232 lines
7.0 KiB
Python
232 lines
7.0 KiB
Python
import json
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import geopandas as gpd
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from bs4 import BeautifulSoup
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from shapely.geometry import Point
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import re
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import pandas as pd
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def extraire_numero_telephone(line):
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# Extraction des nombres dans la ligne
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numbers = re.findall(r'\d+', line)
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if numbers:
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numbers = ''.join(numbers)
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# print('numbers',numbers)
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# Vérification si un numéro de téléphone est présent
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if len(numbers) == 10:
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# Reconstruction du numéro de téléphone
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phone_number = ''.join(numbers)
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return phone_number
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else:
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return None
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def extraire_code_postal(line):
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# Extraction du code postal dans la ligne
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match = re.search(r'\b(\d{5})\b', line)
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if match:
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# Extraction des nombres correspondant au code postal
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code_postal = match.group(1)
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return code_postal
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else:
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return None
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# trouver si la ligne est une adresse en cherchant deux numéros distincts et une virgule
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def extraire_addr_line(line):
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# Extraction des nombres dans la ligne
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numbers = re.findall(r'\d+', line)
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# Vérification si un numéro de téléphone est présent
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if len(numbers) == 2 and ',' in line and len(numbers[1]) == 5:
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return line
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else:
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return None
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# Charger le fichier HTML
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with open("list.html", "r") as file:
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html = file.read()
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# Analyser le code HTML avec BeautifulSoup
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soup = BeautifulSoup(html, "html.parser")
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# Initialiser une liste pour stocker les informations des docteurs
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doctors = []
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# Trouver toutes les balises <article> sur la page
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articles = soup.find_all("article")
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# Parcourir chaque <article> pour extraire les informations des docteurs
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for article in articles:
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# Récupérer le nom du docteur à partir de la balise <h1>
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# print(article.find("h1"))
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name = article.find("h1").find('a').text.strip()
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url = article.find("h1").find('a').get("href")
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# Récupérer l'adresse du docteur à partir de la balise <em> dans la classe "entry-content"
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address = ''
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em = article.find("em")
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if em:
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address = em.text
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# print(address)
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# Vérifier si le contenu de l'article contient "Secteur 1"
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if "Secteur 1" in article.text:
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sector = "1"
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elif "Secteur 2" in article.text:
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sector = "2"
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else:
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sector = None
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# Recherche d'un numéro de téléphone dans l'article
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phone_number = None
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gender = 'unknown'
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trans_friendly = ''
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handles_violence = ''
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visio_meeting = 'no'
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pseudo_science = ''
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premenstrual_syndrome = ''
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accessible_cabinet = ''
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tatoo = ''
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toxico = ''
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sterilisation = ''
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abortion = ''
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endometriosis = ''
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bigbody = ''
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poil = ''
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bi = ''
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lesbian = ''
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pma = ''
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ist = ''
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pregnancy = ''
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ivg = ''
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generaliste = ''
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gyneco = ''
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sage_femme = ''
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auto_prelev = ''
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mycoses = ''
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dyspareunie = ''
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spoken = '' # langues parlées
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diu = '' # dispositif intra utérin
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puma= '' # PUMA (ex-CMU)
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ame= '' # AME (Aide médicale d’état)
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code_postal = ''
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for line in article.stripped_strings:
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found = extraire_code_postal(line)
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if found:
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code_postal = found
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address = line
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found = extraire_numero_telephone(line)
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if found:
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# print(found)
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phone_number = found
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if 'Rdv en ligne possible' in line:
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visio_meeting = 'yes'
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if 'femme soignante' in line:
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gender = 'women'
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if 'homme soignante' in line:
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gender = 'women'
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if 'Trans friendly' in line:
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trans_friendly = 'yes'
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if 'Tattoo' in line:
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tatoo = 'yes'
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if 'Sensibilité violence' in line:
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handles_violence = 'yes'
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if 'naturelles/alternatives' in line:
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pseudo_science = 'yes'
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if 'SPM' in line:
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premenstrual_syndrome = 'yes'
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if 'Poilfriendly' in line:
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poil = 'yes'
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if 'Bifriendly' in line:
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bi = 'yes'
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if 'Stérilisation' in line:
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sterilisation = 'yes'
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if 'DIU' in line:
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diu = 'yes'
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if 'Accompagnement grossesse' in line:
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pregnancy = 'yes'
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if 'suivi des IST' in line:
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ist = 'yes'
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if 'IVG' in line:
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ivg = 'yes'
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if 'Médecin généraliste' in line:
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generaliste = 'yes'
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if 'Sage-femme' in line:
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sage_femme = 'yes'
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if 'auto prélèvement' in line:
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auto_prelev = 'yes'
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if 'Conseils mycoses' in line:
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mycoses = 'yes'
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if 'Lesbiennes friendly' in line:
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lesbian = 'yes'
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if 'PMA' in line:
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pma = 'yes'
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if 'PUMA' in line:
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puma = 'yes'
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if 'AME' in line:
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ame = 'yes'
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if 'français, anglais' in line:
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spoken = 'french;english'
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if 'espagnol' in line:
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spoken = ('french;english;spanish')
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if 'Dyspareunie' in line:
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dyspareunie = 'yes'
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if 'Gynécologue' in line:
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gyneco = 'yes'
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# chercher une adresse si on en a pas
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if not address:
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found = extraire_addr_line(line)
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if found:
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# print(found)
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address = found
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# Ajouter les informations du docteur à la liste
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doctor = {
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"name": name,
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"address": address,
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"address:code_postal": code_postal,
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"ref:FR:convention_secteur": sector,
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"contact:phone": phone_number,
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"contact:website": url,
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"gender": gender,
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"visioconference_meeting": visio_meeting,
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"handles:gender:trans": trans_friendly,
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"handles:violence": handles_violence,
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"handles:premenstrual_syndrome": premenstrual_syndrome,
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"handles:IST": ist,
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"accessible_cabinet": accessible_cabinet,
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"pseudo_science": pseudo_science,
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"speaks": spoken,
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"handles:tatoo": tatoo,
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"handles:toxico": toxico,
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"handles:sterilisation": sterilisation,
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"handles:abortion": abortion,
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"handles:endometriosis": endometriosis,
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"handles:premenstrual_syndrome": premenstrual_syndrome,
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"handles:hairy": poil,
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"handles:bigbody": bigbody,
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"handles:gender:bi": bi,
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"handles:diu": diu,
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"handles:mycoses": mycoses,
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"handles:pregnancy": pregnancy,
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"handles:abortion": ivg,
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"handles:auto_prelevement": auto_prelev,
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"handles:pma": pma,
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"handles:dyspareunie": dyspareunie,
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"handles:gender:lesbian": lesbian,
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"healcare:generaliste": generaliste,
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"healcare:sage_femme": sage_femme,
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"healcare:gynecologist": gyneco,
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}
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doctors.append(doctor)
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# Enregistrer les informations des docteurs au format JSON dans un fichier
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with open("gynandco.json", "w", encoding="utf-8") as f:
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json.dump(doctors, f, ensure_ascii=False, indent=2)
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with open('gynandco.json', 'r') as f:
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data = json.load(f)
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df = pd.DataFrame(data)
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df.to_csv('gynandco.csv', index=False)
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