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Matomo VS NumPy

Compare Matomo VS NumPy and see what are their differences

Matomo

Matomo is an open-source web analytics platform

Rating
5.0 · 1 review
Pricing
Open source
Alternatives Is it good?
NumPy

NumPy is the fundamental package for scientific computing with Python

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
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Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

NumPy might be a bit more popular than Matomo. We know about 122 links to it since March 2021 and only 86 links to Matomo.

social mentions
86 vs 122
Analytics popularity
100% vs 0%
alternatives listed
240+ vs 189

Base details

Website, pricing, platforms and company facts side by side.

Matomo
NumPy
Website matomo.org numpy.org
Pricing
Open source Official pricing
Open source
Company Startup from New Zealand —
Listed in
Analytics Web Analytics Open Source Privacy +1
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1

Features and specs

What each product offers, as listed by its team.

Matomo 6 features
NumPy 5 features
  • Open Source
    Matomo is an open-source platform, allowing for customization and transparency in how data is collected and processed.
  • Data Ownership
    Users have full ownership of their data, ensuring that no third-party entities have access to sensitive information.
  • Privacy Compliance
    Matomo is designed with privacy in mind, making it easier to comply with GDPR, CCPA, and other data protection regulations.
  • Self-Hosting Option
    Matomo can be self-hosted, giving users complete control over their data security and server environment.
  • Feature-Rich
    The platform offers a wide range of features, including A/B testing, heatmaps, session recording, and more.
  • Community Support
    A large community of users and developers contributes plugins, improvements, and support, enriching the ecosystem.

Possible disadvantages

  • Complex Setup
    The initial setup, especially for self-hosted versions, can be complex and time-consuming, requiring technical expertise.
  • Resource Intensive
    Running Matomo, particularly its self-hosted version, can be resource-intensive, requiring significant server capabilities.
  • Cost for Advanced Features
    While the basic version is free, advanced features and cloud hosting come at a cost, which might be expensive for small businesses.
  • Limited Integrations
    Matomo offers fewer integrations with other marketing and analytics tools compared to some other platforms like Google Analytics.
  • Learning Curve
    New users may find the interface and advanced features challenging to learn and navigate initially.
  • Performance Issues
    Some users report performance issues, particularly with large volumes of data or on less powerful servers.
  • 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

  • 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

An editorial look at what each product does well and who it suits.

Matomo
NumPy

Overall verdict

  • Matomo is a good choice for those who prioritize data privacy and require a comprehensive analytics tool that can be self-hosted. It's especially suitable for organizations hesitant about sharing data with third-party services and for those looking for customizable and transparent analytics solutions.

Why this product is good

  • Matomo is a popular open-source web analytics platform that provides in-depth insights into website traffic, user behavior, conversion rates, and more. It's considered a robust alternative to Google Analytics, with a focus on data privacy, as users can host the platform on their own servers or use Matomo's cloud-based service. Matomo offers features such as heatmaps, session recordings, goal tracking, and more, which can be particularly valuable for businesses looking to gain a detailed understanding of their website performance while maintaining control over their data.

Recommended for

  • Privacy-conscious businesses
  • Web developers
  • Data analysts
  • E-commerce sites
  • Organizations with in-house IT capabilities
  • Government and educational institutions
  • Non-profits looking for cost-effective solutions

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.

Videos

Walkthroughs and reviews on video.

Matomo 5 videos + Add
NumPy 3 videos + Add

WP-Matomo (WP-Piwik) Review: Open Source Analytics For WordPress

More videos

  • - Matomo Analytics - Dashboards
  • - AMU WEBD122 - Spohnholtz Piwik Analytics Review
  • - What are the differences between Matomo Analytics and Google Analytics
  • - Matomo On-Premise installation overview

Learn NUMPY in 5 minutes - BEST Python Library!

More videos

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

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Matomo
NumPy
100% 100%
Analytics
0% 0%
0% 0%
Data Science And Machine Learning
100% 100%
100% 100%
Web Analytics
0% 0%
0% 0%
Data Science Tools
100% 100%

User comments

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

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Matomo 5.0 · 1 review
NumPy no reviews yet

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Matomo 86 mentions
NumPy 122 mentions

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