As an auditor or controller, you can use Python to perform analyses that are repeatable, transparent, and efficient. Where Excel sometimes falls short for large datasets or repetitive checks, Python offers power and flexibility.
For more background on why Python fits so well within the audit practice, read Why Python is valuable for auditors.
Installing Python
To get started with Python, you first need to install the language itself. There are two safe options:
Option 1 – Through the Microsoft Store (recommended)
Python is available via the official Microsoft Store.
The advantage: updates are managed automatically, and this installation is allowed in many organizations, unlike running a standalone .exe installer. Additionally, the Store version automatically adds Python to your PATH, allowing you to use it directly in the terminal.
Option 2 – Through python.org
Prefer a manual installation? Download the latest version from python.org/downloads. Make sure to check the box “Add Python to PATH” during installation, so the system recognizes Python everywhere. Also select “Install pip”, which lets you install additional packages later.
Setting up your working environment
After installing Python, you’ll need a place to write your scripts. Several editors work well for auditors:
- Visual Studio Code (VS Code) – great for larger projects and supports Git version control.
- Jupyter Notebook – ideal for visual, step-by-step analyses (useful for reporting).
Both are easy to install:
Installing modules
With pip, you can add extra modules to Python — similar to add-ins in Excel. Open the terminal (in Windows: Command Prompt or PowerShell) and run:
pip install pandas numpy matplotlib seaborn openpyxl
This installs the most common audit-related packages at once. While Python already includes a lot of functionality, these libraries make analytics easier and faster. Let’s briefly look at a few of them:
1. Pandas – Handle data like a pro
import pandas as pd
Pandas makes it easy to perform Excel-like operations such as sorting, filtering, and grouping data.
2. NumPy – Work with numbers and statistics
import numpy as np
Perfect for numerical operations such as calculating averages, variances, and standard deviations.
3. Matplotlib & Seaborn – Visualize your data
import matplotlib.pyplot as plt
import seaborn as sns
With these tools, you can create charts and spot trends or patterns in your data.
Your first Python script
Let’s start with a small example: loading a dataset and displaying the first few rows.
import pandas as pd # For data processing
# Load data from a CSV file
df = pd.read_csv('audit_data.csv')
# View the first five rows
print(df.head())
See? Just a few lines of code already give you insights into your data.
Conclusion
With Python, you can work efficiently, repeatably, and transparently. Hopefully this article will make the installation process simple (especially via the Microsoft Store).
Want to learn more? Check out our articles on Pandas for auditors or Git and version control to learn how to document your analyses professionally.
