iode.Variables.to_frame

Variables.to_frame(self, vars_axis_name: str = 'names', time_axis_name: str = 'time', sample_as_floats: bool = False) → DataFrame

Create a pandas DataFrame from the current Variables database. The index of the returned DataFrame is build from the Variables names and the columns from the periods.

Parameters:

vars_axis_name: str, optional

Name of the axis representing the Variables names. Defaults to ‘names’.

time_axis_name: str, optional

Name of the axis representing the periods. Defaults to ‘time’.

sample_as_floats: bool, optional

Whether or not to export periods as string or float values. Defaults to False (periods are exported as string values).

Warning

IODE and pandas don’t use the same constant to represent NaN values. When exporting IODE variables as a pandas DataFrame, the IODE NaN values (\(NA\)) are converted to pandas NaN values (\(nan\)).

See also

Variables.from_frame

Examples

>>> from iode import SAMPLE_DATA_DIR
>>> from iode import variables
>>> import pandas as pd
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
>>> len(variables)
394
>>> variables.sample
'1960Y1:2015Y1'
>>> variables.nb_periods
56

>>> # Export the IODE Variables database as a pandas DataFrame
>>> df = variables.to_frame()
>>> df.shape
(394, 56)
>>> df.index.to_list()              
['ACAF', 'ACAG', 'AOUC', ..., 'ZKFO', 'ZX', 'ZZF_']
>>> df.columns.to_list()            
['1960Y1', '1961Y1', ..., '2014Y1', '2015Y1']
>>> variables["AOUC"]               
[nan, 0.24783191606766575, ..., 1.4237139558484628, 1.4608626117037322]
>>> df.loc["AOUC"]                  
time
1960Y1         NaN
1961Y1    0.247832
...
2014Y1    1.423714
2015Y1    1.460863
Name: AOUC, dtype: float64    
>>> variables["ZKFO"]               
[1.0, 1.0, ... 1.0159901, 1.0159901]
>>> df.loc["ZKFO"]                  
time
1960Y1    1.00000
1961Y1    1.00000
...
2014Y1    1.01599
2015Y1    1.01599
Name: ZKFO, dtype: float64

>>> # Export a subset of the IODE Variables database as a pandas DataFrame
>>> df = variables["A*;*_"].to_frame()
>>> df.shape
(33, 56)
>>> df.index.to_list()              
['ACAF', 'ACAG', 'AOUC', ..., 'WNF_', 'YDH_', 'ZZF_']
>>> df.columns.to_list()            
['1960Y1', '1961Y1', ..., '2014Y1', '2015Y1']
>>> variables["AOUC"]               
[nan, 0.24783191606766575, ..., 1.4237139558484628, 1.4608626117037322]
>>> df.loc["AOUC"]                  
time
1960Y1         NaN
1961Y1    0.247832
...
2014Y1    1.423714
2015Y1    1.460863
Name: AOUC, dtype: float64    
>>> variables["ZZF_"]               
[0.68840039, 0.68840039, ..., 0.68840039, 0.68840039]
>>> df.loc["ZZF_"]                  
time
1960Y1    0.6884
1961Y1    0.6884
...
2014Y1    0.6884
2015Y1    0.6884
Name: ZZF_, dtype: float64