Python
  • Introduction
  • Chapter 1.Notes from research
    • 1.Introduction of Python
    • 2. Build developer environment
      • 2.1.Sublime Text3
      • 2.2.Jupyter(IPython notebook)
        • 2.2.1.Introduction
        • 2.2.2.Basic usage
        • 2.2.3.some common operations
      • 2.3.Github
        • 2.3.1.Create Github account
        • 2.3.2.Create a new repository
        • 2.3.3.Basic operations: config, clone, push
      • 2.4.Install Python 3.4 in Windows
    • 3. Write Python code
      • 3.1.Hello Python
      • 3.2.Basic knowledges
      • 3.3.撰寫獨立python程式
      • 3.4.Arguments parser
      • 3.5.Class
      • 3.6.Sequence
    • 4. Web crawler
      • 4.1.Introduction
      • 4.2.requests
      • 4.3.beautifulSoup4
      • 3.4.a little web crawler
    • 5. Software testing
      • 5.1. Robot Framework
        • 1.1.Introduction
        • 1.2.What is test-automation framework?
        • 1.3.Robot Framework Architecture
        • 1.4.Robot Framework Library
        • 1.5.Reference
    • 6. encode/ decode
      • 6.1.編碼/解碼器的基本概念
      • 6.2.常見的編碼/ 解碼錯誤訊息與其意義
      • 6.3 .處理文字檔案
    • 7. module
      • 7.1.Write a module
      • 7.2.Common module
        • 7.2.1.sched
        • 7.2.2.threading
    • 8. Integrate IIS with django
      • 8.1.Integrate IIS with django
  • Chapter 2.Courses
    • 2.1.Python for Data Science and Machine Learning Bootcamp
      • 2.1.1.Virtual Environment
      • 2.1.2.Python crash course
      • 2.1.3.Python for Data Analysis - NumPy
        • 2.1.3.1.Numpy arrays
        • 2.1.3.2.Numpy Array Indexing
        • 2.1.3.3.Numpy Operations
      • 2.1.4.Python for Data Analysis - Pandas
        • 2.1.4.1.Introduction
        • 2.1.4.2.Series
        • 2.1.4.3.DataFrames
        • 2.1.4.4.Missing Data
        • 2.1.4.5.GroupBy
        • 2.1.4.6.Merging joining and Concatenating
        • 2.1.4.7.Data input and output
      • 2.1.5.Python for Data Visual Visualization - Pandas Built-in Data Visualization
      • 2.1.6.Python for Data Visualization - Matplotlib
        • 2.1.6.1.Introduction of Matplotlib
        • 2.1.6.2.Matplotlib
      • 2.1.7.Python for Data Visualization - Seaborn
        • 2.1.7.1.Introduction to Seaborn
        • 2.1.7.2.Distribution Plots
        • 2.1.7.3.Categorical Plots
        • 2.1.7.4.Matrix Plots
        • 2.1.7.5.Grids
        • 2.1.7.6.Regression Plots
      • 2.1.8. Python for Data Visualization - Plotly and Cufflinks
        • 2.1.8.1.Introduction to Plotly and Cufflinks
        • 2.1.8.2.Plotly and Cufflinks
      • 2.1.9. Python for Data Visualization - Geographical plotting
        • 2.1.9.1.Choropleth Maps - USA
        • 2.1.9.2.Choropleth Maps - World
      • 2.1.10.Combine data analysis and visualization to tackle real world data sets
        • 911 calls capstone project
      • 2.1.11.Linear regression
        • 2.1.11.1.Introduction to Scikit-learn
        • 2.1.11.2.Linear regression with Python
      • 2.1.12.Logistic regression
        • 2.1.12.1.Logistic regression Theory
        • 2.1.12.2.Logistic regression with Python
      • 2.1.13.K Nearest Neighbors
        • 2.1.13.1.KNN Theory
        • 2.1.13.2.KNN with Python
      • 2.1.14.Decision trees and random forests
        • 2.1.14.1.Introduction of tree methods
        • 2.1.14.2.Decision trees and Random Forests with Python
      • 2.1.15.Support Vector Machines
      • 2.1.16.K means clustering
      • 2.1.17.Principal Component Analysis
    • 2.2. Machine Learning Crash Course Jam
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  • 1. 使用library
  • 2.從實際資料中繪製Choropleth Map

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  1. Chapter 2.Courses
  2. 2.1.Python for Data Science and Machine Learning Bootcamp
  3. 2.1.9. Python for Data Visualization - Geographical plotting

2.1.9.2.Choropleth Maps - World

Previous2.1.9.1.Choropleth Maps - USANext2.1.10.Combine data analysis and visualization to tackle real world data sets

Last updated 5 years ago

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1. 使用library

import plotly.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot
init_notebook_mode(connected = True)

2.從實際資料中繪製Choropleth Map

  • 讀取資料

    • 可在資料中放入想要顯示的country code, z值 (數量), label

      import pandas as pd
df = pd.read_csv('2014_World_GDP')
df.head()

  • Choropleth Map的兩個要素 Data, Layout

    • Data的locationmode預設為country code, 因此若locations是country code則不需指定locationmode

      data = dict(
  type = 'choropleth',
  locations = df['CODE'],
  z = df['GDP (BILLIONS)'],
  text = df['COUNTRY'],
  colorbar = {'title' : 'GDP Billions US'},
) 

layout = dict(
title = '2014 Global GDP',
geo = dict(
  showframe = False,
  projection = {'type':'Mercator'}
)
)

    • 若資料來源是country name, 則必須在指定locationmode中指定: locationmode = "country names"

      data = dict(
  type = 'choropleth',
  locations = df['COUNTRY'],
  locationmode = "country names",
  z = df['GDP (BILLIONS)'],
  text = df['COUNTRY'],
  colorbar = {'title' : 'GDP Billions US'},
)

layout = dict(
title = '2014 Global GDP',
geo = dict(
  showframe = False,
  projection = {'type':'Mercator'}
)
)

  • 製作Choropleth Map

choromap = go.Figure(data = [data], layout = layout)
iplot(choromap)