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NumPy VS Delighted

Compare NumPy VS Delighted and see what are their differences

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Premium Customer Service Software to manage your customer support. Manage support email, publish self-help articles, and automate repetitive tasks. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Delighted logo Delighted

The fastest and easiest way to gather actionable feedback from your customers
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Delighted Landing page
    Landing page //
    2023-10-19

Deliver customer feedback and employee experience surveys across various channels. Control when and where surveys are delivered for point-in-time feedback at key points in the customer journey and employee lifecycle.

No code required.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Delighted features and specs

  • Survey Distribution via Email, Web, Link, SDK and Kiosk
  • Prebuilt Reports and Trend Reports
    Yes
  • AI Suggested Filters
    Yes
  • Customize Branding
    Yes
  • Survey Types: NPS, CSAT, CES, 5-Star, Thumbs, Smiley, eNPS, PMF
  • Send Survey from Your Domain
    Yes
  • 30+ Languages
  • Autopilot to Schedule Delivery
    Yes
  • Survey Templates
    Yes
  • Additional Questions
    Yes
  • Testimonials

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of Delighted

Overall verdict

  • Yes, Delighted is generally considered a good tool for collecting customer feedback and measuring customer satisfaction.

Why this product is good

  • Delighted is known for its simplicity and effectiveness in gathering Net Promoter Score (NPS) data. It offers a user-friendly interface, easy integration with various platforms, and real-time feedback collection. Delighted helps businesses understand customer sentiment and improve their products or services based on the feedback received.

Recommended for

  • Businesses looking to implement NPS surveys quickly and efficiently
  • Companies seeking real-time feedback from customers
  • Organizations that want to integrate feedback tools with existing platforms like Slack, Salesforce, and Shopify
  • Small to medium-sized businesses aiming for a cost-effective customer feedback solution

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Delighted videos

+ Add

Delighted used by Bonobos

More videos:

  • Demo - NPS Setup in 15 Seconds

Category Popularity

0-100% (relative to NumPy and Delighted)

NumPy

Delighted

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Customer Feedback
0 0%
Customer Feedback
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Surveys
0 0%
Surveys
100% 100

User comments

Share your experience with using NumPy and Delighted. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and Delighted

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Delighted Reviews

12 Best SurveySparrow Alternatives With Pricing and Features
A menโ€™s clothing and accessories brand, Bonobos, wanted to know how customers feel about the new shipping process. The brand used a Delighted NPS survey and found that the customers were not happy with the changes.
Source: qualaroo.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Delighted. While we know about 122 links to NumPy, we've tracked only 2 mentions of Delighted. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (122)

View more

Delighted mentions (2)

  • MSP Feedback Survey / Net Promoter - Third Party Executed
    We've used https://delighted.com and been pretty happy with it. It's sent to customers during "key intersects" (onboarding, after projects, etc.) and after events. The results stream into Teams and we also track/analyze them to improve service. Source: over 4 years ago
  • Creating a simpler NPS, CSAT, CES service like Delighted?
    After seeing a business idea newsletter mention a SaaS that help monitor you NPS score, I decided to look a little more into it. There are a LOT of solutions out there, but they're also wildly expensive since I assume they're targeting larger organizations. One service I found, https://delighted.com, provides simple forms and widgets to collect NPS, CSAT, CES and other and displays the results in a simple... Source: about 5 years ago

What are some alternatives?

When comparing NumPy and Delighted, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Survicate - Collect feedback on your website and find out more about your visitors.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

AskNicely - Collect customer experience feedback on a daily basis and empower your team to take immediate action to drive retention, upgrades, reviews and referrals.

OpenCV - OpenCV is the world's biggest computer vision library

Wootric - Wootric is software that allows apps and websites to take customer satisfaction surveys so that you can properly gauge the popularity and success of your app through the eyes of the people using it. Read more about Wootric.

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