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59 lines
2.1 KiB
59 lines
2.1 KiB
import pandas as pd
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from PIL import Image
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import numpy as np
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import jieba
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from wordcloud import WordCloud
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import matplotlib.pyplot as plt
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def generate_beautiful_wordcloud(file_name='ai_danmaku_statistics.xlsx', mask_image_path=None,font_path=r"C:\Users\wyk93\Desktop\苹方黑体-准-简.ttf"):
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# 读取 Excel 文件
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df = pd.read_excel(file_name)
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# 合并所有弹幕内容为一个大字符串
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text = ' '.join(df['弹幕内容'].dropna().astype(str))
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# 使用 jieba 进行分词
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seg_list = jieba.cut(text, cut_all=True)
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segmented_text = " ".join(seg_list)
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# # 打印合并后的文本以检查内容
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# print("Text content:")
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# print(text[:500]) # 打印前500个字符以检查
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# 读取自定义形状图片
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if mask_image_path:
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mask_image = np.array(Image.open(mask_image_path).convert('L'))
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mask_image[mask_image > 200] = 255
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mask_image[mask_image <= 200] = 0
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else:
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mask_image = None
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background_image = Image.open(mask_image_path)
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# 创建词云对象
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wordcloud = WordCloud(
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background_color='white',# 透明背景
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mask=mask_image,
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contour_color=None,
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contour_width=2,
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width=2000, # 词云图的宽度
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height=2000, # 词云图的高度
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colormap='viridis' , # 使用 'viridis' 颜色映射
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max_words=20000,
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# min_font_size=10,
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# max_font_size=100,
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# prefer_horizontal=0.9, # 更高的水平优先级
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font_path=font_path # 如果需要显示中文，需要指定中文字体路径
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).generate(segmented_text) # 这里替换为你自己的文本数据
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# 显示词云图
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plt.figure(figsize=(10, 8))
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plt.imshow(background_image, interpolation='bilinear')
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plt.imshow(wordcloud, interpolation='bilinear', alpha=0.4) # 设置透明度
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plt.axis('off') # 不显示坐标轴
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plt.show()
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# 调用函数生成词云
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# 可选: 使用自定义形状的掩膜图像
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generate_beautiful_wordcloud(mask_image_path=r"C:\Users\wyk93\Desktop\图片素材\sucai3.jpg") |