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Description

Openpyxl is a Python library designed for reading and writing Excel 2010 files in formats such as xlsx, xlsm, xltx, and xltm. The library was developed due to the absence of a native solution for handling Office Open XML files in Python, and it owes its origins to the PHPExcel project. It is important to note that openpyxl does not provide protection against certain vulnerabilities like quadratic blowup or billion laughs XML attacks by default, but these risks can be mitigated by installing the defusedxml library. To install openpyxl, you can use pip, and it's recommended to perform this installation within a Python virtual environment to avoid conflicts with system packages. In some instances, you may want to work with a specific version of the library, especially if there are fixes that have not yet been released officially. Fortunately, you do not need to create an actual file on your filesystem to begin using openpyxl; simply import the Workbook class and begin your tasks. When you create sheets, they are automatically assigned names, and once you rename a worksheet, you can access it using the corresponding key from the workbook. This ease of use makes openpyxl a popular choice for many Python developers working with Excel files.

Description

Pandas is an open-source data analysis and manipulation tool that is not only fast and powerful but also highly flexible and user-friendly, all within the Python programming ecosystem. It provides various tools for importing and exporting data across different formats, including CSV, text files, Microsoft Excel, SQL databases, and the efficient HDF5 format. With its intelligent data alignment capabilities and integrated management of missing values, users benefit from automatic label-based alignment during computations, which simplifies the process of organizing disordered data. The library features a robust group-by engine that allows for sophisticated aggregating and transforming operations, enabling users to easily perform split-apply-combine actions on their datasets. Additionally, pandas offers extensive time series functionality, including the ability to generate date ranges, convert frequencies, and apply moving window statistics, as well as manage date shifting and lagging. Users can even create custom time offsets tailored to specific domains and join time series data without the risk of losing any information. This comprehensive set of features makes pandas an essential tool for anyone working with data in Python.

API Access

Has API

API Access

Has API

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Screenshots View All

Integrations

Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
Daft
Flower
Flyte
Giskard
Kedro
LanceDB
MLJAR Studio
Microsoft Excel
Netdata
Spyder
ThinkData Works
Union Pandera
Yandex Data Proc
skills.ai

Integrations

Activeeon ProActive
Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Cleanlab
Daft
Flower
Flyte
Giskard
Kedro
LanceDB
MLJAR Studio
Microsoft Excel
Netdata
Spyder
ThinkData Works
Union Pandera
Yandex Data Proc
skills.ai

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

openpyxl

Founded

2022

Website

openpyxl.readthedocs.io/en/stable/

Vendor Details

Company Name

pandas

Founded

2008

Website

pandas.pydata.org

Product Features

Product Features

Data Analysis

Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics

Alternatives

Alternatives

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