wordcloud.WordCloud

class wordcloud.WordCloud(font_path=None, width=400, height=200, margin=2, ranks_only=None, prefer_horizontal=0.9, mask=None, scale=1, color_func=None, max_words=200, min_font_size=4, stopwords=None, random_state=None, background_color='black', max_font_size=None, font_step=1, mode='RGB', relative_scaling=0.5, regexp=None, collocations=True, colormap=None, normalize_plurals=True)[source]

Word cloud object for generating and drawing.

Parameters:

font_path : string

Font path to the font that will be used (OTF or TTF). Defaults to DroidSansMono path on a Linux machine. If you are on another OS or don’t have this font, you need to adjust this path.

width : int (default=400)

Width of the canvas.

height : int (default=200)

Height of the canvas.

prefer_horizontal : float (default=0.90)

The ratio of times to try horizontal fitting as opposed to vertical. If prefer_horizontal < 1, the algorithm will try rotating the word if it doesn’t fit. (There is currently no built-in way to get only vertical words.)

mask : nd-array or None (default=None)

If not None, gives a binary mask on where to draw words. If mask is not None, width and height will be ignored and the shape of mask will be used instead. All white (#FF or #FFFFFF) entries will be considerd “masked out” while other entries will be free to draw on. [This changed in the most recent version!]

scale : float (default=1)

Scaling between computation and drawing. For large word-cloud images, using scale instead of larger canvas size is significantly faster, but might lead to a coarser fit for the words.

min_font_size : int (default=4)

Smallest font size to use. Will stop when there is no more room in this size.

font_step : int (default=1)

Step size for the font. font_step > 1 might speed up computation but give a worse fit.

max_words : number (default=200)

The maximum number of words.

stopwords : set of strings or None

The words that will be eliminated. If None, the build-in STOPWORDS list will be used.

background_color : color value (default=”black”)

Background color for the word cloud image.

max_font_size : int or None (default=None)

Maximum font size for the largest word. If None, height of the image is used.

mode : string (default=”RGB”)

Transparent background will be generated when mode is “RGBA” and background_color is None.

relative_scaling : float (default=.5)

Importance of relative word frequencies for font-size. With relative_scaling=0, only word-ranks are considered. With relative_scaling=1, a word that is twice as frequent will have twice the size. If you want to consider the word frequencies and not only their rank, relative_scaling around .5 often looks good.

color_func : callable, default=None

Callable with parameters word, font_size, position, orientation, font_path, random_state that returns a PIL color for each word. Overwrites “colormap”. See colormap for specifying a matplotlib colormap instead.

regexp : string or None (optional)

Regular expression to split the input text into tokens in process_text. If None is specified, r"\w[\w']+" is used.

collocations : bool, default=True

Whether to include collocations (bigrams) of two words.

colormap : string or matplotlib colormap, default=”viridis”

Matplotlib colormap to randomly draw colors from for each word. Ignored if “color_func” is specified.

normalize_plurals : bool, default=True

Whether to remove trailing ‘s’ from words. If True and a word appears with and without a trailing ‘s’, the one with trailing ‘s’ is removed and its counts are added to the version without trailing ‘s’ – unless the word ends with ‘ss’.

Notes

Larger canvases with make the code significantly slower. If you need a large word cloud, try a lower canvas size, and set the scale parameter.

The algorithm might give more weight to the ranking of the words than their actual frequencies, depending on the max_font_size and the scaling heuristic.

Attributes

words_ (dict of string to float) Word tokens with associated frequency. .. versionchanged: 2.0 words_ is now a dictionary
layout_ (list of tuples (string, int, (int, int), int, color))) Encodes the fitted word cloud. Encodes for each word the string, font size, position, orientation and color.

Methods

fit_words(frequencies) Create a word_cloud from words and frequencies.
generate(text) Generate wordcloud from text.
generate_from_frequencies(frequencies[, ...]) Create a word_cloud from words and frequencies.
generate_from_text(text) Generate wordcloud from text.
process_text(text) Splits a long text into words, eliminates the stopwords.
recolor([random_state, color_func, colormap]) Recolor existing layout.
to_array() Convert to numpy array.
to_file(filename) Export to image file.
to_html()
to_image()
__init__(font_path=None, width=400, height=200, margin=2, ranks_only=None, prefer_horizontal=0.9, mask=None, scale=1, color_func=None, max_words=200, min_font_size=4, stopwords=None, random_state=None, background_color='black', max_font_size=None, font_step=1, mode='RGB', relative_scaling=0.5, regexp=None, collocations=True, colormap=None, normalize_plurals=True)[source]
fit_words(frequencies)[source]

Create a word_cloud from words and frequencies.

Alias to generate_from_frequencies.

Parameters:

frequencies : array of tuples

A tuple contains the word and its frequency.

Returns:

self

generate(text)[source]

Generate wordcloud from text.

Alias to generate_from_text.

Calls process_text and generate_from_frequencies.

Returns:self
generate_from_frequencies(frequencies, max_font_size=None)[source]

Create a word_cloud from words and frequencies.

Parameters:

frequencies : dict from string to float

A contains words and associated frequency.

max_font_size : int

Use this font-size instead of self.max_font_size

Returns:

self

generate_from_text(text)[source]

Generate wordcloud from text.

Calls process_text and generate_from_frequencies.

..versionchanged:: 1.2.2
Argument of generate_from_frequencies() is not return of process_text() any more.
Returns:self
process_text(text)[source]

Splits a long text into words, eliminates the stopwords.

Parameters:

text : string

The text to be processed.

Returns:

words : dict (string, int)

Word tokens with associated frequency.

..versionchanged:: 1.2.2

Changed return type from list of tuples to dict.

Notes

There are better ways to do word tokenization, but I don’t want to include all those things.

recolor(random_state=None, color_func=None, colormap=None)[source]

Recolor existing layout.

Applying a new coloring is much faster than generating the whole wordcloud.

Parameters:

random_state : RandomState, int, or None, default=None

If not None, a fixed random state is used. If an int is given, this is used as seed for a random.Random state.

color_func : function or None, default=None

Function to generate new color from word count, font size, position and orientation. If None, self.color_func is used.

colormap : string or matplotlib colormap, default=None

Use this colormap to generate new colors. Ignored if color_func is specified. If None, self.color_func (or self.color_map) is used.

Returns:

self

to_array()[source]

Convert to numpy array.

Returns:

image : nd-array size (width, height, 3)

Word cloud image as numpy matrix.

to_file(filename)[source]

Export to image file.

Parameters:

filename : string

Location to write to.

Returns:

self