2256 lines
116 KiB
Python
2256 lines
116 KiB
Python
#!/usr/bin/env python3
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# -*- coding: UTF-8 -*-
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#Nom : : recup_zh.py
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#Description :
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#Copyright : 2021, CEN38
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#Auteur : Colas Geier
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#Version : 1.0
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import re
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import pandas as pd
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import pandas_access as mdb
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import numpy as np
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from sqlalchemy.sql.expression import column
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from pycen import bdd
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from sqlalchemy import create_engine
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from geoalchemy2 import Geometry
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isin_bdd = True
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# Parametres bdd IN
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user = 'cen_admin'
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pwd = '#CEN38@venir'
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adr = '192.168.0.189'
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base = 'bd-cen-38'
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schema = 'zh'
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table = 'cr_cen38_zh_medwet_v2021'
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con = create_engine('postgresql+psycopg2://{0}:{1}@{2}/{3}'.format(user,pwd,adr,base), echo=False)
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bd = bdd.CEN(
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user = user,
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pwd = pwd,
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adr = adr,
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base = base
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# schema = schema
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)
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# Parametres bdd OUT
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user_zh = 'postgres'
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pwd_zh = 'tutu'
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adr_zh = '192.168.60.10'
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base_zh = 'bd_cen'
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con_zh = create_engine('postgresql+psycopg2://{0}:{1}@{2}/{3}'.format(user_zh,pwd_zh,adr_zh,base_zh), echo=False)
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# Read MS access database
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db_file1 = '/home/colas/Documents/5_BDD/ZONES_HUMIDES/MEDWET_v1.mdb'
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db_file2 = '/home/colas/Documents/5_BDD/ZONES_HUMIDES/MEDWET_V2.mdb'
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df_med1 = mdb.read_table(db_file1, "SITEINFO")
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df_med2 = mdb.read_table(db_file2, "SITEINFO")
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# FILE = db_file2
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# for tab in mdb.list_tables(FILE):
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# if tab not in ['SIG', 'List', 'Switchboard', 'Items'] and not tab.startswith(('DicGen','DIcGen')):
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# # df = mdb.read_table(FILE, tab, keep_default_na=False,skipinitialspace=True)
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# df = mdb.read_table(FILE, tab)
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# if 'SITE_COD' in df.columns or 'SIT_COD' in df.columns:
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# print(tab)
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# FILE = db_file2
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# for tab in mdb.list_tables(FILE):
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# if tab not in ['SIG', 'List', 'Switchboard', 'Items'] and not tab.startswith(('DicGen','DIcGen')):
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# # df = mdb.read_table(FILE, tab, keep_default_na=False,skipinitialspace=True)
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# df = mdb.read_table(FILE, tab)
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# if 'ORG' in df.columns:
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# print(tab)
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df = bd.get_table(
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schema = schema,
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table = table)
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df.sort_values('site_code', inplace=True)
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df.auteur_fiche.fillna('Inconnu', inplace=True)
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df[['auteur_fiche','auteur_fiche_remarque']] = df[['auteur_fiche','auteur_fiche_remarque']].replace(
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['Biron N.','BIRON N\.','Balmain C.','Feuvrier B.','Souvignet N.','Billard G.','BELLUT', 'C. Balmain','E. JOURDAN','E. Jordan','Juton M.','P. Bellut', 'Folgar H.',],
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['BIRON Nicolas','BIRON Nicolas','BALMAIN Céline','FEUVRIER Benoit','SOUVIGNET Nicolas','BILLARD Gilbert','BELLUT P.','BALMAIN Céline','JOURDAN Elise','JOURDAN Elise','JUTON Mathieu','BELLUT P.','FOGLAR Hélène'],
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regex=True)
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# Récupération des structures
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df_structure = pd.DataFrame(
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df['organisme_auteur'].drop_duplicates(), )
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df_structure.rename(columns={'organisme_auteur':'nom'}, inplace=True)
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# df_structure.drop(
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# labels=[
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# # 1092,748,1088,
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# 17],
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# axis=0,
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# inplace=True)
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df_structure['nom_autres'] = None
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df_structure.loc[df_structure.nom == 'Acer campestre', 'nom_autres'] = 'ACER CAMPESTRE'
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df_structure.loc[df_structure.nom == 'FRAPNA Isère', 'nom_autres'] = 'Asso. FRAPNA'
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df_structure.loc[df_structure.nom == 'DRAC NATURE', 'nom_autres'] = 'Asso. Drac Nature'
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df_structure.loc[df_structure.nom == 'Comité Gère vivante', 'nom_autres'] = 'Asso. GERE VIVANTE'
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df_structure.nom.fillna('Inconnu', inplace=True)
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df_structure.reset_index(inplace=True, drop=True)
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# Envoie des structures en bdd
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if not isin_bdd:
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df_structure['nom'].to_sql(
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name='organisme',
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con = con_zh,
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schema='personnes',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Correction des structures dans le df global
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df.organisme_auteur.fillna('Inconnu', inplace=True)
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for d,j in df_structure[~df_structure.nom_autres.isna()].iterrows():
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df.loc[df.organisme_auteur==j.nom_autres, 'organisme_auteur'] = j.nom
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# Récupération des personnes
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df_pers = df[['auteur_fiche', 'organisme_auteur']].drop_duplicates()
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df_pers.auteur_fiche.fillna('Inconnu', inplace=True)
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tmp = [i.split('&') for i in df_pers['auteur_fiche'].dropna().unique() ]
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lst_pers = [item for sublist in tmp for item in sublist]
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tmp = pd.DataFrame(data=lst_pers, columns=['nom_prenom'])
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tmp['nom_prenom'] = tmp.nom_prenom.str.strip()
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tmp[['nom','prenom','autre']] = tmp['nom_prenom'].str.split(' ', 2, expand=True)
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for i,j in tmp[~tmp.autre.isna()].iterrows():
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tmp.loc[tmp.nom==j.nom, 'nom'] = j.nom + ' ' + j.prenom
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tmp.loc[tmp.autre==j.autre, 'prenom'] = j.autre
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tmp.drop(columns=['nom_prenom', 'autre'], inplace=True)
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tmp['organisme'] = None
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for nom in tmp.nom:
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orga = df_pers.loc[df_pers.auteur_fiche.str.contains(nom),'organisme_auteur']
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orga = orga.unique()
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tmp.loc[tmp.nom == nom,'organisme'] = orga[0]
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tmp['id_organisme'] = tmp['organisme']
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tmp['id_organisme'] = tmp['id_organisme'].replace(df_structure.nom.to_list(),df_structure.index.to_list())
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tmp.nom = tmp.nom.str.upper()
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tmp.drop_duplicates(inplace=True)
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df_pers = tmp
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df_pers.drop(columns='organisme', inplace=True)
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df_pers.reset_index(inplace=True, drop=True)
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# Envoie des personnes en bdd
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if not isin_bdd:
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df_pers.to_sql(
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name='personne',
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con = con_zh,
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schema='personnes',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Correction des personnes dans le df global
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df_pers = pd.read_sql_table(
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table_name='personne',
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con = con_zh,
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schema='personnes',
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index_col='id',
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)
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# df.auteur_fiche.fillna('Inconnu', inplace=True)
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# df[['auteur_fiche','auteur_fiche_remarque']] = df[['auteur_fiche','auteur_fiche_remarque']].replace(
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# ['Biron N.','Balmain C.','Feuvrier B.','Souvignet N.','Billard G.','BELLUT', 'C. Balmain','E. JOURDAN','E. Jordan','Juton M.','P. Bellut' ],
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# ['BIRON Nicolas','BALMAIN Céline','FEUVRIER Benoit','SOUVIGNET Nicolas','BILLARD Gilbert','BELLUT P.','BALMAIN Céline','JOURDAN Elise','JOURDAN Elise','JUTON Mathieu','BELLUT P.'],
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# regex=True)
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df_pers['nom_prenom'] = df_pers.nom.str[0] + df_pers.nom.str[1:].str.lower() + ' ' + df_pers.prenom
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for pers in df_pers.nom_prenom.dropna():
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val = df_pers[df_pers.nom_prenom == pers].index[0]
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val = str(val)
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df.auteur_fiche = df.auteur_fiche.str.replace(pers,val, regex=True)
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df_pers['nom_prenom'] = df_pers.nom + ' ' + df_pers.prenom
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for pers in df_pers.nom_prenom.dropna():
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val = df_pers[df_pers.nom_prenom == pers].index[0]
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val = str(val)
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df.auteur_fiche = df.auteur_fiche.str.replace(pers,val, regex=False)
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df.auteur_fiche.replace(df_pers.nom.to_list(),df_pers.index.to_list(), inplace=True)
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NOM = df_pers.nom.str[0] + df_pers.nom.str[1:].str.lower()
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df.auteur_fiche.replace(NOM.to_list(),NOM.index.to_list(), inplace=True)
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df.loc[df.auteur_fiche == 'SETIS Groupe Degaud', 'auteur_fiche'] = df_pers.loc[df_pers.prenom == 'Degaud'].index[0]
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# Récupération des sites
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df_site = df[['site_code', 'date_init', 'name_zone', 'auteur_fiche', 'typo_sdage', ]]
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df_site = df_site.rename(columns={
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'site_code': 'id',
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'date_init': 'date_deb',
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'name_zone': 'nom',
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'auteur_fiche': 'id_auteur',
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'typo_sdage': 'id_typo_sdage'
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})
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df_site.sort_values('date_deb', inplace=True)
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df_site.reset_index(inplace=True, drop=True)
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typ_sdage = pd.read_sql_table(
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table_name = 'typo_sdage',
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con = con_zh,
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schema = 'sites',
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index_col = 'id',
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)
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typ_milieu = pd.read_sql_table(
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table_name = 'type_milieu',
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con = con_zh,
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schema = 'sites',
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index_col = 'id',
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)
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typ_site = pd.read_sql_table(
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table_name = 'type_site',
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con = con_zh,
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schema = 'sites',
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index_col = 'id',
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)
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df_site.id_typo_sdage.replace(typ_sdage.nom.str.lower().to_list(),typ_sdage.index.to_list(), inplace=True)
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df_site.id_typo_sdage.fillna(typ_sdage[typ_sdage.nom.str.lower() == 'inconnu'].index[0], inplace=True)
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df_site.loc[df_site.id_typo_sdage == "bordures de cours d'eau", 'id_typo_sdage'] = typ_sdage[typ_sdage.nom.str.lower().str.contains("cours d'eau")].index[0]
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df_site.loc[df_site.id_typo_sdage == "petits plans d'eau et bordures de plans d'eau", 'id_typo_sdage'] = typ_sdage[typ_sdage.nom.str.lower().str.contains("plans d'eau")].index[0]
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df_site.loc[df_site.id_typo_sdage == 'zones humides de bas-fond en tête de bassin versant', 'id_typo_sdage'] = typ_sdage[typ_sdage.nom.str.lower().str.contains("bas-fond")].index[0]
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df_site.loc[df_site.id_typo_sdage == 'plaines alluviales', 'id_typo_sdage'] = typ_sdage[typ_sdage.nom.str.lower().str.contains("plaines alluviales")].index[0]
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df_site['id_type_milieu'] = typ_milieu[typ_milieu.nom_court.str.contains('humides')].index[0]
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df_site['id_type_site'] = typ_site[typ_site.nom == 'N.D.'].index[0]
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df_site.set_index('id', inplace=True)
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df_site.date_deb.fillna('0001-01-01', inplace=True)
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df_site.nom.fillna('Inconnu', inplace=True)
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# Envoie des sites en bdd
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if not isin_bdd:
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df_site.to_sql(
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name='sites',
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con = con_zh,
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schema='sites',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Récupération des géometries
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df_geomsite = df[['site_code', 'geom', 'date_der_modif', 'link_pdf', 'fonction_majeur', 'interet_patri', 'bilan_menaces', 'orient_act', 'usages_process_natu_comm' ]]
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df_geomsite = df_geomsite.rename(columns={
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'site_code': 'id_site',
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'date_der_modif': 'date',
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'fonction_majeur': 'rmq_fct_majeur',
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'interet_patri': 'rmq_interet_patri',
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'bilan_menaces': 'rmq_bilan_menace',
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'orient_act': 'rmq_orient_act',
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'usages_process_natu_comm': 'rmq_usage_process'
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})
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df_geomsite = df_geomsite.merge(df_site[['id_auteur']].reset_index(), left_on='id_site', right_on='id')
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df_geomsite.drop(columns=['id'], inplace=True)
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df_geomsite.date.fillna('0001-01-01', inplace=True)
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df_geomsite.reset_index(inplace=True, drop=True)
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# Envoie des géometries en bdd
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if not isin_bdd:
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df_geomsite.to_postgis(
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name='r_sites_geom',
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con = con_zh,
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schema='sites',
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index=True,
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index_label='id',
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if_exists='append',
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geom_col='geom'
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)
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print("INSERT ... ok !")
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df_rgsite = df_geomsite[['id_site']]
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df_rgsite.index.name = 'id'
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df_rgsite.reset_index(inplace=True)
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# Récupération des types de connexions
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df_typconex = df[['connex_type']]
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df_typconex = df_typconex.rename(columns={
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'connex_type': 'nom'
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})
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df_typconex.dropna(inplace=True)
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df_typconex = pd.DataFrame(df_typconex.nom.unique(), columns=['nom'])
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df_typconex = pd.concat(
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[ pd.DataFrame(['inconnu'], columns=['nom']),df_typconex ],
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ignore_index=True)
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df_typconex['nom'] = df_typconex.nom.str[0].str.upper() + df_typconex.nom.str[1:]
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# Envoie des types de connexions en bdd
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if not isin_bdd:
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df_typconex.to_sql(
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name='param_type_connect',
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con = con_zh,
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schema='zones_humides',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Récupération des relations sites / types de connexions
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df_Rtypconex = df[['site_code','connex_type']]
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df_Rtypconex = pd.merge(df_Rtypconex, df_rgsite, left_on='site_code', right_on='id_site')
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df_Rtypconex = df_Rtypconex.rename(columns={
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'id': 'id_geom_site',
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'connex_type': 'id_param_connect'
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})
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df_Rtypconex.id_param_connect.fillna('inconnu', inplace=True)
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df_Rtypconex.id_param_connect.replace(df_typconex.nom.str.lower().to_list(),df_typconex.index.to_list(), inplace=True)
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df_Rtypconex.drop(columns=['site_code', 'id_site'], inplace=True)
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# Envoie des relations sites / types de connexions
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if not isin_bdd:
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df_Rtypconex.to_sql(
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name='r_site_type_connect',
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con = con_zh,
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schema='zones_humides',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Incrémentation des types de paramettres fctEcoSocioPatri
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d = {
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'nom': ['Fonctions hydroligiques', 'Fonctions biologiques', 'Valeurs socio-économiques', 'Interêt patrimonial',],
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'nom_court': ['fct_hydro', 'fct_bio', 'val_socioEco', 'int_patri']}
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df_typFct = pd.DataFrame(data=d)
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if not isin_bdd:
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df_typFct.to_sql(
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name='type_param_fct',
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con = con_zh,
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schema='zones_humides',
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index=True,
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index_label='id',
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if_exists='append',
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)
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print("INSERT ... ok !")
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# Récupération des fct Hydro, Bio, Socio-eco, Patri
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columns_fct = ['fct_bio', 'val_socio_eco', 'int_patri', 'fct_hydro']
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df_rSiteFct = df[['site_code'] + columns_fct ]
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df_rSiteFct = pd.merge(df_rSiteFct, df_rgsite, left_on='site_code', right_on='id_site')
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df_rSiteFct = df_rSiteFct.rename(columns={
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'id': 'id_geom_site',
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})
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df_rSiteFct.drop(columns=['site_code', 'id_site'], inplace=True)
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df_rSiteFct.index.name = 'id'
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# df_rSiteFct.dropna(axis=0,subset=columns_fct, inplace=True)
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lst_df = {}
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for col in columns_fct:
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print(col)
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lst_df[col] = df_rSiteFct[['id_geom_site', col]]
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d = lst_df[col][col].str.split('//').apply(pd.Series).stack()
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d = pd.DataFrame(d, columns=[col])
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d.index.name = 'id'
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del lst_df[col][col]
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lst_df[col] = lst_df[col].merge(d, on='id',how='left')
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lst_df[col][[col, col+'_rmq']] = lst_df[col][col].str.split('; Justification :', expand=True)
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lst_df[col][col] = lst_df[col][col].str.replace('Critère :','')
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lst_df[col][col+'_rmq'] = lst_df[col][col+'_rmq'].str.replace('Justification :','')
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lst_df[col][col] = lst_df[col][col].str.strip()
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lst_df[col][col+'_rmq'] = lst_df[col][col+'_rmq'].str.strip()
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lst_df[col].dropna(subset=[col], inplace=True)
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# Isolement des paramètres des fct Hydro, Bio, Socio-eco, Patri
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df_paramFct = pd.DataFrame(columns=['nom', 'type'])
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for col in columns_fct:
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# x = lst_df[col][col].drop_duplicates().dropna()
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# y = pd.Series([col]*len(x))
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x = lst_df[col][col].drop_duplicates().dropna().tolist()
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y = [col]*len(x)
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xy = {'nom': x, 'type': y}
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xy = pd.DataFrame(data=xy)
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df_paramFct = df_paramFct.append(xy, ignore_index=True)
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# Incrémentation des paramètres des fct Hydro, Bio, Socio-eco, Patri
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df_paramFct['id_type'] = df_paramFct.type.copy()
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df_paramFct.id_type.replace('val_socio_eco','val_socioeco', inplace=True)
|
|
df_paramFct.id_type.replace(df_typFct.nom_court.str.lower().to_list(),df_typFct.index.to_list(), inplace=True)
|
|
del df_paramFct['type']
|
|
df_paramFct[['nom','description']] = df_paramFct.nom.str.split('(', n=1, expand=True)
|
|
df_paramFct['description'] = df_paramFct['description'].str.replace(';',',')
|
|
df_paramFct['description'] = df_paramFct['description'].str.strip(')')
|
|
df_paramFct['description'] = df_paramFct['description'].replace('),',':')
|
|
df_paramFct['nom'] = df_paramFct['nom'].str.strip()
|
|
if not isin_bdd:
|
|
df_paramFct.to_sql(
|
|
name='param_fct_eco_socio_patri',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
# Incrémentation des relations sites / paramètres des fct Hydro, Bio, Socio-eco, Patri
|
|
df_paramFct.id_type.replace(df_typFct.index.to_list(), df_typFct.nom_court.str.lower().to_list(), inplace=True)
|
|
for col in columns_fct:
|
|
lst_df[col].rename(columns={
|
|
col : 'id_fct',
|
|
col+'_rmq' : 'description'
|
|
}, inplace=True)
|
|
if col == 'int_patri':
|
|
lst_df[col]['quantite'] = [
|
|
sum(map(int, filter(str.isdigit, row.split())))
|
|
if isinstance(row,str) else None
|
|
for row in lst_df[col]['description'] ]
|
|
for col in columns_fct:
|
|
id_type = col
|
|
if col == 'val_socio_eco':
|
|
id_type = 'val_socioeco'
|
|
tmp = df_paramFct[df_paramFct.id_type==id_type]
|
|
for i,row in tmp.iterrows():
|
|
lst_df[col].loc[lst_df[col]['id_fct'].str.contains(row.nom, na=False), 'id_fct'] = row.name
|
|
df_RsiteFct = pd.DataFrame()
|
|
for col in columns_fct:
|
|
df_RsiteFct = pd.concat([df_RsiteFct, lst_df[col]])
|
|
df_RsiteFct.sort_values('id_geom_site')
|
|
df_RsiteFct.reset_index(drop=True,inplace=True)
|
|
df_RsiteFct['description'] = df_RsiteFct['description'].replace('prioriatire','prioritaire', regex=True)
|
|
if not isin_bdd:
|
|
df_RsiteFct.to_sql(
|
|
name='r_site_fctecosociopatri',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
# Récupération des critères de délimitation
|
|
df_critDelm = df[['site_code','criete_delimit', 'criete_delimit_rmq']]
|
|
df_critDelm = pd.merge(df_critDelm, df_rgsite, left_on='site_code', right_on='id_site')
|
|
df_critDelm.rename(columns={
|
|
'id': 'id_geom_site',
|
|
}, inplace=True)
|
|
df_critDelm.drop(columns=['site_code', 'id_site'], inplace=True)
|
|
df_critDelm.index.name = 'id'
|
|
df_critDelm.dropna(subset=['criete_delimit'], inplace=True)
|
|
d = df_critDelm['criete_delimit'].str.split('//').apply(pd.Series).stack()
|
|
d = pd.DataFrame(d, columns=['criete_delimit'])
|
|
d.index.name = 'id'
|
|
del df_critDelm['criete_delimit']
|
|
df_critDelm = df_critDelm.merge(d, on='id',how='left')
|
|
df_critDelm['criete_delimit'] = df_critDelm['criete_delimit'].str.replace('critère de délimitation ZH : ','')
|
|
df2_critDelm = df_rgsite[['id']].copy()
|
|
df2_critDelm.rename(columns={
|
|
'id': 'id_geom_site',
|
|
}, inplace=True)
|
|
df2_critDelm['criete_delimit'] = 'Non déterminé'
|
|
df_critDelm = pd.concat([df_critDelm, df2_critDelm])
|
|
# Récupération des paramètres de délimitation de fct
|
|
# df_PcritDelm = pd.DataFrame(columns=['id_type','nom_court','nom', 'description'] )
|
|
# df_PcritDelm = df_PcritDelm.append(df_critDelm[['criete_delimit']].drop_duplicates())
|
|
# df_PcritDelm[['nom', 'description']] = df_PcritDelm.criete_delimit.str.split('(', expand=True)
|
|
# del df_PcritDelm['criete_delimit']
|
|
# df_PcritDelm['nom'] = df_PcritDelm['nom'].str.strip()
|
|
# df_PcritDelm['description'] = df_PcritDelm['description'].str.strip(')')
|
|
# df_PcritDelm['id_type'] = 0
|
|
# df_PcritDelm.drop_duplicates(inplace=True)
|
|
# df_PcritDelm.reset_index(drop=True, inplace=True)
|
|
# # Incrémentation des paramètres de délimitation de fct
|
|
# if not isin_bdd:
|
|
# df_PcritDelm.to_sql(
|
|
# name='param_delim_fct',
|
|
# con = con_zh,
|
|
# schema='zones_humides',
|
|
# index=True,
|
|
# index_label='id',
|
|
# if_exists='append',
|
|
# )
|
|
# print("INSERT ... ok !")
|
|
df_PcritDelm = pd.read_sql_table(
|
|
table_name = 'param_delim_fct',
|
|
con = con_zh,
|
|
schema = 'zones_humides',
|
|
index_col = 'id',
|
|
)
|
|
# Récupération des relations sites / paramètres de délimitation de fct
|
|
for i,row in df_PcritDelm.iterrows():
|
|
df_critDelm.loc[df_critDelm['criete_delimit'].astype(str).str.contains(row.nom, na=False), 'criete_delimit'] = row.name
|
|
df_critDelm.rename(columns={
|
|
'criete_delimit' : 'id_crit_delim',
|
|
'criete_delimit_rmq': 'description'
|
|
}, inplace=True)
|
|
# Incrémentation des relations sites / paramètres de délimitation de fct
|
|
df_critDelm.reset_index(drop=True,inplace=True)
|
|
if not isin_bdd:
|
|
df_critDelm.to_sql(
|
|
name='r_site_critdelim',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
# Récupération des codes Corine Biotope
|
|
df_corbio = df[['site_code', 'corine_biotope']]
|
|
df_corbio = pd.merge(df_corbio, df_rgsite, left_on='site_code', right_on='id_site')
|
|
df_corbio = df_corbio.rename(columns={
|
|
'id': 'id_geom_site',
|
|
})
|
|
df_corbio.drop(columns=['site_code', 'id_site'], inplace=True)
|
|
df_corbio.index.name = 'id'
|
|
df_corbio.dropna(subset=['corine_biotope'], inplace=True)
|
|
d = df_corbio['corine_biotope'].str.split(' ; ').apply(pd.Series).stack()
|
|
d = pd.DataFrame(d, columns=['corine_biotope'])
|
|
d.index.name = 'id'
|
|
del df_corbio['corine_biotope']
|
|
df_corbio = df_corbio.merge(d, on='id',how='left')
|
|
df_corbio[['id_cb', 'libelle']] = df_corbio['corine_biotope'].str.split(' : ', expand=True)
|
|
df_corbio = df_corbio[['id_geom_site', 'id_cb']]
|
|
df_corbio.reset_index(inplace=True, drop=True)
|
|
# Incrémentation des relations sites / code corine biotope
|
|
if not isin_bdd:
|
|
df_corbio.to_sql(
|
|
name='r_site_habitat',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
# Récupération des éléments de submersion
|
|
df_sub = df[['site_code','regime_hydrique_freq','regime_hydrique_etendue', 'regime_hydrique_orig']]
|
|
df_sub = pd.merge(df_sub, df_rgsite, left_on='site_code', right_on='id_site')
|
|
df_sub = df_sub.rename(columns={
|
|
'id': 'id_geom_site',
|
|
})
|
|
df_sub.drop(columns=['site_code', 'id_site'], inplace=True)
|
|
df_sub.index.name = 'id'
|
|
df_sub.dropna(subset=['regime_hydrique_freq','regime_hydrique_etendue', 'regime_hydrique_orig'], inplace=True, how='all')
|
|
df_typePsub = pd.read_sql_table(
|
|
table_name = 'type_param_sub',
|
|
con = con_zh,
|
|
schema = 'zones_humides',
|
|
index_col = 'id',
|
|
)
|
|
# Récupération des critères de submersion
|
|
df_paramSub = df_sub[['regime_hydrique_freq','regime_hydrique_etendue']].stack()
|
|
df_paramSub = df_paramSub.reset_index(level=1)
|
|
df_paramSub.set_axis(['id_type','nom'], axis=1, inplace=True)
|
|
df_paramSub = df_paramSub.drop_duplicates().sort_values('id_type')
|
|
df_paramSub.id_type.replace(['regime_hydrique_freq', 'regime_hydrique_etendue'],[0,1], inplace=True)
|
|
df_paramSub.reset_index(inplace=True, drop=True)
|
|
df_paramSub2 = pd.DataFrame({
|
|
'id_type' : [0, 1],
|
|
'nom' : ['Inconnu', 'Inconnu']
|
|
})
|
|
df_paramSub = df_paramSub.append(df_paramSub2, ignore_index=True)
|
|
# Incrémentation des critères de submersion
|
|
if not isin_bdd:
|
|
df_paramSub.to_sql(
|
|
name='param_sub',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
# Récupération des relations sites / critères de submersion
|
|
df_sub.rename(
|
|
columns={'regime_hydrique_orig': 'id_origsub', 'regime_hydrique_etendue': 'id_etendsub', 'regime_hydrique_freq': 'id_freqsub'},
|
|
inplace=True)
|
|
df_sub.replace(df_paramSub.nom.to_list(), df_paramSub.index.to_list(), inplace=True, regex=True)
|
|
df_sub.id_freqsub.fillna(6, inplace=True)
|
|
df_sub.id_etendsub.fillna(7, inplace=True)
|
|
df_sub.reset_index(inplace=True, drop=True)
|
|
# Incrémentation des relations sites / critères de submersion
|
|
if not isin_bdd:
|
|
df_sub.to_sql(
|
|
name='r_site_sub',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
# Récupération des régimes hydriques
|
|
df_hydri0 = df[['site_code','regime_hydrique_entree','regime_hydrique_sortie']]
|
|
df_hydri0 = pd.merge(df_hydri0, df_rgsite, left_on='site_code', right_on='id_site')
|
|
df_hydri0 = df_hydri0.rename(columns={
|
|
'id': 'id_geom_site',
|
|
})
|
|
df_hydri0.drop(columns=['site_code', 'id_site'], inplace=True)
|
|
df_hydri0.index.name = 'id'
|
|
df_hydri0.dropna(subset=['regime_hydrique_entree','regime_hydrique_sortie'], inplace=True, how='all')
|
|
# Mise à plat des entrées d'eau
|
|
df_hydriE = df_hydri0[['id_geom_site','regime_hydrique_entree']].reset_index(drop=True)
|
|
df_hydriE.index.name = 'id'
|
|
d= df_hydriE['regime_hydrique_entree'].str.split(' // ').apply(pd.Series).stack()
|
|
d = pd.DataFrame(d, columns=['regime_hydrique_entree'])
|
|
d.index.name = 'id'
|
|
del df_hydriE['regime_hydrique_entree']
|
|
df_hydriE = df_hydriE.merge(d, on='id',how='left')
|
|
df_hydriE.rename(columns={'regime_hydrique_entree': 'id_reg_hydro'}, inplace=True)
|
|
df_hydriE.id_reg_hydro.replace("Entrée d'eau : ", '', inplace=True, regex=True)
|
|
df_hydriE[['id_reg_hydro', 'id_permanance']] = df_hydriE['id_reg_hydro'].str.split(' ; Permanence : ', expand=True)
|
|
df_hydriE[['id_reg_hydro','id_toponymie']] = df_hydriE['id_reg_hydro'].str.split(' ; Toponymie : ', expand=True)
|
|
df_hydriE = df_hydriE.reset_index(drop=True)
|
|
df_hydriE.index.name = 'id'
|
|
|
|
d = df_hydriE[df_hydriE.id_toponymie.str.contains(';',na=False)]
|
|
d = d.merge(
|
|
pd.DataFrame({'toponymie':d['id_toponymie'].str.split(' ; ').apply(pd.Series).stack()}),
|
|
on='id',how='left') \
|
|
.drop(columns=['id_toponymie']) \
|
|
.rename(columns={'toponymie':'id_toponymie'})
|
|
# df_hydriE = pd.concat([
|
|
# df_hydriE[~df_hydriE.index.isin(d.index)],
|
|
# d ]).sort_values('id_geom_site').reset_index(drop=True)
|
|
|
|
|
|
# d = d.merge(
|
|
# pd.DataFrame({'toponymie':d['id_toponymie'].str.split(', ').apply(pd.Series).stack()}),
|
|
# on='id',how='left') \
|
|
# .drop(columns=['id_toponymie']) \
|
|
# .rename(columns={'toponymie':'id_toponymie'}) \
|
|
# .drop_duplicates()
|
|
# d = d.merge(
|
|
# pd.DataFrame({'toponymie':d['id_toponymie'].str.split(' ou ').apply(pd.Series).stack()}),
|
|
# on='id',how='left') \
|
|
# .drop(columns=['id_toponymie']) \
|
|
# .rename(columns={'toponymie':'id_toponymie'}) \
|
|
# .drop_duplicates()
|
|
|
|
# df_hydriE[['id_toponymie', 'id_permanance']] = df_hydriE['id_toponymie'].str.split(' ; Permanence : ', expand=True)
|
|
# d = df_hydriE['id_toponymie'].str.split(' ; ').apply(pd.Series).stack()
|
|
# d = pd.DataFrame(d, columns=['id_toponymie'])
|
|
# d.index.name = 'id'
|
|
# del df_hydriE['id_toponymie']
|
|
# df_hydriE = df_hydriE.merge(d, on='id',how='left')
|
|
df_hydriE['entree_sortie'] = 0
|
|
# Mise à plat des sorties d'eau
|
|
df_hydriS = df_hydri0[['id_geom_site','regime_hydrique_sortie']].reset_index(drop=True)
|
|
df_hydriS.index.name = 'id'
|
|
d = df_hydriS['regime_hydrique_sortie'].str.split(' // ').apply(pd.Series).stack()
|
|
d = pd.DataFrame(d, columns=['regime_hydrique_sortie'])
|
|
d.index.name = 'id'
|
|
del df_hydriS['regime_hydrique_sortie']
|
|
df_hydriS = df_hydriS.merge(d, on='id',how='left')
|
|
df_hydriS.rename(columns={'regime_hydrique_sortie': 'id_reg_hydro'}, inplace=True)
|
|
df_hydriS.id_reg_hydro.replace("Sortie d'eau : ", '', inplace=True, regex=True)
|
|
df_hydriS[['id_reg_hydro', 'id_permanance']] = df_hydriS['id_reg_hydro'].str.split(' ; Permanence : ', expand=True)
|
|
df_hydriS[['id_reg_hydro','id_toponymie']] = df_hydriS['id_reg_hydro'].str.split(' ; Toponymie : ', expand=True)
|
|
# df_hydriS[['id_toponymie', 'id_permanance']] = df_hydriS['id_toponymie'].str.split(' ; Permanence : ', expand=True)
|
|
# d = df_hydriS['id_toponymie'].str.split(' ; ').apply(pd.Series).stack()
|
|
# d = pd.DataFrame(d, columns=['id_toponymie'])
|
|
# d.index.name = 'id'
|
|
# del df_hydriS['id_toponymie']
|
|
# df_hydriS = df_hydriS.merge(d, on='id',how='left')
|
|
df_hydriS['entree_sortie'] = 1
|
|
# Regroupement des régimes hydriques
|
|
df_hydri = pd.concat([df_hydriE, df_hydriS], ignore_index=True)
|
|
for col in df_hydri.columns:
|
|
if not col in ['id_geom_site', 'entree_sortie']:
|
|
df_hydri[col] = df_hydri[col].str.strip()
|
|
# df_hydri.drop_duplicates(inplace=True)
|
|
# df_hydri.id_toponymie.replace('', None, inplace=True, regex=True)
|
|
|
|
df_hydri = pd.merge(df_hydri, df_rgsite, left_on='id_geom_site', right_on='id')
|
|
# Récupération des critères de régimes hydriques
|
|
df_Preghydro = df_hydri[['id_reg_hydro']].drop_duplicates()
|
|
df_Preghydro.dropna(inplace=True)
|
|
df_Preghydro['nom'] = df_Preghydro.id_reg_hydro.str[0].str.upper() + df_Preghydro.id_reg_hydro.str[1:].str.lower()
|
|
del df_Preghydro['id_reg_hydro']
|
|
df_Preghydro.sort_values('nom', inplace=True)
|
|
df_Preghydro.reset_index(inplace=True, drop=True)
|
|
# Incrémentation des critères de régimes hydriques
|
|
if not isin_bdd:
|
|
df_Preghydro.to_sql(
|
|
name='param_reg_hydro',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
# Récupération des critères de permanances hydriques
|
|
df_PpermHydro = df_hydri[['id_permanance']].drop_duplicates()
|
|
df_PpermHydro.columns = ['nom']
|
|
df_PpermHydro.nom = df_PpermHydro.nom.replace([''],[None])
|
|
df_PpermHydro.dropna(inplace=True)
|
|
df_PpermHydro.sort_values('nom', inplace=True)
|
|
df_PpermHydro.reset_index(inplace=True, drop=True)
|
|
df_PpermHydro.loc[5] = 'inconnu'
|
|
# Incrémentation des critères de permanances hydriques
|
|
if not isin_bdd:
|
|
df_PpermHydro.to_sql(
|
|
name='param_permanence',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
# Récupération des Toponymie
|
|
df_hydri[['id_toponymie']] = df_hydri[['id_toponymie']] \
|
|
.replace(
|
|
['ruisseau','canal','canaux','torrent','catelan','\nRuisseau de la Grande Valloire',', …'],
|
|
['Ruisseau','Canal','Canal','Torrent','Catelan','',''],
|
|
regex=True) \
|
|
.replace(['Ruisseaux'],['Ruisseau'], regex=True)
|
|
# df_topony = df_hydri[['id_toponymie']] \
|
|
# .replace([''],[None]) \
|
|
# .dropna() \
|
|
# .drop_duplicates()
|
|
# df_tron = pd.read_sql_table(
|
|
# table_name='troncon_hydro',
|
|
# con=con_zh,
|
|
# schema='ref_hydro',
|
|
# columns=['id', 'nom'],
|
|
# ).dropna()
|
|
# df_topony[df_topony.id_toponymie.isin(df_tron.nom)]
|
|
# df_topony[~df_topony.id_toponymie.isin(df_tron.nom)].id_toponymie.unique()
|
|
df_RegHydro = df_hydri.drop(columns='id').copy()
|
|
df_RegHydro.rename(columns={
|
|
'id_toponymie':'rmq_toponymie',
|
|
'id_permanance':'id_permanence',
|
|
'entree_sortie': 'in_out'}, inplace=True)
|
|
df_RegHydro.id_reg_hydro = df_RegHydro.id_reg_hydro.str[0].str.upper() + df_RegHydro.id_reg_hydro.str[1:].str.lower()
|
|
df_RegHydro.dropna(subset=['id_reg_hydro'],inplace=True)
|
|
df_RegHydro.id_permanence.fillna('inconnu', inplace=True)
|
|
d1 = dict(df_Preghydro.nom)
|
|
d2 = dict(df_PpermHydro.nom)
|
|
d1 = {v: str(k) for k, v in d1.items()}
|
|
d2 = {v: str(k) for k, v in d2.items()}
|
|
dic = {
|
|
'id_reg_hydro': d1,
|
|
'id_permanence': {'':None, **d2}
|
|
}
|
|
df_RegHydro.in_out = ~df_RegHydro.in_out.astype(bool)
|
|
df_RegHydro = df_RegHydro.replace(dic) \
|
|
.drop(columns='id_site') \
|
|
.reset_index(drop=True)
|
|
# Incrémentation des régimes hydriques
|
|
if not isin_bdd:
|
|
df_RegHydro.to_sql(
|
|
name='r_site_reghydro',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=False,
|
|
# index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
|
|
|
|
|
|
# Récupération des usages et process
|
|
df_Ppostion = pd.read_sql_table(
|
|
table_name = 'param_position',
|
|
con = con_zh,
|
|
schema = 'zones_humides',
|
|
index_col = 'id',
|
|
)
|
|
df.usages_process_natu = df.usages_process_natu.replace(
|
|
['avce', 'Entretine', 'Espèce invasive','espèce invasive'],
|
|
['avec', 'Entretien', 'Espèces invasives','espèces invasives'], regex=True)
|
|
|
|
df_Rprocess = df[['site_code', 'usages_process_natu']].copy()
|
|
df_Rprocess = pd.merge(df_Rprocess, df_rgsite, left_on='site_code', right_on='id_site')
|
|
df_Rprocess = df_Rprocess.rename(columns={
|
|
'id': 'id_geom_site',
|
|
})
|
|
df_Rprocess.drop(columns=['site_code'], inplace=True)
|
|
# df_Rprocess.dropna(subset=['usages_process_natu'], inplace=True)
|
|
df_Rprocess.reset_index(drop=True, inplace=True)
|
|
df_Rprocess.index.name = 'id'
|
|
d = df_Rprocess['usages_process_natu'].str.split('//', expand=True).stack()
|
|
d = pd.DataFrame(d, columns=['activ_humaine'])
|
|
d.index.name = 'id'
|
|
df_Rprocess = df_Rprocess \
|
|
.drop(columns=['usages_process_natu']) \
|
|
.merge(d, on='id', how='left')
|
|
df_Rprocess[['activ_humaine', 'position']] = df_Rprocess['activ_humaine'].str.split(', Localisation :', expand=True)
|
|
df_Rprocess[['activ_humaine', 'rmq_activ_hum']] = df_Rprocess['activ_humaine'].str.split(pat=' \(Remarques :', expand=True)
|
|
df_Rprocess['rmq_activ_hum'] = df_Rprocess['rmq_activ_hum'].str.replace(' \)','', regex=True).str.strip()
|
|
df_Rprocess['rmq_activ_hum'] = df_Rprocess['rmq_activ_hum'].replace([''],[None])
|
|
df_Rprocess.activ_humaine = df_Rprocess.activ_humaine.str.strip()
|
|
df_Rprocess.position = df_Rprocess.position.str.strip()
|
|
df_Rprocess.activ_humaine = df_Rprocess.activ_humaine.str[0].str.upper() + df_Rprocess.activ_humaine.str[1:].str.lower()
|
|
df_Rprocess.activ_humaine.replace(
|
|
['Dépots', 'Dépôt sauvage', 'Atterissement', "Entretien plan d'eau", "Création plan d'eau", 'Déchèterie communale' ,"Canalisation d'eau", 'Surpiétinement'],
|
|
['Dépôts', 'Dépôts sauvages', 'Atterrissement', "Entretien de plan d'eau", "Création de plan d'eau", 'Déchèterie', 'Canalisation', 'Piétinement'],
|
|
regex=True,
|
|
inplace=True
|
|
)
|
|
df_Rprocess.activ_humaine.replace(['Canalisation', 'Step'], ["Canalisation d'eau", 'STEP'],
|
|
regex=True, inplace=True
|
|
)
|
|
# Récupération du dictionnaire activité humaine
|
|
df_ActHum = pd.read_sql_table(
|
|
table_name='param_activ_hum',
|
|
schema = 'zones_humides',
|
|
con=con_zh
|
|
)
|
|
df_Rprocess['activ_hum_autre'] = None
|
|
df_Rprocess.loc[~df_Rprocess.activ_humaine.isin(df_ActHum.nom),'activ_hum_autre'] = df_Rprocess.loc[~df_Rprocess.activ_humaine.isin(df_ActHum.nom),'activ_humaine']
|
|
df_Rprocess.loc[~df_Rprocess.activ_hum_autre.isna(),'activ_humaine'] = df_ActHum.loc[df_ActHum.id==21, 'nom'].reset_index(drop=True)[0]
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BB0073') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position','rmq_activ_hum']] = ["Pas d'activité marquante", 'ZH + EF','Anciennes mines']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BO0013') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BO0081') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BO0112') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BO0303') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0054') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0055') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0056') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0059') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0060') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0104') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0108') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38DA0018') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GC0008') & (df_Rprocess.activ_humaine.isna()), 'activ_humaine'] = "Autre (préciser dans l'encart réservé aux remarques)"
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GC0096') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GC0098') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0003') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GR0006') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0049') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0054') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0058') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0060') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum','position']] = ["Sylviculture",'Coupe des arbres sous THT','ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38QV0020') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38QV0033') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RH0038') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RH0056') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RH0101') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0005') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0023') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0032') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0048') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0055') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0061') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0063') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0065') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0083') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0085') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0086') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0099') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0126') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0143') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0146') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0147') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0160') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0164') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0005') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0007') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0012') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0021') & (df_Rprocess.activ_humaine == "Pas d'activité marquante"),['position']] = ['ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0025') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0227') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0344') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='26PNRV0111') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH + EF']
|
|
|
|
|
|
|
|
# '38RD0163' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0163') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0163') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
"Élevage / pastoralisme",'Equin','ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38RD0162' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0162') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Ovin', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0162') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)','Randonnée', 'ZH + EF']
|
|
# '38RD0161' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0161') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0161') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)','Randonnée', 'ZH + EF']
|
|
# '38RD0159' ADD ['Urbanisation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0159') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = [
|
|
"Élevage / pastoralisme",'ZH + EF']
|
|
# '38RD0158' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0158') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0158') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
"Tourisme et loisirs (camping, zone de stationnement)",'VTT','ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38RD0157' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0157') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0157') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
"Élevage / pastoralisme",'Equin','ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38RD0156' ADD ['Urbanisation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0156') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
"Élevage / pastoralisme",'Bovin','ZH + EF']
|
|
# '38RD0155' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0155') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Bovin', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0155') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)','Ski', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38RD0153' ADD ['Urbanisation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0153') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
"Élevage / pastoralisme",'Bovin','ZH + EF']
|
|
# '38RD0152' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0152') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38RD0152') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Bovin', 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0152') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)','Randonnée, Ski', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1, d2]).sort_values('id_site')
|
|
# '38RD0151' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0151') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38RD0151') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Equin', 'ZH + EF']
|
|
d2[['activ_humaine','rmq_activ_hum', 'position']] = ["Prélèvements d'eau",'Captage', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0151') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)','Randonnée', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1, d2]).sort_values('id_site')
|
|
# '38RD0150' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0150') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Equin', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0150') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Chasse', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38RD0149' ADD ['Urbanisation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0149') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = [
|
|
'Tourisme et loisirs (camping, zone de stationnement)', 'Randonnée','ZH + EF']
|
|
# '38RD0148' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0148') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine','rmq_activ_hum', 'position']] = ["Tourisme et loisirs (camping, zone de stationnement)",'Randonnée', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0148') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Chasse', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38VS0057' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0057') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0057') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
d2[['activ_humaine', 'position']] = ["Tourisme et loisirs (camping, zone de stationnement)", 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0057') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Urbanisation', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# '38VS0056' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0056') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0056') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# '38VS0055' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0055') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0055') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0055') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Sylviculture', 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
d3[['activ_humaine', 'position']] = ["Tourisme et loisirs (camping, zone de stationnement)", 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0055') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Urbanisation', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
# '38VS0054' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d5 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d6 = df_Rprocess[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Sylviculture', 'ZH']
|
|
d2[['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
d3[['activ_humaine', 'position']] = ['Chasse', 'ZH + EF']
|
|
d4[['activ_humaine', 'position']] = ["Tourisme et loisirs (camping, zone de stationnement)", 'ZH + EF']
|
|
d5[['activ_humaine','rmq_activ_hum', 'position']] = ["Prélèvements d'eau",'Irrigation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0054') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4,d5]).sort_values('id_site')
|
|
# '38VS0053' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d5 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d6 = df_Rprocess[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
d2[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'EF']
|
|
d3[['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'EF']
|
|
d4[['activ_humaine','rmq_activ_hum', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)",'Remblais', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0053') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4]).sort_values('id_site')
|
|
# '38VS0052' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d5 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d6 = df_Rprocess[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Sylviculture", 'EF']
|
|
d2[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Equin', 'EF']
|
|
d3[['activ_humaine', 'position']] = ["Pêche", 'ZH']
|
|
d4[['activ_humaine', 'position']] = ['Tourisme et loisirs (camping, zone de stationnement)', 'ZH + EF']
|
|
d5[['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
d6[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0052') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4,d5,d6]).sort_values('id_site')
|
|
# '38VS0051' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0051') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0051') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0051') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Urbanisation", 'EF']
|
|
d2[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Bovin', 'ZH + EF']
|
|
d3[['activ_humaine','rmq_activ_hum', 'position']] = ["Prélèvements d'eau",'Pompage agricole, Abrevoir', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0051') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Agriculture", 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3])
|
|
# '38VS0050' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0050') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0050') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Urbanisation", 'EF']
|
|
d2[['activ_humaine','rmq_activ_hum', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)",'Autoroute', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0050') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Agriculture", 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2])
|
|
# '38VS0049' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d5 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d6 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d7 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d8 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d9 = df_Rprocess[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Sylviculture", 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
d3[['activ_humaine', 'position']] = ["Pêche", 'ZH']
|
|
d4[['activ_humaine', 'position']] = ['Tourisme et loisirs (camping, zone de stationnement)', 'ZH']
|
|
d5[['activ_humaine', 'position']] = ['Urbanisation', 'ZH + EF']
|
|
d6[['activ_humaine', 'position']] = ['Industrie', 'EF']
|
|
d7[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'EF']
|
|
d8[['activ_humaine', 'position','rmq_activ_hum']] = ["Prélèvements d'eau", 'ZH','Pompage, lavoir']
|
|
d9[['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0049') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4,d5,d6,d7,d8,d9]).sort_values('id_site')
|
|
# '38VS0048' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0048') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0048') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0048') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'EF']
|
|
d2[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'EF']
|
|
d3[['activ_humaine', 'position']] = ["Industrie", 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0048') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
# '38VS0047' ADD ["Prélèvements d'eau", 'ZH']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VS0047') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Bovin', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0047') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Agriculture", 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38VS0046' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VS0046') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VS0046') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VS0046') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38VS0046') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'EF']
|
|
d2[['activ_humaine','rmq_activ_hum', 'position']] = ["Élevage / pastoralisme",'Bovin, abeilles', 'ZH + EF']
|
|
d3[['activ_humaine', 'position']] = ["Prélèvements d'eau", 'ZH']
|
|
d4[['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0046') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4]).sort_values('id_site')
|
|
# '38RD0128' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0128') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38RD0128') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ["Chasse", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0128') & (df_Rprocess.activ_humaine.isna()),['activ_humaine','rmq_activ_hum', 'position']] = ["Activité militaire",'Zone de tirs temporaires du Galibier-Grandes Rousses', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2])
|
|
# '38RD0165' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RD0165') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38RD0165') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'EF']
|
|
d2[['activ_humaine', 'position']] = ["Extraction de granulats, mines", 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RD0165') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Activité hydroélectrique, barrage", 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2])
|
|
# '38RH0292' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d4 = df_Rprocess[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d5 = df_Rprocess[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position','activ_hum_autre']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'ZH', 'Remblais']
|
|
d2[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
d3[['activ_humaine', 'position']] = ['Tourisme et loisirs (camping, zone de stationnement)', 'ZH']
|
|
d4[['activ_humaine', 'position']] = ['Urbanisation', 'ZH']
|
|
d5[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RH0292') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3,d4,d5]).sort_values('id_site')
|
|
# '38MA0059' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38MA0059') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38MA0059') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ["Chasse", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0059') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Agriculture", 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2])
|
|
# '38DA0019' ADD ["Prélèvements d'eau", 'ZH']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38DA0019') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38DA0019') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Chasse", 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38DA0020' ADD ["Prélèvements d'eau", 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38DA0020') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38DA0020') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ["Élevage / pastoralisme", 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ["Prélèvements d'eau", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38DA0020') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Chasse", 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2])
|
|
# '38CG0110' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38CG0110') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38CG0110') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38CG0110') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Chasse', 'ZH']
|
|
d2[['activ_humaine', 'position']] = ['Tourisme et loisirs (camping, zone de stationnement)', 'EF']
|
|
d3[['activ_humaine', 'position']] = ["Prélèvements d'eau", 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0110') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
# '26PNRV0208' ADD ["Prélèvements d'eau", 'ZH']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='26PNRV0208') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ["Prélèvements d'eau", 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='26PNRV0208') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ["Pas d'activité marquante", 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38BO0058' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38BO0058') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38BO0058') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38BO0058') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
d2[['activ_humaine', 'position']] = ['Urbanisation', 'EF']
|
|
d3[['activ_humaine', 'position']] = ["Infrastructures linéaires (routes, voies ferrées)", 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BO0058') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
# '38VE0119' ADD ['Agriculture', 'EF']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VE0119') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0119') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Élevage / pastoralisme', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38VE0147' ADD ['Infrastructures linéaires (routes, voies ferrées)', 'EF']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VE0147') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0147') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Élevage / pastoralisme', 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38VE0184' ADD ['Agriculture', 'EF']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VE0184') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ['Agriculture', 'EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0184') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38VE0280' ADD ['Urbanisation', 'ZH']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VE0280') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ['Urbanisation', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0280') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d])
|
|
# '38VE0345' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VE0345') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VE0345') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d3 = df_Rprocess[(df_Rprocess.id_site=='38VE0345') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Élevage / pastoralisme', 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'EF']
|
|
d3[['activ_humaine', 'position','activ_hum_autre']] = ["Autre (préciser dans l'encart réservé aux remarques)", 'ZH + EF', 'Remblais']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0345') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
# '38VE0331' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VE0331') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VE0331') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Élevage / pastoralisme', 'ZH + EF']
|
|
d2[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0331') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Agriculture', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# '38VE0338' ADD ['Urbanisation', 'ZH']
|
|
d = df_Rprocess[(df_Rprocess.id_site=='38VE0338') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d[['activ_humaine', 'position']] = ['Infrastructures linéaires (routes, voies ferrées)', 'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0338') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Élevage / pastoralisme', 'ZH + EF']
|
|
df_Rprocess = pd.concat([df_Rprocess, d]).sort_values('id_site')
|
|
# '38VA0006' ADD ['Urbanisation', 'ZH']
|
|
d1 = df_Rprocess[(df_Rprocess.id_site=='38VA0006') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d2 = df_Rprocess[(df_Rprocess.id_site=='38VA0006') & (df_Rprocess.activ_humaine.isna())].copy()
|
|
d1[['activ_humaine', 'position']] = ['Pêche', 'ZH']
|
|
d2[['activ_humaine', 'position']] = ['Tourisme et loisirs (camping, zone de stationnement)', 'ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VA0006') & (df_Rprocess.activ_humaine.isna()),['activ_humaine', 'position']] = ['Activité hydroélectrique, barrage', 'ZH']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
|
|
|
|
import pymedwet as pym
|
|
# Récupération du dictionnaire des impacts
|
|
df_imp = pym.medwet.__get_DicGenIMP__() \
|
|
.rename(columns={'CODE':'id','DESCR':'nom'})
|
|
df_impact = df_imp.copy()
|
|
df_impact.nom = df_impact.nom.str[0].str.upper() + df_impact.nom.str[1:]
|
|
# Incrémentation du dictionnaire des impacts
|
|
if not isin_bdd:
|
|
df_impact.to_sql(
|
|
name='param_impact',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=False,
|
|
# index_label='id',
|
|
if_exists='append',
|
|
method='multi'
|
|
)
|
|
print("INSERT ... ok !")
|
|
# Récupération des impacts
|
|
df_imp = pd.read_sql_table(
|
|
table_name='param_impact',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
columns=['id','nom']
|
|
)
|
|
med1 = pym.medwet.get_usage_process(pym.db_file1)
|
|
med2 = pym.medwet.get_usage_process(pym.db_file2)
|
|
med = pd.concat([med1, med2]).sort_values('SITE_COD')
|
|
med.ACTIVITE_HUM = med.ACTIVITE_HUM.str[0].str.upper() + med.ACTIVITE_HUM.str[1:].str.lower()
|
|
med.LOCALISATION = med.LOCALISATION.str[0].str.upper() + med.LOCALISATION.str[1:].str.lower()
|
|
med.LOCALISATION = med.LOCALISATION.replace(df_Ppostion.description.to_list(),df_Ppostion.nom.to_list())
|
|
merge_med = med[['SITE_COD', 'ACTIVITE_HUM','LOCALISATION', 'IMPACT']].copy() # 'ACTIV_TYPO'
|
|
# Merge usage_process - impact
|
|
df_Rprocess = pd.merge(df_Rprocess, merge_med,
|
|
left_on = ['id_site','activ_humaine','position'],
|
|
right_on = ['SITE_COD','ACTIVITE_HUM','LOCALISATION'],
|
|
how = 'left') \
|
|
.drop(columns=['SITE_COD','ACTIVITE_HUM','LOCALISATION']) \
|
|
.rename(columns={'IMPACT':'impact'})
|
|
# Ajout des impacts pour les zones
|
|
|
|
|
|
# '38RH0261'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38RH0261') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Élevage / pastoralisme"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'EF']
|
|
# '38VE0281'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0281') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Industrie"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '31.0','nom'].item() ,'ZH']
|
|
d1 = df_Rprocess.loc[(df_Rprocess.id_site=='38VE0281') & (df_Rprocess.activ_humaine=="Élevage / pastoralisme"),].copy()
|
|
d2 = d1.copy()
|
|
d1[['activ_humaine','impact','position']] = ["Élevage / pastoralisme", df_imp.loc[df_imp.id == '45.0','nom'].item() ,'EF']
|
|
d2[['activ_humaine','impact','rmq_activ_hum','position']] = ['Élevage / pastoralisme', df_imp.loc[df_imp.id == '46.0','nom'].item() ,'Fauche','EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VE0281') & (df_Rprocess.activ_humaine=="Élevage / pastoralisme"),
|
|
['rmq_activ_hum']] = ['Prairies labourées, resemées, pour parties les moins pentues']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1, d2]).sort_values('id_site')
|
|
# '38VS0033'
|
|
df_Rprocess.drop(
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38VS0033') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)")].index, inplace=True)
|
|
# '38QV0065'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38QV0065') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Sylviculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '54.0','nom'].item() ,'EF']
|
|
# '38MA0057'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38MA0057') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Sylviculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '54.0','nom'].item() ,'EF']
|
|
# '38FP0084'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0084') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)"),
|
|
['impact','activ_hum_autre', 'position']] = [ df_imp.loc[df_imp.id == '91.4','nom'].item() ,'Espèces invasives','ZH + EF']
|
|
# '38FP0081'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0081') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)") & (df_Rprocess.rmq_activ_hum=="épandage de granules type NPK sur pâture"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '44.0','nom'].item() ,'Drainage','EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0081') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)") & (df_Rprocess.rmq_activ_hum=="solidage"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '91.4','nom'].item() ,'Solidage','EF']
|
|
# '38FP0072'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0072') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '46.0','nom'].item() ,'EF']
|
|
# '38FP0069'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0069') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '34.0','nom'].item() ,'Remblais','EF']
|
|
# '38FP0067'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0067') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Chasse"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '62.0','nom'].item() ,'ZH + EF']
|
|
# '38FP0065'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0065') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)") & (df_Rprocess.rmq_activ_hum=="drainage"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '31.0','nom'].item() ,'Drainage','ZH']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38FP0065') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)") & (df_Rprocess.rmq_activ_hum=="solidage"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '91.4','nom'].item() ,'Solidage','ZH']
|
|
# '38GL0025'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0025') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Prélèvements d'eau"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '36.0','nom'].item() ,'ZH']
|
|
# '38GL0024'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0024') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Élevage / pastoralisme"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'ZH + EF']
|
|
# '38GL0023'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0023') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Élevage / pastoralisme"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0023') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Tourisme et loisirs (camping, zone de stationnement)"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '30','nom'].item() ,'Ski de fond','ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0023') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '15.0','nom'].item() ,'ZH + EF']
|
|
# '38GL0022'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0022') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Agriculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0022') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Tourisme et loisirs (camping, zone de stationnement)"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '30','nom'].item() ,'Ski','ZH + EF']
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0022') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Prélèvements d'eau"),
|
|
['impact','rmq_activ_hum', 'position']] = [ df_imp.loc[df_imp.id == '31.0','nom'].item() ,'Drainage','ZH + EF']
|
|
# '38GL0021'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0021') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Agriculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'ZH + EF']
|
|
# '38GL0020'
|
|
df_Rprocess.drop(
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0020') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Agriculture"),].index,
|
|
inplace=True)
|
|
# '38GL0019'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38GL0019') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Agriculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '45.0','nom'].item() ,'ZH + EF']
|
|
# '38DA0017'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38DA0017') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Pas d'activité marquante"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '0','nom'].item() ,'ZH + EF']
|
|
# '38DA0010'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38DA0010') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Pas d'activité marquante"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '0','nom'].item() ,'ZH + EF']
|
|
# '38CG0142'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0142') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Prélèvements d'eau"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '36.0','nom'].item() ,'ZH']
|
|
# '38CG0135'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0135') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Infrastructures linéaires (routes, voies ferrées)"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '53.0','nom'].item() ,'ZH + EF']
|
|
# '38CG0131'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0131') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Chasse"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '13.0','nom'].item() ,'ZH + EF']
|
|
# '38CG0112'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38CG0112') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Sylviculture"),
|
|
['impact', 'position']] = [ df_imp.loc[df_imp.id == '62.0','nom'].item() ,'ZH + EF']
|
|
# '38BO0271'
|
|
df_Rprocess.drop(df_Rprocess.loc[(df_Rprocess.id_site=='38BO0271') & (df_Rprocess.position=='ERROR') &
|
|
(df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)")].index, inplace=True)
|
|
# '38BI0127'
|
|
df_Rprocess.loc[(df_Rprocess.id_site=='38BI0127') & (df_Rprocess.position=='ERROR') & (df_Rprocess.activ_humaine=="Autre (préciser dans l'encart réservé aux remarques)"),
|
|
['activ_hum_autre','impact', 'position']] = ['Gestion privée', df_imp.loc[df_imp.id == '35.0','nom'].item() ,'ZH']
|
|
|
|
|
|
# 38RD0163
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0163') &
|
|
(df_Rprocess.activ_humaine =="Infrastructures linéaires (routes, voies ferrées)") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0163') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
# df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# 38RD0162
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0162') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
# df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0162') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0162') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# 38RD0161
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0161') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0161') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
# 38RD0159
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0159') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0159') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '91.2','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# 38RD0158
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0158') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0158') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '22.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0158') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0158') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '91.2','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# 38RD0157
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0157') &
|
|
(df_Rprocess.activ_humaine =="Infrastructures linéaires (routes, voies ferrées)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '36.0','nom'].item()
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0157') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0157') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '91.2','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
# 38RD0156
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0156') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
# 38RD0155
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0155') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0155') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
# 38RD0153
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0153') &
|
|
(df_Rprocess.activ_humaine =="Élevage / pastoralisme") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
# 38RD0152
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0152') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0152') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0152') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0152') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0152') &
|
|
(df_Rprocess.activ_humaine =="Urbanisation") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
# 38RD0151
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0151') &
|
|
(df_Rprocess.activ_humaine =="Tourisme et loisirs (camping, zone de stationnement)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0151') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0151') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0151') &
|
|
(df_Rprocess.activ_humaine =="Prélèvements d'eau") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '36.0','nom'].item()
|
|
# 38RD0150
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0150') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0150') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '24.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0150') &
|
|
(df_Rprocess.activ_humaine =="Chasse") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '62.0','nom'].item()
|
|
# 38RD0149
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0149') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
# 38RD0148
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0148') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0148') &
|
|
(df_Rprocess.activ_humaine =="Chasse") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '62.0','nom'].item()
|
|
# 38VS0057
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0057') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0057') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0057') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
# 38VS0056
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0056') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0056') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '34.0','nom'].item()
|
|
# 38VS0055
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0055') &
|
|
(df_Rprocess.activ_humaine =='Sylviculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '51.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0055') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0055') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '16.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0055') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
# 38VS0054
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =='Sylviculture') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '53.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =='Chasse') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '62.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0054') &
|
|
(df_Rprocess.activ_humaine =="Prélèvements d'eau") &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '36.0','nom'].item()
|
|
# 38VS0053
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '17.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '15.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0053') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '34.0','nom'].item()
|
|
# 38VS0052
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '43.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Sylviculture') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '53.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '73.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0052') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
# 38VS0051
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0051') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0051') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0051') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0051') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '17.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0051') &
|
|
(df_Rprocess.activ_humaine =="Prélèvements d'eau") &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '36.0','nom'].item()
|
|
# 38VS0050
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '47.4','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '17.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0050') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '21.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# 38VS0049
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '45.0','nom'].item(), 'Equin']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Industrie') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '12.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Sylviculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '51.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '53.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Sylviculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '55.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH'),].copy()
|
|
d2 = d1.copy()
|
|
d3 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '16.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '32.0','nom'].item()
|
|
d3['impact'] = df_imp.loc[df_imp.id == '61.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '73.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d2 = d1.copy()
|
|
d3 = d1.copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
d2['impact'] = df_imp.loc[df_imp.id == '15.0','nom'].item()
|
|
d3['impact'] = df_imp.loc[df_imp.id == '34.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '17.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d1,d2,d3]).sort_values('id_site')
|
|
d1 = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH'),].copy()
|
|
d1['impact'] = df_imp.loc[df_imp.id == '34.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0049') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '15.0','nom'].item(), 'Plusieurs remblaiement constatés']
|
|
df_Rprocess = pd.concat([df_Rprocess, d1]).sort_values('id_site')
|
|
# 38VS0048
|
|
d = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d['impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '15.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d]).sort_values('id_site')
|
|
d = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'EF'),].copy()
|
|
d['impact'] = df_imp.loc[df_imp.id == '91.2','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '91.4','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =="Industrie") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '12.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0048') &
|
|
(df_Rprocess.activ_humaine =="Infrastructures linéaires (routes, voies ferrées)") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
# 38VS0047
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0047') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0047') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
# 38VS0046
|
|
d = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0046') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),].copy()
|
|
d['impact'] = df_imp.loc[df_imp.id == '17.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0046') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess = pd.concat([df_Rprocess, d]).sort_values('id_site')
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0046') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VS0046') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '81.0','nom'].item()
|
|
# 38RD0128
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0128') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0128') &
|
|
(df_Rprocess.activ_humaine =='Chasse') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '62.0','nom'].item()
|
|
# 38RD0165
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0165') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '13.0','nom'].item(), 'D526']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0165') &
|
|
(df_Rprocess.activ_humaine =='Extraction de granulats, mines') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '34.0','nom'].item(), 'Extraction de gravats']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RD0165') &
|
|
(df_Rprocess.activ_humaine =='Activité hydroélectrique, barrage') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '11.0','nom'].item(), 'Barrage']
|
|
# 38RH0292
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '41.0','nom'].item(), 'Maïs']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '41.0','nom'].item(), 'Equin']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '11.0','nom'].item(), 'Camping, maison']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '16.0','nom'].item(), "Paintball, parcours sur câble"]
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =="Infrastructures linéaires (routes, voies ferrées)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '13.0','nom'].item(), 'Routes']
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38RH0292') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '31.0','nom'].item()
|
|
# 38CG0110
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38CG0110') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38CG0110') &
|
|
(df_Rprocess.activ_humaine =='Chasse') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '62.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38CG0110') &
|
|
(df_Rprocess.activ_humaine =='Tourisme et loisirs (camping, zone de stationnement)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '61.0','nom'].item(), "Tir à l'arc"]
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38CG0110') &
|
|
(df_Rprocess.activ_humaine =="Prélèvements d'eau") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
['impact','rmq_activ_hum']] = [df_imp.loc[df_imp.id == '30','nom'].item(), 'Irrigation']
|
|
# 38BO0058
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38BO0058') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38BO0058') &
|
|
(df_Rprocess.activ_humaine =='Pêche') &
|
|
(df_Rprocess.position == 'ZH'),
|
|
'impact'] = df_imp.loc[df_imp.id == '63.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38BO0058') &
|
|
(df_Rprocess.activ_humaine =='Urbanisation') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '11.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38BO0058') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
# 38VE0331
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0331') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0331') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0331') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '31.0','nom'].item()
|
|
# 38VE0338
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0338') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
# 38VE0345
|
|
d = df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0345') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'].copy()
|
|
d['impact'] = df_imp.loc[df_imp.id == '31.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0345') &
|
|
(df_Rprocess.activ_humaine =='Agriculture') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '41.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0345') &
|
|
(df_Rprocess.activ_humaine =='Élevage / pastoralisme') &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '45.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0345') &
|
|
(df_Rprocess.activ_humaine =='Infrastructures linéaires (routes, voies ferrées)') &
|
|
(df_Rprocess.position == 'EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '13.0','nom'].item()
|
|
df_Rprocess.loc[
|
|
(df_Rprocess.id_site=='38VE0345') &
|
|
(df_Rprocess.activ_humaine =="Autre (préciser dans l'encart réservé aux remarques)") &
|
|
(df_Rprocess.position == 'ZH + EF'),
|
|
'impact'] = df_imp.loc[df_imp.id == '15.0','nom'].item()
|
|
|
|
df_Rprocess.loc[df_Rprocess.activ_humaine=="Pas d'activité marquante",'impact'] = df_imp.loc[df_imp.id == '0','nom'].item()
|
|
|
|
|
|
|
|
|
|
# Remplacement des valeurs par leurs identifiants
|
|
df_Rprocess['id_position'] = df_Rprocess.position.replace(df_Ppostion.nom.to_list(),df_Ppostion.index.to_list())
|
|
df_Rprocess['id_activ_hum'] = df_Rprocess.activ_humaine.replace(df_ActHum.nom.to_list(),df_ActHum.id.to_list())
|
|
df_Rprocess.impact = df_Rprocess.impact.str.lower()
|
|
df_Rprocess['id_impact'] = df_Rprocess.impact.replace(df_imp.nom.str.lower().to_list(),df_imp.id.to_list())
|
|
df_Rprocess.drop(columns=['id_site', 'activ_humaine', 'position','impact'], inplace=True)
|
|
df_Rprocess.rename(
|
|
columns = {'rmq_activ_hum': 'remarques'},
|
|
inplace = True
|
|
)
|
|
df_Rprocess.reset_index(drop=True, inplace=True)
|
|
df_Rprocess.dropna(subset=['id_activ_hum'], inplace=True)
|
|
df_Rprocess.id_activ_hum = df_Rprocess.id_activ_hum.astype(int)
|
|
df_Rprocess.id_position = df_Rprocess.id_position.astype(int)
|
|
# Incrémentation des usages et process
|
|
if not isin_bdd:
|
|
df_Rprocess.to_sql(
|
|
name='r_site_usageprocess',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
method='multi'
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
|
|
|
|
|
|
# df_Rprocess.loc[~df_Rprocess.activ_hum_autre.isna(),['id_site','rmq_activ_hum','activ_hum_autre']]
|
|
# df_Rprocess.loc[~(
|
|
# (df_Rprocess.activ_humaine == df_Rprocess.ACTIV_TYPO) | (df_Rprocess.activ_hum_autre == df_Rprocess.ACTIV_TYPO) | df_Rprocess.ACTIV_TYPO.isna())
|
|
# , ['id_geom_site','id_site','activ_hum_autre','ACTIV_TYPO']] = None
|
|
|
|
|
|
# Recherche des 'activité autres' spécifiée dans medwet et non précisée dans zh
|
|
# lst_zhsit = df_Rprocess[~df_Rprocess.activ_hum_autre.isna()].id_site.tolist()
|
|
# tmp = med1[~med1.ACTIV_TYPO.eq(med1.ACTIVITE_HUM) & (~med1.ACTIV_TYPO.isna()) & (~med1.SITE_COD.isin(lst_zhsit))]
|
|
# df_Rprocess.loc[
|
|
# (df_Rprocess.id_site.isin(tmp.SITE_COD)) &
|
|
# (df_Rprocess.activ_humaine == df_ActHum.loc[df_ActHum.id==21, 'nom'].values[0])
|
|
# , :]
|
|
# , 'activ_hum_autre']
|
|
|
|
# df_Rprocess[df_Rprocess.activ_humaine.isna()].sort_values('id_site')
|
|
# df_Rprocess[df_Rprocess.id_site=='38VE0331']
|
|
# dc[dc.CODE=='13.0'].DESCR
|
|
# '38RD0023' ==> ????? Ne sait pas où c'est ...
|
|
# '38RD0126' ==> ????? Ne sait pas où c'est ...
|
|
# '38VE0227' ==> Disparue en 2021
|
|
# '38VE0331' ==> Inconnu
|
|
# '38VE0338' ==> Inconnu
|
|
# '38VE0344' ==> Inconnu
|
|
# '38VE0345' ==> Inconnu
|
|
# '38VA0006' ==> Dilemme : BDD = "Pas d'activité marquante" / Fiche = "Activité hydroélectrique, barrage"
|
|
|
|
|
|
######## NOT NaN IN activ_humaine FOR NEXT ---> ###########
|
|
df_Rprocess.position.replace(df_Ppostion.nom.to_list(),df_Ppostion.index.to_list(), inplace=True)
|
|
df_Rprocess.activ_humaine.replace(df_ActHum.nom.to_list(),df_ActHum.id.to_list(), inplace=True)
|
|
df_Rprocess.impact.replace(df_imp.nom.to_list(),df_imp.id.to_list(), inplace=True)
|
|
######## WHAT activ_hum_autre ??? FOR NEXT ---> ###########
|
|
|
|
|
|
if not isin_bdd:
|
|
df_Rprocess.to_sql(
|
|
name='r_site_usageprocess',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !")
|
|
|
|
|
|
|
|
|
|
df_Rprocess[['usages_process', 'description']] = df_Rprocess['usages_process_natu'].str.split('\(', expand=True)
|
|
df_Rprocess['description'] = df_Rprocess['description'].str.replace('\)','', regex=True)
|
|
# Récupération des descriptions d'usages
|
|
process_describ = df_Rprocess.loc[~df_Rprocess.description.isna(), ['usages_process','description']]
|
|
process_describ['usages_process'] = process_describ['usages_process'].str.strip()
|
|
process_describ['description'] = process_describ['description'].str.strip()
|
|
process_describ['usages_process'] = process_describ['usages_process'].str[0].str.upper() + process_describ['usages_process'].str[1:].str.lower()
|
|
process_describ.drop_duplicates(inplace=True)
|
|
del df_Rprocess['description']
|
|
del df_Rprocess['usages_process_natu']
|
|
tmp = ['tourisme et loisirs', 'aérodrome, aéroport, héliport', 'extraction de granulats, mines', 'activité hydroélectrique, barrage']
|
|
d = df_Rprocess[['usages_process']]
|
|
splt = '|'.join([',', ' et '])
|
|
dd = d[~d.usages_process.str.lower().str.contains('|'.join(tmp))]
|
|
dd = dd.usages_process.str.split(splt).apply(pd.Series).stack()
|
|
d = pd.concat(
|
|
[d[d.usages_process.str.lower().str.contains('|'.join(tmp))],
|
|
pd.DataFrame(dd, columns=['usages_process'], index=dd.index).droplevel(1) ]
|
|
).sort_index()
|
|
del df_Rprocess['usages_process']
|
|
df_Rprocess = df_Rprocess.merge(d, on='id',how='left')
|
|
for col in df_Rprocess.columns:
|
|
if col != 'id_geom_site':
|
|
df_Rprocess[col] = df_Rprocess[col].str.strip()
|
|
df_Rprocess['usages_process'] = df_Rprocess['usages_process'].str[0].str.upper() + df_Rprocess['usages_process'].str[1:].str.lower()
|
|
df_Rprocess.drop_duplicates(inplace=True)
|
|
df_Rprocess.dropna(subset=['usages_process'], inplace=True)
|
|
df_Rprocess.reset_index(inplace=True)
|
|
df_Rprocess = df_Rprocess.groupby(['id','id_geom_site', 'usages_process', 'position'])['rmq_activ_hum'].apply(' '.join).str.strip()
|
|
df_Rprocess.replace(' ', ', ', inplace=True, regex=True)
|
|
df_Rprocess = df_Rprocess.reset_index(['id_geom_site', 'usages_process', 'position'])
|
|
df_Rprocess['description'] = None
|
|
for i, row in process_describ.iterrows():
|
|
df_Rprocess.loc[df_Rprocess.usages_process == row.usages_process,'description'] = row.description
|
|
df_Rprocess.usages_process.replace(
|
|
['Dépots', 'Dépôt sauvage', 'Atterissement', "Entretien plan d'eau", "Création plan d'eau", 'Déchèterie communale' ,"Canalisation d'eau", 'Surpiétinement'],
|
|
['Dépôts', 'Dépôts sauvages', 'Atterrissement', "Entretien de plan d'eau", "Création de plan d'eau", 'Déchèterie', 'Canalisation', 'Piétinement'],
|
|
regex=True,
|
|
inplace=True
|
|
)
|
|
df_Rprocess.usages_process.replace(['Canalisation', 'Step'], ["Canalisation d'eau", 'STEP'],
|
|
regex=True, inplace=True
|
|
)
|
|
df_Rprocess.impact.replace(
|
|
[', ,'], [', '],
|
|
regex=True, inplace=True
|
|
)
|
|
df_Rprocess.impact.replace({'':None}, inplace=True)
|
|
df_Rprocess.reset_index(inplace=True, drop=True)
|
|
df_Rprocess.index.name = 'id'
|
|
# Loop who slow code ...
|
|
for i, row in df_Rprocess[df_Rprocess.impact.isna()].iterrows():
|
|
# if row.id_geom_site==18:
|
|
# break
|
|
tmp = df_Rprocess.loc[(df_Rprocess.usages_process == row.usages_process) & (df_Rprocess.id_geom_site == row.id_geom_site) & (df_Rprocess.index != row.name)]
|
|
if not tmp.empty:
|
|
df_Rprocess.drop(row.name, inplace=True)
|
|
df_Rprocess.reset_index(inplace=True, drop=True)
|
|
df_Rprocess.index.name = 'id'
|
|
# df_Rprocess.loc[df_Rprocess.usages_process.str.contains("égout"), ['id_geom_site','usages_process', 'rmq_activ_hum', 'position']]
|
|
# Récupération de la liste des usages et process
|
|
df_process = pd.DataFrame(
|
|
{
|
|
'nom': df_Rprocess['usages_process'],
|
|
'description': df_Rprocess['description']
|
|
},
|
|
index = df_Rprocess[['usages_process', 'description']].index)
|
|
df_process.drop_duplicates(inplace=True)
|
|
df_process.sort_values('nom', inplace=True)
|
|
df_process.reset_index(inplace=True, drop=True)
|
|
df_process.index.name = 'id'
|
|
# Incrémentation des usages et process
|
|
# if not isin_bdd:
|
|
# df_process.to_sql(
|
|
# name='param_usageprocess',
|
|
# con = con_zh,
|
|
# schema='zones_humides',
|
|
# index=True,
|
|
# index_label='id',
|
|
# if_exists='append',
|
|
# )
|
|
# print("INSERT ... ok !")
|
|
# # Mise en forme des relations sites / usages et process
|
|
# df_Rprocess.position.replace(df_Ppostion.nom.to_list(),df_Ppostion.index.to_list(), inplace=True)
|
|
# df_Rprocess.usages_process.replace(df_process.nom.to_list(),df_process.index.to_list(), inplace=True)
|
|
# df_Rprocess.reset_index(inplace=True, drop=True)
|
|
# df_Rprocess.index.name = 'id'
|
|
# Incrémentation des relations sites / usages et process
|
|
# if not isin_bdd:
|
|
# df_Rprocess.to_sql(
|
|
# name='r_site_usageprocess',
|
|
# con = con_zh,
|
|
# schema='zones_humides',
|
|
# index=True,
|
|
# index_label='id',
|
|
# if_exists='append',
|
|
# )
|
|
# print("INSERT ... ok !")
|
|
|
|
|
|
|
|
d = df_process.copy()
|
|
d['nom'] = df_process['nom'].str.lower()
|
|
d
|
|
df_process = d
|
|
df_process.reset_index(drop=True, inplace=True)
|
|
df_process.index.name = 'id'
|
|
df_process[['nom','description']] = df_process['nom'].str.split('\(', expand=True)
|
|
df_process['nom'] = df_process['nom'].str.strip()
|
|
df_process['description'] = df_process['description'].str.replace('\)','', regex=True).str.strip()
|
|
d = df_process['nom'].str.split(' et ').apply(pd.Series).stack()
|
|
d = pd.DataFrame(d, columns=['nom'])
|
|
d.index.name = 'id'
|
|
del df_process['nom']
|
|
df_process = df_process.merge(d, on='id',how='left')
|
|
df_process['nom'] = df_process['nom'].str[0].str.upper() + df_process['nom'].str[1:]
|
|
df_process.drop_duplicates(inplace=True)
|
|
|
|
d = d['nom'].str.split([' et ']).apply(pd.Series).stack()
|
|
|
|
if not isin_bdd:
|
|
df_critDelm.to_sql(
|
|
name='r_site_usageprocess',
|
|
con = con_zh,
|
|
schema='zones_humides',
|
|
index=True,
|
|
index_label='id',
|
|
if_exists='append',
|
|
)
|
|
print("INSERT ... ok !") |