💡 作者：韩信子@ShowMeAI
📘 数据分析实战系列：https://www.showmeai.tech/tutorials/40
📘 AI 岗位&攻略系列：https://www.showmeai.tech/tutorials/47
📘 本文地址：https://www.showmeai.tech/article-detail/402
📢 声明：版权所有，转载请联系平台与作者并注明出处
📢 收藏ShowMeAI查看更多精彩内容

💡 引言

数据科学在互联网、医疗、电信、零售、体育、航空、艺术等各个领域仍然越来越受欢迎。在 📘Glassdoor的美国最佳职位列表中，数据科学职位排名第三，2022 年有近 10,071 个职位空缺。

除了数据独特的魅力，数据科学相关岗位的薪资也备受关注，在本篇内容中，ShowMeAI会基于数据对下述问题进行分析：

数据科学中薪水最高的工作是什么？
哪个国家的薪水最高，机会最多？
典型的薪资范围是多少？
工作水平对数据科学家有多重要？
数据科学，全职vs自由职业者
数据科学领域薪水最高的工作是什么？
数据科学领域平均薪水最高的工作是什么？
数据科学专业的最低和最高工资
招聘数据科学专业人员的公司规模如何？
工资是不是跟公司规模有关？
WFH（远程办公）和 WFO 的比例是多少？
数据科学工作的薪水每年如何增长？
如果有人正在寻找与数据科学相关的工作，你会建议他在网上搜索什么？
如果你有几年初级员工的经验，你应该考虑跳槽到什么规模的公司？

💡 数据说明

我们本次用到的数据集是 🏆数据科学工作薪水数据集，大家可以通过 ShowMeAI 的百度网盘地址下载。

🏆 实战数据集下载（百度网盘）：公众号『ShowMeAI研究中心』回复『实战』，或者点击这里获取本文 [37]基于pandasql和plotly的数据科学家薪资分析与可视化『ds_salaries数据集』

⭐ ShowMeAI官方GitHub：https://github.com/ShowMeAI-Hub

数据集包含 11 列，对应的名称和含义如下：

参数	含义
work_year	支付工资的年份
experience_level : 发薪时的经验等级
employment_type	就业类型
job_title	岗位名称
salary	支付的总工资总额
salary_currency	支付的薪水的货币
salary_in_usd	支付的标准化工资（美元）
employee_residence	员工的主要居住国家
remote_ratio	远程完成的工作总量
company_location	雇主主要办公室所在的国家/地区
company_size	根据员工人数计算的公司规模

本篇分析使用到Pandas和SQL，欢迎大家阅读ShowMeAI的数据分析教程和对应的工具速查表文章，系统学习和动手实践：

📘图解数据分析：从入门到精通系列教程

📘编程语言速查表 | SQL 速查表

📘数据科学工具库速查表 | Pandas 速查表

📘数据科学工具库速查表 | Matplotlib 速查表

💡 导入工具库

我们先导入需要使用的工具库，我们使用pandas读取数据，使用 Plotly 和 matplotlib 进行可视化。并且我们在本篇中会使用 SQL 进行数据分析，我们这里使用到了 📘pandasql 工具库。

# For loading data
import pandas as pd
import numpy as np# For SQL queries
import pandasql as ps# For ploting graph / Visualization
import plotly.graph_objects as go
import plotly.express as px
from plotly.offline import iplot
import plotly.figure_factory as ffimport plotly.io as pio
import seaborn as sns
import matplotlib.pyplot as plt# To show graph below the code or on same notebook
from plotly.offline import init_notebook_mode
init_notebook_mode(connected=True)# To convert country code to country name
import country_converter as cocoimport warnings
warnings.filterwarnings('ignore')

💡 加载数据集

我们下载的数据集是 CSV 格式的，所以我们可以使用 read_csv 方法来读取我们的数据集。

# Loading data
salaries = pd.read_csv('ds_salaries.csv')

要查看前五个记录，我们可以使用 salaries.head() 方法。

借助 pandasql完成同样的任务是这样的：

# Function query to execute SQL queries
def query(query):return ps.sqldf(query)# Showing Top 5 rows of data
query("""SELECT * FROM salaries LIMIT 5
""")

输出：

💡 数据预处理

我们数据集中的第1列“Unnamed: 0”是没有用的，在分析之前我们把它剔除：

salaries = salaries.drop('Unnamed: 0', axis = 1)

我们查看一下数据集中缺失值情况：

salaries.isna().sum()

输出：

work_year             0
experience_level      0
employment_type       0
job_title             0
salary                0
salary_currency       0
salary_in_usd         0
employee_residence    0
remote_ratio          0
company_location      0
company_size          0
dtype: int64

我们的数据集中没有任何缺失值，因此不用做缺失值处理，employee_residence 和 company_location 使用的是短国家代码。我们映射替换为国家的全名以便于理解：

# Converting countries code to country names
salaries["employee_residence"] = coco.convert(names=salaries["employee_residence"], to="name")
salaries["company_location"] = coco.convert(names=salaries["company_location"], to="name")

这个数据集中的experience_level代表不同的经验水平，使用的是如下缩写：

CN: Entry Level （入门级）
ML：Mid level （中级）
SE：Senior Level （高级）
EX：Expert Level （资深专家级）

为了更容易理解，我们也把这些缩写替换为全称。

# Replacing values in column - experience_level :
salaries['experience_level'] = query("""SELECT REPLACE(REPLACE(REPLACE(REPLACE(experience_level, 'MI', 'Mid level'), 'SE', 'Senior Level'), 'EN', 'Entry Level'), 'EX', 'Expert Level') FROM salaries""")

同样的方法，我们对工作形式也做全称替换

FT: Full Time （全职）
PT: Part Time （兼职）
CT：Contract （合同制）
FL：Freelance （自由职业）

# Replacing values in column - experience_level :
salaries['employment_type'] = query("""SELECT REPLACE(REPLACE(REPLACE(REPLACE(employment_type, 'PT', 'Part Time'), 'FT', 'Full Time'), 'FL', 'Freelance'), 'CT', 'Contract') FROM salaries""")

数据集中公司规模字段处理如下：

S：Small （小型）
M：Medium （中型）
L：Large （大型）

# Replacing values in column - company_size :
salaries['company_size'] = query("""SELECT REPLACE(REPLACE(REPLACE(company_size, 'M', 'Medium'), 'L', 'Large'), 'S', 'Small') FROM salaries""")

我们对远程比率字段也做一些处理，以便更好理解

# Replacing values in column - remote_ratio :
salaries['remote_ratio'] = query("""SELECT REPLACE(REPLACE(REPLACE(remote_ratio, '100', 'Fully Remote'), '50', 'Partially Remote'), '0', 'Non Remote Work') FROM salaries""")

这是预处理后的最终输出。

💡 数据分析&可视化

💦 数据科学中薪水最高的工作是什么？

top10_jobs = query("""SELECT job_title,Count(*) AS job_countFROM salariesGROUP BY job_titleORDER BY job_count DESCLIMIT 10
""")

我们绘制条形图以便更直观理解：

data = go.Bar(x = top10_jobs['job_title'], y = top10_jobs['job_count'],text = top10_jobs['job_count'], textposition = 'inside',textfont = dict(size = 12,color = 'white'),marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'black',line_width = 1))layout = go.Layout(title = {'text': "<b>Top 10 Data Science Jobs</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Job Title</b>', tickmode = 'array'),yaxis = dict(title = '<b>Total</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 数据科学职位的市场分布

fig = px.pie(top10_jobs, values='job_count', names='job_title', color_discrete_sequence = px.colors.qualitative.Alphabet)fig.update_layout(title = {'text': "<b>Distribution of job positions</b>", 'x':0.5, 'xanchor': 'center'},width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 拥有最多数据科学工作的国家

top10_com_loc = query("""SELECT company_location AS company,Count(*) AS job_countFROM salariesGROUP BY companyORDER BY job_count DESCLIMIT 10
""")data = go.Bar(x = top10_com_loc['company'], y = top10_com_loc['job_count'],textfont = dict(size = 12,color = 'white'),marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'black',line_width = 1))layout = go.Layout(title = {'text': "<b>Top 10 Data Science Countries</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Countries</b>', tickmode = 'array'),yaxis = dict(title = '<b>Total</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

从上图中，我们可以看出美国在数据科学方面的工作机会最多。现在我们来看看世界各地的薪水。大家可以继续运行代码，查看可视化结果。

df = salaries
df["company_country"] = coco.convert(names = salaries["company_location"], to = 'name_short')temp_df = df.groupby('company_country')['salary_in_usd'].sum().reset_index()
temp_df['salary_scale'] = np.log10(df['salary_in_usd'])fig = px.choropleth(temp_df, locationmode = 'country names', locations = "company_country",color = "salary_scale", hover_name = "company_country",hover_data = temp_df[['salary_in_usd']], color_continuous_scale = 'Jet',)fig.update_layout(title={'text':'<b>Salaries across the World</b>', 'xanchor': 'center','x':0.5})
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 平均工资（基于货币计算）

df = salaries[['salary_currency','salary_in_usd']].groupby(['salary_currency'], as_index = False).mean().set_index('salary_currency').reset_index().sort_values('salary_in_usd', ascending = False)#Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'salary_currency',y = 'salary_in_usd',color = 'salary_currency',color_discrete_sequence = px.colors.qualitative.Safe,)fig.update_layout(title={'text':'<b>Average salary as a function of currency</b>', 'xanchor': 'center','x':0.5},xaxis_title = '<b>Currency</b>',yaxis_title = '<b>Mean Salary</b>')
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

人们以美元赚取的收入最多，其次是瑞士法郎和新加坡元。

df = salaries[['company_country','salary_in_usd']].groupby(['company_country'], as_index = False).mean().set_index('company_country').reset_index().sort_values('salary_in_usd', ascending = False)#Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'company_country',y = 'salary_in_usd',color = 'company_country',color_discrete_sequence = px.colors.qualitative.Dark2,)fig.update_layout(title = {'text': "<b>Average salary as a function of company location</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Company Location</b>', tickmode = 'array'),yaxis = dict(title = '<b>Mean Salary</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 数据科学工作经验水平分布

job_exp = query("""SELECT experience_level, Count(*) AS job_countFROM salariesGROUP BY experience_levelORDER BY job_count ASC
""")data = go.Bar(x = job_exp['job_count'], y = job_exp['experience_level'],orientation = 'h', text = job_exp['job_count'],marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'white',line_width = 2))layout = go.Layout(title = {'text': "<b>Jobs on Experience Levels</b>",'x':0.5, 'xanchor':'center'},xaxis = dict(title='<b>Total</b>', tickmode = 'array'),yaxis = dict(title='<b>Experience lvl</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2')
fig.show()

从上图可以看出，大多数数据科学都是 高级水平 ，专家级很少。

💦 数据科学工作就业类型分布

job_emp = query("""
SELECT employment_type,
COUNT(*) AS job_count
FROM salaries
GROUP BY employment_type
ORDER BY job_count ASC
""")data =  go.Bar(x = job_emp['job_count'], y = job_emp['employment_type'], orientation ='h',text = job_emp['job_count'],textposition ='outside',marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'white',line_width = 2))layout = go.Layout(title = {'text': "<b>Jobs on Employment Type</b>",'x':0.5, 'xanchor': 'center'},xaxis = dict(title='<b>Total</b>', tickmode = 'array'),yaxis =dict(title='<b>Emp Type lvl</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_layout(plot_bgcolor = '#f1e7d2', paper_bgcolor = '#f1e7d2')
fig.show()

从上图中，我们可以看到大多数数据科学家从事 全职工作 ，而合同工和自由职业者 则较少

💦 数据科学工作数量趋势

job_year = query("""SELECT work_year, COUNT(*) AS 'job count'FROM salariesGROUP BY work_yearORDER BY 'job count' DESC
""")data = go.Scatter(x = job_year['work_year'], y = job_year['job count'],marker = dict(size = 20,line_width = 1.5,line_color = 'white',color = px.colors.qualitative.Alphabet),line = dict(color = '#ED7D31', width = 4), mode = 'lines+markers')layout  = go.Layout(title = {'text' : "<b><i>Data Science jobs Growth (2020 to 2022)</i></b>",'x' : 0.5, 'xanchor' : 'center'},xaxis = dict(title = '<b>Year</b>'),yaxis = dict(title = '<b>Jobs</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_xaxes(tickvals = ['2020','2021','2022'])
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 数据科学工作薪水分布

salary_usd = query("""SELECT salary_in_usd FROM salaries
""")import matplotlib.pyplot as pltplt.figure(figsize = (20, 8))
sns.set(rc = {'axes.facecolor' : '#f1e7d2','figure.facecolor' : '#f1e7d2'})p = sns.histplot(salary_usd["salary_in_usd"], kde = True, alpha = 1, fill = True,edgecolor = 'black', linewidth = 1)
p.axes.lines[0].set_color("orange")
plt.title("Data Science Salary Distribution \n", fontsize = 25)
plt.xlabel("Salary", fontsize = 18)
plt.ylabel("Count", fontsize = 18)
plt.show()

💦 薪酬最高的 10 大数据科学工作

salary_hi10 = query("""SELECT job_title,MAX(salary_in_usd) AS salaryFROM salariesGROUP BY salaryORDER BY salary DESCLIMIT 10
""")data = go.Bar(x = salary_hi10['salary'],y = salary_hi10['job_title'],orientation = 'h',text = salary_hi10['salary'],textposition = 'inside',insidetextanchor = 'middle',textfont = dict(size = 13,color = 'black'),marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'black',line_width = 1))layout = go.Layout(title = {'text': "<b>Top 10 Highest paid Data Science Jobs</b>",'x':0.5,'xanchor': 'center'},xaxis = dict(title = '<b>salary</b>', tickmode = 'array'),yaxis = dict(title = '<b>Job Title</b>'),width = 900,height = 600)
fig = go.Figure(data = data, layout= layout)
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

首席数据工程师 是数据科学领域的高薪工作。

💦 不同岗位平均薪资与排名

salary_av10 = query("""SELECT job_title,ROUND(AVG(salary_in_usd)) AS salaryFROM salariesGROUP BY job_titleORDER BY salary DESCLIMIT 10
""")data = go.Bar(x = salary_av10['salary'],y = salary_av10['job_title'],orientation = 'h',text = salary_av10['salary'],textposition = 'inside',insidetextanchor = 'middle',textfont = dict(size = 13,color = 'white'),marker = dict(color = px.colors.qualitative.Alphabet,opacity = 0.9,line_color = 'white',line_width = 2))layout = go.Layout(title = {'text': "<b>Top 10 Average paid Data Science Jobs</b>",'x':0.5,'xanchor': 'center'},xaxis = dict(title = '<b>salary</b>', tickmode = 'array'),yaxis = dict(title = '<b>Job Title</b>'),width = 900,height = 600)
fig = go.Figure(data = data, layout = layout)
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 数据科学薪资趋势

salary_year = query("""SELECT ROUND(AVG(salary_in_usd)) AS salary,work_year AS yearFROM salariesGROUP BY yearORDER BY salary DESC
""")data = go.Scatter(x = salary_year['year'],y = salary_year['salary'],marker = dict(size = 20,line_width = 1.5,line_color = 'black',color = '#ED7D31'),line = dict(color = 'black', width = 4), mode = 'lines+markers')layout = go.Layout(title = {'text' : "<b>Data Science Salary Growth (2020 to 2022) </b>",'x' : 0.5,'xanchor' : 'center'},xaxis = dict(title = '<b>Year</b>'),yaxis = dict(title = '<b>Salary</b>'),width = 900,height = 600)fig = go.Figure(data = data, layout = layout)
fig.update_xaxes(tickvals = ['2020','2021','2022'])
fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 经验水平&薪资

salary_exp = query("""SELECT experience_level AS 'Experience Level',salary_in_usd AS SalaryFROM salaries
""")fig = px.violin(salary_exp, x = 'Experience Level', y = 'Salary', color = 'Experience Level', box = True)fig.update_layout(title = {'text': "<b>Salary on Experience Level</b>",'xanchor': 'center','x':0.5},xaxis = dict(title = '<b>Experience level</b>'),yaxis = dict(title = '<b>salary</b>', ticktext = [-300000, 0, 100000, 200000, 300000, 400000, 500000, 600000, 700000]),width = 900,height = 600)fig.update_layout(paper_bgcolor= '#f1e7d2', plot_bgcolor = '#f1e7d2', showlegend = False)
fig.show()

💦 不同经验水平的薪资趋势

tmp_df = salaries.groupby(['work_year', 'experience_level']).median()
tmp_df.reset_index(inplace = True)fig = px.line(tmp_df, x='work_year', y='salary_in_usd', color='experience_level', symbol="experience_level")fig.update_layout(title = {'text': "<b>Median Salary Trend By Experience Level</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Working Year</b>', tickvals = [2020, 2021, 2022], tickmode = 'array'),yaxis = dict(title = '<b>Salary</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

观察 1. 在COVID-19大流行期间（2020 年至 2021 年），专家级员工薪资非常高，但是呈现部分下降趋势。 2. 2021年以后专家级和高级职称人员工资有所上涨。

💦 年份&薪资分布

year_gp = salaries.groupby('work_year')
hist_data = [year_gp.get_group(2020)['salary_in_usd'],year_gp.get_group(2021)['salary_in_usd'],year_gp.get_group(2022)['salary_in_usd']]
group_labels = ['2020', '2021', '2022']fig = ff.create_distplot(hist_data, group_labels, show_hist = False)fig.update_layout(title = {'text': "<b>Salary Distribution By Working Year</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Salary</b>'),yaxis = dict(title = '<b>Kernel Density</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 就业类型&薪资

salary_emp = query("""SELECT employment_type AS 'Employment Type',salary_in_usd AS SalaryFROM salaries
""")fig = px.box(salary_emp,x='Employment Type',y='Salary',color = 'Employment Type')fig.update_layout(title = {'text': "<b>Salary by Employment Type</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Employment Type</b>'),yaxis = dict(title = '<b>Salary</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 公司规模分布

comp_size = query("""SELECT company_size,COUNT(*) AS countFROM salariesGROUP BY company_size
""")import plotly.graph_objects as go
data = go.Pie(labels = comp_size['company_size'], values = comp_size['count'].values,hoverinfo = 'label',hole = 0.5,textfont_size = 16,textposition = 'auto')
fig = go.Figure(data = data)fig.update_layout(title = {'text': "<b>Company Size</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b></b>'),yaxis = dict(title = '<b></b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 不同公司规模的经验水平比例

df = salaries.groupby(['company_size', 'experience_level']).size()
comp_s = np.round(df['Small'].values / df['Small'].values.sum(),2)
comp_m = np.round(df['Medium'].values / df['Medium'].values.sum(),2)
comp_l = np.round(df['Large'].values / df['Large'].values.sum(),2)fig = go.Figure()
categories = ['Entry Level', 'Expert Level','Mid level','Senior Level']fig.add_trace(go.Scatterpolar(r = comp_s,theta = categories,fill = 'toself',name = 'Company Size S'))fig.add_trace(go.Scatterpolar(r = comp_m,theta = categories,fill = 'toself',name = 'Company Size M'))fig.add_trace(go.Scatterpolar(r = comp_l,theta = categories,fill = 'toself',name = 'Company Size L'))fig.update_layout(polar = dict(radialaxis = dict(range = [0, 0.6])),showlegend = True,
)fig.update_layout(title = {'text': "<b>Proportion of Experience Level In Different Company Sizes</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b></b>'),yaxis = dict(title = '<b></b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 不同公司规模&工作薪资

salary_size = query("""SELECT company_size AS 'Company size',salary_in_usd AS SalaryFROM salaries
""")fig = px.box(salary_size, x='Company size', y = 'Salary',color = 'Company size')fig.update_layout(title = {'text': "<b>Salary by Company size</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Company size</b>'),yaxis = dict(title = '<b>Salary</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 WFH（远程办公）和 WFO 的比例

rem_type = query("""SELECT remote_ratio,COUNT(*) AS totalFROM salariesGROUP BY remote_ratio
""")data = go.Pie(labels = rem_type['remote_ratio'], values = rem_type['total'].values,hoverinfo = 'label',hole = 0.4,textfont_size = 18,textposition = 'auto')fig = go.Figure(data = data)fig.update_layout(title = {'text': "<b>Remote Ratio</b>", 'x':0.5, 'xanchor': 'center'},width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 薪水受Remote Type影响程度

salary_remote = query("""SELECT remote_ratio AS 'Remote type',salary_in_usd AS SalaryFrom salaries
""")fig = px.box(salary_remote, x = 'Remote type', y = 'Salary', color = 'Remote type')fig.update_layout(title = {'text': "<b>Salary by Remote Type</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Remote type</b>'),yaxis = dict(title = '<b>Salary</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()

💦 不同经验水平&远程比率

exp_remote = salaries.groupby(['experience_level', 'remote_ratio']).count()
exp_remote.reset_index(inplace = True)fig = px.histogram(exp_remote, x = 'experience_level',y = 'work_year', color = 'remote_ratio',barmode = 'group',text_auto = True)fig.update_layout(title = {'text': "<b>Respondent Count In Different Experience Level Based on Remote Ratio</b>", 'x':0.5, 'xanchor': 'center'},xaxis = dict(title = '<b>Experience Level</b>'),yaxis = dict(title = '<b>Number of Respondents</b>'),width = 900,height = 600)fig.update_layout(plot_bgcolor = '#f1e7d2',paper_bgcolor = '#f1e7d2')
fig.show()