tempo-py 0.0.1

A python library for working with the National Institute of Statistics Tempo Online Database.

tempo.py

A python library for working with the National Institute of Statistics Tempo Online Database. There's also an already existing R package.

🚧 UNDER CONSTRUCTION 🚧

Example usage

from tempo import Node, LeafNode

nodes = Node.get_all()
[print(" " * n.level + n.name) for n in nodes]
# A. STATISTICA SOCIALA
#  A.1 POPULATIE SI STRUCTURA DEMOGRAFICA <a href="...">
#   1. POPULATIA REZIDENTA
#   2. POPULATIA DUPA DOMICILIU
#   3. DATE ISTORICE DE POPULATIE (1968 - 1991)
#  A.2 MISCAREA NATURALA A POPULATIEI
# ...

nodes = Node.get_all('10')
[print(n.name) for n in nodes]
# 1. POPULATIA REZIDENTA
# 2. POPULATIA DUPA DOMICILIU
# 3. DATE ISTORICE DE POPULATIE (1968 - 1991)

from tempo import eq_lambda

nodes = Node.by_property('level', 0, eq_lambda)
[print(n.name) for n in nodes]
# A. STATISTICA SOCIALA
# B. STATISTICA ECONOMICA
# C. FINANTE
# D. JUSTITIE
# E. MEDIU INCONJURATOR
# F. UTILITATI PUBLICE SI ADMINISTRAREA TERITORIULUI
# H. DEZVOLTARE DURABILA - Tinte 2030
# G. DEZVOLTARE DURABILA - Orizont 2020

leaf = LeafNode.by_code('POP105A')
q = leaf.query(
    ('Varste si grupe de varsta', ['Total']),
    ('Sexe', ['Masculin']),
    ('Medii de rezidenta', ['Rural']),
    ('Macroregiuni, regiuni de dezvoltare si judete', ['Bihor']),
    ('Perioade', ['Anul 2016', 'Anul 2017']),
    ('UM: Numar persoane', ['Numar persoane'])
)
# Varste si grupe de varsta, Sexe, Medii de rezidenta, Macroregiuni  regiuni de dezvoltare si judete, Perioade, UM: Numar persoane, Valoare
# Total, Masculin, Rural, Bihor, Anul 2016, Numar persoane, 144439
# Total, Masculin, Rural, Bihor, Anul 2017, Numar persoane, 145335

Concepts

To better understand the concepts of this library it's useful to look at the visual interface of Tempo.

Data organization

All data is organized hierarchically, with all nodes having a specified level, code, parent code, name and more.

API and Data extraction

Extrating data from Tempo is done through a JSON API. Take a look at these URLs for better understanding.

http://statistici.insse.ro:8077/tempo-ins/context/
http://statistici.insse.ro:8077/tempo-ins/context/1010
http://statistici.insse.ro:8077/tempo-ins/matrix/POP105A

Nodes and Leaf Nodes

The differences between nodes and leaf nodes are:

• leaf nodes are located at the bottom of the hierarchy
• leaf nodes are accessed through the tempo-ins/matrix route instead of the tempo-ins/context route
• normal nodes are used just for organization
• leaf nodes actually enable us to query the database

Querying, Dimensions and Options

Querying for data is done by sending a POST request to the URL of the leaf node and providing the following request body:

{
    "language": "ro",
    "arr": [
        [
            {
                "label": "<option label>",
                "nomItemId": 1,
                "offset": 1,
                "parentId": null
            }
        ],
        "..."
    ],
    "matrixName": "<leaf name>",
    "matrixDetails": {
        "nomJud": 0,
        "nomLoc": 0,
        "...": "..."
    }
}

Dimensions are characteristics of the dataset and each dimension has multiple options. Each element of the arr JSON element corresponds to a dimension and contains the data for the selected options.