NumPy VS Seaborn

Compare NumPy VS Seaborn and see what are their differences

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NumPy

NumPy is the fundamental package for scientific computing with Python

Seaborn

Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

Landing page //
2023-05-13

Landing page //
2023-10-20

NumPy

Website: numpy.org
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Seaborn

Website: seaborn.pydata.org
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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.

Seaborn features and specs

High-Level Interface
Seaborn provides a high-level interface for drawing attractive statistical graphics, simplifying the process of creating complex plots with just a few lines of code.
Integration with Pandas
Seaborn automatically works well with Pandas data structures, making it easy to visualize data directly from DataFrames without additional data manipulation.
Built-in Themes
Seaborn offers built-in themes and color palettes that allow users to quickly improve the aesthetics of their plots, making them more appealing and informative.
Statistical Plotting
Seaborn includes a wide array of statistical plots like heatmaps, violin plots, and box plots, which help in understanding data distribution and relationships.
Customization
It provides extensive options for customizing plots, giving users the flexibility to tailor their visualizations to specific needs and preferences.

Possible disadvantages of Seaborn

Dependence on Matplotlib
Seaborn is built on top of Matplotlib, and users may need to understand Matplotlib to handle more intricate customizations that Seaborn does not directly support.
Learning Curve
While Seaborn simplifies plotting, there is still a learning curve involved, especially for users unfamiliar with statistical data visualization.
Limited Interactivity
Seaborn primarily generates static plots, which may not provide the level of interactivity required for dynamic data exploration compared to other tools such as Plotly or Bokeh.
Performance
For very large datasets, Seaborn may become slow, and performance can be an issue compared to more optimized visualization libraries.
3D Plotting Support
Seaborn does not natively support 3D plotting, limiting its use for visualizations that require three-dimensional data representation.

NumPy videos

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Learn NUMPY in 5 minutes - BEST Python Library!

Seaborn videos

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Seaborn Review

Category Popularity

0-100% (relative to NumPy and Seaborn)

Seaborn

Data Science And Machine Learning

89 89%

Data Science And Machine Learning

11% 11

Data Science Tools

95 95%

Data Science Tools

5% 5

Development

0 0%

Development

100% 100

Python Tools

100 100%

Python Tools

0% 0

User comments

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

Reviews

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

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

Seaborn Reviews

5 Best Python Libraries For Data Visualization in 2023

Seaborn is working hard to make visualization a central part of understanding and exploring data. Its dataset-oriented plotting functions run on data frames carrying whole datasets. Seaborn internally performs the necessary semantic mapping and statistical aggregation to provide informative plots. Lastly, Seaborn is fully integrated with the PyData stack including support...

Source: www.technotification.com

Top 8 Python Libraries for Data Visualization

Seaborn is a Python data visualization library that is based on Matplotlib and closely integrated with the NumPy and pandas data structures. Seaborn has various dataset-oriented plotting functions that operate on data frames and arrays that have whole datasets within them. Then it internally performs the necessary statistical aggregation and mapping functions to create...

Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, NumPy should be more popular than Seaborn. It has been mentiond 119 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.

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

Seaborn mentions (37)

How I Hacked Uber’s Hidden API to Download 4379 Rides
Below are the key insights. If you want to see the Python code I used to do this analysis and generate the charts using Seaborn, you can find my full analysis Jupyter notebook on my Github repo here: Tip Analysis.ipynb. - Source: dev.to / 28 days ago
Scientific Visualization: Python and Matplotlib, by Nicolas Rougier
Additionally, Seaborn (https://seaborn.pydata.org/) is a great mention for people that want to use Matplotlib with better default aesthetics, amongst other conveniences: "Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.". - Source: Hacker News / 8 months ago
Data Visualisation Basics
Seaborn: built on top of matplotlib, adds a number of functions to make common statistical visualizations easier to generate. - Source: dev.to / 8 months ago
Useful Python Libraries for AI/ML
Pandas - The standard data analysis and manipulation tool Numpy - scientific computing library Seaborn - statistical data visualization Sklearn - basic machine learning and predictive analysis CausalML - a suite of uplift modeling and causal inference methods PyTorch - professional deep learning framework PivotTablejs - Drag’n’drop Pivot Tables and Charts for Jupyter/IPython Notebook LazyPredict - build... - Source: dev.to / 9 months ago
Essential Deep Learning Checklist: Best Practices Unveiled
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / 11 months ago