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Pandas VS Ceph

Compare Pandas VS Ceph and see what are their differences

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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
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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.

Ceph logo Ceph

Ceph is a distributed object store and file system designed to provide excellent performance...
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Ceph Landing page
    Landing page //
    2022-04-16

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.

Ceph features and specs

  • Scalability
    Ceph is designed to scale horizontally by adding more nodes. This allows for seamless expansion of storage capacity as needs grow.
  • High Availability
    Ceph provides high availability and fault tolerance through its distributed architecture and data replication methods, ensuring data is always accessible.
  • Open Source
    Being an open-source project, Ceph has a large community of developers and users which help in rapid identification and rectification of issues. It also offers lower cost of ownership compared to proprietary solutions.
  • Versatility
    Ceph supports block storage, object storage, and file systems within the same cluster, providing great flexibility and reducing the need for multiple storage solutions.
  • Performance
    Ceph delivers high performance, particularly for large-scale deployments, by balancing loads and efficiently distributing data.

Possible disadvantages of Ceph

  • Complexity
    Setting up and maintaining a Ceph cluster can be complex and requires skilled administrators, which might not be suitable for smaller organizations.
  • Resource Intensive
    Ceph can be resource-heavy, demanding significant CPU, memory, and network resources, which can be a limitation for smaller setups.
  • Documentation
    Despite a rich set of features, Ceph’s documentation can sometimes be lacking or difficult for new users to comprehend, potentially leading to longer learning curves.
  • Hardware Requirements
    Ceph typically requires high-quality, enterprise-grade hardware to achieve optimal performance and reliability, which can entail a higher upfront investment.
  • Operational Overhead
    Day-to-day management, monitoring, and troubleshooting of Ceph clusters require a specialized skill set, leading to possible increases in operational overhead.

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

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

Ceph videos

+ Add

UDS 2013-03: Ceph Review - Part 1/2

More videos:

  • Review - Designing for High Performance Ceph at Scale
  • Review - RHCS 4 Cockpit Ceph Installer

Category Popularity

0-100% (relative to Pandas and Ceph)

Pandas

Ceph

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Cloud Storage
0 0%
Cloud Storage
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Storage
0 0%
Storage
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 Pandas and Ceph

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

Ceph Reviews

Simplyblock as alternative to Ceph: A Comprehensive Comparison
Ceph utilizes its own storage driver (rbd) that is integrated into the Linux Kernel and can also be used on other platforms as a third-party driver. It enables seamless connectivity between hosts and the Ceph cluster. In addition to OpenStack, Ceph offers deep integrations with Kubernetes through a separate CSI driver, as well as other platforms.
Source: www.simplyblock.io
Best & Cheapest Object Storage Providers With S-3 Support
The libraries of Ceph support applications built in Java, C, C++, PHP, Python, and other languages. It also gives these apps access to its object storage platform via a native API.
Source: macpost.net
What are the alternatives to S3?
Ceph is a software-defined storage platform that implements object storage. Its interface is built with the same storage system that provides the librados interface, making it have the same abilities as librados like read-only snapshot and revert to snapshot. The software delivers Object, File, and Block storage in a single, unified system. Ceph is S3 compatible, and its...
Source: www.w6d.io
Ceph Storage Platform Alternatives in 2022
Open-Source software platforms are not free but you can use them as community edition or with limited features. The above storage platforms have same goals but also have some different abilities and capabilities, so choosing or using them is depended to your requirements and budget. About Ceph, I think that Ceph is still the best and there is no limitation for community...
Source: www.teimouri.net
15 FreeNAS Alternatives 2020 | Best Storage Operating System
PetaSAN is a Ceph-based iSCSI cluster, open-source FreeNAS alternative, known widely for its end-to-end integrated solution and scale-out SAN arrangement that offers impressive adaptability and execution. Its latest cloud storage technology makes it corporate-efficient to manage large data storage in one unit; run on the Linux operating system, the program has many nodes...
Source: solutionsuggest.com

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than Ceph. While we know about 219 links to Pandas, we've tracked only 11 mentions of Ceph. 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 / about 1 month 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 / about 2 months 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 / about 2 months 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 / 4 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 / 9 months ago
View more

Ceph mentions (11)

  • 10 open source tools that platform, SRE and DevOps engineers should consider in 2024.
    Ceph stands out in storage technology, offering a scalable and reliable solution where traditional systems fall short. It supports object, block, and file storage in one system, adaptable for various environments including on-premises, cloud, or container-native setups. Key benefits include scalability, enabled by the CRUSH algorithm, allowing for expansion without typical downtime. This makes Ceph suitable for... - Source: dev.to / over 1 year ago
  • iSCSI over WAN / backup of remote site
    With that being said, you better take a look at something more WAN optimized and more secure, like S3 storage. You can build the S3 storage (and gain immutability) using something like MinIO (https://min.io/) or Ceph (https://ceph.io/en/) or check out Object First Ootbi offerings - https://objectfirst.com/object-storage/ (I work for them). Source: almost 2 years ago
  • What's the best AWS S3 protocol alternative?
    I believe Ceph [1] could be a good alternative. It can be self hosted and I believe some cloud providers also offer it. Here are some differences between S3 and Ceph [2]. [1] - https://ceph.io/en/ [2] - https://www.lightbitslabs.com/blog/ceph-storage/. - Source: Hacker News / about 2 years ago
  • Seeking Advice & Opinions: Hybrid NAS/Cloud Storage for Family Use
    Another option is a distributed Ceph cluster https://ceph.io/en/. Source: over 2 years ago
  • First Time NAS buyer for Digital Textile Printing Factory
    There's also cool systems like https://ceph.io/en/ that could be efficient if willing to set up and learn. Source: almost 3 years ago
View more

What are some alternatives?

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

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

Minio - Minio is an open-source minimal cloud storage server.

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

GlusterFS - GlusterFS is a scale-out network-attached storage file system.

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

StorPool - StorPool is designed from the ground up to provide cloud builders, shared hosting providers and MSPs with the most resource efficient storage software on the market.

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