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Pandas VS Plotly.js

Compare Pandas VS Plotly.js and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป 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.

Plotly.js logo Plotly.js

Open-source JavaScript charting library behind Plotly and Dash - plotly/plotly.js
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Plotly.js Landing page
    Landing page //
    2023-09-26

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.

Plotly.js features and specs

  • Interactive Visualizations
    Plotly.js provides highly interactive charting capabilities, allowing users to hover, zoom, and pan easily within charts, enhancing the user experience.
  • Wide Range of Chart Types
    The library supports a comprehensive variety of chart types, from simple line and bar charts to more complex types like histograms, scatter plots, and even 3D and geographic plots.
  • Cross-Platform Compatibility
    Plotly.js works seamlessly across different platforms and browsers, ensuring consistent chart rendering and functionality whether used on desktops or mobile devices.
  • Customizable
    Users have a high degree of control over the appearance and behavior of plots, with numerous options to customize colors, legends, tooltips, and more.
  • Integration with Dash
    Plotly.js integrates well with the Dash framework, enabling the creation of interactive web applications that are highly visual and data-driven.

Possible disadvantages of Plotly.js

  • Performance Concerns
    Rendering very large datasets can be slow, potentially impacting the performance of the application, particularly in resource-constrained environments.
  • Complexity
    For users new to the library, the learning curve can be steep due to the extensive options and configurations available.
  • Size of Library
    Plotly.js has a relatively large file size, which can be a concern for web applications where minimizing load times and data transfer is critical.
  • Limited Free Features
    Certain advanced features and functionalities may require a paid subscription to Plotly's services, which may not be ideal for all users or projects.
  • Dependency Management
    Managing dependencies and ensuring compatibility with other JavaScript libraries can sometimes pose challenges, especially in complex applications.

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

Plotly.js videos

+ Add

[Beginner] Simple Bar Chart | React Plotly.js

More videos:

  • Tutorial - Create Real-time Chart with Javascript | Plotly.js Tutorial

Category Popularity

0-100% (relative to Pandas and Plotly.js)

Pandas

Plotly.js

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Libraries
0 0%
Libraries
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 Plotly.js

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

Plotly.js Reviews

15 JavaScript Libraries for Creating Beautiful Charts
Plotly.js is the first scientific JavaScript charting library for the web. It has been open-source since 2015, meaning anyone can use it for free. Plotly.js supports 20 chart types, including SVG maps, 3D charts, and statistical graphs. Itโ€™s built on top of D3.js and stack.gl.
Source: www.sitepoint.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Plotly.js is a high-level JavaScript library, free and open-source. It is built on D3.js and WebGL, so can be used to create many different chart types including 3D charts to statistical graphs.
Source: hackernoon.com

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than Plotly.js. While we know about 231 links to Pandas, we've tracked only 4 mentions of Plotly.js. 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 (231)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / 3 months ago
  • What Training Exists for Security Professionals Learning AI and Data Science?
    For early-career security practitioners (0-3 years). Start with Python literacy if you do not have it. The free Python Crash Course book and the pandas getting-started guide are enough to bootstrap. Then a hands-on applied course: GTK Cyber's Applied Data Science & AI for Cybersecurity and SANS SEC595 are both reasonable starting points. The goal at this stage is to be able to load a Zeek conn.log into a pandas... - Source: dev.to / 3 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Evaluate the Options
    Python and data engineering for security data. Pandas for ingesting Zeek, Sysmon, EDR, and SIEM exports. Timestamp normalization to UTC, join keys across heterogeneous sources, feature extraction from raw logs. Without this layer, the ML content downstream is theater. - Source: dev.to / 3 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 3 months ago
  • Introduction to Python for Data Analysis: A Beginnerโ€™s Guide
    Pandas url is the most widely used library for data manipulation. - Source: dev.to / 3 months ago
View more

Plotly.js mentions (4)

  • Weekly JavaScript Roundup: Friday Links 17, February 07, 2025
    Plotly.js - Open-source JavaScript charting library behind Plotly and Dash. - Source: dev.to / over 1 year ago
  • Ask HN: What packages can be used to create interactive mathematics simulations?
    Well, MathML[1] support is (nearly) everywhere now, and as the docs say: MathML Core is a subset with increased implementation details based on rules from LaTeX and the Open Font Format. It is tailored for browsers and designed specifically to work well with other web standards including HTML, CSS, DOM, JavaScript. I don't have a lot of experience working with this stuff (yet) but if you can script your... - Source: Hacker News / about 3 years ago
  • What's new in Matplotlib 3.7.0 (Feb 13, 2023)
    Plotly offers multiple options (python, R, javascript). The weby stuff is done with plotly.js and uses d3.js underneath - https://github.com/plotly/plotly.js. - Source: Hacker News / over 3 years ago
  • How to decide between Dash versus Flask + React + Plotly.js?
    So you didn't use Django DRF as the backend? I'm just curious how Dash communicated with Django - did it communicate via plain HTTP calls? I guess you ran non-React Plotly.js (https://github.com/plotly/plotly.js)? Source: about 5 years ago

What are some alternatives?

When comparing Pandas and Plotly.js, you can also consider the following products

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

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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

Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application

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

Chart.js - Easy, object oriented client side graphs for designers and developers.

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