 32.16 36.66 10.16 -11.13 ACAG -28.93 -38.29 -41.16 -14.03 -39.85 AOUC 3.02 3.03 3.03 3.05 3.05 AOUC_ 2.96 2.97 2.98 2.99 3.00 AQC 3.06 3.11 3.15 3.16 3.16
>>> # add a (subsets of) a Variables object to the current (subset of) Variables object >>> 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 56.48 64.32 73.32 20.32 -22.26 ACAG -57.87 -76.57 -82.32 -28.06 -79.69 AOUC 6.05 6.06 6.06 6.09 6.10 AOUC_ 5.93 5.95 5.96 5.98 5.99 AQC 6.13 6.22 6.31 6.31 6.32
>>> # add a pandas Series to a single variable >>> series = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0], index=vars_subset.periods_as_str) >>> series 1991Y1 1.0 1992Y1 2.0 1993Y1 3.0 1994Y1 4.0 1995Y1 5.0 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 57.48 66.32 76.32 24.32 -17.26
>>> # add a pandas Series to the subset corresponding to a single period >>> series = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0], index=vars_subset.names) >>> series ACAF 1.0 ACAG 2.0 AOUC 3.0 AOUC_ 4.0 AQC 5.0 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 -16.26 ACAG -77.69 AOUC 9.10 AOUC_ 9.99 AQC 11.32
>>> # add a pandas DataFrame to the current subset of the Variables object >>> data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0], ... [6.0, 7.0, 8.0, 9.0, 10.0], ... [11.0, 12.0, 13.0, 14.0, 15.0], ... [16.0, 17.0, 18.0, 19.0, 20.0], ... [21.0, 22.0, 23.0, 24.0, 25.0]],) >>> df = pd.DataFrame(data, index=vars_subset.names, columns=vars_subset.periods_as_str) >>> df 1991Y1 1992Y1 1993Y1 1994Y1 1995Y1 ACAF 1.0 2.0 3.0 4.0 5.0 ACAG 6.0 7.0 8.0 9.0 10.0 AOUC 11.0 12.0 13.0 14.0 15.0 AOUC_ 16.0 17.0 18.0 19.0 20.0 AQC 21.0 22.0 23.0 24.0 25.0 >>> 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 58.48 68.32 79.32 28.32 -11.26 ACAG -51.87 -69.57 -74.32 -19.06 -67.69 AOUC 17.05 18.06 19.06 20.09 24.10 AOUC_ 21.93 22.95 23.96 24.98 29.99 AQC 27.13 28.22 29.31 30.31 36.32
>>> # add an larray Array to the current subset of the Variables object >>> 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 3.0 4.0 5.0 ACAG 6.0 7.0 8.0 9.0 10.0 AOUC 11.0 12.0 13.0 14.0 15.0 AOUC_ 16.0 17.0 18.0 19.0 20.0 AQC 21.0 22.0 23.0 24.0 25.0 >>> 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 59.48 70.32 82.32 32.32 -6.26 ACAG -45.87 -62.57 -66.32 -10.06 -57.69 AOUC 28.05 30.06 32.06 34.09 39.10 AOUC_ 37.93 39.95 41.96 43.98 49.99 AQC 48.13 50.22 52.31 54.31 61.32
>>> # WARNING: adding 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). >>> # add a numpy 1D ndarray to a single variable >>> data = np.array([1.0, 2.0, 3.0, 4.0, 5.0]) >>> 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 60.48 72.32 85.32 36.32 -1.26 >>> # add a numpy 1D ndarray to the subset corresponding to a single period >>> 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 -0.26 ACAG -55.69 AOUC 42.10 AOUC_ 53.99 AQC 66.32 >>> # add a numpy 2D ndarray to the current (subset of the) Variables object >>> data = np.array([[1.0, 2.0, 3.0, 4.0, 5.0], ... [6.0, 7.0, 8.0, 9.0, 10.0], ... [11.0, 12.0, 13.0, 14.0, 15.0], ... [16.0, 17.0, 18.0, 19.0, 20.0], ... [21.0, 22.0, 23.0, 24.0, 25.0]]) >>> 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 61.48 74.32 88.32 40.32 4.74 ACAG -39.87 -55.57 -58.32 -1.06 -45.69 AOUC 39.05 42.06 45.06 48.09 57.10 AOUC_ 53.93 56.95 59.96 62.98 73.99 AQC 69.13 72.22 75.31 78.31 91.32