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app.py

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+#docker run -it -v "$(pwd):/home/app" -p 4000:4000 jedha/streamlit-fs-image
+#docker run -it -v "$(pwd):/home/app" -p 4000:4000 jedha/streamlit-fs-image bash
+#docker build . -t NAME_DOCKER
+# docker run -it -p 4000:80 -v "$(pwd):/home/app" -e PORT:80 NAME_DOCKER bash
+
+#http://localhost:4000
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px 
+import plotly.graph_objects as go
+import numpy as np
+
+
+
+DATA_URL = 'https://full-stack-assets.s3.eu-west-3.amazonaws.com/Deployment/get_around_delay_analysis.xlsx'
+
+@st.cache_data
+def load_data(): 
+    data = pd.read_excel(DATA_URL)
+    return data
+
+data = load_data()
+print('state: ',data['state'].value_counts())
+
+st.markdown("""
+    Bienvenue sur ce tableau de bord streamlit du `Projet Get Around`. Nos <a href=DATA_URL style="text-decoration: none;">données</a>
+    illustrent quelques statistiques et visualisations de données. A l'aide de cet un outil permet de suivre et comprendre les données des locations de voitures réalisé par
+    <a href="https://github.com/2nzi" style="text-decoration: none;">@2nzi</a> sur github.
+""", unsafe_allow_html=True)
+
+
+
+if st.checkbox('Show raw data'):
+    st.subheader('Raw data')
+    st.write(data)   
+
+
+data = data.drop(['time_delta_with_previous_rental_in_minutes','previous_ended_rental_id'],axis=1)
+
+st.subheader("Part des différents types de location")
+st.markdown("""
+    Deux types de locations existe. Connect & Mobile.
+""", unsafe_allow_html=True)
+
+fig = px.pie(data, values='car_id',names='checkin_type')
+# fig = go.Figure(data=[go.Pie(labels=data['checkin_type'], values=data['car_id'])])
+st.plotly_chart(fig)    
+
+
+
+st.subheader("Repartition des locations annulées dans chaque type de commande")
+fig = px.histogram(data,x='checkin_type',color='state')
+st.plotly_chart(fig)    
+
+
+col = 'delay_at_checkout_in_minutes'
+col_med = data[col].median()
+col_std = data[col].std()
+lower_bound = col_med - 2 * col_std
+upper_bound = col_med + 2 * col_std
+print(col_med,lower_bound,upper_bound)
+data = data[(data[col] >= lower_bound) & (data[col] <= upper_bound)]
+print('state: ',data['state'].value_counts())
+#utiliser Q1-1.5IQR et Q3+1.5IQR
+
+if st.checkbox('Show on Late',value=True):
+    mini = 0
+    df = data[data['delay_at_checkout_in_minutes']>mini]
+    title_late = 'Late cars'
+
+else:
+    df = data
+    title_late = 'All cars'
+    mini = int(df['delay_at_checkout_in_minutes'].min())
+
+
+st.subheader(title_late)
+trsh = int(df['delay_at_checkout_in_minutes'].max()) #make the max chossable !
+seuil = st.slider("Choose the minute threshold!", mini, int(df['delay_at_checkout_in_minutes'].max()), int(trsh*0.1))
+# seuil = st.slider("Choose the minute threshold!", 0, trsh, int(trsh*0.1))
+
+fig_px = px.histogram(df, color='checkin_type', x='delay_at_checkout_in_minutes')
+fig = go.Figure(fig_px)
+
+x=seuil
+fig.add_shape(
+    type="line",
+    x0=x, x1=x, y0=0, y1=1,
+    line=dict(color="Green", width=2, dash="dash"),
+    xref='x', yref='paper'
+)
+
+fig.add_shape(
+    type="rect",
+    x0=mini, x1=x, y0=0, y1=1,
+    fillcolor="Green",
+    opacity=0.2,
+    line_width=0,
+    xref='x', yref='paper'
+)
+
+fig.update_layout(
+    title="",
+    xaxis_title="Delay at Checkout in Minutes",
+    yaxis_title="Count"
+)
+
+st.plotly_chart(fig)  
+col1, col2 = st.columns(2)
+
+move_upper_mask = df['delay_at_checkout_in_minutes']<seuil
+lower_mask = df['delay_at_checkout_in_minutes']>mini
+global_mask = move_upper_mask & lower_mask
+col1.metric("Number of rent", len(df[global_mask]))
+
+part_of_rent = 100*len(df[move_upper_mask]) / len(df)
+col2.metric("Part of rent", f'{part_of_rent:.2f}%')
+
+# col2.metric("Part of rent", f'{100*len(df[df['delay_at_checkout_in_minutes']<seuil])/len(df['delay_at_checkout_in_minutes']):.2f}%')
+
+
+#IDEE:
+# pouvoir choisir l'id d'une voiture spécifiquement
+
+
+
+
+
+# day_data = data[data['dateRep']== start_time]
+
+
+
+# st.subheader("Analyse par pays")
+
+# country = st.selectbox("Select a country you want to see sales", data["countriesAndTerritories"].sort_values().unique())
+
+
+
+
+# st.write("Current growth rate")
+
+# country_data = data[data["countriesAndTerritories"]==country]
+
+# from random import randrange
+# current_day = randrange(len(country_data))
+# # current_day = int(len(country_data)/2) #take random value
+
+# # st.write(country_data.iloc[current_day]['dateRep'])
+# # st.write(country_data.iloc[current_day]['cases'])
+# # st.write(country_data.iloc[current_day-1]['cases'])
+
+
+# ratio = np.round((country_data.iloc[current_day]['cases'] - country_data.iloc[current_day-1]['cases'])/country_data.iloc[current_day]['cases'],2)
+# ratio2 = np.round((country_data.iloc[current_day]['cases'] - country_data.iloc[current_day-2]['cases'])/country_data.iloc[current_day-1]['cases'],2)
+# diff_ratio = np.round(ratio-ratio2,2)
+# st.metric(label="",value = ratio, delta = diff_ratio)
+# # st.write(f'{ratio:.2f}')
+
+
+# #### Create two columns
+# col1, col2 = st.columns(2)
+
+# with col1:
+#     st.subheader('Cas positifs cases')
+#     fig = go.Figure()
+#     fig.add_trace(go.Scatter(x=data_date["dateRep"], y=data_date["cases"], mode='lines',name='new cases', line=dict(color='blue')))
+#     fig.add_trace(go.Scatter(x=data_date["dateRep"], y=data_date["Rolcases"], mode='lines',name='Rolling 7-day Mean',line=dict(color='red')))
+
+#     st.plotly_chart(fig)
+
+# with col2:
+#     st.subheader('Cas de décès')
+#     fig2 = go.Figure()
+#     fig2.add_trace(go.Scatter(x=data_date["dateRep"], y=data_date["deaths"], mode='lines',name='new cases', line=dict(color='blue')))
+#     fig2.add_trace(go.Scatter(x=data_date["dateRep"], y=data_date["Roldeaths"], mode='lines',name='Rolling 7-day Mean',line=dict(color='red')))
+
+#     st.plotly_chart(fig2)
+
+            
+

requirements.txt

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+streamlit
+pandas
+plotly
+numpy
+openpyxl