WordCloud词云制作以及美化

相关截图

 

 

 

 

 

练习代码

import wordcloud
import pandas as pd
import jieba
import matplotlib.pyplot as plt
from nltk.corpus import brown
font_path = 'C:\Windows\Fonts\msyh.ttc'
text = 'this is shanghai, 李帅, 郭靖, 成龙, 哀牢山 三十六剑'

'''
wc = wordcloud.WordCloud(
    font_path=font_path,
    max_font_size=300,
    width=360,
    height=180,
    mode='RGBA',
    background_color=None, #透明的词云
)

cloudobj = wc.generate(text)

# cloudobj.show()
print(cloudobj)

#     展示词云图
plt.imshow(cloudobj)
# 关闭坐标轴，否则很丑
plt.axis('off')
plt.show()
# 保存高清图片
cloudobj.to_file('词云.png')
'''
raw = pd.read_table('./金庸-射雕英雄传txt精校版.txt',names=['txt'],encoding='GBK')
# print(raw)

# 加入章节标识
# 章节判断用变量预处理
def m_head(tmpstr):
    return tmpstr[:1]  #取第一个字

def m_mid(tmpstr):
    return tmpstr.find("回 ")

# 用apply函数将下面的属性加入到对应列
raw['head'] = raw.txt.apply(m_head)
raw['mid'] = raw.txt.apply(m_mid)
raw['len'] = raw.txt.apply(len)


# 章节判断
chapnum = 0
for i in range(len(raw)):
    if raw['head'][i] == "第" and raw['mid'][i] >0 and raw['len'][i]<30:
        chapnum += 1
    if chapnum >= 40 and raw['txt'][i] == "附录一：成吉思汗家族":
        chapnum=0
    raw.loc[i,'chap'] = chapnum


# 删除临时变量
del raw['head']
del raw['mid']
del raw['len']

# 段落聚合  根据章节聚合
rawgrp = raw.groupby('chap')

chapter = rawgrp.agg(sum)

chapter = chapter[chapter.index != 0]

t = chapter.txt[1]
print("*"*100)
print(t)
print("*"*100)
'''
生成射雕英雄传第一章的词云
'''


# 把停用词.txt的内容读入数据框
stoplist = list(pd.read_csv('./停用词.txt',names=['w'],sep='aaa',encoding='utf-8',engine='python').w)


# print(stoplist)
# print(' '.join(stoplist))

def m_cut(intxt):
    return [w for w in jieba.cut(intxt) if w not in stoplist]


cloudobj = wordcloud.WordCloud(
    font_path=font_path,
    width=1200,
    height=800,
    mode='RGBA',
    background_color=None,
    stopwords=stoplist,
).generate(' '.join(jieba.lcut(chapter.txt[1])))

plt.imshow(cloudobj)
plt.axis('off')
plt.show()



'''
基于分词频数绘制词云
'''

txt_freq = {'张三':100,'李四':90,'王二麻子':50}

cloudobj = wordcloud.WordCloud(
    font_path=font_path,

).fit_words(txt_freq)

plt.imshow(cloudobj)
plt.axis("off")
plt.show()


'''
基于分词频数绘制射雕英雄传的词云
'''
import nltk
from nltk import FreqDist

# 去停用词
tokens = m_cut(chapter.txt[1])
# 生成完备的词条频数词典
fdist = FreqDist(tokens)

print(type(fdist))
# <class 'nltk.probability.FreqDist'>

cloudobj = wordcloud.WordCloud(
    font_path=font_path,
    background_color=None,
    width=1600,
    height=1000,

).fit_words(fdist)

plt.imshow(cloudobj)
plt.axis("off")
plt.show()



'''
词云的美化：
1，设置背景图片
    Mask/遮罩
    用于控制词频的整体形状
    指定mask后，设置的高和宽江北忽略，遮罩形状被指定图形的形状取代。除全白的部分仍然被保留外，
    其余部分会用于绘制词云。因此背景图片的画布一定要设置为白色
    字体的大小，布局和颜色也会基于mask生成
    必要时需要调整颜色以增强可视效果

    # 基本调用方式
    from scipy.misc import imread
    mask = imread('背景图片')
'''


from imageio import imread

def m_cut2(intxt):
    return [w for w in jieba.cut(intxt) if w not in stoplist and len(w)>1]

cloudobj = wordcloud.WordCloud(
    font_path=font_path,
    mask=imread('射雕背景1.png'),
    mode='RGBA',
    background_color=None,

).generate(' '.join(m_cut2(chapter.txt[1])))


plt.imshow(cloudobj)
plt.axis("off")
plt.show()