first commit

Data_prep_functions.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import pickle
+import statsmodels.api as sm
+import numpy as np
+from sklearn.metrics import mean_absolute_error, r2_score,mean_absolute_percentage_error  
+from sklearn.preprocessing import MinMaxScaler
+import matplotlib.pyplot as plt
+from statsmodels.stats.outliers_influence import variance_inflation_factor
+from plotly.subplots import make_subplots
+
+st.set_option('deprecation.showPyplotGlobalUse', False)
+from datetime import datetime
+import seaborn as sns
+
+def calculate_discount(promo_price_series, non_promo_price_series):
+    # Calculate the 4-week moving average of non-promo price
+    window_size = 4
+    base_price = non_promo_price_series.rolling(window=window_size).mean()
+    
+    # Calculate discount_raw
+    discount_raw_series = (1 - promo_price_series / base_price) * 100
+    
+    # Calculate discount_final
+    discount_final_series = discount_raw_series.where(discount_raw_series >= 5, 0)
+    
+    return base_price, discount_raw_series, discount_final_series
+
+
+def create_dual_axis_line_chart(date_series, promo_price_series, non_promo_price_series, base_price_series, discount_series):
+    # Create traces for the primary axis (price vars)
+    trace1 = go.Scatter(
+        x=date_series,
+        y=promo_price_series,
+        name='Promo Price',
+        yaxis='y1'
+    )
+    
+    trace2 = go.Scatter(
+        x=date_series,
+        y=non_promo_price_series,
+        name='Non-Promo Price',
+        yaxis='y1'
+    )
+
+    trace3 = go.Scatter(
+        x=date_series,
+        y=base_price_series,
+        name='Base Price',
+        yaxis='y1'
+    )
+    
+    # Create a trace for the secondary axis (discount)
+    trace4 = go.Scatter(
+        x=date_series,
+        y=discount_series,
+        name='Discount',
+        yaxis='y2'
+    )
+
+    # Create the layout with dual axes
+    layout = go.Layout(
+        title='Price and Discount Over Time',
+        yaxis=dict(
+            title='Price',
+            side='left'
+        ),
+        yaxis2=dict(
+            title='Discount',
+            side='right',
+            overlaying='y',
+            showgrid=False
+        ),
+        xaxis=dict(title='Date'),
+    )
+
+    # Create the figure with the defined traces and layout
+    fig = go.Figure(data=[trace1, trace2, trace3, trace4], layout=layout)
+
+    return fig
+
+
+def to_percentage(value):
+  return f'{value * 100:.1f}%'   
+
+def plot_actual_vs_predicted(date, y, predicted_values, model,target_column=None, flag=None, repeat_all_years=False, is_panel=False):
+    if flag is not None :
+      fig = make_subplots(specs=[[{"secondary_y": True}]])
+    else :
+      fig = go.Figure()
+
+    if is_panel :
+      df=pd.DataFrame()
+      df['date'] = date
+      df['Actual'] = y
+      df['Predicted'] = predicted_values
+      df_agg = df.groupby('date').agg({'Actual':'sum', 'Predicted':'sum'}).reset_index()
+      df_agg.columns = ['date', 'Actual', 'Predicted']
+      assert len(df_agg) == pd.Series(date).nunique()
+      # date = df_agg['date']
+      # y = df_agg['Actual']
+      # predicted_values = df_agg['Predicted']
+      # ymax = df_agg['Actual'].max() # Sprint3 - ymax to set y value for flag
+
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Actual'], mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=df_agg['date'], y=df_agg['Predicted'], mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    else :
+      fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='#08083B')))
+      fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='#11B6BD')))
+
+    line_values=[]
+    if flag:
+      min_date, max_date = flag[0], flag[1]
+      min_week = datetime.strptime(str(min_date), "%Y-%m-%d").strftime("%U")
+      max_week = datetime.strptime(str(max_date), "%Y-%m-%d").strftime("%U")
+      month=pd.to_datetime(min_date).month
+      day=pd.to_datetime(min_date).day
+      #st.write(pd.to_datetime(min_date).week)
+      #st.write(min_week)
+      # Initialize an empty list to store line values
+
+      # Sprint3 change : put flags to secondary axis, & made their y value to 1 instead of 5M
+      if repeat_all_years:
+        #line_values=list(pd.to_datetime((pd.Series(date)).dt.week).map(lambda x: 10000 if x==min_week else 0  ))
+        #st.write(pd.Series(date).map(lambda x: pd.Timestamp(x).week))
+        line_values=list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x).week >=int(min_week)) & (pd.Timestamp(x).week <=int(max_week)) else 0))
+        assert len(line_values) == len(date)
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+      else:
+        line_values = []
+
+        line_values = list(pd.Series(date).map(lambda x: 1 if (pd.Timestamp(x) >= pd.Timestamp(min_date)) and (pd.Timestamp(x) <= pd.Timestamp(max_date)) else 0))
+
+        #st.write(line_values)
+        fig.add_trace(go.Scatter(x=date, y=line_values, mode='lines', name='Flag', line=dict(color='#FF5733')),secondary_y=True)
+
+
+    # Calculate MAPE
+    mape = mean_absolute_percentage_error(y, predicted_values)
+    print('mape*********',mape)
+    # Calculate AdjR2 # Assuming X is your feature matrix
+    r2 = r2_score(y, predicted_values)
+
+    adjr2 = 1 - (1 - r2) * (len(y) - 1) / (len(y) - len(model.params) - 1) #manoj
+
+
+    # Create a table to display the metrics
+    metrics_table = pd.DataFrame({
+      'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+      'Value': [mape, r2, adjr2]
+    })
+    # st.write(metrics_table)
+    fig.update_layout(
+      xaxis=dict(title='Date'),
+      yaxis=dict(title=target_column),
+      xaxis_tickangle=-30
+    )
+    fig.add_annotation(
+    text=f"MAPE: {mape*100:0.1f}%,  Adjr2: {adjr2 *100:.1f}%",
+    xref="paper",
+    yref="paper",
+    x=0.95,  # Adjust these values to position the annotation
+    y=1.2,
+    showarrow=False,
+    )
+    # print("{}{}"*20, len(line_values))
+  #metrics_table.set_index(['Metric'],inplace=True)
+    return metrics_table,line_values, fig
+
+def plot_residual_predicted(actual, predicted, df):
+        df_=df.copy()
+        df_['Residuals'] = actual - pd.Series(predicted)
+        df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+        
+        # Create a Plotly scatter plot
+        fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+        
+        # Add horizontal lines
+        fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+        fig.add_hline(y=2, line_color="red")
+        fig.add_hline(y=-2, line_color="red")
+        
+        fig.update_xaxes(title='Predicted')
+        fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+        
+        # Set the same width and height for both figures
+        fig.update_layout(title='2.3.1 Residuals over Predicted Values', autosize=False, width=600, height=400)
+        
+        return fig
+
+def residual_distribution(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        
+        # Create a Seaborn distribution plot
+        sns.set(style="whitegrid")
+        plt.figure(figsize=(6, 4))
+        sns.histplot(Residuals, kde=True, color="#11B6BD")
+        
+        plt.title('2.3.3 Distribution of Residuals')
+        plt.xlabel('Residuals')
+        plt.ylabel('Probability Density')
+        
+        return plt
+
+
+def qqplot(actual, predicted):
+        Residuals = actual - pd.Series(predicted)
+        Residuals = pd.Series(Residuals)
+        Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+        
+        # Create a QQ plot using Plotly with custom colors
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                mode='markers',
+                                marker=dict(size=5, color="#11B6BD"),
+                                name='QQ Plot'))
+        
+        # Add the 45-degree reference line
+        diagonal_line = go.Scatter(
+            x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+            y=[-2, 2],  # Adjust the y values accordingly
+            mode='lines',
+            line=dict(color='red'),  # Customize the line color and style
+            name=' '
+        )
+        fig.add_trace(diagonal_line)
+        
+        # Customize the layout
+        fig.update_layout(title='2.3.2 QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                          xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+        
+        return fig

Eda_functions.py

@@ -0,0 +1,178 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+import re
+from matplotlib.colors import ListedColormap
+# from st_aggrid import AgGrid, GridOptionsBuilder
+# from src.agstyler import PINLEFT, PRECISION_TWO, draw_grid
+
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+    
+
+def line_plot(data, x_col, y1_cols, y2_cols, title):
+    fig = go.Figure()
+      
+    for y1_col in y1_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y1_col], mode='lines', name=y1_col,line=dict(color='#11B6BD')))
+
+    for y2_col in y2_cols:
+        fig.add_trace(go.Scatter(x=data[x_col], y=data[y2_col], mode='lines', name=y2_col, yaxis='y2',line=dict(color='#739FAE')))
+    if len(y2_cols)!=0:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    else:
+        fig.update_layout(yaxis=dict(), yaxis2=dict(overlaying='y', side='right'))
+    if title:
+        fig.update_layout(title=title)
+    fig.update_xaxes(showgrid=False)
+    fig.update_yaxes(showgrid=False)
+    fig.update_layout(legend=dict(
+        orientation="h",
+        yanchor="top",
+        y=1.1,
+        xanchor="center",
+        x=0.5
+    ))
+
+    return fig
+
+
+def line_plot_target(df,target,title):
+
+    coefficients = np.polyfit(df['date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['date']
+    trendline_y = trendline(df['date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+    fig.update_layout(legend=dict(
+        orientation="h",
+        yanchor="top",
+        y=1.1,
+        xanchor="center",
+        x=0.5
+    ))
+    return fig
+
+def correlation_plot(df,selected_features,target):
+    custom_cmap = ListedColormap(['#08083B', "#11B6BD"])  
+    corr_df=df[selected_features]
+    corr_df=pd.concat([corr_df,df[target]],axis=1)
+    fig, ax = plt.subplots(figsize=(16, 12))
+    sns.heatmap(corr_df.corr(),annot=True, cmap='Blues', fmt=".2f", linewidths=0.5,mask=np.triu(corr_df.corr()))
+    #plt.title('Correlation Plot')
+    plt.xticks(rotation=45)
+    plt.yticks(rotation=0)
+    return fig
+
+def summary(data,selected_feature,spends,Target=None):
+        
+        if Target:
+            sum_df = data[selected_feature]
+            sum_df['Year']=data['date'].dt.year
+            sum_df=sum_df.groupby('Year')[selected_feature].sum()
+            sum_df=sum_df.reset_index()
+            total_sum = sum_df.sum(numeric_only=True)
+            total_sum['Year'] = 'Total'
+            sum_df = pd.concat([sum_df, total_sum.to_frame().T],axis=0,ignore_index=True).copy()
+            #sum_df = sum_df.append(total_sum, ignore_index=True)
+            #st.write(sum_df)
+            sum_df.set_index(['Year'],inplace=True)
+            sum_df=sum_df.applymap(format_numbers)
+            spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+            for col in spends_col:
+                sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+            # st.write(spends_col)
+            # sum_df = sum_df.reindex(sorted(sum_df.columns), axis=1)
+
+            return sum_df
+        else:
+            #selected_feature=list(selected_feature)
+            selected_feature.append(spends)
+        
+            if len(selected_feature)>1:
+                imp_clicks=selected_feature[0]
+                spends_col=selected_feature[1]
+                
+            selected_feature=list(set(selected_feature))
+
+            if len(selected_feature)>1:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                
+                sum_df['CPM/CPC']=(sum_df[spends_col] / sum_df[imp_clicks])*1000
+                sum_df.loc['Grand Total']=sum_df.sum()
+            
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                #spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                sum_df[spends_col]=sum_df[spends_col].map(lambda x: f'${x}')
+                return sum_df
+            else:
+                sum_df = data[selected_feature]
+                sum_df['Year']=data['date'].dt.year
+                sum_df=sum_df.groupby('Year')[selected_feature].agg('sum')
+                sum_df.loc['Grand Total']=sum_df.sum()
+                sum_df=sum_df.applymap(format_numbers)
+                sum_df.fillna('-',inplace=True)
+                sum_df=sum_df.replace({"0.0":'-','nan':'-'})
+                spends_col=[col for col in sum_df.columns if any(keyword in col for keyword in ['spends', 'cost'])]
+                for col in spends_col:
+                    sum_df[col]=sum_df[col].map(lambda x: f'${x}')
+                return sum_df
+
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+
+

Home.py

@@ -0,0 +1,644 @@
+import sqlite3
+import uuid
+import streamlit as st
+from utilities import (
+    load_local_css,
+    set_header,
+)
+
+
+import os
+import datetime
+import shutil
+import pandas as pd
+import pickle
+from pathlib import Path
+import re
+st.set_page_config(layout="wide")
+load_local_css("styles.css")
+set_header()
+
+
+# Define the path to the database file
+database_file = r"DB/User.db"
+
+# Establish a connection to the SQLite database specified by database_file
+conn = sqlite3.connect(database_file, check_same_thread=False)
+
+# Create a cursor object using the connection
+
+c = conn.cursor()
+
+
+
+def update_summary_df():
+    #print("[DEBUG]: Running update_summary_df")
+    
+    """ Function to fetch the project details everytime user 
+    changes the slection box this function is being called on change in 
+    username select box """
+
+    # Execute a SQL query to select distinct project names, the last edited page,
+    # and the last updated time from the 'sessions' table where the owner matches the user's name
+    
+    c.execute(
+        """
+        SELECT project_name, last_edited_page, updated_time as last_updated 
+        FROM (
+            SELECT project_name, last_edited_page, updated_time
+            FROM sessions 
+            WHERE owner=?
+            ORDER BY updated_time DESC
+        ) sub
+        GROUP BY project_name
+        """,
+        (st.session_state["username"],)
+    )
+
+    # Fetch all the results of the query
+    project_summary = c.fetchall()
+    
+
+    # This will hold the user's owned sessions
+
+    # Create a DataFrame from the fetched data with specified column names
+    project_summary_df = pd.DataFrame(
+        project_summary,
+        columns=["Project Name", "Last Page Edited", "Modified Date"],
+    )
+
+    # Convert the 'Modified Date' column to datetime format
+    project_summary_df["Modified Date"] = project_summary_df[
+        "Modified Date"
+    ].map(lambda x: pd.to_datetime(x).date())
+
+    # Sort the DataFrame by 'Modified Date' in descending order
+    session_summary_df = project_summary_df.sort_values(
+        by=["Modified Date"], ascending=False
+    )
+
+    session_summary_df["Last Page Modified"] = session_summary_df[
+        "Last Page Edited"
+    ].map(lambda x: re.sub(r'[_1-9]', ' ', x).replace(".py", ""))
+
+    # Save the resulting DataFrame to the session state
+    st.session_state["session_summary_df"] = session_summary_df
+
+
+    # Add a 'selected' column to the DataFrame and initialize it with False for all rows
+
+    if "selected" not in st.session_state.session_summary_df.columns:
+        st.session_state.session_summary_df["selected"] = [False] * len(
+            st.session_state.session_summary_df
+        )
+
+    # Reset the index of the DataFrame and save it back to the session state
+    st.session_state["session_summary_df"] = (
+        st.session_state["session_summary_df"].reset_index(drop=True).copy()
+    )
+
+
+st.header("Manage Projects")
+# c.execute("PRAGMA table_info(sessions);")
+
+# st.write(c.fetchall())
+users = {
+    "ioannis": "Ioannis Papadopoulos",
+    "sharon": "Sharon Sheng",
+    "herman": "Herman Kwong",
+    "ismail": "Ismail Mohammed",
+    "geetha": "Geetha Krishna",
+    "srishti": "Srishti Verma",
+    "samkeet": "Samkeet Sangai",
+    "manoj": "Manoj P"
+}
+
+
+if 'username' not in st.session_state:
+    st.session_state['username']=''
+
+# first_name_value = [key for key, value in users.items() if value == st.session_state['username']]
+
+# # Extract the first key from the list if the list is not empty
+# first_name_value = first_name_value[0] if first_name_value else ''
+
+
+first_name=st.text_input('Enter Name').lower()
+
+
+if st.button('Login'):
+
+    if first_name not in users.keys():
+        st.warning('Please enter a valid name')
+        st.stop()
+
+    name=users[first_name]
+
+    st.session_state.name=name # storing in session state
+
+    st.session_state["username"]= name 
+
+    update_summary_df() #function call to fetch user saved projects
+
+    #st.success('Projects sucessfully loaded')
+
+if len(first_name)==0 or first_name not in users.keys():
+    st.stop()
+
+
+
+# name=st.session_state['Username']
+
+# c.execute('Delete from sessions')
+# conn.commit()
+if st.session_state["username"] in users.values():
+
+    if "session_summary_df" not in st.session_state:
+        st.session_state.session_summary_df = pd.DataFrame()
+
+    if "project_name" not in st.session_state:
+        st.session_state["project_name"] = None
+
+    cols1 = st.columns([2, 1])
+
+    with cols1[0]:
+        st.markdown(f"**Welcome {st.session_state['username']}**")
+    with cols1[1]:
+        st.markdown(f"**Current Project: {st.session_state['project_name']}**")
+
+
+    # Execute a SQL query to select all project names from the 'sessions' table
+    # where the owner matches the user's name
+    c.execute("SELECT project_name FROM sessions WHERE owner=?", (st.session_state['username'],))
+
+    # Fetch all the results and create a list of project names
+    user_projects = [project[0] for project in c.fetchall()]
+
+
+    c.execute("SELECT DISTINCT username FROM users")
+
+    # Fetch all the results and create a list of usernames excluding the current user's name
+    allowed_users_db = [user[0] for user in c.fetchall() if user[0] != st.session_state['username']]
+
+    page_name = "Home Page"
+
+    # Execute a SQL query to select the email, user_id, and user_type from the 'users' table
+    # where the username matches the current user's name
+    c.execute(
+        "SELECT email, user_id, user_type FROM users WHERE username = ?",
+        (st.session_state['username'],),
+    )
+
+    # Fetch the result of the query (assume there is only one matching row)
+    user_data = c.fetchone()
+
+    # Unpack the fetched data into corresponding variables
+    email, user_id, user_type = user_data
+
+
+
+    folder_path = r"Users"
+    user_folder_path = os.path.join(folder_path, email)
+
+    if not os.path.exists(user_folder_path):
+        os.makedirs(user_folder_path)
+
+
+    def dump_session_details_db(allowed_users, project_name):
+
+        'Function to dump details of project in db when a project is created/modified/cloned '
+
+        created_time = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")  # Get the current time
+        session_id = str(uuid.uuid4())  # Generate a unique session ID
+
+        if len(allowed_users) == 0:
+            # Insert a new session into the database with no allowed users
+            c.execute(
+                "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                (
+                    user_id,
+                    st.session_state['username'],
+                    session_id,
+                    project_name,
+                    page_name,
+                    created_time,
+                    created_time,
+                    None,
+                ),
+            )
+            conn.commit()  # Commit the transaction
+        else:
+            # Insert new sessions for each allowed user
+            for allowed_user in allowed_users:
+                c.execute(
+                    "INSERT INTO sessions VALUES (?, ?, ?, ?, ?, ?, ?,?)",
+                    (
+                        user_id,
+                        st.session_state['username'],
+                        session_id,
+                        project_name,
+                        "1_Home.py",
+                        created_time,
+                        created_time,
+                        allowed_user,
+                    ),
+                )
+            conn.commit()  # Commit the transaction
+
+
+
+    st.markdown(
+        """
+    * **Delete Project:** If you wish to delete a project, select it and click 'Delete Project'.
+    * **Modify User Access:** Make changes to user access permissions as needed.
+push
+    """
+    )
+
+    session_col = st.columns([5, 5])
+
+    # data editor
+    if "selected_row_index" not in st.session_state:
+        st.session_state["selected_row_index"] = None
+
+
+    def selection_change():
+        # Get the edited rows from the session state
+        #print(st.session_state['session_summary_df'])
+        edited_rows: dict = st.session_state['project_selection']["edited_rows"]
+        #print(edited_rows)
+        
+        # Set the selected row index in the session state
+        st.session_state["selected_row_index"] = next(iter(edited_rows))
+
+        # # Set all 'selected' flags to False in the DataFrame
+        # st.session_state["session_summary_df"] = st.session_state[
+        #     "session_summary_df"
+        # ].assign(selected=False)
+
+        # Create a dictionary to update the DataFrame
+        update_dict = {idx: values for idx, values in edited_rows.items()}
+
+        # Update the DataFrame with the edited rows
+        st.session_state["session_summary_df"].update(
+            pd.DataFrame.from_dict(update_dict, orient="index")
+        )
+
+        # Reset the DataFrame index
+        st.session_state['session_summary_df'] = st.session_state['session_summary_df'].reset_index(drop=True)
+        
+
+
+
+    st.markdown("Select Project")
+
+    if len(st.session_state["session_summary_df"])!=0:
+        
+        with st.container():
+            # Display an editable data table using Streamlit's data editor component
+            
+            table = st.data_editor(
+                st.session_state["session_summary_df"].drop(['Last Page Edited'],axis=1).reindex(
+                    columns=['selected','Project Name','Last Page Modified','Modified Date'
+                            ]),  
+                hide_index=True,  
+                on_change=selection_change,  # Function to call when data is edited
+                key="project_selection",  # Key for the data editor component in the session state
+                use_container_width=True,  
+            )
+
+        if len(st.session_state["session_summary_df"]) > 0 and st.session_state["selected_row_index"] is not None :
+
+            selected_row_index = st.session_state["session_summary_df"]["selected"]
+        
+
+            # st.write(st.session_state['selected_row_index'])
+
+            if len(selected_row_index) != 0:
+                
+                try :
+                    project_name = st.session_state["session_summary_df"].at[
+                        st.session_state["selected_row_index"], "Project Name"
+                    ]
+                except Exception as e:
+                    st.session_state["selected_row_index"]=None
+                    st.rerun() 
+
+                last_edited_page = st.session_state["session_summary_df"].at[
+                        st.session_state["selected_row_index"], "Last Page Edited"
+                    ]
+
+                st.session_state["project_name"] = project_name
+
+
+                project_col = st.columns(2)
+
+                with project_col[0]:
+                    
+                    if st.button('Load Project',use_container_width=True):
+                        st.session_state["project_name"] = project_name
+                        st.rerun()
+
+                    project_path = os.path.join(user_folder_path, project_name)
+
+                    st.session_state["project_path"] = project_path  # load project dct
+
+                    project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+                    with open(project_dct_path, "rb") as f:
+                        try:
+                            st.session_state["project_dct"] = pickle.load(f)
+                            st.success('Project Loded')
+
+                        except Exception as e:
+                            st.warning('Something went wrong unable to load saved details / data is lost due to app refresh. Please uncheck the check box and create a new project.')
+                            st.stop()
+
+
+                with project_col[1]:
+
+                    if st.button(f"Delete Project - **{project_name}**",use_container_width=True):
+
+                        project_name_to_delete = project_name
+                        st.warning(
+                            f"{project_name_to_delete} will be deleted permanentaly and all the information regarding the project will be lost"
+                        )
+
+                        try:
+                            c.execute(
+                                "Delete FROM sessions WHERE project_name =? AND owner =?",
+                                (
+                                    project_name_to_delete,
+                                    st.session_state["name"],
+                                ),
+                            )
+                            if os.path.exists(project_path):
+                                shutil.rmtree(project_path)
+
+                            conn.commit()
+                            update_summary_df()
+                            st.rerun()
+
+                        except:
+                            st.warning('Failed to Delete project try refreshing the page or try after some time')
+                            st.stop()
+
+                with st.expander("Add users with access to the selected project"):
+
+                    c.execute(
+                        "SELECT DISTINCT allowed_users FROM sessions WHERE project_name = ?",
+                        (project_name,),
+                    )
+
+                    present_users = c.fetchall()
+
+                    present_users = [
+                        user[0]
+                        for user in present_users
+                        if user[0] != st.session_state['username'] and user[0] is not None
+                    ]
+
+                    present_users = None if len(present_users) == 0 else present_users
+
+                    if present_users is not None:
+
+                        allowed_users = st.multiselect(
+                            "",
+                            list(set(allowed_users_db) - set(present_users)),
+                        )
+                    else:
+                        
+                        allowed_users = st.multiselect(
+                            "",
+                            list(set(allowed_users_db)),
+                        )
+                        
+                    if st.button("Save Changes", use_container_width=True):
+                        dump_session_details_db(allowed_users, project_name)
+                        c.execute("SELECT * from sessions")
+
+    with st.expander("Create New Project"):
+
+        st.markdown(
+            "To create a new project, Enter Project name below, select user who you want to give access of this project and click **Create New Project**"
+        )
+
+        project_col1 = st.columns(2)
+        with project_col1[0]:
+            project_name = st.text_input(
+                "Enter Project Name", key="project_name_box"
+            )
+        if project_name in user_projects:
+            st.warning("Project already exists please enter new name")
+
+        with project_col1[1]:
+
+            allowed_users = st.multiselect(
+                "Select Users who can access to this Project", allowed_users_db
+            )
+            allowed_users = list(allowed_users)
+
+        Create = st.button("Create New Project", use_container_width=True )
+
+        if Create:
+
+            if len(project_name) == 0:
+                st.error("Plase enter a valid project name")
+                st.stop()
+            if project_name in user_projects:
+
+                st.warning("Project already exists please enter new name")
+                st.stop()
+
+            project_path = os.path.join(user_folder_path, project_name)
+            
+            if not os.path.exists(project_path):
+                os.makedirs(project_path)
+
+            else:
+                st.warning("Project already exists please enter new name")
+                st.stop()
+
+            dump_session_details_db(allowed_users, project_name)
+
+            project_dct = {
+                "data_import": {
+                    "granularity_selection": 0,
+                    "cat_dct": {},
+                    "merged_df": None,
+                    "edited_df": None,
+                    "numeric_columns": None,
+                    "files_dict": None,
+                    "formatted_panel1_values": None,
+                    "formatted_panel2_values": None,
+                    "missing_stats_df": None,
+                    "edited_stats_df": None,
+                    "default_df": None,
+                    "final_df": None,
+                    "edited_df": None,
+                },
+                "data_validation": {
+                    "target_column": 0,
+                    "selected_panels": None,
+                    "selected_feature": 0,
+                    "validated_variables": [],
+                    "Non_media_variables": 0,
+                },
+                "transformations": {"Media": {}, "Exogenous": {}},
+                "model_build": {
+                    "sel_target_col": None,
+                    "all_iters_check": False,
+                    "iterations": 0,
+                    "build_button": False,
+                    "show_results_check": False,
+                    "session_state_saved": {},
+                },
+                "model_tuning": {
+                    "sel_target_col": None,
+                    "sel_model": {},
+                    "flag_expander": False,
+                    "start_date_default": None,
+                    "end_date_default": None,
+                    "repeat_default": "No",
+                    "flags": {},
+                    "select_all_flags_check": {},
+                    "selected_flags": {},
+                    "trend_check": False,
+                    "week_num_check": False,
+                    "sine_cosine_check": False,
+                    "session_state_saved": {},
+                },
+                "saved_model_results": {
+                    "selected_options": None,
+                    "model_grid_sel": [1],
+                },
+                "model_result_overview": {},
+                "build_response_curves": {
+                    "response_metrics_selectbox": 0,
+                    "panel_selected_selectbox": 0,
+                    "selected_channel_name_selectbox": 0,
+                    "K_number_input": "default",
+                    "b_number_input": "default",
+                    "a_number_input": "default",
+                    "x0_number_input": "default",
+                },
+                "scenario_planner": {
+                    "panel_selected": 0,
+                    "metrics_selected": 0,
+                    "scenario": None,
+                    "optimization_key_value": None,
+                    "total_spends_change": None,
+                    "optimze_all_channels": False,
+                },
+                "saved_scenarios": {
+                    "selected_scenario_selectbox_key": 0,
+                },
+                "optimized_result_analysis": {
+                    "selected_scenario_selectbox_visualize": 0,
+                    "metric_selectbox_visualize": 0,
+                },
+            }
+
+            st.session_state["project_dct"] = project_dct
+
+            st.session_state["project_path"] = project_path
+            st.session_state["project_name"] = project_name
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "wb") as f:
+                pickle.dump(project_dct, f)
+
+            st.success("Project Created")
+
+            update_summary_df() 
+            
+            st.rerun()
+
+
+    # st.header('Clone Project')
+
+    with st.expander("**Clone saved projects**"):
+
+        c.execute(
+            "SELECT DISTINCT owner FROM sessions WHERE allowed_users=?",
+            (st.session_state['username'],),
+        )  # owner
+        owners = c.fetchall()
+
+        owners = [owner[0] for owner in owners if owner[0]!=st.session_state["username"]]
+
+        if len(owners) == 0:
+
+            st.warning("You dont have any shared project yet!")
+
+            st.stop()
+
+        cols = st.columns(2)
+
+        with cols[0]:
+
+            owner = st.selectbox("Select Owner", owners)
+
+            c.execute("SELECT email FROM users WHERE username=?", (owner,))
+
+            owner_email = c.fetchone()[0]
+
+            owner_folder_path = os.path.join(folder_path, owner_email)
+
+        with cols[1]:
+
+            c.execute(
+                "SELECT project_name FROM sessions WHERE  owner=? AND allowed_users = ?",
+                (owner, st.session_state['username']),
+            )  # available sessions for user
+            project_names = c.fetchall()
+
+            project_name_owner = st.selectbox(
+                "Select a saved Project available for you",
+                [project_name[0] for project_name in project_names],
+            )
+            owner_project_path = os.path.join(owner_folder_path, project_name)
+
+    
+        project_name_user = st.text_input(
+            "Enter Project Name", value=project_name_owner
+        )
+
+        if project_name in user_projects:
+
+            st.warning(
+                "This Project name already exists in your directory Please enter a different name"
+            )
+
+        project_path = os.path.join(user_folder_path, project_name_user)
+
+        owner_project_path = os.path.join(
+            owner_folder_path, project_name_owner
+        )
+
+
+        if st.button("Load Project", use_container_width=True):
+
+            if os.path.exists(project_path):
+
+                st.warning(
+                    "This Project name already exists in your directory Please enter a different name"
+                )
+
+                st.stop()
+
+            shutil.copytree(owner_project_path, project_path)
+
+            project_dct_path = os.path.join(project_path, "project_dct.pkl")
+
+            with open(project_dct_path, "rb") as f:
+                st.session_state["project_dct"] = pickle.load(f)
+
+            st.session_state["project_path"] = project_path
+
+
+            dump_session_details_db([], project_name_user) #passing empty list for allowed users
+            st.success("Project Cloned")
+            st.rerun()
+

Model/model_0.pkl

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+size 29283

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Model/model_8.pkl

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Model/model_9.pkl

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Model_Results_Pretrained.py

@@ -0,0 +1,349 @@
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+from collections import OrderedDict
+import pickle
+import json
+import streamlit as st
+import plotly.express as px
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import r2_score
+import pickle
+import json
+import pandas as pd
+import statsmodels.api as sm
+from sklearn.metrics import mean_absolute_percentage_error
+import sys
+import os
+from utilities import (set_header, 
+                       initialize_data,
+                       load_local_css,
+                       create_channel_summary,
+                       create_contribution_pie,
+                       create_contribuion_stacked_plot,
+                       create_channel_spends_sales_plot,
+                       format_numbers,
+                       channel_name_formating,
+                       load_authenticator)
+import seaborn as sns
+import matplotlib.pyplot as plt
+import sweetviz as sv
+import tempfile
+
+original_stdout = sys.stdout
+sys.stdout = open('temp_stdout.txt', 'w')
+sys.stdout.close()
+sys.stdout = original_stdout
+
+st.set_page_config(layout='wide')
+load_local_css('styles.css')
+set_header()
+
+for k, v in st.session_state.items():
+    if k not in ['logout', 'login','config'] and not k.startswith('FormSubmitter'):
+        st.session_state[k] = v
+
+authenticator = st.session_state.get('authenticator')
+if authenticator is None:
+    authenticator = load_authenticator()
+
+name, authentication_status, username = authenticator.login('Login', 'main')
+auth_status = st.session_state.get('authentication_status')
+
+if auth_status == True:
+    is_state_initiaized = st.session_state.get('initialized',False)
+    if not is_state_initiaized:
+        a=1
+      
+
+    def plot_residual_predicted(actual, predicted, df_):
+            df_['Residuals'] = actual - pd.Series(predicted)
+            df_['StdResidual'] = (df_['Residuals'] - df_['Residuals'].mean()) / df_['Residuals'].std()
+            
+            # Create a Plotly scatter plot
+            fig = px.scatter(df_, x=predicted, y='StdResidual', opacity=0.5,color_discrete_sequence=["#11B6BD"])
+            
+            # Add horizontal lines
+            fig.add_hline(y=0, line_dash="dash", line_color="darkorange")
+            fig.add_hline(y=2, line_color="red")
+            fig.add_hline(y=-2, line_color="red")
+            
+            fig.update_xaxes(title='Predicted')
+            fig.update_yaxes(title='Standardized Residuals (Actual - Predicted)')
+            
+            # Set the same width and height for both figures
+            fig.update_layout(title='Residuals over Predicted Values', autosize=False, width=600, height=400)
+            
+            return fig
+
+    def residual_distribution(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            
+            # Create a Seaborn distribution plot
+            sns.set(style="whitegrid")
+            plt.figure(figsize=(6, 4))
+            sns.histplot(Residuals, kde=True, color="#11B6BD")
+            
+            plt.title(' Distribution of Residuals')
+            plt.xlabel('Residuals')
+            plt.ylabel('Probability Density')
+            
+            return plt
+
+    
+    def qqplot(actual, predicted):
+            Residuals = actual - pd.Series(predicted)
+            Residuals = pd.Series(Residuals)
+            Resud_std = (Residuals - Residuals.mean()) / Residuals.std()
+            
+            # Create a QQ plot using Plotly with custom colors
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=sm.ProbPlot(Resud_std).theoretical_quantiles,
+                                    y=sm.ProbPlot(Resud_std).sample_quantiles,
+                                    mode='markers',
+                                    marker=dict(size=5, color="#11B6BD"),
+                                    name='QQ Plot'))
+            
+            # Add the 45-degree reference line
+            diagonal_line = go.Scatter(
+                x=[-2, 2],  # Adjust the x values as needed to fit the range of your data
+                y=[-2, 2],  # Adjust the y values accordingly
+                mode='lines',
+                line=dict(color='red'),  # Customize the line color and style
+                name=' '
+            )
+            fig.add_trace(diagonal_line)
+            
+            # Customize the layout
+            fig.update_layout(title='QQ Plot of Residuals',title_x=0.5, autosize=False, width=600, height=400,
+                            xaxis_title='Theoretical Quantiles', yaxis_title='Sample Quantiles')
+            
+            return fig
+
+
+    def plot_actual_vs_predicted(date, y, predicted_values, model):
+        fig = go.Figure()
+
+        fig.add_trace(go.Scatter(x=date, y=y, mode='lines', name='Actual', line=dict(color='blue')))
+        fig.add_trace(go.Scatter(x=date, y=predicted_values, mode='lines', name='Predicted', line=dict(color='orange')))
+        
+        # Calculate MAPE
+        mape = mean_absolute_percentage_error(y, predicted_values)*100
+        
+        # Calculate R-squared
+        rss = np.sum((y - predicted_values) ** 2)
+        tss = np.sum((y - np.mean(y)) ** 2)
+        r_squared = 1 - (rss / tss)
+        
+        # Get the number of predictors
+        num_predictors = model.df_model
+        
+        # Get the number of samples
+        num_samples = len(y)
+        
+        # Calculate Adjusted R-squared
+        adj_r_squared = 1 - ((1 - r_squared) * ((num_samples - 1) / (num_samples - num_predictors - 1)))
+        metrics_table = pd.DataFrame({
+        'Metric': ['MAPE', 'R-squared', 'AdjR-squared'],
+        'Value': [mape, r_squared, adj_r_squared]})
+        fig.update_layout(
+            xaxis=dict(title='Date'),
+            yaxis=dict(title='Value'),
+            title=f'MAPE : {mape:.2f}%, AdjR2: {adj_r_squared:.2f}',
+            xaxis_tickangle=-30
+        )
+
+        return metrics_table,fig
+
+
+
+
+    # # Perform linear regression
+    # model = sm.OLS(y, X).fit()
+    eda_columns=st.columns(3)
+    with eda_columns[0]:
+        tactic=st.checkbox('Tactic Level Model')
+    if tactic:
+        with open('mastercard_mmm_model.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard.csv')
+        test=pd.read_csv('test_mastercard.csv')
+        train['Date']=pd.to_datetime(train['Date'])
+        test['Date']=pd.to_datetime(test['Date'])
+        train.set_index('Date',inplace=True)
+        test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+        # sys.stdout.close()
+        # sys.stdout = original_stdout
+
+        # st.set_page_config(layout='wide')
+        # load_local_css('styles.css')
+        # set_header()
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+
+  
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature,verbose=False)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              if os.path.exists(report_file):
+                  with open(report_file, 'rb') as f:
+                      st.download_button(
+                          label="Download EDA Report",
+                          data=f.read(),
+                          file_name="report.html",
+                          mime="text/html"
+                      )
+              else:
+                  st.warning("Report generation failed. Unable to find the report file.")
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            st.plotly_chart(fig)
+
+        with columns[1]:
+            st.empty()
+            fig = qqplot(y_train,model.predict(X_train))
+            st.plotly_chart(fig)
+
+        with columns[0]:
+            fig=residual_distribution(y_train,model.predict(X_train))
+            st.pyplot(fig)
+    else:
+        with open('mastercard_mmm_model_channel.pkl', 'rb') as file:
+            model = pickle.load(file)
+        train=pd.read_csv('train_mastercard_channel.csv')
+        test=pd.read_csv('test_mastercard_channel.csv')
+        # train['Date']=pd.to_datetime(train['Date'])
+        # test['Date']=pd.to_datetime(test['Date'])
+        # train.set_index('Date',inplace=True)
+        # test.set_index('Date',inplace=True)
+        test.dropna(inplace=True)
+        X_train=train.drop(["total_approved_accounts_revenue"],axis=1)
+        y_train=train['total_approved_accounts_revenue']
+        X_test=test.drop(["total_approved_accounts_revenue"],axis=1)
+        X_train=sm.add_constant(X_train)
+        X_test=sm.add_constant(X_test)
+        y_test=test['total_approved_accounts_revenue']
+
+    
+
+        channel_data=pd.read_excel("Channel_wise_imp_click_spends_new.xlsx",sheet_name='Sheet3')
+        target_column='Total Approved Accounts - Revenue'
+        with eda_columns[1]:
+          if st.button('Generate EDA Report'):
+              def generate_report_with_target(channel_data, target_feature):
+                  report = sv.analyze([channel_data, "Dataset"], target_feat=target_feature)
+                  temp_dir = tempfile.mkdtemp()
+                  report_path = os.path.join(temp_dir, "report.html")
+                  report.show_html(filepath=report_path, open_browser=False)  # Generate the report as an HTML file
+                  return report_path
+          
+              report_file = generate_report_with_target(channel_data, target_column)
+          
+              # Provide a link to download the generated report
+              with open(report_file, 'rb') as f:
+                  st.download_button(
+                      label="Download EDA Report",
+                      data=f.read(),
+                      file_name="report.html",
+                      mime="text/html"
+                  )
+
+
+        st.title('Analysis of Result')
+
+        st.write(model.summary(yname='Revenue'))
+
+        metrics_table_train,fig_train= plot_actual_vs_predicted(X_train.index, y_train, model.predict(X_train), model)
+        metrics_table_test,fig_test= plot_actual_vs_predicted(X_test.index, y_test, model.predict(X_test), model)
+
+        metrics_table_train=metrics_table_train.set_index('Metric').transpose()
+        metrics_table_train.index=['Train']
+        metrics_table_test=metrics_table_test.set_index('Metric').transpose()
+        metrics_table_test.index=['test']
+        metrics_table=np.round(pd.concat([metrics_table_train,metrics_table_test]),2)
+
+        st.markdown('Result Overview')
+        st.dataframe(np.round(metrics_table,2),use_container_width=True)
+
+        st.subheader('Actual vs Predicted Plot Train')
+
+        st.plotly_chart(fig_train,use_container_width=True)
+        st.subheader('Actual vs Predicted Plot Test')
+        st.plotly_chart(fig_test,use_container_width=True)
+
+        st.markdown('## Residual Analysis')
+        columns=st.columns(2)
+        Xtrain1=X_train.copy()
+        with columns[0]:
+            fig=plot_residual_predicted(y_train,model.predict(Xtrain1),Xtrain1)
+            st.plotly_chart(fig)
+
+        with columns[1]:
+            st.empty()
+            fig = qqplot(y_train,model.predict(X_train))
+            st.plotly_chart(fig)
+
+        with columns[0]:
+            fig=residual_distribution(y_train,model.predict(X_train))
+            st.pyplot(fig)
+
+elif auth_status == False:
+    st.error('Username/Password is incorrect')
+    
+if auth_status != True:
+    try:
+        username_forgot_pw, email_forgot_password, random_password = authenticator.forgot_password('Forgot password')
+        if username_forgot_pw:
+            st.success('New password sent securely')
+            # Random password to be transferred to user securely
+        elif username_forgot_pw == False:
+            st.error('Username not found')
+    except Exception as e:
+        st.error(e)

README.md

@@ -1,11 +1,11 @@
 ---
-title: RFI
-emoji: 🚀
-colorFrom: yellow
+title: dev-space
+emoji: 🏆
+colorFrom: indigo
 colorTo: pink
 sdk: streamlit
-sdk_version: 1.36.0
-app_file: app.py
+sdk_version: 1.32.1
+app_file: Home.py
 pinned: false
 ---
 

Scenario.py

@@ -0,0 +1,338 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import datetime
+from utilities import set_header,initialize_data,load_local_css
+from scipy.optimize import curve_fit
+import statsmodels.api as sm
+from plotly.subplots import make_subplots
+
+st.set_page_config(
+  page_title="Data Validation",
+  page_icon=":shark:",
+  layout="wide",
+  initial_sidebar_state='collapsed'
+)
+load_local_css('styles.css')
+set_header()
+
+def format_numbers(x):
+    if abs(x) >= 1e6:
+        # Format as millions with one decimal place and commas
+        return f'{x/1e6:,.1f}M'
+    elif abs(x) >= 1e3:
+        # Format as thousands with one decimal place and commas
+        return f'{x/1e3:,.1f}K'
+    else:
+        # Format with one decimal place and commas for values less than 1000
+        return f'{x:,.1f}'
+
+def format_axis(x):
+    if isinstance(x, tuple):
+        x = x[0]  # Extract the numeric value from the tuple
+    if abs(x) >= 1e6:
+        return f'{x / 1e6:.0f}M'
+    elif abs(x) >= 1e3:
+        return f'{x / 1e3:.0f}k'
+    else:
+        return f'{x:.0f}'
+
+
+attributred_app_installs=pd.read_csv("attributed_app_installs.csv")
+attributred_app_installs_tactic=pd.read_excel('attributed_app_installs_tactic.xlsx')
+data=pd.read_excel('Channel_wise_imp_click_spends.xlsx')
+data['Date']=pd.to_datetime(data['Date'])
+st.header('Saturation Curves')
+
+# st.dataframe(data.head(2))
+st.markdown('Data QC')
+
+st.markdown('Channel wise summary')
+summary_df=data.groupby(data['Date'].dt.strftime('%B %Y')).sum()
+summary_df=summary_df.sort_index(key=lambda x: pd.to_datetime(x, format='%B %Y'))
+st.dataframe(summary_df.applymap(format_numbers))
+
+
+
+def line_plot_target(df,target,title):
+    df=df
+    df['Date_unix'] = df['Date'].apply(lambda x: x.timestamp())
+
+# Perform polynomial fitting
+    coefficients = np.polyfit(df['Date_unix'], df[target], 1)
+    # st.dataframe(df)
+    coefficients = np.polyfit(df['Date'].view('int64'), df[target], 1)
+    trendline = np.poly1d(coefficients)
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatter(x=df['Date'], y=df[target], mode='lines', name=target,line=dict(color='#11B6BD')))
+    trendline_x = df['Date']
+    trendline_y = trendline(df['Date'].view('int64'))
+
+
+    fig.add_trace(go.Scatter(x=trendline_x, y=trendline_y, mode='lines', name='Trendline', line=dict(color='#739FAE')))
+
+    fig.update_layout(
+        title=title,
+        xaxis=dict(type='date')
+    )
+
+    for year in df['Date'].dt.year.unique()[1:]:
+
+        january_1 = pd.Timestamp(year=year, month=1, day=1)
+        fig.add_shape(
+            go.layout.Shape(
+                type="line",
+                x0=january_1,
+                x1=january_1,
+                y0=0,
+                y1=1,
+                xref="x",
+                yref="paper",
+                line=dict(color="grey", width=1.5, dash="dash"),
+            )
+        )
+
+    return fig
+channels_d= data.columns[:28]
+channels=list(set([col.replace('_impressions','').replace('_clicks','').replace('_spend','') for col in channels_d if col.lower()!='date']))
+channel= st.selectbox('Select Channel_name',channels)
+target_column = st.selectbox('Select Channel)',[col for col in data.columns if col.startswith(channel)])
+fig=line_plot_target(data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+# st.markdown('## Saturation Curve')
+
+
+st.header('Build saturation curve')
+
+# Your data
+# st.write(len(attributred_app_installs))
+# st.write(len(data))
+# col=st.columns(3)
+# with col[0]:
+col=st.columns(2)
+with col[0]:
+    if st.checkbox('Cap Outliers'):
+        x = data[target_column]
+        x.index=data['Date']
+        # st.write(x)
+        result = sm.tsa.seasonal_decompose(x, model='additive')
+        x_resid=result.resid
+        # fig = make_subplots(rows=1, cols=1, shared_xaxes=True, vertical_spacing=0.02)
+        # trace_x = go.Scatter(x=data['Date'], y=x, mode='lines', name='x')
+        # fig.add_trace(trace_x)
+        # trace_x_resid = go.Scatter(x=data['Date'], y=x_resid, mode='lines', name='x_resid', yaxis='y2',line=dict(color='orange'))
+
+        # fig.add_trace(trace_x_resid)
+        # fig.update_layout(title='',
+        #               xaxis=dict(title='Date'),
+        #               yaxis=dict(title='x', side='left'),
+        #               yaxis2=dict(title='x_resid', side='right'))
+        # st.title('')
+        # st.plotly_chart(fig)
+
+        # x=result.resid
+        # x=x.fillna(0)
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+        lower_threshold = -2.0 
+        upper_threshold = 2.0   
+        x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+    else:
+        x = data[target_column]
+        x_mean = np.mean(x)
+        x_std = np.std(x)
+        x_scaled = (x - x_mean) / x_std
+with col[1]:
+    if st.checkbox('Attributed'):
+        column=[col for col in attributred_app_installs.columns if col in target_column]
+        data['app_installs_appsflyer']=attributred_app_installs[column]
+        y=data['app_installs_appsflyer']
+        title='Attributed-App_installs_appsflyer'
+        # st.dataframe(y)
+        # st.dataframe(x)
+        # st.dataframe(x_scaled)
+    else:
+        y=data["app_installs_appsflyer"]
+        title='App_installs_appsflyer'
+        # st.write(len(y))
+    # Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+columns=st.columns(3)
+
+
+with columns[0]:
+    K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0)
+with columns[1]:
+    a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=0.5)
+with columns[2]:
+    x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0)
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x))+1, value=float(x_mean), step=1.)
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_numbers(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_numbers(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+x_tick_labels = {format_axis(x_scaled[i]): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 30  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=15
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+fig.update_xaxes(tickvals=list(x_tick_labels.keys()), ticktext=list(x_tick_labels.values()))
+# Show the plot using st.plotly_chart
+
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)
+
+
+
+
+st.markdown('Tactic level')
+if channel=='paid_social':
+
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='paid_social_impressions')
+else:
+    tactic_data=pd.read_excel("Tatcic_paid.xlsx",sheet_name='digital_app_display_impressions')  
+target_column = st.selectbox('Select Channel)',[col for col in tactic_data.columns if col!='Date' and col!='app_installs_appsflyer'])
+fig=line_plot_target(tactic_data, target=str(target_column), title=f'{str(target_column)} Over Time')
+st.plotly_chart(fig, use_container_width=True)
+
+if st.checkbox('Cap Outliers',key='tactic1'):
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+    lower_threshold = -2.0 
+    upper_threshold = 2.0   
+    x_scaled = np.clip(x_scaled, lower_threshold, upper_threshold)
+else:
+    x = tactic_data[target_column]
+    x_mean = np.mean(x)
+    x_std = np.std(x)
+    x_scaled = (x - x_mean) / x_std
+
+if st.checkbox('Attributed',key='tactic2'):
+    column=[col for col in attributred_app_installs_tactic.columns if col in target_column]
+    tactic_data['app_installs_appsflyer']=attributred_app_installs_tactic[column]
+    y=tactic_data['app_installs_appsflyer']
+    title='Attributed-App_installs_appsflyer'
+    # st.dataframe(y)
+    # st.dataframe(x)
+    # st.dataframe(x_scaled)
+else:
+    y=data["app_installs_appsflyer"]
+    title='App_installs_appsflyer'
+    # st.write(len(y))
+# Curve fitting function
+def sigmoid(x, K, a, x0):
+    return K / (1 + np.exp(-a * (x - x0)))
+
+# Curve fitting
+# st.dataframe(x_scaled.head(3))
+# # y=y.astype(float)
+# st.dataframe(y.head(3))
+initial_K = np.max(y)
+initial_a = 1
+initial_x0 = 0
+K = st.number_input('K (Amplitude)', min_value=0.01, max_value=2.0 * np.max(y), value=float(initial_K), step=5.0,key='tactic3')
+a = st.number_input('a (Slope)', min_value=0.01, max_value=5.0, value=float(initial_a), step=2.0,key='tactic41')
+x0 = st.number_input('x0 (Center)', min_value=float(min(x_scaled)), max_value=float(max(x_scaled)), value=float(initial_x0), step=2.0,key='tactic4')
+params, _ = curve_fit(sigmoid, x_scaled, y, p0=[K, a, x0], maxfev=20000)
+
+# Slider to vary x
+x_slider = st.slider('X Value', min_value=float(min(x)), max_value=float(max(x)), value=float(x_mean), step=1.,key='tactic7')
+
+# Calculate the corresponding value on the fitted curve
+x_slider_scaled = (x_slider - x_mean) / x_std
+y_slider_fit = sigmoid(x_slider_scaled, *params)
+
+# Display the corresponding value
+st.write(f'{target_column}: {format_axis(x_slider)}')
+st.write(f'Corresponding  App_installs: {format_axis(y_slider_fit)}')
+
+# Scatter plot of your data
+fig = px.scatter(data_frame=data, x=x_scaled, y=y, labels={'x': f'{target_column}', 'y': 'App Installs'}, title=title)
+
+# Add the fitted sigmoid curve to the plot
+x_fit = np.linspace(min(x_scaled), max(x_scaled), 100)  # Generate x values for the curve
+y_fit = sigmoid(x_fit, *params)
+fig.add_trace(px.line(x=x_fit, y=y_fit).data[0])
+fig.data[1].update(line=dict(color='orange'))
+fig.add_vline(x=x_slider_scaled, line_dash='dash', line_color='red', annotation_text=f'{format_numbers(x_slider)}')
+
+
+
+x_tick_labels = {format_axis((x_scaled[i],0)): format_axis(x[i]) for i in range(len(x_scaled))}
+num_points = 50  # Number of points you want to select
+keys = list(x_tick_labels.keys())
+values = list(x_tick_labels.values())
+spacing = len(keys) // num_points  # Calculate the spacing
+if spacing==0:
+    spacing=2
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+else:
+    selected_keys = keys[::spacing]
+    selected_values = values[::spacing]
+
+# Update the x-axis ticks with the selected keys and values
+fig.update_xaxes(tickvals=selected_keys, ticktext=selected_values)
+
+# Round the x-axis and y-axis tick values to zero decimal places
+fig.update_xaxes(tickformat=".f")  # Format x-axis ticks to zero decimal places
+fig.update_yaxes(tickformat=".f")  # Format y-axis ticks to zero decimal places
+
+# Show the plot using st.plotly_chart
+fig.update_xaxes(showgrid=False)
+fig.update_yaxes(showgrid=False)
+fig.update_layout(
+    width=600,  # Adjust the width as needed
+    height=600  # Adjust the height as needed
+)
+st.plotly_chart(fig)

Test/X_test_tuned_trend.csv

@@ -0,0 +1,971 @@
+paid_search_clicks,kwai_clicks,fb_level_achieved_tier_2_clicks_lag_2,fb_level_achieved_tier_1_impressions,ga_app_clicks,digital_tactic_others_impressions_lag_2,programmatic_clicks_lag_3,total_approved_accounts_revenue,date,dma,Trend
+0.0,0.0,0.0,0.0,0.0,0.0,0.06194251734390485,31.746,8/7/2023,Albany/Schenectady/Troy SMM Food,124
+0.0,0.0010625686993517797,0.0,0.0,0.005135905343058742,0.0,0.0,30.66,8/8/2022,Albany/Schenectady/Troy SMM Food,125
+0.0,0.0,0.0,0.0,0.0019373305473274695,0.0,0.0,31.61,8/9/2021,Albany/Schenectady/Troy SMM Food,126
+0.0,0.0,0.006928265811381233,0.0,0.0,0.04710893906935569,0.062438057482656094,31.871,9/11/2023,Albany/Schenectady/Troy SMM Food,127
+0.0,0.0,0.0,0.013920804779456755,0.0,0.0,0.0,34.48,9/12/2022,Albany/Schenectady/Troy SMM Food,128
+0.0,0.0,0.0,0.0,0.0014406267064896794,0.0,0.0,34.3,9/13/2021,Albany/Schenectady/Troy SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06442021803766104,38.922,9/18/2023,Albany/Schenectady/Troy SMM Food,130
+0.0,0.0,0.0,0.014511012797352662,0.0,0.0,0.0,34.78,9/19/2022,Albany/Schenectady/Troy SMM Food,131
+0.0,0.0,0.0,0.0,0.0010515523405034157,0.0,0.0,34.23,9/20/2021,Albany/Schenectady/Troy SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06838453914767095,36.091,9/25/2023,Albany/Schenectady/Troy SMM Food,133
+0.0,0.0,0.0,0.016489078958499923,0.0,0.0,0.0,34.21,9/26/2022,Albany/Schenectady/Troy SMM Food,134
+0.0,0.0,0.0,0.0,0.0011647488571576068,0.0,0.0,33.64,9/27/2021,Albany/Schenectady/Troy SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06045589692765114,31.809,9/4/2023,Albany/Schenectady/Troy SMM Food,136
+0.0,0.0,0.0,0.014564429905722917,0.0,0.0,0.0,30.83,9/5/2022,Albany/Schenectady/Troy SMM Food,137
+0.0,0.0,0.0,0.0,0.0016552670959924358,0.0,0.0,32.16,9/6/2021,Albany/Schenectady/Troy SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06293359762140734,19.524,8/7/2023,Albuquerque/Santa FE SMM Food,124
+0.0,0.0011847395500790977,0.0,0.0,0.005197761363088355,0.0,0.0,17.41,8/8/2022,Albuquerque/Santa FE SMM Food,125
+0.0,0.0,0.0,0.0,0.0011734086999617528,0.0,0.0,20.25,8/9/2021,Albuquerque/Santa FE SMM Food,126
+0.0,0.0,0.006270420242227,0.0,0.0,0.05228225082270357,0.06045589692765114,20.598,9/11/2023,Albuquerque/Santa FE SMM Food,127
+0.0,0.0,0.0,0.013504398628886482,0.0,0.0,0.0,18.96,9/12/2022,Albuquerque/Santa FE SMM Food,128
+0.0,0.0,0.0,0.0,0.0015389777783367637,0.0,0.0,22.17,9/13/2021,Albuquerque/Santa FE SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05252725470763132,20.817,9/18/2023,Albuquerque/Santa FE SMM Food,130
+0.0,0.0,0.0,0.014076952049886973,0.0,0.0,0.0,19.46,9/19/2022,Albuquerque/Santa FE SMM Food,131
+0.0,0.0,0.0,0.0,0.0015210395325281761,0.0,0.0,21.48,9/20/2021,Albuquerque/Santa FE SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.051040634291377604,19.984,9/25/2023,Albuquerque/Santa FE SMM Food,133
+0.0,0.0,0.0,0.015995849296932277,0.0,0.0,0.0,21.41,9/26/2022,Albuquerque/Santa FE SMM Food,134
+0.0,0.0,0.0,0.0,0.0009569126298581084,0.0,0.0,20.66,9/27/2021,Albuquerque/Santa FE SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05153617443012884,19.474,9/4/2023,Albuquerque/Santa FE SMM Food,136
+0.0,0.0,0.0,0.014128771317940983,0.0,0.0,0.0,18.86,9/5/2022,Albuquerque/Santa FE SMM Food,137
+0.0,0.0,0.0,0.0,0.0009513455880554432,0.0,0.0,22.19,9/6/2021,Albuquerque/Santa FE SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.211595639246779,261.197,8/7/2023,Atlanta SMM Food,124
+0.0,0.008222704775902465,0.0,0.0,0.020853520032583325,0.0,0.0,156.16,8/8/2022,Atlanta SMM Food,125
+0.0,0.0,0.0,0.0,0.004707243124253526,0.0,0.0,101.3,8/9/2021,Atlanta SMM Food,126
+0.0,0.0,0.07059033189111122,0.0,0.0,0.25431693894538904,0.1952428146679881,140.383,9/11/2023,Atlanta SMM Food,127
+0.0,0.0,0.0,0.05367166201076247,0.0,0.0,0.0,112.47,9/12/2022,Atlanta SMM Food,128
+0.0,0.0,0.0,0.0,0.0042668282616426835,0.0,0.0,110.36,9/13/2021,Atlanta SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.20069375619425173,132.639,9/18/2023,Atlanta SMM Food,130
+0.0,0.0,0.0,0.05594720898632875,0.0,0.0,0.0,106.1,9/19/2022,Atlanta SMM Food,131
+0.0,0.0,0.0,0.0,0.004526623545767057,0.0,0.0,122.59,9/20/2021,Atlanta SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.17195242814667988,131.807,9/25/2023,Atlanta SMM Food,133
+0.0,0.0,0.0,0.06357364295489033,0.0,0.0,0.0,117.87,9/26/2022,Atlanta SMM Food,134
+0.0,0.0,0.0,0.0,0.0025589835486250767,0.0,0.0,116.38,9/27/2021,Atlanta SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.19127849355797819,138.316,9/4/2023,Atlanta SMM Food,136
+0.0,0.0,0.0,0.05615315864933586,0.0,0.0,0.0,101.43,9/5/2022,Atlanta SMM Food,137
+0.0,0.0,0.0,0.0,0.005283741230929517,0.0,0.0,109.49,9/6/2021,Atlanta SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.08969276511397423,50.534,8/7/2023,Baltimore SMM Food,124
+0.0,0.003010948271471134,0.0,0.0,0.010581709346465842,0.0,0.0,54.66,8/8/2022,Baltimore SMM Food,125
+0.0,0.0,0.0,0.0,0.002178569025442959,0.0,0.0,54.51,8/9/2021,Baltimore SMM Food,126
+0.0,0.0,0.013868768647461295,0.0,0.0,0.09580695439115328,0.06987115956392467,63.01,9/11/2023,Baltimore SMM Food,127
+0.0,0.0,0.0,0.02379803605790658,0.0,0.0,0.0,58.19,9/12/2022,Baltimore SMM Food,128
+0.0,0.0,0.0,0.0,0.002276920097290043,0.0,0.0,61.67,9/13/2021,Baltimore SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06442021803766104,72.253,9/18/2023,Baltimore SMM Food,130
+0.0,0.0,0.0,0.024807014486139218,0.0,0.0,0.0,60.84,9/19/2022,Baltimore SMM Food,131
+0.0,0.0,0.0,0.0,0.001681865184605169,0.0,0.0,56.75,9/20/2021,Baltimore SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07383548067393458,74.369,9/25/2023,Baltimore SMM Food,133
+0.0,0.0,0.0,0.028188578310361815,0.0,0.0,0.0,63.62,9/26/2022,Baltimore SMM Food,134
+0.0,0.0,0.0,0.0,0.0016020709187669687,0.0,0.0,55.3,9/27/2021,Baltimore SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07433102081268583,56.708,9/4/2023,Baltimore SMM Food,136
+0.0,0.0,0.0,0.024898332642610616,0.0,0.0,0.0,57.06,9/5/2022,Baltimore SMM Food,137
+0.0,0.0,0.0,0.0,0.0019039282965114788,0.0,0.0,64.56,9/6/2021,Baltimore SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.028245787908820614,2.491,8/7/2023,Baton Rouge SMM Food,124
+0.0,0.0005060126015939978,0.0,0.0,0.0027847580217331635,0.0,0.0,2.29,8/8/2022,Baton Rouge SMM Food,125
+0.0,0.0,0.0,0.0,0.0007769116115719355,0.0,0.0,2.82,8/9/2021,Baton Rouge SMM Food,126
+0.0,0.0,0.0026347580075683306,0.0,0.0,0.0372315586355452,0.019821605550049554,3.282,9/11/2023,Baton Rouge SMM Food,127
+0.0,0.0,0.0,0.009473010003288533,0.0,0.0,0.0,2.93,9/12/2022,Baton Rouge SMM Food,128
+0.0,0.0,0.0,0.0,0.000550518578263553,0.0,0.0,6.28,9/13/2021,Baton Rouge SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.027254707631318136,3.751,9/18/2023,Baton Rouge SMM Food,130
+0.0,0.0,0.0,0.009874642417058198,0.0,0.0,0.0,1.6,9/19/2022,Baton Rouge SMM Food,131
+0.0,0.0,0.0,0.0,0.0007249525547470607,0.0,0.0,3.84,9/20/2021,Baton Rouge SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.020317145688800792,2.883,9/25/2023,Baton Rouge SMM Food,133
+0.0,0.0,0.0,0.01122070256617374,0.0,0.0,0.0,1.98,9/26/2022,Baton Rouge SMM Food,134
+0.0,0.0,0.0,0.0,0.0005325803324549652,0.0,0.0,2.19,9/27/2021,Baton Rouge SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.02081268582755203,5.023,9/4/2023,Baton Rouge SMM Food,136
+0.0,0.0,0.0,0.009910992383390915,0.0,0.0,0.0,3.56,9/5/2022,Baton Rouge SMM Food,137
+0.0,0.0,0.0,0.0,0.000716911272143211,0.0,0.0,2.76,9/6/2021,Baton Rouge SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07086223984142716,30.899,8/7/2023,Birmingham/Anniston/Tuscaloosa SMM Food,124
+0.0,0.0015968856587974965,0.0,0.0,0.006559212363940131,0.0,0.0,21.32,8/8/2022,Birmingham/Anniston/Tuscaloosa SMM Food,125
+0.0,0.0,0.0,0.0,0.002610324045249656,0.0,0.0,12.39,8/9/2021,Birmingham/Anniston/Tuscaloosa SMM Food,126
+0.0,0.0,0.011830671085553057,0.0,0.0,0.06832539296539691,0.05302279484638256,11.227,9/11/2023,Birmingham/Anniston/Tuscaloosa SMM Food,127
+0.0,0.0,0.0,0.020386457479869002,0.0,0.0,0.0,11.48,9/12/2022,Birmingham/Anniston/Tuscaloosa SMM Food,128
+0.0,0.0,0.0,0.0,0.0017505253668380393,0.0,0.0,12.29,9/13/2021,Birmingham/Anniston/Tuscaloosa SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06095143706640238,11.018,9/18/2023,Birmingham/Anniston/Tuscaloosa SMM Food,130
+0.0,0.0,0.0,0.021250793330832705,0.0,0.0,0.0,9.71,9/19/2022,Birmingham/Anniston/Tuscaloosa SMM Food,131
+0.0,0.0,0.0,0.0,0.002048671383380772,0.0,0.0,12.55,9/20/2021,Birmingham/Anniston/Tuscaloosa SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,9.499,9/25/2023,Birmingham/Anniston/Tuscaloosa SMM Food,133
+0.0,0.0,0.0,0.02414759149407838,0.0,0.0,0.0,9.26,9/26/2022,Birmingham/Anniston/Tuscaloosa SMM Food,134
+0.0,0.0,0.0,0.0,0.001227223437387516,0.0,0.0,11.65,9/27/2021,Birmingham/Anniston/Tuscaloosa SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.051040634291377604,14.16,9/4/2023,Birmingham/Anniston/Tuscaloosa SMM Food,136
+0.0,0.0,0.0,0.021329020533966504,0.0,0.0,0.0,13.7,9/5/2022,Birmingham/Anniston/Tuscaloosa SMM Food,137
+0.0,0.0,0.0,0.0,0.001038562576297197,0.0,0.0,12.02,9/6/2021,Birmingham/Anniston/Tuscaloosa SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.20416253716551042,142.058,8/7/2023,Boston/Manchester SMM Food,124
+0.0,0.006427977432121013,0.0,0.0,0.02215311501340549,0.0,0.0,133.68,8/8/2022,Boston/Manchester SMM Food,125
+0.0,0.0,0.0,0.0,0.006543129798732431,0.0,0.0,118.91,8/9/2021,Boston/Manchester SMM Food,126
+0.0,0.0,0.04895274042536989,0.0,0.0,0.16361847104901653,0.1798810703666997,172.275,9/11/2023,Boston/Manchester SMM Food,127
+0.0,0.0,0.0,0.06149452980004475,0.0,0.0,0.0,167.04,9/12/2022,Boston/Manchester SMM Food,128
+0.0,0.0,0.0,0.0,0.0038660012518507932,0.0,0.0,117.31,9/13/2021,Boston/Manchester SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.19425173439048563,178.545,9/18/2023,Boston/Manchester SMM Food,130
+0.0,0.0,0.0,0.06410174720660182,0.0,0.0,0.0,145.96,9/19/2022,Boston/Manchester SMM Food,131
+0.0,0.0,0.0,0.0,0.0032907402655753953,0.0,0.0,113.79,9/20/2021,Boston/Manchester SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.22398414271555994,156.549,9/25/2023,Boston/Manchester SMM Food,133
+0.0,0.0,0.0,0.07283976560910188,0.0,0.0,0.0,157.5,9/26/2022,Boston/Manchester SMM Food,134
+0.0,0.0,0.0,0.0,0.0044307467147211566,0.0,0.0,114.52,9/27/2021,Boston/Manchester SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.19573835480673935,149.121,9/4/2023,Boston/Manchester SMM Food,136
+0.0,0.0,0.0,0.06433771488842611,0.0,0.0,0.0,138.78,9/5/2022,Boston/Manchester SMM Food,137
+0.0,0.0,0.0,0.0,0.004624356057413845,0.0,0.0,116.39,9/6/2021,Boston/Manchester SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07036669970267592,21.059,8/7/2023,Buffalo SMM Food,124
+0.0,0.001065745719110646,0.0,0.0,0.006125601663532545,0.0,0.0,14.52,8/8/2022,Buffalo SMM Food,125
+0.0,0.0,0.0,0.0,0.002417333262757264,0.0,0.0,16.9,8/9/2021,Buffalo SMM Food,126
+0.0,0.0,0.007447706411528032,0.0,0.0,0.05334496751978329,0.06987115956392467,21.384,9/11/2023,Buffalo SMM Food,127
+0.0,0.0,0.0,0.016659126610822184,0.0,0.0,0.0,16.16,9/12/2022,Buffalo SMM Food,128
+0.0,0.0,0.0,0.0,0.0018525877998869001,0.0,0.0,17.94,9/13/2021,Buffalo SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.07333994053518335,19.469,9/18/2023,Buffalo SMM Food,130
+0.0,0.0,0.0,0.017365432764953035,0.0,0.0,0.0,15.79,9/19/2022,Buffalo SMM Food,131
+0.0,0.0,0.0,0.0,0.0014635134339006364,0.0,0.0,13.96,9/20/2021,Buffalo SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07284440039643211,19.007,9/25/2023,Buffalo SMM Food,133
+0.0,0.0,0.0,0.019732598682000637,0.0,0.0,0.0,17.25,9/26/2022,Buffalo SMM Food,134
+0.0,0.0,0.0,0.0,0.0011474291715493155,0.0,0.0,16.09,9/27/2021,Buffalo SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,24.686,9/4/2023,Buffalo SMM Food,136
+0.0,0.0,0.0,0.01742935740030342,0.0,0.0,0.0,24.05,9/5/2022,Buffalo SMM Food,137
+0.0,0.0,0.0,0.0,0.002140218293024599,0.0,0.0,18.49,9/6/2021,Buffalo SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11248761149653122,91.731,8/7/2023,Charlotte SMM Food,124
+0.0,0.00275216557111256,0.0,0.0,0.01447925716853174,0.0,0.0,105.22,8/8/2022,Charlotte SMM Food,125
+0.0,0.0,0.0,0.0,0.00312867749309781,0.0,0.0,68.53,8/9/2021,Charlotte SMM Food,126
+0.0,0.0,0.019428597524418424,0.0,0.0,0.14467607804531682,0.10257680872150644,109.118,9/11/2023,Charlotte SMM Food,127
+0.0,0.0,0.0,0.0334917711067763,0.0,0.0,0.0,90.2,9/12/2022,Charlotte SMM Food,128
+0.0,0.0,0.0,0.0,0.0033154826735872405,0.0,0.0,71.22,9/13/2021,Charlotte SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09712586719524281,95.013,9/18/2023,Charlotte SMM Food,130
+0.0,0.0,0.0,0.03491174014852976,0.0,0.0,0.0,80.4,9/19/2022,Charlotte SMM Food,131
+0.0,0.0,0.0,0.0,0.0025410453028164894,0.0,0.0,67.35,9/20/2021,Charlotte SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,115.348,9/25/2023,Charlotte SMM Food,133
+0.0,0.0,0.0,0.03967072787903239,0.0,0.0,0.0,69.61,9/26/2022,Charlotte SMM Food,134
+0.0,0.0,0.0,0.0,0.0018940313333067407,0.0,0.0,67.22,9/27/2021,Charlotte SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09514370664023786,64.194,9/4/2023,Charlotte SMM Food,136
+0.0,0.0,0.0,0.035040255247413665,0.0,0.0,0.0,69.04,9/5/2022,Charlotte SMM Food,137
+0.0,0.0,0.0,0.0,0.0029257897474006802,0.0,0.0,95.05,9/6/2021,Charlotte SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.2259663032705649,127.164,8/7/2023,Chicago SMM Food,124
+0.0,0.021210939190104566,0.0,0.0,0.029393362157871656,0.0,0.0,128.34,8/8/2022,Chicago SMM Food,125
+0.0,0.0,0.0,0.0,0.007107256701402499,0.0,0.0,131.14,8/9/2021,Chicago SMM Food,126
+0.0,0.0,0.15988812078214787,0.0,0.0,0.2559436219550531,0.21110009910802774,139.546,9/11/2023,Chicago SMM Food,127
+0.0,0.0,0.0,0.07186262484115848,0.0,0.0,0.0,115.04,9/12/2022,Chicago SMM Food,128
+0.0,0.0,0.0,0.0,0.004144971902184347,0.0,0.0,126.47,9/13/2021,Chicago SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2423191278493558,150.534,9/18/2023,Chicago SMM Food,130
+0.0,0.0,0.0,0.07490942409399909,0.0,0.0,0.0,112.06,9/19/2022,Chicago SMM Food,131
+0.0,0.0,0.0,0.0,0.004689304878444938,0.0,0.0,105.95,9/20/2021,Chicago SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.17443012884043607,140.18,9/25/2023,Chicago SMM Food,133
+0.0,0.0,0.0,0.0851206890876872,0.0,0.0,0.0,113.37,9/26/2022,Chicago SMM Food,134
+0.0,0.0,0.0,0.0,0.005144565185862888,0.0,0.0,112.47,9/27/2021,Chicago SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.17938553022794845,134.87,9/4/2023,Chicago SMM Food,136
+0.0,0.0,0.0,0.07518517635856978,0.0,0.0,0.0,128.32,9/5/2022,Chicago SMM Food,137
+0.0,0.0,0.0,0.0,0.005288071152331588,0.0,0.0,132.52,9/6/2021,Chicago SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1442021803766105,72.288,8/7/2023,Cleveland/Akron/Canton SMM Food,124
+0.0,0.0,0.0,0.0,0.010643565366495456,0.0,0.0,88.44,8/8/2022,Cleveland/Akron/Canton SMM Food,125
+0.0,0.0,0.0,0.0,0.005229307933303457,0.0,0.0,81.13,8/9/2021,Cleveland/Akron/Canton SMM Food,126
+0.0,0.0,0.018360600644668993,0.0,0.0,0.003847068303229848,0.13082259663032705,77.361,9/11/2023,Cleveland/Akron/Canton SMM Food,127
+0.0,0.0,0.0,0.04319655098784653,0.0,0.0,0.0,83.99,9/12/2022,Cleveland/Akron/Canton SMM Food,128
+0.0,0.0,0.0,0.0,0.0038678569324516817,0.0,0.0,81.0,9/13/2021,Cleveland/Akron/Canton SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1367690782953419,74.588,9/18/2023,Cleveland/Akron/Canton SMM Food,130
+0.0,0.0,0.0,0.045027978918762056,0.0,0.0,0.0,68.98,9/19/2022,Cleveland/Akron/Canton SMM Food,131
+0.0,0.0,0.0,0.0,0.004016929940723048,0.0,0.0,79.08,9/20/2021,Cleveland/Akron/Canton SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1337958374628345,75.304,9/25/2023,Cleveland/Akron/Canton SMM Food,133
+0.0,0.0,0.0,0.05116595995981537,0.0,0.0,0.0,73.72,9/26/2022,Cleveland/Akron/Canton SMM Food,134
+0.0,0.0,0.0,0.0,0.003246203931154074,0.0,0.0,72.7,9/27/2021,Cleveland/Akron/Canton SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.12983151635282458,85.988,9/4/2023,Cleveland/Akron/Canton SMM Food,136
+0.0,0.0,0.0,0.04519373333208909,0.0,0.0,0.0,95.79,9/5/2022,Cleveland/Akron/Canton SMM Food,137
+0.0,0.0,0.0,0.0,0.0032332141669478556,0.0,0.0,84.14,9/6/2021,Cleveland/Akron/Canton SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10555004955401387,54.72,8/7/2023,Columbus OH SMM Food,124
+0.0,0.0016355875358600512,0.0,0.0,0.010457378746206322,0.0,0.0,58.12,8/8/2022,Columbus OH SMM Food,125
+0.0,0.0,0.0,0.0,0.003066821473068197,0.0,0.0,54.05,8/9/2021,Columbus OH SMM Food,126
+0.0,0.0,0.012976309777184706,0.0,0.0,0.09535695517348619,0.09563924677898909,56.575,9/11/2023,Columbus OH SMM Food,127
+0.0,0.0,0.0,0.026403905702080413,0.0,0.0,0.0,54.07,9/12/2022,Columbus OH SMM Food,128
+0.0,0.0,0.0,0.0,0.002041867221177515,0.0,0.0,53.22,9/13/2021,Columbus OH SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.0931615460852329,59.875,9/18/2023,Columbus OH SMM Food,130
+0.0,0.0,0.0,0.0275233666153899,0.0,0.0,0.0,51.71,9/19/2022,Columbus OH SMM Food,131
+0.0,0.0,0.0,0.0,0.0023913537343448264,0.0,0.0,47.83,9/20/2021,Columbus OH SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0753221010901883,61.444,9/25/2023,Columbus OH SMM Food,133
+0.0,0.0,0.0,0.031275209501753144,0.0,0.0,0.0,56.01,9/26/2022,Columbus OH SMM Food,134
+0.0,0.0,0.0,0.0,0.0023177450705095877,0.0,0.0,51.58,9/27/2021,Columbus OH SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.0817641228939544,60.308,9/4/2023,Columbus OH SMM Food,136
+0.0,0.0,0.0,0.027624684053467907,0.0,0.0,0.0,61.28,9/5/2022,Columbus OH SMM Food,137
+0.0,0.0,0.0,0.0,0.0019162995005174012,0.0,0.0,56.04,9/6/2021,Columbus OH SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.22150644202180375,73.064,8/7/2023,Dallas/Ft. Worth SMM Food,124
+0.0,0.007823266746219531,0.0,0.0,0.02227744561366501,0.0,0.0,55.67,8/8/2022,Dallas/Ft. Worth SMM Food,125
+0.0,0.0,0.0,0.0,0.00542044303519496,0.0,0.0,58.56,8/9/2021,Dallas/Ft. Worth SMM Food,126
+0.0,0.0,0.053580445593371474,0.0,0.0,0.2960835671223029,0.20366699702675917,82.586,9/11/2023,Dallas/Ft. Worth SMM Food,127
+0.0,0.0,0.0,0.055526458478242974,0.0,0.0,0.0,62.13,9/12/2022,Dallas/Ft. Worth SMM Food,128
+0.0,0.0,0.0,0.0,0.004551365953778902,0.0,0.0,54.28,9/13/2021,Dallas/Ft. Worth SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.20862239841427155,77.309,9/18/2023,Dallas/Ft. Worth SMM Food,130
+0.0,0.0,0.0,0.05788064427948714,0.0,0.0,0.0,58.16,9/19/2022,Dallas/Ft. Worth SMM Food,131
+0.0,0.0,0.0,0.0,0.005119822777851043,0.0,0.0,55.82,9/20/2021,Dallas/Ft. Worth SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.21258671952428146,75.709,9/25/2023,Dallas/Ft. Worth SMM Food,133
+0.0,0.0,0.0,0.06577063414997705,0.0,0.0,0.0,62.33,9/26/2022,Dallas/Ft. Worth SMM Food,134
+0.0,0.0,0.0,0.0,0.003531360183490589,0.0,0.0,56.4,9/27/2021,Dallas/Ft. Worth SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.1734390485629336,80.36,9/4/2023,Dallas/Ft. Worth SMM Food,136
+0.0,0.0,0.0,0.058093711196037776,0.0,0.0,0.0,60.65,9/5/2022,Dallas/Ft. Worth SMM Food,137
+0.0,0.0,0.0,0.0,0.004822913881708902,0.0,0.0,60.16,9/6/2021,Dallas/Ft. Worth SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06095143706640238,19.057,8/7/2023,Des Moines/Ames SMM Food,124
+0.0,0.001062279879373701,0.0,0.0,0.002908470061792389,0.0,0.0,21.69,8/8/2022,Des Moines/Ames SMM Food,125
+0.0,0.0,0.0,0.0,0.0009030978924323453,0.0,0.0,15.48,8/9/2021,Des Moines/Ames SMM Food,126
+0.0,0.0,0.007034601336350359,0.0,0.0,0.038076904404842446,0.0639246778989098,17.536,9/11/2023,Des Moines/Ames SMM Food,127
+0.0,0.0,0.0,0.009818746375673355,0.0,0.0,0.0,19.26,9/12/2022,Des Moines/Ames SMM Food,128
+0.0,0.0,0.0,0.0,0.0007459836015571291,0.0,0.0,16.86,9/13/2021,Des Moines/Ames SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05401387512388503,19.413,9/18/2023,Des Moines/Ames SMM Food,130
+0.0,0.0,0.0,0.010235037169468615,0.0,0.0,0.0,17.91,9/19/2022,Des Moines/Ames SMM Food,131
+0.0,0.0,0.0,0.0,0.0005560856200662181,0.0,0.0,16.58,9/20/2021,Des Moines/Ames SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05797819623389494,20.201,9/25/2023,Des Moines/Ames SMM Food,133
+0.0,0.0,0.0,0.011630224459597573,0.0,0.0,0.0,17.11,9/26/2022,Des Moines/Ames SMM Food,134
+0.0,0.0,0.0,0.0,0.000921036138240933,0.0,0.0,12.87,9/27/2021,Des Moines/Ames SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04707631318136769,20.614,9/4/2023,Des Moines/Ames SMM Food,136
+0.0,0.0,0.0,0.010272713801757724,0.0,0.0,0.0,19.45,9/5/2022,Des Moines/Ames SMM Food,137
+0.0,0.0,0.0,0.0,0.0008585615580110242,0.0,0.0,16.73,9/6/2021,Des Moines/Ames SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14073339940535184,124.957,8/7/2023,Detroit SMM Food,124
+0.0,0.004029038694198784,0.0,0.0,0.01633679345002101,0.0,0.0,129.16,8/8/2022,Detroit SMM Food,125
+0.0,0.0,0.0,0.0,0.005138998144060223,0.0,0.0,98.0,8/9/2021,Detroit SMM Food,126
+0.0,0.0,0.027197842309067383,0.0,0.0,0.16679364832597332,0.1367690782953419,114.674,9/11/2023,Detroit SMM Food,127
+0.0,0.0,0.0,0.05264923653159416,0.0,0.0,0.0,99.78,9/12/2022,Detroit SMM Food,128
+0.0,0.0,0.0,0.0,0.003737959290389495,0.0,0.0,105.22,9/13/2021,Detroit SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13478691774033696,132.01,9/18/2023,Detroit SMM Food,130
+0.0,0.0,0.0,0.05488143517450875,0.0,0.0,0.0,98.35,9/19/2022,Detroit SMM Food,131
+0.0,0.0,0.0,0.0,0.004616314774809995,0.0,0.0,76.97,9/20/2021,Detroit SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.12041625371655104,134.155,9/25/2023,Detroit SMM Food,133
+0.0,0.0,0.0,0.062362588370685694,0.0,0.0,0.0,111.04,9/26/2022,Detroit SMM Food,134
+0.0,0.0,0.0,0.0,0.004344766846879995,0.0,0.0,88.84,9/27/2021,Detroit SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.11397423191278494,147.194,9/4/2023,Detroit SMM Food,136
+0.0,0.0,0.0,0.05508346156574187,0.0,0.0,0.0,125.93,9/5/2022,Detroit SMM Food,137
+0.0,0.0,0.0,0.0,0.00365569078375011,0.0,0.0,109.54,9/6/2021,Detroit SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.09365708622398414,71.874,8/7/2023,Grand Rapids SMM Food,124
+0.0,0.0,0.0,0.0,0.008291799484969583,0.0,0.0,89.36,8/8/2022,Grand Rapids SMM Food,125
+0.0,0.0,0.0,0.0,0.0016558856561927318,0.0,0.0,60.59,8/9/2021,Grand Rapids SMM Food,126
+0.0,0.0,0.010334378341344649,0.0,0.0,0.004593258733825526,0.06937561942517344,59.484,9/11/2023,Grand Rapids SMM Food,127
+0.0,0.0,0.0,0.022162738793584366,0.0,0.0,0.0,51.84,9/12/2022,Grand Rapids SMM Food,128
+0.0,0.0,0.0,0.0,0.0015989781177654881,0.0,0.0,62.76,9/13/2021,Grand Rapids SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10852329038652131,84.959,9/18/2023,Grand Rapids SMM Food,130
+0.0,0.0,0.0,0.023102384621302353,0.0,0.0,0.0,53.77,9/19/2022,Grand Rapids SMM Food,131
+0.0,0.0,0.0,0.0,0.001827226831674759,0.0,0.0,45.22,9/20/2021,Grand Rapids SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08572844400396432,79.122,9/25/2023,Grand Rapids SMM Food,133
+0.0,0.0,0.0,0.02625158212085696,0.0,0.0,0.0,53.48,9/26/2022,Grand Rapids SMM Food,134
+0.0,0.0,0.0,0.0,0.0012488730443978803,0.0,0.0,45.61,9/27/2021,Grand Rapids SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,97.272,9/4/2023,Grand Rapids SMM Food,136
+0.0,0.0,0.0,0.023187427795256017,0.0,0.0,0.0,79.71,9/5/2022,Grand Rapids SMM Food,137
+0.0,0.0,0.0,0.0,0.001495678564316035,0.0,0.0,69.21,9/6/2021,Grand Rapids SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05847373637264618,34.23,8/7/2023,Greensboro SMM Food,124
+0.0,0.0,0.0,0.0,0.008662935605147259,0.0,0.0,41.28,8/8/2022,Greensboro SMM Food,125
+0.0,0.0,0.0,0.0,0.002148878135828745,0.0,0.0,33.05,8/9/2021,Greensboro SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.004162733342605559,0.05302279484638256,40.267,9/11/2023,Greensboro SMM Food,127
+0.0,0.0,0.0,0.01938722492394592,0.0,0.0,0.0,38.46,9/12/2022,Greensboro SMM Food,128
+0.0,0.0,0.0,0.0,0.0019855782429505676,0.0,0.0,34.42,9/13/2021,Greensboro SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,39.972,9/18/2023,Greensboro SMM Food,130
+0.0,0.0,0.0,0.02020919576536063,0.0,0.0,0.0,37.05,9/19/2022,Greensboro SMM Food,131
+0.0,0.0,0.0,0.0,0.0019132066995159206,0.0,0.0,29.9,9/20/2021,Greensboro SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,58.21,9/25/2023,Greensboro SMM Food,133
+0.0,0.0,0.0,0.022964008737958168,0.0,0.0,0.0,31.18,9/26/2022,Greensboro SMM Food,134
+0.0,0.0,0.0,0.0,0.0017647522514448503,0.0,0.0,32.39,9/27/2021,Greensboro SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,28.032,9/4/2023,Greensboro SMM Food,136
+0.0,0.0,0.0,0.020283588694575232,0.0,0.0,0.0,31.02,9/5/2022,Greensboro SMM Food,137
+0.0,0.0,0.0,0.0,0.0016861951060072422,0.0,0.0,55.3,9/6/2021,Greensboro SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.08622398414271557,34.307,8/7/2023,Harrisburg/Lancaster SMM Food,124
+0.0,0.00118647246994757,0.0,0.0,0.010951608346242928,0.0,0.0,37.54,8/8/2022,Harrisburg/Lancaster SMM Food,125
+0.0,0.0,0.0,0.0,0.0027989849063399744,0.0,0.0,31.88,8/9/2021,Harrisburg/Lancaster SMM Food,126
+0.0,0.0,0.009303514502060626,0.0,0.0,0.05938927543516645,0.06838453914767095,47.335,9/11/2023,Harrisburg/Lancaster SMM Food,127
+0.0,0.0,0.0,0.01722270730805324,0.0,0.0,0.0,47.54,9/12/2022,Harrisburg/Lancaster SMM Food,128
+0.0,0.0,0.0,0.0,0.0017567109688410004,0.0,0.0,37.65,9/13/2021,Harrisburg/Lancaster SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06838453914767095,47.204,9/18/2023,Harrisburg/Lancaster SMM Food,130
+0.0,0.0,0.0,0.017952907903006455,0.0,0.0,0.0,47.12,9/19/2022,Harrisburg/Lancaster SMM Food,131
+0.0,0.0,0.0,0.0,0.0023622814049309086,0.0,0.0,39.6,9/20/2021,Harrisburg/Lancaster SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,42.773,9/25/2023,Harrisburg/Lancaster SMM Food,133
+0.0,0.0,0.0,0.02040015539180732,0.0,0.0,0.0,39.34,9/26/2022,Harrisburg/Lancaster SMM Food,134
+0.0,0.0,0.0,0.0,0.0015365035375355792,0.0,0.0,36.2,9/27/2021,Harrisburg/Lancaster SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06987115956392467,49.172,9/4/2023,Harrisburg/Lancaster SMM Food,136
+0.0,0.0,0.0,0.018018995117875386,0.0,0.0,0.0,46.11,9/5/2022,Harrisburg/Lancaster SMM Food,137
+0.0,0.0,0.0,0.0,0.0018253711510738705,0.0,0.0,41.15,9/6/2021,Harrisburg/Lancaster SMM Food,138
+0.0,0.0018337180408220845,0.0,0.0,0.010148098646058258,0.0,0.0,57.79,8/8/2022,Hartford/New Haven SMM Food,125
+0.0,0.0,0.0,0.0,0.0037509490545957138,0.0,0.0,69.72,8/9/2021,Hartford/New Haven SMM Food,126
+0.0,0.0,0.014994996886122389,0.0,0.0,0.08363767112400147,0.0882061446977205,74.813,9/11/2023,Hartford/New Haven SMM Food,127
+0.0,0.0,0.0,0.025926611998646588,0.0,0.0,0.0,80.45,9/12/2022,Hartford/New Haven SMM Food,128
+0.0,0.0,0.0,0.0,0.0030495017874599055,0.0,0.0,68.05,9/13/2021,Hartford/New Haven SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10059464816650149,103.87,9/18/2023,Hartford/New Haven SMM Food,130
+0.0,0.0,0.0,0.027025836833104906,0.0,0.0,0.0,76.63,9/19/2022,Hartford/New Haven SMM Food,131
+0.0,0.0,0.0,0.0,0.0023901166139442343,0.0,0.0,59.62,9/20/2021,Hartford/New Haven SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08721506442021804,89.9,9/25/2023,Hartford/New Haven SMM Food,133
+0.0,0.0,0.0,0.030709859033967845,0.0,0.0,0.0,79.63,9/26/2022,Hartford/New Haven SMM Food,134
+0.0,0.0,0.0,0.0,0.0023709412477350544,0.0,0.0,64.74,9/27/2021,Hartford/New Haven SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09910802775024777,67.473,9/4/2023,Hartford/New Haven SMM Food,136
+0.0,0.0,0.0,0.027125322790759788,0.0,0.0,0.0,68.92,9/5/2022,Hartford/New Haven SMM Food,137
+0.0,0.0,0.0,0.0,0.0025515608262215235,0.0,0.0,67.33,9/6/2021,Hartford/New Haven SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1962338949454906,133.105,8/7/2023,Houston SMM Food,124
+0.0,0.0046540451267612325,0.0,0.0,0.022832912673530933,0.0,0.0,118.76,8/8/2022,Houston SMM Food,125
+0.0,0.0,0.0,0.0,0.005281885550328628,0.0,0.0,103.5,8/9/2021,Houston SMM Food,126
+0.0,0.0,0.01913533089801548,0.0,0.0,0.21760885851354209,0.1684836471754212,140.942,9/11/2023,Houston SMM Food,127
+0.0,0.0,0.0,0.04769953315392536,0.0,0.0,0.0,140.87,9/12/2022,Houston SMM Food,128
+0.0,0.0,0.0,0.0,0.004650335585826283,0.0,0.0,105.88,9/13/2021,Houston SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1620416253716551,129.531,9/18/2023,Houston SMM Food,130
+0.0,0.0,0.0,0.049721876494397986,0.0,0.0,0.0,145.85,9/19/2022,Houston SMM Food,131
+0.0,0.0,0.0,0.0,0.003903114863868561,0.0,0.0,111.54,9/20/2021,Houston SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.15510406342913777,135.992,9/25/2023,Houston SMM Food,133
+0.0,0.0,0.0,0.05649970535943224,0.0,0.0,0.0,150.89,9/26/2022,Houston SMM Food,134
+0.0,0.0,0.0,0.0,0.003332802359195532,0.0,0.0,100.18,9/27/2021,Houston SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.15857284440039643,134.53,9/4/2023,Houston SMM Food,136
+0.0,0.0,0.0,0.04990490981700915,0.0,0.0,0.0,130.92,9/5/2022,Houston SMM Food,137
+0.0,0.0,0.0,0.0,0.0032511524127564434,0.0,0.0,108.11,9/6/2021,Houston SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14172447968285432,48.376,8/7/2023,Indianapolis SMM Food,124
+0.0,0.0024260878158616334,0.0,0.0,0.012808526067531899,0.0,0.0,46.59,8/8/2022,Indianapolis SMM Food,125
+0.0,0.0,0.0,0.0,0.003151564220508767,0.0,0.0,38.95,8/9/2021,Indianapolis SMM Food,126
+0.0,0.0,0.016644041455881685,0.0,0.0,0.11700205142709444,0.11446977205153618,49.036,9/11/2023,Indianapolis SMM Food,127
+0.0,0.0,0.0,0.023691657521331348,0.0,0.0,0.0,35.97,9/12/2022,Indianapolis SMM Food,128
+0.0,0.0,0.0,0.0,0.0022490848882767175,0.0,0.0,37.35,9/13/2021,Indianapolis SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11645193260654113,51.603,9/18/2023,Indianapolis SMM Food,130
+0.0,0.0,0.0,0.0246961257538268,0.0,0.0,0.0,38.52,9/19/2022,Indianapolis SMM Food,131
+0.0,0.0,0.0,0.0,0.0029072329413917966,0.0,0.0,31.83,9/20/2021,Indianapolis SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,53.418,9/25/2023,Indianapolis SMM Food,133
+0.0,0.0,0.0,0.028062573805723218,0.0,0.0,0.0,39.19,9/26/2022,Indianapolis SMM Food,134
+0.0,0.0,0.0,0.0,0.002647437657267423,0.0,0.0,31.38,9/27/2021,Indianapolis SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10505450941526263,55.713,9/4/2023,Indianapolis SMM Food,136
+0.0,0.0,0.0,0.024787035718660904,0.0,0.0,0.0,46.84,9/5/2022,Indianapolis SMM Food,137
+0.0,0.0,0.0,0.0,0.0030024912122374,0.0,0.0,37.3,9/6/2021,Indianapolis SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07383548067393458,96.535,8/7/2023,Jacksonville SMM Food,124
+0.0,0.0008658822942801401,0.0,0.0,0.007301484604295482,0.0,0.0,62.66,8/8/2022,Jacksonville SMM Food,125
+0.0,0.0,0.0,0.0,0.0019435161493304308,0.0,0.0,34.35,8/9/2021,Jacksonville SMM Food,126
+0.0,0.0,0.0069561155917302895,0.0,0.0,0.08053507618258654,0.059960356788899896,27.967,9/11/2023,Jacksonville SMM Food,127
+0.0,0.0,0.0,0.02082072414146757,0.0,0.0,0.0,27.95,9/12/2022,Jacksonville SMM Food,128
+0.0,0.0,0.0,0.0,0.0015767099505548275,0.0,0.0,32.77,9/13/2021,Jacksonville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06491575817641229,29.339,9/18/2023,Jacksonville SMM Food,130
+0.0,0.0,0.0,0.02170347183093534,0.0,0.0,0.0,26.19,9/19/2022,Jacksonville SMM Food,131
+0.0,0.0,0.0,0.0,0.001496297124516331,0.0,0.0,28.8,9/20/2021,Jacksonville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.049554013875123884,28.801,9/25/2023,Jacksonville SMM Food,133
+0.0,0.0,0.0,0.024661976783877985,0.0,0.0,0.0,25.24,9/26/2022,Jacksonville SMM Food,134
+0.0,0.0,0.0,0.0,0.0016119678819717068,0.0,0.0,25.69,9/27/2021,Jacksonville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07433102081268583,51.144,9/4/2023,Jacksonville SMM Food,136
+0.0,0.0,0.0,0.02178336541288693,0.0,0.0,0.0,43.45,9/5/2022,Jacksonville SMM Food,137
+0.0,0.0,0.0,0.0,0.0012927908186189051,0.0,0.0,31.77,9/6/2021,Jacksonville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11248761149653122,29.172,8/7/2023,Kansas City SMM Food,124
+0.0,0.0018331404008659269,0.0,0.0,0.0055076600234367145,0.0,0.0,35.15,8/8/2022,Kansas City SMM Food,125
+0.0,0.0,0.0,0.0,0.0016793909438039844,0.0,0.0,32.17,8/9/2021,Kansas City SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.08131822285155027,0.11050545094152626,33.379,9/11/2023,Kansas City SMM Food,127
+0.0,0.0,0.0,0.020919710852413544,0.0,0.0,0.0,31.54,9/12/2022,Kansas City SMM Food,128
+0.0,0.0,0.0,0.0,0.00130639914302542,0.0,0.0,35.16,9/13/2021,Kansas City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10555004955401387,30.958,9/18/2023,Kansas City SMM Food,130
+0.0,0.0,0.0,0.021806655337224045,0.0,0.0,0.0,30.39,9/19/2022,Kansas City SMM Food,131
+0.0,0.0,0.0,0.0,0.0012995949808221627,0.0,0.0,33.17,9/20/2021,Kansas City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10009910802775024,32.027,9/25/2023,Kansas City SMM Food,133
+0.0,0.0,0.0,0.024779225733846975,0.0,0.0,0.0,30.72,9/26/2022,Kansas City SMM Food,134
+0.0,0.0,0.0,0.0,0.0017282571996273786,0.0,0.0,30.48,9/27/2021,Kansas City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.0882061446977205,31.622,9/4/2023,Kansas City SMM Food,136
+0.0,0.0,0.0,0.021886928747765332,0.0,0.0,0.0,33.43,9/5/2022,Kansas City SMM Food,137
+0.0,0.0,0.0,0.0,0.0012779453738117983,0.0,0.0,34.17,9/6/2021,Kansas City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,28.985,8/7/2023,Knoxville SMM Food,124
+0.0,0.0009172922503781605,0.0,0.0,0.004641057182821841,0.0,0.0,24.48,8/8/2022,Knoxville SMM Food,125
+0.0,0.0,0.0,0.0,0.00217362054384059,0.0,0.0,22.62,8/9/2021,Knoxville SMM Food,126
+0.0,0.0,0.007657423696883808,0.0,0.0,0.05601546016597905,0.06095143706640238,34.993,9/11/2023,Knoxville SMM Food,127
+0.0,0.0,0.0,0.012082336514998791,0.0,0.0,0.0,21.57,9/12/2022,Knoxville SMM Food,128
+0.0,0.0,0.0,0.0,0.0016899064672090188,0.0,0.0,22.64,9/13/2021,Knoxville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05153617443012884,23.653,9/18/2023,Knoxville SMM Food,130
+0.0,0.0,0.0,0.012594597987226747,0.0,0.0,0.0,25.34,9/19/2022,Knoxville SMM Food,131
+0.0,0.0,0.0,0.0,0.0013614510008517755,0.0,0.0,23.4,9/20/2021,Knoxville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,23.93,9/25/2023,Knoxville SMM Food,133
+0.0,0.0,0.0,0.014311428395501457,0.0,0.0,0.0,26.36,9/26/2022,Knoxville SMM Food,134
+0.0,0.0,0.0,0.0,0.0009154690964382679,0.0,0.0,25.75,9/27/2021,Knoxville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05302279484638256,27.206,9/4/2023,Knoxville SMM Food,136
+0.0,0.0,0.0,0.01264096049426039,0.0,0.0,0.0,23.09,9/5/2022,Knoxville SMM Food,137
+0.0,0.0,0.0,0.0,0.0013441313152434838,0.0,0.0,25.4,9/6/2021,Knoxville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05946481665014866,31.532,8/7/2023,Las Vegas SMM Food,124
+0.0,0.002099721240632628,0.0,0.0,0.003961259522696397,0.0,0.0,24.92,8/8/2022,Las Vegas SMM Food,125
+0.0,0.0,0.0,0.0,0.0010515523405034157,0.0,0.0,17.85,8/9/2021,Las Vegas SMM Food,126
+0.0,0.0,0.009369763221981868,0.0,0.0,0.08034252733962806,0.035678889990089196,34.959,9/11/2023,Las Vegas SMM Food,127
+0.0,0.0,0.0,0.0185917027305958,0.0,0.0,0.0,28.18,9/12/2022,Las Vegas SMM Food,128
+0.0,0.0,0.0,0.0,0.0012927908186189051,0.0,0.0,24.83,9/13/2021,Las Vegas SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.048562933597621406,33.115,9/18/2023,Las Vegas SMM Food,130
+0.0,0.0,0.0,0.019379945382538788,0.0,0.0,0.0,26.13,9/19/2022,Las Vegas SMM Food,131
+0.0,0.0,0.0,0.0,0.001155470454153165,0.0,0.0,24.17,9/20/2021,Las Vegas SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05302279484638256,30.334,9/25/2023,Las Vegas SMM Food,133
+0.0,0.0,0.0,0.022021719233974155,0.0,0.0,0.0,25.26,9/26/2022,Las Vegas SMM Food,134
+0.0,0.0,0.0,0.0,0.0013317601112375612,0.0,0.0,24.46,9/27/2021,Las Vegas SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04707631318136769,30.934,9/4/2023,Las Vegas SMM Food,136
+0.0,0.0,0.0,0.019451285727142276,0.0,0.0,0.0,26.34,9/5/2022,Las Vegas SMM Food,137
+0.0,0.0,0.0,0.0,0.0016472258133885859,0.0,0.0,23.98,9/6/2021,Las Vegas SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.0639246778989098,10.063,8/7/2023,Little Rock/Pine Bluff SMM Food,124
+0.0,0.0014186837323228979,0.0,0.0,0.006373025743650996,0.0,0.0,8.89,8/8/2022,Little Rock/Pine Bluff SMM Food,125
+0.0,0.0,0.0,0.0,0.001933619186125693,0.0,0.0,10.01,8/9/2021,Little Rock/Pine Bluff SMM Food,126
+0.0,0.0,0.0067615890956558185,0.0,0.0,0.04907078091276023,0.055004955401387515,10.863,9/11/2023,Little Rock/Pine Bluff SMM Food,127
+0.0,0.0,0.0,0.015617921237149364,0.0,0.0,0.0,9.69,9/12/2022,Little Rock/Pine Bluff SMM Food,128
+0.0,0.0,0.0,0.0,0.0014356782248873107,0.0,0.0,11.67,9/13/2021,Little Rock/Pine Bluff SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.062438057482656094,9.718,9/18/2023,Little Rock/Pine Bluff SMM Food,130
+0.0,0.0,0.0,0.01628008283607892,0.0,0.0,0.0,10.38,9/19/2022,Little Rock/Pine Bluff SMM Food,131
+0.0,0.0,0.0,0.0,0.0015482561813412057,0.0,0.0,9.39,9/20/2021,Little Rock/Pine Bluff SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.05252725470763132,9.803,9/25/2023,Little Rock/Pine Bluff SMM Food,133
+0.0,0.0,0.0,0.018499299465241616,0.0,0.0,0.0,9.19,9/26/2022,Little Rock/Pine Bluff SMM Food,134
+0.0,0.0,0.0,0.0,0.0015946481963634153,0.0,0.0,9.27,9/27/2021,Little Rock/Pine Bluff SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04013875123885034,11.034,9/4/2023,Little Rock/Pine Bluff SMM Food,136
+0.0,0.0,0.0,0.016340012143976613,0.0,0.0,0.0,9.59,9/5/2022,Little Rock/Pine Bluff SMM Food,137
+0.0,0.0,0.0,0.0,0.0017777420156510687,0.0,0.0,11.85,9/6/2021,Little Rock/Pine Bluff SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.3062438057482656,114.624,8/7/2023,Los Angeles SMM Food,124
+0.0,0.019228478860571916,0.0,0.0,0.03378946950137623,0.0,0.0,106.07,8/8/2022,Los Angeles SMM Food,125
+0.0,0.0,0.0,0.0,0.010010778281592518,0.0,0.0,93.99,8/9/2021,Los Angeles SMM Food,126
+0.0,0.0,0.13303797876107476,0.0,0.0,0.3747884838535892,0.2522299306243806,131.228,9/11/2023,Los Angeles SMM Food,127
+0.0,0.0,0.0,0.10969678061239888,0.0,0.0,0.0,115.82,9/12/2022,Los Angeles SMM Food,128
+0.0,0.0,0.0,0.0,0.009888921922134182,0.0,0.0,119.97,9/13/2021,Los Angeles SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.24727452923686818,119.603,9/18/2023,Los Angeles SMM Food,130
+0.0,0.0,0.0,0.11434765534026742,0.0,0.0,0.0,109.13,9/19/2022,Los Angeles SMM Food,131
+0.0,0.0,0.0,0.0,0.00820272681612694,0.0,0.0,99.09,9/20/2021,Los Angeles SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.25966303270564917,114.482,9/25/2023,Los Angeles SMM Food,133
+0.0,0.0,0.0,0.12993493592533384,0.0,0.0,0.0,115.06,9/26/2022,Los Angeles SMM Food,134
+0.0,0.0,0.0,0.0,0.00794355009220286,0.0,0.0,98.9,9/27/2021,Los Angeles SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2477700693756194,135.601,9/4/2023,Los Angeles SMM Food,136
+0.0,0.0,0.0,0.11476858539692965,0.0,0.0,0.0,107.08,9/5/2022,Los Angeles SMM Food,137
+0.0,0.0,0.0,0.0,0.008441491053441243,0.0,0.0,109.3,9/6/2021,Los Angeles SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.049554013875123884,5.978,8/7/2023,Madison WI SMM Food,124
+0.0,0.000593236234973785,0.0,0.0,0.0031558941419108397,0.0,0.0,5.08,8/8/2022,Madison WI SMM Food,125
+0.0,0.0,0.0,0.0,0.0008400047520021403,0.0,0.0,5.58,8/9/2021,Madison WI SMM Food,126
+0.0,0.0,0.004571583640934544,0.0,0.0,0.034073713711936085,0.037165510406342916,7.98,9/11/2023,Madison WI SMM Food,127
+0.0,0.0,0.0,0.00930365997973997,0.0,0.0,0.0,6.03,9/12/2022,Madison WI SMM Food,128
+0.0,0.0,0.0,0.0,0.0004911367990351248,0.0,0.0,8.3,9/13/2021,Madison WI SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.044598612487611496,7.618,9/18/2023,Madison WI SMM Food,130
+0.0,0.0,0.0,0.009698112368307879,0.0,0.0,0.0,6.79,9/19/2022,Madison WI SMM Food,131
+0.0,0.0,0.0,0.0,0.0004218580566019586,0.0,0.0,5.87,9/20/2021,Madison WI SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.03815659068384539,7.501,9/25/2023,Madison WI SMM Food,133
+0.0,0.0,0.0,0.011020108853925868,0.0,0.0,0.0,6.51,9/26/2022,Madison WI SMM Food,134
+0.0,0.0,0.0,0.0,0.0006612408541165596,0.0,0.0,5.48,9/27/2021,Madison WI SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04509415262636274,8.164,9/4/2023,Madison WI SMM Food,136
+0.0,0.0,0.0,0.009733812499659315,0.0,0.0,0.0,5.28,9/5/2022,Madison WI SMM Food,137
+0.0,0.0,0.0,0.0,0.00041381677399810894,0.0,0.0,5.27,9/6/2021,Madison WI SMM Food,138
+0.0,0.005007849599907721,0.0,0.0,0.011139032086932654,0.0,0.0,265.02,8/8/2022,Miami/West Palm Beach SMM Food,125
+0.0,0.0,0.0,0.0,0.0028466140417627763,0.0,0.0,109.22,8/9/2021,Miami/West Palm Beach SMM Food,126
+0.0,0.0,0.018040328170654842,0.0,0.0,0.135383570276492,0.0867195242814668,117.213,9/11/2023,Miami/West Palm Beach SMM Food,127
+0.0,0.0,0.0,0.040042460444501625,0.0,0.0,0.0,103.86,9/12/2022,Miami/West Palm Beach SMM Food,128
+0.0,0.0,0.0,0.0,0.0025509422660212277,0.0,0.0,106.45,9/13/2021,Miami/West Palm Beach SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.08225966303270565,115.904,9/18/2023,Miami/West Palm Beach SMM Food,130
+0.0,0.0,0.0,0.04174016266482536,0.0,0.0,0.0,95.09,9/19/2022,Miami/West Palm Beach SMM Food,131
+0.0,0.0,0.0,0.0,0.002568261951629519,0.0,0.0,104.62,9/20/2021,Miami/West Palm Beach SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07036669970267592,103.499,9/25/2023,Miami/West Palm Beach SMM Food,133
+0.0,0.0,0.0,0.04742996560650661,0.0,0.0,0.0,111.66,9/26/2022,Miami/West Palm Beach SMM Food,134
+0.0,0.0,0.0,0.0,0.0022886727410956695,0.0,0.0,102.39,9/27/2021,Miami/West Palm Beach SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10158572844400396,146.302,9/4/2023,Miami/West Palm Beach SMM Food,136
+0.0,0.0,0.0,0.04189381415960767,0.0,0.0,0.0,147.87,9/5/2022,Miami/West Palm Beach SMM Food,137
+0.0,0.0,0.0,0.0,0.0027909436237361245,0.0,0.0,104.06,9/6/2021,Miami/West Palm Beach SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.0931615460852329,24.866,8/7/2023,Milwaukee SMM Food,124
+0.0,0.001717034769678263,0.0,0.0,0.009219639785413772,0.0,0.0,23.28,8/8/2022,Milwaukee SMM Food,125
+0.0,0.0,0.0,0.0,0.0018352681142786086,0.0,0.0,19.3,8/9/2021,Milwaukee SMM Food,126
+0.0,0.0,0.01275815316445043,0.0,0.0,0.08156726192538118,0.062438057482656094,25.297,9/11/2023,Milwaukee SMM Food,127
+0.0,0.0,0.0,0.021252502340492276,0.0,0.0,0.0,18.62,9/12/2022,Milwaukee SMM Food,128
+0.0,0.0,0.0,0.0,0.0014635134339006364,0.0,0.0,20.78,9/13/2021,Milwaukee SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06689791873141725,28.503,9/18/2023,Milwaukee SMM Food,130
+0.0,0.0,0.0,0.02215355637305754,0.0,0.0,0.0,20.56,9/19/2022,Milwaukee SMM Food,131
+0.0,0.0,0.0,0.0,0.001509905448922846,0.0,0.0,18.05,9/20/2021,Milwaukee SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.08275520317145689,24.272,9/25/2023,Milwaukee SMM Food,133
+0.0,0.0,0.0,0.02517341451994022,0.0,0.0,0.0,19.51,9/26/2022,Milwaukee SMM Food,134
+0.0,0.0,0.0,0.0,0.0013719665242568095,0.0,0.0,20.33,9/27/2021,Milwaukee SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07284440039643211,28.938,9/4/2023,Milwaukee SMM Food,136
+0.0,0.0,0.0,0.02223510677772291,0.0,0.0,0.0,25.25,9/5/2022,Milwaukee SMM Food,137
+0.0,0.0,0.0,0.0,0.001925577903521843,0.0,0.0,24.08,9/6/2021,Milwaukee SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.1709613478691774,37.06,8/7/2023,Minneapolis/St. Paul SMM Food,124
+0.0,0.0038046255712315823,0.0,0.0,0.00971510650585097,0.0,0.0,46.84,8/8/2022,Minneapolis/St. Paul SMM Food,125
+0.0,0.0,0.0,0.0,0.002077125152594394,0.0,0.0,41.5,8/9/2021,Minneapolis/St. Paul SMM Food,126
+0.0,0.0,0.03094870136244261,0.0,0.0,0.1252957809638225,0.17393458870168482,38.324,9/11/2023,Minneapolis/St. Paul SMM Food,127
+0.0,0.0,0.0,0.038681592342424166,0.0,0.0,0.0,51.68,9/12/2022,Minneapolis/St. Paul SMM Food,128
+0.0,0.0,0.0,0.0,0.001091758753522664,0.0,0.0,45.98,9/13/2021,Minneapolis/St. Paul SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1570862239841427,40.978,9/18/2023,Minneapolis/St. Paul SMM Food,130
+0.0,0.0,0.0,0.0403215970895781,0.0,0.0,0.0,40.48,9/19/2022,Minneapolis/St. Paul SMM Food,131
+0.0,0.0,0.0,0.0,0.002432178707564371,0.0,0.0,49.56,9/20/2021,Minneapolis/St. Paul SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.15262636273538155,38.141,9/25/2023,Minneapolis/St. Paul SMM Food,133
+0.0,0.0,0.0,0.04581802852379454,0.0,0.0,0.0,38.38,9/26/2022,Minneapolis/St. Paul SMM Food,134
+0.0,0.0,0.0,0.0,0.0014257812616825726,0.0,0.0,47.81,9/27/2021,Minneapolis/St. Paul SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.1645193260654113,40.776,9/4/2023,Minneapolis/St. Paul SMM Food,136
+0.0,0.0,0.0,0.04047002664468756,0.0,0.0,0.0,44.99,9/5/2022,Minneapolis/St. Paul SMM Food,137
+0.0,0.0,0.0,0.0,0.0022967140236995194,0.0,0.0,43.4,9/6/2021,Minneapolis/St. Paul SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06194251734390485,48.944,8/7/2023,Mobile/Pensacola SMM Food,124
+0.0,0.0007766369210538014,0.0,0.0,0.006063745643502932,0.0,0.0,31.02,8/8/2022,Mobile/Pensacola SMM Food,125
+0.0,0.0,0.0,0.0,0.0015507304221423902,0.0,0.0,18.44,8/9/2021,Mobile/Pensacola SMM Food,126
+0.0,0.0,0.007016034816117654,0.0,0.0,0.06153590867568924,0.05153617443012884,18.764,9/11/2023,Mobile/Pensacola SMM Food,127
+0.0,0.0,0.0,0.016447031490323782,0.0,0.0,0.0,17.84,9/12/2022,Mobile/Pensacola SMM Food,128
+0.0,0.0,0.0,0.0,0.001671349661200135,0.0,0.0,18.48,9/13/2021,Mobile/Pensacola SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05054509415262636,17.263,9/18/2023,Mobile/Pensacola SMM Food,130
+0.0,0.0,0.0,0.01714434533067197,0.0,0.0,0.0,15.41,9/19/2022,Mobile/Pensacola SMM Food,131
+0.0,0.0,0.0,0.0,0.0012228935159854428,0.0,0.0,17.3,9/20/2021,Mobile/Pensacola SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.030723488602576808,16.84,9/25/2023,Mobile/Pensacola SMM Food,133
+0.0,0.0,0.0,0.019481373757693706,0.0,0.0,0.0,14.96,9/26/2022,Mobile/Pensacola SMM Food,134
+0.0,0.0,0.0,0.0,0.0012754711330106138,0.0,0.0,15.57,9/27/2021,Mobile/Pensacola SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.053518334985133795,21.946,9/4/2023,Mobile/Pensacola SMM Food,136
+0.0,0.0,0.0,0.017207456111133626,0.0,0.0,0.0,23.31,9/5/2022,Mobile/Pensacola SMM Food,137
+0.0,0.0,0.0,0.0,0.0013367085928399302,0.0,0.0,17.9,9/6/2021,Mobile/Pensacola SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.08572844400396432,77.43,8/7/2023,Nashville SMM Food,124
+0.0,0.0024515039739325646,0.0,0.0,0.011818829747058097,0.0,0.0,56.6,8/8/2022,Nashville SMM Food,125
+0.0,0.0,0.0,0.0,0.002948676474811637,0.0,0.0,41.75,8/9/2021,Nashville SMM Food,126
+0.0,0.0,0.018863584556427716,0.0,0.0,0.1291559227775106,0.09613478691774033,54.687,9/11/2023,Nashville SMM Food,127
+0.0,0.0,0.0,0.023131705316610268,0.0,0.0,0.0,43.39,9/12/2022,Nashville SMM Food,128
+0.0,0.0,0.0,0.0,0.0023907351741445306,0.0,0.0,39.43,9/13/2021,Nashville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10951437066402378,55.368,9/18/2023,Nashville SMM Food,130
+0.0,0.0,0.0,0.02411243295492175,0.0,0.0,0.0,41.52,9/19/2022,Nashville SMM Food,131
+0.0,0.0,0.0,0.0,0.0027346546455091774,0.0,0.0,42.92,9/20/2021,Nashville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09217046580773042,58.347,9/25/2023,Nashville SMM Food,133
+0.0,0.0,0.0,0.0273993150200572,0.0,0.0,0.0,48.74,9/26/2022,Nashville SMM Food,134
+0.0,0.0,0.0,0.0,0.0031305331736986982,0.0,0.0,43.99,9/27/2021,Nashville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.08225966303270565,54.91,9/4/2023,Nashville SMM Food,136
+0.0,0.0,0.0,0.024201194256313775,0.0,0.0,0.0,43.7,9/5/2022,Nashville SMM Food,137
+0.0,0.0,0.0,0.0,0.002930119668802753,0.0,0.0,44.1,9/6/2021,Nashville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,10.449,8/7/2023,New Orleans SMM Food,124
+0.0,0.0011893606697283578,0.0,0.0,0.006126220223732841,0.0,0.0,13.23,8/8/2022,New Orleans SMM Food,125
+0.0,0.0,0.0,0.0,0.0021148573248124577,0.0,0.0,14.12,8/9/2021,New Orleans SMM Food,126
+0.0,0.0,0.007795828665891241,0.0,0.0,0.0697753770970809,0.040634291377601585,12.088,9/11/2023,New Orleans SMM Food,127
+0.0,0.0,0.0,0.01804932909865379,0.0,0.0,0.0,9.64,9/12/2022,New Orleans SMM Food,128
+0.0,0.0,0.0,0.0,0.0012334090393904772,0.0,0.0,24.18,9/13/2021,New Orleans SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05698711595639247,10.331,9/18/2023,New Orleans SMM Food,130
+0.0,0.0,0.0,0.01881457643298289,0.0,0.0,0.0,9.9,9/19/2022,New Orleans SMM Food,131
+0.0,0.0,0.0,0.0,0.001212996552780705,0.0,0.0,34.03,9/20/2021,New Orleans SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,11.492,9/25/2023,New Orleans SMM Food,133
+0.0,0.0,0.0,0.02137928211724966,0.0,0.0,0.0,8.96,9/26/2022,New Orleans SMM Food,134
+0.0,0.0,0.0,0.0,0.0007688703289680858,0.0,0.0,17.31,9/27/2021,New Orleans SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,18.667,9/4/2023,New Orleans SMM Food,136
+0.0,0.0,0.0,0.018883835570947957,0.0,0.0,0.0,19.88,9/5/2022,New Orleans SMM Food,137
+0.0,0.0,0.0,0.0,0.001574235709753643,0.0,0.0,7.57,9/6/2021,New Orleans SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.4534192269573835,246.856,8/7/2023,New York SMM Food,124
+0.0,0.039101892732193715,0.0,0.0,0.0494452281708712,0.0,0.0,205.05,8/8/2022,New York SMM Food,125
+0.0,0.0,0.0,0.0,0.015521531106030714,0.0,0.0,234.8,8/9/2021,New York SMM Food,126
+0.0,0.0,0.24068159947386805,0.0,0.0,0.520680218910273,0.410802775024777,288.514,9/11/2023,New York SMM Food,127
+0.0,0.0,0.0,0.17120571402939075,0.0,0.0,0.0,254.52,9/12/2022,New York SMM Food,128
+0.0,0.0,0.0,0.0,0.013122136089082036,0.0,0.0,230.57,9/13/2021,New York SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.41873141724479684,509.862,9/18/2023,New York SMM Food,130
+0.0,0.0,0.0,0.1784644168917692,0.0,0.0,0.0,247.62,9/19/2022,New York SMM Food,131
+0.0,0.0,0.0,0.0,0.012023573173356115,0.0,0.0,230.26,9/20/2021,New York SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.42913776015857286,303.344,9/25/2023,New York SMM Food,133
+0.0,0.0,0.0,0.20279176256454842,0.0,0.0,0.0,260.65,9/26/2022,New York SMM Food,134
+0.0,0.0,0.0,0.0,0.011220682033371742,0.0,0.0,237.2,9/27/2021,New York SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.4474727452923687,274.76,9/4/2023,New York SMM Food,136
+0.0,0.0,0.0,0.17912136984330115,0.0,0.0,0.0,223.46,9/5/2022,New York SMM Food,137
+0.0,0.0,0.0,0.0,0.01324461100874067,0.0,0.0,236.95,9/6/2021,New York SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.07482656095143707,53.749,8/7/2023,Norfolk/Portsmouth/Newport News SMM Food,124
+0.0,0.0011087798958443822,0.0,0.0,0.008415511525028807,0.0,0.0,62.84,8/8/2022,Norfolk/Portsmouth/Newport News SMM Food,125
+0.0,0.0,0.0,0.0,0.00208578499539854,0.0,0.0,53.72,8/9/2021,Norfolk/Portsmouth/Newport News SMM Food,126
+0.0,0.0,0.010047019244106654,0.0,0.0,0.08559141854246288,0.062438057482656094,66.739,9/11/2023,Norfolk/Portsmouth/Newport News SMM Food,127
+0.0,0.0,0.0,0.015531940702298817,0.0,0.0,0.0,56.23,9/12/2022,Norfolk/Portsmouth/Newport News SMM Food,128
+0.0,0.0,0.0,0.0,0.0010960886749247368,0.0,0.0,57.58,9/13/2021,Norfolk/Portsmouth/Newport News SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06045589692765114,57.374,9/18/2023,Norfolk/Portsmouth/Newport News SMM Food,130
+0.0,0.0,0.0,0.016190456939540337,0.0,0.0,0.0,55.17,9/19/2022,Norfolk/Portsmouth/Newport News SMM Food,131
+0.0,0.0,0.0,0.0,0.0014499051094941215,0.0,0.0,47.98,9/20/2021,Norfolk/Portsmouth/Newport News SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06442021803766104,71.472,9/25/2023,Norfolk/Portsmouth/Newport News SMM Food,133
+0.0,0.0,0.0,0.01839745623572133,0.0,0.0,0.0,47.55,9/26/2022,Norfolk/Portsmouth/Newport News SMM Food,134
+0.0,0.0,0.0,0.0,0.0011863984641679714,0.0,0.0,51.74,9/27/2021,Norfolk/Portsmouth/Newport News SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05797819623389494,49.663,9/4/2023,Norfolk/Portsmouth/Newport News SMM Food,136
+0.0,0.0,0.0,0.01625005632294706,0.0,0.0,0.0,49.62,9/5/2022,Norfolk/Portsmouth/Newport News SMM Food,137
+0.0,0.0,0.0,0.0,0.0014202142198799074,0.0,0.0,80.86,9/6/2021,Norfolk/Portsmouth/Newport News SMM Food,138
+0.0,0.0012719631834588849,0.0,0.0,0.004641057182821841,0.0,0.0,2.43,8/8/2022,Oklahoma City SMM Food,125
+0.0,0.0,0.0,0.0,0.001920629421919474,0.0,0.0,2.79,8/9/2021,Oklahoma City SMM Food,126
+0.0,0.0,0.008075170402119658,0.0,0.0,0.05973936106606689,0.0639246778989098,5.321,9/11/2023,Oklahoma City SMM Food,127
+0.0,0.0,0.0,0.017613566913413724,0.0,0.0,0.0,4.4,9/12/2022,Oklahoma City SMM Food,128
+0.0,0.0,0.0,0.0,0.0016657826193974697,0.0,0.0,5.31,9/13/2021,Oklahoma City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,4.506,9/18/2023,Oklahoma City SMM Food,130
+0.0,0.0,0.0,0.01836033899387075,0.0,0.0,0.0,3.98,9/19/2022,Oklahoma City SMM Food,131
+0.0,0.0,0.0,0.0,0.0015340292967343948,0.0,0.0,3.66,9/20/2021,Oklahoma City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.06095143706640238,6.145,9/25/2023,Oklahoma City SMM Food,133
+0.0,0.0,0.0,0.02086312538457761,0.0,0.0,0.0,3.72,9/26/2022,Oklahoma City SMM Food,134
+0.0,0.0,0.0,0.0,0.0010830989107185184,0.0,0.0,4.46,9/27/2021,Oklahoma City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06541129831516353,8.982,9/4/2023,Oklahoma City SMM Food,136
+0.0,0.0,0.0,0.018427926025991807,0.0,0.0,0.0,2.67,9/5/2022,Oklahoma City SMM Food,137
+0.0,0.0,0.0,0.0,0.002021454734567743,0.0,0.0,2.99,9/6/2021,Oklahoma City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.04311199207135778,12.325,8/7/2023,Omaha SMM Food,124
+0.0,0.0009476183480764309,0.0,0.0,0.003898166382266192,0.0,0.0,15.49,8/8/2022,Omaha SMM Food,125
+0.0,0.0,0.0,0.0,0.000536291693656742,0.0,0.0,12.09,8/9/2021,Omaha SMM Food,126
+0.0,0.0,0.00745909950348901,0.0,0.0,0.04438741589312105,0.049554013875123884,13.324,9/11/2023,Omaha SMM Food,127
+0.0,0.0,0.0,0.009233480542505387,0.0,0.0,0.0,11.97,9/12/2022,Omaha SMM Food,128
+0.0,0.0,0.0,0.0,0.0005041265632413435,0.0,0.0,13.54,9/13/2021,Omaha SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.06045589692765114,12.73,9/18/2023,Omaha SMM Food,130
+0.0,0.0,0.0,0.0096249574987297,0.0,0.0,0.0,11.64,9/19/2022,Omaha SMM Food,131
+0.0,0.0,0.0,0.0,0.0006804162203257396,0.0,0.0,14.6,9/20/2021,Omaha SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.04558969276511397,13.377,9/25/2023,Omaha SMM Food,133
+0.0,0.0,0.0,0.010936981891184227,0.0,0.0,0.0,11.62,9/26/2022,Omaha SMM Food,134
+0.0,0.0,0.0,0.0,0.0004911367990351248,0.0,0.0,12.9,9/27/2021,Omaha SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.05004955401387512,14.921,9/4/2023,Omaha SMM Food,136
+0.0,0.0,0.0,0.00966038834039735,0.0,0.0,0.0,12.01,9/5/2022,Omaha SMM Food,137
+0.0,0.0,0.0,0.0,0.000555467059865922,0.0,0.0,13.82,9/6/2021,Omaha SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.15807730426164518,296.409,8/7/2023,Orlando/Daytona Beach/Melborne SMM Food,124
+0.0,0.0035978304669271864,0.0,0.0,0.014912249308739028,0.0,0.0,177.13,8/8/2022,Orlando/Daytona Beach/Melborne SMM Food,125
+0.0,0.0,0.0,0.0,0.005805187479779151,0.0,0.0,65.31,8/9/2021,Orlando/Daytona Beach/Melborne SMM Food,126
+0.0,0.0,0.024174453275719036,0.0,0.0,0.18581466537428856,0.1238850346878097,77.936,9/11/2023,Orlando/Daytona Beach/Melborne SMM Food,127
+0.0,0.0,0.0,0.04488872730475567,0.0,0.0,0.0,68.78,9/12/2022,Orlando/Daytona Beach/Melborne SMM Food,128
+0.0,0.0,0.0,0.0,0.003562906753705691,0.0,0.0,66.88,9/13/2021,Orlando/Daytona Beach/Melborne SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13627353815659068,75.008,9/18/2023,Orlando/Daytona Beach/Melborne SMM Food,130
+0.0,0.0,0.0,0.046791899365486236,0.0,0.0,0.0,58.18,9/19/2022,Orlando/Daytona Beach/Melborne SMM Food,131
+0.0,0.0,0.0,0.0,0.003940847036086625,0.0,0.0,59.08,9/20/2021,Orlando/Daytona Beach/Melborne SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.11149653121902874,68.939,9/25/2023,Orlando/Daytona Beach/Melborne SMM Food,133
+0.0,0.0,0.0,0.053170328912353564,0.0,0.0,0.0,66.79,9/26/2022,Orlando/Daytona Beach/Melborne SMM Food,134
+0.0,0.0,0.0,0.0,0.004718377207858856,0.0,0.0,57.95,9/27/2021,Orlando/Daytona Beach/Melborne SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.17244796828543113,108.309,9/4/2023,Orlando/Daytona Beach/Melborne SMM Food,136
+0.0,0.0,0.0,0.0469641470254011,0.0,0.0,0.0,84.61,9/5/2022,Orlando/Daytona Beach/Melborne SMM Food,137
+0.0,0.0,0.0,0.0,0.004595902288200223,0.0,0.0,60.35,9/6/2021,Orlando/Daytona Beach/Melborne SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.028245787908820614,6.148,8/7/2023,Paducah KY/Cape Girardeau MO SMM Food,124
+0.0,0.00036420199235732373,0.0,0.0,0.0035888862821181287,0.0,0.0,5.95,8/8/2022,Paducah KY/Cape Girardeau MO SMM Food,125
+0.0,0.0,0.0,0.0,0.0016602155775948047,0.0,0.0,6.97,8/9/2021,Paducah KY/Cape Girardeau MO SMM Food,126
+0.0,0.0,0.0033689794894980066,0.0,0.0,0.02940059048808853,0.036669970267591674,5.015,9/11/2023,Paducah KY/Cape Girardeau MO SMM Food,127
+0.0,0.0,0.0,0.008585794855104294,0.0,0.0,0.0,5.9,9/12/2022,Paducah KY/Cape Girardeau MO SMM Food,128
+0.0,0.0,0.0,0.0,0.0006470139695097488,0.0,0.0,5.93,9/13/2021,Paducah KY/Cape Girardeau MO SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.0421209117938553,5.725,9/18/2023,Paducah KY/Cape Girardeau MO SMM Food,130
+0.0,0.0,0.0,0.008949811523566999,0.0,0.0,0.0,4.53,9/19/2022,Paducah KY/Cape Girardeau MO SMM Food,131
+0.0,0.0,0.0,0.0,0.001383719168062436,0.0,0.0,5.28,9/20/2021,Paducah KY/Cape Girardeau MO SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.03815659068384539,4.811,9/25/2023,Paducah KY/Cape Girardeau MO SMM Food,133
+0.0,0.0,0.0,0.010169803507137157,0.0,0.0,0.0,6.14,9/26/2022,Paducah KY/Cape Girardeau MO SMM Food,134
+0.0,0.0,0.0,0.0,0.0010738205077140764,0.0,0.0,5.48,9/27/2021,Paducah KY/Cape Girardeau MO SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.024777006937561942,6.809,9/4/2023,Paducah KY/Cape Girardeau MO SMM Food,136
+0.0,0.0,0.0,0.008982757054847872,0.0,0.0,0.0,7.88,9/5/2022,Paducah KY/Cape Girardeau MO SMM Food,137
+0.0,0.0,0.0,0.0,0.0006903131835304776,0.0,0.0,7.07,9/6/2021,Paducah KY/Cape Girardeau MO SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.2522299306243806,136.614,8/7/2023,Philadelphia SMM Food,124
+0.0,0.006726039649498299,0.0,0.0,0.0256807638356943,0.0,0.0,135.86,8/8/2022,Philadelphia SMM Food,125
+0.0,0.0,0.0,0.0,0.007499423868390242,0.0,0.0,130.86,8/9/2021,Philadelphia SMM Food,126
+0.0,0.0,0.04194430100389289,0.0,0.0,0.2407575070389005,0.21754212091179384,175.883,9/11/2023,Philadelphia SMM Food,127
+0.0,0.0,0.0,0.06751901436081623,0.0,0.0,0.0,157.21,9/12/2022,Philadelphia SMM Food,128
+0.0,0.0,0.0,0.0,0.006012405146878353,0.0,0.0,149.83,9/13/2021,Philadelphia SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.20416253716551042,235.367,9/18/2023,Philadelphia SMM Food,130
+0.0,0.0,0.0,0.07038165514606878,0.0,0.0,0.0,153.26,9/19/2022,Philadelphia SMM Food,131
+0.0,0.0,0.0,0.0,0.0061806535213589,0.0,0.0,146.17,9/20/2021,Philadelphia SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.2358771060455897,157.971,9/25/2023,Philadelphia SMM Food,133
+0.0,0.0,0.0,0.07997571812552183,0.0,0.0,0.0,135.14,9/26/2022,Philadelphia SMM Food,134
+0.0,0.0,0.0,0.0,0.00549714450003168,0.0,0.0,145.33,9/27/2021,Philadelphia SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2309217046580773,165.02,9/4/2023,Philadelphia SMM Food,136
+0.0,0.0,0.0,0.07064074003892257,0.0,0.0,0.0,151.89,9/5/2022,Philadelphia SMM Food,137
+0.0,0.0,0.0,0.0,0.0056214751002912015,0.0,0.0,155.51,9/6/2021,Philadelphia SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.17195242814667988,79.849,8/7/2023,Phoenix/Prescott SMM Food,124
+0.0,0.0,0.0,0.0,0.014418019708702422,0.0,0.0,66.86,8/8/2022,Phoenix/Prescott SMM Food,125
+0.0,0.0,0.0,0.0,0.005271370026923594,0.0,0.0,46.12,8/9/2021,Phoenix/Prescott SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.001808959402470655,0.13627353815659068,80.322,9/11/2023,Phoenix/Prescott SMM Food,127
+0.0,0.0,0.0,0.036806299803701564,0.0,0.0,0.0,73.57,9/12/2022,Phoenix/Prescott SMM Food,128
+0.0,0.0,0.0,0.0,0.0034243492688393585,0.0,0.0,56.9,9/13/2021,Phoenix/Prescott SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.1630327056491576,72.968,9/18/2023,Phoenix/Prescott SMM Food,130
+0.0,0.0,0.0,0.0383667967368777,0.0,0.0,0.0,67.09,9/19/2022,Phoenix/Prescott SMM Food,131
+0.0,0.0,0.0,0.0,0.002687025510086375,0.0,0.0,54.17,9/20/2021,Phoenix/Prescott SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1367690782953419,71.044,9/25/2023,Phoenix/Prescott SMM Food,133
+0.0,0.0,0.0,0.04359675990047249,0.0,0.0,0.0,75.15,9/26/2022,Phoenix/Prescott SMM Food,134
+0.0,0.0,0.0,0.0,0.0027371288863103616,0.0,0.0,54.23,9/27/2021,Phoenix/Prescott SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.14866204162537167,80.716,9/4/2023,Phoenix/Prescott SMM Food,136
+0.0,0.0,0.0,0.038508030393034846,0.0,0.0,0.0,67.72,9/5/2022,Phoenix/Prescott SMM Food,137
+0.0,0.0,0.0,0.0,0.0037181653639800187,0.0,0.0,56.91,9/6/2021,Phoenix/Prescott SMM Food,138
+0.0,0.0022663703679840757,0.0,0.0,0.00748705266438432,0.0,0.0,50.92,8/8/2022,Pittsburgh SMM Food,125
+0.0,0.0,0.0,0.0,0.003074244195471751,0.0,0.0,59.65,8/9/2021,Pittsburgh SMM Food,126
+0.0,0.0,0.009616613547803052,0.0,0.0,0.0779249674907126,0.11050545094152626,57.215,9/11/2023,Pittsburgh SMM Food,127
+0.0,0.0,0.0,0.025851136933559334,0.0,0.0,0.0,57.81,9/12/2022,Pittsburgh SMM Food,128
+0.0,0.0,0.0,0.0,0.002534859700813528,0.0,0.0,64.84,9/13/2021,Pittsburgh SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.10604558969276512,50.418,9/18/2023,Pittsburgh SMM Food,130
+0.0,0.0,0.0,0.026947161800410718,0.0,0.0,0.0,49.22,9/19/2022,Pittsburgh SMM Food,131
+0.0,0.0,0.0,0.0,0.002456921115576216,0.0,0.0,55.17,9/20/2021,Pittsburgh SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.10059464816650149,54.89,9/25/2023,Pittsburgh SMM Food,133
+0.0,0.0,0.0,0.030620459431281187,0.0,0.0,0.0,46.36,9/26/2022,Pittsburgh SMM Food,134
+0.0,0.0,0.0,0.0,0.002446405592171182,0.0,0.0,58.95,9/27/2021,Pittsburgh SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.08870168483647176,59.663,9/4/2023,Pittsburgh SMM Food,136
+0.0,0.0,0.0,0.027046358148388426,0.0,0.0,0.0,56.47,9/5/2022,Pittsburgh SMM Food,137
+0.0,0.0,0.0,0.0,0.0023140337093078105,0.0,0.0,57.77,9/6/2021,Pittsburgh SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10257680872150644,31.759,8/7/2023,Portland OR SMM Food,124
+0.0,0.002691513375716019,0.0,0.0,0.007488289784784913,0.0,0.0,32.69,8/8/2022,Portland OR SMM Food,125
+0.0,0.0,0.0,0.0,0.0017870204186555107,0.0,0.0,36.91,8/9/2021,Portland OR SMM Food,126
+0.0,0.0,0.020675086178223175,0.0,0.0,0.10859833173904791,0.09613478691774033,41.43,9/11/2023,Portland OR SMM Food,127
+0.0,0.0,0.0,0.025917320120911743,0.0,0.0,0.0,37.08,9/12/2022,Portland OR SMM Food,128
+0.0,0.0,0.0,0.0,0.0024290859065628904,0.0,0.0,40.37,9/13/2021,Portland OR SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11050545094152626,36.716,9/18/2023,Portland OR SMM Food,130
+0.0,0.0,0.0,0.02701615099834603,0.0,0.0,0.0,34.13,9/19/2022,Portland OR SMM Food,131
+0.0,0.0,0.0,0.0,0.0006643336551180404,0.0,0.0,39.78,9/20/2021,Portland OR SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.099603567888999,39.153,9/25/2023,Portland OR SMM Food,133
+0.0,0.0,0.0,0.030698852877443856,0.0,0.0,0.0,34.55,9/26/2022,Portland OR SMM Food,134
+0.0,0.0,0.0,0.0,0.0015080497683219573,0.0,0.0,36.01,9/27/2021,Portland OR SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10356788899900891,45.084,9/4/2023,Portland OR SMM Food,136
+0.0,0.0,0.0,0.027115601306424007,0.0,0.0,0.0,32.45,9/5/2022,Portland OR SMM Food,137
+0.0,0.0,0.0,0.0,0.0023134151491075146,0.0,0.0,41.15,9/6/2021,Portland OR SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.05153617443012884,35.059,8/7/2023,Providence RI/New Bedford MA SMM Food,124
+0.0,0.0014989756862287948,0.0,0.0,0.006621068383969743,0.0,0.0,36.29,8/8/2022,Providence RI/New Bedford MA SMM Food,125
+0.0,0.0,0.0,0.0,0.0017084632732179026,0.0,0.0,35.9,8/9/2021,Providence RI/New Bedford MA SMM Food,126
+0.0,0.0,0.005980529346775456,0.0,0.0,0.05103785298168852,0.05004955401387512,39.575,9/11/2023,Providence RI/New Bedford MA SMM Food,127
+0.0,0.0,0.0,0.016572692519385028,0.0,0.0,0.0,47.61,9/12/2022,Providence RI/New Bedford MA SMM Food,128
+0.0,0.0,0.0,0.0,0.0012748525728103176,0.0,0.0,36.32,9/13/2021,Providence RI/New Bedford MA SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.05450941526263627,46.736,9/18/2023,Providence RI/New Bedford MA SMM Food,130
+0.0,0.0,0.0,0.01727533407353234,0.0,0.0,0.0,43.68,9/19/2022,Providence RI/New Bedford MA SMM Food,131
+0.0,0.0,0.0,0.0,0.0017511439270383351,0.0,0.0,32.15,9/20/2021,Providence RI/New Bedford MA SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.053518334985133795,44.89,9/25/2023,Providence RI/New Bedford MA SMM Food,133
+0.0,0.0,0.0,0.01963021820698563,0.0,0.0,0.0,43.55,9/26/2022,Providence RI/New Bedford MA SMM Food,134
+0.0,0.0,0.0,0.0,0.0016719682214004312,0.0,0.0,32.14,9/27/2021,Providence RI/New Bedford MA SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.052031714568880075,36.566,9/4/2023,Providence RI/New Bedford MA SMM Food,136
+0.0,0.0,0.0,0.017338927045711716,0.0,0.0,0.0,40.72,9/5/2022,Providence RI/New Bedford MA SMM Food,137
+0.0,0.0,0.0,0.0,0.0022255796006654645,0.0,0.0,34.4,9/6/2021,Providence RI/New Bedford MA SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10208126858275521,74.844,8/7/2023,Raleigh/Durham/Fayetteville SMM Food,124
+0.0,0.002229401410789994,0.0,0.0,0.014169977068383676,0.0,0.0,95.13,8/8/2022,Raleigh/Durham/Fayetteville SMM Food,125
+0.0,0.0,0.0,0.0,0.0034323905514432084,0.0,0.0,67.86,8/9/2021,Raleigh/Durham/Fayetteville SMM Food,126
+0.0,0.0,0.017138164073892976,0.0,0.0,0.12866985361914052,0.09217046580773042,92.348,9/11/2023,Raleigh/Durham/Fayetteville SMM Food,127
+0.0,0.0,0.0,0.03276343729674159,0.0,0.0,0.0,84.3,9/12/2022,Raleigh/Durham/Fayetteville SMM Food,128
+0.0,0.0,0.0,0.0,0.0024866120051904306,0.0,0.0,74.59,9/13/2021,Raleigh/Durham/Fayetteville SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09464816650148662,87.201,9/18/2023,Raleigh/Durham/Fayetteville SMM Food,130
+0.0,0.0,0.0,0.034152526763903276,0.0,0.0,0.0,76.26,9/19/2022,Raleigh/Durham/Fayetteville SMM Food,131
+0.0,0.0,0.0,0.0,0.0025843445168372186,0.0,0.0,62.15,9/20/2021,Raleigh/Durham/Fayetteville SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09266600594648167,108.314,9/25/2023,Raleigh/Durham/Fayetteville SMM Food,133
+0.0,0.0,0.0,0.03880802245868132,0.0,0.0,0.0,60.35,9/26/2022,Raleigh/Durham/Fayetteville SMM Food,134
+0.0,0.0,0.0,0.0,0.00243712718916674,0.0,0.0,64.26,9/27/2021,Raleigh/Durham/Fayetteville SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.07730426164519326,60.95,9/4/2023,Raleigh/Durham/Fayetteville SMM Food,136
+0.0,0.0,0.0,0.03427824709389885,0.0,0.0,0.0,65.5,9/5/2022,Raleigh/Durham/Fayetteville SMM Food,137
+0.0,0.0,0.0,0.0,0.0019868153633511593,0.0,0.0,98.44,9/6/2021,Raleigh/Durham/Fayetteville SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.6694747274529237,288.005,8/7/2023,Rem US East North Central SMM Food,124
+0.0,0.010814575259181308,0.0,0.0,0.07214515040133847,0.0,0.0,308.06,8/8/2022,Rem US East North Central SMM Food,125
+0.0,0.0,0.0,0.0,0.02009021674541791,0.0,0.0,247.25,8/9/2021,Rem US East North Central SMM Food,126
+0.0,0.0,0.07424624851147829,0.0,0.0,0.5637364309077629,0.6283448959365708,263.553,9/11/2023,Rem US East North Central SMM Food,127
+0.0,0.0,0.0,0.1610144344327281,0.0,0.0,0.0,240.58,9/12/2022,Rem US East North Central SMM Food,128
+0.0,0.0,0.0,0.0,0.016926281320903215,0.0,0.0,240.68,9/13/2021,Rem US East North Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.5926660059464817,298.802,9/18/2023,Rem US East North Central SMM Food,130
+0.0,0.0,0.0,0.16784105194697213,0.0,0.0,0.0,230.68,9/19/2022,Rem US East North Central SMM Food,131
+0.0,0.0,0.0,0.0,0.016729579177209047,0.0,0.0,202.81,9/20/2021,Rem US East North Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.5812685827552032,297.755,9/25/2023,Rem US East North Central SMM Food,133
+0.0,0.0,0.0,0.1907202755058764,0.0,0.0,0.0,247.8,9/26/2022,Rem US East North Central SMM Food,134
+0.0,0.0,0.0,0.0,0.014327709919459187,0.0,0.0,202.26,9/27/2021,Rem US East North Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.5827552031714569,348.214,9/4/2023,Rem US East North Central SMM Food,136
+0.0,0.0,0.0,0.16845889879914241,0.0,0.0,0.0,298.88,9/5/2022,Rem US East North Central SMM Food,137
+0.0,0.0,0.0,0.0,0.015420087233182148,0.0,0.0,258.8,9/6/2021,Rem US East North Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.28344895936570863,84.899,8/7/2023,Rem US Middle Atlantic SMM Food,124
+0.0,0.004143700225496054,0.0,0.0,0.0231415742134787,0.0,0.0,71.81,8/8/2022,Rem US Middle Atlantic SMM Food,125
+0.0,0.0,0.0,0.0,0.006617357022767966,0.0,0.0,78.23,8/9/2021,Rem US Middle Atlantic SMM Food,126
+0.0,0.0,0.028037555383228337,0.0,0.0,0.20463726833171583,0.267591674925669,91.266,9/11/2023,Rem US Middle Atlantic SMM Food,127
+0.0,0.0,0.0,0.05392961586570856,0.0,0.0,0.0,79.8,9/12/2022,Rem US Middle Atlantic SMM Food,128
+0.0,0.0,0.0,0.0,0.005358587015165347,0.0,0.0,82.84,9/13/2021,Rem US Middle Atlantic SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2968285431119921,95.654,9/18/2023,Rem US Middle Atlantic SMM Food,130
+0.0,0.0,0.0,0.056216099457233604,0.0,0.0,0.0,79.34,9/19/2022,Rem US Middle Atlantic SMM Food,131
+0.0,0.0,0.0,0.0,0.005923332478035712,0.0,0.0,79.18,9/20/2021,Rem US Middle Atlantic SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.288404360753221,92.07,9/25/2023,Rem US Middle Atlantic SMM Food,133
+0.0,0.0,0.0,0.06387918718332888,0.0,0.0,0.0,76.85,9/26/2022,Rem US Middle Atlantic SMM Food,134
+0.0,0.0,0.0,0.0,0.004886007022139106,0.0,0.0,78.62,9/27/2021,Rem US Middle Atlantic SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.25421209117938554,92.244,9/4/2023,Rem US Middle Atlantic SMM Food,136
+0.0,0.0,0.0,0.05642303894016692,0.0,0.0,0.0,90.03,9/5/2022,Rem US Middle Atlantic SMM Food,137
+0.0,0.0,0.0,0.0,0.005541680834453002,0.0,0.0,88.71,9/6/2021,Rem US Middle Atlantic SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.3865213082259663,129.148,8/7/2023,Rem US Mountain SMM Food,124
+0.0,0.00753329148822845,0.0,0.0,0.02747273273595218,0.0,0.0,113.3,8/8/2022,Rem US Mountain SMM Food,125
+0.0,0.0,0.0,0.0,0.007616950306446507,0.0,0.0,115.63,8/9/2021,Rem US Mountain SMM Food,126
+0.0,0.0,0.023499729051807798,0.0,0.0,0.3230963384624684,0.3295341922695738,135.595,9/11/2023,Rem US Mountain SMM Food,127
+0.0,0.0,0.0,0.08969502904489553,0.0,0.0,0.0,124.27,9/12/2022,Rem US Mountain SMM Food,128
+0.0,0.0,0.0,0.0,0.005267040105521521,0.0,0.0,115.0,9/13/2021,Rem US Mountain SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.3637264618434093,137.112,9/18/2023,Rem US Mountain SMM Food,130
+0.0,0.0,0.0,0.09349787851239232,0.0,0.0,0.0,126.58,9/19/2022,Rem US Mountain SMM Food,131
+0.0,0.0,0.0,0.0,0.004318168758267262,0.0,0.0,118.66,9/20/2021,Rem US Mountain SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.3399405351833498,137.733,9/25/2023,Rem US Mountain SMM Food,133
+0.0,0.0,0.0,0.10624302537960408,0.0,0.0,0.0,125.46,9/26/2022,Rem US Mountain SMM Food,134
+0.0,0.0,0.0,0.0,0.0057761151503652325,0.0,0.0,115.09,9/27/2021,Rem US Mountain SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.31714568880079286,153.818,9/4/2023,Rem US Mountain SMM Food,136
+0.0,0.0,0.0,0.09384205752517928,0.0,0.0,0.0,123.49,9/5/2022,Rem US Mountain SMM Food,137
+0.0,0.0,0.0,0.0,0.005829929887790996,0.0,0.0,119.33,9/6/2021,Rem US Mountain SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.14816650148662042,95.632,8/7/2023,Rem US New England SMM Food,124
+0.0,0.0027053767346638,0.0,0.0,0.012870382087561513,0.0,0.0,92.11,8/8/2022,Rem US New England SMM Food,125
+0.0,0.0,0.0,0.0,0.0037107426415764655,0.0,0.0,93.54,8/9/2021,Rem US New England SMM Food,126
+0.0,0.0,0.016692145621939145,0.0,0.0,0.10162023020001978,0.14717542120911795,96.91,9/11/2023,Rem US New England SMM Food,127
+0.0,0.0,0.0,0.02729342543083433,0.0,0.0,0.0,108.12,9/12/2022,Rem US New England SMM Food,128
+0.0,0.0,0.0,0.0,0.003017336657044507,0.0,0.0,105.63,9/13/2021,Rem US New England SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.14568880079286423,104.654,9/18/2023,Rem US New England SMM Food,130
+0.0,0.0,0.0,0.028450599800614283,0.0,0.0,0.0,107.99,9/19/2022,Rem US New England SMM Food,131
+0.0,0.0,0.0,0.0,0.001866196124293415,0.0,0.0,103.16,9/20/2021,Rem US New England SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1377601585728444,103.237,9/25/2023,Rem US New England SMM Food,133
+0.0,0.0,0.0,0.032328838321626044,0.0,0.0,0.0,110.46,9/26/2022,Rem US New England SMM Food,134
+0.0,0.0,0.0,0.0,0.002290528421696558,0.0,0.0,93.03,9/27/2021,Rem US New England SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.13181367690782952,95.746,9/4/2023,Rem US New England SMM Food,136
+0.0,0.0,0.0,0.028555330519776528,0.0,0.0,0.0,94.74,9/5/2022,Rem US New England SMM Food,137
+0.0,0.0,0.0,0.0,0.0022682602544858974,0.0,0.0,92.71,9/6/2021,Rem US New England SMM Food,138
+0.0,0.006359527097316345,0.0,0.0,0.02227744561366501,0.0,0.0,56.31,8/8/2022,Rem US Pacific SMM Food,125
+0.0,0.0,0.0,0.0,0.006462098412493638,0.0,0.0,52.58,8/9/2021,Rem US Pacific SMM Food,126
+0.0,0.0,0.04350177887159544,0.0,0.0,0.2505022822201761,0.26957383548067393,68.969,9/11/2023,Rem US Pacific SMM Food,127
+0.0,0.0,0.0,0.07303394106363369,0.0,0.0,0.0,64.99,9/12/2022,Rem US Pacific SMM Food,128
+0.0,0.0,0.0,0.0,0.005756939784156053,0.0,0.0,60.43,9/13/2021,Rem US Pacific SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.2755203171456888,59.672,9/18/2023,Rem US Pacific SMM Food,130
+0.0,0.0,0.0,0.07613040119907764,0.0,0.0,0.0,59.55,9/19/2022,Rem US Pacific SMM Food,131
+0.0,0.0,0.0,0.0,0.006201684568168969,0.0,0.0,50.8,9/20/2021,Rem US Pacific SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.25173439048562934,59.2,9/25/2023,Rem US Pacific SMM Food,133
+0.0,0.0,0.0,0.08650810352908421,0.0,0.0,0.0,58.94,9/26/2022,Rem US Pacific SMM Food,134
+0.0,0.0,0.0,0.0,0.00586766206000906,0.0,0.0,56.23,9/27/2021,Rem US Pacific SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.2512388503468781,68.417,9/4/2023,Rem US Pacific SMM Food,136
+0.0,0.0,0.0,0.07641064808426322,0.0,0.0,0.0,64.25,9/5/2022,Rem US Pacific SMM Food,137
+0.0,0.0,0.0,0.0,0.006398386711863137,0.0,0.0,56.78,9/6/2021,Rem US Pacific SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.6902874132804757,361.601,8/7/2023,Rem US South Atlantic SMM Food,124
+0.0,0.012989967334070573,0.0,0.0,0.07146720842181392,0.0,0.0,306.04,8/8/2022,Rem US South Atlantic SMM Food,125
+0.0,0.0,0.0,0.0,0.021449812065668792,0.0,0.0,216.74,8/9/2021,Rem US South Atlantic SMM Food,126
+0.0,0.0,0.07827180767102376,0.0,0.0,0.7032422127774433,0.5802775024777007,251.083,9/11/2023,Rem US South Atlantic SMM Food,127
+0.0,0.0,0.0,0.17196903338655478,0.0,0.0,0.0,233.99,9/12/2022,Rem US South Atlantic SMM Food,128
+0.0,0.0,0.0,0.0,0.015825244164376112,0.0,0.0,221.21,9/13/2021,Rem US South Atlantic SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.6040634291377601,232.236,9/18/2023,Rem US South Atlantic SMM Food,130
+0.0,0.0,0.0,0.17926009905695495,0.0,0.0,0.0,215.08,9/19/2022,Rem US South Atlantic SMM Food,131
+0.0,0.0,0.0,0.0,0.017987112064411077,0.0,0.0,197.7,9/20/2021,Rem US South Atlantic SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.5708622398414271,309.873,9/25/2023,Rem US South Atlantic SMM Food,133
+0.0,0.0,0.0,0.20369590803657825,0.0,0.0,0.0,210.58,9/26/2022,Rem US South Atlantic SMM Food,134
+0.0,0.0,0.0,0.0,0.014305441752248528,0.0,0.0,213.12,9/27/2021,Rem US South Atlantic SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.5802775024777007,234.028,9/4/2023,Rem US South Atlantic SMM Food,136
+0.0,0.0,0.0,0.17991998108708984,0.0,0.0,0.0,215.63,9/5/2022,Rem US South Atlantic SMM Food,137
+0.0,0.0,0.0,0.0,0.01699123014193431,0.0,0.0,279.36,9/6/2021,Rem US South Atlantic SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.9940535183349851,402.383,8/7/2023,Rem US South Central SMM Food,124
+0.0,0.014466126242031146,0.0,0.0,0.09628013229649275,0.0,0.0,349.19,8/8/2022,Rem US South Central SMM Food,125
+0.0,0.0,0.0,0.0,0.026810254761435028,0.0,0.0,321.57,8/9/2021,Rem US South Central SMM Food,126
+0.0,0.0,0.0887623135688707,0.0,0.0,0.8841922317358683,0.8275520317145688,406.505,9/11/2023,Rem US South Central SMM Food,127
+0.0,0.0,0.0,0.23375662511916412,0.0,0.0,0.0,389.86,9/12/2022,Rem US South Central SMM Food,128
+0.0,0.0,0.0,0.0,0.023796629465592297,0.0,0.0,338.41,9/13/2021,Rem US South Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.9340931615460852,374.323,9/18/2023,Rem US South Central SMM Food,130
+0.0,0.0,0.0,0.24366733338763769,0.0,0.0,0.0,410.27,9/19/2022,Rem US South Central SMM Food,131
+0.0,0.0,0.0,0.0,0.023312915388960728,0.0,0.0,322.93,9/20/2021,Rem US South Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.8875123885034688,376.732,9/25/2023,Rem US South Central SMM Food,133
+0.0,0.0,0.0,0.2768828031055804,0.0,0.0,0.0,415.53,9/26/2022,Rem US South Central SMM Food,134
+0.0,0.0,0.0,0.0,0.021042799453873943,0.0,0.0,312.75,9/27/2021,Rem US South Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.7715559960356789,385.963,9/4/2023,Rem US South Central SMM Food,136
+0.0,0.0,0.0,0.24456430760086398,0.0,0.0,0.0,384.02,9/5/2022,Rem US South Central SMM Food,137
+0.0,0.0,0.0,0.0,0.021850639075460684,0.0,0.0,348.91,9/6/2021,Rem US South Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.4821605550049554,81.439,8/7/2023,Rem US West North Central SMM Food,124
+0.0,0.005221576383686008,0.0,0.0,0.032486163159352294,0.0,0.0,85.9,8/8/2022,Rem US West North Central SMM Food,125
+0.0,0.0,0.0,0.0,0.010447481783001583,0.0,0.0,67.85,8/9/2021,Rem US West North Central SMM Food,126
+0.0,0.0,0.04495249924795993,0.0,0.0,0.32640646345165913,0.42170465807730423,81.258,9/11/2023,Rem US West North Central SMM Food,127
+0.0,0.0,0.0,0.08643118883692774,0.0,0.0,0.0,90.01,9/12/2022,Rem US West North Central SMM Food,128
+0.0,0.0,0.0,0.0,0.006995915865349195,0.0,0.0,80.77,9/13/2021,Rem US West North Central SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.4405351833498513,77.838,9/18/2023,Rem US West North Central SMM Food,130
+0.0,0.0,0.0,0.09009565954761295,0.0,0.0,0.0,77.07,9/19/2022,Rem US West North Central SMM Food,131
+0.0,0.0,0.0,0.0,0.007515506433597942,0.0,0.0,73.56,9/20/2021,Rem US West North Central SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.4400396432111001,78.46,9/25/2023,Rem US West North Central SMM Food,133
+0.0,0.0,0.0,0.10237703355668529,0.0,0.0,0.0,82.7,9/26/2022,Rem US West North Central SMM Food,134
+0.0,0.0,0.0,0.0,0.007104782460601314,0.0,0.0,74.24,9/27/2021,Rem US West North Central SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.38503468780971256,87.813,9/4/2023,Rem US West North Central SMM Food,136
+0.0,0.0,0.0,0.09042731448975182,0.0,0.0,0.0,85.04,9/5/2022,Rem US West North Central SMM Food,137
+0.0,0.0,0.0,0.0,0.007015091231558375,0.0,0.0,87.6,9/6/2021,Rem US West North Central SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.04112983151635283,43.858,8/7/2023,Richmond/Petersburg SMM Food,124
+0.0,0.0009487736279887459,0.0,0.0,0.007115916544206644,0.0,0.0,41.87,8/8/2022,Richmond/Petersburg SMM Food,125
+0.0,0.0,0.0,0.0,0.0012606256882035066,0.0,0.0,34.56,8/9/2021,Richmond/Petersburg SMM Food,126
+0.0,0.0,0.005540418423986577,0.0,0.0,0.04781667750388989,0.03964321110009911,41.396,9/11/2023,Richmond/Petersburg SMM Food,127
+0.0,0.0,0.0,0.01266418225172223,0.0,0.0,0.0,36.0,9/12/2022,Richmond/Petersburg SMM Food,128
+0.0,0.0,0.0,0.0,0.0013101105042271969,0.0,0.0,35.96,9/13/2021,Richmond/Petersburg SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.04162537165510406,37.841,9/18/2023,Richmond/Petersburg SMM Food,130
+0.0,0.0,0.0,0.013201112554278954,0.0,0.0,0.0,34.82,9/19/2022,Richmond/Petersburg SMM Food,131
+0.0,0.0,0.0,0.0,0.00111155267993214,0.0,0.0,33.78,9/20/2021,Richmond/Petersburg SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0421209117938553,52.209,9/25/2023,Richmond/Petersburg SMM Food,133
+0.0,0.0,0.0,0.015000619892522049,0.0,0.0,0.0,37.32,9/26/2022,Richmond/Petersburg SMM Food,134
+0.0,0.0,0.0,0.0,0.0012204192751842583,0.0,0.0,33.61,9/27/2021,Richmond/Petersburg SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.03617443012884043,37.615,9/4/2023,Richmond/Petersburg SMM Food,136
+0.0,0.0,0.0,0.013249707732199589,0.0,0.0,0.0,38.38,9/5/2022,Richmond/Petersburg SMM Food,137
+0.0,0.0,0.0,0.0,0.0012822752952138712,0.0,0.0,47.08,9/6/2021,Richmond/Petersburg SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.11793855302279485,26.418,8/7/2023,Sacramento/Stockton/Modesto SMM Food,124
+0.0,0.0036908304998685493,0.0,0.0,0.006869111024288489,0.0,0.0,24.42,8/8/2022,Sacramento/Stockton/Modesto SMM Food,125
+0.0,0.0,0.0,0.0,0.00287321213037551,0.0,0.0,23.32,8/9/2021,Sacramento/Stockton/Modesto SMM Food,126
+0.0,0.0,0.017499367285692864,0.0,0.0,0.09899835674878281,0.08275520317145689,27.508,9/11/2023,Sacramento/Stockton/Modesto SMM Food,127
+0.0,0.0,0.0,0.029183182904838144,0.0,0.0,0.0,25.44,9/12/2022,Sacramento/Stockton/Modesto SMM Food,128
+0.0,0.0,0.0,0.0,0.0019026911761108865,0.0,0.0,24.91,9/13/2021,Sacramento/Stockton/Modesto SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.11298315163528246,22.085,9/18/2023,Sacramento/Stockton/Modesto SMM Food,130
+0.0,0.0,0.0,0.030420478364843156,0.0,0.0,0.0,24.71,9/19/2022,Sacramento/Stockton/Modesto SMM Food,131
+0.0,0.0,0.0,0.0,0.0023127965889072188,0.0,0.0,23.73,9/20/2021,Sacramento/Stockton/Modesto SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.07879088206144698,27.12,9/25/2023,Sacramento/Stockton/Modesto SMM Food,133
+0.0,0.0,0.0,0.034567240528453246,0.0,0.0,0.0,26.42,9/26/2022,Sacramento/Stockton/Modesto SMM Food,134
+0.0,0.0,0.0,0.0,0.0016187720441749643,0.0,0.0,22.87,9/27/2021,Sacramento/Stockton/Modesto SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.10257680872150644,30.477,9/4/2023,Sacramento/Stockton/Modesto SMM Food,136
+0.0,0.0,0.0,0.030532460487995166,0.0,0.0,0.0,25.38,9/5/2022,Sacramento/Stockton/Modesto SMM Food,137
+0.0,0.0,0.0,0.0,0.0018699074854951917,0.0,0.0,25.66,9/6/2021,Sacramento/Stockton/Modesto SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.13280475718533202,31.866,8/7/2023,Salt Lake City SMM Food,124
+0.0,0.0,0.0,0.0,0.005755702663755461,0.0,0.0,30.89,8/8/2022,Salt Lake City SMM Food,125
+0.0,0.0,0.0,0.0,0.002216919757861319,0.0,0.0,30.55,8/9/2021,Salt Lake City SMM Food,126
+0.0,0.0,0.0,0.0,0.0,0.0020036799212335816,0.12338949454905847,35.521,9/11/2023,Salt Lake City SMM Food,127
+0.0,0.0,0.0,0.024569078193799245,0.0,0.0,0.0,28.16,9/12/2022,Salt Lake City SMM Food,128
+0.0,0.0,0.0,0.0,0.0018668146844937111,0.0,0.0,34.69,9/13/2021,Salt Lake City SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.12091179385530228,39.734,9/18/2023,Salt Lake City SMM Food,130
+0.0,0.0,0.0,0.0256107469124291,0.0,0.0,0.0,20.08,9/19/2022,Salt Lake City SMM Food,131
+0.0,0.0,0.0,0.0,0.002090114916800613,0.0,0.0,30.04,9/20/2021,Salt Lake City SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.11446977205153618,36.76,9/25/2023,Salt Lake City SMM Food,133
+0.0,0.0,0.0,0.02910187138286449,0.0,0.0,0.0,28.2,9/26/2022,Salt Lake City SMM Food,134
+0.0,0.0,0.0,0.0,0.0013169146664304542,0.0,0.0,31.92,9/27/2021,Salt Lake City SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.12933597621407333,38.673,9/4/2023,Salt Lake City SMM Food,136
+0.0,0.0,0.0,0.025705023731608795,0.0,0.0,0.0,29.76,9/5/2022,Salt Lake City SMM Food,137
+0.0,0.0,0.0,0.0,0.0014486679890935294,0.0,0.0,30.71,9/6/2021,Salt Lake City SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,27.155,8/7/2023,San Diego SMM Food,124
+0.0,0.0020376249453456933,0.0,0.0,0.006930348484117806,0.0,0.0,23.05,8/8/2022,San Diego SMM Food,125
+0.0,0.0,0.0,0.0,0.0011183568421353973,0.0,0.0,18.52,8/9/2021,San Diego SMM Food,126
+0.0,0.0,0.008977334498881523,0.0,0.0,0.06158408920762952,0.058969276511397425,31.97,9/11/2023,San Diego SMM Food,127
+0.0,0.0,0.0,0.020400456588260608,0.0,0.0,0.0,26.77,9/12/2022,San Diego SMM Food,128
+0.0,0.0,0.0,0.0,0.0014214513402804997,0.0,0.0,22.06,9/13/2021,San Diego SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.055996035678889985,29.921,9/18/2023,San Diego SMM Food,130
+0.0,0.0,0.0,0.021265385972826126,0.0,0.0,0.0,23.46,9/19/2022,San Diego SMM Food,131
+0.0,0.0,0.0,0.0,0.0010942329943238486,0.0,0.0,18.64,9/20/2021,San Diego SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.059960356788899896,29.193,9/25/2023,San Diego SMM Food,133
+0.0,0.0,0.0,0.02416417332539202,0.0,0.0,0.0,27.29,9/26/2022,San Diego SMM Food,134
+0.0,0.0,0.0,0.0,0.001024335691690386,0.0,0.0,21.74,9/27/2021,San Diego SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.06739345887016848,34.927,9/4/2023,San Diego SMM Food,136
+0.0,0.0,0.0,0.021343666889227886,0.0,0.0,0.0,24.65,9/5/2022,San Diego SMM Food,137
+0.0,0.0,0.0,0.0,0.0013292858704363766,0.0,0.0,22.7,9/6/2021,San Diego SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.10852329038652131,37.539,8/7/2023,San Francisco/Oakland/San Jose SMM Food,124
+0.0,0.007054716784551935,0.0,0.0,0.010025623726399625,0.0,0.0,43.76,8/8/2022,San Francisco/Oakland/San Jose SMM Food,125
+0.0,0.0,0.0,0.0,0.0026325922124603163,0.0,0.0,35.86,8/9/2021,San Francisco/Oakland/San Jose SMM Food,126
+0.0,0.0,0.03269606409616145,0.0,0.0,0.08548204542111204,0.08919722497522299,39.91,9/11/2023,San Francisco/Oakland/San Jose SMM Food,127
+0.0,0.0,0.0,0.047694445908865776,0.0,0.0,0.0,39.0,9/12/2022,San Francisco/Oakland/San Jose SMM Food,128
+0.0,0.0,0.0,0.0,0.0020171248131656697,0.0,0.0,39.68,9/13/2021,San Francisco/Oakland/San Jose SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09712586719524281,39.834,9/18/2023,San Francisco/Oakland/San Jose SMM Food,130
+0.0,0.0,0.0,0.04971657356674364,0.0,0.0,0.0,41.01,9/19/2022,San Francisco/Oakland/San Jose SMM Food,131
+0.0,0.0,0.0,0.0,0.002183517507045328,0.0,0.0,37.16,9/20/2021,San Francisco/Oakland/San Jose SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.0817641228939544,38.973,9/25/2023,San Francisco/Oakland/San Jose SMM Food,133
+0.0,0.0,0.0,0.056493679565598955,0.0,0.0,0.0,40.14,9/26/2022,San Francisco/Oakland/San Jose SMM Food,134
+0.0,0.0,0.0,0.0,0.0021674349418376285,0.0,0.0,36.73,9/27/2021,San Francisco/Oakland/San Jose SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09068384539147671,45.739,9/4/2023,San Francisco/Oakland/San Jose SMM Food,136
+0.0,0.0,0.0,0.049899587365703414,0.0,0.0,0.0,42.29,9/5/2022,San Francisco/Oakland/San Jose SMM Food,137
+0.0,0.0,0.0,0.0,0.0014294926228843492,0.0,0.0,39.55,9/6/2021,San Francisco/Oakland/San Jose SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.176412289395441,54.498,8/7/2023,Seattle/Tacoma SMM Food,124
+0.0,0.006289632662621285,0.0,0.0,0.014541731748761649,0.0,0.0,38.02,8/8/2022,Seattle/Tacoma SMM Food,125
+0.0,0.0,0.0,0.0,0.0025020760101978337,0.0,0.0,44.59,8/9/2021,Seattle/Tacoma SMM Food,126
+0.0,0.0,0.04408198262886745,0.0,0.0,0.13501876631890186,0.14073339940535184,56.797,9/11/2023,Seattle/Tacoma SMM Food,127
+0.0,0.0,0.0,0.03795459470136038,0.0,0.0,0.0,45.0,9/12/2022,Seattle/Tacoma SMM Food,128
+0.0,0.0,0.0,0.0,0.0019701142379431645,0.0,0.0,47.26,9/13/2021,Seattle/Tacoma SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.15163528245787908,53.988,9/18/2023,Seattle/Tacoma SMM Food,130
+0.0,0.0,0.0,0.03956377652701973,0.0,0.0,0.0,42.63,9/19/2022,Seattle/Tacoma SMM Food,131
+0.0,0.0,0.0,0.0,0.0010663977853105227,0.0,0.0,44.68,9/20/2021,Seattle/Tacoma SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.14767096134786917,57.516,9/25/2023,Seattle/Tacoma SMM Food,133
+0.0,0.0,0.0,0.04495690578008078,0.0,0.0,0.0,43.4,9/26/2022,Seattle/Tacoma SMM Food,134
+0.0,0.0,0.0,0.0,0.002235476563870203,0.0,0.0,47.9,9/27/2021,Seattle/Tacoma SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.13429137760158572,69.688,9/4/2023,Seattle/Tacoma SMM Food,136
+0.0,0.0,0.0,0.039709416436374406,0.0,0.0,0.0,41.17,9/5/2022,Seattle/Tacoma SMM Food,137
+0.0,0.0,0.0,0.0,0.0016051637197684492,0.0,0.0,42.94,9/6/2021,Seattle/Tacoma SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.13875123885034688,41.124,8/7/2023,St. Louis SMM Food,124
+0.0,0.0016459850550708866,0.0,0.0,0.010519234766235935,0.0,0.0,33.68,8/8/2022,St. Louis SMM Food,125
+0.0,0.0,0.0,0.0,0.0028880575751826162,0.0,0.0,35.24,8/9/2021,St. Louis SMM Food,126
+0.0,0.0,0.013647236303775618,0.0,0.0,0.11378140992305427,0.09266600594648167,41.021,9/11/2023,St. Louis SMM Food,127
+0.0,0.0,0.0,0.028725686548902455,0.0,0.0,0.0,46.76,9/12/2022,St. Louis SMM Food,128
+0.0,0.0,0.0,0.0,0.002591148679040476,0.0,0.0,37.37,9/13/2021,St. Louis SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.09266600594648167,38.672,9/18/2023,St. Louis SMM Food,130
+0.0,0.0,0.0,0.029943585281161426,0.0,0.0,0.0,41.99,9/19/2022,St. Louis SMM Food,131
+0.0,0.0,0.0,0.0,0.0026987781538920018,0.0,0.0,33.82,9/20/2021,St. Louis SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.09217046580773042,52.498,9/25/2023,St. Louis SMM Food,133
+0.0,0.0,0.0,0.034025339850661744,0.0,0.0,0.0,38.58,9/26/2022,St. Louis SMM Food,134
+0.0,0.0,0.0,0.0,0.003129296053298106,0.0,0.0,35.39,9/27/2021,St. Louis SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.09514370664023786,44.922,9/4/2023,St. Louis SMM Food,136
+0.0,0.0,0.0,0.03005381188828102,0.0,0.0,0.0,51.7,9/5/2022,St. Louis SMM Food,137
+0.0,0.0,0.0,0.0,0.0020622797077872873,0.0,0.0,45.12,9/6/2021,St. Louis SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.18434093161546083,392.534,8/7/2023,Tampa/Ft. Myers SMM Food,124
+0.0,0.002718951273633502,0.0,0.0,0.014108739608554358,0.0,0.0,249.67,8/8/2022,Tampa/Ft. Myers SMM Food,125
+0.0,0.0,0.0,0.0,0.005458175207413023,0.0,0.0,94.56,8/9/2021,Tampa/Ft. Myers SMM Food,126
+0.0,0.0,0.018081680874809502,0.0,0.0,0.19101996581778166,0.1377601585728444,112.223,9/11/2023,Tampa/Ft. Myers SMM Food,127
+0.0,0.0,0.0,0.05247086431152936,0.0,0.0,0.0,92.25,9/12/2022,Tampa/Ft. Myers SMM Food,128
+0.0,0.0,0.0,0.0,0.004128889336976647,0.0,0.0,93.22,9/13/2021,Tampa/Ft. Myers SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.13181367690782952,105.58,9/18/2023,Tampa/Ft. Myers SMM Food,130
+0.0,0.0,0.0,0.05469550040636205,0.0,0.0,0.0,85.97,9/19/2022,Tampa/Ft. Myers SMM Food,131
+0.0,0.0,0.0,0.0,0.004280436586049198,0.0,0.0,86.54,9/20/2021,Tampa/Ft. Myers SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.13875123885034688,98.576,9/25/2023,Tampa/Ft. Myers SMM Food,133
+0.0,0.0,0.0,0.062151307947871305,0.0,0.0,0.0,92.27,9/26/2022,Tampa/Ft. Myers SMM Food,134
+0.0,0.0,0.0,0.0,0.004065796196546442,0.0,0.0,83.16,9/27/2021,Tampa/Ft. Myers SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.18533201189296333,155.563,9/4/2023,Tampa/Ft. Myers SMM Food,136
+0.0,0.0,0.0,0.0548968423482816,0.0,0.0,0.0,131.88,9/5/2022,Tampa/Ft. Myers SMM Food,137
+0.0,0.0,0.0,0.0,0.004370127815092137,0.0,0.0,85.36,9/6/2021,Tampa/Ft. Myers SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.033201189296333006,14.745,8/7/2023,Tucson/Sierra Vista SMM Food,124
+0.0,0.0008540406751789106,0.0,0.0,0.0032796061819700653,0.0,0.0,11.07,8/8/2022,Tucson/Sierra Vista SMM Food,125
+0.0,0.0,0.0,0.0,0.0014684619155030051,0.0,0.0,10.23,8/9/2021,Tucson/Sierra Vista SMM Food,126
+0.0,0.0,0.004325155281482285,0.0,0.0,0.03514997428852929,0.033201189296333006,13.086,9/11/2023,Tucson/Sierra Vista SMM Food,127
+0.0,0.0,0.0,0.008438669343202216,0.0,0.0,0.0,15.07,9/12/2022,Tucson/Sierra Vista SMM Food,128
+0.0,0.0,0.0,0.0,0.0005659825832709562,0.0,0.0,12.89,9/13/2021,Tucson/Sierra Vista SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.04558969276511397,13.845,9/18/2023,Tucson/Sierra Vista SMM Food,130
+0.0,0.0,0.0,0.008796448244863359,0.0,0.0,0.0,12.4,9/19/2022,Tucson/Sierra Vista SMM Food,131
+0.0,0.0,0.0,0.0,0.0005641269026700677,0.0,0.0,11.2,9/20/2021,Tucson/Sierra Vista SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.028741328047571853,14.286,9/25/2023,Tucson/Sierra Vista SMM Food,133
+0.0,0.0,0.0,0.009995534545410896,0.0,0.0,0.0,14.46,9/26/2022,Tucson/Sierra Vista SMM Food,134
+0.0,0.0,0.0,0.0,0.0005597969812679949,0.0,0.0,13.72,9/27/2021,Tucson/Sierra Vista SMM Food,135
+0.0,0.0,0.0,0.0,0.0,0.0,0.036669970267591674,14.191,9/4/2023,Tucson/Sierra Vista SMM Food,136
+0.0,0.0,0.0,0.008828829229391247,0.0,0.0,0.0,13.52,9/5/2022,Tucson/Sierra Vista SMM Food,137
+0.0,0.0,0.0,0.0,0.0008028911399843727,0.0,0.0,12.27,9/6/2021,Tucson/Sierra Vista SMM Food,138
+0.0,0.0,0.0,0.0,0.0,0.0,0.17443012884043607,117.859,8/7/2023,Washington DC/Hagerstown SMM Food,124
+0.0,0.008450294918628531,0.0,0.0,0.01769886301107308,0.0,0.0,121.38,8/8/2022,Washington DC/Hagerstown SMM Food,125
+0.0,0.0,0.0,0.0,0.005262091623919152,0.0,0.0,114.34,8/9/2021,Washington DC/Hagerstown SMM Food,126
+0.0,0.0,0.05609156745484475,0.0,0.0,0.19144403459184595,0.155599603567889,160.362,9/11/2023,Washington DC/Hagerstown SMM Food,127
+0.0,0.0,0.0,0.05177122745529417,0.0,0.0,0.0,144.07,9/12/2022,Washington DC/Hagerstown SMM Food,128
+0.0,0.0,0.0,0.0,0.0036185771717323423,0.0,0.0,123.88,9/13/2021,Washington DC/Hagerstown SMM Food,129
+0.0,0.0,0.0,0.0,0.0,0.0,0.14519326065411298,165.895,9/18/2023,Washington DC/Hagerstown SMM Food,130
+0.0,0.0,0.0,0.05396620066002498,0.0,0.0,0.0,138.59,9/19/2022,Washington DC/Hagerstown SMM Food,131
+0.0,0.0,0.0,0.0,0.0035734222771107256,0.0,0.0,116.24,9/20/2021,Washington DC/Hagerstown SMM Food,132
+0.0,0.0,0.0,0.0,0.0,0.0,0.1620416253716551,159.074,9/25/2023,Washington DC/Hagerstown SMM Food,133
+0.0,0.0,0.0,0.06132259383502524,0.0,0.0,0.0,132.64,9/26/2022,Washington DC/Hagerstown SMM Food,134
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Test/output_df.csv

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Test/overall_contributions.csv

@@ -0,0 +1,143 @@
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Test/test_contr.csv

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Test/x_test_contribution_non_panel.csv

@@ -0,0 +1,38 @@
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Transformation_functions.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from Eda_functions import format_numbers,line_plot,summary
+import numpy as np
+import re
+
+def sanitize_key(key, prefix=""):
+    # Use regular expressions to remove non-alphanumeric characters and spaces
+    key = re.sub(r'[^a-zA-Z0-9]', '', key)
+    return f"{prefix}{key}"
+
+
+def check_box(options, ad_stock_value,lag_value,num_columns=4, prefix=""):
+    num_rows = -(-len(options) // num_columns)  # Ceiling division to calculate rows
+
+    selected_options = []
+    adstock_info = {}  # Store adstock and lag info for each selected option
+    if ad_stock_value!=0:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum adstock values
+                        adstock = col.slider('Select Adstock Range', 0.0, 1.0, ad_stock_value, step=0.05, format="%.2f",key= f"adstock_{key}" )
+
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # Create a dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'adstock': adstock,
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'adstock': ad_stock_value,
+                            'lag': lag_value}
+
+        return selected_options, adstock_info   
+    else:
+        for row in range(num_rows):
+            cols = st.columns(num_columns)
+            for col in cols:
+                if options:
+                    option = options.pop(0)
+                    key = sanitize_key(f"{option}_{row}", prefix=prefix)
+                    selected = col.checkbox(option, key=key)
+                    if selected:
+                        selected_options.append(option)
+                        
+                        # Input minimum and maximum lag values
+                        lag = col.slider('Select Lag Range', 0, 7, lag_value, step=1,key=f"lag_{key}" )
+
+                        # dictionary to store adstock and lag info for the option
+                        option_info = {
+                            'lag': lag}
+                        # Append the dictionary to the adstock_info list
+                        adstock_info[option]=option_info
+
+                    else:adstock_info[option]={
+                            'lag': lag_value}
+                        
+        return selected_options, adstock_info 
+
+def apply_lag(X, features,lag_dict):
+    #lag_data=pd.DataFrame()
+    for col in features:
+        for lag in range(lag_dict[col]['lag'][0], lag_dict[col]['lag'][1] + 1):
+            if lag>0:
+                X[f'{col}_lag{lag}'] = X[col].shift(periods=lag, fill_value=0)
+    return X
+
+def apply_adstock(X, variable_name, decay):
+    values = X[variable_name].values
+    adstock = np.zeros(len(values))
+    
+    for row in range(len(values)):
+        if row == 0:
+            adstock[row] = values[row]
+        else:
+            adstock[row] = values[row] + adstock[row - 1] * decay
+    
+    return adstock
+
+def top_correlated_features(df,target,media_data):
+    corr_df=df.drop(target,axis=1)
+    #corr_df[target]=df[target]
+    #st.dataframe(corr_df)
+    for i in media_data:
+        #st.write(media_data[2])
+        #st.dataframe(corr_df.filter(like=media_data[2]))
+        d=(pd.concat([corr_df.filter(like=i),df[target]],axis=1)).corr()[target]
+        d=d.sort_values(ascending=False)
+        d=d.drop(target,axis=0)
+        corr=pd.DataFrame({'Feature_name':d.index,"Correlation":d.values})
+        corr.columns = pd.MultiIndex.from_product([[i], ['Feature_name', 'Correlation']])
+
+    return corr
+
+def top_correlated_features(df,variables,target):
+    correlation_df=pd.DataFrame()
+    for col in variables:
+        d=pd.concat([df.filter(like=col),df[target]],axis=1).corr()[target]
+        #st.dataframe(d)
+        d=d.sort_values(ascending=False).iloc[1:]
+        corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+        corr_df.columns=pd.MultiIndex.from_tuples([(col, 'Variable'), (col, 'Correlation')])
+        correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return correlation_df
+
+def top_correlated_feature(df,variable,target):
+    d=pd.concat([df.filter(like=variable),df[target]],axis=1).corr()[target]
+    # st.dataframe(d)
+    d=d.sort_values(ascending=False).iloc[1:]
+    # st.dataframe(d)
+    corr_df=pd.DataFrame({'Media_channel':d.index,'Correlation':d.values})
+    corr_df['Adstock']=corr_df['Media_channel'].map(lambda x:x.split('_adst')[1] if len(x.split('_adst'))>1 else '-')
+    corr_df['Lag']=corr_df['Media_channel'].map(lambda x:x.split('_lag')[1][0] if len(x.split('_lag'))>1 else '-' )
+    corr_df.drop(['Correlation'],axis=1,inplace=True)
+    corr_df['Correlation']=np.round(d.values,2)
+    sorted_corr_df= corr_df.loc[corr_df['Correlation'].abs().sort_values(ascending=False).index]
+    #corr_df.columns=pd.MultiIndex.from_tuples([(variable, 'Variable'), (variable, 'Correlation')])
+    #correlation_df=pd.concat([corr_df,correlation_df],axis=1)
+    return sorted_corr_df

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