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Create beautiful tag clouds as images or HTML

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atizo/PyTagCloud

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README.rst

PyTagCloud let you create simple tag clouds inspired by http://www.wordle.net/

Currently, the following output formats have been written and are working:

If you have ideas for other formats please let us know.

You can install PyTagCloud either via the Python Package Index (PyPI) or from source.

To install using easy_install:

$ easy_install -U pytagcloud

Downloading and installing from source

Download the latest version of PyTagCloud from http://pypi.python.org/pypi/pytagcloud/

You can install it by doing the following,:

$ tar xfz pytagcloud-*.tar.gz $ cd pytagcloud-*/ $ python setup.py build $ python setup.py install # as root

$ apt-get install python-pygame

You probably want to see some code by now, so here’s an example:

from pytagcloud import create_tag_image, make_tags from pytagcloud.lang.counter import get_tag_counts YOUR_TEXT = "A tag cloud is a visual representation for text data, typically\ used to depict keyword metadata on websites, or to visualize free form text." tags = make_tags(get_tag_counts(YOUR_TEXT), maxsize=80) create_tag_image(tags, 'cloud_large.png', size=(900, 600), fontname='Lobster') import webbrowser webbrowser.open('cloud_large.png') # see results

More examples can be found in test.py.

Development of pytagcloud happens at Github: https://github.com/atizo/PyTagCloud

You are highly encouraged to participate in the development of pytagcloud. If you don’t like Github (for some reason) you’re welcome to send regular patches.

About

Create beautiful tag clouds as images or HTML

Источник

Creating Word Clouds with Python Libraries

Learn how to create word clouds with the help of WordCloud, Matplotlib, and Pandas libraries.

Contents

A word cloud (or tag cloud) is a figure filled with words in different sizes, which represent the frequency or the importance of each word. It’s useful if you want to explore text data or make your report livelier.

Prerequisites

To create a word cloud, we’ll need the following:

• Python installed on your machine
• Pip: package management system (it comes with Python)
• Jupyter Notebook: an online editor for data visualization
• Pandas: a library to prepare data for plotting
• Matplotlib: a plotting library
• WordCloud: a word cloud generator library

You can download the latest version of Python for Windows on the official website.

To get other tools, you’ll need to install recommended Scientific Python Distributions. Type this in your terminal:

 pip install numpy scipy matplotlib ipython jupyter pandas sympy nose wordcloud 

Getting Started

Create a folder that will contain your notebook (e.g. “wordcloud”) and open Jupyter Notebook by typing this command in your terminal (don’t forget to change the path):

 cd C:\Users\Shark\Documents\code\wordcloud py -m notebook 

This will automatically open the Jupyter home page at http://localhost:8888/tree. Click on the “New” button in the top right corner, select the Python version installed on your machine, and a notebook will open in a new browser window.

In the first line of the notebook, import all the necessary libraries:

 from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt import pandas as pd %matplotlib notebook 

You need the last line — %matplotlib notebook — to display plots in input cells.

Data Preparation

We’ll create a word cloud showing the most commonly used words in Nightwish lyrics, a symphonic metal band from Finland. We’ll create a word cloud from a .csv file, which you can download on Kaggle (nightwish_lyrics.csv).

On the second line in your Jupyter notebook, type this code to read the file:

 df = pd.read_csv('nightwish_lyrics.csv') df.head() 

This will show the first 5 lines of the .csv file:

For plotting, we’ll use the first column ( data[‘lyric’] ) with lines from song lyrics.

Plotting

We’ll create a word cloud in several steps.

1. Set stop words

 comment_words = '' stopwords = set(STOPWORDS) stopwords = ['nan', 'NaN', 'Nan', 'NAN'] + list(STOPWORDS) 

The last line is optional. Add NaN values to the stop list if your file or array contains them.

2. Iterate through the .csv file

 values = df['lyric'].values for val in values: val = str(val) tokens = val.split() for i in range(len(tokens)): tokens[i] = tokens[i].lower() comment_words += ' '.join(tokens)+' ' 

Here we convert each val (lyric line) to string, split the values (words), and convert each token into a lowercase.

If you’re generating a word cloud not from a .csv file but from a list of words only, you don’t need this part. You can set comment_words and then use the WordCloud() function.

By the way, if you want to get a list of the most frequent words in a text, you can use this Python code:

 text = """. """ # your text text.split() count = <> for word in text.split(): count.setdefault(word, 0) count[word] += 1 list_count = list(count.items()) list_count.sort(key=lambda i: i[1], reverse=True) for i in list_count: print(i[0], ':', i[1]) 

3. Generate a word cloud

 facecolor = 'black' wordcloud = WordCloud(width=1000, height=600, background_color=facecolor, stopwords=stopwords, min_font_size=10).generate(comment_words) 

4. Plot the WordCloud image

 plt.figure(figsize=(10,6), facecolor=facecolor) plt.imshow(wordcloud) plt.axis('off') plt.tight_layout(pad=2) 

We’re creating a 1000 × 600 px image so we set figsize=(10,6) . This is the size of the figure that can be larger than the word cloud itself. In our code, they’re equal as we previously set WordCloud(width=1000, height=600) . In addition, pad=2 allows us to set padding to our figure.

5. Save the chart as an image

 filename = 'wordcloud' plt.savefig(filename+'.png', facecolor=facecolor) 

You might need to repeat facecolor in savefig() . Otherwise, plt.savefig might ignore it.

That’s it, our word cloud is ready. You can download the notebook on GitHub to get the full code.

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License

roshansingh/python-tagcloud

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README.md

A tag cloud image generator in python. Inspired from jqcloud.

You will need PIL to run this code. To install pil simply run pip install pil

t = TagCloud() words = [,] print t.draw(words)` 

The list words should be sorted on weight before using, and the given format should be used.

It takes the list as input and normalizes the weights. Then it calculates the size of the word that will be drawn based on the weight. It then places the word on the center of the photo and then keeps moving it in a circle and gradually increasing the radius till it finds a place where it does not collide with any other word.

Once all the positions are calculated, the photo is written to a file.

You can confiure the font, color schemes and font size.

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