Python统计词频并绘制图片(附完整代码)

效果 Python统计词频并绘制图片(附完整代码)
文章图片

Python统计词频并绘制图片(附完整代码)
文章图片

Python统计词频并绘制图片(附完整代码)
文章图片

1 实现代码 读取txt文件:

def readText(text_file_path):with open(text_file_path, encoding='gbk') as f: #content = f.read()return content

得到文章的词频:
def getRecommondArticleKeyword(text_content,key_word_need_num = 10, custom_words = [], stop_words =[], query_pattern = 'searchEngine'):''':param text_content: 文本字符串:param key_word_need_num: 需要的关键词数量:param custom_words: 自定义关键词:param stop_words: 不查询关键词:param query_pattern:precision:精确模式————试图将句子最精确地切开,适合文本分析;entire:全模式————把句子中所有的可以成词的词语都扫描出来, 速度非常快,但是不能解决歧义;searchEngine:搜索引擎模式————在精确模式的基础上,对长词再次切分,提高召回率,适合用于搜索引擎分词;paddle模式————利用PaddlePaddle深度学习框架,训练序列标注(双向GRU)网络模型实现分词。同时支持词性标注。:return:'''# jieba.enable_paddle()# paddle.fluid.install_check.run_check()if not isinstance(text_content, str):raise ValueError('文本字符串类型错误!')if not isinstance(key_word_need_num, int):raise ValueError('关键词个数类型错误!')if not isinstance(custom_words, list):raise ValueError('自定义关键词类型错误!')if not isinstance(stop_words, list):raise ValueError('屏蔽关键词类型错误!')if not isinstance(query_pattern, str):raise ValueError('查询模式类型错误!')# 添加自定义关键词for word in custom_words:jieba.add_word(word)if query_pattern == 'searchEngine':key_words = jieba.cut_for_search(text_content)elif query_pattern == 'entire':key_words = jieba.cut(text_content, cut_all=True, use_paddle=True)elif query_pattern == 'precision':key_words = jieba.cut(text_content, cut_all=False, use_paddle=True)else:return []# print("拆分后的词: %s" % " ".join(key_words))# 过滤后的关键词stop_words = set(stop_words)word_count = Counter()for word in key_words:if len(word) > 1 and word not in stop_words:word_count[word] += 1# res_words = list()# for data in word_count.most_common(key_word_need_num):#res_words.append(data[0])# return res_wordsreturn word_count

绘制图片:
def drawWordsCloud(word_count, save_img_filePath='', img_mask_filePath=''):# print(word_count)# print(type(word_count))if len(img_mask_filePath) != 0:img_mask = np.array(Image.open(img_mask_filePath)) #打开遮罩图片,将图片转换为数组wc = wordcloud.WordCloud(font_path='/Library/Fonts/Arial Unicode.ttf',# 设置中文字体,词云默认字体是“DroidSansMono.ttf字体库”,不支持中文background_color="white",# 设置背景颜色max_words=200,# 设置最大显示的字数max_font_size=50,# 设置字体最大值random_state=30,# 设置有多少种随机生成状态,即有多少种配色方案width=400,height=200,mask=img_mask)else:wc = wordcloud.WordCloud(font_path='/Library/Fonts/Arial Unicode.ttf',# 设置中文字体,词云默认字体是“DroidSansMono.ttf字体库”,不支持中文background_color="white",# 设置背景颜色max_words=200,# 设置最大显示的字数max_font_size=50,# 设置字体最大值random_state=30,# 设置有多少种随机生成状态,即有多少种配色方案width=400,height=200)# 绘图wc.generate_from_frequencies(word_count)#从字典生成词云plt.imshow(wc)#显示词云plt.axis('off')#关闭坐标轴plt.show()#显示图像# 保存图片if len(save_img_filePath) != 0:wc.to_file(save_img_filePath)else:pass

2 完整代码
#-*- coding : utf-8-*-import jiebafrom collections import Counterimport paddleimport wordcloud#词云展示库import matplotlib.pyplot as plt#图像展示库import timefrom PIL import Imageimport numpy as npdef timer(func):def calculateTime(*args, **kwargs):t = time.perf_counter()result = func(*args, **kwargs)print(f'func {func.__name__} coast time:{time.perf_counter() - t:.8f} s')return resultreturn calculateTimedef readText(text_file_path):with open(text_file_path, encoding='gbk') as f: #content = f.read()return content@timerdef getRecommondArticleKeyword(text_content,key_word_need_num = 10, custom_words = [], stop_words =[], query_pattern = 'searchEngine'):''':param text_content: 文本字符串:param key_word_need_num: 需要的关键词数量:param custom_words: 自定义关键词:param stop_words: 不查询关键词:param query_pattern:precision:精确模式————试图将句子最精确地切开,适合文本分析;entire:全模式————把句子中所有的可以成词的词语都扫描出来, 速度非常快,但是不能解决歧义;searchEngine:搜索引擎模式————在精确模式的基础上,对长词再次切分,提高召回率,适合用于搜索引擎分词;paddle模式————利用PaddlePaddle深度学习框架,训练序列标注(双向GRU)网络模型实现分词。同时支持词性标注。:return:'''# jieba.enable_paddle()# paddle.fluid.install_check.run_check()if not isinstance(text_content, str):raise ValueError('文本字符串类型错误!')if not isinstance(key_word_need_num, int):raise ValueError('关键词个数类型错误!')if not isinstance(custom_words, list):raise ValueError('自定义关键词类型错误!')if not isinstance(stop_words, list):raise ValueError('屏蔽关键词类型错误!')if not isinstance(query_pattern, str):raise ValueError('查询模式类型错误!')# 添加自定义关键词for word in custom_words:jieba.add_word(word)if query_pattern == 'searchEngine':key_words = jieba.cut_for_search(text_content)elif query_pattern == 'entire':key_words = jieba.cut(text_content, cut_all=True, use_paddle=True)elif query_pattern == 'precision':key_words = jieba.cut(text_content, cut_all=False, use_paddle=True)else:return []# print("拆分后的词: %s" % " ".join(key_words))# 过滤后的关键词stop_words = set(stop_words)word_count = Counter()for word in key_words:if len(word) > 1 and word not in stop_words:word_count[word] += 1# res_words = list()# for data in word_count.most_common(key_word_need_num):#res_words.append(data[0])# return res_wordsreturn word_countdef drawWordsCloud(word_count, save_img_filePath='', img_mask_filePath=''):# print(word_count)# print(type(word_count))if len(img_mask_filePath) != 0:img_mask = np.array(Image.open(img_mask_filePath)) #打开遮罩图片,将图片转换为数组wc = wordcloud.WordCloud(font_path='/Library/Fonts/Arial Unicode.ttf',# 设置中文字体,词云默认字体是“DroidSansMono.ttf字体库”,不支持中文background_color="white",# 设置背景颜色max_words=200,# 设置最大显示的字数max_font_size=50,# 设置字体最大值random_state=30,# 设置有多少种随机生成状态,即有多少种配色方案width=400,height=200,mask=img_mask)else:wc = wordcloud.WordCloud(font_path='/Library/Fonts/Arial Unicode.ttf',# 设置中文字体,词云默认字体是“DroidSansMono.ttf字体库”,不支持中文background_color="white",# 设置背景颜色max_words=200,# 设置最大显示的字数max_font_size=50,# 设置字体最大值random_state=30,# 设置有多少种随机生成状态,即有多少种配色方案width=400,height=200)# 绘图wc.generate_from_frequencies(word_count)#从字典生成词云plt.imshow(wc)#显示词云plt.axis('off')#关闭坐标轴plt.show()#显示图像# 保存图片if len(save_img_filePath) != 0:wc.to_file(save_img_filePath)else:passif __name__ == '__main__':pass# /Users/mac/Downloads/work/retailSoftware/公司项目/test.txttext_file_path = "/Users/mac/Downloads/电子书/编程思想/相约星期二/相约星期二.txt"# text_file_path = "/Users/mac/Downloads/work/retailSoftware/公司项目/test3.txt"text_content = readText(text_file_path)# print(text_content)# print(JNI_API_getRecommondArticleKeyword(text_content))img_mask_filePath = '/Users/mac/Desktop/截屏2021-08-20 下午4.02.10.png'img_save_filePath = '/Users/mac/Downloads/test9.png'drawWordsCloud(getRecommondArticleKeyword(text_content), img_save_filePath, img_mask_filePath)

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