mirror of
https://github.com/Steffo99/sdmx-sandbox.git
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555 lines
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22 KiB
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555 lines
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22 KiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# PandaSDMX\n",
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"\n",
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"- [Documentazione aggiornata (v1.4.1)](https://pandasdmx.readthedocs.io/en/latest/)\n",
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"- [Esempio breve (con poche spiegazioni)](https://pandasdmx.readthedocs.io/en/master/example.html)\n",
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"- [Esempio approfondito (ma non troppo aggiornato)](https://pandasdmx.readthedocs.io/en/latest/walkthrough.html)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Installazione\n",
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"\n",
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"- L'ultima versione non funziona con Pydantic 1.8.1 ma richiede 1.7 ([dr-leo/pandaSDMX#204](https://github.com/dr-leo/pandaSDMX/issues/204))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: pandasdmx in ./venv/lib/python3.9/site-packages (1.4.1)\r\n",
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"Requirement already satisfied: pydantic==1.7 in ./venv/lib/python3.9/site-packages (1.7)\r\n",
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"Requirement already satisfied: requests>=2.7 in ./venv/lib/python3.9/site-packages (from pandasdmx) (2.25.1)\r\n",
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"Requirement already satisfied: pandas>=1.0 in ./venv/lib/python3.9/site-packages (from pandasdmx) (1.2.3)\r\n",
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"Requirement already satisfied: lxml>=3.6 in ./venv/lib/python3.9/site-packages (from pandasdmx) (4.6.2)\r\n",
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"Requirement already satisfied: pytz>=2017.3 in ./venv/lib/python3.9/site-packages (from pandas>=1.0->pandasdmx) (2021.1)\r\n",
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"Requirement already satisfied: numpy>=1.16.5 in ./venv/lib/python3.9/site-packages (from pandas>=1.0->pandasdmx) (1.20.1)\r\n",
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"Requirement already satisfied: python-dateutil>=2.7.3 in ./venv/lib/python3.9/site-packages (from pandas>=1.0->pandasdmx) (2.8.1)\r\n",
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"Requirement already satisfied: six>=1.5 in ./venv/lib/python3.9/site-packages (from python-dateutil>=2.7.3->pandas>=1.0->pandasdmx) (1.15.0)\r\n",
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"Requirement already satisfied: chardet<5,>=3.0.2 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (4.0.0)\r\n",
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"Requirement already satisfied: idna<3,>=2.5 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (2.10)\r\n",
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"Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (2020.12.5)\r\n",
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"Requirement already satisfied: urllib3<1.27,>=1.21.1 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (1.26.3)\r\n"
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]
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}
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],
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"source": [
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"!pip install pandasdmx pydantic==1.7"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Esempio"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/mnt/tera/ext4/code/sdmx-sandbox/venv/lib/python3.9/site-packages/pandasdmx/remote.py:11: RuntimeWarning: optional dependency requests_cache is not installed; cache options to Session() have no effect\n",
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" warn(\n"
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]
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}
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],
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"source": [
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"import pandas\n",
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"import pandasdmx\n",
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"\n",
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"# Per type annotations\n",
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"import pandasdmx.message\n",
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"import pandasdmx.model\n",
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"import pandasdmx.source\n",
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"import pandasdmx.source.estat"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"È possibile selezionare tra più fonti di dati, tra i quali Eurostat (`ESTAT`)."
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"> __Request__: client di comunicazione tra `pandasdmx` e un server di dati come Eurostat\n",
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"\n",
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"Come prima cosa, è necessario creare un'istanza di `pandasdmx.Request`:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"data": {
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"text/plain": "<pandasdmx.api.Request at 0x7f4410c71940>"
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"eurostat: pandasdmx.Request = pandasdmx.Request(\"ESTAT\")\n",
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"eurostat"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"> __Dataflow__: set di metadati relativi a una misura effettuata (ad esempio, `educ_enrl1ad - Students by ISCED level, study intensity and sex`)\n",
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"\n",
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"> __Message__: risposta HTTPS ricevuta in seguito a una richiesta effettuata ad un server di dati\n",
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"\n",
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"Poi, scarichiamo _tutti_ i dataflow disponibili usando `.dataflow()` sul client creato in precedenza per effettuare una richiesta al server Eurostat, creando un `pandasdmx.message.Message`:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"outputs": [
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{
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"data": {
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"text/plain": "<pandasdmx.StructureMessage>\n <Header>\n id: 'IDREF382067'\n prepared: '2021-03-15T01:45:49.005000+00:00'\n receiver: <Agency Unknown>\n sender: <Agency Unknown>\n source: \n test: False\n response: <Response [200]>\n DataflowDefinition (6573): DS-018995 DS-022469 DS-032655 DS-043227 DS...\n DataStructureDefinition (6573): DSD_DS-018995 DSD_DS-022469 DSD_DS-03..."
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"all_flows_msg: pandasdmx.message.Message = eurostat.dataflow()\n",
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"all_flows_msg"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"> __Series__: una specie di `dict` più veloce e avanzato implementato da `pandas`\n",
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"\n",
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"PandaSDMX ha la funzionalità che cercavamo di cercare dataset per keyword!\n",
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"\n",
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"Per effettuare la ricerca, usiamo il metodo `.to_pandas()` per convertire il `Message` in oggetti Python e/o `pandas`, poi usiamo i metodi \"nativi\" per trovare quello che ci serve:"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"source": [
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"# Converte i risultati in due Series di pandas, una con i dataflow e una con la loro relativa struttura\n",
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"_dict: dict[str, pandas.Series] = all_flows_msg.to_pandas()\n",
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"all_flows: pandas.Series = _dict[\"dataflow\"]\n",
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"all_structs: pandas.Series = _dict[\"structure\"]\n",
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"all_flows, all_structs"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"execution_count": 5,
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"outputs": [
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{
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"data": {
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"text/plain": "(DS-018995 EU trade since 1988 by SITC\n DS-022469 EXTRA EU trade since 1999 by mode of transport...\n DS-032655 EU trade since 1988 by BEC\n DS-043227 EFTA trade since 1995 by SITC\n DS-066341 Sold production, exports and imports by PRODCO...\n ... \n yth_incl_120 Young people living in households with very lo...\n yth_part_010 Frequency of getting together with relatives o...\n yth_part_020 Frequency of contacts with relatives or friend...\n yth_part_030 Participation of young people in activities of...\n yth_volunt_010 Participation of young people in informal volu...\n Length: 6573, dtype: object,\n DSD_DS-018995 \n DSD_DS-022469 \n DSD_DS-032655 \n DSD_DS-043227 \n DSD_DS-066341 \n ..\n DSD_yth_incl_120 \n DSD_yth_part_010 \n DSD_yth_part_020 \n DSD_yth_part_030 \n DSD_yth_volunt_010 \n Length: 6573, dtype: object)"
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"outputs": [
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{
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"data": {
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"text/plain": "educ_enrl1ad Students by ISCED level, study intensity and sex\neduc_enrl1at Students by ISCED level, type of institution a...\neduc_enrl1tl Students by ISCED level, age and sex\neduc_enrl5 Tertiary students (ISCED 5-6) by field of educ...\neduc_enrl6 Tertiary students (ISCED 5-6) non-citizens, n...\neduc_enrl8 Tertiary students (ISCED 5-6) by country of ci...\neduc_enrllng1 Students in ISCED 1-3 by modern foreign langua...\neduc_enrllng2 Students in ISCED 1-3 by number of modern fore...\neduc_fiaid Financial aid to students\neduc_ilev Distribution of pupils/ students by level\neduc_iste Pupil/ student - teacher ratio and average cla...\neduc_mofo_dst Foreign students by level of education and cou...\neduc_mofo_fld Foreign students by level and field of education\neduc_mofo_gen Foreign students by level of education and sex\neduc_mofo_orig Foreign students by level of education and cou...\neduc_momo_dst Students going abroad by level of education an...\neduc_momo_fld Students from abroad by level and field of edu...\neduc_momo_gen Students from abroad by level of education and...\neduc_momo_orig Students from abroad by level of education and...\neduc_outc_pisa Underachieving 15-year-old students by sex and...\neduc_renrlrg1 Students by level of education, orientation, s...\neduc_renrlrg3 Students by age, sex and NUTS 2 regions\neduc_thmob Student mobility\neduc_uoe_enra01 Pupils and students enrolled by education leve...\neduc_uoe_enra02 Pupils and students enrolled by education leve...\neduc_uoe_enra03 Pupils and students enrolled by education leve...\neduc_uoe_enra04 Pupils and students by education level - as % ...\neduc_uoe_enra05 Pupils and students in education by age groups...\neduc_uoe_enra06 Pupils and students in education aged 30 and o...\neduc_uoe_enra07 Expected school years of pupils and students b...\neduc_uoe_enra08 Students in post-compulsory education - as % o...\neduc_uoe_enra09 Students participation at the end of compulsor...\neduc_uoe_enra11 Pupils and students enrolled by education leve...\neduc_uoe_enra12 Pupils and students enrolled by sex, age and N...\neduc_uoe_enra13 Distribution of pupils and students enrolled i...\neduc_uoe_enra16 Pupils and students enrolled by education leve...\neduc_uoe_enrt01 Students enrolled in tertiary education by edu...\neduc_uoe_enrt02 Students enrolled in tertiary education by edu...\neduc_uoe_enrt03 Students enrolled in tertiary education by edu...\neduc_uoe_enrt04 Distribution of students enrolled at tertiary ...\neduc_uoe_enrt05 Ratio of the proportion of tertiary students o...\neduc_uoe_enrt06 Students enrolled in tertiary education by edu...\neduc_uoe_enrt07 Students in tertiary education by age groups -...\neduc_uoe_enrt08 Students in tertiary education - as % of 20-24...\neduc_uoe_fina01 Financial aid to students by education level -...\neduc_uoe_fine09 Public expenditure on education per pupil/stud...\neduc_uoe_fine10 Pupils and students enrolled by education leve...\neduc_uoe_fini04 Annual expenditure on educational institutions...\neduc_uoe_fini06 Ratio of annual expenditure per student at the...\neduc_uoe_mobs01 Mobile students from abroad enrolled by educat...\neduc_uoe_mobs02 Mobile students from abroad enrolled by educat...\neduc_uoe_mobs03 Share of mobile students from abroad enrolled ...\neduc_uoe_mobs04 Distribution of mobile students from abroad en...\neduc_uoe_perp04 Ratio of pupils and students to teachers and a...\nhrst_fl_tefor Participation of foreign students in tertiary ...\ntsc00028 Doctorate students in science and technology f...\ndtype: object"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Cerchiamo nella Series i allflows la cui descrizione contiene \"student\"\n",
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"# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.contains.html\n",
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"student_flows: pandas.Series = all_flows[all_flows.str.contains(\"student\", case=False)]\n",
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"student_flows"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Per continuare gli esperimenti, prendiamo il primo dataflow tra quelli contenenti `\"student\"` nel label:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
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{
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"data": {
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"text/plain": "'educ_enrl1ad'"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_flow_label = student_flows.index[0]\n",
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"my_flow_label"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Usiamo il label per chiamare di nuovo `.dataflow()`, specificando però stavolta il dataflow di cui ci interessano i dettagli:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"outputs": [
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{
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"data": {
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"text/plain": "<DataflowDefinition ESTAT:educ_enrl1ad(1.0): Students by ISCED level, study intensity and sex>"
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_flow_msg: pandasdmx.message.Message = eurostat.dataflow(my_flow_label)\n",
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"my_flow: pandasdmx.model.DataflowDefinition = my_flow_msg.dataflow[my_flow_label]\n",
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"my_flow"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"> __Structure__: metadati su come sono strutturate le misure di un dataflow (cosa è stato misurato, quali filtri è possibile applicare, note, etc)\n",
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"\n",
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"_Particolarità di Eurostat: la structure va richiesta separatamente dal dataflow, in quanto tutti i campi a parte `id` di `dataflow.structure` sono sempre vuoti._\n",
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"\n",
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"Scopriamo prima il label della structure, poi scarichiamo da Eurostat la structure del dataflow che ci interessa con il metodo `.datastructure()`:"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"outputs": [
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{
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"data": {
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"text/plain": "<DataStructureDefinition ESTAT:DSD_educ_enrl1ad(1.0): DSWS Data Structure Definition>"
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"my_struct_label: pandasdmx.source.DataStructureDefinition = my_flow.structure.id\n",
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"my_struct_msg: pandasdmx.message.Message = eurostat.datastructure(my_struct_label)\n",
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"my_struct: pandasdmx.source.DataStructureDefinition = my_struct_msg.structure[my_struct_label]\n",
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"my_struct"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"Ispezioniamo la structure che abbiamo scaricato, visualizzandola contemporaneamente [sul Data Explorer di Eurostat](https://ec.europa.eu/eurostat/databrowser/view/educ_enrl1ad/default/table?lang=en)\n",
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"\n",
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"> __Measures__: valori aggregati relativi alle misure effettuate, simili a `COUNT(*)` dell'SQL\n",
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"\n",
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"> __Dimensions__: filtri applicabili ai dati raccolti in modo simile all'`HAVING` dell'SQL\n",
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"\n",
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"> __Attributes__: ???\n",
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"\n",
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"> __Annotations__: commenti che possono essere aggiunti al dataflow"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "code",
|
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"execution_count": 10,
|
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"outputs": [
|
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{
|
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"data": {
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"text/plain": "([],\n <MeasureDescriptor: <PrimaryMeasure OBS_VALUE>>,\n <AttributeDescriptor: <DataAttribute OBS_FLAG>; <DataAttribute OBS_STATUS>>,\n <DimensionDescriptor: <Dimension FREQ>; <Dimension UNIT>; <Dimension ISCED97>; <Dimension SEX>; <Dimension WORKTIME>; <Dimension GEO>; <TimeDimension TIME_PERIOD>>)"
|
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},
|
|
"execution_count": 10,
|
|
"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"my_struct.annotations, my_struct.measures, my_struct.attributes, my_struct.dimensions"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
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"pycharm": {
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"name": "#%%\n"
|
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}
|
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}
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},
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{
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"cell_type": "markdown",
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"source": [
|
|
"Infine, richiediamo i dati da Eurostat, limitandoli a quelli dell'`IT`alia dal 2010 in poi e selezionando solo il `WORKTIME` `TOTAL`, e convertiamoli in una Series multi-chiave:"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
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"name": "#%% md\n"
|
|
}
|
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}
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "FREQ UNIT ISCED97 SEX WORKTIME GEO TIME_PERIOD\nA NR ED0 F TOTAL IT 2010 808706.0\n 2011 811615.0\n 2012 815656.0\n M TOTAL IT 2010 872281.0\n 2011 876225.0\n ... \n UNK M TOTAL IT 2011 NaN\n 2012 NaN\n T TOTAL IT 2010 NaN\n 2011 NaN\n 2012 NaN\nName: value, Length: 279, dtype: float64"
|
|
},
|
|
"execution_count": 28,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"my_data_msg: pandasdmx.message.Message = eurostat.data(my_flow_label, key={\"GEO\": \"IT\", \"WORKTIME\": \"TOTAL\"}, params={\"startPeriod\": \"2010\"})\n",
|
|
"my_data: pandas.Series = my_data_msg.to_pandas()\n",
|
|
"my_data"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"Abbiamo ricevuto i dati, e possiamo manipolarli come una qualsiasi series di `pandas` (le quali sono molto simili a tabelle SQL in-memory):"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": "SEX TIME_PERIOD\nF 2010 808706.0\n 2011 811615.0\n 2012 815656.0\nM 2010 872281.0\n 2011 876225.0\n 2012 879256.0\nT 2010 1680987.0\n 2011 1687840.0\n 2012 1694912.0\nName: value, dtype: float64"
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Il numero di studenti [M]aschi, [F]emmine e [T]otali in Italia nel [2010], [2011] e [2012]\n",
|
|
"my_data.groupby([\"SEX\", \"TIME_PERIOD\"]).first()"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%%\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Sorgenti dati\n",
|
|
"\n",
|
|
"Tra le sorgenti di dati di cui abbiamo parlato, sono [completamente supportate](https://pandasdmx.readthedocs.io/en/latest/sources.html):\n",
|
|
"\n",
|
|
"- `ESTAT` - Eurostat\n",
|
|
"- `ISTAT` - ISTAT\n",
|
|
"\n",
|
|
"Queste sorgenti non supportano lo standard `SDMX-MD` ma solo lo standard `SDMX-JSON`, che [non supporta query di metadati e struttura](https://pandasdmx.readthedocs.io/en/latest/sources.html#data-source-limitations):\n",
|
|
"\n",
|
|
"- `OECD` - Organisation for Economic Cooperation and Development"
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Archiviazione dati\n",
|
|
"\n",
|
|
"Se si vogliono replicare dati provenienti da queste fonti, si potrebbe usare tranquillamente un database **relazionale** (SQL) le cui tabelle sono generate a runtime in base alla struttura del dataflow desiderato.\n",
|
|
"\n",
|
|
"[SQLAlchemy](https://www.sqlalchemy.org/) potrebbe essere utile in questo caso; non sono particolarmente familiare con l'[ORM di Django](https://docs.djangoproject.com/en/3.1/topics/db/models/), ma sembrano molto simili (anche se [si direbbe che SQLAlchemy supporti query più complesse](https://stackoverflow.com/questions/18199053/example-of-what-sqlalchemy-can-do-and-django-orm-cannot))."
|
|
],
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"pycharm": {
|
|
"name": "#%% md\n"
|
|
}
|
|
}
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
} |