import pandas as pd
import pkuseg
import re
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image,ImageSequence
from wordcloud import WordCloud,ImageColorGenerator
def count_words(sp, n):
w = {}
# sp = s.split()
# print(sp)
# TODO: `q Count the number of occurences of each word in s
for i in sp:
if i not in w:
w[i] = 1
else:
w[i] += 1
# TODO: Sort the occurences in descending order (alphabetically in case of ties)
top = sorted(w.items(), key=lambda item:(-item[1], item[0]))
top_n = top[:n]
# TODO: Return the top n most frequent words.
return top_n
def filter_label(l):#过滤符号和单个词
temp=[]
for i in l:
if len(i)!=1 and i.find('nbsp')<0:
temp.append(i)
return temp
def DrawWordcloud(read_name):#生成词云
image = Image.open('back.jpg')#作为背景形状的图
graph = np.array(image)
#参数分别是指定字体、背景颜色、最大的词的大小、使用给定图作为背景形状
wc = WordCloud(font_path = 'simsun.ttc', background_color = 'White', max_words = 50, mask = graph)
# fp = pd.read_csv(read_name)#读取词频文件
# name = list(fp.name)#词
# value = fp.val#词的频率
name=[]
value=[]
for t in read_name:
name.append(t[0])
value.append(t[1])
for i in range(len(name)):
name[i] = str(name[i])
# print(name[i])
#注意因为要显示中文，所以需要转码
name[i] = name[i].encode('gb2312').decode('gb2312')
dic = dict(zip(name, value))#词频以字典形式存储
print(dic)
wc.generate_from_frequencies(dic)#根据给定词频生成词云
image_color = ImageColorGenerator(graph)
plt.imshow(wc)
plt.axis("off")#不显示坐标轴
plt.show()
wc.to_file('Wordcloud.png')#保存的图片命名为Wordcloud.png
#1.读入excel
df=pd.read_excel('2019all.xlsx')#这个会直接默认读取到这个Excel的第一个表单
data=df.ix[:,['反映内容']].values#0表示第一行 这里读取数据并不包含表头，要注意哦！
#2.分词
seg = pkuseg.pkuseg() # 以默认配置加载模型
texts=[]
for t in range(len(data)):
###去重
if t+1!=len(data):
if len(data[t][0])==len(data[t+1][0]):
continue
text = seg.cut(str(data[t][0])) # 进行分词
#3.过滤分词
text=filter_label(text)
# print(text)
texts.extend(text)
#4.统计
# print(texts)
top_n=count_words(texts,500)
DrawWordcloud(top_n)
