六、日期时间预处理
作者：Chris Albon
译者：飞龙
协议：CC BY-NC-SA 4.0
把日期和时间拆成多个特征
# 加载库
import pandas as pd
# 创建数据帧
df = pd.DataFrame()
# 创建五个日期
df['date'] = pd.date_range('1/1/2001', periods=150, freq='W')
# 为年月日，时分秒创建特征
df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month
df['day'] = df['date'].dt.day
df['hour'] = df['date'].dt.hour
df['minute'] = df['date'].dt.minute
# 展示三行
df.head(3)
|
date |
year |
month |
day |
hour |
minute |
| 0 |
2001-01-07 |
2001 |
1 |
7 |
0 |
0 |
| 1 |
2001-01-14 |
2001 |
1 |
14 |
0 |
0 |
| 2 |
2001-01-21 |
2001 |
1 |
21 |
0 |
0 |
计算日期时间之间的差
# 加载库
import pandas as pd
# 创建数据帧
df = pd.DataFrame()
# 创建两个 datetime 特征
df['Arrived'] = [pd.Timestamp('01-01-2017'), pd.Timestamp('01-04-2017')]
df['Left'] = [pd.Timestamp('01-01-2017'), pd.Timestamp('01-06-2017')]
# 计算特征之间的间隔
df['Left'] - df['Arrived']
'''
0 0 days
1 2 days
dtype: timedelta64[ns]
'''
# 计算特征之间的间隔
pd.Series(delta.days for delta in (df['Left'] - df['Arrived']))
'''
0 0
1 2
dtype: int64
'''
将字符串转换为日期
# 加载库
import numpy as np
import pandas as pd
# 创建字符串
date_strings = np.array(['03-04-2005 11:35 PM',
'23-05-2010 12:01 AM',
'04-09-2009 09:09 PM'])
如果errors="coerce"那么任何问题都不会产生错误（默认行为），而是将导致错误的值设置为NaT（即缺失值）。
| 代码 |
描述 |
示例 |
%Y |
整年 |
2001 |
%m |
零填充的月份 |
04 |
%d |
零填充的日期 |
09 |
%I |
零填充的小时（12 小时） |
02 |
%p |
AM 或 PM |
AM |
%M |
零填充的分钟 |
05 |
%S |
零填充的秒钟 |
09 |
# 转换为 datetime
[pd.to_datetime(date, format="%d-%m-%Y %I:%M %p", errors="coerce") for date in date_strings]
'''
[Timestamp('2005-04-03 23:35:00'),
Timestamp('2010-05-23 00:01:00'),
Timestamp('2009-09-04 21:09:00')]
'''
转换 pandas 列的时区
# 加载库
import pandas as pd
from pytz import all_timezones
# 展示十个时区
all_timezones[0:10]
'''
['Africa/Abidjan',
'Africa/Accra',
'Africa/Addis_Ababa',
'Africa/Algiers',
'Africa/Asmara',
'Africa/Asmera',
'Africa/Bamako',
'Africa/Bangui',
'Africa/Banjul',
'Africa/Bissau']
'''
# 创建十个日期
dates = pd.Series(pd.date_range('2/2/2002', periods=10, freq='M'))
# 设置时区
dates_with_abidjan_time_zone = dates.dt.tz_localize('Africa/Abidjan')
# 查看 pandas 序列
dates_with_abidjan_time_zone
'''
0 2002-02-28 00:00:00+00:00
1 2002-03-31 00:00:00+00:00
2 2002-04-30 00:00:00+00:00
3 2002-05-31 00:00:00+00:00
4 2002-06-30 00:00:00+00:00
5 2002-07-31 00:00:00+00:00
6 2002-08-31 00:00:00+00:00
7 2002-09-30 00:00:00+00:00
8 2002-10-31 00:00:00+00:00
9 2002-11-30 00:00:00+00:00
dtype: datetime64[ns, Africa/Abidjan]
'''
# 转换时区
dates_with_london_time_zone = dates_with_abidjan_time_zone.dt.tz_convert('Europe/London')
# 查看 pandas 序列
dates_with_london_time_zone
'''
0 2002-02-28 00:00:00+00:00
1 2002-03-31 00:00:00+00:00
2 2002-04-30 01:00:00+01:00
3 2002-05-31 01:00:00+01:00
4 2002-06-30 01:00:00+01:00
5 2002-07-31 01:00:00+01:00
6 2002-08-31 01:00:00+01:00
7 2002-09-30 01:00:00+01:00
8 2002-10-31 00:00:00+00:00
9 2002-11-30 00:00:00+00:00
dtype: datetime64[ns, Europe/London]
'''
编码星期
# 加载库
import pandas as pd
# 创建数据集
dates = pd.Series(pd.date_range('2/2/2002', periods=3, freq='M'))
# 查看数据
dates
'''
0 2002-02-28
1 2002-03-31
2 2002-04-30
dtype: datetime64[ns]
'''
# 查看星期
dates.dt.weekday_name
'''
0 Thursday
1 Sunday
2 Tuesday
dtype: object
'''
处理时间序列中的缺失值
# 加载库
import pandas as pd
import numpy as np
# 创建日期
time_index = pd.date_range('01/01/2010', periods=5, freq='M')
# 创建数据帧，设置索引
df = pd.DataFrame(index=time_index)
# 创建带有一些缺失值的特征
df['Sales'] = [1.0,2.0,np.nan,np.nan,5.0]
# 对缺失值执行插值
df.interpolate()
|
Sales |
| 2010-01-31 |
1.0 |
| 2010-02-28 |
2.0 |
| 2010-03-31 |
3.0 |
| 2010-04-30 |
4.0 |
| 2010-05-31 |
5.0 |
# 前向填充
df.ffill()
|
Sales |
| 2010-01-31 |
1.0 |
| 2010-02-28 |
2.0 |
| 2010-03-31 |
2.0 |
| 2010-04-30 |
2.0 |
| 2010-05-31 |
5.0 |
# 后向填充
df.bfill()
|
Sales |
| 2010-01-31 |
1.0 |
| 2010-02-28 |
2.0 |
| 2010-03-31 |
5.0 |
| 2010-04-30 |
5.0 |
| 2010-05-31 |
5.0 |
# 对缺失值执行插值
df.interpolate(limit=1, limit_direction='forward')
|
Sales |
| 2010-01-31 |
1.0 |
| 2010-02-28 |
2.0 |
| 2010-03-31 |
3.0 |
| 2010-04-30 |
NaN |
| 2010-05-31 |
5.0 |
处理时区
# 加载库
import pandas as pd
from pytz import all_timezones
# 展示十个时区
all_timezones[0:10]
'''
['Africa/Abidjan',
'Africa/Accra',
'Africa/Addis_Ababa',
'Africa/Algiers',
'Africa/Asmara',
'Africa/Asmera',
'Africa/Bamako',
'Africa/Bangui',
'Africa/Banjul',
'Africa/Bissau']
'''
# 创建 datetime
pd.Timestamp('2017-05-01 06:00:00', tz='Europe/London')
# Timestamp('2017-05-01 06:00:00+0100', tz='Europe/London')
# 创建 datetime
date = pd.Timestamp('2017-05-01 06:00:00')
# 设置时区
date_in_london = date.tz_localize('Europe/London')
# 修改时区
date_in_london.tz_convert('Africa/Abidjan')
# Timestamp('2017-05-01 05:00:00+0000', tz='Africa/Abidjan')
平移时间特征
# 加载库
import pandas as pd
# 创建数据帧
df = pd.DataFrame()
# 创建数据
df['dates'] = pd.date_range('1/1/2001', periods=5, freq='D')
df['stock_price'] = [1.1,2.2,3.3,4.4,5.5]
# 将值平移一行
df['previous_days_stock_price'] = df['stock_price'].shift(1)
# 展示数据帧
df
|
dates |
stock_price |
previous_days_stock_price |
| 0 |
2001-01-01 |
1.1 |
NaN |
| 1 |
2001-01-02 |
2.2 |
1.1 |
| 2 |
2001-01-03 |
3.3 |
2.2 |
| 3 |
2001-01-04 |
4.4 |
3.3 |
| 4 |
2001-01-05 |
5.5 |
4.4 |
滑动时间窗口
# 加载库
import pandas as pd
# 创建 datetime
time_index = pd.date_range('01/01/2010', periods=5, freq='M')
# 创建数据帧，设置索引
df = pd.DataFrame(index=time_index)
# 创建特征
df['Stock_Price'] = [1,2,3,4,5]
# 计算滑动均值
df.rolling(window=2).mean()
|
Stock_Price |
| 2010-01-31 |
NaN |
| 2010-02-28 |
1.5 |
| 2010-03-31 |
2.5 |
| 2010-04-30 |
3.5 |
| 2010-05-31 |
4.5 |
# 识别滑动时间窗口中的最大值
df.rolling(window=2).max()
|
Stock_Price |
| 2010-01-31 |
NaN |
| 2010-02-28 |
2.0 |
| 2010-03-31 |
3.0 |
| 2010-04-30 |
4.0 |
| 2010-05-31 |
5.0 |
选择日期时间范围
# 加载库
import pandas as pd
# 创建数据帧
df = pd.DataFrame()
# 创建 datetime
df['date'] = pd.date_range('1/1/2001', periods=100000, freq='H')
如果数据帧未按时间索引，请使用此方法。
# 选择两个日期时间之间的观测
df[(df['date'] > '2002-1-1 01:00:00') & (df['date'] <= '2002-1-1 04:00:00')]
|
date |
| 8762 |
2002-01-01 02:00:00 |
| 8763 |
2002-01-01 03:00:00 |
| 8764 |
2002-01-01 04:00:00 |
如果数据帧按时间索引，请使用此方法。
# 设置索引
df = df.set_index(df['date'])
# 选择两个日期时间之间的观测
df.loc['2002-1-1 01:00:00':'2002-1-1 04:00:00']
|
date |
| date |
|
| 2002-01-01 01:00:00 |
2002-01-01 01:00:00 |
| 2002-01-01 02:00:00 |
2002-01-01 02:00:00 |
| 2002-01-01 03:00:00 |
2002-01-01 03:00:00 |
| 2002-01-01 04:00:00 |
2002-01-01 04:00:00 |
