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  5. D3.js vs Pandas

D3.js vs Pandas

OverviewDecisionsComparisonAlternatives

Overview

D3.js
D3.js
Stacks2.0K
Followers1.7K
Votes653
GitHub Stars111.7K
Forks22.9K
Pandas
Pandas
Stacks2.1K
Followers1.3K
Votes23

D3.js vs Pandas: What are the differences?

Key Differences between D3.js and Pandas

D3.js and Pandas are both popular tools used in data analysis and visualization. However, they have some key differences that set them apart. Here are six specific differences between D3.js and Pandas:

  1. Data Processing and Manipulation: While both D3.js and Pandas allow for data processing and manipulation, they have different approaches. D3.js is a JavaScript library primarily focused on creating interactive data visualizations. It provides flexible tools for data manipulation and transformation. On the other hand, Pandas is a Python library that specializes in data manipulation and analysis. It offers extensive data structures and functions for working with structured data.

  2. Programming Language: D3.js is a JavaScript library, meaning it is used with JavaScript programming language. This makes it suitable for web-based data visualizations as it can directly interact with HTML and CSS. In contrast, Pandas is a Python library, making it ideal for data analysis and manipulation within a Python environment. It benefits from Python's rich ecosystem and other related libraries.

  3. Visualization Capabilities: D3.js is renowned for its powerful and flexible data visualization capabilities. It provides a wide range of options for creating interactive charts, graphs, maps, and other visualizations. On the contrary, Pandas focuses more on data analysis and manipulation rather than visualization. It does offer basic plotting functionality, but it may not have the same level of flexibility and customization as D3.js.

  4. Integration with Other Libraries: D3.js is designed to work seamlessly with other JavaScript libraries and frameworks. This makes it highly compatible and easily integratable within web applications. In contrast, Pandas is closely integrated with the Python ecosystem and works well with other libraries such as NumPy and Matplotlib. This allows for efficient and streamlined data analysis workflows.

  5. Learning Curve: D3.js has a steeper learning curve compared to Pandas. It requires a solid understanding of JavaScript and web development concepts such as HTML, CSS, and SVG (Scalable Vector Graphics). Pandas, being a Python library, is relatively easier to learn for those already familiar with Python programming and data analysis.

  6. Community and Support: Due to its popularity and extensive use, both D3.js and Pandas have large and active communities. However, D3.js has a larger community primarily centered around web development and data visualization. It has a vast number of online resources, tutorials, and community-driven projects. Pandas, being a specialized library, also has a strong community dedicated to data analysis and has good support from the broader Python community.

In summary, D3.js is a powerful JavaScript library focused on creating interactive data visualizations, while Pandas is a Python library primarily used for data analysis and manipulation. D3.js is more suitable for web-based visualizations and has a steeper learning curve, while Pandas offers extensive data processing capabilities within the Python ecosystem.

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Advice on D3.js, Pandas

Vinay
Vinay

Oct 10, 2020

Decided

We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.

5.82k views5.82k
Comments
cfvedova
cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

72.8k views72.8k
Comments
Yuchen
Yuchen

Oct 11, 2020

Decided

ML Model Training and Benchmarking

We choose python for ML and data analysis. Because:

  • Simple syntax and easy to use
  • ML Library and framework support

The python libraries and frameworks we choose for ML are:

  1. TensorFlow
  • High performance (GPU support/ highly parallel)
  • Easy to debug
  • visualization support
  1. Numpy
  • Easy matrix manipulation
  • datatype with high compatibility
  1. Pandas
  • High efficiency when handling large data
  • Dataset manipulation and customization
  1. Matplotlib
  • Simple and easy to use
12.5k views12.5k
Comments

Detailed Comparison

D3.js
D3.js
Pandas
Pandas

It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

Declarative Approach for Individual Nodes Manipulation; Functions Factory; Web Standards; Built-in ELement Inspector to Debug; Uses SVG, Canvas, and HTML; Data-driven approach to DOM Manipulation; Voronoi Diagrams; Maps and topo.
Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data;Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects;Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Statistics
GitHub Stars
111.7K
GitHub Stars
-
GitHub Forks
22.9K
GitHub Forks
-
Stacks
2.0K
Stacks
2.1K
Followers
1.7K
Followers
1.3K
Votes
653
Votes
23
Pros & Cons
Pros
  • 195
    Beautiful visualizations
  • 103
    Svg
  • 92
    Data-driven
  • 81
    Large set of examples
  • 61
    Data-driven documents
Cons
  • 11
    Beginners cant understand at all
  • 6
    Complex syntax
Pros
  • 21
    Easy data frame management
  • 2
    Extensive file format compatibility
Integrations
JavaScript
JavaScript
React Native
React Native
AngularJS
AngularJS
React
React
Bootstrap
Bootstrap
Python
Python

What are some alternatives to D3.js, Pandas?

Highcharts

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

Plotly.js

Plotly.js

It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.

Chart.js

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

ZingChart

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

amCharts

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.

ApexCharts

ApexCharts

A modern JavaScript charting library to build interactive charts and visualizations with simple API.

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