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")