#!/usr/bin/env python3 # -*- coding: UTF-8 -*- #Nom : : recup_zh.py #Description : #Copyright : 2021, CEN38 #Auteur : Colas Geier #Version : 1.0 import re import pandas as pd import pandas_access as mdb import numpy as np from sqlalchemy.sql.expression import column from pycen import bdd from sqlalchemy import create_engine from geoalchemy2 import Geometry isin_bdd = True # Parametres bdd IN user = 'cen_admin' pwd = '#CEN38@venir' adr = '192.168.0.189' base = 'bd-cen-38' schema = 'zh' table = 'cr_cen38_zh_medwet_v2021' con = create_engine('postgresql+psycopg2://{0}:{1}@{2}/{3}'.format(user,pwd,adr,base), echo=False) bd = bdd.CEN( user = user, pwd = pwd, adr = adr, base = base # schema = schema ) # Parametres bdd OUT user_zh = 'postgres' pwd_zh = 'tutu' adr_zh = '192.168.60.10' base_zh = 'bd_cen' con_zh = create_engine('postgresql+psycopg2://{0}:{1}@{2}/{3}'.format(user_zh,pwd_zh,adr_zh,base_zh), echo=False) # Read MS access database db_file1 = '/home/colas/Documents/5_BDD/ZONES_HUMIDES/MEDWET_v1.mdb' db_file2 = '/home/colas/Documents/5_BDD/ZONES_HUMIDES/MEDWET_V2.mdb' df_med1 = mdb.read_table(db_file1, "SITEINFO") df_med2 = mdb.read_table(db_file2, "SITEINFO") # FILE = db_file2 # for tab in mdb.list_tables(FILE): # if tab not in ['SIG', 'List', 'Switchboard', 'Items'] and not tab.startswith(('DicGen','DIcGen')): # # df = mdb.read_table(FILE, tab, keep_default_na=False,skipinitialspace=True) # df = mdb.read_table(FILE, tab) # if 'SITE_COD' in df.columns or 'SIT_COD' in df.columns: # print(tab) # FILE = db_file2 # for tab in mdb.list_tables(FILE): # if tab not in ['SIG', 'List', 'Switchboard', 'Items'] and not tab.startswith(('DicGen','DIcGen')): # # df = mdb.read_table(FILE, tab, keep_default_na=False,skipinitialspace=True) # df = mdb.read_table(FILE, tab) # if 'ORG' in df.columns: # print(tab) df = bd.get_table( schema = schema, table = table) df.sort_values('site_code', inplace=True) df.auteur_fiche.fillna('Inconnu', inplace=True) df[['auteur_fiche','auteur_fiche_remarque']] = df[['auteur_fiche','auteur_fiche_remarque']].replace( ['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.',], ['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'], regex=True) # Récupération des structures df_structure = pd.DataFrame( df['organisme_auteur'].drop_duplicates(), ) df_structure.rename(columns={'organisme_auteur':'nom'}, inplace=True) # df_structure.drop( # labels=[ # # 1092,748,1088, # 17], # axis=0, # inplace=True) df_structure['nom_autres'] = None df_structure.loc[df_structure.nom == 'Acer campestre', 'nom_autres'] = 'ACER CAMPESTRE' df_structure.loc[df_structure.nom == 'FRAPNA Isère', 'nom_autres'] = 'Asso. FRAPNA' df_structure.loc[df_structure.nom == 'DRAC NATURE', 'nom_autres'] = 'Asso. Drac Nature' df_structure.loc[df_structure.nom == 'Comité Gère vivante', 'nom_autres'] = 'Asso. GERE VIVANTE' df_structure.nom.fillna('Inconnu', inplace=True) df_structure.reset_index(inplace=True, drop=True) # Envoie des structures en bdd if not isin_bdd: df_structure['nom'].to_sql( name='organisme', con = con_zh, schema='personnes', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Correction des structures dans le df global df.organisme_auteur.fillna('Inconnu', inplace=True) for d,j in df_structure[~df_structure.nom_autres.isna()].iterrows(): df.loc[df.organisme_auteur==j.nom_autres, 'organisme_auteur'] = j.nom # Récupération des personnes df_pers = df[['auteur_fiche', 'organisme_auteur']].drop_duplicates() df_pers.auteur_fiche.fillna('Inconnu', inplace=True) tmp = [i.split('&') for i in df_pers['auteur_fiche'].dropna().unique() ] lst_pers = [item for sublist in tmp for item in sublist] tmp = pd.DataFrame(data=lst_pers, columns=['nom_prenom']) tmp['nom_prenom'] = tmp.nom_prenom.str.strip() tmp[['nom','prenom','autre']] = tmp['nom_prenom'].str.split(' ', 2, expand=True) for i,j in tmp[~tmp.autre.isna()].iterrows(): tmp.loc[tmp.nom==j.nom, 'nom'] = j.nom + ' ' + j.prenom tmp.loc[tmp.autre==j.autre, 'prenom'] = j.autre tmp.drop(columns=['nom_prenom', 'autre'], inplace=True) tmp['organisme'] = None for nom in tmp.nom: orga = df_pers.loc[df_pers.auteur_fiche.str.contains(nom),'organisme_auteur'] orga = orga.unique() tmp.loc[tmp.nom == nom,'organisme'] = orga[0] tmp['id_organisme'] = tmp['organisme'] tmp['id_organisme'] = tmp['id_organisme'].replace(df_structure.nom.to_list(),df_structure.index.to_list()) tmp.nom = tmp.nom.str.upper() tmp.drop_duplicates(inplace=True) df_pers = tmp df_pers.drop(columns='organisme', inplace=True) df_pers.reset_index(inplace=True, drop=True) # Envoie des personnes en bdd if not isin_bdd: df_pers.to_sql( name='personne', con = con_zh, schema='personnes', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Correction des personnes dans le df global df_pers = pd.read_sql_table( table_name='personne', con = con_zh, schema='personnes', index_col='id', ) # df.auteur_fiche.fillna('Inconnu', inplace=True) # df[['auteur_fiche','auteur_fiche_remarque']] = df[['auteur_fiche','auteur_fiche_remarque']].replace( # ['Biron N.','Balmain C.','Feuvrier B.','Souvignet N.','Billard G.','BELLUT', 'C. Balmain','E. JOURDAN','E. Jordan','Juton M.','P. Bellut' ], # ['BIRON Nicolas','BALMAIN Céline','FEUVRIER Benoit','SOUVIGNET Nicolas','BILLARD Gilbert','BELLUT P.','BALMAIN Céline','JOURDAN Elise','JOURDAN Elise','JUTON Mathieu','BELLUT P.'], # regex=True) df_pers['nom_prenom'] = df_pers.nom.str[0] + df_pers.nom.str[1:].str.lower() + ' ' + df_pers.prenom for pers in df_pers.nom_prenom.dropna(): val = df_pers[df_pers.nom_prenom == pers].index[0] val = str(val) df.auteur_fiche = df.auteur_fiche.str.replace(pers,val, regex=True) df_pers['nom_prenom'] = df_pers.nom + ' ' + df_pers.prenom for pers in df_pers.nom_prenom.dropna(): val = df_pers[df_pers.nom_prenom == pers].index[0] val = str(val) df.auteur_fiche = df.auteur_fiche.str.replace(pers,val, regex=False) df.auteur_fiche.replace(df_pers.nom.to_list(),df_pers.index.to_list(), inplace=True) NOM = df_pers.nom.str[0] + df_pers.nom.str[1:].str.lower() df.auteur_fiche.replace(NOM.to_list(),NOM.index.to_list(), inplace=True) df.loc[df.auteur_fiche == 'SETIS Groupe Degaud', 'auteur_fiche'] = df_pers.loc[df_pers.prenom == 'Degaud'].index[0] # Récupération des sites df_site = df[['site_code', 'date_init', 'name_zone', 'auteur_fiche', 'typo_sdage', ]] df_site = df_site.rename(columns={ 'site_code': 'id', 'date_init': 'date_deb', 'name_zone': 'nom', 'auteur_fiche': 'id_auteur', 'typo_sdage': 'id_typo_sdage' }) df_site.sort_values('date_deb', inplace=True) df_site.reset_index(inplace=True, drop=True) typ_sdage = pd.read_sql_table( table_name = 'typo_sdage', con = con_zh, schema = 'sites', index_col = 'id', ) typ_milieu = pd.read_sql_table( table_name = 'type_milieu', con = con_zh, schema = 'sites', index_col = 'id', ) typ_site = pd.read_sql_table( table_name = 'type_site', con = con_zh, schema = 'sites', index_col = 'id', ) df_site.id_typo_sdage.replace(typ_sdage.nom.str.lower().to_list(),typ_sdage.index.to_list(), inplace=True) df_site.id_typo_sdage.fillna(typ_sdage[typ_sdage.nom.str.lower() == 'inconnu'].index[0], inplace=True) 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] 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] 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] 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] df_site['id_type_milieu'] = typ_milieu[typ_milieu.nom_court.str.contains('humides')].index[0] df_site['id_type_site'] = typ_site[typ_site.nom == 'N.D.'].index[0] df_site.set_index('id', inplace=True) df_site.date_deb.fillna('0001-01-01', inplace=True) df_site.nom.fillna('Inconnu', inplace=True) # Envoie des sites en bdd if not isin_bdd: df_site.to_sql( name='sites', con = con_zh, schema='sites', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Récupération des géometries df_geomsite = df[['site_code', 'geom', 'date_der_modif', 'link_pdf', 'fonction_majeur', 'interet_patri', 'bilan_menaces', 'orient_act', 'usages_process_natu_comm' ]] df_geomsite = df_geomsite.rename(columns={ 'site_code': 'id_site', 'date_der_modif': 'date', 'fonction_majeur': 'rmq_fct_majeur', 'interet_patri': 'rmq_interet_patri', 'bilan_menaces': 'rmq_bilan_menace', 'orient_act': 'rmq_orient_act', 'usages_process_natu_comm': 'rmq_usage_process' }) df_geomsite = df_geomsite.merge(df_site[['id_auteur']].reset_index(), left_on='id_site', right_on='id') df_geomsite.drop(columns=['id'], inplace=True) df_geomsite.date.fillna('0001-01-01', inplace=True) df_geomsite.reset_index(inplace=True, drop=True) # Envoie des géometries en bdd if not isin_bdd: df_geomsite.to_postgis( name='r_sites_geom', con = con_zh, schema='sites', index=True, index_label='id', if_exists='append', geom_col='geom' ) print("INSERT ... ok !") df_rgsite = df_geomsite[['id_site']] df_rgsite.index.name = 'id' df_rgsite.reset_index(inplace=True) # Récupération des types de connexions df_typconex = df[['connex_type']] df_typconex = df_typconex.rename(columns={ 'connex_type': 'nom' }) df_typconex.dropna(inplace=True) df_typconex = pd.DataFrame(df_typconex.nom.unique(), columns=['nom']) df_typconex = pd.concat( [ pd.DataFrame(['inconnu'], columns=['nom']),df_typconex ], ignore_index=True) df_typconex['nom'] = df_typconex.nom.str[0].str.upper() + df_typconex.nom.str[1:] # Envoie des types de connexions en bdd if not isin_bdd: df_typconex.to_sql( name='param_type_connect', con = con_zh, schema='zones_humides', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Récupération des relations sites / types de connexions df_Rtypconex = df[['site_code','connex_type']] df_Rtypconex = pd.merge(df_Rtypconex, df_rgsite, left_on='site_code', right_on='id_site') df_Rtypconex = df_Rtypconex.rename(columns={ 'id': 'id_geom_site', 'connex_type': 'id_param_connect' }) df_Rtypconex.id_param_connect.fillna('inconnu', inplace=True) df_Rtypconex.id_param_connect.replace(df_typconex.nom.str.lower().to_list(),df_typconex.index.to_list(), inplace=True) df_Rtypconex.drop(columns=['site_code', 'id_site'], inplace=True) # Envoie des relations sites / types de connexions if not isin_bdd: df_Rtypconex.to_sql( name='r_site_type_connect', con = con_zh, schema='zones_humides', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Incrémentation des types de paramettres fctEcoSocioPatri d = { 'nom': ['Fonctions hydroligiques', 'Fonctions biologiques', 'Valeurs socio-économiques', 'Interêt patrimonial',], 'nom_court': ['fct_hydro', 'fct_bio', 'val_socioEco', 'int_patri']} df_typFct = pd.DataFrame(data=d) if not isin_bdd: df_typFct.to_sql( name='type_param_fct', con = con_zh, schema='zones_humides', index=True, index_label='id', if_exists='append', ) print("INSERT ... ok !") # Récupération des fct Hydro, Bio, Socio-eco, Patri columns_fct = ['fct_bio', 'val_socio_eco', 'int_patri', 'fct_hydro'] df_rSiteFct = df[['site_code'] + columns_fct ] df_rSiteFct = pd.merge(df_rSiteFct, df_rgsite, left_on='site_code', right_on='id_site') df_rSiteFct = df_rSiteFct.rename(columns={ 'id': 'id_geom_site', }) df_rSiteFct.drop(columns=['site_code', 'id_site'], inplace=True) df_rSiteFct.index.name = 'id' # df_rSiteFct.dropna(axis=0,subset=columns_fct, inplace=True) lst_df = {} for col in columns_fct: print(col) lst_df[col] = df_rSiteFct[['id_geom_site', col]] d = lst_df[col][col].str.split('//').apply(pd.Series).stack() d = pd.DataFrame(d, columns=[col]) d.index.name = 'id' del lst_df[col][col] lst_df[col] = lst_df[col].merge(d, on='id',how='left') lst_df[col][[col, col+'_rmq']] = lst_df[col][col].str.split('; Justification :', expand=True) lst_df[col][col] = lst_df[col][col].str.replace('Critère :','') lst_df[col][col+'_rmq'] = lst_df[col][col+'_rmq'].str.replace('Justification :','') lst_df[col][col] = lst_df[col][col].str.strip() lst_df[col][col+'_rmq'] = lst_df[col][col+'_rmq'].str.strip() lst_df[col].dropna(subset=[col], inplace=True) # Isolement des paramètres des fct Hydro, Bio, Socio-eco, Patri df_paramFct = pd.DataFrame(columns=['nom', 'type']) for col in columns_fct: # x = lst_df[col][col].drop_duplicates().dropna() # y = pd.Series([col]*len(x)) x = lst_df[col][col].drop_duplicates().dropna().tolist() y = [col]*len(x) xy = {'nom': x, 'type': y} xy = pd.DataFrame(data=xy) df_paramFct = df_paramFct.append(xy, ignore_index=True) # Incrémentation des paramètres des fct Hydro, Bio, Socio-eco, Patri df_paramFct['id_type'] = df_paramFct.type.copy() 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 !")