The document discusses xlwings, an open-source Python library designed for integrating Python with Excel, showcased at the Python Quants Conference in New York City. It highlights xlwings' features, including easy installation, cross-platform compatibility, and support for user-defined functions, along with practical applications such as Monte Carlo simulations and correlation analysis. The author, with a background in finance and asset management, emphasizes xlwings' capabilities for analytical web applications and data manipulation.

1. xlwings: Python & Excel
• For Python Quants Conference
• New York City
May 6th, 2016 Felix Zumstein

2. Material
• For the material presented during the
demos (Part 1 &3), see:
https://github.com/ZoomerAnalytics/xlwings_noteboo
ks/tree/master/for_python_quants_conference_nyc_2
0160506
2

3. About me
• Consultancy (Zurich):
– Analytical apps for Excel & web
– Open-source: xlwings
• Previously:
– 9yrs in Banking /Asset Management
– Background: Finance & Economics
3

4. The open-source Python/Excel Landscape
4
Reading/
Writing
Programming/
Interacting

5. xlwings Features
5
Scripting/Interaction
Macros
User Defined Functions (UDFs)
1
2
3

6. 1 Scripting/Interaction

7. GDP per capita
7
Source of xls file:
http://data.worldbank.org/indicator/NY.GDP.PCAP.CD
+

8. 2 Macros

9. Monte Carlo Simulation
9
simulation.py
xlwings_app.py web_app.py
Source Code: https://github.com/ZoomerAnalytics/simulation-demo
Hosted Sample: www.zoomeranalytics.com/simulation-demo

10. 3 UDFs

11. DJIA: Correlation Analysis
11
=CORREL(array1, array2)
Excel’s Correlation formula accepts just 2 data sets:
Here’s how we’re going to fix this:
• Add CORREL2 to get the full correlation matrix
• Visual representation: Heatmap

12. Summary

13. (1) Easy Installation
13
• pip/conda install xlwings
• included in:

14. (2) Cross-Platform
14
+
(except UDFs)

15. (3) Flexibility
15
Version
2.6
2.7
3.3
3.4
3.5
32-bit
64-bit
32-bit
64-bit
2003
2010
2011 (Mac)
2013
2016 (Win + Mac)
ArchitectureVersion Architecture

16. (4) Simplicity
16
>>> from xlwings import Workbook, Range
>>> wb = Workbook()
>>> Range("A1").value = my_variable
• Strings
• Numbers
• DateTime
• Lists (nested)
• NumPy arrays
• Pandas DataFrames

17. (5) Powerful built-in Converters/Options
17
Range("A1").options(pd.DataFrame).value
@xw.func
@xw.arg("x", pd.DataFrame)
def myfunction(x):
return x

18. (6) Prototype analytical web apps
18
vs.

19. Questions?