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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  • 0. Distribution Plots的繪圖種類
  • 1. 使用library
  • 2.Distribution plot
  • 3. Joint plot
  • 4.Pair plot
  • 5.Rug plot

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  1. Chapter 2.Courses
  2. 2.1.Python for Data Science and Machine Learning Bootcamp
  3. 2.1.7.Python for Data Visualization - Seaborn

2.1.7.2.Distribution Plots

Previous2.1.7.1.Introduction to SeabornNext2.1.7.3.Categorical Plots

Last updated 5 years ago

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0. Distribution Plots的繪圖種類

  • sns.distplot (kde, bin)

  • sns.jointplot

  • sns.pairplot

  • sns.rugplot

1. 使用library

import seaborn as sns

  • 將圖表直接嵌入到Notebook之中

%matplotlib inline

  • 讀入資料

tips = sns.load_dataset('tips')
tips.head()
  •   tips['total_bill'].hist(bins = 30)
  tips['total_bill'].plot(kind='hist', bins = 30)
  tips['total_bill'].plot.hist(bins = 30)

  • kde (Kernel distribution estimation)預設為true

    • 也可以設為False

      sns.distplot(tips['total_bill'], kde=False)
sns.distplot(tips['total_bill'])

  • bin

sns.distplot(tips['total_bill'], kde=False, bins=30)

  • 比較資料來源中的兩個屬性之間的相關性或非相關性

    • 繪圖前可以先設定背景的方格線

      sns.set_style('whitegrid')

    • 例如比較資料中帳單金額與小費間的關聯性

      sns.jointplot(x='total_bill',y='tip',data=tips)

    • 用顏色深淺來代表相關性 (顏色越深, 相關性越高)

      sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')

    • 線性回歸圖

      sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')

    • kde (Kernel distribution estimation)

      sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde')

4.Pair plot

  • 可以將資料來源中的數值欄位一一比較, 畫出關聯圖

    • 輸入資料來源

      sns.pairplot(tips)

    • 加入類別欄位的比較

      • 例如比較性別在資料上的差異

      sns.pairplot(tips,hue='sex')

    • 調整色系

      sns.pairplot(tips,hue='sex',palette='coolwarm')

  • 以stick的方式表現資料

sns.rugplot(tips['total_bill'])

2.Distribution plot
等義於使用pandas內建的plot.hist或是plot(kind = 'hist')
3. Joint plot
5.Rug plot