Excel is a widely-used format for data analysis and reporting, and from our website or portal is most important feature nowadays, many libraries allow developers to export data in Excel format. There are several libraries and inbuilt features in programming languages which help us to export data in excel format, some languages and their libraries are listed below –
Python
Nodejs
PHP
- PhpSpreadsheet
- (inbuilt .csv import method)
Note: I have only mentioned most used and versatile libraries which has all the features and fast in terms of speed with these features.
Although there are tonnes of libraries for excel but above library has the required features like – text hyperlink, combine columns/rows and text decorations and width controls etc, which is very hard to find in any other library.
Let's try how can we export data in excel using xlsxwriter in Python.
Now before jump into creating an export to excel code, we need to learn some basics about excel.
.xls: The.xls file extension is used for Microsoft Excel 97-2003 workbooks. It supports Excel versions 97-2003 and has a maximum row and column count of 65,536. This extension, however, does not support certain Excel features such as macros, pivot tables, and data validation.
.xlsx: Microsoft Excel 2007 and later versions use the.xlsx file extension. It can hold up to 1,048,576 rows and 16,384 columns. This file format also supports advanced Excel features like macros, pivot tables, and data validation.
.xlsm: Similar to.xlsx, the.xlsm file extension is used for workbooks that contain macros. It has the same row and column constraints as.xlsx.
.xlsb: Also known as “Binary Excel,” this file extension is faster and uses less memory than other Excel file formats. It is advised to use it with large datasets and complex calculations. It is, however, not human-readable and cannot be opened with a text editor.
.csv: This is a plain-text file format that can be opened by Excel, but it does not retain the original excel file's formatting and data types. It can only store data in tabular format and does not support formulas or styles.
.Odds: This is an OpenDocument Spreadsheet file; it is an open spreadsheet standard that can be used by many spreadsheet programmes, including Apache OpenOffice Calc and LibreOffice Calc.
It is not fully compatible with Excel, but it can open.ods files in Excel with some restrictions.
Finally, the file extension you choose will be determined by the specific needs of your project and what you intend to do with the Excel file. . The most commonly used formats for compatibility and functionality are.xlsx and.xlsm, but.csv and .ods can be used for data exchange or open-source project compatibility. Because of their limitations,.xls and.xlsb are less commonly and less strongly recommended.
Let's built a basic application with xlsxwriter.
First we need to import xlsxwriter library with the help of PyPi
pip install XlsxWriter
then we can import the library and write some basic export to excel application.
import xlsxwriter # Create a new workbook and add a worksheet workbook = xlsxwriter.Workbook('example.xlsx') worksheet = workbook.add_worksheet() # Prepare data to be written to the worksheet data = [['Name', 'Age', 'City'], ['John', 25, 'New York'], ['Mike', 32, 'Los Angeles'], ['Mary', 28, 'Chicago']] # Write data to the worksheet for row_num, row_data in enumerate(data): for col_num, cell_data in enumerate(row_data): worksheet.write(row_num, col_num, cell_data) # Save the workbook workbook.close() Some feature about this library is it can also be accessible with Pandas too and we can use it with all the databases like –SQL, MongoDB, Postgres etc.
xlsxwriter is a low-level library that provides a lot of flexibility and control over the Excel file format, which makes it useful for advanced Excel manipulation. However, it also makes the code slightly more complex when compared to other libraries such as pandas.
As you continue to explore the topic of data export to Excel using different libraries in programming languages, I invite you to reach out to me through comments, direct messaging here, or on Twitter. if you have any further questions or would like to request additional in-depth coverage of specific libraries in Node.js or PHP.