Best Python library for excel

In the realm of data manipulation and analysis, Excel remains a ubiquitous tool, and integrating it seamlessly with Python has become a paramount necessity for many developers and data scientists. Python, with its expansive ecosystem of libraries, offers a variety of options for working with Excel files. Selecting the best Python library for Excel depends on the specific requirements of the task at hand. In this exploration, we delve into two standout libraries renowned for their effectiveness in handling Excel files: Pandas and Openpyxl. Each of these libraries brings its own set of capabilities to the table, catering to different needs in the realm of Excel file manipulation. Whether the focus is on data analysis, manipulation, or intricate Excel file operations, the choice between Pandas and Openpyxl often boils down to the intricacies of the project and the desired outcomes.

1. Pandas:

Description:

Pandas is a versatile and powerful Python library designed for data manipulation and analysis. Its core data structure, the DataFrame, provides a convenient and efficient way to handle structured data. While not exclusively designed for Excel, Pandas seamlessly integrates with Excel files, offering robust support for reading, writing, and manipulating data.

Key Features:

Seamless Integration: Pandas provides easy-to-use functions like read_excel and to_excel for seamless integration with Excel files.
Data Manipulation Capabilities: Pandas excels in data manipulation, allowing users to perform a wide range of operations on the data using its powerful DataFrame structure.
File Format Support: Beyond Excel, Pandas supports various file formats, making it a versatile tool for data analysis across different data sources.

Example:

import pandas as pd
# Read Excel file
df = pd.read_excel('example.xlsx')
# Perform data manipulation
df['new_column'] = df['existing_column'] * 2
# Write back to Excel
df.to_excel('output.xlsx', index=False)

2. Openpyxl:

Description:

Openpyxl is a dedicated Python library specifically crafted for Excel file manipulation. It provides comprehensive features for reading, writing, and modifying Excel spreadsheets, and it includes capabilities for styling and formatting.

Key Features:

Direct Access to Excel Elements: Openpyxl enables direct access to Excel workbook, worksheets, and cells, providing fine-grained control over Excel elements.
Styling and Formatting: The library includes features for styling and formatting, allowing users to enhance the visual presentation of Excel sheets.
Modern Format Support: Openpyxl is compatible with the newer .xlsx format, making it suitable for working with contemporary Excel files.

Example:

from openpyxl import Workbook, load_workbook

# Create a new workbook
wb = Workbook()

# Access the default sheet
sheet = wb.active

# Write data to a cell
sheet['A1'] = 'Hello, Openpyxl!'

# Save the workbook
wb.save('example.xlsx')

# Load an existing workbook
existing_wb = load_workbook('example.xlsx')

3. xlrd:

Description:

xlrd is a library focused on reading data and formatting information from Excel files. It supports both the older .xls and the newer .xlsx formats, making it a versatile choice for extracting data from Excel sheets.

Key Features:

Data Reading: xlrd specializes in reading data from Excel files, providing functions to extract information from cells, rows, and columns.
Format Support: Supporting both .xls and .xlsx formats, xlrd offers flexibility in handling Excel files created in different versions.
Versatility: While not designed for writing, xlrd's primary strength lies in its versatility for extracting data from Excel files.

Example:

import xlrd

# Open the Excel file
workbook = xlrd.open_workbook('example.xlsx')

# Access the default sheet
sheet = workbook.sheet_by_index(0)

# Read data from a cell
data = sheet.cell_value(0, 0)

4. xlwt:

Description:

xlwt serves as a companion library to xlrd and is specifically designed for writing data and formatting information to Excel files in the older .xls format.

Key Features:

Writing to Excel: xlwt excels in writing data to Excel files, providing functions to create worksheets, insert data, and apply formatting.
Format Support: Tailored for the .xls format, xlwt is optimized for scenarios where compatibility with older Excel versions is required.

Example:

import xlwt

# Create a new workbook and add a sheet
workbook = xlwt.Workbook()
sheet = workbook.add_sheet('Sheet1')

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

# Save the workbook
workbook.save('output.xls')

5. xlwt-future:

Description:

xlwt-future is a fork of the original xlwt with added Python 3 support. This library enables writing data to Excel files in the .xls format, offering compatibility with both Python 2 and 3.

Key Features:

Python 3 Compatibility: xlwt-future ensures compatibility with Python 3, making it suitable for projects migrating to the latest Python version.
Continued .xls Support: Like its predecessor, xlwt-future is geared towards writing data to Excel files in the .xls format.

Example:

import xlwt

# Create a new workbook and add a sheet
workbook = xlwt.Workbook()
sheet = workbook.add_sheet('Sheet1')

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

# Save the workbook
workbook.save('output.xls')

6. openpyxl-styles:

Description:

openpyxl-styles is an extension to Openpyxl, enhancing its styling and formatting capabilities for Excel files. It provides additional features for customizing the appearance of Excel sheets.

Key Features:

Extended Styling Options: openpyxl-styles introduces additional styling options beyond the standard Openpyxl features, allowing for more intricate formatting.
Improved Aesthetics: The library aims to enhance the visual appeal of Excel sheets by providing users with a broader set of styling tools.

Example:

from openpyxl import Workbook
from openpyxl.styles import Font, Alignment

# Create a new workbook
wb = Workbook()
sheet = wb.active

# Apply custom styles to cells
cell = sheet['A1']
cell.value = 'Styled Cell'
cell.font = Font(size=14, bold=True)
cell.alignment = Alignment(horizontal='center', vertical='center')

# Save the workbook
wb.save('styled_output.xlsx')

7. pyexcel:

Description:

pyexcel is a versatile library that offers a uniform API for reading, writing, and manipulating various spreadsheet formats, including Excel. It simplifies working with different spreadsheet formats through a consistent interface.

Key Features:

Uniform API: pyexcel provides a consistent API for interacting with various spreadsheet formats, including Excel, making it easy to switch between formats.
Format Agnosticism: The library supports multiple spreadsheet formats, allowing users to work with Excel files alongside other formats seamlessly.

Example:

import pyexcel as pe

# Create a sheet
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 22]}
book = pe.Book(data)

# Save as Excel file
book.save_as('output.xlsx')

8. pyexcel-xlsxw:

Description:

pyexcel-xlsxw is a plugin for pyexcel that facilitates writing Excel files specifically in the .xlsx format. It extends pyexcel's capabilities to include the newer Excel format.

Key Features:

.xslx Format Support: pyexcel-xlsxw extends pyexcel to support the .xlsx format, providing users with the ability to write Excel files in the modern format.
Seamless Integration: The plugin seamlessly integrates with the pyexcel library, allowing users to work with Excel files in a consistent manner.

Example:

import pyexcel as pe

# Create a sheet
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 22]}
book = pe.Book(data)

# Save as Excel file in .xlsx format
book.save_as('output.xlsx')

9. xlsxwriter:

Description:

xlsxwriter is a Python module specifically designed for writing Excel files. It offers extensive formatting options and supports the creation of charts within Excel sheets.

Key Features:

Advanced Formatting: xlsxwriter provides advanced formatting options, allowing users to customize the appearance of cells, rows, and columns.
Chart Support: The library supports the creation of charts within Excel sheets, making it a comprehensive solution for data presentation.

Example:

import xlsxwriter

# Create a new Excel workbook
workbook = xlsxwriter.Workbook('output.xlsx')
worksheet = workbook.add_worksheet()

# Write data and create a chart
data = [10, 20, 30, 40, 50]
worksheet.write_column('A1', data)

chart = workbook.add_chart({'type': 'line'})
chart.add_series({'values': '=Sheet1!$A$1:$A$5'})

worksheet.insert_chart('C1', chart)

# Save the workbook
workbook.close()

10. pyxlsb:

Description:

pyxlsb is a library designed for reading and writing Binary Excel files (.xlsb), an Excel binary workbook format. It provides functionality specifically tailored to this binary format.

Key Features:

Binary Excel Support: pyxlsb is focused on working with Binary Excel files (.xlsb), providing support for reading and writing in this binary format.
Specialized Functionality: The library offers functionality that caters specifically to the unique aspects of Binary Excel files.

Example:

import pyxlsb

# Example usage specific to Binary Excel files
# (Detailed usage may vary based on the library's API)

11. pandas-profiling:

Description:

While Pandas-centric, pandas-profiling is a library that generates profile reports from a DataFrame, including interactive reports in Excel format. It offers a comprehensive overview of the data within a Pandas DataFrame.

Key Features:

Profile Reports: pandas-profiling generates detailed profile reports that include statistical summaries, data visualizations, and interactive charts.
Interactive Excel Reports: The library allows users to export profiling results to interactive Excel reports, facilitating in-depth data exploration.

Example:

import pandas as pd
from pandas_profiling import ProfileReport

# Create a DataFrame
df = pd.read_csv('example.csv')

# Generate a profile report
profile = ProfileReport(df)

# Export the report to an interactive Excel file
profile.to_file("data_profile_report.xlsx")

12. DataNitro:

Description:

DataNitro is a library that transforms Microsoft Excel into a powerful Python Integrated Development Environment (IDE). It allows the seamless execution of Python code directly within Excel, bridging the gap between the spreadsheet functionality of Excel and the scripting capabilities of Python.

Key Features:

Python IDE Integration: DataNitro turns Excel into a Python IDE, enabling users to write, execute, and debug Python code directly within Excel.
Bi-Directional Interaction: Provides a bi-directional interaction, allowing Python code to manipulate Excel data and Excel to display results from Python calculations.

13. Xlwings:

Description:

xlwings is a library designed to facilitate the interaction between Python and Excel. It allows users to call Python functions from Excel, manipulate Python objects within Excel, and vice versa, seamlessly integrating Python and Excel workflows.

Key Features:

Python-Excel Interaction: Enables calling Python functions and manipulating Python objects from Excel and vice versa, fostering a smooth integration of the two environments.
Automation of Excel Tasks: Provides a Pythonic interface for automating Excel tasks, making it easy to control Excel from Python scripts.

Example:

import xlwings as xw

# Call a Python function from Excel
@xw.func
def add_numbers(x, y):
    return x + y

14. tablib:

Description:

tablib is a library designed for working with tabular data. While not exclusive to Excel, tablib supports importing and exporting data to and from Excel files, offering a convenient solution for managing tabular data.

Key Features:

Tabular Data Handling: tablib provides a comprehensive set of tools for managing tabular data, including reading from and writing to Excel files.
Format Agnostic: Supports multiple formats, making it versatile for handling tabular data in different file formats, including Excel.

Example:

import tablib

# Load data from an Excel file
data = tablib.Dataset().load('example.xlsx')

# Perform operations on the data
# (Detailed usage may vary based on the library's API)

15. pyxlsb:

Description:

pyxlsb is a specialized library for reading and writing Binary Excel files (.xlsb), which is an Excel binary workbook format. It provides functionality specifically tailored to this binary format.

Key Features:

Binary Excel Support: pyxlsb is focused on working with Binary Excel files (.xlsb), providing support for reading and writing in this binary format.
Specialized Functionality: The library offers functionality that caters specifically to the unique aspects of Binary Excel files.

Example:

import pyxlsb

# Example usage specific to Binary Excel files
# (Detailed usage may vary based on the library's API)

16. excelerator:

Description:

excelerator is a Python module designed for creating Excel files in the .xlsx format. It offers support for formulas, formatting options, and various features to facilitate the creation of complex Excel spreadsheets.

Key Features:

Excel File Creation: excelerator focuses on creating Excel files in the .xlsx format, providing tools for generating complex spreadsheets.
Formula Support: The library supports the inclusion of formulas in Excel files, enabling the creation of dynamic and calculated cells.

Example:

import excelerator as xl

# Create a new workbook
wb = xl.Workbook()

# Add a sheet and write data
sheet = wb.add_sheet('Sheet1')
sheet.write(0, 0, 'Hello, excelerator!')

# Save the workbook
wb.save('output.xlsx')

17. pyexcel-odsr:

Description:

pyexcel-odsr is a plugin for pyexcel that extends its capabilities to support the Open Document Spreadsheet (ODS) format used by LibreOffice and OpenOffice.

Key Features:

ODS Format Support: pyexcel-odsr extends pyexcel to support the Open Document Spreadsheet (ODS) format, enabling compatibility with LibreOffice and OpenOffice.
Seamless Integration: The plugin integrates seamlessly with the pyexcel library, allowing users to work with ODS files in a consistent manner.

Example:

import pyexcel as pe

# Create a sheet
data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 22]}
book = pe.Book(data)

# Save as ODS file
book.save_as('output.ods')

18. pandasgui:

Description:

pandasgui, while primarily a Pandas GUI, provides a user interface for exploring and analyzing Pandas DataFrames. It also allows for exporting DataFrames to Excel with an interactive interface.

Key Features:

Pandas GUI: pandasgui offers a graphical user interface for exploring and interacting with Pandas DataFrames.
Export to Excel: The library includes functionality to export DataFrames to Excel, enhancing the interactive data exploration process.

Example:

from pandasgui import show

# Create a DataFrame
df = pd.DataFrame({'Column1': [1, 2, 3], 'Column2': ['A', 'B', 'C']})

# Show the DataFrame in the GUI
gui = show(df)

# Export the DataFrame to Excel through the GUI
gui.export(df, to='excel', filename='output.xlsx')