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What is pandas?

Pandas is a versatile open-source library for data analysis and manipulation that excels in speed and power while maintaining a user-friendly interface within the Python ecosystem. It supports a wide range of data formats for both importing and exporting, such as CSV, text documents, Microsoft Excel, SQL databases, and the efficient HDF5 format. The library stands out with its intelligent data alignment features and its adept handling of missing values, allowing for seamless label-based alignment during calculations, which greatly aids in the organization of chaotic datasets. Moreover, pandas includes a sophisticated group-by engine that facilitates complex aggregation and transformation tasks, making it simple for users to execute split-apply-combine operations on their data. In addition to these capabilities, pandas is equipped with extensive time series functions that allow for the creation of date ranges, frequency conversions, and moving window statistics, as well as managing date shifting and lagging. Users also have the flexibility to define custom time offsets for specific applications and merge time series data without losing any critical information. Ultimately, the comprehensive array of features offered by pandas solidifies its status as an indispensable resource for data professionals utilizing Python, ensuring they can efficiently handle a diverse range of data-related tasks.

What is Stata?

Stata delivers everything you need for reproducible data analysis—powerful statistics, visualization, data manipulation, and automated reporting—all in one intuitive platform. Known for its speed and precision, Stata features an extensive graphical interface that simplifies usability while allowing for full programmability. The software combines the convenience of menus, dialogs, and buttons, giving users a flexible approach to data management. Its drag-and-drop functionality and point-and-click capabilities make accessing Stata's vast array of statistical and graphical tools straightforward. Additionally, users can quickly execute commands using Stata's user-friendly command syntax, which enhances efficiency. Furthermore, Stata logs every action and result, ensuring that all analyses maintain reproducibility and integrity, regardless of whether menu options or dialog boxes are used. Complete command-line programming and capabilities, including a robust matrix language, are also part of Stata's offerings. This versatility allows users to utilize all pre-installed commands, facilitating the creation of new commands or the scripting of complex analyses, thereby broadening the scope of what can be achieved within the software.

Media

Media

Integrations Supported

MLJAR Studio
ApertureDB
Cleanlab
Dagster
Dash
Flower
GLM-5.1
GLM-5.3
LanceDB
Netdata
RunCode
ThinkData Works
Train in Data
Union Pandera
Yandex Data Proc

Integrations Supported

MLJAR Studio
Buffer Editor
OpenCage Geocoding API
PyCharm

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided

Pricing Information

$48.00/6-month/student
Free Trial Offered?

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

pandas

Date Founded

2008

Company Website

pandas.pydata.org

Company Facts

Organization Name

StataCorp LLC

Date Founded

1985

Company Location

United States

Company Website

www.stata.com

Categories and Features

Component Libraries

Not specified

Data Analysis

Not specified

Categories and Features

Big Data

Data Cleansing
Data Visualization
Predictive Analytics

Data Analysis

Data Visualization
Predictive Analytics
Regression Analysis
Statistical Modeling

Data Preparation

Data Access
Data Blending
Data Cleansing
Data Governance
Data Modeling
Data Transformation
Machine Learning

Data Science

Access Control
Advanced Modeling
Audit Logs
Data Ingestion
Data Preparation
Data Visualization
Model Deployment
Reports

Data Visualization

Analytics
Simulation Models

Master Data Management

Data Governance
Data Source Integrations
Hierarchy Management
Match & Merge
Visualization

Predictive Analytics

AI / Machine Learning
Data Blending
For Education
For Healthcare
Modeling & Simulation

Reporting

Drag & Drop
Forecasting
Report Export
Scheduled / Automated Reports

Statistical Analysis

Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Simulation
Survival Analysis
Time Series
Visualization

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