20180307【充实计划】第639期

昨日阅读1小时，累积阅读52小时

昨日阅读1小时，累计阅读102小时

2018.3.7
继续阅读《投机者的扑克：操盘18年手记》
阅读心得：本书是国内较难得的对顺势而为的正确交易理念的继承，书中大多数观点都是极为宝贵的经验之谈，但没对顺势交易及关键点展开描述（可理解）并提出较好的改变错误的交易思维而走上正道的好的学习、进阶办法。
昨日阅读2.5小时，累计阅读8.5小时
阅读到的有价值的内容： 1、认识自己的缺点比市场还重要！ 2、投机市场，不要爱上你的头寸；3、痛是宝贵的财富；4、让风险意识融进你的血液，镌刻在大脑里；5、不抄底不抓顶，不做短线。

昨日阅读0.5小时，累计阅读88小时

昨日阅读1小时，累计阅读486小时

第07天

1.主题：正则表达式
《正则表达式必知必会》——Ben Forta著
Python for Data Analysis 2nd Edition

2.摘要
正则表达式的两种用途：查找和替换；

对比：
array0将匹配array0
array[0]将匹配array0
array\[0\]将匹配array[0]
\d匹配0-9的数字，等价于[0-9]
\D匹配0-9之外的字符，等价于^[0-9]
\s匹配空白字符，等价于[\f\n\r\t\v]
\S匹配非空白字符，等价于[^\f\n\r\t\v]
\w匹配数字字母或下划线，等价于[a-zA-Z0-9_]
\W匹配非数字字母或非下划线，等价于[^a-zA-Z0-9_]

Preparation
Cleaning, munging, combining, normalizing, reshaping, slicing and dicing, and transforming data for analysis

%quickref or %magic. 在jupyter notebook中查询快捷键或魔法键

Using NumPy arrays enables you to express many kinds of data processing tasks as concise array expressions that might otherwise require writing loops. This practice of replacing explicit loops with array expressions is commonly referred to as vectoriza‐ tion.

While pandas adopts many coding idioms from NumPy, the biggest difference is that pandas is designed for working with tabular or heterogeneous data. NumPy, by con‐ trast, is best suited for working with homogeneous numerical array data.

pandas slicing with labels behaves differently than normal Python slicing in that the end‐
point is inclusive:
    In [125]: obj['b':'c']
    Out[125]:
    b    1.0
    c    2.0

The re module functions fall into three categories: pattern matching, substitution, and splitting. Naturally these are all related; a regex describes a pattern to locate in the text, which can then be used for many purposes.  


3.心得感悟
快速快速学习数据处理预备知识，飞速达到核心知识点：时间序列；
此后暂停，消化一下~~

4.时间统计
昨日阅读5小时，累计280小时

昨日阅读1小时，累计7小时

昨日阅读1小时，累计阅读414小时。

昨天阅读时间2小时，累计阅读时间770小时。

昨日阅读2小时，累积阅读187小时