• 基礎數據分析-2026(2)

人口結構分佈

• 講義

• 台灣歷年人口(.csv) population_taiwan_age2024.csv

  世界0-14歲歷年人口(.csv) population_world_age2024_0_14.csv

  世界15-64歲歷年人口(.csv) population_world_age2024_15_64.csv

  世界65歲以上歷年人口(.csv) population_world_age2024_65_plus.csv

  # 匯入numpy套件，為了簡化程式碼，將它套件另外命名成np
  import numpy as np
  # 匯入pandas套件，為了簡化程式碼，將它套件另外命名成pd
  import pandas as pd
  # 匯入 csv 套件
  import csv
  
  # 匯入plotly套件，以便繪製視覺化圖形
  import plotly.graph_objs as go
  import plotly.io as pio
  
  # 1. 針對 Colab 的核心設定：指定渲染模式
  pio.renderers.default = 'colab'
  
  # 2. 讀取人口數資料
  data1 = pd.read_csv('population_taiwan_age2024.csv')
  data2 = pd.read_csv('population_world_age2024_0_14.csv')
  data3 = pd.read_csv('population_world_age2024_15_64.csv')
  data4 = pd.read_csv('population_world_age2024_65_plus.csv')
  
  country = ['臺灣', '日本', '南韓', '中國']
  
  # 4個國家0-14歲的人口比例
  p1 = []
  # 4個國家15-64歲的人口比例
  p2 = []
  # 4個國家65歲以上的人口比例
  p3 = []
  
  # 臺灣
  taiwan_p1 = data1.loc[0]['2024'] # Changed from [65] to ['2024']
  taiwan_p2 = data1.loc[1]['2024'] # Changed from [65] to ['2024']
  taiwan_p3 = data1.loc[2]['2024'] # Changed from [65] to ['2024']
  taiwan_all = taiwan_p1 + taiwan_p2 + taiwan_p3
  
  p1.append(taiwan_p1 / taiwan_all * 100)
  p2.append(taiwan_p2 / taiwan_all * 100)
  p3.append(taiwan_p3 / taiwan_all * 100)
  
  # 日本
  country_p1 = data2.loc[120]['2024'] # Changed from [65] to ['2024']
  country_p2 = data3.loc[120]['2024'] # Changed from [65] to ['2024']
  country_p3 = data4.loc[120]['2024'] # Changed from [65] to ['2024']
  country_all = country_p1 + country_p2 + country_p3
  
  p1.append(country_p1 / country_all * 100)
  p2.append(country_p2 / country_all * 100)
  p3.append(country_p3 / country_all * 100)
  
  # 南韓
  country_p1 = data2.loc[125]['2024'] # Changed from [65] to ['2024']
  country_p2 = data3.loc[125]['2024'] # Changed from [65] to ['2024']
  country_p3 = data4.loc[125]['2024'] # Changed from [65] to ['2024']
  country_all = country_p1 + country_p2 + country_p3
  
  p1.append(country_p1 / country_all * 100)
  p2.append(country_p2 / country_all * 100)
  p3.append(country_p3 / country_all * 100)
  
  # 中國 
  country_p1 = data2.loc[45]['2024'] # Changed from [65] to ['2024']
  country_p2 = data3.loc[45]['2024'] # Changed from [65] to ['2024']
  country_p3 = data4.loc[45]['2024'] # Changed from [65] to ['2024']
  country_all = country_p1 + country_p2 + country_p3
  
  p1.append(country_p1 / country_all * 100)
  p2.append(country_p2 / country_all * 100)
  p3.append(country_p3 / country_all * 100)
  
  # 3. 準備繪圖資料
  trace1 = go.Bar(
      x = country,
      y = p1,
      name = '0-14'
  )
  trace2 = go.Bar(
      x = country,
      y = p2,
      name = '15-64'
  )
  trace3 = go.Bar(
      x = country,
      y = p3,
      name = '65up'
  )
  
  traces = [trace1, trace2, trace3]
  
  # 4. 建立 Layout
  layout = go.Layout(
      title='2024 四國人口結構分佈',  
      xaxis={'title': '國家'}, 
      yaxis={'title': '區間'}, 
      barmode='stack'
  )
  
  # 5. 組合並顯示
  fig = go.Figure(data=traces, layout=layout)
  fig.show()