import requests
from bs4 import BeautifulSoup
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import re
import jieba
import numpy as np
from wordcloud import WordCloud, ImageColorGenerator
url = "https://comment.bilibili.com/92542241.xml"
r = requests.get(url)
r.encoding = 'utf8'
soup = BeautifulSoup(r.text,'lxml')
d = soup.find_all('d')
dlst = []
n = 0
for i in d:
n += 1
danmuku = {}
danmuku['弹幕'] = i.text
danmuku['网址'] = url
danmuku['时间'] = datetime.date.today()
dlst.append(danmuku)
df = pd.DataFrame(dlst)
with open('sign.txt','w',encoding='utf8') as f:
for text in df['弹幕'].values:
pattern = re.compile(r'[一-龥]+')
filter_data = re.findall(pattern,text)
f.write("".join(filter_data))
with open('sign.txt', 'r', encoding='utf8') as f:
data = f.read()
segment = jieba.lcut(data)
words_df = pd.DataFrame({"segment": segment})
word_stat = words_df.groupby(by=['segment'])['segment'].agg({'计数':np.size})
words_stat = word_stat.reset_index().sort_values(by=['计数'],ascending=False)
wordcloud = WordCloud(
font_path="/Library/Application Support/Apple/Fonts/iLife/BalegaRegular.otf", # mac上没有该字体
# font_path="C:\Windows\Fonts\simkai.ttf",
# 设置字体可以显示中文
background_color="white", # 背景颜色
max_words=3000, # 词云显示的最大词数
max_font_size=200, # 字体最大值
random_state=100,
width=1000, height=860, margin=2,
# 设置图片默认的大小,但是如果使用背景图片的话, # 那么保存的图片大小将会按照其大小保存,margin为词语边缘距离
)
# 生成词云, 可以用generate输入全部文本,也可以我们计算好词频后使用generate_from_frequencies函数
word_frequence = {x[0]: x[1] for x in words_stat.head(500).values}
word_frequence_dict = {}
for key in word_frequence:
word_frequence_dict[key] = word_frequence[key]
wordcloud.generate_from_frequencies(word_frequence_dict)
# 从背景图片生成颜色值
# image_colors = ImageColorGenerator(color_mask)
# 重新上色
# wordcloud.recolor(color_func=image_colors)
# 保存图片
wordcloud.to_file('output.png')
plt.imshow(wordcloud)
plt.axis("off")
plt.show()
原文地址:https://www.cnblogs.com/wuxiaoshi/p/11048761.html
时间: 2024-10-03 08:27:12