基于Python的詞云生成類庫,很好用,而且功能強大.博主個人比較推薦

github:https://github.com/amueller/word_cloud

官方地址:https://amueller.github.io/word_cloud/

寫這篇文章花費一個半小時,閱讀需要十五分鐘,讀完本篇文章后您將能上手wordcloud

中文詞云與其他要點,我將會在下一篇文章中介紹

快速生成詞云from wordcloud import WordCloud

f = open(u'txt/AliceEN.txt','r').read()

wordcloud = WordCloud(background_color="white",width=1000, height=860, margin=2).generate(f)

# width,height,margin可以設置圖片屬性

# generate 可以對全部文本進行自動分詞,但是他對中文支持不好,對中文的分詞處理請看我的下一篇文章

#wordcloud = WordCloud(font_path = r'D:\Fonts\simkai.ttf').generate(f)

# 你可以通過font_path參數來設置字體集

#background_color參數為設置背景顏色,默認顏色為黑色

import matplotlib.pyplot as plt

plt.imshow(wordcloud)

plt.axis("off")

plt.show()

wordcloud.to_file('test.png')

# 保存圖片,但是在第三模塊的例子中 圖片大小將會按照 mask 保存

快速生成詞云

自定義字體顏色

這段代碼主要來自wordcloud的github,你可以在github下載該例子#!/usr/bin/env python

"""

Colored by Group Example

========================

Generating a word cloud that assigns colors to words based on

a predefined mapping from colors to words

"""

from wordcloud import (WordCloud, get_single_color_func)

import matplotlib.pyplot as plt

class SimpleGroupedColorFunc(object):

"""Create a color function object which assigns EXACT colors

to certain words based on the color to words mapping

Parameters

----------

color_to_words : dict(str -> list(str))

A dictionary that maps a color to the list of words.

default_color : str

Color that will be assigned to a word that's not a member

of any value from color_to_words.

"""

def __init__(self, color_to_words, default_color):

self.word_to_color = {word: color

for (color, words) in color_to_words.items()

for word in words}

self.default_color = default_color

def __call__(self, word, **kwargs):

return self.word_to_color.get(word, self.default_color)

class GroupedColorFunc(object):

"""Create a color function object which assigns DIFFERENT SHADES of

specified colors to certain words based on the color to words mapping.

Uses wordcloud.get_single_color_func

Parameters

----------

color_to_words : dict(str -> list(str))

A dictionary that maps a color to the list of words.

default_color : str

Color that will be assigned to a word that's not a member

of any value from color_to_words.

"""

def __init__(self, color_to_words, default_color):

self.color_func_to_words = [

(get_single_color_func(color), set(words))

for (color, words) in color_to_words.items()]

self.default_color_func = get_single_color_func(default_color)

def get_color_func(self, word):

"""Returns a single_color_func associated with the word"""

try:

color_func = next(

color_func for (color_func, words) in self.color_func_to_words

if word in words)

except StopIteration:

color_func = self.default_color_func

return color_func

def __call__(self, word, **kwargs):

return self.get_color_func(word)(word, **kwargs)

text = """The Zen of Python, by Tim Peters

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.

Special cases aren't special enough to break the rules.

Although practicality beats purity.

Errors should never pass silently.

Unless explicitly silenced.

In the face of ambiguity, refuse the temptation to guess.

There should be one-- and preferably only one --obvious way to do it.

Although that way may not be obvious at first unless you're Dutch.

Now is better than never.

Although never is often better than *right* now.

If the implementation is hard to explain, it's a bad idea.

If the implementation is easy to explain, it may be a good idea.

Namespaces are one honking great idea -- let's do more of those!"""

# Since the text is small collocations are turned off and text is lower-cased

wc = WordCloud(collocations=False).generate(text.lower())

# 自定義所有單詞的顏色

color_to_words = {

# words below will be colored with a green single color function

'#00ff00': ['beautiful', 'explicit', 'simple', 'sparse',

'readability', 'rules', 'practicality',

'explicitly', 'one', 'now', 'easy', 'obvious', 'better'],

# will be colored with a red single color function

'red': ['ugly', 'implicit', 'complex', 'complicated', 'nested',

'dense', 'special', 'errors', 'silently', 'ambiguity',

'guess', 'hard']

}

# Words that are not in any of the color_to_words values

# will be colored with a grey single color function

default_color = 'grey'

# Create a color function with single tone

# grouped_color_func = SimpleGroupedColorFunc(color_to_words, default_color)

# Create a color function with multiple tones

grouped_color_func = GroupedColorFunc(color_to_words, default_color)

# Apply our color function

# 如果你也可以將color_func的參數設置為圖片,詳細的說明請看 下一部分

wc.recolor(color_func=grouped_color_func)

# Plot

plt.figure()

plt.imshow(wc, interpolation="bilinear")

plt.axis("off")

plt.show()

Apply our color function

利用背景圖片生成詞云,設置停用詞詞集

該段代碼主要來自于wordcloud的github,你同樣可以在github下載該例子以及原圖片與效果圖#!/usr/bin/env python

"""

Image-colored wordcloud

=======================

You can color a word-cloud by using an image-based coloring strategy

implemented in ImageColorGenerator. It uses the average color of the region

occupied by the word in a source image. You can combine this with masking -

pure-white will be interpreted as 'don't occupy' by the WordCloud object when

passed as mask.

If you want white as a legal color, you can just pass a different image to

"mask", but make sure the image shapes line up.

"""

from os import path

from PIL import Image

import numpy as np

import matplotlib.pyplot as plt

from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

d = path.dirname(__file__)

# Read the whole text.

text = open(path.join(d, 'alice.txt')).read()

# read the mask / color image taken from

# http://jirkavinse.deviantart.com/art/quot-Real-Life-quot-Alice-282261010

alice_coloring = np.array(Image.open(path.join(d, "alice_color.png")))

# 設置停用詞

stopwords = set(STOPWORDS)

stopwords.add("said")

# 你可以通過 mask 參數 來設置詞云形狀

wc = WordCloud(background_color="white", max_words=2000, mask=alice_coloring,

stopwords=stopwords, max_font_size=40, random_state=42)

# generate word cloud

wc.generate(text)

# create coloring from image

image_colors = ImageColorGenerator(alice_coloring)

# show

# 在只設置mask的情況下,你將會得到一個擁有圖片形狀的詞云

plt.imshow(wc, interpolation="bilinear")

plt.axis("off")

plt.figure()

# recolor wordcloud and show

# we could also give color_func=image_colors directly in the constructor

# 我們還可以直接在構造函數中直接給顏色

# 通過這種方式詞云將會按照給定的圖片顏色布局生成字體顏色策略

plt.imshow(wc.recolor(color_func=image_colors), interpolation="bilinear")

plt.axis("off")

plt.figure()

plt.imshow(alice_coloring, cmap=plt.cm.gray, interpolation="bilinear")

plt.axis("off")

plt.show()

展示效果如下:

愛麗絲的原圖

按照形狀生成詞云

按照圖片顏色生成詞云字體顏色def friends_signature():

signature = get_data("Signature")

wash_signature=[]

for item in signature:

#去除emoji表情等非文字

if "emoji" in item:

continue

rep = re.compile("1f\d+\w*|[<>/=【】『』♂ω]")

item=rep.sub("", item)

wash_signature.append(item)

words="".join(wash_signature)

print(wash_signature)

wordlist = jieba.cut(words, cut_all=True)

word_space_split = " ".join(wordlist)

# 圖片的作用：生成的圖片是這個圖片的兩倍大小

coloring = np.array(Image.open("img/num.jpg"))

# simkai.ttf 必填項 識別中文的字體，例：simkai.ttf，

my_wordcloud = WordCloud(background_color="white", max_words=800,

mask=coloring, max_font_size=120, random_state=30, scale=2,font_path="fonts/STKAITI.TTF").generate(word_space_split)

image_colors = ImageColorGenerator(coloring)

plt.imshow(my_wordcloud.recolor(color_func=image_colors))

plt.imshow(my_wordcloud)

plt.axis("off")

plt.show()

# 保存圖片

my_wordcloud.to_file('Signature/signature.png')

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