unimore/tirocinio-canali-steffo-sdmx-sandbox
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Steffo 2021-03-20 23:58:26 +01:00
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@ -10,10 +10,7 @@
"- [Esempio approfondito (ma non troppo aggiornato)](https://pandasdmx.readthedocs.io/en/latest/walkthrough.html)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
@ -24,36 +21,13 @@
"- 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))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 1,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pandasdmx in ./venv/lib/python3.9/site-packages (1.4.1)\r\n",
"Requirement already satisfied: pydantic==1.7 in ./venv/lib/python3.9/site-packages (1.7)\r\n",
"Requirement already satisfied: requests>=2.7 in ./venv/lib/python3.9/site-packages (from pandasdmx) (2.25.1)\r\n",
"Requirement already satisfied: pandas>=1.0 in ./venv/lib/python3.9/site-packages (from pandasdmx) (1.2.3)\r\n",
"Requirement already satisfied: lxml>=3.6 in ./venv/lib/python3.9/site-packages (from pandasdmx) (4.6.2)\r\n",
"Requirement already satisfied: pytz>=2017.3 in ./venv/lib/python3.9/site-packages (from pandas>=1.0->pandasdmx) (2021.1)\r\n",
"Requirement already satisfied: numpy>=1.16.5 in ./venv/lib/python3.9/site-packages (from pandas>=1.0->pandasdmx) (1.20.1)\r\n",
"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",
"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",
"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",
"Requirement already satisfied: idna<3,>=2.5 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (2.10)\r\n",
"Requirement already satisfied: certifi>=2017.4.17 in ./venv/lib/python3.9/site-packages (from requests>=2.7->pandasdmx) (2020.12.5)\r\n",
"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"
]
}
],
"execution_count": null,
"outputs": [],
"source": [
"!pip install pandasdmx pydantic==1.7"
],
@ -70,25 +44,13 @@
"## Esempio"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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",
" warn(\n"
]
}
],
"execution_count": null,
"outputs": [],
"source": [
"import pandas\n",
"import pandasdmx\n",
@ -112,10 +74,7 @@
"È possibile selezionare tra più fonti di dati, tra i quali Eurostat (`ESTAT`)."
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
@ -126,25 +85,13 @@
"Come prima cosa, è necessario creare un'istanza di `pandasdmx.Request`:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"data": {
"text/plain": "<pandasdmx.api.Request at 0x7f4410c71940>"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"eurostat: pandasdmx.Request = pandasdmx.Request(\"ESTAT\")\n",
"eurostat"
@ -166,25 +113,13 @@
"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`:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [
{
"data": {
"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..."
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"all_flows_msg: pandasdmx.message.Message = eurostat.dataflow()\n",
"all_flows_msg"
@ -211,6 +146,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Converte i risultati in due Series di pandas, una con i dataflow e una con la loro relativa struttura\n",
"_dict: dict[str, pandas.Series] = all_flows_msg.to_pandas()\n",
@ -223,32 +160,12 @@
"pycharm": {
"name": "#%%\n"
}
},
"execution_count": 5,
"outputs": [
{
"data": {
"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)"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
]
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"data": {
"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"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"# Cerchiamo nella Series i allflows la cui descrizione contiene \"student\"\n",
"# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.contains.html\n",
@ -268,25 +185,13 @@
"Per continuare gli esperimenti, prendiamo il primo dataflow tra quelli contenenti `\"student\"` nel label:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [
{
"data": {
"text/plain": "'educ_enrl1ad'"
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"my_flow_label = student_flows.index[0]\n",
"my_flow_label"
@ -304,25 +209,13 @@
"Usiamo il label per chiamare di nuovo `.dataflow()`, specificando però stavolta il dataflow di cui ci interessano i dettagli:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [
{
"data": {
"text/plain": "<DataflowDefinition ESTAT:educ_enrl1ad(1.0): Students by ISCED level, study intensity and sex>"
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"my_flow_msg: pandasdmx.message.Message = eurostat.dataflow(my_flow_label)\n",
"my_flow: pandasdmx.model.DataflowDefinition = my_flow_msg.dataflow[my_flow_label]\n",
@ -345,25 +238,13 @@
"Scopriamo prima il label della structure, poi scarichiamo da Eurostat la structure del dataflow che ci interessa con il metodo `.datastructure()`:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [
{
"data": {
"text/plain": "<DataStructureDefinition ESTAT:DSD_educ_enrl1ad(1.0): DSWS Data Structure Definition>"
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"my_struct_label: pandasdmx.source.DataStructureDefinition = my_flow.structure.id\n",
"my_struct_msg: pandasdmx.message.Message = eurostat.datastructure(my_struct_label)\n",
@ -391,25 +272,13 @@
"> __Annotations__: commenti che possono essere aggiunti al dataflow"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [
{
"data": {
"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>>)"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"execution_count": null,
"outputs": [],
"source": [
"my_struct.annotations, my_struct.measures, my_struct.attributes, my_struct.dimensions"
],
@ -426,28 +295,42 @@
"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": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"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"
}
],
"execution_count": null,
"outputs": [],
"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_series: pandas.Series = my_data_msg.to_pandas()\n",
"my_data_series"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"> __DataFrame__: Tabella di dati di `pandas`, implementata come array di Series\n",
"\n",
"Per avere una rappresentazione migliore dei dati sul notebook, convertiamo la Series a un DataFrame:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"my_data: pandas.DataFrame = my_data_series.to_frame()\n",
"my_data"
],
"metadata": {
@ -460,31 +343,43 @@
{
"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):"
"Inoltre, per semplificarne le query, \"appiattiamo\" il [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) trasformandolo in normalissime colonne:"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
"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"
}
"execution_count": null,
"outputs": [],
"source": [
"my_data.reset_index(inplace=True)\n",
"my_data"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Abbiamo finalmente i dati, e possiamo manipolarli come un qualsiasi DataFrame di `pandas`, in modo molto simile a una tabella SQL:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"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()"
"my_data.groupby([\"FREQ\", \"TIME_PERIOD\", \"SEX\"]).first()"
],
"metadata": {
"collapsed": false,
@ -508,10 +403,7 @@
"- `OECD` - Organisation for Economic Cooperation and Development"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
"collapsed": false
}
},
{
@ -523,10 +415,193 @@
"\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
}
},
{
"cell_type": "markdown",
"source": [
"## Filtraggio in base a `TIME_PERIOD`\n",
"\n",
"È possibile capire se un DataFrame ha una colonna `TIME_PERIOD` in questo modo:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"\"TIME_PERIOD\" in list(my_data.columns)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"I `TIME_PERIOD` possono essere misurati in modi diversi: anni, quadrimestri, giorni, etc...\n",
"\n",
"I valori possibili sono:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"list(my_struct.dimensions.get(\"FREQ\").local_representation.enumerated)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Per capire quali sono disponibili, si può effettuare una query aggregata:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"list(my_data.groupby([\"FREQ\"]).any().index)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"In questo caso, è disponibile solo `A`, il che significa che le misurazioni sono **eseguite solo annualmente**.\n",
"\n",
"Possiamo trovare il \"periodo\" più recente con una query sulla tabella:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"latest_period = my_data[\"TIME_PERIOD\"].max()\n",
"latest_period"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Possiamo filtrare i dati in modo da avere solo quelli del periodo desiderato:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"my_data.loc[my_data[\"TIME_PERIOD\"] == latest_period]"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"In generale, possiamo applicare ulteriori filtri effettuando accessi agli elementi (`__getitem__`) della proprietà `loc` del dataframe:"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"my_data.loc[my_data[\"SEX\"] == \"M\"]"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 26,
"outputs": [
{
"data": {
"text/plain": " FREQ UNIT ISCED97 SEX WORKTIME GEO TIME_PERIOD value\n5 A NR ED0 M TOTAL IT 2012 879256.0",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>FREQ</th>\n <th>UNIT</th>\n <th>ISCED97</th>\n <th>SEX</th>\n <th>WORKTIME</th>\n <th>GEO</th>\n <th>TIME_PERIOD</th>\n <th>value</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>5</th>\n <td>A</td>\n <td>NR</td>\n <td>ED0</td>\n <td>M</td>\n <td>TOTAL</td>\n <td>IT</td>\n <td>2012</td>\n <td>879256.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 26,
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}
],
"source": [
"(\n",
" my_data\n",
" .loc[my_data[\"TIME_PERIOD\"] == latest_period]\n",
" .loc[my_data[\"SEX\"] == \"M\"]\n",
" .loc[my_data[\"ISCED97\"] == \"ED0\"]\n",
")"
],
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"name": "#%%\n"
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{
"cell_type": "code",
"execution_count": null,
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