Python 疫情数据的可视化与分析（二）

Python处理数据 疫情词云

Python 疫情数据的可视化与分析第二弹来袭。
上次通过数据的预处理已经将疫情的数据清洗到excel中，详情见
https://blog.csdn.net/qq_42695315/article/details/111411789
这次将通过处理得到词云。

import openpyxl

from wordcloud import WordCloud

# 读取数据

wb = openpyxl.load_workbook('data.xlsx')
# 获取工作表
ws = wb['国内疫情']
frequency = {}
for row in ws.values:
    if row[0] == '省份':
        pass
    else:
        frequency[row[0]] = float(row[1])

wordcloud = WordCloud(font_path="C:/Windows/Fonts/SIMLI.TTF",
                      background_color="white",
                      width=1920, height=1080)

# 根据确诊病例生成词云

wordcloud.generate_from_frequencies(frequency)

# 保存词云
wordcloud.to_file('wordcloude.png')

得到如图的中国词云

同理生成全球的词云

wb = openpyxl.load_workbook('data1.xlsx')
frequency_out = {}
sheet_name = wb.sheetnames
for each in sheet_name:
    if "洲" in each:
        ws = wb[each]
        for row in ws.values:
            if row[0] == '国家':
                pass
            else:
                frequency[row[0]] = float(row[1])

        wordcloud = WordCloud(font_path="C:/Windows/Fonts/SIMLI.TTF",
                              background_color="white",
                              width=1920, height=1080)
wordcloud.generate_from_frequencies(frequency)

# 保存词云
wordcloud.to_file('wordcloude1.png')

这就是全球疫情词云了。
好了词云制作就到这里了。