Pandas for General Ledger Review | 30 Days of Python for Accountants (Day 3) 📈

Welcome to Day 3 of the 30 Days of Python for Accountants challenge!

Today, we are moving away from traditional Excel-style filtering and pivoting, and stepping into the world of large-scale General Ledger (GL) analytics using Pandas.

Using a public data set from the State of Oklahoma's General Ledger, we'll walk through how to load, clean, profile, and filter over 250,000 rows of transactional financial data in seconds. We'll also handle real-world debugging issues on the fly, including how to override explicit data types (dtypes) like converting numerical account strings that Pandas tries to read as floats.

What You'll Learn Today:
How to handle 403 Forbidden/Server errors by downloading data locally to Google Colab. Follow along here! https://colab.research.google.com/gist/danshorstein/72016b0a425e9c1b676be1b6cc2f053c/day_03_pandas_public_gl_review_colab.ipynb

Standardizing columns into a Python-friendly lowercase format.

Engineering a master financial "Analysis Amount" field.

Summarizing financial dimensions like an absolute pro (Groupby vs. Excel Pivot Tables).

Automating risk-based audit procedures like finding large absolute values, round numbers, and flag-worthy transaction keywords.

📂 Resources:

Look out for Day 4 where we jump right back into Excel—but this time, automating the workbook build dynamically with openpyxl.

If you're finding this series helpful, make sure to like, subscribe, and leave a comment with what accounting workflow you want to automate next!

Timestamps
00:00 - Intro: Welcome to Day 3 of Python for Accountants
00:09 - Today's Challenge: Analyzing the Oklahoma GL Dataset
01:10 - Importing Libraries (Pandas, NumPy, Pathlib)
01:26 - Troubleshooting: Handling a 403 Forbidden Error in Colab
02:12 - Manual Workaround: Uploading the Local CSV to the Environment
03:22 - First-Pass Inspection: Reading the Shape and Column Info
04:24 - Data Type Correction: Overriding dtypes with a Dictionary
05:10 - Debugging a Syntax Error (Don't Forget Your Commas!)
06:28 - Column Cleaning: Transforming to Lowercase with Underscores
06:38 - Building a Basic Profile Report
07:03 - Isolating "Money-Like" Columns
07:56 - Engineering a Custom Analysis Amount Field
08:37 - Run-Through: Analysis Amount Sanity Check
09:06 - Investigating Top 10 Absolute Financial Quantities
09:48 - Grouping Financial Data by Account, Agency, and Ledger Dimensions
10:55 - Summarizing to Pinpoint Period Activity
11:39 - Audit Filtering: Catching High-Dollar and Round-Dollar Exceptions
13:20 - Keyword Search Strategy for Risk Mitigation
14:12 - Consolidating Flags into a Compact Review Exception File
14:33 - Exporting Reviewer-Friendly CSV Outputs
14:43 - Recap & Teaser for Day 4: Working with openpyxl

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