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NumPy VS Whatagraph

Compare NumPy VS Whatagraph and see what are their differences

NumPy

NumPy is the fundamental package for scientific computing with Python

Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
Whatagraph

Whatagraph is the most visual multi-source marketing reporting platform. Built in collaboration with digital marketing agencies

Rating
0 reviews
Alternatives Is it good?
Data Analytic System
Sourced crypto market data with indicator monitoring, market regime context and research. BTC, ETH, XRP, SOL + major indices monitored 24/7. Educational content — not investment advice. sponsored
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?

Based on our record, NumPy seems to be a lot more popular than Whatagraph. While we know about 122 links to NumPy, we've tracked only 4 mentions of Whatagraph.

social mentions
122 vs 4
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
189 vs 240+

Base details

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

NumPy
Whatagraph
Website numpy.org whatagraph.com
Pricing
Open source
Official pricing
Company — Startup from The Netherlands · 10 - 19 employees
Listed in
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1
Business Intelligence Data Dashboard Data Visualization Data Analytics +1

Features and specs

What each product offers, as listed by its team.

NumPy 5 features
Whatagraph 6 features
  • 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.
  • User-Friendly Interface
    Whatagraph's intuitive design makes it easy for users, even those without technical expertise, to create and understand comprehensive reports.
  • Customization
    Offers extensive customization options for reports, allowing users to tailor them to specific needs and branding requirements.
  • Integrations
    Seamlessly integrates with popular marketing tools and platforms such as Google Analytics, Facebook, and Mailchimp, providing a centralized reporting solution.
  • Automation
    Enables automated reporting, saving time and ensuring that reports are consistently delivered on schedule.
  • Collaboration
    Facilitates collaboration by allowing multiple users to access and edit reports, streamlining team workflows.
  • Visual Appeal
    Produces visually appealing, professional reports that can enhance presentations and client communications.

Possible disadvantages

  • Pricing
    Whatagraph may be considered expensive for small businesses or startups due to its subscription model.
  • Learning Curve
    While relatively user-friendly, some users may experience a learning curve when first starting out with the platform.
  • Template Limitations
    Some users have reported limited flexibility in template designs, which may not suit highly specific reporting needs.
  • Data Sync Delays
    There can be occasional delays in data syncing from integrated platforms, which might affect the timeliness of reports.
  • Customer Support
    Some users have indicated that customer support can be slow to respond or not as helpful as desired.

Analysis

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

NumPy
Whatagraph

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.

Overall verdict

  • Whatagraph is generally considered a good solution for marketing teams that need to consolidate and simplify their reporting processes. Its intuitive interface and robust features make it an attractive option for both small businesses and larger enterprises looking to enhance their data-driven decision-making.

Why this product is good

  • Whatagraph is a marketing reporting tool that aggregates data from multiple sources and presents it in visually appealing formats. It's highly valued for its ease of use, customization options, and the ability to automate report creation, saving marketing teams significant time. The platform supports integration with a wide range of marketing tools, which allows for comprehensive reporting across different channels and metrics.

Recommended for

  • Marketing agencies looking for a streamlined reporting solution
  • Businesses seeking to automate and customize their marketing reports
  • Teams that require integration across multiple marketing platforms
  • Professionals who value visually appealing and easy-to-understand reports

Videos

Walkthroughs and reviews on video.

NumPy 3 videos + Add
Whatagraph 3 videos + Add

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

Top 4 Whatagraph Features Released in 2019

More videos

  • - Whatagraph Reviews - Honest thoughts after using the whatagraph tool (whatagraph review)
  • - whatagraph review - Everything You Need To Know About The Tool (whatagraph review 2019)

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
NumPy
Whatagraph
100% 100%
Data Science And Machine Learning
0% 0%
24% 24%
Data Dashboard
76% 76%
100% 100%
Data Science Tools
0% 0%
0% 0%
Business Intelligence
100% 100%

User comments

Share your experience with using NumPy and Whatagraph. 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.

NumPy no reviews yet
Whatagraph no reviews yet

View more

  • 8 Databox Alternatives: Which One Is The Best?
    hockeystack.com · Dec 2021

    Customers mainly use Whatagraph for tracking campaign results from various channels. The platform provides visualizations, reports, and data insights in the manner of leading your company’s success. It offers some...

  • 25 Best Reporting Tools for 2022
    hevodata.com · Nov 2021

    Whatagraph is known as a reporting tool that allows you to compare and monitor the performance of various campaigns. It also allows you to transfer custom data from API and Google Sheets.

Social recommendations and mentions

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

NumPy 122 mentions
Whatagraph 4 mentions

View more

  • Linking visibility and positions data in google data studio
    I recommend pulling this easily into whatagraph.com through drag & drop functionality. Amazing integration depth, also! Source: over 5 years ago
  • Does this tool exist?
    Try whatagraph.com. Should do the job for you. Source: over 5 years ago
  • V2.0 of Google Data Studio
    Hey everyone, Just like the title says that's what Whatagraph.com is - those of you who are looking to significantly improve your data aggregation, visualization, and reporting capabilities, I would love to invite you to our webinar... Source: over 5 years ago

View more

Alternatives to NumPy and Whatagraph

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

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