Create README.md

README.md

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+import pandas as pd
+import streamlit as st
+import plotly.express as px
+from plotly import graph_objs as go
+st.title("Demand Trend Analysis")
+
+df = pd.read_csv("data/cleaned_data.csv",parse_dates=['Order Date'],index_col='Order Date')
+df_train = df.index< '2018-01-01'
+
+df_test = df.index>= '2018-01-01'
+df_train = df[df_train]
+df_test = df[df_test]
+time_pred = ["Past","Future"]
+
+#display the years of data as a slider 2015-2017 for past and 2018 for future
+
+k = st.sidebar.selectbox("Time",time_pred)
+if k == "Past":
+    n_years = st.sidebar.slider("Years of data", 2015, 2016, 2017)
+    
+    periods = 12*n_years
+else:
+    n_years = st.sidebar.slider("Years of data", 2018,2019)
+    periods = 12
+
+@st.cache_data
+def load_data():
+    data = df.copy()
+    
+    return data
+
+
+data_load_state = st.text("Loading data...")
+data = load_data()
+data_load_state.text("Loading data...done!")
+
+st.subheader("Raw data")
+st.write(data.head())
+
+def plot_raw_data_year(input:str):
+    
+    
+    if input == "Past":
+        
+        df_yearly= df_train.groupby(pd.Grouper(freq='Y'))['Sales'].sum()
+        df_yearly = pd.DataFrame(df_yearly)
+    else:
+        df_yearly = df_test.groupby(pd.Grouper(freq='Y'))['Sales'].sum()
+        df_yearly = pd.DataFrame(df_yearly)
+        
+    fig = go.Figure()
+    fig.add_trace(go.Bar(x=df_yearly.index, y=df_yearly.Sales,name='Yearly Sales' ,))
+    fig.update_layout(title_text='Yearly Sales',plot_bgcolor='white',xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+    
+plot_raw_data_year(k)
+
+
+def plot_raw_data_month(input:str):
+    if input == "Past":
+        df_monthly= df_train.groupby(pd.Grouper(freq='M'))['Sales'].sum()
+        df_monthly = pd.DataFrame(df_monthly)
+    else:
+        df_monthly = df_test.groupby(pd.Grouper(freq='M'))['Sales'].sum()
+        df_monthly = pd.DataFrame(df_monthly)
+        
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df_monthly.index, y=df_monthly.Sales,name='Monthly Sales' ))
+    fig.update_layout(title_text= 'Monthly Sales',plot_bgcolor='white',xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+ 
+   
+plot_raw_data_month(k)
+
+    
+def plot_raw_data_day(input:str):
+    if input == "Past":
+        df_daily= df_train.groupby(pd.Grouper(freq='D'))['Sales'].sum()
+        df_daily = pd.DataFrame(df_daily)
+    else:
+        df_daily = df_test.groupby(pd.Grouper(freq='D'))['Sales'].sum()
+        df_daily = pd.DataFrame(df_daily)
+        
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=df_daily.index, y=df_daily.Sales,name='Daily Sales' ))
+    fig.update_layout(title_text= 'Daily Sales',plot_bgcolor='white',xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+    
+plot_raw_data_day(k)
+
+def plot_raw_yearly_sales_by_segment(input:str):
+    
+    if input == "Past":
+        df_yearly_segment = df_train.groupby([pd.Grouper(freq='Y'), 'Segment'])['Sales'].sum().reset_index()
+
+      
+        df_yearly_segment = pd.DataFrame(df_yearly_segment)
+    else:
+        df_yearly_segment = df_test.groupby([pd.Grouper(freq='Y'), 'Segment'])['Sales'].sum().reset_index()
+
+       
+        df_yearly_segment = pd.DataFrame(df_yearly_segment)
+    color_scale = px.colors.sequential.Viridis
+
+# create a dictionary that maps each unique value in the Segment column to a color from the color scheme
+    color_map = {segment: color_scale[i % len(color_scale)] for i, segment in enumerate(df_yearly_segment['Segment'].unique())}
+
+# use the color_map dictionary to map the Segment values to colors
+    colors = df_yearly_segment['Segment'].map(color_map)
+
+# create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=df_yearly_segment['Order Date'], y=df_yearly_segment['Sales'], marker={'color': colors},hovertext=df_yearly_segment['Segment']))
+    fig.update_layout(title_text='Yearly Sales by Segment', plot_bgcolor='white')
+    
+    st.plotly_chart(fig)
+    
+    
+plot_raw_yearly_sales_by_segment(k)
+def plot_raw_yearly_sales_by_region(input:str):
+    
+    if input == "Past":
+        df_yearly_segment = df_train.groupby([pd.Grouper(freq='Y'), 'Region'])['Sales'].sum().reset_index()
+
+      
+        df_yearly_segment = pd.DataFrame(df_yearly_segment)
+    else:
+        df_yearly_segment = df_test.groupby([pd.Grouper(freq='Y'), 'Region'])['Sales'].sum().reset_index()
+
+       
+        df_yearly_segment = pd.DataFrame(df_yearly_segment)
+    color_scale = px.colors.sequential.Viridis
+
+# create a dictionary that maps each unique value in the Segment column to a color from the color scheme
+    color_map = {segment: color_scale[i % len(color_scale)] for i, segment in enumerate(df_yearly_segment['Region'].unique())}
+
+# use the color_map dictionary to map the Segment values to colors
+    colors = df_yearly_segment['Region'].map(color_map)
+
+# create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=df_yearly_segment['Order Date'], y=df_yearly_segment['Sales'], marker={'color': colors},hovertext=df_yearly_segment['Region']))
+    fig.update_layout(title_text='Yearly Sales by  Region', plot_bgcolor='white')
+    st.plotly_chart(fig)
+    
+    
+plot_raw_yearly_sales_by_region(k)
+
+def plot_raw_yearly_sales_by_Category(input:str):
+    
+    if input == "Past":
+        df_yearly_segment = df_train.groupby([pd.Grouper(freq='Y'), 'Category'])['Sales'].sum().reset_index()
+
+      
+        
+    else:
+        df_yearly_segment = df_test.groupby([pd.Grouper(freq='Y'), 'Category'])['Sales'].sum().reset_index()
+
+       
+    df_yearly_segment = pd.DataFrame(df_yearly_segment)
+    color_scale = px.colors.sequential.Viridis
+
+# create a dictionary that maps each unique value in the Segment column to a color from the color scheme
+    color_map = {segment: color_scale[i % len(color_scale)] for i, segment in enumerate(df_yearly_segment['Category'].unique())}
+
+# use the color_map dictionary to map the Segment values to colors
+    colors = df_yearly_segment['Category'].map(color_map)
+
+# create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=df_yearly_segment['Order Date'], y=df_yearly_segment['Sales'], marker={'color': colors},hovertext=df_yearly_segment['Category']))
+    fig.update_layout(title_text='Yearly Sales by  Category', plot_bgcolor='white')
+    st.plotly_chart(fig)
+    
+plot_raw_yearly_sales_by_Category(k)
+
+def plot_raw_yearly_sales_by_State(input:str, number:int):
+    
+    if input == "Past":
+        df_yearly_state = df_train.groupby([pd.Grouper(freq='Y'), 'State'])['Sales'].sum().reset_index()
+    else:
+        df_yearly_state = df_test.groupby([pd.Grouper(freq='Y'), 'State'])['Sales'].sum().reset_index()
+        
+    df_yearly_state = pd.DataFrame(df_yearly_state)
+    color_scale = px.colors.sequential.Viridis
+    topN_states = df_yearly_state.groupby('State').sum().sort_values('Sales', ascending=False).head(number).index.tolist()
+    top_states_df = df_yearly_state[df_yearly_state['State'].isin(topN_states)]
+
+    # create a dictionary that maps each unique value in the State column to a color from the color scheme
+    color_map = {state: color_scale[i % len(color_scale)] for i, state in enumerate(top_states_df['State'].unique())}
+
+    # use the color_map dictionary to map the State values to colors
+    colors = top_states_df['State'].map(color_map)
+
+    # create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=top_states_df['Order Date'], y=top_states_df['Sales'], marker={'color': colors},hovertext=top_states_df['State']))
+    fig.update_layout(title_text=f'Top {number} states with highest sales', plot_bgcolor='white')
+    st.plotly_chart(fig)
+
+
+# initialize Streamlit slider for selecting number of subcategories to display
+number_st = st.slider('Select the number of States', 1, 10, 3)
+
+plot_raw_yearly_sales_by_State(k,number_st)
+
+def plot_raw_yearly_sales_by_Sub_Cat(input:str, number:int):
+    
+    if input == "Past":
+        df_yearly_state = df_train.groupby([pd.Grouper(freq='Y'), 'Sub-Category'])['Sales'].sum().reset_index()
+    else:
+        df_yearly_state = df_test.groupby([pd.Grouper(freq='Y'), 'Sub-Category'])['Sales'].sum().reset_index()
+        
+    df_yearly_state = pd.DataFrame(df_yearly_state)
+    color_scale = px.colors.sequential.Viridis
+    topN_states = df_yearly_state.groupby('Sub-Category').sum().sort_values('Sales', ascending=False).head(number).index.tolist()
+    top_states_df = df_yearly_state[df_yearly_state['Sub-Category'].isin(topN_states)]
+
+    # create a dictionary that maps each unique value in the State column to a color from the color scheme
+    color_map = {state: color_scale[i % len(color_scale)] for i, state in enumerate(top_states_df['Sub-Category'].unique())}
+
+    # use the color_map dictionary to map the State values to colors
+    colors = top_states_df['Sub-Category'].map(color_map)
+
+    # create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=top_states_df['Order Date'], y=top_states_df['Sub-Category'], marker={'color': colors},hovertext=top_states_df['Sub-Category']))
+    fig.update_layout(title_text=f'Top {number} sub categories with highest sales', plot_bgcolor='white')
+    st.plotly_chart(fig)
+
+
+# initialize Streamlit slider for selecting number of subcategories to display
+number_sub_cat = st.slider('Select the number of Sub-Category', 1, 10, 3)
+
+plot_raw_yearly_sales_by_Sub_Cat(k,number_sub_cat)
+
+
+
+
+
+def plot_raw_yearly_sales_by_Product(input:str,number:int):
+    
+    if input == "Past":
+        df_yearly_product = df_train.groupby([pd.Grouper(freq='Y'), 'Product Name'])['Sales'].sum().reset_index()
+    else:
+        df_yearly_product = df_test.groupby([pd.Grouper(freq='Y'), 'Product Name'])['Sales'].sum().reset_index()
+       
+    df_yearly_product = pd.DataFrame(df_yearly_product)
+    color_scale = px.colors.sequential.Viridis
+    topN_products = df_yearly_product.groupby('Product Name').sum().sort_values('Sales', ascending=False).head(number).index.tolist()
+    top_product_df = df_yearly_product[df_yearly_product['Product Name'].isin(topN_products)]
+
+    # create a dictionary that maps each unique value in the Product Name column to a color from the color scheme
+    color_map = {product: color_scale[i % len(color_scale)] for i, product in enumerate(top_product_df['Product Name'].unique())}
+
+    # use the color_map dictionary to map the Product Name values to colors
+    colors = top_product_df['Product Name'].map(color_map)
+
+    # create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=top_product_df['Order Date'], y=top_product_df['Sales'], marker={'color': colors},hovertext=top_product_df['Product Name']))
+    fig.update_layout(title_text=f'Top {number} best-selling products', plot_bgcolor='white')
+    st.plotly_chart(fig)
+
+# initialize Streamlit slider for selecting number of products to display
+number_p = st.slider('Select the number of products to display', 1, 10, 3)
+plot_raw_yearly_sales_by_Product(k,number_p)
+
+
+def plot_raw_yearly_sales_by_City(input:str, number:int):
+    
+    if input == "Past":
+        df_yearly_state = df_train.groupby([pd.Grouper(freq='Y'), 'City'])['Sales'].sum().reset_index()
+    else:
+        df_yearly_state = df_test.groupby([pd.Grouper(freq='Y'), 'City'])['Sales'].sum().reset_index()
+        
+    df_yearly_state = pd.DataFrame(df_yearly_state)
+    color_scale = px.colors.sequential.Viridis
+    topN_states = df_yearly_state.groupby('City').sum().sort_values('Sales', ascending=False).head(number).index.tolist()
+    top_states_df = df_yearly_state[df_yearly_state['City'].isin(topN_states)]
+
+    # create a dictionary that maps each unique value in the State column to a color from the color scheme
+    color_map = {state: color_scale[i % len(color_scale)] for i, state in enumerate(top_states_df['City'].unique())}
+
+    # use the color_map dictionary to map the State values to colors
+    colors = top_states_df['City'].map(color_map)
+
+    # create the plot using plotly.graph_objects
+    fig = go.Figure(data=go.Bar(x=top_states_df['Order Date'], y=top_states_df['City'], marker={'color': colors},hovertext=top_states_df['City']))
+    fig.update_layout(title_text=f'Top {number} states with highest sales', plot_bgcolor='white')
+    st.plotly_chart(fig)
+
+
+# initialize Streamlit slider for selecting number of subcategories to display
+number_city = st.slider('Select the number of Cities', 1, 10, 3)
+
+plot_raw_yearly_sales_by_City(k,number_city)
+
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