Need advice about which tool to choose?Ask the StackShare community!

AWS Data Wrangler

4
24
+ 1
0
NumPy

1.6K
682
+ 1
10
Add tool

NumPy vs AWS Data Wrangler: What are the differences?

Developers describe NumPy as "Fundamental package for scientific computing with Python". Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. On the other hand, AWS Data Wrangler is detailed as "Move pandas/spark dataframes across AWS services". It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).

NumPy and AWS Data Wrangler can be primarily classified as "Data Science" tools.

NumPy and AWS Data Wrangler are both open source tools. It seems that NumPy with 12.6K GitHub stars and 4.11K forks on GitHub has more adoption than AWS Data Wrangler with 378 GitHub stars and 35 GitHub forks.

Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of AWS Data Wrangler
Pros of NumPy
    Be the first to leave a pro
    • 8
      Great for data analysis
    • 2
      Faster than list

    Sign up to add or upvote prosMake informed product decisions

    What is AWS Data Wrangler?

    It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).

    What is NumPy?

    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

    Need advice about which tool to choose?Ask the StackShare community!

    Jobs that mention AWS Data Wrangler and NumPy as a desired skillset
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United States of America Texas Houston
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    Philippines National Capital Region Makati City
    What companies use AWS Data Wrangler?
    What companies use NumPy?
    See which teams inside your own company are using AWS Data Wrangler or NumPy.
    Sign up for StackShare EnterpriseLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with AWS Data Wrangler?
    What tools integrate with NumPy?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    GitHubPythonReact+42
    48
    39822
    What are some alternatives to AWS Data Wrangler and NumPy?
    Pandas
    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
    SciPy
    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
    Anaconda
    A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
    Dataform
    Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
    PySpark
    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
    See all alternatives