Class Note: Using Python in Excel with External Data

Objective

• Explain how to effectively use Python in Excel, especially for working with external data (like CSV files), without importing the entire dataset into the spreadsheet.

---

Common Misconceptions

• Misconception: You must load external data fully into Excel to analyze it with Python.
• Reality: You only need to create a connection to the data, not import it directly into the worksheet.

---

Steps for Best Practice

1. Importing Data Properly

• Open Excel → Go to Data → Select Get Data from Text/CSV.
• Locate your CSV file and click Import.
• Important: Instead of clicking Load, click the down arrow next to Load and select Load To → Only Create Connection.
• This keeps your Excel file small and decluttered.

2. Analyzing Data with Python in Excel

• Enter Python mode in Excel using =PY.
• Use the "xl" function in quotes to list and reference all external connections/tables.
• Example: Select your external data (e.g., Satisfaction Survey) and preview as a DataFrame.
• Use DataFrame methods like .describe() to get summary statistics.

3. Finding Relationships in the Data

• Use .corr(numeric_only=True) to compute correlations among numeric columns.
  • +1: perfect positive correlation
  • -1: perfect negative correlation
  • Example insight: More meeting hours per week correlate negatively with employee satisfaction.
• To visualize correlations, use Seaborn’s heatmap() in Python (already initialized as sns).
  • Add annot=True for annotated heatmap visual.
  • Darker colors: high negative correlation; lighter colors: high positive correlation.

4. Giving Actionable Insights

• To further analyze a specific relationship, use Seaborn’s linear model plot (sns.lmplot()).
  • Example: Plot Meeting Hours/Week (X-axis) vs. Employee Satisfaction (1-10) (Y-axis).
  • You can now make recommendations: e.g., to achieve satisfaction level 9, limit meetings to 4–6 hours/week.

5. Keeping Data Up-to-Date

• Your data connection can be refreshed when the source CSV changes via Queries & Connections.
• For data cleaning, use Power Query Editor (better for cleaning than Python in Excel).

---

Additional Notes

• You can also connect to data in another Excel workbook: Get Data → From File → Excel Workbook → Import → Load To → Only Create Connection.
• After establishing the connection, access and analyze the dataset using Python in Excel as above.

---

Key Takeaways

• Don’t import raw data directly into Excel worksheets—create a connection instead.
• Python in Excel enables advanced, visual, and actionable data analysis.
• Keeping data external leads to smaller files and easier updates.
• Visualization and statistics (correlation and regression) are straightforward with built-in Python libraries.

---

Resources

• Complete Power Query and Python in Excel courses (links provided in original content).

---

Conclusion

• Using Python in Excel with external connections opens vast opportunities for data analysis without bloating your Excel files.
• Practical statistical analysis and visualization are now at your fingertips, and recommendations for management become data-backed and immediate.

---

Let me know if you have more questions!