博客
关于我
理解Python系统下的时间格式
阅读量:348 次
发布时间:2019-03-04

本文共 3102 字,大约阅读时间需要 10 分钟。

  • Overview

    pandas/numpy/datetime/time,这四个module是常用的时间相关模块。timestampdatetimestr是三大类常用的数据类型。需要理顺彼此之间错综复杂的关系。

    The Python world has a number of avaiable representations of dates, times, deltas, and timespans.

  • Native Python dates and times: datetime and dateutil

    Python’s basic objects for working with dates and times reside in the built-in datetime module.

    Third-party dateutil can be used to parse dates from a variety of string formats.

    • The datetime module supplies classes for manipulating dates and times.

    • The dateutil module provides powerful extensions to the standard datetime module.

  • Typed arrays of times: Numpy's datetime64

    The weaknesses of Python’s datetime format inspired the Numpy team to add a set of native time series date type to Numpy.

    The datetime64 dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented very compactly.

    The datetime64 requires a very specific input format.

    Because of the uniform type in NumPy datetime64 arrays, this type of operation can be accomplished much more quickly than if we were working directly with Python’s datetime objects.

    • Starting in NumPy 1.7, there are core array date types which natively support datetime functionality. The data type is called “datetime64”, so named because “datetime” is already taken by datetime library included in Python.

      The most basic way to create datetimes is from strings in ISO8601 date or datetime format.

      The Unit for internal storage is :

      1. automatically selected from the form of the string,
      2. and can be either :
        1. a unit: Y M W D
        2. a time unit: h m s ms us ns ps fs as

      datetime64 is the data type; datetime64[ns] or datetime64[s] or datetime64[unit] is datetime64 with unit.

      Finally, we will note that while the datetime64 data type addresses some of the deficiencies of the built-in Python datetime type, it lacks many of the convenient methods and functions provided by datetime and especially dateutil.

  • Dates and times in pandas: best of both worlds

    Pandas builds upon all the tools just discussed to provide Timestamp object, which combines the ease-of-use of datetime and dateutil with the efficient storage and vectorized interface of numpy.datetime64.

    From a group of these Timestamp objects, Pandas can construct a DatetimeIndex that can be used to index data in a Series or DataFrame.

    Pandas Time Series: Indexing by Time

    Where the Pandas time series tools become useful is when you begin to index data by timestamps.

    Pandas Time Series Data Structures

    For timestamps, Pandas provides the Timestamp type: it is essentially a replacement for Python’s native datetime, but is based on the more efficient numpy.datetime64 date type.

    For time Periods, Pandas provides the Period type, based on numpy.datetime64.

    For time deltas or durations, Pandas provides the Timedelta type, based on numpy.timedelta64, more efficient replacement for Python’s native datetime.timedelta type.

  • 汇总

    Python native is datetime.datetime data type from module: datetime;

    更高效的是datetime64 data type from module: NumPy;

    结合上述两者优点的是TimeStamp / Timedelta data type from module: Pandas;

  • 不同数据类型之间的转换

    在这里插入图片描述

  • References

转载地址:http://vtge.baihongyu.com/

你可能感兴趣的文章
Mysql 分页语句 Limit原理
查看>>
MySql 创建函数 Error Code : 1418
查看>>
MySQL 创建新用户及授予权限的完整流程
查看>>
mysql 创建表,不能包含关键字values 以及 表id自增问题
查看>>
mysql 删除日志文件详解
查看>>
mysql 判断表字段是否存在,然后修改
查看>>
MySQL 到底能不能放到 Docker 里跑?
查看>>
mysql 前缀索引 命令_11 | Mysql怎么给字符串字段加索引?
查看>>
MySQL 加锁处理分析
查看>>
mysql 协议的退出命令包及解析
查看>>
mysql 参数 innodb_flush_log_at_trx_commit
查看>>
mysql 取表中分组之后最新一条数据 分组最新数据 分组取最新数据 分组数据 获取每个分类的最新数据
查看>>
MySQL 命令和内置函数
查看>>
mysql 四种存储引擎
查看>>
MySQL 在并发场景下的问题及解决思路
查看>>
MySQL 基础架构
查看>>
MySQL 基础模块的面试题总结
查看>>
MySQL 备份 Xtrabackup
查看>>
mYSQL 外键约束
查看>>
mysql 多个表关联查询查询时间长的问题
查看>>