pandas小记：pandas时间序列分析和处理Timeseries

r囧r小猫 2022-09-25 06:20 263阅读 0赞

http://[blog.csdn.net/pipisorry/article/details/52209377][blog.csdn.net_pipisorry_article_details_52209377]

pandas 最基本的时间序列类型就是以时间戳（TimeStamp）为 index 元素的 Series 类型。

## 其它时间序列处理相关的包 ##

\[[P4J 0.6: Periodic light curve analysis tools based on Information Theory][P4J 0.6_ Periodic light curve analysis tools based on Information Theory]\]

\[[p4j github][]\]

## pandas时序数据文件读取 ##

dateparse = lambda dates: pd.datetime.strptime(dates, '%Y-%m')  
data = pd.read\_csv('AirPassengers.csv', parse\_dates='Month', index\_col='Month',date\_parser=dateparse)  
print data.head()

### read\_csv时序参数    ###

parse\_dates:这是指定含有时间数据信息的列。正如上面所说的，列的名称为“月份”。  
index\_col:使用pandas 的时间序列数据背后的关键思想是：目录成为描述时间数据信息的变量。所以该参数告诉pandas使用“月份”的列作为索引。  
date\_parser：指定将输入的字符串转换为可变的时间数据。Pandas默认的数据读取格式是‘YYYY-MM-DD HH:MM:SS’？如需要读取的数据没有默认的格式，就要人工定义。这和dataparse的功能部分相似，这里的定义可以为这一目的服务。The default uses dateutil.parser.parser to do the conversion.

\[[pandas.read\_csv][pandas.read_csv]\]

\[[python模块：时间处理模块][python]\]

# 时间序列分析和处理Time Series #

pandas has simple, powerful, and efficient functionality for performingresampling operations during frequency conversion (e.g., converting secondlydata into 5-minutely data). This is extremely common in, but not limited to,financial applications.

## 时序数据生成和表示 ##

c = pandas.Timestamp('2012-01-01 00:00:08')

In [103]: rng = pd.date_range('1/1/2012', periods=100, freq='S')
    
    In [104]: ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)
    
    In [105]: ts.resample('5Min', how='sum')
    Out[105]: 
    2012-01-01    25083
    Freq: 5T, dtype: int32

Time zone representation

In [106]: rng = pd.date_range('3/6/2012 00:00', periods=5, freq='D')
    
    In [107]: ts = pd.Series(np.random.randn(len(rng)), rng)
    
    In [108]: ts
    Out[108]: 
    2012-03-06    0.464000
    2012-03-07    0.227371
    2012-03-08   -0.496922
    2012-03-09    0.306389
    2012-03-10   -2.290613
    Freq: D, dtype: float64
    
    In [109]: ts_utc = ts.tz_localize('UTC')
    
    In [110]: ts_utc
    Out[110]: 
    2012-03-06 00:00:00+00:00    0.464000
    2012-03-07 00:00:00+00:00    0.227371
    2012-03-08 00:00:00+00:00   -0.496922
    2012-03-09 00:00:00+00:00    0.306389
    2012-03-10 00:00:00+00:00   -2.290613
    Freq: D, dtype: float64

## 时序转换    ##

Convert to another time zone

In [111]: ts_utc.tz_convert('US/Eastern')
    Out[111]: 
    2012-03-05 19:00:00-05:00    0.464000
    2012-03-06 19:00:00-05:00    0.227371
    2012-03-07 19:00:00-05:00   -0.496922
    2012-03-08 19:00:00-05:00    0.306389
    2012-03-09 19:00:00-05:00   -2.290613
    Freq: D, dtype: float64

Converting between time span representations

In [112]: rng = pd.date_range('1/1/2012', periods=5, freq='M')
    In [113]: ts = pd.Series(np.random.randn(len(rng)), index=rng)
    In [114]: ts
    Out[114]: 
    2012-01-31   -1.134623
    2012-02-29   -1.561819
    2012-03-31   -0.260838
    2012-04-30    0.281957
    2012-05-31    1.523962
    Freq: M, dtype: float64
    
    In [115]: ps = ts.to_period()
    In [116]: ps
    Out[116]: 
    2012-01   -1.134623
    2012-02   -1.561819
    2012-03   -0.260838
    2012-04    0.281957
    2012-05    1.523962
    Freq: M, dtype: float64
    
    In [117]: ps.to_timestamp()
    Out[117]: 
    2012-01-01   -1.134623
    2012-02-01   -1.561819
    2012-03-01   -0.260838
    2012-04-01    0.281957
    2012-05-01    1.523962
    Freq: MS, dtype: float64

Converting between period and timestamp enables some convenient arithmeticfunctions to be used. In the following example, we convert a quarterlyfrequency with year ending in November to 9am of the end of the month followingthe quarter end:

In [118]: prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')
    In [119]: ts = pd.Series(np.random.randn(len(prng)), prng)
    In [120]: ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
    In [121]: ts.head()
    Out[121]: 
    1990-03-01 09:00   -0.902937
    1990-06-01 09:00    0.068159
    1990-09-01 09:00   -0.057873
    1990-12-01 09:00   -0.368204
    1991-03-01 09:00   -1.144073
    Freq: H, dtype: float64

\[[pandas-docs/stable/timeseries][pandas-docs_stable_timeseries]\]

\[[pandas cookbook Timeseries][]\]

## 时序处理相关函数    ##

** pandas.[to\_datetime][to_datetime](\*args, \*\*kwargs)**

Convert argument to datetime.

lz觉得此函数一般是对不明确类型进行转换，如果直接对datetime类型转换太慢不值得，有其它方法。

**Series.dt.normalize(\*args, \*\*kwargs)**  
    Return DatetimeIndex with times to midnight. Length is unaltered.直接调用应该就是只取datetime的date部分，即重置time部分，而保持datetime类型不变。

**DatetimeIndex.normalize()**

Return DatetimeIndex with times to midnight. Length is unaltered

### DatetimeIndex相关的函数 ###

Note: pandas时间序列series的index必须是DatetimeIndex不能是Index，也就是如果index是从dataframe列中转换来的，其类型必须是datetime类型，不能是date、object等等其它类型！否则在很多处理包中会报错。

ts.index

返回DatetimeIndex(\['2015-07-02', '2015-07-03', '2015-07-04', '2015-07-05', ... '2016-10-26', '2016-10-27', '2016-10-28', '2016-10-29'\],  
              dtype='datetime64\[ns\]', name='time', length=481, freq=None)

说明index类型是DatetimeIndex没错，但是freq=None说明datetime的频率没有自动推断出来，这里看起来应该freq=1也就是一天。

freq也可以通过ts.index.inferred\_freq查看

## series对象的dt存取器 .dt accessor    ##

Series.[dt][]()  
    Accessor object for datetimelike properties of the Series values.

**示例**

s.dt.date

>>> s.dt.hour  
    >>> s.dt.second  
    >>> s.dt.quarter  
Returns a Series indexed like the original Series. Raises TypeError if the Series does not contain datetimelike values.

### Series datetime类型处理    ###

**示例：将series中datetime类型只保留date部分而不改变数据类型，即数据类型仍为datetime**

s.map(pd.Timestamp.date) 或者 s.map(lambda x: pd.to\_datetime(x.date()))

但是pd.Timestamp.date会将数据的类型从datetime类型转换成date类型，在pd中显示是object类型；而转换成datetime的函数pd.to\_datetime特别慢而耗时。

一个最好的转换方式是直接进行类型转换

Converting to datetime64\[D\]:df.dates.values.astype('M8\[D\]') Though re-assigning that to a DataFrame col will revert it back to \[ns\].

所以可以这样实现user\_pay\_df\['time'\] = user\_pay\_df\['time'\].dt.date.astype('M8\[D\]')

或者user\_pay\_df\['time'\] = user\_pay\_df\['time'\].dt.date.astype(np.datetime64)

不过这仍不是最快的方法，lz找到一个更快的方法

**重置datetime的time部分**

user_pay_df['time'] = user_pay_df['time'].dt.normalize()

\[ [Reset time part of a pandas timestamp][]\]

\[[Keep only date part when using pandas.to\_datetime][Keep only date part when using pandas.to_datetime]\]

uid   sid                time  
0  22127870  1862 2015-12-25 17:00:00  
1   3434231  1862 2016-10-05 11:00:00  
2  16955285  1862 2016-02-10 15:00:00  
uid              int64  
sid              int64  
time    datetime64\[ns\]  
dtype: object  
        uid   sid       time  
0  22127870  1862 2015-12-25  
1   3434231  1862 2016-10-05  
2  16955285  1862 2016-02-10  
uid              int64  
sid              int64  
time    datetime64\[ns\]

[皮皮blog][blog]

## pandas时序类型    ##

### pandas 的 TimeStamp ###

pandas 最基本的时间日期对象是一个从 Series 派生出来的子类 TimeStamp，这个对象与 datetime 对象保有高度兼容性，可通过 pd.to\_datetime() 函数转换。（一般是从 datetime 转换为 Timestamp）

lang:python
    >>> pd.to_datetime(now)
    Timestamp('2014-06-17 15:56:19.313193', tz=None)
    >>> pd.to_datetime(np.nan)
    NaT

### pandas 的时间序列 ###

pandas 最基本的时间序列类型就是以时间戳（TimeStamp）为 index 元素的 Series 类型。

lang:python
    >>> dates = [datetime(2011,1,1),datetime(2011,1,2),datetime(2011,1,3)]
    >>> ts = Series(np.random.randn(3),index=dates)
    >>> ts
    2011-01-01    0.362289
    2011-01-02    0.586695
    2011-01-03   -0.154522
    dtype: float64
    >>> type(ts)
    <class 'pandas.core.series.Series'>
    >>> ts.index
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2011-01-01, ..., 2011-01-03]
    Length: 3, Freq: None, Timezone: None
    >>> ts.index[0]
    Timestamp('2011-01-01 00:00:00', tz=None)

时间序列之间的算术运算会自动按时间对齐。

### 时间序列的索引、选取、子集构造 ###

时间序列只是 index 比较特殊的 Series ，因此一般的索引操作对时间序列依然有效。其特别之处在于对时间序列索引的操作优化。

先在序列对象选择一个特殊值。可以通过以下两种方式实现：

1 使用各种字符串进行索引：

lang:python
    >>> ts['20110101']
    0.36228897878097266
    >>> ts['2011-01-01']
    0.36228897878097266
    >>> ts['01/01/2011']
    0.36228897878097266

2 Import the datetime library and use 'datetime' function:

from datetime import datetime
    ts[datetime(1949,1,1)]

3 你需要1949年所有的值，可以这样做：ts\['1949'\]。可见，月份部分已经省略。如果你要获得某月所有的日期，日期部分也可以省略。

对于较长的序列，还可以只传入 “年” 或 “年月” 选取切片：

>>> ts
    2011-01-01    0.362289
    2011-01-02    0.586695
    2011-01-03   -0.154522
    2012-12-25    0.111869
    dtype: float64
    >>> ts['2012']
    2012-12-25    0.111869
    dtype: float64
    >>> ts['2011-1-2':'2012-12']
    2011-01-02    0.586695
    2011-01-03   -0.154522
    2012-12-25    0.111869
    dtype: float64

4 除了这种字符串切片方式外，还有一种实例方法可用：ts.truncate(after='2011-01-03')。

值得注意的是，切片时使用的字符串时间戳并不必存在于 index 之中，如 ts.truncate(before='3055') 也是合法的。

##  ##

### Time/Date Components ###

There are several time/date properties that one can access from Timestamp or a collection of timestamps like a DateTimeIndex.

<table>     
 <thead> 
  <tr> 
   <th>Property</th> 
   <th>Description</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>year</td> 
   <td>The year of the datetime</td> 
  </tr> 
  <tr> 
   <td>month</td> 
   <td>The month of the datetime</td> 
  </tr> 
  <tr> 
   <td>day</td> 
   <td>The days of the datetime</td> 
  </tr> 
  <tr> 
   <td>hour</td> 
   <td>The hour of the datetime</td> 
  </tr> 
  <tr> 
   <td>minute</td> 
   <td>The minutes of the datetime</td> 
  </tr> 
  <tr> 
   <td>second</td> 
   <td>The seconds of the datetime</td> 
  </tr> 
  <tr> 
   <td>microsecond</td> 
   <td>The microseconds of the datetime</td> 
  </tr> 
  <tr> 
   <td>nanosecond</td> 
   <td>The nanoseconds of the datetime</td> 
  </tr> 
  <tr> 
   <td>date</td> 
   <td>Returns datetime.date</td> 
  </tr> 
  <tr> 
   <td>time</td> 
   <td>Returns datetime.time</td> 
  </tr> 
  <tr> 
   <td>dayofyear</td> 
   <td>The ordinal day of year</td> 
  </tr> 
  <tr> 
   <td>weekofyear</td> 
   <td>The week ordinal of the year</td> 
  </tr> 
  <tr> 
   <td>week</td> 
   <td>The week ordinal of the year</td> 
  </tr> 
  <tr> 
   <td>dayofweek</td> 
   <td>The numer of the day of the week with Monday=0, Sunday=6</td> 
  </tr> 
  <tr> 
   <td>weekday</td> 
   <td>The number of the day of the week with Monday=0, Sunday=6</td> 
  </tr> 
  <tr> 
   <td>weekday_name</td> 
   <td>The name of the day in a week (ex: Friday)</td> 
  </tr> 
  <tr> 
   <td>quarter</td> 
   <td>Quarter of the date: Jan=Mar = 1, Apr-Jun = 2, etc.</td> 
  </tr> 
  <tr> 
   <td>days_in_month</td> 
   <td>The number of days in the month of the datetime</td> 
  </tr> 
  <tr> 
   <td>is_month_start</td> 
   <td>Logical indicating if first day of month (defined by frequency)</td> 
  </tr> 
  <tr> 
   <td>is_month_end</td> 
   <td>Logical indicating if last day of month (defined by frequency)</td> 
  </tr> 
  <tr> 
   <td>is_quarter_start</td> 
   <td>Logical indicating if first day of quarter (defined by frequency)</td> 
  </tr> 
  <tr> 
   <td>is_quarter_end</td> 
   <td>Logical indicating if last day of quarter (defined by frequency)</td> 
  </tr> 
  <tr> 
   <td>is_year_start</td> 
   <td>Logical indicating if first day of year (defined by frequency)</td> 
  </tr> 
  <tr> 
   <td>is_year_end</td> 
   <td>Logical indicating if last day of year (defined by frequency)</td> 
  </tr> 
 </tbody> 
</table>

Furthermore, if you have a Series with datetimelike values, then you can access these properties via the .dt accessor, see the [docs][].

\[Time/Date Components[¶][Link 1]\]

## 日期的范围、频率以及移动 ##

pandas 中的时间序列一般被默认为不规则的，即没有固定的频率。但出于分析的需要，我们可以通过插值的方式将序列转换为具有固定频率的格式。一种快捷方式是使用 .resample(rule) 方法：

lang:python
    >>> ts
    2011-01-01    0.362289
    2011-01-02    0.586695
    2011-01-03   -0.154522
    2011-01-06    0.222958
    dtype: float64
    >>> ts.resample('D')
    2011-01-01    0.362289
    2011-01-02    0.586695
    2011-01-03   -0.154522
    2011-01-04         NaN
    2011-01-05         NaN
    2011-01-06    0.222958
    Freq: D, dtype: float64

### 生成日期范围 ###

pd.date\_range() 可用于生成指定长度的 DatetimeIndex。参数可以是起始结束日期，或单给一个日期，加一个时间段参数。日期是包含的。

lang:python
    >>> pd.date_range('20100101','20100110')
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2010-01-01, ..., 2010-01-10]
    Length: 10, Freq: D, Timezone: None
    >>> pd.date_range(start='20100101',periods=10)
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2010-01-01, ..., 2010-01-10]
    Length: 10, Freq: D, Timezone: None
    >>> pd.date_range(end='20100110',periods=10)
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2010-01-01, ..., 2010-01-10]
    Length: 10, Freq: D, Timezone: None

默认情况下，date\_range 会按天计算时间点。这可以通过 freq 参数进行更改，如 “BM” 代表 bussiness end of month。

lang:python
    >>> pd.date_range('20100101','20100601',freq='BM')
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2010-01-29, ..., 2010-05-31]
    Length: 5, Freq: BM, Timezone: None

### 频率和日期偏移量 ###

pandas 中的频率是由一个基础频率和一个乘数组成的。基础频率通常以一个字符串别名表示，如上例中的 “BM”。对于每个基础频率，都有一个被称为日期偏移量（date offset）的对象与之对应。可以通过实例化日期偏移量来创建某种频率：

lang:python
    >>> Hour()
    <Hour>
    >>> Hour(2)
    <2 * Hours>
    >>> Hour(1) + Minute(30)
    <90 * Minutes>

但一般来说不必这么麻烦，使用前面提过的字符串别名来创建频率就可以了：

lang:python
    >>> pd.date_range('00:00','12:00',freq='1h20min')
    <class 'pandas.tseries.index.DatetimeIndex'>
    [2014-06-17 00:00:00, ..., 2014-06-17 12:00:00]
    Length: 10, Freq: 80T, Timezone: None

可用的别名，可以通过 help() 或 文档来查询，这里就不写了。

### 移动（超前和滞后）数据 ###

移动（shifting）指的是沿着时间轴将数据前移或后移。Series 和 DataFrame 都有一个 .shift() 方法用于执行单纯的移动操作，index 维持不变：

lang:python
    >>> ts
    2011-01-01    0.362289
    2011-01-02    0.586695
    2011-01-03   -0.154522
    2011-01-06    0.222958
    dtype: float64
    >>> ts.shift(2)
    2011-01-01         NaN
    2011-01-02         NaN
    2011-01-03    0.362289
    2011-01-06    0.586695
    dtype: float64
    >>> ts.shift(-2)
    2011-01-01   -0.154522
    2011-01-02    0.222958
    2011-01-03         NaN
    2011-01-06         NaN
    dtype: float64

上例中因为移动操作产生了 NA 值，另一种移动方法是移动 index，而保持数据不变。这种移动方法需要额外提供一个 freq 参数来指定移动的频率：

lang:python
    >>> ts.shift(2,freq='D')
    2011-01-03    0.362289
    2011-01-04    0.586695
    2011-01-05   -0.154522
    2011-01-08    0.222958
    dtype: float64
    >>> ts.shift(2,freq='3D')
    2011-01-07    0.362289
    2011-01-08    0.586695
    2011-01-09   -0.154522
    2011-01-12    0.222958
    dtype: float64

## 时期及其算术运算 ##

本节使用的时期（period）概念不同于前面的时间戳（timestamp），指的是一个时间段。但在使用上并没有太多不同，pd.Period 类的构造函数仍需要一个时间戳，以及一个 freq 参数。freq 用于指明该 period 的长度，时间戳则说明该 period 在公园时间轴上的位置。

lang:python
    >>> p = pd.Period(2010,freq='M')
    >>> p
    Period('2010-01', 'M')
    >>> p + 2
    Period('2010-03', 'M')

上例中我给 period 的构造器传了一个 “年” 单位的时间戳和一个 “Month” 的 freq，pandas 便自动把 2010 解释为了 2010-01。

period\_range 函数可用于创建规则的时间范围：

lang:python
    >>> pd.period_range('2010-01','2010-05',freq='M')
    <class 'pandas.tseries.period.PeriodIndex'>
    freq: M
    [2010-01, ..., 2010-05]
    length: 5

PeriodIndex 类保存了一组 period，它可以在任何 pandas 数据结构中被用作轴索引：

lang:python
    >>> Series(np.random.randn(5),index=pd.period_range('201001','201005',freq='M'))
    2010-01    0.755961
    2010-02   -1.074492
    2010-03   -0.379719
    2010-04    0.153662
    2010-05   -0.291157
    Freq: M, dtype: float64

### 时期的频率转换 ###

Period 和 PeriodIndex 对象都可以通过其 .asfreq(freq, method=None, how=None) 方法被转换成别的频率。

lang:python
    >>> p = pd.Period('2007',freq='A-DEC')
    >>> p.asfreq('M',how='start')
    Period('2007-01', 'M')
    >>> p.asfreq('M',how='end')
    Period('2007-12', 'M')
    >>> ts = Series(np.random.randn(1),index=[p])
    >>> ts
    2007   -0.112347
    Freq: A-DEC, dtype: float64
    >>> ts.asfreq('M',how='start')
    2007-01   -0.112347
    Freq: M, dtype: float64

### 时间戳与时期间相互转换 ###

以时间戳和以时期为 index 的 Series 和 DataFrame 都有一对 .to\_period() 和 to\_timestamp(how='start') 方法用于互相转换 index 的类型。因为从 period 到 timestamp 的转换涉及到一个取端值的问题，所以需要一个额外的 how 参数，默认为 'start'：

lang:python
    >>> ts = Series(np.random.randn(5),index=pd.period_range('201001','201005',freq='M'))
    >>> ts
    2010-01   -0.312160
    2010-02    0.962652
    2010-03   -0.959478
    2010-04    1.240236
    2010-05   -0.916218
    Freq: M, dtype: float64
    >>> ts.to_timestamp()
    2010-01-01   -0.312160
    2010-02-01    0.962652
    2010-03-01   -0.959478
    2010-04-01    1.240236
    2010-05-01   -0.916218
    Freq: MS, dtype: float64
    >>> ts.to_timestamp(how='end')
    2010-01-31   -0.312160
    2010-02-28    0.962652
    2010-03-31   -0.959478
    2010-04-30    1.240236
    2010-05-31   -0.916218
    Freq: M, dtype: float64
    >>> ts.to_timestamp().to_period()
    2010-01-01 00:00:00.000   -0.312160
    2010-02-01 00:00:00.000    0.962652
    2010-03-01 00:00:00.000   -0.959478
    2010-04-01 00:00:00.000    1.240236
    2010-05-01 00:00:00.000   -0.916218
    Freq: L, dtype: float64
    >>> ts.to_timestamp().to_period('M')
    2010-01   -0.312160
    2010-02    0.962652
    2010-03   -0.959478
    2010-04    1.240236
    2010-05   -0.916218
    Freq: M, dtype: float64

## 重采样及频率转换 ##

重采样（resampling）指的是将时间序列从一个频率转换到另一个频率的过程。pandas 对象都含有一个.resample(freq, how=None, axis=0, fill\_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0) 方法用于实现这个过程。

本篇最前面曾用 resample 规整化过时间序列。当时进行的是插值操作，因为原索引的频率与给出的 freq 参数相同。resample 方法更多的应用场合是 freq 发生改变的时候，这时操作就分为升采样（upsampling）和降采样（downsampling）两种。具体的区别都体现在参数里。

lang:python
    >>> ts
    2010-01   -0.312160
    2010-02    0.962652
    2010-03   -0.959478
    2010-04    1.240236
    2010-05   -0.916218
    Freq: M, dtype: float64
    >>> ts.resample('D',fill_method='ffill')#升采样
    2010-01-01   -0.31216
    2010-01-02   -0.31216
    2010-01-03   -0.31216
    2010-01-04   -0.31216
    2010-01-05   -0.31216
    2010-01-06   -0.31216
    2010-01-07   -0.31216
    2010-01-08   -0.31216
    2010-01-09   -0.31216
    2010-01-10   -0.31216
    2010-01-11   -0.31216
    2010-01-12   -0.31216
    2010-01-13   -0.31216
    2010-01-14   -0.31216
    2010-01-15   -0.31216
    ...
    2010-05-17   -0.916218
    2010-05-18   -0.916218
    2010-05-19   -0.916218
    2010-05-20   -0.916218
    2010-05-21   -0.916218
    2010-05-22   -0.916218
    2010-05-23   -0.916218
    2010-05-24   -0.916218
    2010-05-25   -0.916218
    2010-05-26   -0.916218
    2010-05-27   -0.916218
    2010-05-28   -0.916218
    2010-05-29   -0.916218
    2010-05-30   -0.916218
    2010-05-31   -0.916218
    Freq: D, Length: 151
    >>> ts.resample('A-JAN',how='sum')#降采样
    2010   -0.312160
    2011    0.327191
    Freq: A-JAN, dtype: float64

\[[pandas 时间序列操作][pandas]\]

from: [http://blog.csdn.net/pipisorry/article/details/52209377][blog.csdn.net_pipisorry_article_details_52209377]

ref: \[[时间序列预测全攻略（附带Python代码）][Python]\]

\[[概率论：假设检验-t检验和Augmented Dickey–Fuller test ][-t_Augmented Dickey_Fuller test]\]

\[[Complete guide to create a Time Series Forecast (with Codes in Python)][Complete guide to create a Time Series Forecast _with Codes in Python]\]

[blog.csdn.net_pipisorry_article_details_52209377]: http://blog.csdn.net/pipisorry/article/details/52209377
[P4J 0.6_ Periodic light curve analysis tools based on Information Theory]: https://pypi.python.org/pypi/P4J/0.6
[p4j github]: https://github.com/phuijse/P4J
[pandas.read_csv]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html#pandas.read_csv
[python]: http://blog.csdn.net/pipisorry/article/details/53067168
[pandas-docs_stable_timeseries]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html
[pandas cookbook Timeseries]: http://pandas.pydata.org/pandas-docs/stable/cookbook.html#timeseries
[to_datetime]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html
[dt]: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.html?highlight=dt#pandas.Series.dt
[Reset time part of a pandas timestamp]: http://stackoverflow.com/questions/26882499/reset-time-part-of-a-pandas-timestamp
[Keep only date part when using pandas.to_datetime]: http://stackoverflow.com/questions/16176996/keep-only-date-part-when-using-pandas-to-datetime
[blog]: http://blog.csdn.net/pipisorry
[docs]: http://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dt-accessors
[Link 1]: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#time-date-components
[pandas]: http://my.oschina.net/lionets/blog/280855
[Python]: http://www.36dsj.com/archives/44065
[-t_Augmented Dickey_Fuller test]: http://blog.csdn.net/pipisorry/article/details/51184556
[Complete guide to create a Time Series Forecast _with Codes in Python]: https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/