python:从excel中提取高频词生成词云

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)