for col in ['class', 'employer_type', 'industry', 'work_year',]: tmp=train_public[col].value_counts().rename_axis(col).reset_index(name='counts') fig = px.pie(tmp, values='counts', names=col,title=f'ratio of counts {col}') fig.show() 改为matplotlib.pyplot下画出的饼图的代码

以下是使用matplotlib库来绘制饼图的代码：

import matplotlib.pyplot as plt

# 遍历数据集中需要绘制饼图的变量
for col in ['class', 'employer_type', 'industry', 'work_year']:
    # 计算每个取值的出现次数
    tmp = train_public[col].value_counts()
    # 将结果转换为DataFrame格式
    tmp_df = pd.DataFrame({'counts': tmp.values}, index=tmp.index)
    
    # 绘制饼图
    plt.pie(tmp_df['counts'], labels=tmp_df.index, autopct='%1.1f%%')
    plt.title(f'ratio of counts {col}')
    plt.show()

这段代码的功能与上面的代码相同，只是使用了matplotlib库来绘制饼图。首先遍历数据集中需要绘制饼图的变量，然后计算每个取值的出现次数，并将结果转换为DataFrame格式。最后使用plt.pie()函数来绘制饼图，其中labels参数表示饼图的标签，autopct参数表示饼图上每个扇形所占的比例。最后通过plt.title()函数来设置图表标题，通过plt.show()函数来显示图表。

相关问题

#6. 查询"empName"为"李华"的数据 GET es_employer/_search { "query": { "term": { "empName": "李华" } } }这段代码对不对

这段代码是正确的，可以查询到"empName"为"李华"的数据。如果想要查询多个条件，可以使用bool查询，例如：

GET es_employer/_search
{
  "query": {
    "bool": {
      "must": [
        {
          "term": {
            "empName": "李华"
          }
        },
        {
          "range": {
            "salary": {
              "gte": 5000
            }
          }
        }
      ]
    }
  }
}

这个查询可以同时查询"empName"为"李华"且"salary"大于等于5000的数据。

总是报错SystemError: Negative size passed to PyUnicode_New

Sure, I can help you with that. Here's an article that answers your question:

Comparing the Treatment of People with Good Looks and Those with Average Looks in Job Applications

There is no doubt that people with good looks tend to receive more positive attention and admiration from others. But when it comes to job applications, do they also have an advantage over those with average looks? Let's take a closer look and compare the treatment of these two groups of people.

Firstly, it is widely acknowledged that appearance plays a role in the initial stage of job applications, where recruiters often screen through resumes and cover letters. People with good looks may have the advantage of grabbing recruiters' attention and making a good first impression. However, this advantage may not last long if their skills and experiences are not up to par. In contrast, people with average looks may not have the same visual appeal, but they may still have the right qualifications and experiences that make them suitable for the job.

Secondly, during job interviews, recruiters may unconsciously show bias towards people with good looks. They may be more likely to engage in small talk and make positive comments about their appearance, which can create a more favorable impression. However, the same bias may also make them overlook some of their flaws, such as lack of relevant skills or experiences. For people with average looks, their performance during the interview may be the sole focus, and they may have to work harder to make an impression.

Thirdly, the attitude of the prospective employer also matters. Some employers may value appearance more than others, and this can impact the treatment of job applicants. For example, in jobs where physical appearance and social skills are seen as crucial, such as sales and customer service, people with good looks may have an edge. However, in jobs where technical skills and knowledge are more important, such as engineering or finance, appearance may not play a significant role.

In conclusion, while people with good looks may have some advantages in the initial stage of job applications, such as attracting attention and making a good first impression, this advantage may not necessarily translate into job offers. Ultimately, it is one's qualifications, experiences, and performance that determine whether they are suitable for the job. Recruiters and employers should focus on these factors rather than being swayed by superficial qualities such as appearance.