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import pandas as pd
def load_excel_data(filepath):
"""
Reads an Excel file and loads specific sheets into individual dataframes.
Extracts a weightage dictionary from the 'Weightage' sheet.
Parameters:
filepath (str): Path to the Excel file.
Returns:
- weightage_dict (dict): A dictionary mapping variable names to their corresponding weightages.
- priority_df (DataFrame): Data from the 'Priority Data' sheet.
- warehouse_df (DataFrame): Data from the 'Warehouse Data' sheet.
- order_df (DataFrame): Data from the 'Order Data' sheet.
- cost_df (DataFrame): Data from the 'Cost Data' sheet.
- distance_df (DataFrame): Data from the 'Distance Data' sheet.
- days_df (DataFrame): Data from the 'Days Data' sheet.
"""
# Define the sheet names to be read
sheets = [
'Weightage', 'Priority Data', 'Warehouse Data', 'Order Data',
'Cost Data', 'Distance Data', 'Days Data'
]
# Read the 'Weightage' sheet and create a dictionary
weightage_df = pd.read_excel(filepath, sheet_name='Weightage')
weightage_dict = dict(zip(weightage_df['Variable'], weightage_df['Weightage']))
# Read all other sheets into separate dataframes
priority_df = pd.read_excel(filepath, sheet_name='Priority Data')
warehouse_df = pd.read_excel(filepath, sheet_name='Warehouse Data')
order_df = pd.read_excel(filepath, sheet_name='Order Data')
cost_df = pd.read_excel(filepath, sheet_name='Cost Data')
distance_df = pd.read_excel(filepath, sheet_name='Distance Data')
days_df = pd.read_excel(filepath, sheet_name='Days Data')
return weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df
if __name__ == "__main__":
filepath = 'Intelligent_Sourcing.xlsx' # Example file path
weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df = load_excel_data(filepath)
# Print output for verification
print("Weightage Dictionary:")
print(weightage_dict)
print("\nLoaded DataFrames:")
for name, df in zip([
"Priority Data", "Warehouse Data", "Order Data", "Cost Data", "Distance Data", "Days Data"
], [priority_df, warehouse_df, order_df, cost_df, distance_df, days_df]):
print(f"\n{name}: {df.shape} rows and columns")
print(df.head())
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