Excel Parsing
Excel parsing refers to the process of extracting and manipulating data from Excel spreadsheets. With the right tools and techniques, you can take unstructured Excel data and turn it into meaningful information. This guide will walk you through everything you need to know to implement robust Excel parsing in your applications.
Why Parse Excel Files?
There are several key reasons you may want to parse Excel spreadsheets:
-
Extract data – Excel files often contain useful data but aren’t optimized for analysis. By parsing into formats like CSV or JSON, you can extract and transform Excel data for downstream use.
-
Data integration – Parsing Excel files allows integrating siloed data with other systems like databases, web apps and analytics tools. This unlocks new reporting and automation capabilities.
-
Implement business logic – You can add validation, calculations, aggregations etc. to implement complex logic as you parse Excel data. This can fix errors and enrich the data.
-
Build workflows – Parsing Excel can be a key component in workflows like reporting pipelines, ETL processes and application integrations.
Overall, parsing unlocks Excel data so you can use it programmatically for a wide range of purposes.
Reading Excel Files
The first step in Excel parsing is accessing the Excel file contents. There are several options:
Python
- pandas – provides
read_excel()to load spreadsheet data into a Pandas DataFrame. - OpenPyXL – full featured library to read/write Excel files.
load_workbook()loads an Excel file.
JavaScript
- xlsx – parses Excel files in the browser with SheetJS. Offers
readFile()method. - ExcelJS – comprehensive library for browser/NodeJS Excel parsing.
Java
- Apache POI – standard Java API for Excel parsing. Use
WorkbookFactory.create()to load files.
These libraries handle parsing the Excel binary file format efficiently. They also offer methods to easily access sheets, cells, styles etc.
Raw Binary Parsing
You can also directly parse the Excel binary file format (XLS/XLSX). However, this is complex and generally not recommended.
Structuring & Transforming Data
Once the Excel file is loaded, the next step is processing the data:
Structuring
- Loop through rows/columns to extract cell values into lists or objects
- Convert into standard formats like CSV, JSON or XML for easier manipulation
Transforming
- Clean invalid data, trim strings, handle errors etc.
- Map column names for standard schemas
- Aggregate values like sum, average, count etc.
- Add calculations, apply business logic
- Merge data across multiple sheets
Pandas, POI and other libraries provide vectorized functions to efficiently structure and transform Excel data without slow for loops.
Exporting Parsed Data
After parsing and transforming, export the Excel data into a usable format:
- CSV – for additional analysis and machine learning
- JSON – for web apps and REST APIs
- Relational databases – integrate into OLTP apps
- Data warehouses – load into systems like BigQuery for BI
- Reports/visualizations – render parsed data into dashboards and applications
Proper data modeling is important when exporting parsed Excel data for downstream consumption.
Conclusion
Parsing Excel data allows you to unlock dispersed spreadsheets for programmatic usage. With the right libraries like Python’s pandas or Java’s POI, you can efficiently load, structure, transform and export Excel data. Integrating parsed Excel data into databases, apps and analytics systems enables new opportunities like reporting automation, predictive modeling and application modernization. Excel parsing is a key ETL and data integration skill worth learning.
Professional data parsing via ZennoPoster, Python, creating browser and keyboard automation scripts. SEO-promotion and website creation: from a business card site to a full-fledged portal.