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foppaInit.py
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foppaInit.py
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## Foppa Initialization
"""Imports"""
import math
import json
import sqlite3
import pandas as pd
import numpy as np
import os
import re
import time
from os import walk
from datetime import date
#from blazingsql import BlazingContext
from rapidfuzz import fuzz
from rapidfuzz import process
import logging
import optparse
import dedupe
from unidecode import unidecode
import csv
import urllib.request
import shutil
import zipfile
def downloadFiles():
"""Download of the various files in the FOPPA database"""
os.mkdir("data/contractNotices") #Contract notices from TED
os.mkdir("data/contractAwards") #Contract award from TED
os.mkdir("data/opening") #Opening dates from SIRENE
os.mkdir("data/Etab") #All facilities from SIRENE
os.mkdir("data/geolocate") #For localisation of SIRENE entities
#### Contract award
urls = ["https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2010.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2011.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2012.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2013.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2014.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2015.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2016.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2017.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2018.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2019.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-award-notices-2020.zip",
]
for url in urls:
urllib.request.urlretrieve(url,"Award.zip")
with zipfile.ZipFile("Award.zip", 'r') as zip_ref:
zip_ref.extractall("data/contractAwards")
#### Contract Notices
urls = ["https://data.europa.eu/api/hub/store/data/ted-contract-notices-2008.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2009.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2010.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2011.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2012.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2013.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2014.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2015.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2016.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2017.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2018.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2019.zip",
"https://data.europa.eu/api/hub/store/data/ted-contract-notices-2020.zip"
]
for url in urls:
urllib.request.urlretrieve(url,"Contract.zip")
with zipfile.ZipFile("Contract.zip", 'r') as zip_ref:
zip_ref.extractall("data/contractNotices")
os.remove("Contract.zip")
os.remove("Award.zip")
######SIRENE:
#Opening Dates
urls = ["https://files.data.gouv.fr/insee-sirene/StockEtablissementHistorique_utf8.zip"]
for url in urls:
urllib.request.urlretrieve(url,"HistoEtab.zip")
with zipfile.ZipFile("HistoEtab.zip", 'r') as zip_ref:
zip_ref.extractall("data/opening")
chunksize = 10 ** 6
compteur=0
compt=-1
with pd.read_csv("data/opening/StockEtablissementHistorique_utf8.csv", chunksize=chunksize,dtype="str") as reader:
for chunk in reader:
if compt>-1:
compt=compt+1
chunk = chunk[["siret","dateDebut","dateFin"]]
name = "data/opening/HistoPart"+str(compteur)+".csv"
datefin = np.array(chunk["dateFin"])
for j in range(len(datefin)):
if len(str(datefin[j]))<4:
datefin[j]="2030-01-01"
chunk["dateFin"]=datefin
chunk.to_csv(name,index=False)
compteur=compteur+1
os.remove("data/opening/StockEtablissementHistorique_utf8.csv")
os.remove("HistoEtab.zip")
#Facilities
urls = ["https://files.data.gouv.fr/insee-sirene/StockEtablissement_utf8.zip",
"https://files.data.gouv.fr/insee-sirene/StockUniteLegale_utf8.zip"]
for url in urls:
urllib.request.urlretrieve(url,"Sirene.zip")
with zipfile.ZipFile("Sirene.zip", 'r') as zip_ref:
zip_ref.extractall("data/Etab")
os.remove("Sirene.zip")
chunksize = 10 ** 6
compteur=0
allCol = ["siret","siren","nomEnseigne","nomUnite","num","typevoie","libelle","cp","ville","TypeActivite","CatJuridique"]
chunksize = 10 ** 6
newDF = pd.DataFrame(index=range(0,chunksize),columns=allCol)
diconom = {}
dicotype = {}
filename = "data/Etab/StockUniteLegale_utf8.csv"
chunksize = 10 ** 6
compt=-1
for chunk in pd.read_csv(filename, chunksize=chunksize,dtype = str):
if compt>-2:
compt=compt+1
for l in range(len(chunk)):
if not(str(chunk["denominationUniteLegale"][chunksize*compt+l]) == "nan"):
temp = str(chunk["denominationUniteLegale"][chunksize*compt+l])
elif not(str(chunk["denominationUsuelle1UniteLegale"][chunksize*compt+l]) == "nan"):
temp = str(chunk["denominationUsuelle1UniteLegale"][chunksize*compt+l])
elif not(str(chunk["denominationUsuelle2UniteLegale"][chunksize*compt+l]) == "nan"):
temp = str(chunk["denominationUsuelle2UniteLegale"][chunksize*compt+l])
elif not(str(chunk["denominationUsuelle3UniteLegale"][chunksize*compt+l]) == "nan"):
temp = str(chunk["denominationUsuelle3UniteLegale"][chunksize*compt+l])
else:
temp = str(chunk["prenom1UniteLegale"][chunksize*compt+l]) + " " + str(chunk["nomUniteLegale"][chunksize*compt+l])
diconom[str(chunk["siren"][chunksize*compt+l])] = temp
dicotype[str(chunk["siren"][chunksize*compt+l])] = str(chunk["categorieJuridiqueUniteLegale"][chunksize*compt+l])
numeroFichier=0
os.remove("data/opening/StockUniteLegale_utf8.csv")
filename = "data/Etab/StockEtablissement_utf8.csv"
chunksize = 10 ** 6
compt=-1
for chunk in pd.read_csv(filename, chunksize=chunksize,dtype = str):
a=0
# Insert a row of data
compt=compt+1
for l in range(len(chunk)):
siren = str(chunk["siren"][chunksize*compt+l])
siret = str(chunk["siret"][chunksize*compt+l])
num = str(chunk["numeroVoieEtablissement"][chunksize*compt+l])
typevoie = str(chunk["typeVoieEtablissement"][chunksize*compt+l])
libelle = str(chunk["libelleVoieEtablissement"][chunksize*compt+l])
cp = str(chunk["codePostalEtablissement"][chunksize*compt+l])
ville = str(chunk["libelleCommuneEtablissement"][chunksize*compt+l])
e1= str(chunk["enseigne1Etablissement"][chunksize*compt+l])
denom= str(chunk["denominationUsuelleEtablissement"][chunksize*compt+l])
e2= str(chunk["enseigne2Etablissement"][chunksize*compt+l])
e3= str(chunk["enseigne3Etablissement"][chunksize*compt+l])
nom = diconom.get(siren,"0")
typ = dicotype.get(siren,"0")
activite = str(chunk["activitePrincipaleEtablissement"][chunksize*compt+l])
if not(e1=="nan"):
nome = e1
elif not(denom=="nan"):
nome = denom
elif not(e2=="nan"):
nome = e2
elif not(e3=="nan"):
nome = e3
else:
nome = nom
params = (siret,siren,nome,nom,num,typevoie,libelle,cp,ville,activite,typ)
newDF.iloc[a] = params
a = a+1
del siren,siret,num,typevoie,libelle,cp,ville,nom,nome,e1,denom,e2,e3
newDF["ville"] = newDF["ville"].replace(regex=r"[0-9]+",value=r"")
newDF["ville"] = newDF["ville"].replace(regex=r"CEDEX",value=r"")
newDF["ville"] = newDF["ville"].replace(regex=r"-",value=r" ")
newDF["ville"] = newDF["ville"].replace(regex=r"'",value=r" ")
newDF["ville"] = newDF["ville"].str.lstrip()
newDF["ville"] = newDF["ville"].str.rstrip()
nomFD = "data/Etab/EtabPart"+str(numeroFichier)+".csv"
newDF.to_csv(nomFD,index=False)
numeroFichier = numeroFichier+1
newDF = pd.DataFrame(index=range(0,chunksize),columns=allCol)
os.remove("data/opening/StockEtablissement_utf8.csv")
datas = pd.read_csv("data/Etab/EtabPart0.csv",dtype=str)
for l in range(len(datas)):
if datas["nomEnseigne"][l] == "MAIRIE":
temp = datas["ville"][l]
temp = "MAIRIE DE "+datas["ville"][l]
datas["nomEnseigne"][l] = temp
datas.to_csv("data/Etab/EtabPart0.csv",index=False)
#Geolocalisation
urls = ["https://files.data.gouv.fr/insee-sirene-geo/GeolocalisationEtablissement_Sirene_pour_etudes_statistiques_utf8.zip"]
for url in urls:
urllib.request.urlretrieve(url,"Sirene.zip")
with zipfile.ZipFile("Sirene.zip", 'r') as zip_ref:
zip_ref.extractall("data/geolocate")
def databaseCreation(nameDatabase):
"""Creation of the tables of the database"""
database = sqlite3.connect(nameDatabase)
cursor = database.cursor()
request = "DROP TABLE IF EXISTS Lots"
sql = cursor.execute(request)
request = "CREATE TABLE Lots(lotId INTEGER,tedCanId INTEGER,correctionsNB INTEGER,cancelled INTEGER,awardDate TEXT,awardEstimatedPrice NUMERIC,awardPrice NUMERIC,cpv TEXT,tenderNumber INTEGER,onBehalf TINYINT,jointProcurement TINYINT,fraAgreement TINYINT,fraEstimated TEXT,lotsNumber INTEGER,accelerated TINYINT,outOfDirectives TINYINT,contractorSme TINYINT,numberTendersSme INTEGER,subContracted TINYINT,gpa TINYINT,multipleCae TINYINT,typeOfContract TEXT,topType TEXT,renewal TINYINT, contractDuration NUMERIC, publicityDuration NUMERIC,PRIMARY KEY(lotId))"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS AgentsBase"
sql = cursor.execute(request)
request = "CREATE TABLE AgentsBase(agentId INTEGER,name TEXT,siret TEXT,address TEXT,city TEXT,zipcode TEXT,country TEXT, date TEXT,type TEXT,PRIMARY KEY(agentId))"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS Agents"
sql = cursor.execute(request)
request = "CREATE TABLE Agents(agentId INTEGER,name TEXT,siret TEXT,address TEXT,city TEXT,zipcode TEXT,country TEXT, department TEXT,PRIMARY KEY(agentId))"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS AgentsLink"
sql = cursor.execute(request)
request = "CREATE TABLE AgentsLink(temporalagentId INTEGER,agentId INTEGER)"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS CriteriaTemp"
sql = cursor.execute(request)
request = "CREATE TABLE CriteriaTemp (lotId INTEGER,CRIT_PRICE_WEIGHT TEXT,CRIT_WEIGHTS TEXT, CRIT_CRITERIA TEXT)"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS LotBuyers"
sql = cursor.execute(request)
request = "CREATE TABLE LotBuyers(lotId INTEGER,agentId INTEGER,FOREIGN KEY(agentId) REFERENCES Agents(agentId) ON UPDATE CASCADE,FOREIGN KEY(lotId) REFERENCES Lots(lotId) ON UPDATE CASCADE)"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS LotWinners"
sql = cursor.execute(request)
request = "CREATE TABLE LotWinners(lotId INTEGER,agentId INTEGER,FOREIGN KEY(agentId) REFERENCES Agents(agentId) ON UPDATE CASCADE,FOREIGN KEY(lotId) REFERENCES Lots(lotId) ON UPDATE CASCADE)"
sql = cursor.execute(request)
request = "DROP TABLE IF EXISTS Names"
sql = cursor.execute(request)
request = "CREATE TABLE Names(agentId INTEGER,name TEXT)"
sql = cursor.execute(request)
database.commit()
return database
def load_csv_files():
"""Extraction of the csv files into the database"""
listeFichiers = []
newDF = []
for (repertoire, sousRepertoires, fichiers) in walk("data/contractAwards"):
listeFichiers.extend(fichiers)
for j in listeFichiers:
datas = pd.read_csv("data/contractAwards/"+j,dtype=str)
# We only keep french contracts
newDF.append(datas[datas["ISO_COUNTRY_CODE"].str.contains("FR")])
result = pd.concat(newDF)
return result
def firstCleaning(datas,database):
"""Insert into database + some preliminar cleaning"""
columns = datas.columns
# First cleaning : Normalize + Upper strings
for column in columns:
datas[column] = datas[column].str.upper()
datas[column]= datas[column].str.normalize('NFKD').str.encode('ascii', errors='ignore').str.decode('utf-8')
# Parenthesis deletion
datas["CAE_NAME"] = datas["CAE_NAME"].replace(regex=r'\([^)]*\)',value=r"")
# Replace "Y" by 1 and "N" by 0 on boolean columns
datas["B_ON_BEHALF"] = datas["B_ON_BEHALF"].replace(regex=r'Y',value=r"1")
datas["B_ON_BEHALF"] = datas["B_ON_BEHALF"].replace(regex=r'N',value=r"0")
datas["B_INVOLVES_JOINT_PROCUREMENT"] = datas["B_INVOLVES_JOINT_PROCUREMENT"].replace(regex=r'Y',value=r"1")
datas["B_INVOLVES_JOINT_PROCUREMENT"] = datas["B_INVOLVES_JOINT_PROCUREMENT"].replace(regex=r'N',value=r"0")
datas["B_FRA_AGREEMENT"] = datas["B_FRA_AGREEMENT"].replace(regex=r'Y',value=r"1")
datas["B_FRA_AGREEMENT"] = datas["B_FRA_AGREEMENT"].replace(regex=r'N',value=r"0")
datas["B_ACCELERATED"] = datas["B_ACCELERATED"].replace(regex=r'Y',value=r"1")
datas["B_ACCELERATED"] = datas["B_ACCELERATED"].replace(regex=r'N',value=r"0")
datas["B_OUT_OF_DIRECTIVES"] = datas["B_OUT_OF_DIRECTIVES"].replace(regex=r'Y',value=r"1")
datas["B_OUT_OF_DIRECTIVES"] = datas["B_OUT_OF_DIRECTIVES"].replace(regex=r'N',value=r"0")
datas["B_CONTRACTOR_SME"] = datas["B_CONTRACTOR_SME"].replace(regex=r'Y',value=r"1")
datas["B_CONTRACTOR_SME"] = datas["B_CONTRACTOR_SME"].replace(regex=r'N',value=r"0")
datas["B_SUBCONTRACTED"] = datas["B_SUBCONTRACTED"].replace(regex=r'Y',value=r"1")
datas["B_SUBCONTRACTED"] = datas["B_SUBCONTRACTED"].replace(regex=r'N',value=r"0")
datas["B_GPA"] = datas["B_GPA"].replace(regex=r'Y',value=r"1")
datas["B_GPA"] = datas["B_GPA"].replace(regex=r'N',value=r"0")
datas["B_MULTIPLE_CAE"] = datas["B_MULTIPLE_CAE"].replace(regex=r'Y',value=r"1")
datas["B_MULTIPLE_CAE"] = datas["B_MULTIPLE_CAE"].replace(regex=r'N',value=r"0")
nameCAE = np.array(datas["CAE_NAME"])
siretCAE = np.array(datas["CAE_NATIONALID"])
addressCAE = np.array(datas["CAE_ADDRESS"])
townCAE = np.array(datas["CAE_TOWN"])
postalCodeCAE = np.array(datas["CAE_POSTAL_CODE"])
countryCAE = np.array(datas["ISO_COUNTRY_CODE"])
nameWIN = np.array(datas["WIN_NAME"])
siretWIN = np.array(datas["WIN_NATIONALID"])
addressWIN = np.array(datas["WIN_ADDRESS"])
townWIN = np.array(datas["WIN_TOWN"])
postalCodeWIN = np.array(datas["WIN_POSTAL_CODE"])
countryWIN = np.array(datas["WIN_COUNTRY_CODE"])
tedCanId = np.array(datas["ID_NOTICE_CAN"])
correctionsNB = np.array(datas["CORRECTIONS"])
cancelled = np.array(datas["CANCELLED"])
awardEstimatedPrice = np.array(datas["AWARD_EST_VALUE_EURO"])
awardPrice = np.array(datas["AWARD_VALUE_EURO_FIN_1"])
cpv = np.array(datas["CPV"])
tenderNumber = np.array(datas["NUMBER_OFFERS"])
onBehalf = np.array(datas["B_ON_BEHALF"])
jointProcurement = np.array(datas["B_INVOLVES_JOINT_PROCUREMENT"])
fraAgreement = np.array(datas["B_FRA_AGREEMENT"])
fraEstimated = np.array(datas["FRA_ESTIMATED"])
lotsNumber = np.array(datas["ID_LOT_AWARDED"])
accelerated = np.array(datas["B_ACCELERATED"])
outOfDirectives = np.array(datas["OUT_OF_DIRECTIVES"])
contractorSme = np.array(datas["B_CONTRACTOR_SME"])
numbersTendersSme = np.array(datas["NUMBER_TENDERS_SME"])
subContracted = np.array(datas["B_SUBCONTRACTED"])
gpa = np.array(datas["B_GPA"])
multipleCae = np.array(datas["B_MULTIPLE_CAE"])
typeofContract = np.array(datas["TYPE_OF_CONTRACT"])
topType = np.array(datas["TOP_TYPE"])
criteria = np.array(datas["CRIT_CRITERIA"])
criteriaW = np.array(datas["CRIT_WEIGHTS"])
criteriaP = np.array(datas["CRIT_PRICE_WEIGHT"])
### Change date format
dico = {"JAN":"01","FEB":"02","MAR":"03","APR":"04","MAY":"05","JUN":"06","JUL":"07","AUG":"08","SEP":"09","OCT":"10","NOV":"11","DEC":"12"}
for key in dico:
datas["DT_AWARD"] = datas["DT_AWARD"].replace(regex=key,value=dico[key])
datas["DT_DISPATCH"] = datas["DT_DISPATCH"].replace(regex=key,value=dico[key])
awardDate = np.array(datas["DT_AWARD"])
dispatchDate = np.array(datas["DT_DISPATCH"])
for i in range(len(awardDate)):
if len(str(awardDate[i]))>4:
temp = awardDate[i].split("-")
awardDate[i] = "20"+temp[2]+"-"+temp[1]+"-"+temp[0]
if len(str(dispatchDate[i]))>4:
temp = dispatchDate[i].split("-")
dispatchDate[i] = "20"+temp[2]+"-"+temp[1]+"-"+temp[0]
cur = database.cursor()
compteurAgent=0
for i in range(len(datas)):
sql = ''' INSERT INTO CriteriaTemp(lotId,CRIT_PRICE_WEIGHT,CRIT_WEIGHTS, CRIT_CRITERIA)
VALUES (?,?,?,?) '''
val =(i,criteriaP[i],criteriaW[i],criteria[i])
cur.execute(sql,val)
sql = ''' INSERT INTO Lots(lotId,tedCanId,correctionsNB,cancelled,awardDate,awardEstimatedPrice,awardPrice,cpv,tenderNumber,onBehalf,jointProcurement,fraAgreement,fraEstimated,lotsNumber,accelerated,outOfDirectives,contractorSme,numberTendersSme,subContracted,gpa,multipleCae,typeOfContract,topType)
VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?) '''
val = (i,tedCanId[i],correctionsNB[i],cancelled[i],awardDate[i],awardEstimatedPrice[i],awardPrice[i],cpv[i],tenderNumber[i],onBehalf[i],jointProcurement[i],fraAgreement[i],fraEstimated[i],lotsNumber[i],accelerated[i],outOfDirectives[i],contractorSme[i],numbersTendersSme[i],subContracted[i],gpa[i],multipleCae[i],typeofContract[i],topType[i])
cur.execute(sql,val)
date = None
if len(str(awardDate[i]))>4:
date = awardDate[i]
else:
date = dispatchDate[i]
names = nameCAE[i].split("---")
sirets = None
addresses = None
towns = None
pcs = None
countrys = None
if not(str(siretCAE[i])=="nan"):
sirets = siretCAE[i].split("---")
if not(str(addressCAE[i])=="nan"):
addresses = addressCAE[i].split("---")
if not(str(townCAE[i])=="nan"):
towns = townCAE[i].split("---")
if not(str(postalCodeCAE[i])=="nan"):
pcs = postalCodeCAE[i].split("---")
if not(str(countryCAE[i])=="nan"):
countrys = countryCAE[i].split("---")
for k in range(len(names)):
tempName = names[k]
tempSiret = None
tempAddress = None
tempTown = None
tempPC = None
tempCountry = None
if not(sirets==None) and k<len(sirets):
tempSiret = sirets[k]
if not(addresses==None) and k<len(addresses):
tempAddress = addresses[k]
if not(towns==None) and k<len(towns):
tempTown = towns[k]
if not(pcs==None) and k<len(pcs):
tempPC = pcs[k]
if not(countrys==None) and k<len(countrys):
tempCountry = countrys[k]
sql = ''' INSERT INTO AgentsBase(agentId,name,siret,address,city,zipcode,country,date,type)
VALUES (?,?,?,?,?,?,?,?,?)'''
val = (compteurAgent,tempName,tempSiret,tempAddress,tempTown,tempPC,tempCountry,date,"CAE")
cur.execute(sql,val)
sql = ''' INSERT INTO LotBuyers(lotId,agentId)
VALUES (?,?)'''
val = (i,compteurAgent)
cur.execute(sql,val)
compteurAgent = compteurAgent+1
names=["INFRUCTUEUX"]
if not(str(nameWIN[i])=="nan"):
names = nameWIN[i].split("---")
names2 = nameWIN[i].split("/")
sirets = None
addresses = None
towns = None
pcs = None
countrys = None
if len(names)>1 or len(names2)<2:
if not(str(siretWIN[i])=="nan"):
sirets = siretWIN[i].split("---")
if not(str(addressWIN[i])=="nan"):
addresses = addressWIN[i].split("---")
if not(str(townWIN[i])=="nan"):
towns = townWIN[i].split("---")
if not(str(postalCodeWIN[i])=="nan"):
pcs = postalCodeWIN[i].split("---")
if not(str(countryWIN[i])=="nan"):
countrys = countryWIN[i].split("---")
else:
names=names2
if not(str(siretWIN[i])=="nan"):
sirets = siretWIN[i].split("/")
if not(str(addressWIN[i])=="nan"):
addresses = addressWIN[i].split("/")
if not(str(townWIN[i])=="nan"):
towns = townWIN[i].split("/")
if not(str(postalCodeWIN[i])=="nan"):
pcs = postalCodeWIN[i].split("/")
if not(str(countryWIN[i])=="nan"):
countrys = countryWIN[i].split("/")
for k in range(len(names)):
tempName = names[k]
tempSiret = None
tempAddress = None
tempTown = None
tempPC = None
tempCountry = None
if not(sirets==None) and k<len(sirets):
tempSiret = sirets[k]
if not(addresses==None) and k<len(addresses):
tempAddress = addresses[k]
if not(towns==None) and k<len(towns):
tempTown = towns[k]
if not(pcs==None) and k<len(pcs):
tempPC = pcs[k]
if not(countrys==None) and k<len(countrys):
tempCountry = countrys[k]
sql = ''' INSERT INTO AgentsBase(agentId,name,siret,address,city,zipcode,country,date,type)
VALUES (?,?,?,?,?,?,?,?,?)'''
val = (compteurAgent,tempName,tempSiret,tempAddress,tempTown,tempPC,tempCountry,date,"WIN")
cur.execute(sql,val)
sql = ''' INSERT INTO LotWinners(lotId,agentId)
VALUES (?,?)'''
val = (i,compteurAgent)
cur.execute(sql,val)
compteurAgent = compteurAgent+1
database.commit()
return database
def mainCleaning(database):
datas = pd.read_sql_query("SELECT * FROM AgentsBase", database,dtype=str)
###Nettoyage des donnees
##Nettoyage du Nom
datas["name"] = datas["name"].str.replace("'"," ")
datas["name"] = datas["name"].str.replace("-"," ")
datas["name"] = datas["name"].replace(regex=r"&APOS;",value=r" ")
datas["name"] = datas["name"].replace(regex=r";[A-Z, ]+",value=r"")
datas["name"] = datas["name"].replace(regex=r"- [A-Z, ]+",value=r"")
datas["name"] = datas["name"].replace(regex=r'\([^)]*\)',value=r"")
datas["name"] = datas["name"].replace(regex=r"-",value=r" ")
datas["name"] = datas["name"].replace(regex=r"'",value=r" ")
datas["name"] = datas["name"].str.lstrip()
datas["name"] = datas["name"].str.rstrip()
#Nettoyage du SIRET
datas["siret"]=datas["siret"].replace(regex='[^\d]+',value="")
datas["siret"] = datas["siret"].str.lstrip()
datas["siret"] = datas["siret"].str.rstrip()
sirets = np.array(datas["siret"])
for i in range(len(sirets)):
if not(len(str(sirets[i]))==14):
sirets[i]="NULL_IDENTIFIER"
datas["siret"]=sirets
#Nettoyage de l'adresse
datas["address"] = datas["address"].replace(regex=r"&APOS;",value=r" ")
datas["address"] = datas["address"].replace(regex=r"&",value=r"&")
datas["address"] = datas["address"].replace(regex=r"(\d);",value=r"\1 ")
datas["address"] = datas["address"].replace(regex=r"(\d)-(\d):",value=r"\1 ")
datas["address"] = datas["address"].replace(regex=r"(\d)/(\d)",value=r"\1 ")
datas["address"] = datas["address"].replace(regex=r"(\d);(\d)",value=r"\1 ")
datas["address"] = datas["address"].replace(regex="BIS",value="")
datas["address"] = datas["address"].replace(regex=r"([A-Z])-([A-Z])",value=r"\1 \2")
datas["address"] = datas["address"].replace(regex="'",value=" ")
datas["address"] = datas["address"].replace(regex=";",value=" ; ")
datas["address"] = datas["address"].replace(regex=r"(^[A-Z])",value=r" \1")
datas["address"] = datas["address"].replace(regex=r" +",value=r" ")
datas["address"] = datas["address"].replace(regex=r" BP [0-9, ]+",value=r"")
datas["address"] = datas["address"].replace(regex=r" CEDEX [0-9, ]+",value=r" ")
datas["address"] = datas["address"].replace(regex=r" CS [0-9, ]+",value=r" ")
datas["address"] = datas["address"].str.lstrip()
datas["address"] = datas["address"].str.rstrip()
### Nettoyage Partie Ville
replaceVille = {"CEDEX":"","-":" "," SP ":"","&APOS;":" ","'":" ",'\d':""}
for key in replaceVille:
datas["city"]=datas["city"].replace(regex=key,value=replaceVille[key])
datas["city"] = datas["city"].str.lstrip()
datas["city"] = datas["city"].str.rstrip()
datas["city"] = datas["city"].replace(regex=r"\)",value=r"")
datas["city"] = datas["city"].replace(regex=r"\(",value=r"")
#Nettoyage du code Postal
datas["zipcode"]=datas["zipcode"].replace(regex='[^\d]+',value="")
datas["zipcode"] = datas["zipcode"].str.lstrip()
datas["zipcode"] = datas["zipcode"].str.rstrip()
zips = np.array(datas["zipcode"])
for i in range(len(zips)):
if not(len(str(zips[i]))==5):
zips[i]=None
datas["zipcode"]=zips
datas['name'] = datas['name'].fillna("NULL_IDENTIFIER")
datas['address'] = datas['address'].fillna("NULL_IDENTIFIER")
datas['city'] = datas['city'].fillna("NULL_IDENTIFIER")
datas2 = datas.groupby(["name",'address','city','siret'],as_index=False).agg({'agentId':'-'.join,'name':'first','siret':'first','address':'first','city':'first','zipcode':'first','country':'first','date':'first','type':'first'})
datas = datas2
cursor = database.cursor()
request = "DROP TABLE IF EXISTS AgentsTemp"
sql = cursor.execute(request)
request = "CREATE TABLE IF NOT EXISTS AgentsTemp(agentId INTEGER,name TEXT,siret TEXT,address TEXT,city TEXT,zipcode TEXT,country TEXT, date TEXT,ids TEXT,type TEXT,PRIMARY KEY(agentId))"
sql = cursor.execute(request)
types = np.array(datas["type"])
ids = np.array(datas["agentId"])
names = np.array(datas["name"])
sirets = np.array(datas["siret"])
addresses = np.array(datas["address"])
citys = np.array(datas["city"])
zipcodes = np.array(datas["zipcode"])
countrys = np.array(datas["country"])
dates = np.array(datas["date"])
for i in range(len(ids)):
sql = ''' INSERT INTO AgentsTemp(agentId,name,siret,address,city,zipcode,country,date,ids,type)
VALUES (?,?,?,?,?,?,?,?,?,?)'''
val = (i,names[i],sirets[i],addresses[i],citys[i],zipcodes[i],countrys[i],dates[i],ids[i],types[i])
cursor.execute(sql,val)
database.commit()
return database
def fineTuningAgents(database):
datas = pd.read_sql_query("SELECT * FROM AgentsTemp", database,dtype=str)
cursor = database.cursor()
## La table a siretiser au final :
request = "DROP TABLE IF EXISTS AgentsSiretiser"
sql = cursor.execute(request)
request = "CREATE TABLE IF NOT EXISTS AgentsSiretiser(agentId INTEGER,name TEXT,siret TEXT,address TEXT,newAddress TEXT,city TEXT,zipcode TEXT,country TEXT, date TEXT,catJuridique TEXT,company TEXT,ids TEXT,type TEXT, PRIMARY KEY(agentId))"
sql = cursor.execute(request)
### 1 : Supprimer ce qu'on ne veut pas siretiser
name = np.array(datas["name"])
country = np.array(datas["country"])
delete = ["INFRUCT","SANS SUITE","ABANDON","SANS OBJET","EN ATTENTE","AUCUNE OFFRE"]
for i in range(len(name)):
for pattern in delete:
if pattern in name[i]:
country[i]="INFRUCTUEUX"
datas["country"] = country
### 2 : Hexaposte :
pcs = np.array(datas["zipcode"])
hexaposte = pd.read_csv("data/foppaFiles/hexaposte.csv",dtype=str,sep=";")
hexaposte["libelle_d_acheminement"] = hexaposte["libelle_d_acheminement"].str.replace("-"," ")
citys = np.array(datas["city"])
country = np.array(datas["country"])
for i in range(len(citys)):
if not(len(str(pcs[i]))==5) and not(str(citys[i])=="NULL_IDENTIFIER"): ##Hexaposte may be usefull
temp = hexaposte[hexaposte["libelle_d_acheminement"]==citys[i]]
temp = temp.reset_index()
if len(temp)>0:
if country[i] =="FR" or str(country[i])=="nan":
pcs[i] = temp["code_postal"][0]
country[i] = "FR"
datas["zipcode"] = pcs
datas["country"] = country
datas.assign(newCP=0)
datas["newCP"]=pcs
##No search on countries
siret = np.array(datas["siret"])
country = np.array(datas["country"])
pcs = np.array(datas["zipcode"])
for i in range(len(pcs)):
country[i] = str(country[i])
if not(country[i] == "None" or country[i] == "nan" or country[i] == "FR" or country[i] == "PF" or country[i] == "WF" or country[i] == "NC" or country[i] == "YT" or country[i] == "RE" or country[i] == "GF" or country[i] == "MQ" or country[i] == "GP"):
pcs[i] = None
siret[i] = None
datas["zipcode"] = pcs
datas["siret"] = siret
#datas.to_csv("AgentHexaposted.csv",index=False)
datas["newCP"]=pcs
##Fine Tuning du nom
datas["name"] = datas["name"].replace(regex=r"CONSEIL DEPARTEMENTAL",value=r"DEPARTEMENT")
datas["name"] = datas["name"].replace(regex=r"CONSEIL GENERAL",value=r"DEPARTEMENT")
datas["name"] = datas["name"].replace(regex=r"CONSEIL REGIONAL",value=r"REGION")
datas["name"] = datas["name"].replace(regex=r"^CHU ",value=r"CENTRE HOSPITALIER UNIVERSITAIRE ")
datas["name"] = datas["name"].replace(regex=r"^CH ",value=r"CENTRE HOSPITALIER ")
datas["name"] = datas["name"].replace(regex=r"^CC D[U|E]",value=r"COMMUNAUTE DE COMMUNE ")
datas["name"] = datas["name"].replace(regex=r"^CDC D[U|E]",value=r"COMMUNAUTE DE COMMUNE ")
datas["name"] = datas["name"].replace(regex=r"^CA ",value=r"COMMUNAUTE D AGGLOMERATION ")
datas["name"] = datas["name"].replace(regex=r"^VILLE D[U|E]",value=r"MAIRIE D ")
datas["name"] = datas["name"].replace(regex=r"^COMMUNE D[U|E]",value=r"MAIRIE D ")
datas["name"] = datas["name"].replace(regex=r"^CCAS ",value=r"CENTRE COMMUNAL D ACTION SOCIALE ")
datas["name"] = datas["name"].replace(regex=r"^SIVU ",value=r"SYNDICAT INTERCOM ")
datas["name"] = datas["name"].replace(regex=r"^CAF SA ",value=r" CAF SA ")
datas["name"] = datas["name"].replace(regex=r"^CAF FRANCE ",value=r" CAF FRANCE")
datas["name"] = datas["name"].replace(regex=r"^CAF ",value=r"CAISSE D ALLOCATION FAMILIALES ")
convertisseur = pd.read_csv("data/foppaFiles/CatJuridique.csv",dtype=str,sep=";")
datas = datas.assign(catJuridique=None)
for i in range(len(convertisseur)):
temp = convertisseur["Nom"][i]
datas.loc[datas["name"].str.contains(temp,regex=True,na=False),'catJuridique' ] = convertisseur["catJuridique"][i]
import copy as cp
#### Adresses
datasetEntites=cp.deepcopy(datas)
libellesConvert = pd.read_csv("data/foppaFiles/Libelle.csv",dtype=str)
for k in range(len(libellesConvert)): ##Transformation des types (Avenue--Av)
Seekingpattern = " "+str(libellesConvert["Nom_Voie"][k]) + " "
Appliedpattern = " "+str(libellesConvert["Nom_Voie_Nomalise"][k]) + " "
datasetEntites["address"] = datasetEntites["address"].replace(regex=Seekingpattern,value=Appliedpattern)
datasetEntites = datasetEntites.assign(NumVoie=0)
datasetEntites = datasetEntites.assign(CodeVoie=0) ##Separation de l'adresse en 3 (Num + Type + Libelle)
datasetEntites = datasetEntites.assign(LibelVoie=0)
datasetEntites = datasetEntites.assign(NewAdress=0)
listeLibel = list(libellesConvert["Nom_Voie_Nomalise"])
listeLibel = "|".join(listeLibel)
##### Les cas qu'on recherche : Libelle seul , type + libelle, num + type + libelle
endingschar = ["-",";"," BP "," CS ","$","."]
endingschar = "|".join(endingschar)
datasetEntites["NewAdress"] = datasetEntites["address"]
reg="(" +listeLibel + ") ([A-Z,0-9, ]+)("+endingschar+")"
datasetEntites["NewAdress"] = datasetEntites["address"]
datasetExtraction = datasetEntites["NewAdress"].str.extract(reg)
datasetEntites["CodeVoie"] = datasetExtraction[0]
datasetEntites["LibelVoie"] = datasetExtraction[1]
##Regex num+type+libelle
datasetEntites["NewAdress"] = datasetEntites["address"]
reg="([0-9]+) (" +listeLibel + ") ([A-Z,0-9, ]+)("+endingschar+")"
datasetEntites["NewAdress"] = datasetEntites["address"]
datasetExtraction = datasetEntites["NewAdress"].str.extract(reg)
datasetEntites2 = datasetEntites.copy()
datasetEntites2["NumVoie"] = datasetExtraction[0]
datasetEntites2["CodeVoie"] = datasetExtraction[1]
datasetEntites2["LibelVoie"] = datasetExtraction[2]
adresses = []
for i in range(len(datasetEntites2)):
if not(str(datasetEntites2["CodeVoie"][i])=="nan") and not(str(datasetEntites2["LibelVoie"][i])=="nan"):
adresses.append(str(datasetEntites2["NumVoie"][i])+" "+datasetEntites2["CodeVoie"][i]+" "+datasetEntites2["LibelVoie"][i])
else:
adresses.append(datasetEntites["address"][i])
datas.assign(NewAdress=0)
datas["NewAdress"] = adresses
ids = np.array(datas["ids"])
types = np.array(datas["type"])
names = np.array(datas["name"])
sirets = np.array(datas["siret"])
addresses = np.array(datas["address"])
Newaddresses = np.array(datas["NewAdress"])
citys = np.array(datas["city"])
zipcodes = np.array(datas["newCP"])
countrys = np.array(datas["country"])
dates = np.array(datas["date"])
catJuridique = np.array(datas["catJuridique"])
for i in range(len(ids)):
sql = ''' INSERT INTO AgentsSiretiser(agentId,name,siret,address,newAddress,city,zipcode,country,date,catJuridique,ids,type)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?)'''
val = (i,names[i],sirets[i],addresses[i],Newaddresses[i],citys[i],zipcodes[i],countrys[i],dates[i],catJuridique[i],ids[i],types[i])
cursor.execute(sql,val)
tempID = str(ids[i]).split("-")[0]
sql = ''' INSERT INTO Names(agentId,name)
VALUES (?,?)'''
val = (int(tempID),names[i])
cursor.execute(sql,val)
request = "UPDATE AgentsSiretiser SET name = NULLIF(name,'NULL_IDENTIFIER')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET siret = NULLIF(siret,'NULL_IDENTIFIER')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET address = NULLIF(address,'NULL_IDENTIFIER')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET city = NULLIF(city,'NULL_IDENTIFIER')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET zipcode = NULLIF(zipcode,'NULL_IDENTIFIER')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET name = NULLIF(name,'None')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET siret = NULLIF(siret,'None')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET address = NULLIF(address,'None')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET city = NULLIF(city,'None')"
sql = cursor.execute(request)
request = "UPDATE AgentsSiretiser SET zipcode = NULLIF(zipcode,'None')"
sql = cursor.execute(request)
request = "DELETE FROM AgentsSiretiser WHERE country='INFRUCTUEUX'"
sql = cursor.execute(request)
database.commit()
return database
def safe_cast(val, to_type, default=None):
try:
return to_type(val)
except (ValueError, TypeError):
return default
def findType(chaine):
"""Matching between strings in Criteria and criteria Type"""
lchaine = str.upper(chaine)
lchaine = unidecode(lchaine)
if "ENVIRONNEMENT" in lchaine: ##Specific case : Environnemental and Social aspect mixed together
if "SOCIA" in lchaine:
return("MIXTE")
if "TECHNIQUE" in lchaine:
return("TECHNICAL")
if "TEHNIQUE" in lchaine:
return("TECHNICAL")
if "PRIX" in lchaine:
return("PRICE")
if "DELAI" in lchaine:
return("DELAY")
if "ENVIRONNEMENT" in lchaine:
return("ENVIRONMENTAL")
if "REMISE" in lchaine:
return("PRICE")
if "MONTANT" in lchaine:
return("PRICE")
if "ECONOMIQUE" in lchaine:
return("PRICE")
if "DURABLE" in lchaine:
return("ENVIRONMENTAL")
if "COUT" in lchaine:
return("PRICE")
if "TARIF" in lchaine:
return("PRICE")
if "FINANCIER" in lchaine:
return("PRICE")
if "SOCIAL" in lchaine:
return("SOCIAL")
if "QUALITE" in lchaine:
return("TECHNICAL")
if "QUALITATI" in lchaine:
return("TECHNICAL")
if "HUMAIN" in lchaine:
return("SOCIAL")
if "PERSONNEL" in lchaine:
return("SOCIAL")
if "FONCTIONNALITE" in lchaine:
return("TECHNICAL")
return("OTHER")
def criteriaProcessing(database):
"""Split, clean and Normalization of each criterion"""
tripleDash=0
hyphens=0
noC=0
priceWithOther=0
notWeighted=0
ValueswithName=0
totx=0
manyLots=0
critid=0
cursor = database.cursor()
request = "DROP TABLE IF EXISTS Criteria"
sql = cursor.execute(request)
request = "CREATE TABLE Criteria (criterionId INTEGER,lotId INTEGER,name TEXT,weight INTEGER,type TEXT,PRIMARY KEY(criterionId))"
sql = cursor.execute(request)
datas = pd.read_sql_query("SELECT * FROM CriteriaTemp", database,dtype=str)
datas["CRIT_PRICE_WEIGHT"] =datas["CRIT_PRICE_WEIGHT"].str.replace("-","",regex=True)
datas["CRIT_PRICE_WEIGHT"] = datas["CRIT_PRICE_WEIGHT"].str.replace("[a-z]+","",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("/","---",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("---","&",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("[a-z]+","",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("%","",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("; ","$",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace(";",".",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace(r" - ",r"$",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("-","",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace(r" - ",r"$",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace(r"(\d\d) à (\d\d)",r"\1",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("[éùà-]","",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("([\(\[]).*?([\)\]])", "",regex=True)
datas["CRIT_WEIGHTS"] = datas["CRIT_WEIGHTS"].str.replace("^[0&]+","",regex=True)
for i in range(len(datas)):
prix = datas.loc[i,"CRIT_PRICE_WEIGHT"]
weights = datas.loc[i,"CRIT_WEIGHTS"]
criteria = datas.loc[i,"CRIT_CRITERIA"]
if str(criteria).count('LOT')>2:
manyLots=manyLots+1
somme=0
listecrit = []
if len(str(prix))>0 and not(str(prix)=="nan"):
temp = safe_cast(prix,float)
if not(temp==None):
totx=totx+temp
if not(temp==None):
listecrit.append(("PRIX",temp,"PRICE"))
somme=temp+somme
if len(str(weights))>0 and not(str(weights)=="nan"):
critsW = str(weights).split("&")
critsN = str(criteria).split("---")
if len(critsN)>1:
tripleDash = tripleDash+1
for z in range(min(len(critsW),len(critsN))):
temp = safe_cast(critsW[z],float)
if not(temp==None):
totx = totx+temp
else:
totx = totx+ 0
if not(temp==None):
if temp==0.0:
temp = 1/min(len(critsW),len(critsN))
listecrit.append((critsN[z],temp,findType(critsN[z])))
if findType(critsN[z])=="PRICE":
priceWithOther=priceWithOther+1
somme=temp+somme
elif len(str(criteria))>3:
boolean = True
temp = criteria.split("---")
if len(temp)>1:
tripleDash = tripleDash+1
for l in range(len(temp)):
nombres = re.findall("\d+", temp[l])
critere = temp[l]
if len(nombres)>0:
if boolean:
ValueswithName = ValueswithName+1
boolean=False
poids = float(nombres[0])
totx = totx+poids
if poids ==0:
poids = 1/len(temp)
else:
poids= 1/len(temp)
somme = somme+poids
listecrit.append((critere,poids,findType(critere)))
if findType(critsN[z])=="PRICE":
priceWithOther=priceWithOther+1
if not(float(totx)==float(100)):
notWeighted=notWeighted+1
if len(listecrit)==1:
if listecrit[0][2]=="MIXTE":
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[0][0],50,"ENVIRONMENTAL"))
critid = critid+1
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[0][0],50,"SOCIAL"))
critid = critid+1
else:
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[0][0],100,listecrit[0][2]))
critid = critid+1
elif len(listecrit)>1:
for j in range(len(listecrit)):
if listecrit[j][2]=="MIXTE":
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[j][0],50*listecrit[j][1]/somme,"ENVIRONMENTAL"))
critid = critid+1
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[j][0],50*listecrit[j][1]/somme,"SOCIAL"))
critid = critid+1
else:
cursor.execute("INSERT or IGNORE INTO Criteria values (?,?,?,?,?)",
(critid,i,listecrit[j][0],100*listecrit[j][1]/somme,listecrit[j][2]))
critid = critid+1
database.commit()
return database
def siretization(database):
"""Siretization step"""
cursor = database.cursor()
datas = pd.read_sql_query("SELECT * FROM AgentsSiretiser", database,dtype=str)
request = "DROP TABLE IF EXISTS AgentsSiretiser"
sql = cursor.execute(request)
request = "CREATE TABLE IF NOT EXISTS AgentsSiretiser(agentId INTEGER,name TEXT,siret TEXT,address TEXT,city TEXT,zipcode TEXT,country TEXT, date TEXT,catJuridique TEXT,ids TEXT,type TEXT, PRIMARY KEY(agentId))"
sql = cursor.execute(request)
start = time.time()
###Creation of the databases
cwd = os.getcwd()
data_path = cwd+'/data/opening/*.csv' ##Opening database
d_p = cwd+'/data/Etab/*.csv' # Every facilities in SIRENE
d_s =cwd +'/data/foppaFiles/Sigles.csv' # Acronyms
data_path_noms =cwd +'/data/foppaFiles/ChangementsNoms.csv' #Name modification during time
bc = BlazingContext()
col_types = ["str","str","str","str","str","str","str","str","str","str","str","str"]
bc.create_table("etablissement",d_p,dtype=col_types)
col_types = ["str","str"]
bc.create_table('noms',data_path_noms,dtype=col_types)
col_types = ["str","str","str"]
bc.create_table('ouvertures',data_path,dtype=col_types)
col_types = ["str","str"]
bc.create_table('sigles',d_s,dtype=col_types)
taille=len(datas)
datas = datas.assign(siretPropose=0)
datas = datas.assign(score=0)
datas = datas.assign(NomSirene=0)
datas = datas.assign(AdresseSirene=0)
datas = datas.assign(VilleSirene=0)
datas = datas.assign(CPSirene=0)
dicoAssociation ={}
####Creation of specific datasets for CAEs
##CAF
dfCAF = bc.sql("select * from etablissement WHERE TypeActivite = '84.30C' AND CatJuridique='8110'")
dicoAssociation["8110"] = dfCAF
##CCI
dfCCI= bc.sql("select * from etablissement WHERE TypeActivite = '94.11Z' AND CatJuridique='7381'")
dicoAssociation["7381"] = dfCCI
#dfSIVU
dfSyndic= bc.sql("select * from etablissement WHERE CatJuridique='7353'")
dicoAssociation["7353"] = dfSyndic
#dfSyndicatMixte
dfSyndicMixte= bc.sql("select * from etablissement WHERE CatJuridique='7354' OR CatJuridique='7355'")
dicoAssociation["7354"] = dfSyndicMixte
##CCAS
dfCCAS= bc.sql("select * from etablissement WHERE TypeActivite = '88.99B' AND CatJuridique='7361'")
dicoAssociation["7361"] = dfCCAS
##Mairies
dfMairie = bc.sql("select * from etablissement WHERE TypeActivite = '84.11Z' AND CatJuridique='7210'")
dicoAssociation["7210"] = dfMairie
##Departement
dfDepartement = bc.sql("select * from etablissement WHERE TypeActivite = '84.11Z' AND CatJuridique='7220'")
dicoAssociation["7220"] = dfDepartement
##Region
dfRegion = bc.sql("select * from etablissement WHERE TypeActivite = '84.11Z' AND CatJuridique='7230'")
dicoAssociation["7230"] = dfRegion
##Communauté d'agglo
dfComAgglo = bc.sql("select * from etablissement WHERE TypeActivite = '84.11Z' AND (CatJuridique='7348' OR CatJuridique='7344') ")
dicoAssociation["7348"] = dfComAgglo
dicoAssociation["7344"] = dfComAgglo
##Communauté de Communes
dfComCom = bc.sql("select * from etablissement WHERE TypeActivite = '84.11Z' AND CatJuridique='7346'")
dicoAssociation["7346"] = dfComCom
##Centre hospitalier
dfChu = bc.sql("select * from etablissement WHERE TypeActivite = '86.10Z' AND CatJuridique='7364'")
dicoAssociation["7364"] = dfChu
end = time.time()
for j in range(0,taille):
############## If the Siret is here, we replace the values immediatly:
if len(str(datas["siret"][j]))>9:
request = "SELECT nomUnite,num,typevoie,libelle,ville,cp FROM newtable WHERE siret = '"+str(datas["siret"][j])+ "'"
gdf = bc.sql(request)
if len(gdf)>0:
datas["siretPropose"][j] = gdf['siret'][0]
datas["score"][j] = maxi
datas["NomSirene"][j] = gdf['nomUnite'][0]
datas["AdresseSirene"][j] = str(gdf['num'][0])+" "+ str(gdf['typevoie'][0])+" "+ str(gdf['libelle'][0])
datas["VilleSirene"][j] = gdf['ville'][0]
datas["CPSirene"][j] = gdf['cp'][0]
else:
print("Error : the Siret is incorrect. We will try to siretize")
datas["siret"][j]=""
else:
start = time.time()
gdf2 = pd.DataFrame()