Name: pandas
Rating: 9.9 (2 reviews)
Author: Open Source

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pandas is an open-source Python package designed for data analysis and manipulation. According to the vendor, it provides fast, flexible, and expressive data structures that make working with relational or labeled data easy and intuitive. pandas is said to be suitable for companies of all sizes, from small startups to large enterprises. It is widely used by data scientists, data analysts, financial analysts, statisticians, and social scientists across various industries.

## Key Features

**Data structures**: According to the vendor, pandas provides two main data structures, Series and DataFrame, which allow for efficient and organized handling of data.

**Easy handling of missing data**: pandas is said to offer easy handling of missing data, represented as NaN, in both floating-point and non-floating-point data, ensuring accurate analysis and computations.

**Size mutability**: According to the vendor, columns can be easily inserted and deleted from DataFrame and higher dimensional objects, allowing for size mutability and flexibility in data manipulation.

**Automatic and explicit data alignment**: Users can choose to explicitly align objects to a set of labels or let pandas automatically align the data for computations, ensuring accurate and consistent results, as claimed by the vendor.

**Powerful group by functionality**: pandas is said to provide powerful group by functionality for split-apply-combine operations on data sets, allowing for both aggregating and transforming data, making complex data analysis tasks more accessible, according to the vendor.

**Conversion of data from other Python and NumPy structures**: According to the vendor, pandas simplifies the conversion of ragged, differently-indexed data from other Python and NumPy data structures into DataFrame objects, enabling seamless integration with existing data sources.

**Intelligent label-based slicing and indexing**: pandas is said to offer intelligent label-based slicing, fancy indexing, and subsetting of large data sets, making it easy to extract specific data for analysis and visualization purposes, as claimed by the vendor.

**Merging and joining data sets**: According to the vendor, pandas provides intuitive merging and joining capabilities, allowing for the combination of multiple data sources and enabling comprehensive analysis from diverse data sets.

**Flexible reshaping and pivoting of data sets**: According to the vendor, users can easily reshape and pivot data sets with pandas, enabling efficient data restructuring for further analysis and exploration.

**Hierarchical labeling of axes**: According to the vendor, pandas supports hierarchical labeling of axes, allowing for multiple labels per tick, providing enhanced data visualization and analysis capabilities.

pandas is an open source, BSD-licensed library providing high-performance data structures and data analysis tools for the Python programming language. pandas is a Python package providing expressive data structures designed to make working with “relational” or “labeled” data both easier. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

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Wolfram's flagship product Mathematica is a modern technical computing application featuring a flexible symbolic coding language and a wide array of graphing and data visualization capabilities.

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pandas

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Hey you guys!!! goonies back with another demo vid for all of my pyro fans out there. this is Angry Pandas by Winda fireworks... WOW... so I decided to do a vid of the 2013 500 gram cake from winda. I have to say I was very impressed with it. let me know what you guys think in the comments below.. or hit me up on facebook

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Angry Pandas 500 gram cake by Winda Fireworks demo and review

PandasGUI Demo

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Quick Data Visualization of Excel Data Demo | Python Pandas Tutorial

bamboolib - a GUI for pandas - short demo video

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Demo: Getting started with pandas | Even More Python for Beginners - Data Tools [6 of 31]

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Platform Connectivity

Ability to connect to a wide variety of data sources

Connect to Multiple Data Sources

Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

Extend Existing Data Sources

Use R or Python to create custom connectors for any APIs or databases

Automatic Data Format Detection

Automatic detection of data formats and schemas

MDM Integration

Integration with MDM and metadata dictionaries

Data Exploration

Ability to explore data and develop insights

Visualization

The product’s support and tooling for analysis and visualization of data.

Interactive Data Analysis

Ability to analyze data interactively using Python or R Notebooks

Data Preparation

Interactive Data Cleaning and Enrichment

Access to visual processors for data wrangling

Data Transformations

Use visual tools for standard transformations

Data Encryption

Built-in Processors

Library of processors for data quality checks

Platform Data Modeling

Multiple Model Development Languages and Tools

Access to multiple popular languages, tools, and packages such as R, Python, SAS, Jupyter, RStudio, etc.

Automated Machine Learning

Tools to help automate algorithm development

Single platform for multiple model development

Single place to build, validate, deliver, and monitor many different models

Self-Service Model Delivery

Multiple model delivery modes to comply with existing workflows

Model Deployment

Tools for deploying models into production

Flexible Model Publishing Options

Publish models as REST APIs, hosted interactive web apps or as scheduled jobs for generating reports or running ETL tasks.

Security, Governance, and Cost Controls

Built-in controls to mitigate compliance and audit risk with user activity tracking

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