Welcome to PandAudit

🐼 Our Mission at PandAudit

PandAudit exists to bridge the gap between traditional accounting and modern data tools.

We believe accountants shouldn’t have to choose between deep financial expertise and technical fluency. Our mission is to empower CPAs and finance professionals to harness the power of Python and Pandas — without needing to become programmers.

We simplify the shift from Excel by offering relatable examples, clean code templates, and a mindset rooted in audit logic. Whether you’re exploring your first groupby or validating data across legacy systems, PandAudit is your guide for building confidence in code — one line at a time.

No fluff. Just tools, clarity, and support designed with accountants in mind.

📬 Reach us anytime at hello@pandaudit.com — and start turning your spreadsheets into streamlined, intelligent systems.

🧪 Try a Pandas Demo

📚 Explore PandAudit

• 📊 Excel to Pandas Cheatsheet
• 🧪 Try a Live Pandas Demo
• 📣 Read Success Stories

📈 From spreadsheets to scripts — PandAudit helps you evolve.

• Streamline Data Analysis With Pythons's Merge_as_of Function

  Posted on July 2, 2023

  Title: Streamline Data Analysis with Python’s “merge_as_of” Function: Empowering Internal Audit, Financial Reporting, and the Finance Industry [Read More]

• Unveiling The Limitations Of Data Sampling

  Posted on July 1, 2023

  Title: Unveiling the Limitations of Data Sampling: Empowering Auditors and Finance Professionals with Cutting-Edge Data Services [Read More]

• Streamlining Data From Legacy And Web Based Systems

  Posted on July 1, 2023

  Title: Streamlining Data from Legacy and Web-Based Systems: Unleashing the Power of Data Analytics [Read More]

• Converting Legacy System Unstructured Documents Into Workable Excel Formats

  Posted on July 1, 2023

  Title: Unlocking Data Analytics: Converting Legacy System Unstructured Documents into Workable Excel Formats [Read More]

• Extracting Monetary Amount From Text

  Posted on December 31, 2022

  [Read More]

• Merge/Join tables on the closest date in time

  Merge_asof pandas example

  Posted on October 6, 2022

  

  

  In this post, we demonstrate how to use the merge_asof() function in the Pandas library to merge two dataframes, df1 and df2, where df1 contains names and IDs for certain individuals and df2 contains historical addresses for those same individuals. The goal is to match each person in df1 with a corresponding address in df2 that is closest to a specified cutoff date. [Read More]

  Tags:

  • pandas
  • merge
  • merge_asof
  • dataframe
  • closest match

• Amount in US dollars/cents extracted from a string

  A method for extracting US dollar amounts into a separate column of a dataframe.

  Posted on September 18, 2022

  

  

  If you have an unstructured file with messy strings containing amounts, and you want to extract just the amounts, you might consider the following approach: [Read More]

  Tags:

  • pandas
  • US dollars
  • regex
  • dataframe
  • extract

• Formatting legacy system negative balances

  Convert old legacy system balances ending with '-' OR 'CR' to negative values

  Posted on September 17, 2022

  

  

  This post is about formatting negative balances in a legacy system so that they can be easily processed in a modern system. The post provides a method using the pandas library in Python to convert old legacy system balances, which may be formatted with a ‘-‘ or ‘CR’ suffix, to negative values. The method involves reading the legacy system values into a pandas dataframe, creating a mask to identify rows with negative balances, and then using the mask to replace the ‘-‘ or ‘CR’ suffix with a negative symbol. The resulting values are then formatted by removing any commas and... [Read More]

  Tags:

  • pandas
  • Excel
  • totals
  • formulas
  • hardcoded

• Locating rows with empty spaces (missing data)

  Posted on September 5, 2022

  Removing rows with empty values (nan) This code is performing the following operations on a Pandas DataFrame called df: [Read More]

• Assign unique ID to a group in a dataframe

  Assign an id that is unique to each element in a group

  Posted on May 5, 2020

  

  

  The purpose of this code is to group the elements in the ‘A’ column of a Pandas DataFrame and assign a sequential ID to each group. The ID is assigned starting with one and increasing by one for each group. [Read More]

  Tags:

  • pandas
  • Excel
  • unique
  • unique ID
  • group
  • dataframe

• The as of merge - python [pandas]

  Merging datasets on the nearest or closest mathing value

  Posted on May 5, 2020

  

  

  This post demonstrates how to use the merge_asof() function in the Pandas library to merge two dataframes based on a shared column and the closest date in time. The function takes in two dataframes, data_set_A and data_set_B, and a shared column, ‘key_column’, and returns a new dataframe that includes all rows from data_set_A and only those rows from data_set_B that match the values in data_set_A. If there is no exact match, the function uses the last row in data_set_B whose ‘key_column’ value is less than the ‘key_column’ value in data_set_A. This type of merge is known as a “backward” search.... [Read More]

  Tags:

  • pandas
  • as_of merge
  • regex
  • dataframe
  • merge_as_of
  • pd.merge_asof()

• Unique list of items contained in a dataframe column

  Create a new column in the dataframe that contains a list of unique items from the 'source' column.

  Posted on May 4, 2020

  

  

  import pandas as pd df=pd.DataFrame({'state':['NY','CA','CT','NJ','NY','FL','FL','CA'],\ 'source':['source_a','source_a','source_a','source_b','source_b','source_b',\ 'source_d','source_c']}) df [Read More]

  Tags:

  • pandas
  • unique ID
  • dataframe
  • assign