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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.

Description

Waiting is a compact library designed to facilitate the process of waiting for specific conditions to be met. It fundamentally pauses execution until a designated function returns True, offering various operational modes. Additionally, Waiting is designed to work seamlessly with flux for simulating timelines. The simplest way to utilize it is by providing a function to monitor. It’s straightforward to wait indefinitely; if your predicate yields a value, that value will be returned as the output of wait(). You can also set a timeout, and if this period lapses without the predicate being satisfied, an exception will occur. The library polls the predicate at a default interval of one second, which can be adjusted using the sleep_seconds parameter. When dealing with multiple predicates, Waiting offers two efficient methods for aggregation: any and all. These methods are similar to Python's built-in any() and all(), but they ensure that a predicate is not invoked more than necessary, which is particularly beneficial when working with predicates that are resource-intensive and time-consuming. By streamlining these functions, Waiting enhances both the efficiency and user experience of handling asynchronous operations.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

3LC
ApertureDB
Avanzai
Cleanlab
Daft
DagsHub
Dagster+
Dash
Flux
Flyte
Giskard
Kedro
LanceDB
MLJAR Studio
Netdata
Python
RunCode
TeamStation
Union Pandera

Integrations

3LC
ApertureDB
Avanzai
Cleanlab
Daft
DagsHub
Dagster+
Dash
Flux
Flyte
Giskard
Kedro
LanceDB
MLJAR Studio
Netdata
Python
RunCode
TeamStation
Union Pandera

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

pandas

Founded

2008

Website

pandas.pydata.org

Vendor Details

Company Name

Python Software Foundation

Country

United States

Website

pypi.org/project/waiting/

Product Features

Data Analysis

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

Product Features

Alternatives

Alternatives

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requests

Python Software Foundation
ML.NET Reviews

ML.NET

Microsoft