def main():
""" starting import """
# get argument
parser = argparse.ArgumentParser()
parser.add_argument("input_json", type=str, help="JSON file to process")
args = parser.parse_args()
# transform data into dataframe
for row in data_iter(args.input_json):
subset_amterdam.append(row)
result = pd.DataFrame(subset_amterdam)
# lower all column names
result.columns = result.columns.str.lower()
# aggregating numbers per group
result = result.groupby(
['date_of_publication', 'municipality_code', 'municipality_name', 'province']).agg(
total_reported = ('total_reported','sum'),
hospital_admission = ('hospital_admission','sum'),
deceased = ('deceased','sum'),
).reset_index()
log.info(f"Starting import {args.input_json}")
engine = get_engine()
result.to_sql(
"corona_gevallen_new",
engine,
dtype={
"index": Integer(),
"date_of_publication": Date(),
"municipality_code": String(),
"municipality_name": String(),
"province": String(),
"total_reported": Integer(),
"hospital_admission": Integer(),
"deceased": Integer(),
},
)
log.info("Data loaded into DB")
ProvenanceRenameOperator(
task_id="provenance_rename",
dataset_name="corona",
prefix_table_name="corona_",
postfix_table_name="_new",
rename_indexes=False,
pg_schema="public",
).execute()
log.info("Renamed columns based on provenance.")
def import_scooter_data(
table_name: str,
felyx_api_endpoint: str,
felyx_api_header: Dict,
ridecheck_token_endpoint: str,
ridecheck_token_payload: Dict,
ridecheck_token_header: Dict,
ridecheck_data_endpoint: str,
ridecheck_data_header: Dict,
) -> None:
"""Union resources felyx and RIDECHECK and imports data into database table
Args:
felyx_api_endpoint: an URL specification for the data service to call
felyx_api_header: a dictionary of headers key value pairs used when calling
the data endpoint
ridecheck_token_endpoint: an URL specification for the token service to retreive a
(time limited) access key
ridecheck_token_payload: a dictionary of key value pairs elements used in the data body
when calling the token service
ridecheck_token_header: a dictionary of headers key value pairs used when calling
the token endpoint
ridecheck_data_endpoint: an URL specification of the data service to call
ridecheck_data_header: a dictionary of headers key value pairs used when
calling the data endpoint
Executes:
SQL insert statements of scooter data into database table
"""
ridecheck = get_data_ridecheck(
ridecheck_token_endpoint,
ridecheck_token_payload,
ridecheck_token_header,
ridecheck_data_endpoint,
ridecheck_data_header,
)
felyx = get_data_scooter_felyx(felyx_api_endpoint, felyx_api_header)
scooter_dataset = chain([row for row in ridecheck], [row for row in felyx])
df = pd.DataFrame([vars(row) for row in scooter_dataset])
df["geometrie"] = df["geometrie"].apply(lambda g: WKTElement(g.wkt, srid=4326))
db_engine = get_engine()
df.to_sql(
table_name,
db_engine,
if_exists="replace",
index=False,
dtype={"geometrie": Geometry(geometry_type="POINT", srid=4326)},
)
with db_engine.connect() as connection:
connection.execute(f"ALTER TABLE {table_name} ADD PRIMARY KEY (id)")
def load_grex_from_dwh(table_name):
db_engine = get_engine()
dwh_ora_engine = get_ora_engine("oracle_dwh_stadsdelen")
with dwh_ora_engine.connect() as connection:
df = pd.read_sql(
"""
SELECT PLANNR as ID
, PLANNAAM
, STARTDATUM
, PLANSTATUS
, OPPERVLAKTE
, GEOMETRIE_WKT AS GEOMETRY
FROM DMDATA.GREX_GV_PLANNEN_V2
""",
connection,
index_col="id",
coerce_float=True,
params=None,
parse_dates=["startdatum"],
columns=None,
chunksize=None,
)
df["geometry"] = df["geometry"].apply(wkt_loads_wrapped)
grex_rapportage_dtype = {
"id": Integer(),
"plannaam": Text(),
"startdatum": Date(),
"planstatus": Text(),
"oppervlakte": Float(),
"geometry": Geometry(geometry_type="GEOMETRY", srid=4326),
}
df.to_sql(table_name,
db_engine,
if_exists="replace",
dtype=grex_rapportage_dtype)
with db_engine.connect() as connection:
connection.execute(
f"ALTER TABLE {table_name} ADD PRIMARY KEY (id)")
connection.execute(f"""
UPDATE {table_name}
SET geometry = ST_CollectionExtract(ST_Makevalid(geometry), 3)
WHERE ST_IsValid(geometry) = False
OR ST_GeometryType(geometry) != 'ST_MultiPolygon';
COMMIT;
""")
connection.execute(f"""
ALTER TABLE {table_name}
ALTER COLUMN geometry TYPE geometry(MultiPolygon,28992)
USING ST_Transform(geometry,28992);
""")
connection.execute(
f"DELETE FROM {table_name} WHERE geometry is NULL")
def _load_geojson(postgres_conn_id):
"""As airflow executes tasks at different hosts,
these tasks need to happen in a single call.
Otherwise, the (large) file is downloaded by one host,
and stored in the XCom table to be shared between tasks.
"""
tmp_dir = Path(f"/tmp/{dag_id}")
tmp_dir.mkdir(parents=True, exist_ok=True)
# 1. download files
files = {}
for route in ROUTES:
dest = f"{tmp_dir}/{route.name}.geojson"
logger.info("Downloading %s to %s", route.url, dest)
download_file(route.url, dest, http_conn_id=None)
files[route.name] = dest
# 2. generate schema ("schema introspect geojson *.geojson")
schema = introspect_geojson_files(
"gevaarlijke-routes", files=list(files.values())
)
schema = DatasetSchema.from_dict(schema) # TODO: move to schema-tools?
# XXX This is not running as one transaction atm, but autocommitting per chunk
# 3. import data
db_engine = get_engine()
importer = GeoJSONImporter(schema, db_engine, logger=logger)
for route in ROUTES:
geojson_path = files[route.name]
logger.info(
"Importing %s into %s", route.name, route.tmp_db_table_name
)
importer.generate_db_objects(
table_name=route.schema_table_name,
db_table_name=route.tmp_db_table_name,
truncate=True, # when reexecuting the same task
ind_tables=True,
ind_extra_index=False,
)
importer.load_file(
geojson_path,
)
if route.post_process:
hook = PostgresHook(postgres_conn_id=postgres_conn_id)
hook.run(route.post_process)
def load_grex(input_csv, table_name):
db_engine = get_engine()
df = pd.read_csv(
input_csv,
delimiter=";",
decimal=",",
encoding="latin_1",
parse_dates=["STARTDATUM"],
dayfirst=True,
index_col="PLANNR",
)
df.rename(columns=lambda x: "geometry" if x == "GEOMETRIE" else x.lower(),
inplace=True)
df.index.name = "id"
df["geometry"] = df["geometry"].apply(wkt_loads_wrapped)
grex_rapportage_dtype = {
"id": Integer(),
"plannaam": Text(),
"planbeheercode": Text(),
"startdatum": Date(),
"planstatus": Text(),
"oppervlakte": Float(),
"geometry": Geometry(geometry_type="MULTIPOLYGON", srid=4326),
}
df.to_sql(table_name,
db_engine,
if_exists="replace",
dtype=grex_rapportage_dtype)
with db_engine.connect() as connection:
connection.execute(f"ALTER TABLE {table_name} ADD PRIMARY KEY (id)")
connection.execute(f"""
ALTER TABLE {table_name}
ALTER COLUMN geometry TYPE geometry(MultiPolygon,28992)
USING ST_Transform(geometry,28992)
""")
connection.execute(f"DELETE FROM {table_name} WHERE geometry is NULL")
connection.execute(f"""
UPDATE {table_name}
SET geometry = ST_CollectionExtract(ST_Makevalid(geometry), 3)
WHERE ST_IsValid(geometry) = False;
""")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input_csv", type=str, help="CSV file to process")
args = parser.parse_args()
df = pd.read_csv(
args.input_csv,
sep=";",
names=["organisatie", "type_interventie", "aantal", "week_nummer", "jaar"],
converters={
"organisatie": strip,
"type_interventie": strip,
},
header=0,
)
df.index.name = "id"
engine = get_engine()
df.to_sql(
"corona_handhaving_new",
engine,
dtype={"id": Integer(), "aantal": Integer(), "week_nummer": Integer(), "jaar": Integer()},
)
def import_auto_data(
table_name: str,
mywheels_api_endpoint: str,
mywheels_api_header: Dict,
mywheels_api_payload: Dict,
) -> None:
"""Union resources MYWHEELS and [...] and imports data into database table
Args:
mywheels_api_endpoint: an URL specification for the data service to call
mywheels_api_header: a dictionary of headers key value pairs used when calling
the data endpoint
mywheels_api_payload: a dictionary of key value pairs elements used in the data body
when calling the token service
Executes:
SQL insert statements of auto data into database table
"""
mywheels = get_data_auto_mywheels(
mywheels_api_endpoint, mywheels_api_header, mywheels_api_payload
)
auto_dataset = [row for row in mywheels]
df = pd.DataFrame([vars(row) for row in auto_dataset])
df["geometrie"] = df["geometrie"].apply(lambda g: WKTElement(g.wkt, srid=4326))
db_engine = get_engine()
df.to_sql(
table_name,
db_engine,
if_exists="replace",
index=False,
dtype={"geometrie": Geometry(geometry_type="POINT", srid=4326)},
)
with db_engine.connect() as connection:
connection.execute(f"ALTER TABLE {table_name} ADD PRIMARY KEY (id)")
def load_from_dwh(table_name):
db_engine = get_engine()
dwh_ora_engine = get_ora_engine("oracle_dwh_stadsdelen")
with dwh_ora_engine.connect() as connection:
df = pd.read_sql(
"""
select ZAAKNUMMER
, ZAAKNAAM
, ADRES
, ZAAK_CATEGORIE
, ZAAK_SPECIFICATIE
, BEGINDATUM
, EINDDATUM
, OPENINGSTIJDEN_ZO_DO_VAN
, OPENINGSTIJDEN_ZO_DO_TOT
, OPENINGSTIJDEN_VR_ZA_VAN
, OPENINGSTIJDEN_VR_ZA_TOT
, O_TIJDEN_TERRAS_ZO_DO_VAN
, O_TIJDEN_TERRAS_ZO_DO_TOT
, O_TIJDEN_TERRAS_VR_ZA_VAN
, O_TIJDEN_TERRAS_VR_ZA_TOT
, LOCATIE_WKT AS LOCATIE
, TERRASGEOMETRIE_WKT AS TERRASGEOMETRIE
, POSTCODE
, STATUS_VERGUNNING
, STATUS_TIJDELIJK_TERRAS
, TOESTEMMING_TIJDELIJK_TERRAS
, PUBL_BESLUIT_TIJDELIJK_TERRAS
, TIJDELIJK_TERRAS_DETAILS
, STATUS_VERLENG_TIJDELK_TERRAS
, VERLENG_TIJDELK_TERRAS_DETAIL
from DMDATA.HORECA_EXPLOITATIEVERGUNNING_V
""",
connection,
coerce_float=True,
params=None,
parse_dates=["begindatum", "einddatum"],
columns=None,
chunksize=None,
)
df["locatie"] = df["locatie"].apply(wkt_loads_wrapped)
df["terrasgeometrie"] = df["terrasgeometrie"].apply(wkt_loads_wrapped)
dtype = {
"zaaknummer": Integer(),
"zaaknaam": Text(),
"zaakcategorie": Text(),
"zaakspecificatie": Text(),
"begindatum": Date(),
"einddatum": Date(),
"openingstijden_zo_do_van": Text(),
"openingstijden_zo_do_tot": Text(),
"openingstijden_vr_za_van": Text(),
"openingstijden_vr_za_tot": Text(),
"o_tijden_terras_zo_do_van": Text(),
"o_tijden_terras_zo_do_tot": Text(),
"o_tijden_terras_vr_za_van": Text(),
"o_tijden_terras_vr_za_tot": Text(),
"locatie": Geometry(geometry_type="POINT", srid=4326),
"terrasgeometrie": Geometry(geometry_type="MULTIPOLYGON", srid=4326),
"postcode": Text(),
"status_vergunning": Text(),
"status_tijdelijk_terras": Text(),
"toestemming_tijdelijk_terras": Text(),
"publ_besluit_tijdelijk_terras": Text(),
"tijdelijk_terras_details": Text(),
"status_verleng_tijdelk_terras": Text(),
"verleng_tijdelk_terras_detail": Text(),
}
df.to_sql(
table_name, db_engine, if_exists="replace", index_label="id", dtype=dtype
)
with db_engine.connect() as connection:
connection.execute(f"ALTER TABLE {table_name} ADD PRIMARY KEY (id)")
connection.execute(
f"""
ALTER TABLE {table_name}
ALTER COLUMN terrasgeometrie TYPE geometry(MultiPolygon,28992)
USING ST_Transform(terrasgeometrie,28992)
"""
)
connection.execute(
f"""
ALTER TABLE {table_name}
ALTER COLUMN locatie TYPE geometry(Point,28992)
USING ST_Transform(locatie,28992)
"""
)
# connection.execute(f"DELETE FROM {table_name} WHERE terrasgeometrie is NULL")
connection.execute(
f"""
UPDATE {table_name}
SET terrasgeometrie = ST_CollectionExtract(ST_Makevalid(terrasgeometrie), 3)
WHERE ST_IsValid(terrasgeometrie) = False;
"""
)
connection.execute(
f"""
ALTER TABLE {table_name}
RENAME COLUMN VERLENG_TIJDELK_TERRAS_DETAIL to VERLENGING_TIJDELIJK_TERRAS_DETAILS;
ALTER TABLE {table_name}
RENAME COLUMN STATUS_VERLENG_TIJDELK_TERRAS to STATUS_VERLENGING_TIJDELIJK_TERRAS;
"""
)
def load_from_dwh(table_name: str) -> None:
"""Loads data from an Oracle database source into a Postgres database
Args:
table_name: The target table where the source data will be stored
Executes:
SQL INSERT statements for the data and post-processing
an ALTER statement to a contraint.
Note: The SQL processing is done with SQLAlchemy
Note2: Data is filterd on unit OIS 'Onderzoek, Informatie en Statistiek' by code 380000
"""
db_engine = get_engine()
dwh_ora_engine = get_ora_engine("oracle_dwh_ami")
date_fmt = "%Y%d%m"
with dwh_ora_engine.connect() as connection:
df = pd.read_sql(
"""
SELECT
gb.DATUM_BOEKING as "datum_boeking"
,gb.BEDRAG_BOEKING as "bedrag_boeking"
,gb.OMSCHRIJVING as "boeking_omschrijving"
,gb.OPMERKING as "boeking_opmerking"
,gbr.HOOFDREKENING_CODE as "hoofdrekening_code"
,gbr.REKENING_OMSCHRIJVING as "rekening_omschrijving"
,gbr.BEDRIJFSEENHEID_CODE as "bedrijfseenheid_code"
,gbr.BEDRIJFSEENHEID_OMSCHRIJVING as "bedrijfseenheid_omschrijving"
,gb.SUBGROOTBOEK as "subgrootboek"
,gb.SUBGROOTBOEKTYPE as "subgrootboektype"
,vpr.VPL_VERPLICHTINGNUMMER as "vpl_verplichtingnummer"
,vpr.VPL_OMSCHRIJVING as "vpl_omschrijving"
,vpr.VPL_JAAR as "vpl_jaar"
,vpr.VPL_INKOOPCONTRACTCODE as "vpl_inkoopcontractcode"
,vpr.VPL_STATUS as "vpl_status"
,vpr.VPR_DTM_LSTE_AFBOEKING as "vpr_datum_laatste_afboeking"
,vpr.VPR_BEDRAG_OORSPRONKELIJK as "vpr_bedrag_oorspronkelijk"
,vpr.VPR_BEDRAG_AFGEBOEKT as "vpr_bedrag_afgeboekt"
,vpr.VPR_BEDRAG_RESTANT as "vpr_bedrag_restant"
,vpr.VPR_REGELNUMMER as "vpr_regelnummer"
,vpr.VPR_OMSCHRIJVING as "vpr_omschrijving"
,vpr.VPR_OPMERKING as "vpr_opmerking"
,vpr.VPR_DATUM_GROOTBOEK as "vpr_datum_grootboek"
,vpr.VPR_DATUMVERPLICHTING as "vpr_datumverplichting"
,wor.CODE as "werkorder_code"
,wor.OMSCHRIJVING as "werkorder_omschrijving"
,wor.STATUS as "werkorder_status"
,wor.STATUS_OPMERKING as "werkorder_status_opmerking"
,rel.code as "relatie_code"
,rel.naam as "relatie_naam"
,rel.IND_CREDITEUR as "indicatie_crediteur"
,rel.IND_DEBITEUR as "indicatie_debiteur"
,org.SUBTEAM_CODE as "subteam_code"
,org.SUBTEAM_NAAM as "subteam_naam"
,org.TEAM_CODE as "team_code"
,org.TEAM_NAAM as "team_naam"
,org.AFDELING_CODE as "afdeling_code"
,org.AFDELING_NAAM as "afdeling_naam"
,org.RVE_CODE as "rve_code"
,org.RVE_NAAM as "rve_naam"
,org.CLUSTER_CODE as "cluster_code"
,org.CLUSTER_NAAM as "cluster_naam"
FROM DATAHUB.FI2_DHB_FCT_GROOTBOEK GB
INNER JOIN DATAHUB.FI2_DHB_DIM_RELATIE REL ON REL.ID = GB.REL_ID
INNER JOIN DATAHUB.FI2_DHB_DIM_ORGANISATIE org ON ORG.ID = GB.ORG_ID
INNER JOIN DATAHUB.FI2_DHB_DIM_GROOTBOEKREKENING gbr ON gbr.ID = GB.gbr_ID
INNER JOIN DATAHUB.FI2_DHB_DIM_VERPLICHTINGREGEL vpr ON vpr.ID = GB.VPR_ID
INNER JOIN DATAHUB.FI2_DHB_DIM_WERKORDER wor ON wor.ID = GB.WOR_ID
WHERE 1=1
AND RVE_CODE = '380000'
""",
connection,
coerce_float=True,
params=None,
parse_dates={
"datum_boeking": date_fmt,
"vpr_datum_grootboek": date_fmt,
"vpr_datumverplichting": date_fmt,
"vpr_dtm_lste_afboeking": date_fmt,
},
columns=None,
chunksize=None,
)
dtype = {
"datum_boeking": Date(),
"bedrag_boeking": Numeric(),
"boeking_omschrijving": Text(),
"boeking_opmerking": Text(),
"hoofdrekening_code": Text(),
"rekening_omschrijving": Text(),
"bedrijfseenheid_code": Text(),
"bedrijfseenheid_omschrijving": Text(),
"subgrootboek": Text(),
"subgrootboektype": Text(),
"vpl_verplichtingnummer": Text(),
"vpl_omschrijving": Text(),
"vpl_jaar": Integer(),
"vpl_inkoopcontractcode": Text(),
"vpl_status": Text(),
"vpr_datum_laatste_afboeking": Date(),
"vpr_bedrag_oorspronkelijk": Numeric(),
"vpr_bedrag_afgeboekt": Numeric(),
"vpr_bedrag_restant": Numeric(),
"vpr_regelnummer": Integer(),
"vpr_omschrijving": Text(),
"vpr_opmerking": Text(),
"vpr_datum_grootboek": Date(),
"vpr_datumverplichting": Date(),
"werkorder_code": Text(),
"werkorder_omschrijving": Text(),
"werkorder_status": Text(),
"werkorder_status_opmerking": Text(),
"relatie_code": Text(),
"relatie_naam": Text(),
"indicatie_crediteur": Boolean(),
"indicatie_debiteur": Boolean(),
"subteam_code": Text(),
"subteam_naam": Text(),
"team_code": Text(),
"team_naam": Text(),
"afdeling_code": Text(),
"afdeling_naam": Text(),
"rve_code": Text(),
"rve_naam": Text(),
"cluster_code": Text(),
"cluster_naam": Text(),
}
df.to_sql(table_name,
db_engine,
if_exists="replace",
index_label="id",
index=True,
dtype=dtype)
with db_engine.connect() as connection:
connection.execute(
f"ALTER TABLE {table_name} ADD PRIMARY KEY (ID)")
def load_from_dwh(table_name: str) -> None:
"""Loads data from an Oracle database source into a Postgres database
Args:
table_name: The target table where the source data will be stored
Executes:
SQL INSERT statements for the data and post-processing
an ALTER statement to a contraint.
Note: The SQL processing is done with SQLAlchemy
"""
db_engine = get_engine()
dwh_ora_engine = get_ora_engine("oracle_dwh_stadsdelen")
with dwh_ora_engine.connect() as connection:
df = pd.read_sql(
"""
select ADWH_VERSIE_ID
, SOORT_WERKZAAMHEDEN
, KENTEKEN
, ADWH_KENTEKEN
, CATEGORIE
, ADWH_ACTIVITEIT
, WERKZAAMHEDEN_CODE
, WERKZAAMHEDEN_OMSCHRIJVING
, WERKZAAMHEDEN_DATUM
, WERKZAAMHEDEN_DATUM_REF_ID
, WERKZAAMHEDEN_STARTTIJD
, WERKZAAMHEDEN_EINDTIJD
, WERKZAAMHEDEN_UREN_GEPLAND
, PAUZE_STARTTIJD
, PAUZE_EINDTIJD
, PAUZE_UREN_GEPLAND
, INHUUR
, FASE
, MEMO
, TEAM
, AANTAL_MEDEWERKERS
, UREN_INZET_MEDEWERKER_INTERN
, UREN_INZET_MEDEWERKER_INHUUR
, UREN_INZET_VOERTUIG
, AANTAL_MEDEWERKERS_INTERN
, AANTAL_MEDEWERKERS_INHUUR
, ADWH_LAATST_GEZIEN
, ADWH_LAATST_GEZIEN_BRON
from DMDATA.AFVAL_INZML_VOERTUIGPLAN_V2
""",
connection,
coerce_float=True,
params=None,
parse_dates=[
"WERKZAAMHEDEN_DATUM",
"ADWH_LAATST_GEZIEN",
"ADWH_LAATST_GEZIEN_BRON",
],
columns=None,
chunksize=None,
)
dtype = {
"ADWH_VERSIE_ID": Integer(),
"SOORT_WERKZAAMHEDEN": Text(),
"KENTEKEN": Text(),
"ADWH_KENTEKEN": Text(),
"CATEGORIE": Text(),
"ADWH_ACTIVITEIT": Text(),
"WERKZAAMHEDEN_CODE": Text(),
"WERKZAAMHEDEN_OMSCHRIJVING": Text(),
"WERKZAAMHEDEN_DATUM": Date(),
"WERKZAAMHEDEN_DATUM_REF_ID": Integer(),
"WERKZAAMHEDEN_STARTTIJD": Text(),
"WERKZAAMHEDEN_EINDTIJD": Text(),
"WERKZAAMHEDEN_UREN_GEPLAND": Numeric(),
"PAUZE_STARTTIJD": Text(),
"PAUZE_EINDTIJD": Text(),
"PAUZE_UREN_GEPLAND": Text(),
"INHUUR": Text(),
"FASE": Text(),
"MEMO": Text(),
"TEAM": Text(),
"AANTAL_MEDEWERKERS": Text(),
"UREN_INZET_MEDEWERKER_INTERN": Numeric(),
"UREN_INZET_MEDEWERKER_INHUUR": Numeric(),
"UREN_INZET_VOERTUIG": Numeric(),
"AANTAL_MEDEWERKERS_INTERN": Numeric(),
"AANTAL_MEDEWERKERS_INHUUR": Numeric(),
"ADWH_LAATST_GEZIEN": DateTime(),
"ADWH_LAATST_GEZIEN_BRON": DateTime(),
}
df.to_sql(table_name,
db_engine,
if_exists="replace",
dtype=dtype,
index=False)
with db_engine.connect() as connection:
connection.execute(
f"ALTER TABLE {table_name} ADD PRIMARY KEY (ADWH_VERSIE_ID)")
def load_from_dwh(table_name: str) -> None:
"""Loads data from an Oracle database source into a Postgres database
Args:
table_name: The target table where the source data will be stored
Executes:
SQL INSERT statements for the data and post-processing
an ALTER statement to a contraint.
Note: The SQL processing is done with SQLAlchemy
"""
db_engine = get_engine()
dwh_ora_engine = get_ora_engine("oracle_dwh_stadsdelen")
with dwh_ora_engine.connect() as connection:
df = pd.read_sql(
"""
select "ID"
,"DATUM"
,"DAG_VAN_WEEK_NUMMER"
,"DAG_VAN_WEEK_NAAM"
,"DAG_VAN_WEEK_KNAAM"
,"VORIGE_DAG_VAN_WEEK_NAAM"
,"VORIGE_DAG_VAN_WEEK_KNAAM"
,"WEEKEND_IND"
,"FEESTDAG_IND"
,"FEESTDAG_ADAM_IND"
,"AANTAL_WERKDAGEN"
,"AANTAL_WERKDAGEN_ADAM"
,"SEIZOEN"
,"WEEK_IN_MAAND_NUMMER"
,"WEEK_IN_MAAND_START_DATUM"
,"WEEK_IN_MAAND_EINDE_DATUM"
,"WEEK_IN_JAAR_NUMMER"
,"ISO_WEEK_NUMMER"
,"JAAR_ISO_WEEKNR"
,"ISO_WEEK_START_DATUM"
,"ISO_WEEK_EINDE_DATUM"
,"DAG_VAN_MAAND_NUMMER"
,"MAAND_WAARDE"
,"MAAND_NAAM"
,"MAAND_KNAAM"
,"JAARMAAND"
,"MAAND_START_DATUM"
,"MAAND_EINDE_DATUM"
,"DAGEN_IN_MAAND"
,"LAATSTE_DAG_VAN_MAAND_IND"
,"DAG_VAN_KWARTAAL_NUMMER"
,"KWARTAAL_WAARDE"
,"KWARTAAL_NAAM"
,"JAARKWARTAAL"
,"KWARTAAL_START_DATUM"
,"KWARTAAL_EINDE_DATUM"
,"DAGEN_IN_KWARTAAL"
,"LAATSTE_DAG_VAN_KWARTAAL_IND"
,"TERTAAL"
,"JAAR_TERTAAL"
,"DAG_IN_TERTAAL_NUMMER"
,"DAGEN_IN_TERTAAL"
,"LAATSTE_DAG_VAN_TERTAAL_IND"
,"DAG_VAN_JAAR_NUMMER"
,"JAAR_WAARDE"
,"JAAR_NAAM"
,"JAAR_KNAAM"
,"JAAR_START_DATUM"
,"JAAR_EINDE_DATUM"
,"DAGEN_IN_JAAR"
,"LAATSTE_DAG_VAN_JAAR_IND"
,"JAARNAAM_KORT"
,"JULIAANSE_DATUM"
,CAST(NULL AS NUMBER) AS "SCHOOLVAKANTIE_NL_NOORD_IND"
,CAST(NULL AS NUMBER) AS "SCHOOLVAKANTIE_NL_MIDDEN_IND"
,CAST(NULL AS NUMBER) AS "SCHOOLVAKANTIE_NL_ZUID_IND"
,"NIVEAUCODE"
from DMDATA.ALG_DIM_DATUM_V2
""",
connection,
coerce_float=True,
params=None,
parse_dates=[
"DATUM",
"ISO_WEEK_START_DATUM",
"ISO_WEEK_EIND_DATUM",
"WEEK_IN_MAAND_START_DATUM",
"WEEK_IN_MAAND_EINDE_DATUM",
"MAAND_START_DATUM",
"MAAND_EINDE_DATUM",
"KWARTAAL_START_DATUM",
"KWARTAAL_EINDE_DATUM",
"JAAR_START_DATUM",
"JAAR_EINDE_DATUM",
],
columns=None,
chunksize=None,
)
dtype = {
"ID": Integer(),
"DATUM": Date(),
"DAG_VAN_WEEK_NUMMER": Integer(),
"DAG_VAN_WEEK_NAAM": Text(),
"DAG_VAN_WEEK_KNAAM": Text(),
"VORIGE_DAG_VAN_WEEK_NAAM": Text(),
"VORIGE_DAG_VAN_WEEK_KNAAM": Text(),
"WEEKEND_IND": Integer(),
"FEESTDAG_IND": Integer(),
"FEESTDAG_ADAM_IND": Integer(),
"AANTAL_WERKDAGEN": Integer(),
"AANTAL_WERKDAGEN_ADAM": Integer(),
"SEIZOEN": Text(),
"WEEK_IN_MAAND_NUMMER": Integer(),
"WEEK_IN_MAAND_START_DATUM": Date(),
"WEEK_IN_MAAND_EINDE_DATUM": Date(),
"WEEK_IN_JAAR_NUMMER": Integer(),
"ISO_WEEK_NUMMER": Integer(),
"JAAR_ISO_WEEKNR": Integer(),
"ISO_WEEK_START_DATUM": Date(),
"ISO_WEEK_EINDE_DATUM": Date(),
"DAG_VAN_MAAND_NUMMER": Integer(),
"MAAND_WAARDE": Integer(),
"MAAND_NAAM": Text(),
"MAAND_KNAAM": Text(),
"JAARMAAND": Integer(),
"MAAND_START_DATUM": Text(),
"MAAND_EINDE_DATUM": Text(),
"DAGEN_IN_MAAND": Integer(),
"LAATSTE_DAG_VAN_MAAND_IND": Integer(),
"DAG_VAN_KWARTAAL_NUMMER": Integer(),
"KWARTAAL_WAARDE": Integer(),
"KWARTAAL_NAAM": Text(),
"JAARKWARTAAL": Integer(),
"KWARTAAL_START_DATUM": Date(),
"KWARTAAL_EINDE_DATUM": Date(),
"DAGEN_IN_KWARTAAL": Integer(),
"LAATSTE_DAG_VAN_KWARTAAL_IND": Integer(),
"TERTAAL": Integer(),
"JAAR_TERTAAL": Text(),
"DAG_IN_TERTAAL_NUMMER": Integer(),
"DAGEN_IN_TERTAAL": Integer(),
"LAATSTE_DAG_VAN_TERTAAL_IND": Integer(),
"DAG_VAN_JAAR_NUMMER": Integer(),
"JAAR_WAARDE": Integer(),
"JAAR_NAAM": Text(),
"JAAR_KNAAM": Text(),
"JAAR_START_DATUM": Date(),
"JAAR_EINDE_DATUM": Date(),
"DAGEN_IN_JAAR": Integer(),
"LAATSTE_DAG_VAN_JAAR_IND": Integer(),
"JAARNAAM_KORT": Text(),
"JULIAANSE_DATUM": Integer(),
"SCHOOLVAKANTIE_NL_NOORD_IND": Integer(),
"SCHOOLVAKANTIE_NL_MIDDEN_IND": Integer(),
"SCHOOLVAKANTIE_NL_ZUID_IND": Integer(),
"NIVEAUCODE": Text(),
}
df.to_sql(table_name,
db_engine,
if_exists="replace",
dtype=dtype,
index=False)
with db_engine.connect() as connection:
connection.execute(
f"ALTER TABLE {table_name} ADD PRIMARY KEY (ID)")