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A little word cloud generator in Python

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Quick Overview

Word Cloud is a Python library for creating word cloud images from text data. It generates visually appealing representations of text where the size of each word indicates its frequency or importance. The library is easy to use and offers customization options for creating unique word cloud designs.

Pros

  • Simple and intuitive API for creating word clouds
  • Supports various input formats, including plain text and preprocessed word frequencies
  • Offers customization options for colors, fonts, and shapes
  • Integrates well with other data analysis and visualization libraries

Cons

  • Limited advanced features compared to some other text visualization tools
  • May struggle with very large datasets or complex layouts
  • Requires additional dependencies for certain functionalities (e.g., matplotlib for colormaps)
  • Documentation could be more comprehensive for advanced use cases

Code Examples

  1. Creating a basic word cloud:
from wordcloud import WordCloud
import matplotlib.pyplot as plt

text = "Python is a powerful programming language for data analysis and visualization"
wordcloud = WordCloud().generate(text)

plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
  1. Customizing the word cloud appearance:
from wordcloud import WordCloud
import matplotlib.pyplot as plt

wordcloud = WordCloud(width=800, height=400,
                      background_color='white',
                      min_font_size=10).generate(text)

plt.figure(figsize=(8, 4))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()
  1. Using a mask to create a shaped word cloud:
from wordcloud import WordCloud
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt

mask = np.array(Image.open("mask_image.png"))
wordcloud = WordCloud(width=800, height=400, background_color="white", mask=mask).generate(text)

plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation="bilinear")
plt.axis("off")
plt.show()

Getting Started

To get started with Word Cloud, follow these steps:

  1. Install the library:

    pip install wordcloud

  2. Import the necessary modules:

    from wordcloud import WordCloud
import matplotlib.pyplot as plt

  3. Create a word cloud from your text:

    text = "Your text here..."
wordcloud = WordCloud().generate(text)

  4. Display the word cloud:

    plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Competitor Comparisons

jasondavies logo

d3-cloud

3,823

Create word clouds in JavaScript.

Pros of d3-cloud

  • Browser-based rendering, allowing for interactive and dynamic word clouds
  • Seamless integration with D3.js for advanced data visualization capabilities
  • Supports custom fonts and layouts for more flexible design options

Cons of d3-cloud

  • Requires JavaScript knowledge and browser environment to use
  • May have performance limitations for very large datasets
  • Less straightforward for simple, static word cloud generation

Code Comparison

word_cloud:

from wordcloud import WordCloud
wordcloud = WordCloud().generate(text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

d3-cloud:

d3.layout.cloud()
  .size([800, 300])
  .words(words)
  .padding(5)
  .rotate(function() { return ~~(Math.random() * 2) * 90; })
  .font("Impact")
  .fontSize(function(d) { return d.size; })
  .on("end", draw)
  .start();

Summary

word_cloud is a Python library for generating static word clouds, ideal for data analysis and reporting. d3-cloud is a JavaScript library for creating interactive, browser-based word clouds, better suited for web applications and dynamic visualizations. The choice between them depends on the specific use case, programming language preference, and desired level of interactivity.

minimaxir logo

big-list-of-naughty-strings

46,126

The Big List of Naughty Strings is a list of strings which have a high probability of causing issues when used as user-input data.

Pros of big-list-of-naughty-strings

  • Comprehensive collection of edge-case strings for testing
  • Useful for security testing and input validation
  • Regularly updated with community contributions

Cons of big-list-of-naughty-strings

  • Limited functionality beyond providing a list of strings
  • Requires integration into existing testing frameworks
  • May not cover all possible edge cases for specific applications

Code comparison

word_cloud:

from wordcloud import WordCloud
import matplotlib.pyplot as plt

wordcloud = WordCloud().generate(text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")

big-list-of-naughty-strings:

with open('blns.txt', 'r') as f:
    naughty_strings = f.readlines()

for string in naughty_strings:
    test_input(string.strip())

Summary

word_cloud is a Python library for creating word cloud visualizations, while big-list-of-naughty-strings is a collection of strings for testing input validation and security. word_cloud offers more specific functionality for text visualization, whereas big-list-of-naughty-strings provides a valuable resource for testing edge cases in various applications. The choice between the two depends on the specific needs of the project: visualization vs. input testing.

timdream logo

wordcloud2.js

2,353

Tag cloud/Wordle presentation on 2D canvas or HTML

Pros of wordcloud2.js

  • Browser-based rendering, allowing for interactive and dynamic word clouds
  • Supports various shape options for the word cloud (e.g., circle, cardioid)
  • Lightweight and easy to integrate into web applications

Cons of wordcloud2.js

  • Limited customization options compared to word_cloud
  • May have performance issues with very large datasets
  • Lacks some advanced features like collocations and bigrams

Code Comparison

word_cloud (Python):

from wordcloud import WordCloud
wordcloud = WordCloud(width=800, height=400).generate(text)
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

wordcloud2.js (JavaScript):

WordCloud(document.getElementById('canvas'), { 
  list: [['foo', 12], ['bar', 6]],
  weightFactor: 1,
  fontFamily: 'Times, serif'
});

Summary

wordcloud2.js is ideal for web-based applications requiring interactive word clouds, while word_cloud offers more robust customization and processing capabilities for Python environments. The choice between the two depends on the specific project requirements and the preferred development ecosystem.

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README

licence DOI

word_cloud

A little word cloud generator in Python. Read more about it on the blog post or the website.

The code is tested against Python 3.7, 3.8, 3.9, 3.10, 3.11, 3.12.

Installation

If you are using pip:

pip install wordcloud

If you are using conda, you can install from the conda-forge channel:

conda install -c conda-forge wordcloud

Installation notes

wordcloud depends on numpy, pillow, and matplotlib.

If there are no wheels available for your version of python, installing the package requires having a C compiler set up. Before installing a compiler, report an issue describing the version of python and operating system being used.

Examples

Check out examples/simple.py for a short intro. A sample output is:

Constitution

Or run examples/masked.py to see more options. A sample output is:

Alice in Wonderland

Getting fancy with some colors: Parrot with rainbow colors

Generating wordclouds for Arabic:

Arabic wordlcloud

Command-line usage

The wordcloud_cli tool can be used to generate word clouds directly from the command-line:

$ wordcloud_cli --text mytext.txt --imagefile wordcloud.png

If you're dealing with PDF files, then pdftotext, included by default with many Linux distribution, comes in handy:

$ pdftotext mydocument.pdf - | wordcloud_cli --imagefile wordcloud.png

In the previous example, the - argument orders pdftotext to write the resulting text to stdout, which is then piped to the stdin of wordcloud_cli.py.

Use wordcloud_cli --help so see all available options.

Licensing

The wordcloud library is MIT licenced, but contains DroidSansMono.ttf, a true type font by Google, that is apache licensed. The font is by no means integral, and any other font can be used by setting the font_path variable when creating a WordCloud object.