| 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) | |