Hello! If you're an office worker, you can probably relate to the struggle of repetitive Excel tasks. Particularly, tasks like entering accounting data into an ERP system—which involve a lot of numbers and require precision—take a long time and carry a significant risk of errors. I faced this pain too, but then I created my own Python pandas automation script that dramatically reduced my work time. Tasks that took hours can now be completed in an instant, allowing me to quickly finish the accounting work I dislike and have more time to focus on what I enjoy.

Is it limited to just accounting tasks? No, it isn’t. In fact, pandas is a magical tool that can be utilized wherever data-driven work is involved. While the extent of Excel usage may vary across different fields, there are very few places that don’t use Excel at all. Thus, spreadsheets like Excel are essential tools in modern work, and I can confidently say that the capabilities of pandas are boundless.

Now, let’s take a closer look at how pandas can efficiently streamline your work in various situations.

pandas: What Is It?

pandas is an open-source library in Python designed to efficiently handle and analyze data. It is especially optimized for dealing with tabular data (spreadsheets, database tables, etc.) and supports reading and writing Excel files ( .xls, .xlsx) very robustly. The core of pandas is its data structure called DataFrame, which allows you to easily work with table-like data composed of rows and columns, just like an Excel sheet.

pandas: Where Else Can It Be Used Besides Accounting Tasks?

Besides processing accounting data as in my experience, pandas serves as a powerful automation tool across various fields.

pandas automating spreadsheet for worker

1. Data Cleaning and Preprocessing

Data often comes to us in a messy state. Missing values, duplicate rows, incorrect data types, etc.! pandas offers remarkable features to solve these problems.

  • Handling Missing Values: You can find empty cells (NaN, None, etc.) and fill them with specific values (e.g., fillna()) or delete the respective rows/columns (dropna()).

    • Use Case: In customer survey data, fill unanswered items with 'No Response,' or filter out customer data with missing required information to create a follow-up request list.

  • Removing Duplicates: Find and eliminate duplicate rows to ensure data integrity (drop_duplicates()).

    • Use Case: Filter out duplicate email addresses during registration or ensure that only one entry of a repeatedly registered inventory item remains to accurately assess the stock count.

  • Data Type Conversion: Convert data types that are incorrectly recognized, such as numbers being seen as strings or dates being recognized as plain text (e.g., astype(), to_datetime()).

    • Use Case: If sales data is stored as text and cannot be summed, convert it to numbers or change dates from the format '20230101' to an actual date format to facilitate period analysis.

  • Outlier Detection and Treatment: Use statistical methods (IQR, Z-score, etc.) to identify outliers in the data and remove or replace them with other values.

    • Use Case: Identify abnormal transactions that deviate significantly from the average selling price for fraud or data entry errors and detect sudden value changes in sensor data to ascertain equipment anomalies.

  • Standardizing Data Formats: When data from various sources differs in format, you can unify it to a specific format (e.g., standardizing date formats).

    • Use Case: If one file records dates as 'YYYY-MM-DD' and another as 'MM/DD/YYYY', standardizing them to 'YYYY-MM-DD' helps consolidate and analyze the data.

2. Data Integration and Merging

pandas is incredibly useful for combining data scattered across different files or databases into a single analysis.

  • Merging Multiple Files: You can combine multiple Excel or CSV files with the same structure into a single DataFrame (e.g., pd.concat()).

    • Use Case: Load point-wise sales report files generated monthly all at once to aggregate total annual sales or consolidate daily log files to analyze weekly/monthly traffic.

  • Data Merging: Merge different DataFrames based on specific keys (columns) to create new information (e.g., pd.merge()). (Similar to SQL's JOIN)

    • Use Case: Merge the 'Customer Basic Info' file and 'Customer Purchase History' file based on customer IDs to analyze total purchase amounts or preferred product categories by customer. You could also automate the generation of individual pay statements by merging 'Employee Info' and 'Salary Info' based on employee IDs.

  • Restructuring Data: Create pivot tables (pivot_table()), or transform data to long (melt()) or wide (pivot()) formats as necessary for your analysis.

    • Use Case: Create a pivot table that lists product sales data by month, with 'Product' as rows and 'Month' as columns, facilitating a quick overview of monthly product sales trends or restructuring survey response data for easier analysis.

3. Data Analysis and Report Automation

Automate repetitive analysis and report generation to save time and effort.

  • Generating Summary Statistics: Quickly summarize data and extract key metrics using various statistical functions like describe(), mean(), sum(), count().

    • Use Case: Rapidly determine the average, minimum/maximum, and standard deviation of total sales to summarize business performance or calculate average scores from survey responses to gauge satisfaction levels.

  • Group-wise Analysis: Use groupby() to group data based on specific criteria (e.g., by region, product) and perform aggregations (sum, average, count, etc.) for each group.

    • Use Case: Automatically aggregate total sales by each location, average profit margins by product categories, or departmental expenditure to include in reports.

  • Time Series Data Analysis: Efficiently handle date/time data for monthly and quarterly trend analysis and moving average calculations.

    • Use Case: Calculate daily fluctuations in stock price data or analyze daily sales trends of a specific product to provide foundational data for forecasting future sales.

  • Conditional Filtering and Selection: Extract data that meets specific criteria, narrowing down the analysis target.

    • Use Case: Retrieve data only for certain periods (e.g., last quarter) or filter transactions above a specific amount (e.g., over a million won) to extract a VIP customer list.

  • Generating Customized Reports: Utilize analyzed DataFrames to automatically create reports in desired formats (Excel, CSV, PDF, etc.), possibly including charts as needed.

    • Use Case: Set up a system to automatically analyze last week's sales performance data every Monday morning and generate a 'Weekly Sales Report' Excel file, which is then emailed to relevant departments.

Conclusion

pandas is not just a tool that replaces Excel tasks; it is a powerful weapon for understanding and utilizing data. Escape from repetitive and tedious data work, and seize the opportunity to maximize your work efficiency through pandas and concentrate on more important tasks.

In the next post, we will delve into the specific methods to read from and write to Excel files using pandas, along with various options. Stay tuned!