爬取哔哩哔哩的弹幕，http://comment.bilibili.com/6315651.xml
需要知道cid,可以F12,F5刷新，找cid，找到之后拼接url
也可以写代码，解析response获取cid,然后再拼接
使用requests或者urllib都可以
我是用requests，请求该链接获取到xml文件
代码：获取xml
def get_data():
res = requests.get('http://comment.bilibili.com/6315651.xml')
res.encoding = 'utf8'
with open('gugongdanmu.xml', 'a', encoding='utf8') as f:
f.writelines(res.text)解析xml,
def analyze_xml():
f1 = open("gugongdanmu.xml", "r", encoding='utf8')
f2 = open("tanmu2.txt", "w", encoding='utf8')
count = 0
# 正则匹配解决xml的多余的字符
dr = re.compile(r'<[^>]+>', re.S)
while 1:
line = f1.readline()
if not line:
break
pass
# 匹配到之后用空代替
dd = dr.sub('', line)
# dd = re.findall(dr, line)
count = count+1
f2.writelines(dd)
print(count)去掉无用的字符和数字，找出所有的汉字
def analyze_hanzi():
f1 = open("tanmu2.txt", "r", encoding='utf8')
f2 = open("tanmu3.txt", "w", encoding='utf8')
count = 0
# dr = re.compile(r'<[^>]+>',re.S)
# 所有的汉字[一-龥]
dr = re.compile(r'[一-龥]+',re.S)
while 1:
line = f1.readline()
if not line:
break
pass
# 找出无用的符号和数字
# dd = dr.sub('',line)
dd = re.findall(dr, line)
count = count+1
f2.writelines(dd)
print(count)
# pattern = re.compile(r'[一-龥]+')使用jieba分词，生成词云
def show_sign():
content = read_txt_file()
segment = jieba.lcut(content)
words_df = pd.DataFrame({'segment': segment})
stopwords = pd.read_csv("stopwords.txt", index_col=False, quoting=3, sep=" ", names=['stopword'], encoding='utf-8')
words_df = words_df[~words_df.segment.isin(stopwords.stopword)]
print(words_df)
print('-------------------------------')
words_stat = words_df.groupby(by=['segment'])['segment'].agg(numpy.size)
words_stat = words_stat.to_frame()
words_stat.columns = ['计数']
words_stat = words_stat.reset_index().sort_values(by=["计数"], ascending=False)
# 设置词云属性
color_mask = imread('ciyun.png')
wordcloud = WordCloud(font_path="simhei.ttf", # 设置字体可以显示中文
background_color="white", # 背景颜色
max_words=1000, # 词云显示的最大词数
mask=color_mask, # 设置背景图片
max_font_size=100, # 字体最大值
random_state=42,
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(1000).values}
print(word_frequence)
# for key,value in word_frequence:
# write_txt_file(word_frequence)
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()运行程序，结果：
统计的结果
完成！
pip的换源，原来的太慢，然后将你自己没有库装上