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Average Ratings 0 Ratings

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features
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support

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Write a Review

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

All components of a URL, including scheme, user, password, host, port, path, query, and fragment, can be accessed through their respective properties. Every manipulation of a URL results in a newly generated URL object, and the strings provided to the constructor or modification functions are automatically encoded to yield a canonical format. While standard properties return percent-decoded values, the raw_ variants should be used to obtain encoded strings. A human-readable version of the URL can be accessed using the .human_repr() method. Binary wheels for yarl are available on PyPI for operating systems such as Linux, Windows, and MacOS. In cases where you wish to install yarl on different systems like Alpine Linux—which does not comply with manylinux standards due to the absence of glibc—you will need to compile the library from the source using the provided tarball. This process necessitates having a C compiler and the necessary Python headers installed on your machine. It is important to remember that the uncompiled, pure-Python version is significantly slower. Nevertheless, PyPy consistently employs a pure-Python implementation, thus remaining unaffected by performance variations. Additionally, this means that regardless of the environment, PyPy users can expect consistent behavior from the library.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Amazon SageMaker Data Wrangler Yes 
ApertureDB Yes 
Avanzai Yes 
Coiled Yes 
Daft Yes 
DagsHub Yes 
Dagster Yes 
Flower Yes 
GLM-5.1 Yes 
GLM-5.3 Yes 
Giskard Yes 
Kedro Yes 
Netdata Yes 
OrcaSheets Yes 
Python No 
RunCode Yes 
Sliq Yes 
TeamStation Yes 
Train in Data Yes 

Integrations

Amazon SageMaker Data Wrangler No 
ApertureDB No 
Avanzai No 
Coiled No 
Daft No 
DagsHub No 
Dagster No 
Flower No 
GLM-5.1 No 
GLM-5.3 No 
Giskard No 
Kedro No 
Netdata No 
OrcaSheets No 
Python Yes 
RunCode No 
Sliq No 
TeamStation No 
Train in Data No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

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/yarl/

Product Features

Data Analysis

Data Discovery No 
Data Visualization No 
High Volume Processing No 
Predictive Analytics No 
Regression Analysis No 
Sentiment Analysis No 
Statistical Modeling No 
Text Analytics No 

Product Features

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