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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 Altair SLC?

In the past twenty years, a myriad of organizations has developed SAS language programs vital to their operations. Altair SLC directly utilizes these programs in SAS language syntax, which negates the necessity for translation or dependence on external licensing, resulting in substantial reductions in both capital and operational expenses due to its remarkable ability to handle large volumes of work. Furthermore, Altair SLC includes an integrated compiler for SAS language that runs both SAS and SQL scripts, and it also supports compilers for Python and R, which allows for the seamless operation of Python and R codes and enables smooth interactions between SAS datasets and Pandas as well as R data frames. This software is adaptable, able to operate on IBM mainframes, cloud infrastructures, and various servers and workstations across multiple operating systems. Additionally, it provides capabilities for remote job submission and facilitates data transfer between mainframe, cloud, and on-premises systems, thereby increasing its versatility and effectiveness in diverse environments. With such features, Altair SLC has become an indispensable tool for organizations aiming to optimize their data processing workflows.

Media

Media

Integrations Supported

Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Coiled
DagsHub
Dash
Kedro
LanceDB
MLJAR Studio
Netdata
OrcaSheets
RunCode
Spyder
TeamStation
ThinkData Works
Union Pandera
Yandex Data Proc
skills.ai

Integrations Supported

Amazon SageMaker Data Wrangler
ApertureDB
Avanzai
Coiled
DagsHub
Dash
Kedro
LanceDB
MLJAR Studio
Netdata
OrcaSheets
RunCode
Spyder
TeamStation
ThinkData Works
Union Pandera
Yandex Data Proc
skills.ai

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

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

Altair

Date Founded

1985

Company Location

United States

Company Website

altair.com/altair-slc

Categories and Features

Data Analysis

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

Categories and Features

Data Analysis

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

Data Mining

Data Extraction
Data Visualization
Fraud Detection
Linked Data Management
Machine Learning
Predictive Modeling
Semantic Search
Statistical Analysis
Text Mining

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Predictive Analytics

AI / Machine Learning
Benchmarking
Data Blending
Data Mining
Demand Forecasting
For Education
For Healthcare
Modeling & Simulation
Sentiment Analysis

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