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. Categorical Plots的繪圖種類
  • 1. 使用library
  • 2.bar plot
  • 3.countplot(計數圖)
  • 4.boxplot
  • 5.stripplot
  • 6.violinplot
  • 7.swarmplot
  • 8.factorplot

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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

2.1.7.3.Categorical Plots

Previous2.1.7.2.Distribution PlotsNext2.1.7.4.Matrix Plots

Last updated 5 years ago

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0. Categorical Plots的繪圖種類

  • sns.barplot

  • sns.countplot

  • sns.boxplot

  • sns.stripplot

  • sns.violinplot

  • sns.swarmplot

  • sns.factorplot

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()

2.bar plot

  • 矩形高度默認為平均值 (可以用estimator調整), 誤差棒長度為允許誤差的範圍, 默認為95 (可以用ci調整)

sns.barplot(x='sex',y='total_bill',data=tips)
import numpy as np
sns.barplot(x='sex',y='total_bill',data=tips,estimator=np.std)

3.countplot(計數圖)

  • 只要給x就好了

    • 可應用在比較不同類別間的數量

      sns.countplot(x='sex', hue ='size', data=tips)

4.boxplot

sns.boxplot(x='day',y='total_bill',data=tips)

  • 也可以指定想要比較的類別

sns.boxplot(x='day',y='total_bill',data=tips, hue='smoker')

5.stripplot

  • 散點圖, 用來表示數據分佈情形

sns.stripplot(x='day',y='total_bill',data=tips)

  • 增加抖動程度

sns.stripplot(x='day',y='total_bill',data=tips,jitter=True)

  • 類別間的比較

sns.stripplot(x='day',y='total_bill',data=tips,jitter=True,hue='sex')

6.violinplot

  • boxplot決定了四分位數的位置, violinplot展示了任意位置的密度, 通過violinplot可以知道哪些位置的密度較高

sns.violinplot(x='day',y='total_bill',data=tips)

  • 類別間的比較

sns.violinplot(x='day',y='total_bill',data=tips,hue='sex',split=True)

7.swarmplot

  • 有分布趨勢的散點圖

sns.swarmplot(x='day',y='total_bill',data=tips)

  • 類別間的比較

sns.swarmplot(x='day',y='total_bill',data=tips, hue='sex')

  • violinplot + swarmplot

sns.violinplot(x='day',y='total_bill',data=tips)
sns.swarmplot(x='day',y='total_bill',data=tips, color='black')

8.factorplot

  • 萬用的plot

sns.factorplot(x='day',y='total_bill',data=tips, kind='bar')

參考資料
參考資料
由上到下分別代表上邊緣, 上四分位數, 中位數, 下四分位數, 下邊緣, 點為outlier