Excel Data Processing for Startup Performance Tracking

Pushkar Gupta

0

Data Scraper

Automation Engineer

Data Analyst

Microsoft Excel

pandas

Python

Project Overview

Problem:
Small businesses and startups often struggle with tracking their financial and operational performance. They may have Excel sheets with data but lack the tools to analyze trends, profitability, or customer growth efficiently.
Solution:
A Python script that:
Reads an Excel file containing performance data
Cleans and processes the data
Computes key business metrics (e.g., profit, customer retention rate)
Generates summary reports and visualizes trends
import pandas as pd
import matplotlib.pyplot as plt
# Load Excel data
def load_data(file_path):
try:
df = pd.read_excel(file_path)
return df
except Exception as e:
print("Error loading file:", e)
return None
# Process data
def process_data(df):
df['Profit'] = df['Revenue'] - df['Expenses']
df['Customer Growth Rate'] = df['New Customers'] / df['Total Customers'] * 100
return df
# Generate summary statistics
def generate_summary(df):
summary = {
"Total Revenue": df['Revenue'].sum(),
"Total Expenses": df['Expenses'].sum(),
"Total Profit": df['Profit'].sum(),
"Average Customer Growth Rate": df['Customer Growth Rate'].mean()
}
return summary
# Visualize trends
def plot_trends(df):
plt.figure(figsize=(10,5))
plt.plot(df['Month'], df['Revenue'], label='Revenue', marker='o')
plt.plot(df['Month'], df['Expenses'], label='Expenses', marker='o')
plt.plot(df['Month'], df['Profit'], label='Profit', marker='o')
plt.xlabel('Month')
plt.ylabel('Amount')
plt.title('Startup Financial Performance')
plt.legend()
plt.grid(True)
# Main function
def main():
file_path = 'startup_data.xlsx' # Update with actual file path
df = load_data(file_path)
if df is not None:
df = process_data(df)
summary = generate_summary(df)
print("Summary Report:", summary)
plot_trends(df)
if name == "__main__":
main()

How It Works:

Reads Data: Loads an Excel file with columns like Month, Revenue, Expenses, New Customers, and Total Customers.
Processes Data:
Calculates Profit (Revenue - Expenses)
Computes Customer Growth Rate
Generates Summary: Aggregates total revenue, expenses, profit, and average customer growth.
Visualizes Trends: Plots Revenue, Expenses, and Profit over time.

Next Steps

Create a sample startup_data.xlsx file with test data.
Enhance with more KPIs like retention rate or CAC (Customer Acquisition Cost).
Automate sending reports via email.
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Posted Feb 14, 2025

Pushkar designed an Excel automation project that processed large datasets, enabling startups to analyze performance metrics efficiently.

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Tags

Data Scraper

Automation Engineer

Data Analyst

Microsoft Excel

pandas

Python

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