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Pandas VS SHARK

Compare Pandas VS SHARK and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Pandas logo Pandas

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

SHARK logo SHARK

See sharks everywhere with this AR app 🦈
  • Pandas Landing page
    Landing page //
    2023-05-12
  • SHARK Landing page
    Landing page //
    2020-02-11

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

SHARK features and specs

  • Versatility
    SHARK (Sophisticated High-dimensional Additive Regression toolkit) supports a wide range of machine learning algorithms, including regression, classification, clustering, and optimization algorithms. This makes it a versatile tool for various types of machine learning tasks.
  • Modular Design
    The library has a modular design which allows users to use just the components they need. This modularity helps in efficiently managing and optimizing resources.
  • Performance
    SHARK is designed for high performance, with many algorithms optimized for speed and efficiency. It can handle large datasets and complex computations relatively quickly.
  • Open Source
    Being an open-source project, SHARK is freely available for use and modification. This fosters a collaborative environment where users can contribute to and improve the toolkit.
  • Documentation
    SHARK provides comprehensive documentation, including tutorials and API references. This makes it easier for users to understand and implement its functionalities.

Possible disadvantages of SHARK

  • Steep Learning Curve
    Despite the good documentation, SHARK can have a steep learning curve, especially for beginners who are new to machine learning or to the specifics of this library.
  • Limited Community Support
    SHARK does not have as large a user community as other popular machine learning libraries like TensorFlow or scikit-learn. This can make it more challenging to find help and resources online.
  • Lack of Integration
    There are fewer third-party integrations available for SHARK compared to more widely-used libraries. This might limit its interoperability with other tools or platforms commonly used in machine learning workflows.
  • Maintenance and Updates
    As with many open-source projects, the frequency and reliability of updates can be variable. Users might face issues if the toolkit is not actively maintained or updated to fix bugs and improve features.

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

SHARK videos

+ Add

Marine Biologist Breaks Down Shark Attack Scenes from Movies | GQ

More videos:

  • Review - Shark vacuum cleaner test and review
  • Review - Marine Scientist Reviews Shark Attack Scenes, from 'Jaws' to 'Open Water' | Vanity Fair

Category Popularity

0-100% (relative to Pandas and SHARK)

Pandas

SHARK

Data Science And Machine Learning
87 87%
Data Science And Machine Learning
13% 13
Data Science Tools
86 86%
Data Science Tools
14% 14
Python Tools
82 82%
Python Tools
18% 18
Data Dashboard
100 100%
Data Dashboard
0% 0

User comments

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Reviews

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

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

SHARK Reviews

We have no reviews of SHARK yet.
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Social recommendations and mentions

Based on our record, Pandas seems to be more popular. It has been mentiond 219 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.

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 3 days ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 19 days ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 23 days ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 8 months ago
View more

SHARK mentions (0)

We have not tracked any mentions of SHARK yet. Tracking of SHARK recommendations started around Mar 2021.

What are some alternatives?

When comparing Pandas and SHARK, you can also consider the following products

NumPy - NumPy is the fundamental package for scientific computing with Python

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

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

Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

htm.java - htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.

Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

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