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. Chapter 1.Notes from research
  2. 2. Build developer environment

2.1.Sublime Text3

Previous2. Build developer environmentNext2.2.Jupyter(IPython notebook)

Last updated 5 years ago

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  • 0.Prerequisite

    • 1.Install sublime Text 3:

  • 1.Introduction

    • Anaconda是一個plugin, 可幫助我們在開發Python時有較高的生產力, 並且可以幫助開發時的style及正確性

    • 主要功能:

      • 1.語法檢測(Lint)

        • PEP 8

          • PEP 8 是Python的代碼風格指南(Style Guide for Python Code)，其中很多内容都来自Guido van Rossum。PEP 8 从代碼格式、注釋、空白、變量名、參考及權利聲明等方面出發。

        • 當語法或格式出現問題時, 最左方會出現白點

        • 畫面 -> 右鍵選擇Anaconda -> Show error list

          • 舉例來說, 當import一個lib但沒使用時, 即會出現警告

      • 2.閱讀Pydoc的文件

        • 可以閱讀Pydoc的文件以及自己function或是class method定義的doc的文件解釋

        • Pydoc

          • pydoc是Python自帶的module，主要用於從python module中自動生成文檔，也可以從瀏覽器中看這些文檔

          • 用法

            • 1.Help

                    python -m pydoc -h

            • 2.在命令列中查看該module的用法

                    python -m pydoc <module>

            • 3.從瀏覽器打開pydoc

                    python -m pydoc -p 1234

      • 3.Go to definition

      • 4.自動提示與補齊功能(Auto Complete)

        • 推測後續可能的文字並顯示出來

  • 2.安裝

    • 1.Preference -> Package Control -> Package Control: Install package

    • 2.在搜尋欄輸入Anaconda

  • 3.設定

    • 1.修改Preference -> Package Settings -> Anaconda -> Settings Default的內容

      • 1.修改python_interpreter

      • 2.修改complete_all_parameters, complete_parameters

    • 2.將Preference -> Package Settings -> Anaconda -> Settings Default的內容複製到Preference -> Package Settings -> Anaconda -> Settings User

    • 3.重開sublime

https://jenhsuan.gitbooks.io/web-front-end/content/chapter1/11install-sublime-text3-and-packages.html
https://www.gitbook.com/book/jenhsuan/web-front-end/edit#/edit/master/chapter1/12sublime-plugin/121package-install-control.md?_k=7v5tq7
http://sighingnow.github.io/%E7%BC%96%E7%A8%8B%E8%AF%AD%E8%A8%80/pep_8.html