?? 作者：韓信子@ShowMeAI
?? 資料分析實戰系列：https://www.showmeai.tech/tutorials/40
?? AI 崗位&攻略系列：https://www.showmeai.tech/tutorials/47
?? 本文地址：https://www.showmeai.tech/article-detail/402
?? 宣告：著作權所有，轉載請聯系平臺與作者并注明出處
?? 收藏ShowMeAI查看更多精彩內容

?? 引言

資料科學在互聯網、醫療、電信、零售、體育、航空、藝術等各個領域仍然越來越受歡迎，在 ??Glassdoor的美國最佳職位串列中，資料科學職位排名第三，2022 年有近 10,071 個職位空缺，

除了資料獨特的魅力，資料科學相關崗位的薪資也備受關注，在本篇內容中，ShowMeAI會基于資料對下述問題進行分析：

資料科學中薪水最高的作業是什么？
哪個國家的薪水最高，機會最多？
典型的薪資范圍是多少？
作業水平對資料科學家有多重要？
資料科學，全職vs自由職業者
資料科學領域薪水最高的作業是什么？
資料科學領域平均薪水最高的作業是什么？
資料科學專業的最低和最高工資
招聘資料科學專業人員的公司規模如何？
工資是不是跟公司規模有關？
WFH（遠程辦公）和 WFO 的比例是多少？
資料科學作業的薪水每年如何增長？
如果有人正在尋找與資料科學相關的作業，你會建議他在網上搜索什么？
如果你有幾年初級員工的經驗，你應該考慮跳槽到什么規模的公司？

?? 資料說明

我們本次用到的資料集是 ??資料科學作業薪水資料集，大家可以通過 ShowMeAI 的百度網盤地址下載，

?? 實戰資料集下載（百度網盤）：公眾號『ShowMeAI研究中心』回復『實戰』，或者點擊這里獲取本文 [37]基于pandasql和plotly的資料科學家薪資分析與可視化『ds_salaries資料集』

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

資料集包含 11 列，對應的名稱和含義如下：

引數	含義
work_year	支付工資的年份
experience_level : 發薪時的經驗等級
employment_type	就業型別
job_title	崗位名稱
salary	支付的總工資總額
salary_currency	支付的薪水的貨幣
salary_in_usd	支付的標準化工資（美元）
employee_residence	員工的主要居住國家
remote_ratio	遠程完成的作業總量
company_location	雇主主要辦公室所在的國家/地區
company_size	根據員工人數計算的公司規模

本篇分析使用到Pandas和SQL，歡迎大家閱讀ShowMeAI的資料分析教程和對應的工具速查表文章，系統學習和動手實踐：

??圖解資料分析：從入門到精通系列教程

??編程語言速查表 | SQL 速查表

??資料科學工具庫速查表 | Pandas 速查表

??資料科學工具庫速查表 | Matplotlib 速查表

?? 匯入工具庫

我們先匯入需要使用的工具庫，我們使用pandas讀取資料，使用 Plotly 和 matplotlib 進行可視化，并且我們在本篇中會使用 SQL 進行資料分析，我們這里使用到了 ??pandasql 工具庫，

# 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 ff

import 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 coco

import warnings
warnings.filterwarnings('ignore')

?? 加載資料集

我們下載的資料集是 CSV 格式的，所以我們可以使用 read_csv 方法來讀取我們的資料集，

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

要查看前五個記錄，我們可以使用 salaries.head() 方法，

借助 pandasql完成同樣的任務是這樣的：

# 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
""")

輸出：

?? 資料預處理

我們資料集中的第1列“Unnamed: 0”是沒有用的，在分析之前我們把它剔除：

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

我們查看一下資料集中缺失值情況：

salaries.isna().sum()

輸出：

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

我們的資料集中沒有任何缺失值，因此不用做缺失值處理，employee_residence 和 company_location 使用的是短國家代碼，我們映射替換為國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")

這個資料集中的experience_level代表不同的經驗水平，使用的是如下縮寫：

CN: Entry Level （入門級）
ML：Mid level （中級）
SE：Senior Level （高級）
EX：Expert Level （資深專家級）

為了更容易理解，我們也把這些縮寫替換為全稱，

# 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""")

同樣的方法，我們對作業形式也做全稱替換

FT: Full Time （全職）
PT: Part Time （兼職）
CT：Contract （合同制）
FL：Freelance （自由職業）

# 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""")

資料集中公司規模欄位處理如下：

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""")

我們對遠程比率欄位也做一些處理，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""")

這是預處理后的最終輸出，

?? 資料分析&可視化

?? 資料科學中薪水最高的作業是什么？

top10_jobs = query("""
                    SELECT job_title,
                    Count(*) AS job_count
                    FROM salaries
                    GROUP BY job_title
                    ORDER BY job_count DESC
                    LIMIT 10
""")

我們繪制條形圖以便更直觀理解：

data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

?? 資料科學職位的市場分布

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()

?? 擁有最多資料科學作業的國家

top10_com_loc = query("""
                    SELECT company_location AS company,
                    Count(*) AS job_count
                    FROM salaries
                    GROUP BY company
                    ORDER BY job_count DESC
                    LIMIT 10
""")


data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

從上圖中，我們可以看出美國在資料科學方面的作業機會最多，現在我們來看看世界各地的薪水，大家可以繼續運行代碼，查看可視化結果，

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 = https://www.cnblogs.com/showmeai/p/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()

?? 平均工資（基于貨幣計算）

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()

人們以美元賺取的收入最多，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()

?? 資料科學作業經驗水平分布

job_exp = query("""
            SELECT experience_level, Count(*) AS job_count
            FROM salaries
            GROUP BY experience_level
            ORDER BY job_count ASC
""")



data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2', 
                  paper_bgcolor = '#f1e7d2')
fig.show()

從上圖可以看出，大多數資料科學都是 高級水平 ，專家級很少，

?? 資料科學作業就業型別分布

job_emp = query("""
SELECT employment_type,
COUNT(*) AS job_count
FROM salaries
GROUP BY employment_type
ORDER BY job_count ASC
""")


data =  https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2', 
                  paper_bgcolor = '#f1e7d2')
fig.show()

從上圖中，我們可以看到大多數資料科學家從事 全職作業 ，而合同工和自由職業者 則較少

?? 資料科學作業數量趨勢

job_year = query("""
    SELECT work_year, COUNT(*) AS 'job count'
    FROM salaries
    GROUP BY work_year
    ORDER BY 'job count' DESC
""")


data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_xaxes(tickvals = ['2020','2021','2022'])
fig.update_layout(plot_bgcolor = '#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

?? 資料科學作業薪水分布

salary_usd = query("""
                    SELECT salary_in_usd 
                    FROM salaries
""")


import matplotlib.pyplot as plt

plt.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 大資料科學作業

salary_hi10 = query("""
    SELECT job_title,
    MAX(salary_in_usd) AS salary
    FROM salaries
    GROUP BY salary
    ORDER BY salary DESC
    LIMIT 10
""")

data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout
                = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

首席資料工程師 是資料科學領域的高薪作業，

?? 不同崗位平均薪資與排名

salary_av10 = query("""
    SELECT job_title,
    ROUND(AVG(salary_in_usd)) AS salary
    FROM salaries
    GROUP BY job_title
    ORDER BY salary DESC
    LIMIT 10
""")

data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_layout(plot_bgcolor ='#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

?? 資料科學薪資趨勢

salary_year = query("""
    SELECT ROUND(AVG(salary_in_usd)) AS salary,
    work_year AS year
    FROM salaries
    GROUP BY year
    ORDER BY salary DESC
""")

data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/data, layout = layout)
fig.update_xaxes(tickvals = ['2020','2021','2022'])
fig.update_layout(plot_bgcolor = '#f1e7d2',
                 paper_bgcolor = '#f1e7d2')
fig.show()

?? 經驗水平&薪資

salary_exp = query("""
    SELECT experience_level AS 'Experience Level',
    salary_in_usd AS Salary
    FROM 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()

?? 不同經驗水平的薪資趨勢

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()

觀察 1. 在COVID-19大流行期間（2020 年至 2021 年），專家級員工薪資非常高，但是呈現部分下降趨勢， 2. 2021年以后專家級和高級職稱人員工資有所上漲，

?? 年份&薪資分布

year_gp = salaries.groupby('work_year')
hist_data = https://www.cnblogs.com/showmeai/p/[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()

?? 就業型別&薪資

salary_emp = query("""
    SELECT employment_type AS 'Employment Type',
    salary_in_usd AS Salary
    FROM 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()

?? 公司規模分布

comp_size = query("""
                SELECT company_size,
                COUNT(*) AS count
                FROM salaries
                GROUP BY company_size
""")


import plotly.graph_objects as go
data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/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()

?? 不同公司規模的經驗水平比例

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()

?? 不同公司規模&作業薪資

salary_size = query("""
    SELECT company_size AS 'Company size',
    salary_in_usd AS Salary
    FROM 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（遠程辦公）和 WFO 的比例

rem_type = query("""
    SELECT remote_ratio,
    COUNT(*) AS total
    FROM salaries
    GROUP BY remote_ratio
""")


data = https://www.cnblogs.com/showmeai/p/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 = https://www.cnblogs.com/showmeai/p/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影響程度

salary_remote = query("""
    SELECT remote_ratio AS 'Remote type',
    salary_in_usd AS Salary
    From 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()

?? 不同經驗水平&遠程比率

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()

?? 分析結論

資料科學領域Top3多的職位是資料科學家、資料工程師和資料分析師，
資料科學作業越來越受歡迎，員工比例從2020年的11.9%增加到2022年的52.4%，
美國是資料科學公司最多的國家，
工資分布的IQR在62.7k和150k之間，
在資料科學員工中，大多數是高級水平，而專家級則更少，
大多數資料科學員工都是全職作業，很少有合同工和自由職業者，
首席資料工程師是薪酬最高的資料科學作業，
資料科學的最低工資（入門級經驗）為4000美元，具有專家級經驗的資料科學的最高工資為60萬美元，
公司構成：53.7%中型公司，32.6%大型公司，13.7%小型資料科學公司，
工資也受公司規模影響，規模大的公司支付更高的薪水，
62.8%的資料科學是完全遠程作業，20.9%是非遠程作業，16.3%是部分遠程作業，
資料科學薪水隨時間和經驗積累而增長，

參考資料

?? Glassdoor
?? pandasql
?? 資料科學作業薪水資料集（Kaggle）
?? 圖解資料分析：從入門到精通系列教程：https://www.showmeai.tech/tutorials/33
?? 編程語言速查表 | SQL 速查表：https://www.showmeai.tech/article-detail/99
?? 資料科學工具庫速查表 | Pandas 速查表：https://www.showmeai.tech/article-detail/101
?? 資料科學工具庫速查表 | Matplotlib 速查表：https://www.showmeai.tech/article-detail/103

資料科學家賺多少？基于pandasql和plotly的薪資分析與可視化 ?