iode.Tables.__getitem__

Tables.__getitem__(key: str | List[str]) → Table | Self[source]

Return the (subset of) table(s) referenced by key.

The key can represent a single object name (e.g. “ACAF”) or a list of object names (“ACAF;ACAG;AOUC”) or a pattern (e.g. “A*”) or a list of sub-patterns (e.g. “A*;*_”).

If the key represents a list of object names or of sub-patterns, each name or sub-pattern is separated by a separator character which is either a whitespace ` , or a comma `,, or a semi-colon ;, or a tabulation t, or a newline n.

A (sub-)`pattern` is a list of characters representing a group of object names. It includes some special characters which have a special meaning:

  • * : any character sequence, even empty

  • ? : any character (one and only one)

  • @ : any alphanumerical char [A-Za-z0-9]

  • & : any non alphanumerical char

  • | : any alphanumeric character or none at the beginning and end of a string

  • ! : any non-alphanumeric character or none at the beginning and end of a string

  • `` : escape the next character

Note that the key can contain references to IODE lists which are prefixed with the symbol $.

Parameters:
key: str or list(str)

(the list of) name(s) of the table(s) to get. The list of tables to get can be specified by a pattern or by a list of sub-patterns (e.g. “A*;*_”).

Returns:
Single table or a subset of the database.

Examples

>>> from iode import SAMPLE_DATA_DIR
>>> from iode import tables
>>> tables.load(f"{SAMPLE_DATA_DIR}/fun.tbl")
Loading .../fun.tbl
46 objects loaded

>>> # a) get one table
>>> tables["YDH"]
DIVIS |                                                          1 |                                    PC_*40.34
TITLE |                        "Tableau B-3. Revenu disponible des ménages à prix constant"
----- | ---------------------------------------------------------------------------------------------------------
CELL  |                                                            |                     "#S"
CELL  | "Revenus primaires"                                        |                            WBU_+YN+GOSH_+IDH
CELL  | "   Masse salariale totale"                                |                                         WBU_
CELL  | "   Revenu net du travail en provenance du Reste du monde" |                                           YN
CELL  | "   Surplus brut d'exploitation"                           |                                        GOSH_
CELL  | "   Revenu net de la propriété"                            |                                          IDH
CELL  | "Cotisations sociales et impôts"                           |                                  SSF+SSH+DTH
CELL  | "   Cotisations patronales"                                |                                          SSF
CELL  | "   Cotisations personnelles"                              |                                          SSH
CELL  | "IPP"                                                      |                                          DTH
CELL  | "Prestations sociales "                                    |                                     SBH+OCUH
CELL  | "   Sécurité sociale"                                      |                                          SBH
CELL  | "   Diverses prestations"                                  |                                         OCUH
CELL  | "Total"                                                    | (WBU_+YN+GOSH_+IDH)-(SSF+SSH+DTH)+(SBH+OCUH)
----- | ---------------------------------------------------------------------------------------------------------
FILES |
DATE  |

nb lines: 19
nb columns: 2
language: 'ENGLISH'
gridx: 'MAJOR'
gridy: 'MAJOR'
graph_axis: 'VALUES'
graph_alignment: 'LEFT'

>>> # b) get a subset of the Tables database using a pattern
>>> tables_subset = tables["C8_*"]
>>> tables_subset.names
['C8_1', 'C8_10', 'C8_11', 'C8_13', 'C8_14', 'C8_2', 'C8_3', 'C8_4', 'C8_5', 'C8_6', 'C8_7', 'C8_8', 'C8_9']

>>> # c) get a subset of the Tables database using a list of names
>>> tables_subset = tables[["C8_1", "C8_2", "C8_4", "C8_5", "C8_7"]]
>>> tables_subset.names
['C8_1', 'C8_2', 'C8_4', 'C8_5', 'C8_7']