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Listmonk VS NumPy

Compare Listmonk VS NumPy and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Listmonk logo Listmonk

Send e-mail campaigns from a powerful dashboard. High performance and features packed into one app.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Listmonk Landing page
    Landing page //
    2024-08-30
  • NumPy Landing page
    Landing page //
    2023-05-13

Listmonk features and specs

  • Open Source
    Listmonk is free to use, modify, and distribute. This can offer significant cost savings compared to proprietary email marketing solutions.
  • Self-Hosted
    Allows you to have full control over the software and your data, providing enhanced security and customization possibilities.
  • Scalability
    Designed to handle millions of subscribers and messages effectively, making it suitable for both small and large-scale email marketing campaigns.
  • Multilingual Support
    Supports multiple languages, making it accessible for users from different linguistic backgrounds.
  • API Access
    Provides a comprehensive API, allowing for excellent integration with other software and platforms.
  • Flexible Database
    Supports both PostgreSQL and SQLite, catering to different user needs and technical requirements.

Possible disadvantages of Listmonk

  • Technical Expertise Required
    Being a self-hosted solution, it requires a certain level of technical knowledge for setup, maintenance, and troubleshooting.
  • Hosting Costs
    Although the software itself is free, you will need to incur costs for hosting and maintaining the server where Listmonk is installed.
  • Limited Built-In Templates
    May not come with a wide variety of pre-designed email templates, requiring users to create their own or import from other sources.
  • Community Support
    Unlike commercial solutions, support is primarily community-driven, which may lead to slower issue resolution compared to dedicated support teams.
  • Learning Curve
    The initial setup and configuration can be complex and may require a time investment to understand all its features and functionalities.

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.

Analysis of Listmonk

Overall verdict

  • Yes, Listmonk is a good option for those who prefer a self-hosted, open-source mailing list manager. It provides powerful features without recurring subscription costs and allows for full control over your data.

Why this product is good

  • Listmonk is a self-hosted newsletter and mailing list manager that is appreciated for its open-source nature, flexibility, and robust features. It offers advanced segmentation, analytics, and a user-friendly interface, making it suitable for businesses looking for a customizable and privacy-focused solution.

Recommended for

  • Businesses or individuals looking for a cost-effective solution without recurring fees
  • Those who require a high degree of customization and flexibility
  • Organizations that prioritize data privacy and security
  • Developers or tech-savvy users comfortable with self-hosting applications

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.

Listmonk videos

+ Add

Listmonk

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

Category Popularity

0-100% (relative to Listmonk and NumPy)

Listmonk

NumPy

Email Marketing
100 100%
Email Marketing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Newsletter Marketing
100 100%
Newsletter Marketing
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Listmonk and NumPy. 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 Listmonk and NumPy

Listmonk Reviews

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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

Social recommendations and mentions

Based on our record, NumPy should be more popular than Listmonk. It has been mentiond 122 times since March 2021. 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.

Listmonk mentions (32)

  • Email Marketing Automation in 2026: 5 Tools (and 1 Self-Hosted) Through Their APIs
    Listmonk is open-source, Go-based, single-binary or Docker. No task counter, no contact limit, no premium feature gates. Drop this on a $6 VPS:. - Source: dev.to / about 1 month ago
  • 5 Open Source Alternatives Worth Considering Before Renewing Expensive SaaS Tools
    Email marketing platforms are another category where the commercial pricing has drifted well above the value delivered for most use cases. Listmonk is a self-hosted newsletter and mailing list manager written in Go that handles transactional email, campaigns, and subscriber management. The source is on GitHub and it runs as a single binary on any modern Linux host. - Source: dev.to / 3 months ago
  • Newsletters
    Listmonk is a high-performance, self-hosted newsletter and mailing list manager written in Go. It ships as a single binary backed by PostgreSQL and handles millions of subscribers without flinching. The web UI is fast and uncluttered โ€” managing lists, writing campaigns, and reviewing analytics all feel responsive even on modest hardware. - Source: dev.to / 4 months ago
  • How We Built a Welcome Email That Actually Gets Sent
    We use Listmonk for transactional email โ€” it's open source, self-hosted, and speaks a simple HTTP API. Our Go backend already had an email client with methods for password resets, comment notifications, view notifications, and confirmation emails. Adding a welcome email followed the same pattern. - Source: dev.to / 4 months ago
  • Show HN: Built an email marketing platform after paying $200/month to self-host
    I run a few instances of listmonk [0], what makes fertit different/better? One thing I donโ€™t particularly like about listmonk is that it doesnโ€™t really support multitenancy. Itโ€™s lightweight enough that I can spin up multiple instances for different domains, but itโ€™d be nice not to. https://listmonk.app/. - Source: Hacker News / 12 months ago
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NumPy mentions (122)

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What are some alternatives?

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

MailChimp - MailChimp is the best way to design, send, and share email newsletters.

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

Sendy - Sendy is a self hosted newsletter app that sends emails 100x cheaper viaย Amazon SES

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

Brevo - Innovative online Email Marketing solution to manage your contacts, create & send your newsletters and track your results. More than 80 000 clients. Best prices and attractive features.

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

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