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NumPy VS Klipfolio

Compare NumPy VS Klipfolio and see what are their differences

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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Klipfolio logo Klipfolio

Klipfolio is an online dashboard platform for building powerful real-time business dashboards for your team or your clients.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Klipfolio Landing page
    Landing page //
    2023-07-15

  www.klipfolio.comSoftware by Klipfolio

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Release Date
-
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Klipfolio

Website
klipfolio.com
Pricing URL
Official Klipfolio Pricing
$ Details
-
Release Date
2001 January
Startup details
Country
Canada
State
Ontario
City
Ottawa
Founder(s)
Allan Wille
Employees
100 - 249
Edit details

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.

Klipfolio features and specs

  • User-Friendly Interface
    Klipfolio offers an intuitive and easy-to-navigate interface, which makes it accessible for users of varying technical expertise. The drag-and-drop feature simplifies the process of creating dashboards.
  • Customizable Dashboards
    Users can customize their dashboards extensively with various pre-built widgets and data visualization options. This makes it easier to tailor the dashboards to specific needs.
  • Wide Range of Data Sources
    Klipfolio supports integration with numerous data sources, including cloud services, spreadsheets, databases, and web services. This flexibility ensures that users can aggregate data from multiple platforms.
  • Real-Time Data Updates
    The platform supports real-time data updates, allowing users to make timely decisions based on the most current information available.
  • Collaboration Features
    Klipfolio offers collaboration features such as sharing dashboards with team members and stakeholders, which fosters better teamwork and data-driven decision-making.
  • Scalability
    Klipfolio can scale with the needs of small businesses to larger enterprises, providing a versatile solution for various organizational sizes.

Possible disadvantages of Klipfolio

  • Learning Curve
    While the interface is user-friendly, mastering the full range of features and customization options can take some time and effort.
  • Pricing
    For small businesses or startups, the cost of Klipfolio can be relatively high, especially when opting for advanced features and more extensive data integrations.
  • Performance Issues
    Some users have reported performance issues, particularly with large data sets or complex visualizations, which can slow down the dashboards.
  • Limited Offline Capabilities
    The platform primarily operates online, and its functionality is significantly limited without an active internet connection.
  • Advanced Customization Complexity
    While customization is one of Klipfolio's strengths, achieving highly-specific custom configurations may require a more advanced understanding of the platform and potentially some coding knowledge.
  • Customer Support
    Customer support response times can be slow, and the quality of support may vary, which can be a drawback for businesses that require immediate assistance.

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 Klipfolio

Overall verdict

  • Klipfolio is generally well-regarded as an effective and versatile business intelligence and dashboard tool, especially for small to medium-sized businesses seeking to streamline their data analytics processes.

Why this product is good

  • Klipfolio is considered a good tool due to its robust data visualization capabilities, allowing users to create interactive and customizable dashboards. It enables businesses to integrate multiple data sources, offering real-time data monitoring and analysis which supports better decision-making. Additionally, it has a user-friendly interface and various templates that make it accessible for non-technical users.

Recommended for

  • Small to medium-sized businesses
  • Marketing teams looking for real-time analytics
  • Financial analysts who need to consolidate multiple data sources
  • Managers who require quick insights through intuitive visualizations

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

Klipfolio videos

+ Add

This is Klipfolio

More videos:

  • Review - Databox vs Klipfolio: Analytics software comparison #MartechTuesday

Category Popularity

0-100% (relative to NumPy and Klipfolio)

NumPy

Klipfolio

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Data Dashboard
22 22%
Data Dashboard
78% 78
Data Science Tools
100 100%
Data Science Tools
0% 0
Business Intelligence
0 0%
Business Intelligence
100% 100

User comments

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Reviews

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

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

Klipfolio Reviews

Best Affordable Alternatives to Supermetrics
While Klipfolio’s flexibility is unparalleled, it does come with a flat learning curve, particularly for those who aren’t proficient in Excel or Google Sheets. On the other hand, if you have experience with either, it is a potent instrument for graphical data representation. If you work in an agency, constantly creating reports for clients, Klipfolio is a great tool....
Source: adsbot.co
8 Databox Alternatives: Which One Is The Best?
Klipfolio is an analysis tool made for upgrading the analysis of your company to a higher and more dynamic level. It can be considered as a modern BI platform aiming to provide better solutions to businesses. It is used by thousands of businesses.
Source: hockeystack.com
27 dashboards you can easily display on your office screen with Airtame 2
Used by more than 11,000 companies, and integrating data from Dropbox, Marketo, Moz, and so forth, our partner, Klipfolio makes sure that your web analytics, sales and finances, and project management goals are transparent throughout your company. I personally have a screen dedicated to Klipfolio dashboards in front of my desk and it really makes my life easier.
Source: airtame.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Klipfolio. While we know about 119 links to NumPy, we've tracked only 1 mention of Klipfolio. 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 (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 4 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 8 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

Klipfolio mentions (1)

  • What would you do if the upper management wants you to work with 30 excel files that are being used as database?
    If those excel files change over time, ask the maintainers to upload in to cloud and add all those excel files as data source in klipfolio (klipfolio.com) and create a dashboard there. Your creation will last forever (as long as the structures of those files last). Source: over 3 years ago

What are some alternatives?

When comparing NumPy and Klipfolio, 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.

Databox - Databox is an easy-to-use analytics platform that helps growing businesses centralize their data, and use it to make better decisions and improve performance.

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

Geckoboard - Get to know Geckoboard: Instant access to your most important metrics displayed on a real-time dashboard.

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

Grow - Grow is a business intelligence software that empowers businesses to become data-driven and...

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