使用SQL分析数据科学职业发展趋势

SELECT experience_level, AVG(salary_in_usd) AS avg_salary
FROM salary_data
GROUP BY experience_level;
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# 导入绘图所需的库
import matplotlib.pyplot as plt
import seaborn as sns
# 设置图表样式
sns.set(style="whitegrid")

# 初始化用于存储图形的列表
graphs = []

plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=df, estimator=lambda x: sum(x) / len(x))
plt.title('Average Salary by Experience Level')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotatinotallow=45)
graphs.append(plt.gcf())
plt.show()
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# 筛选入门级和资深级的数据
entry_experienced = df[df['experience_level'].isin(['Entry_Level', 'Experienced'])]

# 筛选中级和高级数据
mid_senior = df[df['experience_level'].isin(['Mid-Level', 'Senior'])]

# 绘制入门级与资深级的对比图表
plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=entry_experienced, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0)
plt.title('Average Salary: Entry_Level vs Experienced')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotatinotallow=45)
graphs.append(plt.gcf())
plt.show()
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# 绘制中级和高级的图表
plt.figure(figsize=(10, 6))
sns.barplot(x='experience_level', y='salary_in_usd', data=mid_senior, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0)
plt.title('Average Salary: Mid-Level vs Senior')
plt.xlabel('Experience Level')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotatinotallow=45)
graphs.append(plt.gcf())
plt.show()
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SELECT job_title, COUNT(*) AS job_count
FROM salary_data
GROUP BY job_title
ORDER BY job_count DESC
LIMIT 10;
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plt.figure(figsize=(12, 8))
sns.countplot(y='job_title', data=df, order=df['job_title'].value_counts().index[:10])
plt.title('Most Common Job Titles in Data Science')
plt.xlabel('Job Count')
plt.ylabel('Job Title')
graphs.append(plt.gcf())
plt.show()
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SELECT company_size, AVG(salary_in_usd) AS avg_salary, MIN(salary_in_usd) AS min_salary, MAX(salary_in_usd) AS max_salary
FROM salary_data
GROUP BY company_size;
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plt.figure(figsize=(12, 8))
sns.barplot(x='company_size', y='salary_in_usd', data=df, estimator=lambda x: sum(x) / len(x) if len(x) != 0 else 0, order=['Small', 'Medium', 'Large'])
plt.title('Salary Distribution by Company Size')
plt.xlabel('Company Size')
plt.ylabel('Average Salary (USD)')
plt.xticks(rotatinotallow=45)
graphs.append(plt.gcf())
plt.show()
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SELECT company_location, COUNT(*) AS job_count
FROM salary_data
GROUP BY company_location
ORDER BY job_count DESC
LIMIT 10;
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plt.figure(figsize=(12, 8))
sns.countplot(y='company_location', data=df, order=df['company_location'].value_counts().index[:10])
plt.title('Geographical Distribution of Data Science Jobs')
plt.xlabel('Job Count')
plt.ylabel('Company Location')
graphs.append(plt.gcf())
plt.show()
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SELECT job_title, AVG(salary_in_usd) AS avg_salary
FROM salary_data
GROUP BY job_title
ORDER BY avg_salary DESC
LIMIT 10;
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<SQL Query here>

Create a Python graph to visualize the top 10 highest-paying job titles in Data Science, similar to the insights gathered from the given SQL query above.

# <SQL查询代码>

# 创建一个Python图形来可视化数据科学领域中薪资最高的前10个职位头衔，类似于上述给定SQL查询所得到的见解。
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