Century Stairs Biz Plan National Sales Dept. 2025

Step-by-Step Instructions on Where to Run the Code and Export the Data: Option 1: Google Colab (Recommended for Ease of Use) Create a Google Colab Notebook: Go to Google Colab. Start a new notebook: File > New Notebook. Paste the Python code (all sections: Data Setup, NPV, ROI, etc.) into separate cells. Run the Code: To run the code, click on each cell and press Shift + Enter or click the "Play" button next to each cell. Once you run the code, it will generate: Financial tables. Calculations for NPV, ROI, etc. Visualizations such as line charts and bar charts. Save and Export the Data: Charts: To save the charts, you can use the following in the notebook: python Copy plt.savefig('sales_revenue_growth.png') Download CSV: To download the financial data as a CSV file: python Copy data.to_csv('financials.csv') This will save the data as a CSV file which you can download by running the following: python Copy from google.colab import files files.download('financials.csv') Export the Charts: Right-click on the chart and save the image to your computer. You can insert the saved images into your presentation (Google Slides or PowerPoint). Share the Notebook: After running the code, you can share the notebook itself with stakeholders via Google Colab. Use the Share button in the top-right corner to get a shareable link or send it directly via email. Option 2: Jupyter Notebook (Local Setup or Cloud) If you have Jupyter Notebook installed locally, you can: Install Jupyter (if not already installed): Install Jupyter using Anaconda or pip: nginx Copy pip install jupyter Create a New Notebook: Open Jupyter in your browser by typing jupyter notebook in the terminal. Create a new notebook and paste the code into separate cells. Run the Code: Execute each cell by pressing Shift + Enter. Export the Data: To save the data as CSV or export charts, you can use the same code mentioned above for Google Colab. Use plt.savefig() to export images and files.download() for CSV files. Option 3: Python Locally (On Your Own Machine) If you prefer to run the code on your local Python environment: Install Python and Libraries: Install Python from python.org. Install the necessary libraries using pip: nginx Copy pip install pandas numpy matplotlib Create a Python Script: Open a text editor (like VS Code, Sublime Text, or PyCharm) and create a new file with the .py extension (e.g., financials.py). Copy and paste the Python code into this file. Run the Script: Open your terminal (Command Prompt or Terminal in macOS/Linux). Navigate to the folder where you saved the script. Run the script with: nginx Copy python financials.py Export Data and Charts: The script will automatically generate the financial data and charts. You can save them to your local machine by using plt.savefig() to export the charts and to_csv() for the CSV data. Step 2: Where to Export the Data for Other AI Tools Based on the tools I mentioned earlier (like Tableau, Power BI, or other data visualization tools), here's how you can export and use the data for each platform: Option 1: Google Data Studio (or any other BI Tool like Tableau, Power BI) Export to CSV: Once the data is saved as a CSV from the Python environment (Google Colab, Jupyter, or local Python), you can upload the CSV to Google Data Studio, Tableau, or Power BI. These tools allow you to import data from a CSV file, and you can use them to create more interactive and dynamic dashboards with graphs, charts, and visual analytics. Steps for Google Data Studio: In Google Data Studio, select Create New Report. Choose CSV File as the data source and upload your CSV file. Create customized reports and visuals (e.g., bar charts, line graphs) using the data. Option 2: Tableau or Power BI Steps to Import CSV into Tableau/Power BI: Open Tableau or Power BI. Choose to Import Data (CSV or Excel format). Select your CSV file exported from Python. Drag and drop fields to create dashboards with advanced visualizations. Option 3: Google Sheets (for basic data manipulation) Upload the CSV to Google Sheets: If you're looking for an easier tool to manipulate the data, you can import the CSV into Google Sheets. From there, use Google Sheets' built-in charting features to create bar charts, line graphs, etc. Step 3: Final Presentation Creation Once you have the charts, tables, and calculations, you can create a compelling presentation. Here’s how: Create the Presentation (Google Slides or PowerPoint): Insert the charts (images saved from the Python environment). Add financial tables (copy from the CSV file or Google Sheets). Explain the insights from the NPV and ROI calculations. Share the Presentation: You can share the presentation as a Google Slides link or as a PowerPoint file. Final Summary of Steps: Run the Python code in Google Colab, Jupyter Notebook, or your local Python environment. Export financial data (as CSV) and charts (using plt.savefig()). Upload the CSV data to Google Data Studio, Tableau, Power BI, or Google Sheets for further visualization. Create your presentation in Google Slides or PowerPoint using the charts and data. Share or present the results to the executive board.