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Google Ad Manager VS NumPy

Compare Google Ad Manager VS NumPy and see what are their differences

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Google Ad Manager logo Google Ad Manager

Grow revenue wherever your users are with an integrated ad management platform that surfaces insights for smarter business decisions.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Google Ad Manager Landing page
    Landing page //
    2022-10-02
  • NumPy Landing page
    Landing page //
    2023-05-13

Google Ad Manager features and specs

  • Comprehensive Ad Management
    Google Ad Manager integrates various facets of ad placement, trafficking, and reporting into a single platform, making it easier for publishers to manage their advertisements across multiple channels.
  • Advanced Targeting Capabilities
    It offers advanced targeting options based on factors like demographics, interests, and behaviors, allowing for personalized ad experiences and increased relevance for the audience.
  • Robust Reporting Tools
    Google Ad Manager provides detailed reporting and analytics, helping users measure performance, track revenue, and optimize their ad strategies in real-time.
  • Scalability
    Suitable for organizations of any size, from small businesses to large enterprises, making it a versatile solution that can grow with your business needs.
  • Integration with Google Ecosystem
    Seamlessly integrates with other Google products like Google Analytics and Google Marketing Platform, creating a cohesive digital marketing ecosystem.

Possible disadvantages of Google Ad Manager

  • Complexity
    The platform can be overwhelming for beginners due to its comprehensive features and interface, requiring time and effort to fully understand and utilize.
  • Cost
    While it offers a free tier, advanced features and higher usage levels can lead to significant costs, which might not be suitable for small businesses with limited budgets.
  • Learning Curve
    Even experienced marketers may face a steep learning curve when initially using the platform, which may require additional training or support.
  • Data Privacy Concerns
    Given its extensive data collection and targeting capabilities, there may be concerns regarding data privacy and compliance with global regulations like GDPR and CCPA.
  • Dependence on Google Ecosystem
    Heavy reliance on the Google ecosystem could be a drawback for those looking to diversify their digital marketing tools or who have concerns about vendor lock-in.

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.

Google Ad Manager videos

+ Add

Google Ad Manager reporting integration

More videos:

  • Review - Troubleshoot bad ads - Review and Manage Ads in Google Ad Manager- Google Ad Manager course 2020
  • Review - Opportunities And Experiments In Google Ad Manager

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 Google Ad Manager and NumPy)

Google Ad Manager

NumPy

Ad Networks
100 100%
Ad Networks
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Advertising
100 100%
Advertising
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Google Ad Manager 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 Google Ad Manager and NumPy

Google Ad Manager 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 seems to be a lot more popular than Google Ad Manager. While we know about 119 links to NumPy, we've tracked only 4 mentions of Google Ad Manager. 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.

Google Ad Manager mentions (4)

  • What is Prebid.js & how to debug it using Requestly!
    Prebid.js is an open-source header bidding wrapper that enables publishers to conduct auctions for their ad inventory across multiple demand sources. It integrates seamlessly with ad servers like Google Ad Manager, allowing publishers to increase competition and, consequently, ad revenue. - Source: dev.to / 9 months ago
  • (need tool) How to better sell ad spaces to my direct ad customers?
    I read somewhere that Google Ad Manager can solve my Problems. Is that true? Can someone send me a link of their documentation where they show this feature? Because I don't understand really Google Ad Manager, I saw their website https://admanager.google.com/home/ but it feels like AdSense, just for businesses who have more ad space with different interest groups and so on with better ad network Management or... Source: over 3 years ago
  • 15 Million page views but rejected from GAM
    Thanks for that info! Yes, I filled out a form at https://admanager.google.com/home/ and that's when this Google person reached out to us. Source: almost 4 years ago
  • 15 Million page views but rejected from GAM
    You sure you just signed up at https://admanager.google.com/home/ ? Just register an Adsense account, then a free GAM account and you should be good to go. Source: almost 4 years ago

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 / 3 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 / 7 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 / 8 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 / 9 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 / 9 months ago
View more

What are some alternatives?

When comparing Google Ad Manager and NumPy, you can also consider the following products

OpenX - Ad technology platform available as a hosted service or as an open source download.

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

Kevel - Kevel's APIs make it easy for engineers and PMs to quickly launch a fully-customized, white-labeled, server-side ad server.

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

AdSense - Earn money with website monetization from Google AdSense. We'll optimize your ad sizes to give them more chance to be seen and clicked.

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

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