[Data analysis actual combat][37] Data scientist salary analysis and visualization based on pandasql and plotly@ShowMeAI

References:

How Much Do Data Scientists Earn? Full Data Analysis and Visualization-ShowMeAI

foreword

The code has actually been run through, and there are some errors in the original text, which have been revised. Most revisions are marked with "Modification". To view the corresponding content, it is recommended to use the search "modification" to view.

Table of Contents

import tool library

# 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')

load dataset

# 利用pd.read_csv读取数据集
salaries = pd.read_csv("./ds_salaries数据集/ds_salaries.csv")
salaries.head()

# Function query to execute SQL queries
def query(query):
    return ps.sqldf(query)

query("""
        select *
        from salaries
        limit 5
""")

data preprocessing

# 去掉无用的"Unnamed: 0"这一列
salaries = salaries.drop("Unnamed: 0", axis=1)
salaries

# 查看数据中缺失值的情况
salaries.isna().sum()

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

"""
将experience_level缩写变成全称
CN: Entry level(入门级)<br>
MI: Mid Level(中级)<br>
SE: Senior Level(高级)<br>
EX: Expert Level(资深专家级)
"""
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""")
salaries

"""
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）
"""
salaries['employment_type'] = query("""SELECT 
                                          REPLACE(
                                            REPLACE(
                                              REPLACE(
                                                REPLACE(
                                                  employment_type, 'PT', 'Part Time'), 
                                                                    'FT', 'Full Time'), 
                                                                    'FL', 'Freelance'), 
                                                                    'CT', 'Contract') 
                                        FROM 
                                          salaries""")
salaries

"""
数据集中公司规模字段处理
S：Small （小型）
M：Medium （中型）
L：Large （大型）
采用salaries.repalce({company_size: {}})函数来做替换
注意inplace=True进行本地修改
"""
replace_rule = {
    
    "S": "Small", "M": "Medium", "L": "Large"}
salaries.replace({
    
    "company_size": replace_rule}, inplace=True)

salaries

"""
对远程比率字段也做一些处理
采用salaries["remote_ratio"].repalce()函数来做替换
"""
replace_rule = {
    
    100: 'Fully Remote', 50: 'Partially Remote', 0: 'Non Remote Work'}
salaries["remote_ratio"].replace(replace_rule, inplace=True)

salaries

Data Analysis & Visualization

# 数据科学领域Top10多的职位是？
top10_jobs = query("""\
    select job_title, count(*) as job_count
    from salaries
    group by job_title
    order by job_count desc
    limit 10
""")

# 绘制条形图
data = go.Bar(x = top10_jobs["job_title"], y = top10_jobs["job_count"], # 横轴，纵轴数据
             text = top10_jobs["job_count"], textposition = "outside",  # 标记在纵轴上的文本，位置在内部
             textfont = dict(size = 12, color = "black"),               # 字号是12，颜色是白色
             marker = dict(color = px.colors.qualitative.Alphabet,      # 条形图颜色
                          opacity = 0.9,        # 不透明度
                          line_color = "black", # 条形图外框线的颜色
                          line_width = 1)       # 条形图外框线的宽度
             )

layout = go.Layout(title = {
    
    'text': "<b>Top 10 Data Science Jobs</b>",  # 粗体标题
                            'x':0.5},                                   # 居中显示
                   xaxis = dict(title = '<b>Job Title</b>'), # x轴标题
                   yaxis = dict(title = '<b>Total</b>'),     # y轴标题
                   width = 900,  # 宽
                   height = 600) # 高


fig = go.Figure(data = data, layout = layout) # 生成画图对象
fig.update_layout(plot_bgcolor = '#f1e7d2',   # 图像背景
                 paper_bgcolor = '#f1e7d2')   # 画布背景
fig.show()                                    # 展示图像

# 饼图展示
fig = px.pie(top10_jobs, values="job_count", names="job_title", color_discrete_sequence=px.colors.qualitative.Alphabet) # 生成图片对象

fig.update_layout(title = {
    
    'text': "<b>Distribution of job positions</b>",  # 标题
                            'x':0.5}, # 居中
                   width = 900,
                   height = 600)

fig.update_layout(plot_bgcolor = '#f1e7d2', # 这行其实没用，因为饼图没有背景
                 paper_bgcolor = '#f1e7d2')
fig.show()

# 拥有最多数据科学家的国家
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 = go.Bar(x = top10_com_loc['company'], y = top10_com_loc['job_count'],
             text = top10_com_loc["job_count"], textposition = "outside",
             textfont = dict(size = 12,
                            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 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()

# 统计各国支付的标准化工资(美元)的总和，并绘制地形图
temp_df = salaries.groupby('company_location')['salary_in_usd'].sum().reset_index() # reset_index将series变成了dataframe
temp_df['salary_scale'] = np.log10(temp_df['salary_in_usd']) # 对salar_in_usd取对数,用于表示颜色，修改，这里应该是temp_df


fig = px.choropleth(temp_df, locationmode = 'country names', locations = "company_location", # 数据源，指定位置的模式，位置的数据
                   color = "salary_scale", hover_name = "company_location", # 对应的颜色， 对应的名称
                   hover_data = temp_df[['salary_in_usd']],  # 挂的数字，必须是dataframe
                    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(按照货币类型分组),选择top14
df = salaries.groupby('salary_currency', as_index = False)['salary_in_usd'].mean().sort_values('salary_in_usd', ascending = False) # 修改
# 第一个salary_in_usd最好加上两层中括号，使得到的是一个dataframe，这里只加了一层是因为as_index=False保证了它是一个dataframe
# 在分组的时候指定as_index = False再聚合 和 先分组聚合reset_index()的效果是一样的

# Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'salary_currency', # px.bar也可以画柱状图
            y = 'salary_in_usd',
            color = 'salary_currency',
            text = round(df['salary_in_usd']),
            color_discrete_sequence = px.colors.qualitative.Safe,
            )

fig.update_traces(textposition="outside") # 将文本放在外面
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()

# 平均工资(按照所在地分组),选择top14
df = salaries.groupby(['company_location'], as_index = False)[['salary_in_usd']].mean().sort_values('salary_in_usd', ascending = False)


#Selecting top 14
df = df.iloc[:14]
fig = px.bar(df, x = 'company_location',
            y = 'salary_in_usd',
            text = df['salary_in_usd'].apply(lambda x: str(round(x/1000, 2))+"k".format(x)), # 修改
            color = 'company_location',
            color_discrete_sequence = px.colors.qualitative.Dark2,
            )

fig.update_traces(textposition="outside") # 将文本放在外面
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_count
            FROM salaries
            GROUP BY experience_level
            ORDER BY job_count ASC
""")


# 绘制水平柱状图
data = go.Bar(x = job_exp['job_count'], y = job_exp['experience_level'],
              orientation = 'h', text = job_exp['job_count'], # orientation表示朝向,'h'表示水平
              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>'),
                  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()

# 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()

# 数据科学工作数量趋势(2020-2022)
job_year = query("""
    SELECT work_year, COUNT(*) AS 'job count'
    FROM salaries
    GROUP BY work_year
    ORDER BY 'job count' DESC
""")


data = go.Scatter(x = job_year['work_year'], y = job_year['job count'], # go模块绘制散点图
                  marker = dict(size = 20,            # 散点大小
                                line_width = 1.5,     # 散点的外框线宽
                                line_color = 'white', # 散点的外框线颜色
                                color = px.colors.qualitative.Alphabet), # 散点颜色
                  line = dict(color = '#ED7D31', width = 4), 
                  mode = 'lines+markers') # 散点和线都画


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
""")

# 绘制直方图和核密度曲线
plt.figure(figsize = (20, 8))
sns.set(rc = {
    
    'axes.facecolor' : '#f1e7d2',   # 背景颜色
             'figure.facecolor' : '#f1e7d2'}) # 图形颜色

p = sns.histplot(salary_usd["salary_in_usd"], 
                kde = True,         # 计算核密度估计,用一条或多条线来近似分布
                alpha = 1,          # 透明度,如果是distplot则没有这个参数
                fill = True,        # 填充直方图下面的空间,默认为True
                edgecolor = 'black',# 直方图的外框颜色为黑色
                linewidth = 1       # 直方图的外框线宽
                )
p.axes.lines[0].set_color("orange") # 核密度曲线的颜色标为橙色
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大数据分析工作
salary_hi10 = query("""
    SELECT job_title,
    salary
    FROM salaries
    ORDER BY salary_in_usd DESC
    LIMIT 10
""") # 修改

data = go.Bar(x = salary_hi10['salary'],
             y = salary_hi10['job_title'],
             orientation = 'h',
             text = salary_hi10['salary'],
             textposition = 'inside',
             insidetextanchor = 'middle', # 文本锚点在中间
             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()

# 不同岗位平均薪资与排名
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 = 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 = 'black'),
              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 year
    FROM salaries
    GROUP BY work_year
    ORDER BY salary DESC
""") # 修改

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 Salary
    FROM salaries
""")

# 绘制小提琴图
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<br>经验水平&薪资</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() # 按照工作年份和经验水平分组,只有数字类型会被求中位数
tmp_df.reset_index(inplace = True)
display(tmp_df.head()) # 修改:打印dataframe的开头五行

fig = px.line(tmp_df, x='work_year', y='salary_in_usd', color='experience_level', symbol="experience_level") # 绘制多条折线图

fig.update_layout(title = {
    
    'text': "<b>Median Salary Trend By Experience Level<br>不同经验水平的薪资趋势</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()

# 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) #  绘制多条核密度曲线


fig.update_layout(title = {
    
    'text': "<b>Salary Distribution By Working Year<br>年份&薪资分布</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 Salary
    FROM salaries
""")

# 绘制箱线图
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 count
                FROM salaries
                GROUP BY company_size
""")

# 绘制环形图(饼图中间镂空)
data = go.Pie(labels = comp_size['company_size'], 
              values = comp_size['count'].values, # .values可写可不写
              hoverinfo = 'label',                # 移动到饼图上时会显示的信息
              hole = 0.5, # 中间镂空
              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() # size()聚合方法是考虑有多少行,一定返回series,count()会考虑每列,如果有空值就不计入
comp_s = np.round(df['Small'].values / df['Small'].values.sum(),2) # values得到的是ndarray,这里的计算用到广播
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']

# 绘制极坐标图
fig.add_trace(go.Scatterpolar( # add_trace方法增加一条轨迹
    r = comp_s,
    theta = categories, # 设置角坐标
    fill = 'toself',    # 起点和终点连线,形成闭环的图形
    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])), # 极坐标幅度为[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 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（远程办公）和 WFO 的比例
rem_type = query("""
    SELECT remote_ratio,
    COUNT(*) AS total
    FROM salaries
    GROUP 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 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()

# 不同经验水平&远程比率
exp_remote = salaries.groupby(['experience_level', 'remote_ratio']).size() # 修改:得到series
display(exp_remote.head())
exp_remote = exp_remote.reset_index().rename(columns={
    
    0: 'cnt'})
display(exp_remote.head())
print(exp_remote.index)
fig = px.histogram(exp_remote, x = 'experience_level',
                  y = 'cnt', color = 'remote_ratio',
                  barmode = 'group', # 同一组的条形图不堆叠
                  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()

Analysis conclusion

The top 3 jobs in data science are data scientist, data engineer, and data analyst.

Data science jobs are becoming more and more popular. The proportion of employees will increase from 11.9% in 2020 to 52.4% in 2022.

The United States is the country with the most data science companies.

The IQR of the salary distribution is between 62.7k and 150k.

Among data science employees, most are at the senior level, with fewer at the expert level.

Most data science employees work full-time, with few contractors and freelancers.

Lead data engineer is the highest paying data science job.

The minimum salary for data science (entry-level experience) is $4000, and the maximum salary for data science with expert-level experience is $600,000.

Company composition: 53.7% mid-sized companies, 32.6% large companies, 13.7% small data science companies.

Salaries are also affected by company size, with larger companies paying higher salaries.

62.8% of data science jobs were fully remote, 20.9% were non-remote, and 16.3% were partially remote.

Data science salaries grow with time and experience