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# Introduction
Excel Data remains relevant for work, but a significant portion of time spent using it is purely mechanical. Tasks such as combining files from multiple sources, tracking duplicate records, reformatting conflicting exports, and splitting master sheets into separate files are not complicated, but they are time-consuming and prone to human error.
This five The python Scripts help automate these tasks. Each is self-contained, configurable, and designed to work with messy real-world data.
You can find all the scripts on GitHub..
# Merging multiple Excel files
// Pain point
When combining data from multiple Excel or comma-separated values (CSV) files, the manual process—opening each file, copying the data, and pasting it into the mastersheet—is slow and prone to misalignment errors, especially when the column order differs between files.
// What does the script do?
This script scans a folder for .xlsx and .csv files, stacks all their data into a single unified sheet, and writes a clean merged output file. It can optionally add a source column so you always know which row came from which file, and it handles mismatched column orders automatically.
// How it works
Uses a script. Pandey To read each file in the target directory, aligns the columns by name rather than position, and concatenates everything into one. DataFrame. A configurable add_source_column flag adds the actual file name to each row. Column matches are logged so you know if some files have extra or missing fields. is written with the output. openpyxl And it includes a summary tab that shows file-by-file row counts.
⏩ Get the script for merging Excel files.
# Finding and flagging duplicate rows
// Pain point
Duplicate records are common in datasets that have been exported and re-imported across systems. Exact matches are easy to find, but close duplicates — the same record, slightly different formatting or spacing — are difficult to manually scale.
// What does the script do?
This script scans the Excel file for duplicate rows based on the columns you specify, flags exact duplicates and near duplicates via fuzzy matches on string fields, and writes an annotated output file highlighting each suspected duplicate group with color coding and a confidence score.
// How it works
Uses a script. pandas To detect exact duplicates and RapidFuzz For fuzzy string matching on configurable key columns. Each row is assigned a duplicate group ID and match confidence percentage. The output uses an Excel file. openpyxl Formatting to highlight duplicate clusters. A separate summary sheet shows total duplicates broken down by match type.
⏩ Get the Duplicate Finder script.
# Cleaning and standardization of dirty exported data
// Pain point
Data exported from external systems often comes with inconsistently mixed date formats, inconsistent capitalization, phone numbers with different separators, and trailing whitespace. Manually cleaning any analysis before it is added exponentially.
// What does the script do?
This script applies a configurable set of cleaning rules to an Excel or CSV file. These include standardizing dates, trimming whitespace, correcting capitalization, normalizing phone numbers and postcodes, removing blank rows, and flagging incorrectly displayed cells. It outputs a clean file and a change log that shows what was modified.
// How it works
The script reads a configuration file that maps column names to cleanup operations: date_format, title_case, strip_whitespace, phone_normalize, remove_blank_rowsand others. Each operation is implemented in sequence. A side-by-side transformation log is written to the output on a second sheet, showing the original vs. cleaned values for each transformed cell. Nothing is left in silence. If a value cannot be parsed, it is flagged in a. _clean_errors Column
⏩ Get the data cleaner script.
# Splitting a sheet into separate files by column value
// Pain point
A master dataset often needs to be divided into separate files—such as per region, department, or category. Doing this manually involves repeated filtering, copying and saving with a high risk of mixing data between files.
// What does the script do?
This script reads an Excel sheet and splits it into separate output files—per unique value in a specific column. Each output file contains only rows for that value, preserving the original formatting. Filenames are automatically generated from the column values. Optionally, it can send each file as an email attachment using the email mapping to the name you provide.
// How it works
The script groups. DataFrame Using the target column pandasthen writes each group to its own .xlsx file. openpyxl. A naming template, eg Sales_Report_{value}_{date}.xlsxallows you to control the output filename format. Column headers, data types, and basic formatting are preserved in each output file. Reads a CSV mapping of an optional email mode. {value} → {email address} and sends through each file. Simple Mail Transfer Protocol (SMTP).
⏩ Get the sheet splitter script.
# Generating a summary pivot report from raw data
// Pain point
Generating a summary report from raw data—by category, monthly trends, or top performers—involves creating pivot tables, formatting them, and copying the results into a presentable format. When the source data is updated regularly, this process is repeated from the beginning each time.
// What does the script do?
This script reads a raw data Excel file, creates a configurable pivot summary, and writes a formatted multitab summary report. Charts are generated and embedded in the output file. You can replay it whenever the source data changes.
// How it works
A configuration file defines the date field, value field, grouping columns, and specific collections to run. Uses a script. pandas For all sum logic and openpyxl with the matplotlib For chart generation. Each summary type is given its own tab. Conditional formatting highlights the highest and lowest values. The report is designed for on-demand regeneration, and running the script again cleanly overwrites the previous output.
⏩ Get the Pivot Report Generator script.
# wrap up
These five scripts cover common Excel tasks that are straightforward to automate but painful to perform manually. Pick whichever one works most frequently in your workflow and start from there. Here’s a quick overview:
| The name of the script | Purpose | Key Features | Best use case |
|---|---|---|---|
| Integration of Excel files | Combine multiple Excel/CSV files. | Column alignment, source tracking, summary sheet | Integrating data from multiple sources |
| Duplicate Finder | Identify valid and ambiguous duplicates. | Fuzzy matching, confidence scores, color highlighting | Cleaning datasets with repeated records |
| Data cleaner | Normalize messy exported data | Formatting rules, normalization, change log | Preprocessing of raw external data |
| Sheet splitter | Split a sheet into multiple files. | Automatic file naming, grouping, optional email sending | Category/Area wise distribution of reports |
| Pivot Report Generator | Create summary reports from raw data | Automatic pivots, charts, multi-tab output | Recurring reporting and dashboards |
Happy automation!
Bala Priya c is a developer and technical writer from India. She loves working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding and coffee! Currently, she’s working on learning lessons and sharing her knowledge with the developer community, writing tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource reviews and coding tutorials.