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Description

Parsel is a Python library licensed under BSD that facilitates the extraction and removal of data from HTML and XML documents using XPath and CSS selectors, with the option to integrate regular expressions. To begin, you create a selector object for the HTML or XML content you wish to analyze. After that, you can utilize either CSS or XPath expressions to target specific elements. CSS serves as a styling language for HTML, defining selectors that link styles to designated HTML elements, while XPath is utilized for selecting nodes within XML documents and can also be applied to HTML. Although both CSS and XPath can be used, CSS tends to offer greater readability, whereas XPath provides capabilities that may not be achievable through CSS alone. Built on top of lxml, parsel selectors incorporate some EXSLT extensions and come with pre-registered namespaces available for use in XPath queries. Furthermore, parsel selectors allow for the chaining of selectors, enabling users to primarily select by class using CSS and seamlessly transition to XPath when the situation demands it, enhancing flexibility in data extraction tasks. This dual capability makes parsel a powerful tool for developers working with web data.

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

Screenshots View All

Screenshots View All

Integrations

3LC
Activeeon ProActive
ApertureDB
Avanzai
Coiled
Daft
Dagster
Flower
Flyte
Giskard
Kedro
LanceDB
Netdata
Python
RunCode
TeamStation
ThinkData Works
Train in Data
Union Pandera
skills.ai

Integrations

3LC
Activeeon ProActive
ApertureDB
Avanzai
Coiled
Daft
Dagster
Flower
Flyte
Giskard
Kedro
LanceDB
Netdata
Python
RunCode
TeamStation
ThinkData Works
Train in Data
Union Pandera
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

Python Software Foundation

Country

United States

Website

pypi.org/project/parsel/

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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