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• Overview

  pandas/numpy/datetime/time,这四个module是常用的时间相关模块。timestamp，datetime，str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

  

• References