iode.ComputedTable

class iode.ComputedTable

Object returned by the method compute(). It represents the computation of an IODE table given a generalized sample.

Examples

>>> from iode import SAMPLE_DATA_DIR
>>> from iode import Table, tables, variables
>>> tables.load(f"{SAMPLE_DATA_DIR}/fun.tbl")
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")
>>> tables["C8_1"]          
DIVIS | 1                                  |
TITLE |      "Déterminants de l'output potentiel"
----- | ---------------------------------------------
CELL  |                                    |   "#s"
----- | ---------------------------------------------
CELL  | "Output potentiel"                 |  Q_F+Q_I
CELL  | "Stock de capital"                 | KNFF[-1]
CELL  | "Intensité de capital"             |    KLFHP
CELL  | "Productivité totale des facteurs" |  TFPFHP_

nb lines: 8
nb columns: 2
language: 'ENGLISH'
gridx: 'MAJOR'
gridy: 'MAJOR'
graph_axis: 'VALUES'
graph_alignment: 'LEFT'

>>> # simple time series (current workspace) - 6 observations - 4 decimals
>>> computed_table = tables["C8_1"].compute("2000:6", nb_decimals=4)
>>> computed_table              
   line title \ period[file]     |     00    |     01    |     02    |     03    |     04    |     05
---------------------------------------------------------------------------------------------------------
Output potentiel                 | 5495.2128 | 5627.8589 | 5748.7804 | 5857.9529 | 5966.1999 | 6103.6318
Stock de capital                 | 8083.5517 | 8359.8908 | 8647.9354 | 8910.3393 | 9175.8106 | 9468.8865
Intensité de capital             |    0.5032 |    0.4896 |    0.4758 |    0.4623 |    0.4481 |    0.4349
Productivité totale des facteurs |    0.9938 |    1.0037 |    1.0137 |    1.0239 |    1.0341 |    1.0445
        
>>> # two time series (current workspace) - 5 observations - 2 decimals
>>> computed_table = tables["C8_1"].compute("(2010;2010/2009):5")
>>> computed_table              
   line title \ period[file]     |    10    | 10/09 |    11    | 11/10 |    12    | 12/11 |    13    | 13/12 |    14    | 14/13
--------------------------------------------------------------------------------------------------------------------------------
Output potentiel                 |  6936.11 |  1.74 |  7045.34 |  1.57 |  7161.54 |  1.65 |  7302.29 |  1.97 |  7460.12 |  2.16
Stock de capital                 | 11293.85 |  2.82 | 11525.01 |  2.05 | 11736.78 |  1.84 | 11975.49 |  2.03 | 12263.95 |  2.41
Intensité de capital             |     0.39 | -2.17 |     0.38 | -2.05 |     0.37 | -1.91 |     0.36 | -1.86 |     0.36 |  -1.9
Productivité totale des facteurs |      1.1 |   1.0 |     1.11 |   1.0 |     1.12 |   1.0 |     1.13 |   1.0 |     1.14 |   1.0

>>> # simple time series (current workspace + one extra file) - 5 observations - 2 decimals
>>> computed_table = tables["C8_1"].compute("2010[1;2]:5", extra_files=f"{SAMPLE_DATA_DIR}/ref.av")
>>> computed_table              
   line title \ period[file]     |  10[1]   |  10[2]   |  11[1]   |  11[2]   |  12[1]   |  12[2]   |  13[1]   |  13[2]   |  14[1]   |  14[2]    
----------------------------------------------------------------------------------------------------------------------------------------------- 
Output potentiel                 |  6936.11 |  6797.39 |  7045.34 |  6904.44 |  7161.54 |  7018.31 |  7302.29 |  7156.24 |  7460.12 |  7310.91  
Stock de capital                 | 11293.85 | 11067.97 | 11525.01 | 11294.51 | 11736.78 | 11502.05 | 11975.49 | 11735.98 | 12263.95 | 12018.67  
Intensité de capital             |     0.39 |     0.38 |     0.38 |     0.37 |     0.37 |     0.36 |     0.36 |     0.36 |     0.36 |     0.35  
Productivité totale des facteurs |      1.1 |     1.08 |     1.11 |     1.09 |     1.12 |      1.1 |     1.13 |     1.11 |     1.14 |     1.12  

Attributes:

columns: columns: List[str]
files: files: List[str]
lines: lines: List[str]
nb_columns: nb_columns: int
nb_decimals: nb_decimals: int
nb_files: nb_files: int
nb_lines: nb_lines: int
nb_operations_between_files: nb_operations_between_files: int
nb_periods: nb_periods: int
sample: sample: Sample
title: title: str

Methods

`is_editable`(self, row, column)	Check if a cell in the computed table is editable.
`to_array`(self)	Convert the computed table to a larray Array.
`to_frame`(self)	Convert the computed table to a pandas DataFrame.

__init__(*args, **kwargs)

Methods

`__init__`(args, *kwargs)
`is_editable`(self, row, column)	Check if a cell in the computed table is editable.
`to_array`(self)	Convert the computed table to a larray Array.
`to_frame`(self)	Convert the computed table to a pandas DataFrame.

Attributes

`columns`	columns: List[str]
`files`	files: List[str]
`lines`	lines: List[str]
`nb_columns`	nb_columns: int
`nb_decimals`	nb_decimals: int
`nb_files`	nb_files: int
`nb_lines`	nb_lines: int
`nb_operations_between_files`	nb_operations_between_files: int
`nb_periods`	nb_periods: int
`sample`	sample: Sample
`title`	title: str