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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+DATA.xlsx filter=lfs diff=lfs merge=lfs -text
+data2.xlsx filter=lfs diff=lfs merge=lfs -text

DATA.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88c4851deeb7b111070b9df7ed19e4b6dac93536c5ff5ba703a589bbc18b3c49
+size 3162873

Dashboard.py

@@ -0,0 +1,512 @@
+import streamlit as st
+import pandas as pd
+from streamlit_option_menu import option_menu
+from streamlit_echarts import st_pyecharts
+from pyecharts.charts import Bar
+from pyecharts.charts import Pie
+from pyecharts import options as opts
+
+liste_vaccin = ['YELLOW FEVER', 'Penta', 'VPO', 'VPI', 'nOPV2', 'TD','Mosquirix', 'PCV13', 'MEASLES', 'BCG', 'Janssen', 'Rota', 'MENINGO', 'HPV', 'Mencevax']
+
+region_district = {
+    "ADAMAOUA" : ["Bankim","Banyo","Belel","Dang","Djohong","Meiganga","Ngaoundal","Ngaoundere Rural","Ngaoundere Urbain","Tibati","Tignere"],
+    "CENTRE" : ["Akonolinga","Awae","Ayos","Bafia","Biyem Assi","Cite Verte","Djoungolo","Ebebda","Efoulan","Elig Mfomo","Eseka","Esse","Evodoula","Mbalmayo","Mbandjock","Mbankomo","Mfou","Monatele","Mvog-Ada","Nanga Eboko","Ndikinimeki","Ngog Mapubi","Ngoumou","Nkolbisson","Nkolndongo","Ntui","Obala","Odza","Okola","Sa'a","Soa","Yoko"],
+    "EST": ["Abong_Mbang","Batouri","Belabo","Bertoua","Betare_Oya","Doume","Garoua_Boulai","Kette","Lomie","Mbang","Messamena","Moloundou","Ndelele","Nguelemendouka","Yokadouma"],
+    "EXTREME NORD" : ["Bogo","Bourha","Fotokol","Gazawa","Goulfey","Guere","Guidiguis","Hina","Kaele","Kar Hay","Kolofata","Kousseri","Koza","Mada","Maga","Makary","Maroua 1","Maroua 2","Maroua 3","Meri","Mindif","Mogode","Mokolo","Mora","Moulvoudaye","Moutourwa","Mozogo","Pette","Roua","Tokombere","Vele","Waza","Yagoua"],
+    "LITTORAL": ["Abo","Bangue","Boko","Bonassama","Cite Des Palmiers","Deido","Dibombari","Edea","Japoma","Logbaba","Loum","Manjo","Manoka","Mbanga","Melong","Ndom","New Bell","Ngambe","Njombe Penja","Nkondjock","Nkongsamba","Nylon","Pouma","Yabassi"],
+    "NORD" : ["Bibemi","Figuil","Garoua I","Garoua II","Gaschiga","Golombe","Guider","Lagdo","Mayo Oulo","Ngong","Pitoa","Poli","Rey Bouba","Tchollire","Touboro"],
+    "NORD OUEST" : ["Ako","Bafut","Bali","Bamenda","Bamenda 3","Batibo","Benakuma","Fundong","Kumbo East","Kumbo West","Mbengwi","Misaje","Ndop","Ndu","Njikwa","Nkambe","Nwa","Oku","Santa","Tubah","Wum"],
+    "OUEST" : ["Bafang","Baham","Bamendjou","Bandja","Bandjoun","Bangangte","Bangourain","Batcham","Dschang","Foumban","Foumbot","Galim","Kekem","Kouoptamo","Malantouen","Massangam","Mbouda","Mifi","Penka Michel","Santchou"],
+    "SUD" : ["Ambam","Djoum","Ebolowa","Kribi","Kye-ossi","Lolodorf","Meyomessala","Mintom","Mvangan","Niete","Olamze","Sangmelima","Zoetele"],
+    "SUD OUEST": ["Akwaya","Bakassi","Bamusso","Bangem","Buea","Ekondo Titi","Eyumodjock","Fontem","Konye","Kumba-North","Kumba-South","Limbe","Mamfe","Mbonge","Mundemba","Muyuka","Nguti","Tiko","Toko","Tombel","Wabane"]
+}
+
+list_tranche_dage = ["[0-1[","[01-05[","[05-15[","[15-18[","[18-25[","[25-35[","[35-45[","[45-55[","[55-65[","65etPlus"]
+
+nav_bar1 = option_menu(None,
+                   ["global", "Resume"],
+                    icons=['file-earmark-bar-graph-fill'],
+                    menu_icon="cast",
+                    default_index=0,
+                    orientation="horizontal")
+
+# Initialization
+if 'data' not in st.session_state:
+    st.session_state['data'] = None
+
+if 'dataframe_mappi_region' not in st.session_state:
+    st.session_state['dataframe_mappi_region'] = None
+
+if st.session_state['data'] is None:
+    st.warning("Veuillez entrer les données")
+else:
+
+    data = st.session_state['data']
+    temp = liste_vaccin.copy()
+    temp.append("Global")
+
+    if nav_bar1 == "Resume":
+        ## resume mappi grave
+        # filtre des donnees pour garder les mappi grave
+        filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)]  
+        filtered_df_2 = filtered_df[filtered_df['seriousness/serious'] == 1.0]
+        df = (filtered_df_2[["admininfo/states","admininfo/district",'patien',"admininfo/safetyreportid","Age","Tranche_Age","Sexe","type_Vaccin", "event/reactionstartdate", "event/primarysourcereaction","reporter/reportergivename", "reporter/reportertel", "reporter/reporteremail", "_index",
+                "Semaine_Epid","seriousness/seriousnessdeath", "healthproduct/drugadmin/intervention"]])
+        df['devenir'] = df["seriousness/seriousnessdeath"].apply(lambda x: 'décédé' if x == 1.0 else 'vivant')
+        df.loc[df["type_Vaccin"] == "nOPV2", "healthproduct/drugadmin/intervention"] = "masscampaign"
+        df["event/reactionstartdate"] = pd.to_datetime(df["event/reactionstartdate"], origin="1899-12-30", unit="D").dt.date
+        
+        df = df.rename(columns={"admininfo/states": "REGIONS"})
+        df = df.rename(columns={"admininfo/district": "DISTRICT"})
+        df = df.rename(columns={"patien": "INITIAUX DU PATIENT"})
+        df = df.rename(columns={"admininfo/safetyreportid": "NUM EPID"})
+        df = df.rename(columns={"Age": "AGE"})
+        df = df.rename(columns={"Tranche_Age": "TRANCHE AGE"})
+        df = df.rename(columns={"Sexe": "SEXE"})
+        df = df.rename(columns={"type_Vaccin": "TYPE VACCIN"})
+        df = df.rename(columns={"event/reactionstartdate": "DATE DEBUT REACTION"})
+        df = df.rename(columns={"event/primarysourcereaction": "PREMIERS SIGNES"})
+        df = df.rename(columns={"reporter/reportergivename": "NOTIFICATEURS"})
+        df = df.rename(columns={"reporter/reportertel": "NUMERO NOTIFICATEURS"})
+        df = df.rename(columns={"reporter/reporteremail": "ADRESSE NOTIFICATEURS"})
+        df = df.rename(columns={"_index": "NUMERO INDEX"})
+        df = df.rename(columns={"Semaine_Epid": "SEMAINE EPI"})
+        df = df.rename(columns={"healthproduct/drugadmin/intervention": "CIRCONSTANCE DE VACCINATION"})
+        df = df.rename(columns={"devenir": "DEVENIR"})
+
+        st.write(df[["REGIONS","DISTRICT",'INITIAUX DU PATIENT',"NUM EPID","AGE","TRANCHE AGE","SEXE","TYPE VACCIN","DATE DEBUT REACTION", "PREMIERS SIGNES","NOTIFICATEURS", "NUMERO NOTIFICATEURS", "ADRESSE NOTIFICATEURS", "NUMERO INDEX",
+                     "SEMAINE EPI", "CIRCONSTANCE DE VACCINATION", "DEVENIR"]])
+        st.write("___")
+        col1,col2, col3, col4, col5 = st.columns([1,1,1,2,2])
+        with col1:
+            nbr_region_contnair = st.container(border=True)
+            with nbr_region_contnair:
+                st.write("Nombre de regions")
+                st.subheader(len(list(df["REGIONS"].unique())))
+        
+        with col2:
+            nbr_district_contnair = st.container(border=True)
+            with nbr_district_contnair:
+                st.write("Nombre de district")
+                st.subheader(len(list(df["DISTRICT"].unique())))
+
+        with col3:
+            nbr_cas_contnair = st.container(border=True)
+            with nbr_cas_contnair:
+                st.write("Nombre de cas")
+                st.subheader(df.shape[0])
+
+        with col4:
+            repartition_sex_contnair = st.container(border=True)
+            with repartition_sex_contnair:
+                st.write("Repartitions pas sexe")
+                st.subheader(f"{len(df[df.SEXE == 'Masculin'])} Hommes / {len(df[df.SEXE == 'Féminin'])} Femmes")
+
+        with col5:
+            nbr_mort_contnair = st.container(border=True)
+            with nbr_mort_contnair:
+                st.write("Rapport mort/survivant")
+                st.subheader(f"{df[df.DEVENIR == 'décédé'].shape[0]} mort / {df[df.DEVENIR != 'décédé'].shape[0]} survivants")
+
+        col1, col2 = st.columns([2.3,4])
+        with col1:
+            circonstance_vaccination_contnair = st.container(border=True)
+            with circonstance_vaccination_contnair:
+                st.write("Circonstances de vaccination")
+                st.subheader(f"{len(df[df['CIRCONSTANCE DE VACCINATION'] == 'masscampaign'])} masscampaign / {len(df[df['CIRCONSTANCE DE VACCINATION'] != 'masscampaign'])} routineimmunization")
+
+        with col2:
+            reppartition_type_vaccin = st.expander("Repartission des vaccins",expanded=True)
+            with reppartition_type_vaccin:
+                for i in sorted(list(df["TYPE VACCIN"].unique())):
+                    st.write(f"{i} : {df[df['TYPE VACCIN'] == i].shape[0]}")
+    else:
+
+
+    
+        vaccin_type = st.selectbox("Choissisez le type de vaccin", options= temp, index=len(temp)-1)
+
+        # filtre de donnees pour isolees les mappi
+        filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)]      
+        if vaccin_type == "Global":
+            filtered_df_2 = filtered_df
+        else:
+            # filtre les donnees par vaccin
+            filtered_df_2 = filtered_df[filtered_df["type_Vaccin"] == vaccin_type]
+
+        # affichage de la carte et des indicateur par type de vaccin
+        col1, col2 = st.columns([4,6])
+        with col1:
+            filtre_carte = st.pills("filtre de carte", options=["Mappi grave", "Mappi non grave"], label_visibility="collapsed")
+            if filtre_carte is None:
+                st.map(filtered_df_2, latitude= "_gps_beginning_latitude", longitude= "_gps_beginning_longitude", use_container_width=True, height=608)
+            elif filtre_carte == "Mappi grave":
+                filtered_df_5 = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0]
+                st.map(filtered_df_5, latitude= "_gps_beginning_latitude", longitude= "_gps_beginning_longitude", use_container_width=True, height=608)
+            elif filtre_carte == "Mappi non grave":
+                filtered_df_5 = filtered_df_2[filtered_df_2["seriousness/serious"] == 2.0]
+                st.map(filtered_df_5, latitude= "_gps_beginning_latitude", longitude= "_gps_beginning_longitude", use_container_width=True, height=608)
+            # affichage des card (nbr mappi , nbr mappi grave, nbr mappi non grave)
+
+        with col2:
+            col = st.columns(4)
+            nbr_mappi = filtered_df_2.shape[0]
+            nbr_mappi_grave = filtered_df_2[ (filtered_df_2['seriousness/serious'] == 1.0) ].shape[0]
+            nbr_mappi_non_grave = filtered_df_2[ (filtered_df_2['seriousness/serious'] == 2.0) ].shape[0]
+            nbr_desces = filtered_df_2[filtered_df_2["seriousness/seriousnessdeath"] == 1.0].shape[0]
+            # nbr mappi
+            with col[0]:
+                nbr_mappi_card = st.container(border=True)
+                with nbr_mappi_card :
+                    st.markdown("Nombre de MAPPI")
+                    st.header(f"{nbr_mappi}")
+            # nbr mappi grave
+            with col[1]:
+                nbr_mappi_grave_card = st.container(border=True)
+                with nbr_mappi_grave_card:
+                    st.markdown("Nombre de MAPPI grave")
+                    st.header(f"{nbr_mappi_grave}")
+            # nbr mappi non grave
+            with col[2]:
+                nbr_mappi_non_grave_card = st.container(border=True)
+                with nbr_mappi_non_grave_card:
+                    st.markdown("Nombre de MAPPI non grave")
+                    st.header(f"{nbr_mappi_non_grave}")
+            # nbr desces
+            with col[3]:
+                nbr_desces_card = st.container(border=True)
+                with nbr_desces_card:
+                    st.markdown("Nombre de desces")
+                    st.header(f"{nbr_desces}")
+
+            # graphe repartition des mappi
+            graph_mappi = st.container(border=True)
+            with graph_mappi:
+                col1, col2, col3, col4 = st.columns([1,1,1,1])
+                with col1:
+                    filtre_1 = st.pills(" ", options=["Distribution"], label_visibility="collapsed")
+                with col2:    
+                    filtre_2 = st.toggle("Sexe")
+                with col3:    
+                    filtre_3 = st.toggle("Tranche d'age")
+                with col4:
+                    if filtre_1 is None:
+                        filtre = st.pills("", options=["Par region", "Par semaine vaccinal"], label_visibility= "collapsed")
+                    else:
+                        filtre = st.pills("", options=["Par region", "Par semaine vaccinal"], label_visibility= "collapsed", disabled=True)
+
+                if filtre is None and filtre_1 is None:
+                    if filtre_2 == True and filtre_3 == False:
+                        # filtrer par sexe
+                        # mappi grave
+                        filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0]
+                        mappi_grave_homme = filtered_df_3[filtered_df_3["Sexe"] == "Masculin"].shape[0]
+                        mappi_grave_femme = filtered_df_3[filtered_df_3["Sexe"] != "Masculin"].shape[0]
+                        # mappi non grave
+                        filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 2.0]
+                        mappi_grave_non_homme = filtered_df_3[filtered_df_3["Sexe"] == "Masculin"].shape[0]
+                        mappi_grave_non_femme = filtered_df_3[filtered_df_3["Sexe"] != "Masculin"].shape[0]
+                        # desces
+                        filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/seriousnessdeath"] == 1.0]
+                        desces_homme = filtered_df_3[filtered_df_3["Sexe"] == "Masculin"].shape[0]
+                        desces_femme = filtered_df_3[filtered_df_3["Sexe"] != "Masculin"].shape[0]
+
+                        ## afficher les graphes
+                        col = st.columns(2)
+                        with col[0]:
+                            st.subheader("Homme")
+                            Nbr_mappi_type_vaccin = (
+                                Bar()
+                                .add_xaxis(["Mappi grave", "Mappi non grave", "Desces"])  # Assurez-vous que c'est une liste
+                                .add_yaxis("Nombre de MAPPI grave", [mappi_grave_homme, mappi_grave_non_homme, desces_homme])
+                            )
+                            # Affichage du graphique
+                            st_pyecharts(Nbr_mappi_type_vaccin, height="330px")
+
+                        with col[1]:
+                            st.subheader("Femme")
+                            Nbr_mappi_type_vaccin = (
+                                 Bar()
+                                 .add_xaxis(["Mappi grave", "Mappi non grave", "Desces"])  # Assurez-vous que c'est une liste
+                                 .add_yaxis("Nombre de MAPPI grave", [mappi_grave_femme, mappi_grave_non_femme, desces_femme])
+                             )
+                             # Affichage du graphique
+                            st_pyecharts(Nbr_mappi_type_vaccin, height="303px")
+
+                    elif filtre_3 == True and filtre_2 == False:
+                        _0_1_grave = None
+                        _1_5_grave = None
+                        _5_15_grave = None
+                        _15_18_grave = None
+                        _18_25_grave = None
+                        _25_35_grave = None
+                        _35_45_grave = None
+                        _45_55_grave = None
+                        _55_65_grave = None
+                        _65etPlus_grave = None
+
+                        _0_1_non_grave = None
+                        _1_5_non_grave = None
+                        _5_15_non_grave = None
+                        _15_18_non_grave = None
+                        _18_25_non_grave = None
+                        _25_35_non_grave = None
+                        _35_45_non_grave = None
+                        _45_55_non_grave = None
+                        _55_65_non_grave = None
+                        _65etPlus_non_grave = None
+
+                        filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0]
+                        filtered_df_4 = filtered_df_2[filtered_df_2["seriousness/serious"] == 2.0]
+
+                        for i in list_tranche_dage:
+                            _ = filtered_df_3[filtered_df_3.Tranche_Age == f"{i}"].shape[0]
+                            if i == '[0-1[':
+                                _0_1_grave = _
+                            elif i == '[01-05[':
+                                _1_5_grave = _
+                            elif i == '[05-15[':
+                                _5_15_grave = _
+                            elif i == '[15-18[':
+                                _15_18_grave = _
+                            elif i == '[18-25[':
+                                _18_25_grave = _
+                            elif i == '[25-35[':
+                                _25_35_grave = _
+                            elif i == '[35-45[':
+                                _35_45_grave = _
+                            elif i == '[45-55[':
+                                _45_55_grave = _
+                            elif i == '[55-65[':
+                                _55_65_grave = _
+                            elif i == '65etPlus':
+                                _65etPlus_grave = _
+
+                        for i in list_tranche_dage:
+                            _ = filtered_df_4[filtered_df_4.Tranche_Age == f"{i}"].shape[0]
+                            if i == '[0-1[':
+                                _0_1_non_grave = _
+                            elif i == '[01-05[':
+                                _1_5_non_grave = _
+                            elif i == '[05-15[':
+                                _5_15_non_grave = _
+                            elif i == '[15-18[':
+                                _15_18_non_grave = _
+                            elif i == '[18-25[':
+                                _18_25_non_grave = _
+                            elif i == '[25-35[':
+                                _25_35_non_grave = _
+                            elif i == '[35-45[':
+                                _35_45_non_grave = _
+                            elif i == '[45-55[':
+                                _45_55_non_grave = _
+                            elif i == '[55-65[':
+                                _55_65_non_grave = _
+                            elif i == '65etPlus':
+                                _65etPlus_non_grave = _
+
+                        col = st.columns(2)  
+                        with col[0]:  
+                            # Création du graphique
+                            Nbr_mappi_tranche_age_grave = (
+                                Bar()
+                                .add_xaxis(list_tranche_dage)
+                                .add_yaxis(
+                                    "Mappi grave",
+                                    [_0_1_grave, _1_5_grave, _5_15_grave, _15_18_grave, _18_25_grave, 
+                                     _25_35_grave, _35_45_grave, _45_55_grave, _55_65_grave, _65etPlus_grave]
+                                )
+                            )
+
+                            # Affichage avec Streamlit
+                            st_pyecharts(Nbr_mappi_tranche_age_grave, height="370px")
+                        with col[1]:
+                            Nbr_mappi_tranche_age_grave = (
+                                Bar()
+                                .add_xaxis(list_tranche_dage)
+                                .add_yaxis(
+                                    "Mappi non grave",
+                                    [_0_1_non_grave, _1_5_non_grave, _5_15_non_grave, _15_18_non_grave, _18_25_non_grave, 
+                                     _25_35_non_grave, _35_45_non_grave, _45_55_non_grave, _55_65_non_grave, _65etPlus_non_grave]
+                                )
+                            )
+                            # Affichage avec Streamlit
+                            st_pyecharts(Nbr_mappi_tranche_age_grave, height="370px")
+                    else:
+                        Nbr_mappi_type_vaccin = (
+                            Bar()
+                            .add_xaxis([vaccin_type])  # Assurez-vous que c'est une liste
+                            .add_yaxis("Nombre de MAPPI grave", [filtered_df_2[filtered_df_2['seriousness/serious'] == 1.0].shape[0]])
+                            .add_yaxis("Nombre de MAPPI non grave", [filtered_df_2[filtered_df_2['seriousness/serious'] == 2.0].shape[0]], itemstyle_opts=opts.ItemStyleOpts(color="#F1F54F"))
+                            .add_yaxis("Nombre decses", [filtered_df_2[filtered_df_2["seriousness/seriousnessdeath"] == 1.0].shape[0]], itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                        )
+                        # Affichage du graphique
+                        st_pyecharts(Nbr_mappi_type_vaccin, height="370px")
+
+                elif filtre == "Par region" and filtre_1 is None:
+                    ## recalcule des elements par region
+                    # donnees selon le vaccin "filtered_df_2"
+                    # liste des mappi grave par region
+                    mappi_grave = []
+                    mappi_non_grave = []
+                    desces = []
+                    for i in list(region_district.keys()):
+                       mappi_grave.append(filtered_df_2[(filtered_df_2['seriousness/serious'] == 1.0) & (filtered_df_2["admininfo/states"] == f"{i}")].shape[0])
+                       mappi_non_grave.append(filtered_df_2[(filtered_df_2['seriousness/serious'] == 2.0) & (filtered_df_2["admininfo/states"] == f"{i}")].shape[0])
+                       desces.append(filtered_df_2[(filtered_df_2['seriousness/seriousnessdeath'] == 1.0) & (filtered_df_2["admininfo/states"] == f"{i}")].shape[0])
+                    # afficher le graphe
+                    district_silencieux_bar = (
+                            Bar()
+                            .add_xaxis(list(region_district.keys()))
+                            .add_yaxis("MAPPI NON GRAVE", mappi_non_grave, itemstyle_opts=opts.ItemStyleOpts(color="#F1F54F"))
+                            .add_yaxis("MAPPI GRAVE",mappi_grave, itemstyle_opts=opts.ItemStyleOpts(color="#ECA412"))
+                            .add_yaxis("Desces", desces, itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                            .set_global_opts(title_opts=opts.TitleOpts(title="Distribution des Régions"))
+                        )
+                        # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_bar, height="370px")
+
+                elif filtre == "Par semaine vaccinal" and filtre_1 is None:
+                    # repartition des MAAPI par semaines vaccinal
+                    semaine_epidemiologique = list(filtered_df["Semaine_Epid"].unique())
+                    semaine_epidemiologique.sort()
+                    _ = []
+                    for i in semaine_epidemiologique:
+                        if vaccin_type == "Global":
+                           temp = filtered_df_2[(filtered_df_2["Semaine_Epid"] == f"{i}")] 
+                        else:
+                            temp = filtered_df_2[(filtered_df_2["Semaine_Epid"] == f"{i}") & (filtered_df_2["type_Vaccin"] == vaccin_type)]
+                        _.append(temp.shape[0])
+                    # evolution des mappi par semaine vaccinal
+                    MAPPI_semaine_vaccinal_bar = (
+                            Bar()
+                            .add_xaxis(semaine_epidemiologique)
+                            .add_yaxis("Nombre d'apparitions", _)
+                            .set_global_opts(title_opts=opts.TitleOpts(title="Nombre de MAPPI par semaines vaccinal"))
+                    )
+                    st_pyecharts(MAPPI_semaine_vaccinal_bar, width="100%", height="370px")
+
+                elif filtre_1 is not None:
+                    # Définition des catégories et valeurs
+                    labels = ["MAPPI grave", "MAPPI non grave", "Décès"]
+                    values = [
+                        filtered_df_2[filtered_df_2['seriousness/serious'] == 1.0].shape[0],
+                        filtered_df_2[filtered_df_2['seriousness/serious'] == 2.0].shape[0],
+                        filtered_df_2[filtered_df_2["seriousness/seriousnessdeath"] == 1.0].shape[0]
+                    ]               
+
+                    # Création du Pie Chart
+                    pie_chart = (
+                        Pie()
+                        .add("", [list(z) for z in zip(labels, values)])
+                        .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {d}%"))  # Affichage des pourcentages
+                    )               
+
+                    # Affichage du graphique dans Streamlit
+                    st_pyecharts(pie_chart, height="370px")
+        st.write("___")
+        # gestion des district silentieux
+        col1,col2= st.columns([3.5,2])
+        with col1:
+            compte_district_silencieux = st.container(border=True)
+            with compte_district_silencieux:
+                #distribution = st.pills("filtre distribution", options=["Distribution"], label_visibility="collapsed", key= 1)
+                if vaccin_type == "Global":
+                    region = []
+                    district = []
+                    for i in list(region_district.keys()):
+                        for k in region_district[f"{i}"] :
+                            if data[data["admininfo/district"] == f"{k}".upper()].shape[0] == 0 :
+                                region.append(i)
+                                district.append(k)
+                    district_silencieux = {
+                        "Region" : region,
+                        "District": district
+                    }
+                    district_silencieux = pd.DataFrame(district_silencieux)
+                    region_counts = district_silencieux.Region.value_counts().to_dict()
+                    regions = list(region_counts.keys())
+                    counts = list(region_counts.values())
+                    # Création du graphique
+                    # bar chart
+                    district_silencieux_bar = (
+                        Bar()
+                        .add_xaxis(regions)
+                        .add_yaxis("Nombre d'apparitions", counts)
+                        .set_global_opts(title_opts=opts.TitleOpts(title="Repartition district silencieux"))
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_bar, height="534px")
+                else:
+                    region = []
+                    district = []
+                    for i in list(region_district.keys()):
+                        for k in region_district[f"{i}"] :
+                            if filtered_df_2[filtered_df_2["admininfo/district"] == f"{k}".upper()].shape[0] == 0 :
+                                region.append(i)
+                                district.append(k)
+                    district_silencieux = {
+                        "Region" : region,
+                        "District": district
+                    }
+                    district_silencieux = pd.DataFrame(district_silencieux)
+                    region_counts = district_silencieux.Region.value_counts().to_dict()
+                    regions = list(region_counts.keys())
+                    counts = list(region_counts.values())
+                    # Création du graphique
+                    # bar chart
+                    district_silencieux_bar = (
+                        Bar()
+                        .add_xaxis(regions)
+                        .add_yaxis("Nombre d'apparitions", counts)
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_bar, height="534px")
+
+        with col2:
+            # carte du nombre de district silencieux
+            col = st.columns(2)
+            with col[0]:
+                nbr_district_silencieux_card = st.container(border=True)
+                with nbr_district_silencieux_card:
+                    st.write("Nombre de district manquant")
+                    st.subheader(f"{district_silencieux.shape[0]}")
+
+            with col[1]:
+                taux_dabstention_card = st.container(border=True)
+                with taux_dabstention_card:
+                    st.write("Taux de district manquant")
+                    st.subheader(f"{round((district_silencieux.shape[0]/205)*100,2)} %")
+
+            st.write("___")
+            taux_abstention_region = st.container(border=True)
+            with taux_abstention_region:
+                _ = []
+                for i in list(region_district.keys()): # liste des regions
+                    # nombre de district de la region
+                    nbr_region_district = len(region_district[f"{i}"])
+                    # nombre de district silencieux de la region
+                    nbr_silenc_region_district = (district_silencieux[district_silencieux.Region == f"{i}"].shape[0])
+                    _.append((nbr_silenc_region_district/nbr_region_district)*100)
+
+                taux_de_district_silencieux = {
+                    "region" : list(region_district.keys()),
+                    "taux de district silencieux" : [round(k,2) for k in _]
+                }
+                taux_de_district_silencieux = pd.DataFrame(taux_de_district_silencieux)          
+                st.dataframe(taux_de_district_silencieux, use_container_width=True)
+        # liste des district manquants
+        expander_1 = st.expander("Liste des district manquant", expanded=True)
+        with expander_1:
+            col1, col2 = st.columns([4,1])
+            with col1:
+                region = st.selectbox("Choisir la region",options=list(region_district.keys()))
+            with col2:
+                st.write("")
+                st.write("")
+                btn_1 = st.button("Appliquer", use_container_width=True)
+            if btn_1:
+                district_silencieux = district_silencieux[district_silencieux["Region"] == f"{region}"]
+                st.dataframe(district_silencieux, use_container_width=True)
+            else:
+                st.dataframe(district_silencieux, use_container_width=True)

Data.py

@@ -0,0 +1,132 @@
+import streamlit as st
+import pandas as pd
+
+liste_vaccin = ['YELLOW FEVER', 'Penta', 'VPO', 'VPI', 'nOPV2', 'TD','Mosquirix', 'PCV13', 'MEASLES', 'BCG', 'Janssen', 'Rota', 'MENINGO', 'HPV', 'Mencevax']
+
+data_ = None
+
+# Initialization
+if 'data' not in st.session_state:
+    st.session_state['data'] = None
+
+if 'data_file' not in st.session_state:
+    st.session_state['data_file'] = None
+
+if 'auto_clean_state' not in st.session_state:
+    st.session_state['auto_clean_state'] = None
+
+if 'dataframe_mappi_region' not in st.session_state:
+    st.session_state['dataframe_mappi_region'] = None
+
+# chargement des donnees
+@st.cache_data
+def load_data(data_file):
+    data = pd.read_excel(data_file)
+    return data
+
+# fontion de retraits des colonnes vide
+def empty_columns_cleaner(data):
+    temp = dict(data.isna().sum())
+    _ = []
+    for i in list(data.columns):
+        if temp[f"{i}"] == data.shape[0] :
+            _.append(i)
+            data.drop(columns=f"{i}", inplace=True)
+    return _
+
+# fonction de retrait des colonnes avec une valeur unique
+def remove_one_columns_values(data):
+    _ = []
+    for i in list(data.columns):
+        if len(list(data[f"{i}"].unique())) == 1:
+            data.drop(columns=f"{i}", inplace=True)
+            _.append(i)
+    return _
+
+
+## gestion de l'importation et de la manipulation des donnees
+
+# importation et previsualisation des donnees
+if st.session_state['data_file'] is None:
+    data_file = "DATA.xlsx"
+    st.session_state['data_file'] = data_file
+else:
+    data_file = st.session_state['data_file']
+
+if data_file :
+    data = load_data(data_file)
+    # remplacement 
+    st.session_state["data"] = data
+
+     # afficher le jeu de donnees initiale
+    st.header("Previsualisation des donnees pre-nettoyage")
+    # creation des filtres et des fonctions d'explorations
+    form_1 = st.form("filtre")
+    with form_1:
+        list_colonne = st.multiselect("Choisir les colonnes a appliquer", options=list(data.columns), )
+        list_ligne = st.select_slider("Choisir les lignes a afficher", options=[i for i in range(0,data.shape[0])], value=(0,int(data.shape[0]-1)))
+        col3, col4 = st.columns([4,1])
+        with col3:
+            btn_3 = st.form_submit_button("Appliquer", use_container_width=True)
+        if btn_3:
+            if list_colonne :
+                data_ = data.loc[:,list(list_colonne)]
+                if list_ligne:
+                    data_ = data_.iloc[[i for i in range(list_ligne[0], list_ligne[1]+1)]]
+            else:
+                if list_ligne:
+                    data_ = data
+                    data_ = data_.iloc[[i for i in range(list_ligne[0], list_ligne[1]+1)]]
+        with col4:
+            btn_4 = st.form_submit_button("Reinitialiser",use_container_width=True)
+            if btn_4:
+                list_colonne.clear()
+                list_ligne = ()
+    
+    # choix des colonnees a afficher
+
+    expander_1 = st.expander("Previsualisation des donnees pre-nettoyage", expanded=True)
+    with expander_1:
+        if data_ is None:
+            st.dataframe(data)
+        else:
+            st.dataframe(data_)
+    st.write("___")
+
+    if st.session_state['auto_clean_state'] == None :
+        form_2 = st.form(key="form_1")
+        with form_2:
+            form_2.subheader("Souhaiter vous nettoyer automatiquement les donnees ?")
+            col1,col2 = st.columns([1,1])
+            with col1:
+                btn_1 = st.form_submit_button("Accepter", use_container_width=True)
+                if btn_1:
+                    st.session_state['auto_clean_state'] = True
+                    st.rerun()
+            with col2:
+                btn_2 = st.form_submit_button("Refuser", use_container_width=True)
+                if btn_2:
+                    st.session_state['auto_clean_state'] = False
+                    st.rerun()
+        st.write("___")
+
+    if st.session_state['auto_clean_state'] == True :
+        # netoyage des donnees
+        st.header("Nettoyage des donnees")
+        expander_2 = st.expander("Liste des operations")
+        with expander_2:
+            st.subheader("Retrait des colonnes vides")
+            st.write("Liste des colonnes vides supprimer")
+            _ = empty_columns_cleaner(data)
+            st.write(_)
+            st.subheader("Retrait des colonnes mono-value")
+            st.write("Liste des colonnes mono-value")
+            _ = remove_one_columns_values(data)
+            st.write(_)
+            st.session_state["data"] = data
+        st.write("___")
+        # afficher le je de donnees nettoyer
+
+else : 
+    st.warning("Veuillez selectionner le fichier a traiter")
+

Visualisation.py

@@ -0,0 +1,552 @@
+import streamlit as st
+import pandas as pd
+from streamlit_option_menu import option_menu
+import random
+from streamlit_echarts import st_pyecharts
+from streamlit_echarts import st_echarts
+from pyecharts.charts import Bar
+from pyecharts.charts import Pie
+from pyecharts import options as opts
+
+region_district = {
+                "ADAMAOUA" : ["Bankim","Banyo","Belel","Dang","Djohong","Meiganga","Ngaoundal","Ngaoundere Rural","Ngaoundere Urbain","Tibati","Tignere"],
+                "CENTRE" : ["Akonolinga","Awae","Ayos","Bafia","Biyem Assi","Cite Verte","Djoungolo","Ebebda","Efoulan","Elig Mfomo","Eseka","Esse","Evodoula","Mbalmayo","Mbandjock","Mbankomo","Mfou","Monatele","Mvog-Ada","Nanga Eboko","Ndikinimeki","Ngog Mapubi","Ngoumou","Nkolbisson","Nkolndongo","Ntui","Obala","Odza","Okola","Sa'a","Soa","Yoko"],
+                "EST": ["Abong Mbang","Batouri","Belabo","Bertoua","Betare Oya","Doume","Garoua Boulai","Kette","Lomie","Mbang","Messamena","Moloundou","Ndelele","Nguelemendouka","Yokadouma"],
+                "EXTREME NORD" : ["Bogo","Bourha","Fotokol","Gazawa","Goulfey","Guere","Guidiguis","Hina","Kaele","Kar Hay","Kolofata","Kousseri","Koza","Mada","Maga","Makary","Maroua 1","Maroua 2","Maroua 3","Meri","Mindif","Mogode","Mokolo","Mora","Moulvoudaye","Moutourwa","Mozogo","Pette","Roua","Tokombere","Vele","Waza","Yagoua"],
+                "LITTORAL": ["Abo","Bangue","Boko","Bonassama","Cite Des Palmiers","Deido","Dibombari","Edea","Japoma","Logbaba","Loum","Manjo","Manoka","Mbanga","Melong","Ndom","New Bell","Ngambe","Njombe Penja","Nkondjock","Nkongsamba","Nylon","Pouma","Yabassi"],
+                "NORD" : ["Bibemi","Figuil","Garoua I","Garoua II","Gaschiga","Golombe","Guider","Lagdo","Mayo Oulo","Ngong","Pitoa","Poli","Rey Bouba","Tchollire","Touboro"],
+                "NORD OUEST" : ["Ako","Bafut","Bali","Bamenda","Bamenda 3","Batibo","Benakuma","Fundong","Kumbo East","Kumbo West","Mbengwi","Misaje","Ndop","Ndu","Njikwa","Nkambe","Nwa","Oku","Santa","Tubah","Wum"],
+                "OUEST" : ["Bafang","Baham","Bamendjou","Bandja","Bandjoun","Bangangte","Bangourain","Batcham","Dschang","Foumban","Foumbot","Galim","Kekem","Kouoptamo","Malantouen","Massangam","Mbouda","Mifi","Penka Michel","Santchou"],
+                "SUD" : ["Ambam","Djoum","Ebolowa","Kribi","Kye ossi","Lolodorf","Meyomessala","Mintom","Mvangan","Niete","Olamze","Sangmelima","Zoetele"],
+                "SUD OUEST": ["Akwaya","Bakassi","Bamusso","Bangem","Buea","Ekondo Titi","Eyumodjock","Fontem","Konye","Kumba North","Kumba South","Limbe","Mamfe","Mbonge","Mundemba","Muyuka","Nguti","Tiko","Toko","Tombel","Wabane"]
+}
+liste_vaccin = ['YELLOW FEVER', 'Penta', 'VPO', 'VPI', 'nOPV2', 'TD','Mosquirix', 'PCV13', 'MEASLES', 'BCG', 'Janssen', 'Rota', 'MENINGO', 'HPV', 'Mencevax']
+liste_signe_symptome = {
+
+    "code" : [    10022044,10001497,10002198,10002034,10002424,10061093,10002847,10002855,10003553,10049535,10069633,10060800,10006482,10005169,10005177,10019211,10002199,10011042,10010741,
+                  10012373,10039906,10039083,10011703,10047555,10012174,10040844,10038687,10012735,10033371,10022086,10008479,10000081,10003239,10028411,10000084,10013968,10014625,10000087,
+                  10015150,10041232,10042772,10028372,10016256,10037660,10033775,10008531,10025197,10018762,10019465,10018910,10039424,10020642,10020772,10071552,10021097,10023126,10022075,
+                  10061218,10022437,10024264,10020937,10068319,10021089,10028813,10029864,10030095,10070774,10052139,10016029,10030302,10076569,10016062,10033799,10034037,10061428,10024855,
+                  10017577,10061461,10005191,10042128,10011469,10037087,10052140,10052904,10037844,10020751,10058605,10080283,10030071,10039101,10022061,10030041,10055798,10040047,10015727,
+                  10041349,10042241,10011878,10043071,10043089,10069381,10011224,10027327,10053425,10046735,10069555,10047340,10047513,100475132,10047700],
+
+    "symptome" : ["Abcès au point d'injection", "Agitation", "Anaphylaxie", "Anémie", "Angioedème (généralisé ou localisé)", "Anomalies des nerfs crâniens", "Anurie",
+                  "Anxiété", "Asthme", "Autres", "Baisse ou absence du contact visuel", "Bouffées de chaleur", "Bronchospame", "Cécité", "Cécité corticale",
+                  "Céphalées", "Choc anaphylactique", "Clignement", "Conjonctivite", "Conscience altérée", "Convulsions", "Coryza", "Cyanose", "Déficit du champ visuel",
+                  "Déshydratation", "Desquamation", "Détresse respiratoire", "Diarrhée", "Douleur", "Douleur au point d'injection", "Douleur thoracique", "Douleurs abdominales",
+                  "Douleurs articulaires", "Douleurs musculaires", "Douleurs pelviennes", "Dyspnée", "Encéphalopathie", "Epigastralgie", "Erythème","Eternuement","Evanouissement/Syncope",
+                  "Faiblesse motrice", "Fatigue / Prostration", "Fièvre", "Fourmillements", "Frissons", "Ganglion", "Geignement", "Hémiparésie", "Hémolyse", "Hypersalivation / hypersialorrhée",
+                  "Hypersudation / Hyperhidrose", "Hypertension", "Hyporéactivité", "Hypotension", "Ictère", "Induration au point d'injection", "Inflammation", "Insomnie",
+                  "Léthargie", "Lourdeur des membres", "Maux de gorge", "Modification des réflexes tendineux (hypo / hyper asymetrie)", "Nausée", "Nystagmus cérébelleux",
+                  "Œdème", "Œdème des voies respiratoires hautes (lèvres, gorge, palais ou larynx)", "Œdème des yeux", "Œdème facial", "Oligurie", "Ophtalmoparésie", "Paralysie faciale",
+                  "Paralysie flasque aiguë", "Parotidite", "Perte d'appétit (anorexie)", "Perte de connaissance", "Perte de l'équilibre", "Perturbations de la fonction érectile",
+                  "Phlyctènes", "Plaies buccales", "Pleurs persistentes (> 3 heures)", "Prurit", "Prurit occulaire", "Raideur nucale", "Rash", "Réaction allergique", "Réflexe de moue succion",
+                  "Refus de téter","Révulsion oculaire","Rhume", "Rougeur au point d'injection", "Rougeur des yeux", "Saignement (hémorrhagie)", "Sepsis", "Signes de Babinski", "Somnolence", "Stridor", "Surdité",
+                  "Tachycardie, palpitations", "Tachypnée", "Temps de remplissage capillaire > 3s", "Toux", "Troubles du cycle menstruel", "Tuméfaction au point d'injection",
+                  "Urticaire", "Utilisation excessive des muscles respiratoires accessoires", "Vertige", "Vision floue", "Voix rauque", "Vomissements"]
+}
+
+# Initialization
+if 'data' not in st.session_state:
+    st.session_state['data'] = None
+
+if 'data_file_2' not in st.session_state:
+    st.session_state['data_file_2'] = None
+
+if 'data_2' not in st.session_state:
+    st.session_state['data_2'] = None
+
+if 'dataframe_mappi_region' not in st.session_state:
+    st.session_state['dataframe_mappi_region'] = None
+
+if st.session_state['data'] is None:
+    st.warning("Veuillez entrer les données")
+else :
+    data = st.session_state['data']
+    nav_bar1 = option_menu(None,
+                       ["District silencieux", "Repartition des MAPPI"],
+                        icons=['file-earmark-bar-graph-fill'],
+                        menu_icon="cast",
+                        default_index=0,
+                        orientation="horizontal")
+
+
+    ## gestion des districts silencieux
+    if nav_bar1 == "District silencieux":
+        region = []
+        district = []
+        col1, col2, col3 = st.columns([2,5,1])
+        # creation du csv des district silencieux
+        with col1 :
+            for i in list(region_district.keys()):
+                for k in region_district[f"{i}"] :
+                    if data[data["admininfo/district"] == f"{k}".upper()].shape[0] == 0 :
+                        region.append(i)
+                        district.append(k)
+            district_silencieux = {
+                "Region" : region,
+                "District": district
+            }
+            district_silencieux = pd.DataFrame(district_silencieux)
+            st.write(district_silencieux)
+            st.write("Nombre de district silencieux : ", district_silencieux.shape[0])
+            st.write("Nombre de district present : ", len(list(data["admininfo/district"].unique())))
+
+        # graphe de visualisation des district silencieux
+        with col2:
+            region_counts = district_silencieux.Region.value_counts().to_dict()
+            regions = list(region_counts.keys())
+            counts = list(region_counts.values())
+            # Création du graphique
+            # bar chart
+            district_silencieux_bar = (
+                Bar()
+                .add_xaxis(regions)
+                .add_yaxis("Nombre d'apparitions", counts)
+                .set_global_opts(title_opts=opts.TitleOpts(title="Distribution des Régions"))
+            )
+            # Affichage avec Streamlit
+            st_pyecharts(district_silencieux_bar, height="470px")
+
+        with col3:
+            # liste des district par region
+            seleteur_1 = st.selectbox("Liste des district silencieux par region", options = list(district_silencieux["Region"].unique()))
+            _ = []
+            for i in list(district_silencieux.District[district_silencieux.Region == f"{seleteur_1}"]):
+                _.append(i)
+            st.write(_)
+
+        st.write("___")
+
+        col1, col2 = st.columns([1,4])
+        with col2:
+            # Création du pie chart complet (sans donut)
+            district_silencieux_pie = (
+                Pie()
+                .add("", [list(z) for z in zip(regions, counts)])
+                .set_global_opts(title_opts=opts.TitleOpts(title=""))
+                .set_series_opts(
+                    label_opts=opts.LabelOpts(formatter = "{b}: ({d}%)",
+                                            position = "inside",  # Met le texte à l'intérieur du camembert
+                                            font_size = 12,
+                                            rotate = "radial")  # Affichage des pourcentages
+                )
+            )
+            st_pyecharts(district_silencieux_pie, height="590px")
+        with col1:
+            # Taux de district silencieux par region
+            st.subheader("Taux d'abstention par region")
+            _ = []
+            for i in list(region_district.keys()): # liste des regions
+                # nombre de district de la region
+                nbr_region_district = len(region_district[f"{i}"])
+                # nombre de district silencieux de la region
+                nbr_silenc_region_district = (district_silencieux[district_silencieux.Region == f"{i}"].shape[0])
+                _.append((nbr_silenc_region_district/nbr_region_district)*100)
+
+            taux_de_district_silencieux = {
+                "region" : list(region_district.keys()),
+                "taux de district silencieux" : [f"{round(k,2)}%" for k in _]
+            }
+            taux_de_district_silencieux = pd.DataFrame(taux_de_district_silencieux)
+            st.dataframe(taux_de_district_silencieux, use_container_width = False)
+        expander_2 = st.expander("District silentieux ", expanded=True)
+        with expander_2:
+            if st.session_state['data_file_2'] is None:
+                data_file_2 = "data2.xlsx"
+                st.session_state['data_file_2'] = data_file_2
+            else:
+                data_file_2 = st.session_state['data_file_2']
+
+            if data_file_2:
+                data_2 = pd.read_excel(data_file_2, engine="openpyxl")
+                data_2['q6_district'] = data_2['q6_district'].str.replace('_', ' ')
+                data_2['q6_district'] = data_2['q6_district'].str.replace('-', ' ')
+                # determiner les district silencieux
+                region_2 = []
+                district_2 = []
+                col1, col2, col3 = st.columns([2,5,1])
+                # creation du csv des district silencieux
+                with col1 :
+                    for i in list(region_district.keys()):
+                        for k in region_district[f"{i}"] :
+                            if data_2[data_2["q6_district"] == f"{k}"].shape[0] == 0 :
+                                region_2.append(i)
+                                district_2.append(k)
+                    district_silencieux_2 = {
+                        "Region" : region_2,
+                        "District": district_2
+                    }
+                col1, col2 = st.columns([5,9])
+                with col1:
+                    district_silencieux_2 = pd.DataFrame(district_silencieux_2)
+                    st.write(district_silencieux_2)
+                    st.write("Nombre de district silencieux : ", district_silencieux_2.shape[0])
+                    st.write()
+                with col2:
+                    region_2_counts = district_silencieux_2.Region.value_counts().to_dict()
+                    regions_2 = list(region_2_counts.keys())
+                    counts_2 = list(region_2_counts.values())
+                    district_silencieux_2_bar = (
+                        Bar()
+                        .add_xaxis(regions_2)
+                        .add_yaxis("Nombre d'apparitions", counts_2)
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_2_bar, height="370px")
+        
+        st.write("___")
+        if data_file_2:      
+            expander_3 = st.expander("District silencieux absolue",expanded=True)
+            with expander_3:
+                st.header("Synthese")
+                col1, col2 = st.columns([5,9])
+                with col1:
+                    df_common = district_silencieux.merge(district_silencieux_2, how='inner')
+                    st.write(df_common)
+                    st.write("Nombre de district silencieux : ", df_common.shape[0])
+                    st.write()
+                with col2:
+                    region_2_counts = df_common .Region.value_counts().to_dict()
+                    regions_2 = list(region_2_counts.keys())
+                    counts_2 = list(region_2_counts.values())
+                    district_silencieux_2_bar = (
+                        Bar()
+                        .add_xaxis(regions_2)
+                        .add_yaxis("Nombre d'apparitions", counts_2)
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_2_bar, height="370px")
+
+
+    if nav_bar1 == "Repartition des MAPPI":
+        nav_bar2 = option_menu(None,
+                            ["Carte" ,"Par region", "Par semaine vaccinal", "Par vaccin"],
+                                icons=['file-earmark-bar-graph-fill','file-earmark-bar-graph-fill', 'file-earmark-bar-graph-fill', 'file-earmark-bar-graph-fill'],
+                                menu_icon="cast",
+                                default_index=1,
+                                orientation="horizontal")
+
+        if nav_bar2 == "Par region" :
+            expander_1 = st.expander("database Mapp par region", expanded=False)
+            with expander_1:
+
+                nbr_mappi_region = []
+
+                for i in list(region_district.keys()):
+                    x = data[data["admininfo/states"] == f"{i}"]
+                    filtered_df = x[x['type_Vaccin'].isin(liste_vaccin)]
+                    nbr_mappi_region.append(filtered_df.shape[0])
+
+                # filtrage MAPPI grave
+                filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)] # filtrage du jeu de donnees
+                # compter le nombre de mappi grave par region
+                mappi_grave = []
+                for i in list(region_district.keys()):
+                    temp = filtered_df[filtered_df["admininfo/states"] == f"{i}"]
+                    mappi_grave.append(temp[temp["seriousness/serious"] == 1.0].shape[0])
+                # deduire le nombre de MAPPI non grave
+                mappi_non_grave = []
+                for i in range(0,10):
+                    mappi_non_grave.append(nbr_mappi_region[i] - mappi_grave[i])
+
+                # nombre de deces
+                nbr_deces = []
+                for i in list(region_district.keys()):
+                    temp = filtered_df[filtered_df["admininfo/states"] == f"{i}"]
+                    nbr_deces.append(temp[temp["seriousness/seriousnessdeath"] == 1.0].shape[0])
+
+                dataframe_mappi_region = {
+                        "region": list(region_district.keys()),
+                        "Nombre de MAPPI" : nbr_mappi_region,
+                        "Nombre de MAPPI non grave": mappi_non_grave,
+                        "Nombre de MAPPI grave": mappi_grave,
+                        "Nombre de deces" : nbr_deces
+                    }
+                dataframe_mappi_region = pd.DataFrame(dataframe_mappi_region)
+                st.session_state['dataframe_mappi_region'] = dataframe_mappi_region
+
+                st.dataframe(dataframe_mappi_region, use_container_width = True)
+                col = st.columns(4)
+                with col[0]:
+                    st.write("Nombre totale de MAPPI : ", sum(nbr_mappi_region))
+                with col[1]:
+                    st.write("Nombre totale de MAPPI grave: ", sum(mappi_grave))
+                with col[2]:
+                    st.write("Nombre totale de MAPPI non grave: ", sum(mappi_non_grave))
+                with col[3]:
+                    st.write("Nombre de deces", sum(nbr_deces))
+            st.write("___")
+
+            col1, col2 = st.columns([4,1])
+            with col2:
+                # filtre
+                form_1 = st.form("Filtre")
+                with form_1:
+                    seleteur_2 = st.multiselect("choisisez la/les regions", options=list(region_district.keys()))
+                    btn_1 = st.form_submit_button("Appliquer", use_container_width=True)
+                    btn_2 = st.form_submit_button("Reinitialiser", use_container_width=True)
+
+            if btn_2:
+                btn_1 = False
+
+            with col1:
+                if btn_1 and len(seleteur_2) > 0:
+                    _1_ = []
+                    _2_ = []
+                    _3_ = []
+
+                    for i in seleteur_2:
+                        _1_.append(mappi_non_grave[list(region_district.keys()).index(i)])
+                        _2_.append(mappi_grave[list(region_district.keys()).index(i)])
+                        _3_.append(nbr_deces[list(region_district.keys()).index(i)])
+
+                    district_silencieux_bar = (
+                            Bar()
+                            .add_xaxis(seleteur_2)
+                            .add_yaxis("MAPPI NON GRAVE", _1_, itemstyle_opts=opts.ItemStyleOpts(color="#F1F54F"))
+                            .add_yaxis("MAPPI GRAVE", _2_, itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                            .add_yaxis("Desces", _3_, itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                            .set_global_opts(title_opts=opts.TitleOpts(title="Distribution des Régions"))
+                        )
+                        # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_bar, height="470px")
+
+                else:
+                    district_silencieux_bar = (
+                            Bar()
+                            .add_xaxis(list(region_district.keys()))
+                            .add_yaxis("MAPPI NON GRAVE", mappi_non_grave, itemstyle_opts=opts.ItemStyleOpts(color="#F1F54F"))
+                            .add_yaxis("MAPPI GRAVE", mappi_grave, itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                            .add_yaxis("Desces", nbr_deces, itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                            .set_global_opts(title_opts=opts.TitleOpts(title="Distribution des Régions"))
+                        )
+                        # Affichage avec Streamlit
+                    st_pyecharts(district_silencieux_bar, height="470px")
+
+        elif nav_bar2 == "Par semaine vaccinal" :
+
+            # filtrage MAPPI grave
+            filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)] # filtrage du jeu de donnees
+            # liste des semaines vaccinales
+            semaine_epidemiologique = list(filtered_df["Semaine_Epid"].unique())
+            semaine_epidemiologique.sort()
+
+            selecteur_3 = st.selectbox("Choisissez le type de vacin", options = liste_vaccin)
+            # repartition des MAAPI par semaines vaccinal
+            _ = []
+            for i in semaine_epidemiologique:
+                temp = filtered_df[(filtered_df["Semaine_Epid"] == f"{i}") & (filtered_df["type_Vaccin"] == selecteur_3)]
+                _.append(temp.shape[0])
+            # evolution des mappi par semaine vaccinal
+            MAPPI_semaine_vaccinal_bar = (
+                    Bar()
+                    .add_xaxis(semaine_epidemiologique)
+                    .add_yaxis("Nombre d'apparitions", _)
+                    .set_global_opts(title_opts=opts.TitleOpts(title="Nombre de MAPPI par semaines vaccinal"))
+            )
+            st_pyecharts(MAPPI_semaine_vaccinal_bar, width="100%", height="600px")
+
+        elif nav_bar2 == "Par vaccin":
+            selecteur_5 = st.selectbox("Choisir le Vaccin", options=liste_vaccin, index=0)
+            col = st.columns(2)
+            with col[0]:
+                # filtre par sex:
+                filtre_sex = st.toggle("Sexe")
+            with col[1]:
+                # filtre par tranche d'age
+                list_tranche_dage = list(dict(data.Tranche_Age.value_counts()).keys())
+                filtre_tranche_age = st.toggle("Tranche d'Age")
+
+
+            # filtre des donnees selon les vaccin
+            filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)]
+            # filtre selon le vaccin choisis
+            filtered_df_2 = filtered_df[filtered_df["type_Vaccin"] == selecteur_5]
+            # compte du nombre de MAPPI
+            nbr_mappi = filtered_df_2.shape[0]
+            # determination du nombre de mappi grave
+            nbr_mappi_grave = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0].shape[0]
+            # determination du nombre de mappi non grave
+            nbr_mappi_non_grave = nbr_mappi - nbr_mappi_grave
+            col = st.columns(3)
+            with col[0]:
+                st.write(f"Nombre de MAPPI : {nbr_mappi}")
+            with col[1]:
+                st.write(f"Nombre de mappi grave : {nbr_mappi_grave}")
+            with col[2]:
+                st.write(f"Nombre de mappi non grave : {nbr_mappi_non_grave}")
+            st.write("___")
+
+            if filtre_sex == True and filtre_tranche_age == False:
+                ## filtre du nombre de mappi grave par sexe
+                filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0]
+                mappi_grave_homme = filtered_df_3[filtered_df_3["Sexe"] == "Masculin"].shape[0]
+                mappi_grave_femme = filtered_df_3[filtered_df_3["Sexe"] != "Masculin"].shape[0]
+                ## filtre mappi non grave par sexe
+                filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 2.0]
+                mappi_non_grave_homme = filtered_df_3[filtered_df_3["Sexe"] == "Masculin"].shape[0]
+                mappi_non_grave_femme = filtered_df_3[filtered_df_3["Sexe"] != "Masculin"].shape[0]
+                col = st.columns(2)
+                with col[0]:
+                    st.subheader("HOMME")
+                    ## graphe
+                    Nbr_mappi_type_vaccin_homme = (
+                        Bar()
+                        .add_xaxis([selecteur_5])  # X-axis avec le vaccin sélectionné
+                        .add_yaxis("Nombre de MAPPI non grave (HOMME)", [mappi_non_grave_homme])
+                        .add_yaxis("Nombre de MAPPI grave (HOMME)", [mappi_grave_homme], itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                    )
+
+                    # Affichage avec Streamlit
+                    st_pyecharts(Nbr_mappi_type_vaccin_homme, height="470px")
+
+                with col[1]:
+                    st.subheader("FEMME")
+                    Nbr_mappi_type_vaccin_femme = (
+                        Bar()
+                        .add_xaxis([selecteur_5])  # X-axis avec le vaccin sélectionné
+                        .add_yaxis("Nombre de MAPPI non grave (FEMME)", [mappi_non_grave_femme])
+                        .add_yaxis("Nombre de MAPPI grave (FEMME)", [mappi_grave_femme], itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                    )
+
+                    # Affichage avec Streamlit
+                    st_pyecharts(Nbr_mappi_type_vaccin_femme, height="470px")
+
+            elif filtre_sex == False and filtre_tranche_age == True:
+                _0_1_grave = []
+                _1_5_grave = []
+                _5_15_grave = []
+                _15_18_grave = []
+                _18_25_grave = []
+                _25_35_grave = []
+                _35_45_grave = []
+                _45_55_grave = []
+                _55_65_grave = []
+                _65etPlus_grave = []
+
+                _0_1_non_grave = []
+                _1_5_non_grave = []
+                _5_15_non_grave = []
+                _15_18_non_grave = []
+                _18_25_non_grave = []
+                _25_35_non_grave = []
+                _35_45_non_grave = []
+                _45_55_non_grave = []
+                _55_65_non_grave = []
+                _65etPlus_non_grave = []
+
+                filtered_df_3 = filtered_df_2[filtered_df_2["seriousness/serious"] == 1.0]
+                filtered_df_4 = filtered_df_2[filtered_df_2["seriousness/serious"] == 2.0]
+                for i in list_tranche_dage:
+                    _ = filtered_df_3[filtered_df_3.Tranche_Age == f"{i}"].shape[0]
+
+                    if i == '[0-1[':
+                        _0_1_grave.append(_)
+                    elif i == '[01-05[':
+                        _1_5_grave.append(_)
+                    elif i == '[05-15[':
+                        _5_15_grave.append(_)
+                    elif i == '[15-18[':
+                        _15_18_grave.append(_)
+                    elif i == '[18-25[':
+                        _18_25_grave.append(_)
+                    elif i == '[25-35[':
+                        _25_35_grave.append(_)
+                    elif i == '[35-45[':
+                        _35_45_grave.append(_)
+                    elif i == '[45-55[':
+                        _45_55_grave.append(_)
+                    elif i == '[55-65[':
+                        _55_65_grave.append(_)
+                    elif i == '65etPlus':
+                        _65etPlus_grave.append(_)
+
+                for i in list_tranche_dage:
+                    _ = filtered_df_4[filtered_df_4.Tranche_Age == f"{i}"].shape[0]
+
+                    if i == '[0-1[':
+                        _0_1_non_grave.append(_)
+                    elif i == '[01-05[':
+                        _1_5_non_grave.append(_)
+                    elif i == '[05-15[':
+                        _5_15_non_grave.append(_)
+                    elif i == '[15-18[':
+                        _15_18_non_grave.append(_)
+                    elif i == '[18-25[':
+                        _18_25_non_grave.append(_)
+                    elif i == '[25-35[':
+                        _25_35_non_grave.append(_)
+                    elif i == '[35-45[':
+                        _35_45_non_grave.append(_)
+                    elif i == '[45-55[':
+                        _45_55_non_grave.append(_)
+                    elif i == '[55-65[':
+                        _55_65_non_grave.append(_)
+                    elif i == '65etPlus':
+                        _65etPlus_non_grave.append(_)
+
+                # afficher la figure
+                col = st.columns(2)
+                with col[1]:
+                    st.subheader("Mappi Grave")
+                    Nbr_mappi_tranche_age_grave = (
+                        Bar()
+                        .add_xaxis([selecteur_5])
+                        .add_yaxis("[0-1[", _0_1_grave)
+                        .add_yaxis("[1-5[", _1_5_grave)
+                        .add_yaxis("[5-15[", _5_15_grave)
+                        .add_yaxis("[12-18[", _15_18_grave)
+                        .add_yaxis("[18-25[", _18_25_grave)
+                        .add_yaxis("[25-35[", _25_35_grave)
+                        .add_yaxis("[35-45[", _35_45_grave)
+                        .add_yaxis("[45-55[", _45_55_grave)
+                        .add_yaxis("[55-65[", _55_65_grave)
+                        .add_yaxis("65etPlus", _65etPlus_grave)
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(Nbr_mappi_tranche_age_grave, height="470px")
+                with col[0]:
+                    st.subheader("Mappi non Grave")
+                    Nbr_mappi_tranche_age_non_grave = (
+                        Bar()
+                        .add_xaxis([selecteur_5])  # Doit être une liste
+                        .add_yaxis("[0-1[", _0_1_non_grave)
+                        .add_yaxis("[1-5[", _1_5_non_grave)
+                        .add_yaxis("[5-15[", _5_15_non_grave)
+                        .add_yaxis("[12-18[", _15_18_non_grave)
+                        .add_yaxis("[18-25[", _18_25_non_grave)
+                        .add_yaxis("[25-35[", _25_35_non_grave)
+                        .add_yaxis("[35-45[", _35_45_non_grave)
+                        .add_yaxis("[45-55[", _45_55_non_grave)
+                        .add_yaxis("[55-65[", _55_65_non_grave)
+                        .add_yaxis("65etPlus", _65etPlus_non_grave)
+                    )
+                    # Affichage avec Streamlit
+                    st_pyecharts(Nbr_mappi_tranche_age_non_grave, height="470px")
+
+            elif  filtre_sex == True and filtre_tranche_age == True:
+                filtre_sex = False
+            else:
+                # Création du graphique
+                Nbr_mappi_type_vaccin = (
+                    Bar()
+                    .add_xaxis([selecteur_5])  # Doit être une liste
+                    .add_yaxis("Nombre de MAPPI non grave", [nbr_mappi_non_grave])
+                    .add_yaxis("Nombre de MAPPI grave", [nbr_mappi_grave], itemstyle_opts=opts.ItemStyleOpts(color="#FF4B4B"))
+                    .set_global_opts(title_opts=opts.TitleOpts(title="Distribution des MAPPI par Vaccin"))
+                )
+
+                # Affichage avec Streamlit
+                st_pyecharts(Nbr_mappi_type_vaccin, height="470px")
+
+        elif nav_bar2 == "Carte":
+            latitude = []
+            longitude = []
+            selecteur_6 = st.selectbox("Choisir le Vaccin", options=liste_vaccin)
+            # filtre par vaccin
+            filtered_df = data[data['type_Vaccin'].isin(liste_vaccin)]
+            # filtre sur le vaccin specifique
+            filtered_df_2 = filtered_df[filtered_df['type_Vaccin'] == selecteur_6]
+
+            st.map(filtered_df_2, latitude= "_gps_beginning_latitude", longitude= "_gps_beginning_longitude")

data2.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b0214e55222b5558569ef6f128906364948e82af8789d381c6852fcf6555156
+size 375811

main.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from streamlit_option_menu import option_menu
+
+st.set_page_config(page_title="My Streamlit App", layout="wide")
+
+# initialisation des autres pages
+Visualisation_page = st.Page("Visualisation.py", title= "Data Visualisation")
+Dashboard_page = st.Page("Dashboard.py", title= "Dashboard page")
+Data_page = st.Page("Data.py", title= "Data page")
+# barre de navigation
+nav_bar1 = option_menu(None, 
+                       ["Donnees", "Visualisation des donnees", 'Dashboard'], 
+                        icons=['file-earmark-bar-graph-fill', 'pie-chart-fill', "activity"], 
+                        menu_icon="cast", 
+                        default_index=0, 
+                        orientation="horizontal")
+
+if nav_bar1 == "Visualisation des donnees":
+    pg = st.navigation([Visualisation_page])
+    pg.run()
+elif nav_bar1 == "Dashboard":
+    pg = st.navigation([Dashboard_page])
+    pg.run()
+elif nav_bar1 == "Donnees":
+    pg = st.navigation([Data_page])
+    pg.run()
+