First commit streamlit application

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from PIL import Image
+import datetime
+import streamlit as st
+
+#####################################################################################################################################
+st.set_page_config(layout='wide')
+
+# Sidebar:  Image + main info on dataset
+def data_subset(data, beginning='2010-12-01', end='2011-12-09'):
+    
+    beginning = pd.to_datetime(beginning)
+    end = pd.to_datetime(end)
+    
+    # Subsetting
+    data = data[(data['InvoiceDate'] >= beginning) & (data['InvoiceDate'] <= end)]
+
+    return data
+
+# Loading datasets
+df_info = pd.read_csv('static/customer_info.csv')
+df_info['InvoiceDate'] = pd.to_datetime(df_info['InvoiceDate'])
+
+with st.sidebar:
+    col1, col2, col3 = st.columns(3)
+    with col2:
+        random_image = Image.open('static/logo_random.png')
+        st.image(random_image)
+
+    # Showing top products
+    if st.checkbox('Check to see top products sold in a selected timeframe'):
+        start = st.date_input('Input beginning of the wanted timeframe', datetime.date(2010, 12, 1),
+                              min_value=datetime.date(2010, 12, 1), max_value=datetime.date(2011, 12, 9), key=1)
+        end = st.date_input('Input beginning of the wanted timeframe', datetime.date(2011, 12, 9),
+                            min_value=start, max_value=datetime.date(2011, 12, 9), key=2)
+
+        df_top_products = df_info.copy()
+        df_subset_products = data_subset(df_top_products, start, end)
+
+        df_subset_products = df_top_products.groupby('Description')['Quantity'].sum()
+        number_chosen_products = st.number_input('How many top products sold do you want to see?', value=5)
+        df_subset_products_top = pd.DataFrame(df_top_products.sort_values(by='Quantity', ascending=False)).iloc[:number_chosen_products,:]
+        df_subset_products_top = df_subset_products_top[['Description', 'Quantity']]
+        st.dataframe(df_subset_products_top)
+
+    # Showing most recent clients
+    if st.checkbox('Check to see the most recent customers in a selected timeframe'):
+        start_clts = st.date_input('Input beginning of the wanted timeframe', datetime.date(2010, 12, 1),
+                              min_value=datetime.date(2010, 12, 1), max_value=datetime.date(2011, 12, 9), key=3)
+        end_clts = st.date_input('Input beginning of the wanted timeframe', datetime.date(2011, 12, 9),
+                            min_value=start_clts, max_value=datetime.date(2011, 12, 9), key=4)
+        df_recent_customers = df_info.copy()
+        df_subset_recent_customers = data_subset(df_recent_customers, start_clts, end_clts)
+
+        df_subset_recent_customers = df_subset_recent_customers.groupby('CustomerID')['Recency'].min()
+        number_chosen_recency = st.number_input('How many recent customers do you want to see?', value=5)
+        df_subset_recent_customers_top = pd.DataFrame(df_subset_recent_customers.sort_values()).iloc[:number_chosen_recency,:]
+        st.dataframe(df_subset_recent_customers_top)
+
+    # Showing most prolific customers
+    if st.checkbox('Check to see the top customers in a selected timeframe'):
+        start_top = st.date_input('Input beginning of the wanted timeframe', datetime.date(2010, 12, 1),
+                              min_value=datetime.date(2010, 12, 1), max_value=datetime.date(2011, 12, 9), key=5)
+        end_top = st.date_input('Input beginning of the wanted timeframe', datetime.date(2011, 12, 9),
+                            min_value=start_top, max_value=datetime.date(2011, 12, 9), key=6)
+        df_top_customers = df_info.copy()
+        df_subset_top_customers = data_subset(df_top_customers, start_top, end_top)
+
+        df_subset_top_customers = df_subset_top_customers.groupby('CustomerID')['Monetary'].sum()
+        number_chosen_top_clts = st.number_input('How many top customers do you want to see?', value=5)
+        df_subset_top_customers_top = pd.DataFrame(df_subset_top_customers.sort_values(ascending=False)).iloc[:number_chosen_top_clts,:]
+        st.dataframe(df_subset_top_customers_top)
+
+#####################################################################################################################################
+st.title('E-commerce: client dashboard')
+st.write("---")
+
+# Loading dataset
+df_info_customer = df_info.copy()
+customer_id_default = int(df_info_customer['CustomerID'].min())
+
+# We choose a CustomerID
+st.number_input('CustomerID', min_value=customer_id_default, value=customer_id_default, step=1, format="%d", key='customer_id')
+customer_id = st.session_state.customer_id
+
+if customer_id not in df_info_customer['CustomerID'].values:
+    st.write('This CustomerID is not available right now, please find another.')
+
+else:
+    start_info = st.date_input('Input beginning of the wanted timeframe', datetime.date(2010, 12, 1),
+                                min_value=datetime.date(2010, 12, 1), max_value=datetime.date(2011, 12, 9), key=7)
+    end_info = st.date_input('Input beginning of the wanted timeframe', datetime.date(2011, 12, 9),
+                                min_value=start_info, max_value=datetime.date(2011, 12, 9), key=8)
+    df_subset_info_customer = data_subset(df_info_customer, start_info, end_info)
+
+    # Main info (recency, number of orders, how much the customer spent)
+    df_subset_info_customer = df_subset_info_customer[df_subset_info_customer['CustomerID'] == customer_id]
+    df_main_info = df_subset_info_customer.groupby('CustomerID').agg(Recency=('Recency', 'min'), NbOrder=('NbOrder', 'max'), MonetaryTotal=('Monetary', 'sum'))
+
+    # GroupBy to get the mean value of each order for the customer
+    df_mean_order = df_subset_info_customer.groupby(['InvoiceNo', 'CustomerID']).agg(TotalOrderValue=('Monetary', 'sum'))
+    df_mean_order = df_mean_order.groupby('CustomerID').agg(MeanOrderValue=('TotalOrderValue', 'mean'))
+
+    # GroupBy to get the most bought product and its quantity
+    df_product_clts = pd.DataFrame(df_info.groupby(['CustomerID','Description'])['Quantity'].sum())
+    df_product_clts = df_product_clts.reset_index()
+    df_product_clts = df_product_clts[df_product_clts['CustomerID'] == customer_id]
+    ids, values = df_product_clts.groupby('CustomerID')['Quantity'].max().index, df_product_clts.groupby('CustomerID')['Quantity'].max().values
+    df_product_clts = df_product_clts[(df_product_clts['CustomerID'] == ids[0]) & (df_product_clts['Quantity'] == values[0])]
+
+    # Now we create the columns we want
+    df_main_info['MeanOrderValue'] = df_mean_order['MeanOrderValue'].values[0]
+    df_main_info['MostOrderedProduct'] = df_product_clts['Description'].values[0]
+    df_main_info['MostOrderedProductQuantity'] = df_product_clts['Quantity'].values[0]
+
+    # We can show it now that it's complete
+    st.dataframe(df_main_info)
+
+    st.write("---")
+    #####################################################################################################################################
+    st.subheader('Similarity between customers:')
+    with st.expander('Choose a number of similar customers to compare:'):
+
+        if st.checkbox('Only similar customers:'):
+            options_similar = ['Recency', 'NbOrder', 'MonetaryTotal', 'MeanOrderValue']
+            option_similar = st.selectbox('Choose a feature to plot:', tuple(options_similar))
+
+            df_similar_customer = df_info.copy()
+
+            # Main info (recency, number of orders, how much the customer spent)
+            df_similar_customer_grouped = df_similar_customer.groupby('CustomerID').agg(Recency=('Recency', 'min'), NbOrder=('NbOrder', 'max'), MonetaryTotal=('Monetary', 'sum'))
+
+            # GroupBy to get the mean value of each order for the customer
+            df_mean_order_similar = df_similar_customer.groupby(['InvoiceNo', 'CustomerID']).agg(TotalOrderValue=('Monetary', 'sum'))
+            df_mean_order_similar = df_mean_order_similar.groupby('CustomerID').agg(MeanOrderValue=('TotalOrderValue', 'mean'))
+
+            # Now we create the column we want
+            df_similar_customer_grouped['MeanOrderValue'] = df_mean_order_similar['MeanOrderValue'].values
+
+            # We select the client
+            df_similar_customer_grouped = df_similar_customer_grouped.reset_index()
+            df_selected_clt = df_similar_customer_grouped[df_similar_customer_grouped['CustomerID'] == customer_id]
+
+            # We calculate distances (euclidean)
+            distances = []
+            for i in range(df_similar_customer_grouped.shape[0]):
+                distance = np.linalg.norm(df_similar_customer_grouped.drop('CustomerID', axis=1).values[i] - df_selected_clt.drop('CustomerID', axis=1).values)
+                distances.append(distance)
+
+            n_neighbors = st.slider("Number of similar customers:", min_value=5, max_value=30, value=10)
+            neighbors = sorted(distances)[:n_neighbors]
+
+            # We get the indices of the similar customers
+            indices_neighbors = []
+            for i in range(len(neighbors)):
+                indices_neighbors.append(distances.index(neighbors[i]))
+
+            df_neighbors_selected = df_similar_customer_grouped.iloc[indices_neighbors, :]
+
+            fig2, ax = plt.subplots()
+            ax.set_xlabel('Customers', fontsize=17)
+            ax.set_ylabel(option_similar, fontsize=17)
+            ax.axhline(y=df_selected_clt[option_similar].values, color='r', label='axhline - full height')
+            ax = plt.boxplot(df_neighbors_selected[option_similar], showfliers=False)
+
+            st.pyplot(fig2)
+
+        if st.checkbox('Compare to all customers:'):
+            options_all = ['Recency', 'NbOrder', 'MonetaryTotal', 'MeanOrderValue']
+            option_all = st.selectbox('Choose a feature to plot:', tuple(options_all))
+
+            df_all_customer = df_info.copy()
+
+            # Main info (recency, number of orders, how much the customer spent)
+            df_all_customer_grouped = df_all_customer.groupby('CustomerID').agg(Recency=('Recency', 'min'), NbOrder=('NbOrder', 'max'), MonetaryTotal=('Monetary', 'sum'))
+
+            # GroupBy to get the mean value of each order for the customer
+            df_mean_order_all = df_all_customer.groupby(['InvoiceNo', 'CustomerID']).agg(TotalOrderValue=('Monetary', 'sum'))
+            df_mean_order_all = df_mean_order_all.groupby('CustomerID').agg(MeanOrderValue=('TotalOrderValue', 'mean'))
+
+            # Now we create the column we want
+            df_all_customer_grouped['MeanOrderValue'] = df_mean_order_all['MeanOrderValue'].values
+
+            # We select the client
+            df_selected_clt_all = df_all_customer_grouped.reset_index()
+            df_selected_clt_all = df_selected_clt_all[df_selected_clt_all['CustomerID'] == customer_id]
+
+            # We calculate distances (euclidean)
+            distances = []
+            for i in range(df_all_customer_grouped.shape[0]):
+                distance = np.linalg.norm(df_all_customer_grouped.values[i] - df_selected_clt_all.drop('CustomerID', axis=1).values)
+                distances.append(distance)
+
+            fig2, ax = plt.subplots()
+            ax.set_xlabel('Customers', fontsize=17)
+            ax.set_ylabel(option_all, fontsize=17)
+            ax.axhline(y=df_selected_clt_all[option_all].values, color='r', label='axhline - full height')
+            ax = plt.boxplot(df_all_customer_grouped[option_all], showfliers=False)
+
+            st.pyplot(fig2)
+
+    st.write("---")
+    #####################################################################################################################################
+    st.subheader('Barplot of top selected products in the selected timeframe:')
+    with st.expander('Select to choose how many top products you want to see and in which timeframe'):
+
+        start_product_date = st.date_input('Input beginning of the wanted timeframe', datetime.date(2010, 12, 1),
+                                min_value=datetime.date(2010, 12, 1), max_value=datetime.date(2011, 12, 9), key=9)
+        end_product_date = st.date_input('Input beginning of the wanted timeframe', datetime.date(2011, 12, 9),
+                                min_value=start_product_date, max_value=datetime.date(2011, 12, 9), key=10)
+        df_top_products_plot = df_info.copy()
+        df_subset_products = data_subset(df_top_products_plot, start_product_date, end_product_date)
+        start_product, end_product = st.select_slider('Select a range of top product', options=[x for x in range(1, 21)], value=(1, 10))
+        df_subset_products = df_subset_products.groupby('Description')['Quantity'].sum()
+        df_subset_products = df_subset_products.reset_index()
+        df_slider_products = df_subset_products.sort_values(by='Quantity', ascending=False)
+        df_slider_products = df_slider_products.iloc[start_product-1:end_product, :]
+
+        fig, ax = plt.subplots()
+        bars = plt.barh(y=df_slider_products['Description'], width=df_slider_products['Quantity'], color=['darkmagenta', 'darkblue', 'darkgreen', 'darkred', 'darkgrey', 'darkorange'])
+
+        ax.bar_label(bars)
+        ax = plt.gca().invert_yaxis()
+
+        st.subheader('Selected top products:')
+        st.pyplot(fig)
+
+    st.write("---")
+    #####################################################################################################################################
+
+    st.subheader('Barplot of sales:')
+    with st.expander('Select to choose the periodicity:'):
+        options_similar = ['Months', 'Days', 'Hours']
+        option_similar = st.selectbox('Choose a periodicity:', tuple(options_similar))
+
+        if option_similar == 'Months':
+            df_months = df_info.copy()
+            df_months = df_months.merge(pd.DataFrame(df_months.groupby('CustomerID')['Monetary'].sum()), on='CustomerID')
+            df_months['Periodicity'] = pd.DatetimeIndex(df_months['InvoiceDate']).month
+            df_months = df_months.sort_values('Recency')
+            df_months = df_months.drop_duplicates(subset='CustomerID')
+
+            fig1, ax1 = plt.subplots()
+            ax1 = sns.barplot(x=df_months['Periodicity'], y=df_months['Monetary_y'], errorbar=None)
+            plt.title('Sales per Months')
+            plt.xlabel('Periodicity: Months')
+            plt.ylabel('TotalOrderValue')
+            st.pyplot(fig1)
+
+        elif option_similar == 'Days':
+            df_days = df_info.copy()
+            df_days = df_days.merge(pd.DataFrame(df_days.groupby('CustomerID')['Monetary'].sum()), on='CustomerID')
+            df_days['Periodicity'] = pd.DatetimeIndex(df_days['InvoiceDate']).day
+            df_days = df_days.sort_values('Recency')
+            df_days = df_days.drop_duplicates(subset='CustomerID')
+
+            fig2, ax2 = plt.subplots()
+            ax2 = sns.barplot(x=df_days['Periodicity'], y=df_days['Monetary_y'], errorbar=None)
+            plt.title('Sales per Days')
+            plt.xlabel('Periodicity: Days')
+            plt.xticks(rotation=90)
+            plt.ylabel('TotalOrderValue')
+            st.pyplot(fig2)
+
+        elif option_similar == 'Hours':
+            df_hours = df_info.copy()
+            df_hours = df_hours.merge(pd.DataFrame(df_hours.groupby('CustomerID')['Monetary'].sum()), on='CustomerID')
+            df_hours['Periodicity'] = pd.DatetimeIndex(df_hours['InvoiceDate']).hour
+            df_hours = df_hours.sort_values('Recency')
+            df_hours = df_hours.drop_duplicates(subset='CustomerID')
+
+            fig3, ax3 = plt.subplots()
+            ax3 = sns.barplot(x=df_hours['Periodicity'], y=df_hours['Monetary_y'], errorbar=None)
+            plt.title('Sales per Hours')
+            plt.xlabel('Periodicity: Hours')
+            plt.ylabel('TotalOrderValue')
+            st.pyplot(fig3)
+
+
+    st.write("---")    
+#####################################################################################################################################
+
+col1, col2, col3, col4, col5 = st.columns(5)
+with col5:
+    logo_artefact = Image.open('static/logo_artefact.png')
+    st.image(logo_artefact)

requirements.txt

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+streamlit
+pandas
+numpy
+matplotlib
+datetime
+Pillow
+plotly