7 Python Excel Libraries: In-Depth Review for Developers

Top 7 Python Excel Libraries

1. IronXL
2. Pandas
3. OpenPyXL
4. XlsxWriter
5. Xlrd
6. Xlwt
7. Pyexcel

Handling Excel files is a common task in various fields, from data analysis to report generation. Python, with its extensive ecosystem, offers numerous libraries to work with Excel files, making it a go-to language for such tasks. These libraries provide various functionalities, from basic reading and writing of Excel files to more advanced operations like data manipulation, formatting, and integration with Excel itself. In this article, we will explore some of the most popular and powerful Python libraries for handling Excel files and also take a look at IronXL for Python, which provides an excellent solution for Excel-related tasks. Understanding these libraries will equip you with the tools necessary to handle your Excel processing needs efficiently and effectively.

1. IronXL for Python

IronXL for Python is an advanced library that excels in creating, editing, and extracting data from Excel files. It provides a robust solution for users who need to work extensively with Excel files in a .NET framework context.

Key Features:

1. Reads and writes Excel files in .xls, .xlsx, and .csv formats.
2. Supports advanced formatting and styling.
3. Allows creating and manipulating charts and pivot tables.
4. Integrates seamlessly with other .NET libraries.

Example:

Reading an Excel file:

To read data from an Excel file using IronXL:

from ironxl import *

# Reading an Excel file
workbook = WorkBook.Load("Employee_data.xlsx")
# Select worksheet at index 0
worksheet = workbook.WorkSheets[0]

# Reading data from a specific cell
data = worksheet["A1:N6"]

print(data)

This code snippet reads 'Employee_data.xlsx', accesses the first sheet, and prints all the values in the range A1:N6.

Writing to an Excel file:

To create and manipulate an Excel file using IronXL:

from ironxl import *

workbook = WorkBook.Create()

# Create a blank WorkSheet
worksheet = workbook.CreateWorkSheet("new_sheet")

# Add data and styles to the new worksheet
worksheet["A1"].Value = "Hello, IronXL!"

# Save the excel file as XLS, XLSX, CSV, TSV, JSON, XML, HTML and streams
workbook.SaveAs("excel_IronXL.xlsx")

This code snippet creates 'excel_IronXL.xlsx', writes "Hello, IronXL!" to cell A1, and saves the workbook.

2. Pandas

Pandas is a powerful data manipulation and analysis library that provides easy-to-use data structures and data analysis tools. It includes the read_excel and to_excel functions to read from and write to Excel files. It leverages third-party libraries like OpenPyXL and xlrd to read from and write to Excel files.

Key Features:

1. Handles large datasets efficiently.
2. Supports complex data operations and transformations.
3. Provides easy-to-use data structures like DataFrame.
4. Integrates with other data analysis libraries like NumPy and Matplotlib.

Example

Reading an Excel file:

Here is how you can read data from an Excel file using Pandas.

import pandas as pd

# Reading an Excel file
df = pd.read_excel('Employee data.xlsx')
print(df)

This code snippet reads data from 'Employee data.xlsx' into a DataFrame and displays the first few rows of the data.

Writing to an Excel file:

To write data to an Excel file using Pandas:

# Writing to an Excel file (requires OpenPyXL for .xlsx files)
df.to_excel('Excel_Pandas.xlsx', index=False, engine='openpyxl')

This code snippet writes the DataFrame back to 'Excel_Pandas.xlsx'.

3. OpenPyXL

OpenPyXL is a library for reading and writing Excel 2010 xlsx/xlsm/xltx/xltm files. It is widely used for its simplicity and effectiveness in handling Excel files.

Key Features:

1. Supports formatting of cells.
2. Allows creation and modification of charts and images.
3. Handles named styles and number formats.
4. Provides functionality for merging cells and managing sheet properties.

Example:

Reading an Excel file:

To read data from an Excel file using OpenPyXL:

from openpyxl import load_workbook

# Reading an Excel file
wb = load_workbook('Employee Data.xlsx')
ws = wb.active

# Reading data from a specific cell
data = ws['A2'].value
print("First Name = ",data)

This code snippet opens 'Employee Data.xlsx', reads its active sheet, and prints the value of cell A2.

Writing to an Excel file:

To write data to an Excel file using OpenPyXL:

from openpyxl import Workbook

# Creating a new workbook and adding a worksheet
wb_new = Workbook()
ws_new = wb_new.active

# Writing data to a cell
ws_new['A1'] = 'Hello, OpenPyXL!'

# Saving the workbook
wb_new.save('Excel_OpenpyXL.xlsx')

This code snippet creates a new Excel sheet, writes 'Hello, OpenPyXL!' in it, and saves it with the name 'Excel_OpenpyXL.xlsx'.

4. XlsxWriter

XlsxWriter is a Python library for creating Excel 2007 xlsx files. It is particularly well-suited for writing complex formulas and creating sophisticated charts.

Key Features:

1. Supports various Excel features like charts, conditional formatting, and custom formats.
2. Allows embedding images and creating worksheets.
3. Handles large files efficiently.
4. Supports writing rich strings with multiple formats.

Example:

Reading an Excel file:

XlsxWriter does not support reading Excel files, it is primarily used for writing files.

Writing to an Excel file:

To create an Excel file and write data to it using XlsxWriter:

import xlsxwriter

# Creating an Excel file
workbook = xlsxwriter.Workbook('excel_xlsxWriter.xlsx')
worksheet = workbook.add_worksheet()

# Writing data to a cell
worksheet.write('A1', 'Hello, XlsxWriter!')

# Closing the workbook
workbook.close()

This code snippet creates 'excel_xlsxWriter.xlsx', writes "Hello, XlsxWriter!" to cell A1, and closes the workbook.

5. xlrd

Xlrd is a library for reading data and formatting information from Excel files in the historical .xls format. It was widely used before the .xlsx format became popular.

Key Features:

1. Reads data from .xls files.
2. Extracts formatting information from Excel files.
3. Handles large files efficiently.
4. Supports reading cell values, formatting, and merging cell

Example:

Reading an Excel file:

To read data from an Excel file using Xlrd:

import xlrd

# Opening an Excel file
workbook = xlrd.open_workbook('Employee_data.xls')
sheet = workbook.sheet_by_index(0)

# Print values in the range A1:N6
for row in range(6):  # Rows 0 to 5
    for col in range(14):  # Columns A to N (0 to 13)
        cell_value = sheet.cell_value(row, col)
        print(f"Value at ({row+1}, {col+1}): {cell_value}")

This code snippet opens 'Employee_data.xls', accesses the first excel spreadsheet, and prints all the values from the range A1:N6

Writing an Excel file:

Xlrd does not support writing Excel files, it is primarily used for Reading existing Excel files.

6. Xlwt

xlwt is a python excel library library for writing data and formatting information to Excel files in the historical .xls format.

Key Features:

1. Writes data to .xls files.
2. Supports various formatting options for cells.
3. Creates multiple sheets within a workbook.
4. Allows specifying cell formats, including fonts and colors.

Example:

Reading an Excel file:

xlwt does not support reading Excel files, it is primarily used for writing Excel Workbook.

Writing to an Excel file:

To create an Excel file and write data to it using xlwt:

import xlwt

# Creating a new workbook and adding a worksheet
workbook = xlwt.Workbook()
sheet = workbook.add_sheet('Sheet1')

# Writing data to a cell
sheet.write(0, 0, 'Hello, xlwt!')

# Saving the workbook
workbook.save('excel_xlwt.xls')

This code snippet creates a new Excel file with the name 'excel_xlwt.xls', writes "Hello, xlwt!" to cell A1, and saves the workbook.

7. Pyexcel

Pyexcel provides one API to read, manipulate, and write data in various Excel formats. It focuses on data presentation rather than file formats, making it versatile and easy to use.

Key Features:

1. Supports multiple Excel formats (.xls, .xlsx, .ods).
2. Provides simple data manipulation and conversion.
3. Allows integration with various Python data structures.
4. Facilitates easy export and import of data.

Example:

Reading an Excel file:

To read data from an Excel file using Pyexcel:

import pyexcel as pe

# Reading data from an Excel file
sheet = pe.get_sheet(file_name="Employee_data.xls");

# Printing the data
print(sheet)

This code snippet reads data from 'Employee_data.xls' and prints it.

Writing to an Excel file:

To write data to an Excel file using pyexcel:

# Saving the data to a new Excel file
sheet.save_as('excel_pyexcel.xlsx')

This code snippet saves the data to 'excel_pyexcel.xlsx'.

Working with Excel spreadsheets is streamlined with the use of various Python packages. These Python packages offer powerful tools and functionalities for manipulating and analyzing Excel data efficiently. Utilizing a suitable Python package simplifies tasks involving Excel spreadsheets, enhancing productivity and accuracy.

Conclusion:

Python offers a wide range of libraries to handle Excel files, each with its unique features and strengths. Whether you need to read and write data, create complex charts, or integrate Python with Excel, there is a library that fits your needs. IronXL stands out for its robust features and seamless integration with the .NET framework, making it an excellent choice for extensive Excel file manipulation tasks. IronXL also provides a free trial, allowing users to evaluate its capabilities before committing to a commercial license, which may be required for extensive or commercial use. Exploring and understanding these libraries will enable you to choose the right tool for your specific requirements and enhance your productivity in handling Excel files.