Pandas - 11.2 datetime

import pandas as pd

ebola = pd.read_csv('data/country_timeseries.csv', parse_dates=[0])
print(ebola.iloc[:5, :5])
'''
        Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
0 2015-01-05  289        2776.0            NaN            10030.0
1 2015-01-04  288        2775.0            NaN             9780.0
2 2015-01-03  287        2769.0         8166.0             9722.0
3 2015-01-02  286           NaN         8157.0                NaN
4 2014-12-31  284        2730.0         8115.0             9633.0
'''

基于日期數(shù)據(jù)獲取子集

print(ebola.loc[(ebola.Date.dt.year == 2014) & (ebola.Date.dt.month == 6)].iloc[:,:5])
'''
         Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
79 2014-06-30  100         413.0          107.0              239.0
80 2014-06-22   92           NaN           51.0                NaN
81 2014-06-20   90         390.0            NaN              158.0
82 2014-06-19   89           NaN           41.0                NaN
83 2014-06-18   88         390.0            NaN              136.0
84 2014-06-17   87           NaN            NaN               97.0
85 2014-06-16   86         398.0           33.0                NaN
86 2014-06-10   80         351.0           13.0               89.0
87 2014-06-05   75           NaN           13.0               81.0
88 2014-06-03   73         344.0           13.0                NaN
89 2014-06-01   71         328.0           13.0               79.0
'''

DatetimeIndex 對(duì)象

處理包含datetime的數(shù)據(jù)時(shí)逸爵，經(jīng)常把datetime對(duì)象設(shè)置成DataFrame的索引

ebola.index = ebola['Date']
print(ebola.index)
'''
DatetimeIndex(['2015-01-05', '2015-01-04', '2015-01-03', '2015-01-02',
               '2014-12-31', '2014-12-28', '2014-12-27', '2014-12-24',
               '2014-12-21', '2014-12-20',
               ...
               '2014-04-04', '2014-04-01', '2014-03-31', '2014-03-29',
               '2014-03-28', '2014-03-27', '2014-03-26', '2014-03-25',
               '2014-03-24', '2014-03-22'],
              dtype='datetime64[ns]', name='Date', length=122, freq=None)
'''

# 指定年份抽取行數(shù)據(jù)
print(ebola['2015'].iloc[:,:5])
'''
                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
Date                                                                      
2015-01-05 2015-01-05  289        2776.0            NaN            10030.0
2015-01-04 2015-01-04  288        2775.0            NaN             9780.0
2015-01-03 2015-01-03  287        2769.0         8166.0             9722.0
2015-01-02 2015-01-02  286           NaN         8157.0                NaN
'''

# 指定年份月份抽取數(shù)據(jù)
print(ebola['2014-06'].iloc[:,:5])
'''
                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
Date                                                                      
2014-06-30 2014-06-30  100         413.0          107.0              239.0
2014-06-22 2014-06-22   92           NaN           51.0                NaN
2014-06-20 2014-06-20   90         390.0            NaN              158.0
2014-06-19 2014-06-19   89           NaN           41.0                NaN
2014-06-18 2014-06-18   88         390.0            NaN              136.0
2014-06-17 2014-06-17   87           NaN            NaN               97.0
2014-06-16 2014-06-16   86         398.0           33.0                NaN
2014-06-10 2014-06-10   80         351.0           13.0               89.0
2014-06-05 2014-06-05   75           NaN           13.0               81.0
2014-06-03 2014-06-03   73         344.0           13.0                NaN
2014-06-01 2014-06-01   71         328.0           13.0               79.0
'''

TimedeltaIndex 對(duì)象

用日期運(yùn)算的結(jié)果作為index后荡陷，可以直接用TimedeltaIndex對(duì)象作為索引拍屑，但是必須要注意index順序，從上到下场刑。

ebola['outbreak_d'] = ebola['Date'] - ebola['Date'].min()

ebola.index = ebola['outbreak_d']

print(ebola.index)
'''
TimedeltaIndex(['289 days', '288 days', '287 days', '286 days', '284 days',
                '281 days', '280 days', '277 days', '274 days', '273 days',
                ...
                 '13 days',  '10 days',   '9 days',   '7 days',   '6 days',
                  '5 days',   '4 days',   '3 days',   '2 days',   '0 days'],
               dtype='timedelta64[ns]', name='outbreak_d', length=122, freq=None)
'''

print(ebola.iloc[:5, :5])
'''
                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
outbreak_d                                                                
289 days   2015-01-05  289        2776.0            NaN            10030.0
288 days   2015-01-04  288        2775.0            NaN             9780.0
287 days   2015-01-03  287        2769.0         8166.0             9722.0
286 days   2015-01-02  286           NaN         8157.0                NaN
284 days   2014-12-31  284        2730.0         8115.0             9633.0
'''

print(ebola['289 days': '280 days'].iloc[:, :5])
'''
                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
outbreak_d                                                                
289 days   2015-01-05  289        2776.0            NaN            10030.0
288 days   2015-01-04  288        2775.0            NaN             9780.0
287 days   2015-01-03  287        2769.0         8166.0             9722.0
286 days   2015-01-02  286           NaN         8157.0                NaN
284 days   2014-12-31  284        2730.0         8115.0             9633.0
281 days   2014-12-28  281        2706.0         8018.0             9446.0
'''

# 索引順序錯(cuò)誤
print(ebola['280 days': '289 days'].iloc[:, :5])
'''
Empty DataFrame
Columns: [Date, Day, Cases_Guinea, Cases_Liberia, Cases_SierraLeone]
Index: []
'''

日期范圍

2015-01-01和2014-03-23的數(shù)據(jù)是缺失的

ebola = pd.read_csv('data/country_timeseries.csv', parse_dates=[0])

print(ebola.iloc[:5, :5])
'''
        Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
0 2015-01-05  289        2776.0            NaN            10030.0
1 2015-01-04  288        2775.0            NaN             9780.0
2 2015-01-03  287        2769.0         8166.0             9722.0
3 2015-01-02  286           NaN         8157.0                NaN
4 2014-12-31  284        2730.0         8115.0             9633.0
'''

print(ebola.iloc[-5:, :5])
'''
          Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
117 2014-03-27    5         103.0            8.0                6.0
118 2014-03-26    4          86.0            NaN                NaN
119 2014-03-25    3          86.0            NaN                NaN
120 2014-03-24    2          86.0            NaN                NaN
121 2014-03-22    0          49.0            NaN                NaN
'''

創(chuàng)建一個(gè)日期范圍來(lái)為數(shù)據(jù)集重建索引

head_range = pd.date_range(start='2014-12-31', end='2015-01-05')
print(head_range)
'''
DatetimeIndex(['2014-12-31', '2015-01-01', '2015-01-02', '2015-01-03',
               '2015-01-04', '2015-01-05'],
              dtype='datetime64[ns]', freq='D')
'''

在這個(gè)例子中蔽豺，只取前5行數(shù)據(jù),想把head_range設(shè)置為ebola_5的索引，需要先把日期設(shè)置為ebola_5的索引棘利，然后為數(shù)據(jù)重建索引

ebola_5 = ebola.head()
ebola_5.index = ebola_5['Date']
ebola_5.reindex(head_range)
print(ebola_5.iloc[:, :5])
'''
                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
Date                                                                      
2015-01-05 2015-01-05  289        2776.0            NaN            10030.0
2015-01-04 2015-01-04  288        2775.0            NaN             9780.0
2015-01-03 2015-01-03  287        2769.0         8166.0             9722.0
2015-01-02 2015-01-02  286           NaN         8157.0                NaN
2014-12-31 2014-12-31  284        2730.0         8115.0             9633.0
'''

頻率

在head_range函數(shù)中有一個(gè)參數(shù)freq,其默認(rèn)值為D（代表day）橱野，表示日期范圍內(nèi)的值是逐日遞增的。

print(pd.date_range('2017-01-01', '2017-01-07', freq='B'))
'''
DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04', '2017-01-05',
               '2017-01-06'],
              dtype='datetime64[ns]', freq='B')
'''

偏移量

偏移量是在基本頻率上做的一點(diǎn)調(diào)整善玫，例如可以向剛剛創(chuàng)建的工作日范圍添加一個(gè)偏移量水援，這樣就可以隔一個(gè)工作日取一個(gè)工作日。
在基本頻率前加一個(gè)倍數(shù)值就創(chuàng)建出了該偏移量

# 從2017年1月1日這周隔一天取一個(gè)工作日
print(pd.date_range('2017-01-01', '2017-01-07', freq='2B'))
# DatetimeIndex(['2017-01-02', '2017-01-04', '2017-01-06'], dtype='datetime64[ns]', freq='2B')

偏移量可以和其他基本頻率結(jié)合使用

# 每月的第一個(gè)星期五
print(pd.date_range('2017-01-01', '2017-12-31', freq='WOM-1THU'))
'''
DatetimeIndex(['2017-01-05', '2017-02-02', '2017-03-02', '2017-04-06',
               '2017-05-04', '2017-06-01', '2017-07-06', '2017-08-03',
               '2017-09-07', '2017-10-05', '2017-11-02', '2017-12-07'],
              dtype='datetime64[ns]', freq='WOM-1THU')
'''

移動(dòng)

有時(shí)需要更改數(shù)據(jù)的日期茅郎，例如修正數(shù)據(jù)中的某個(gè)測(cè)量誤差蜗元，或者對(duì)數(shù)據(jù)的開始日期進(jìn)行標(biāo)準(zhǔn)化，以便比較趨勢(shì)只洒。
比如需要比較不同國(guó)家的疫情傳播速度许帐，但是不同國(guó)家爆發(fā)疫情的時(shí)間不同,很難比較各國(guó)疫情的爆發(fā)情況劳坑。

ebola_sub = ebola[['Day', 'Cases_Guinea', 'Cases_Liberia']]
print(ebola_sub.tail(10))
'''
     Day  Cases_Guinea  Cases_Liberia
112   13         143.0           18.0
113   10         127.0            8.0
114    9         122.0            8.0
115    7         112.0            7.0
116    6         112.0            3.0
117    5         103.0            8.0
118    4          86.0            NaN
119    3          86.0            NaN
120    2          86.0            NaN
121    0          49.0            NaN
'''

最好所有的日期都從常用的0天開始毕谴。
（1）由于有些日期沒(méi)有列出來(lái)，所以需要為數(shù)據(jù)集的所有日期創(chuàng)建一個(gè)日期范圍距芬。
（2）需要計(jì)算數(shù)據(jù)集中最早日期和每列最早有效日期（非NaN）之間的插值涝开。
（3）然后根據(jù)計(jì)算結(jié)果移動(dòng)每列。
開始之前框仔，首先讀取ebola數(shù)據(jù)集的一個(gè)副本舀武。同時(shí)把Date解析為date對(duì)象，并把日期指派給index离斩。本例中會(huì)解析日期并直接設(shè)置為索引银舱。

ebola = pd.read_csv('data/country_timeseries.csv', index_col='Date', parse_dates=['Date'])
print(ebola.head().iloc[:, :4])
'''
            Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
Date                                                           
2015-01-05  289        2776.0            NaN            10030.0
2015-01-04  288        2775.0            NaN             9780.0
2015-01-03  287        2769.0         8166.0             9722.0
2015-01-02  286           NaN         8157.0                NaN
2014-12-31  284        2730.0         8115.0             9633.0
'''

new_idx = pd.date_range(ebola.index.min(), ebola.index.max())
print(new_idx)
'''
DatetimeIndex(['2014-03-22', '2014-03-23', '2014-03-24', '2014-03-25',
               '2014-03-26', '2014-03-27', '2014-03-28', '2014-03-29',
               '2014-03-30', '2014-03-31',
               ...
               '2014-12-27', '2014-12-28', '2014-12-29', '2014-12-30',
               '2014-12-31', '2015-01-01', '2015-01-02', '2015-01-03',
               '2015-01-04', '2015-01-05'],
              dtype='datetime64[ns]', length=290, freq='D')
'''

new_idx = reversed(new_idx)
ebola = ebola.reindex(new_idx)
print(ebola.head().iloc[:, :4])
'''

              Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone
Date                                                             
2015-01-05  289.0        2776.0            NaN            10030.0
2015-01-04  288.0        2775.0            NaN             9780.0
2015-01-03  287.0        2769.0         8166.0             9722.0
2015-01-02  286.0           NaN         8157.0                NaN
2015-01-01    NaN           NaN            NaN                NaN
'''

# 每列最早有效日期，Series的last_valid_index方法返回最后一個(gè)非缺失值或非空值的索引值
# 類似的還有first_valid_index（返回第一個(gè)非缺失值或非空值的索引值）
last_valid = ebola.apply(pd.Series.last_valid_index)
print(last_valid)
'''
Day                   2014-03-22
Cases_Guinea          2014-03-22
Cases_Liberia         2014-03-27
Cases_SierraLeone     2014-03-27
Cases_Nigeria         2014-07-23
Cases_Senegal         2014-08-31
Cases_UnitedStates    2014-10-01
Cases_Spain           2014-10-08
Cases_Mali            2014-10-22
Deaths_Guinea         2014-03-22
Deaths_Liberia        2014-03-27
Deaths_SierraLeone    2014-03-27
Deaths_Nigeria        2014-07-23
Deaths_Senegal        2014-09-07
Deaths_UnitedStates   2014-10-01
Deaths_Spain          2014-10-08
Deaths_Mali           2014-10-22
dtype: datetime64[ns]
'''

# 獲取數(shù)據(jù)中最早的日期
earliest_date = ebola.index.min()
print(earliest_date)
# 2014-03-22 00:00:00

# 計(jì)算最早日期和每列最早有效期日的差值
shift_values = last_valid - earliest_date
print(shift_values)
'''
Day                     0 days
Cases_Guinea            0 days
Cases_Liberia           5 days
Cases_SierraLeone       5 days
Cases_Nigeria         123 days
Cases_Senegal         162 days
Cases_UnitedStates    193 days
Cases_Spain           200 days
Cases_Mali            214 days
Deaths_Guinea           0 days
Deaths_Liberia          5 days
Deaths_SierraLeone      5 days
Deaths_Nigeria        123 days
Deaths_Senegal        169 days
Deaths_UnitedStates   193 days
Deaths_Spain          200 days
Deaths_Mali           214 days
dtype: timedelta64[ns]
'''

# 歷遍各樂(lè)趣跛梗，根據(jù)shift_values中相應(yīng)的值使用shift方法把列下移寻馏。（shift_values中的數(shù)字都是正數(shù)，若是負(fù)數(shù)核偿，會(huì)把值上移）
ebola_dict = {}
for idx, col in enumerate(ebola):
    d = shift_values[idx].days
    shifted = ebola[col].shift(d)
    ebola_dict[col] = shifted

ebola_shift = pd.DataFrame(ebola_dict)
# dict是無(wú)序的诚欠，傳入原來(lái)的ebola的列來(lái)重新排列
ebola_shift = ebola_shift[ebola.columns]

# 每列的最后一行都有值
print(ebola_shift.tail())
'''
            Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \
Date                                                              
2014-03-26  4.0          86.0            8.0                2.0   
2014-03-25  3.0          86.0            NaN                NaN   
2014-03-24  2.0          86.0            7.0                NaN   
2014-03-23  NaN           NaN            3.0                2.0   
2014-03-22  0.0          49.0            8.0                6.0   

            Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \
Date                                                                        
2014-03-26            1.0            NaN                 1.0          1.0   
2014-03-25            NaN            NaN                 NaN          NaN   
2014-03-24            NaN            NaN                 NaN          NaN   
2014-03-23            NaN            NaN                 NaN          NaN   
2014-03-22            0.0            1.0                 1.0          1.0   

            Cases_Mali  Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  \
Date                                                                        
2014-03-26         NaN           62.0             4.0                 2.0   
2014-03-25         NaN           60.0             NaN                 NaN   
2014-03-24         NaN           59.0             2.0                 NaN   
2014-03-23         NaN            NaN             3.0                 2.0   
2014-03-22         1.0           29.0             6.0                 5.0   

            Deaths_Nigeria  Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  \
Date                                                                            
2014-03-26             1.0             NaN                  0.0           1.0   
2014-03-25             NaN             NaN                  NaN           NaN   
2014-03-24             NaN             NaN                  NaN           NaN   
2014-03-23             NaN             NaN                  NaN           NaN   
2014-03-22             0.0             0.0                  0.0           1.0   

            Deaths_Mali  
Date                     
2014-03-26          NaN  
2014-03-25          NaN  
2014-03-24          NaN  
2014-03-23          NaN  
2014-03-22          1.0  
'''

每一行的索引已經(jīng)失效，可以將其刪除，然后指定正確的列轰绵，即日期粉寞。Day不再表示日期爆發(fā)的第一天，而是指特定國(guó)家疫情爆發(fā)的第一天

ebola_shift.index = ebola_shift['Day']
ebola_shift = ebola_shift.drop(['Day'], axis=1)

print(ebola_shift.tail())
'''
     Cases_Guinea  Cases_Liberia  Cases_SierraLeone  Cases_Nigeria  \
Day                                                                  
4.0          86.0            8.0                2.0            1.0   
3.0          86.0            NaN                NaN            NaN   
2.0          86.0            7.0                NaN            NaN   
NaN           NaN            3.0                2.0            NaN   
0.0          49.0            8.0                6.0            0.0   

     Cases_Senegal  Cases_UnitedStates  Cases_Spain  Cases_Mali  \
Day                                                               
4.0            NaN                 1.0          1.0         NaN   
3.0            NaN                 NaN          NaN         NaN   
2.0            NaN                 NaN          NaN         NaN   
NaN            NaN                 NaN          NaN         NaN   
0.0            1.0                 1.0          1.0         1.0   

     Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  Deaths_Nigeria  \
Day                                                                      
4.0           62.0             4.0                 2.0             1.0   
3.0           60.0             NaN                 NaN             NaN   
2.0           59.0             2.0                 NaN             NaN   
NaN            NaN             3.0                 2.0             NaN   
0.0           29.0             6.0                 5.0             0.0   

     Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  
Day                                                                  
4.0             NaN                  0.0           1.0          NaN  
3.0             NaN                  NaN           NaN          NaN  
2.0             NaN                  NaN           NaN          NaN  
NaN             NaN                  NaN           NaN          NaN  
0.0             0.0                  0.0           1.0          1.0  
'''

重采樣

下采樣：從高頻率到低頻率（比如從每天到每月）
上采樣：從低頻率到高頻率（比如從每月到每天）
原樣采樣：采樣頻率不變（比如每月的第一個(gè)星期四到每月的最后一個(gè)星期五）

resample函數(shù)有一個(gè)rule參數(shù)左腔，用于接收偏移量字符串唧垦。

# 下采樣：從每天到每月
# 這里有多個(gè)值，需要把結(jié)果居合起來(lái)
down = ebola.resample('M').mean()
print(down.iloc[:5, :5])
'''
                   Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \
Date                                                                     
2014-03-31    4.500000     94.500000       6.500000           3.333333   
2014-04-30   24.333333    177.818182      24.555556           2.200000   
2014-05-31   51.888889    248.777778      12.555556           7.333333   
2014-06-30   84.636364    373.428571      35.500000         125.571429   
2014-07-31  115.700000    423.000000     212.300000         420.500000   

            Cases_Nigeria  
Date                       
2014-03-31            NaN  
2014-04-30            NaN  
2014-05-31            NaN  
2014-06-30            NaN  
2014-07-31       1.333333
'''

# 這里對(duì)下采樣得到的值進(jìn)行上采樣
# 請(qǐng)注意填充了多少確實(shí)日期
# 使用缺失值進(jìn)行填充
up = down.resample('D').mean()
print(up.iloc[:5, :5])
'''
            Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  Cases_Nigeria
Date                                                                          
2014-03-31  4.5          94.5            6.5           3.333333            NaN
2014-04-01  NaN           NaN            NaN                NaN            NaN
2014-04-02  NaN           NaN            NaN                NaN            NaN
2014-04-03  NaN           NaN            NaN                NaN            NaN
2014-04-04  NaN           NaN            NaN                NaN            NaN
'''

時(shí)區(qū)

import pytz
import re

# 在pandas中處理時(shí)區(qū)液样，最簡(jiǎn)單的方法是使用pytz.all_timezones給出的字符串名
regex = re.compile(r'^US')
selected_files = filter(regex.search, pytz.common_timezones)
print(list(selected_files))
# ['US/Alaska', 'US/Arizona', 'US/Central', 'US/Eastern', 'US/Hawaii', 'US/Mountain', 'US/Pacific']

# 指定時(shí)區(qū)
depart = pd.Timestamp('2017-08-29 07:00', tz='US/Eastern')
print(depart)
# 2017-08-29 07:00:00-04:00

# 對(duì)時(shí)區(qū)編碼的另一種方法是調(diào)用‘空’時(shí)間戳的tz_localize方法
arrive = pd.Timestamp('2017-08-29 09:57')
print(arrive)

arrive = arrive.tz_localize('US/Pacific')
print(arrive)
# 2017-08-29 09:57:00-07:00

# 把航班到達(dá)時(shí)間轉(zhuǎn)換回東部時(shí)區(qū)
arrive = arrive.tz_convert('US/Eastern')
print(arrive)
# 2017-08-29 12:57:00-04:00

# 對(duì)兩個(gè)時(shí)間點(diǎn)計(jì)算時(shí)間差业崖，之前的版本需要調(diào)整成同一個(gè)時(shí)區(qū)才可計(jì)算，現(xiàn)在不需要
# duration = arrive.tz_convert('US/Eastern') - depart
duration = arrive - depart
print(duration)
# 0 days 05:57:00

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