极客教程使用Pandas数据框架的处理时间
Pandas是为金融建模而创建的，所以正如你所期望的，它包含了大量用于处理日期和时间的工具。有时，我们的数据集中给定的日期和时间格式不能直接用于分析，所以我们对这些时间值进行预处理，以获得日期、月份、年份、小时、分钟和秒等特征。
让我们讨论一下用Pandas数据框架处理日期和时间的所有不同方法。
将日期和时间划分为多个功能：
使用pd.date_range创建五个日期和时间，生成固定频率的日期和时间跨度的序列。然后我们使用pandas.Series.dt来提取特征。
# Load library
import pandas as pd
# calling DataFrame constructor
df = pd.DataFrame()
# Create 6 dates
df['time'] = pd.date_range('2/5/2019', periods = 6, freq ='2H')
print(df['time']) # print dataframe
# Extract features - year, month, day, hour, and minute
df['year'] = df['time'].dt.year
df['month'] = df['time'].dt.month
df['day'] = df['time'].dt.day
df['hour'] = df['time'].dt.hour
df['minute'] = df['time'].dt.minute
# Show six rows
df.head(6)
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输出:
0 2019-02-05 00:00:00
1 2019-02-05 02:00:00
2 2019-02-05 04:00:00
3 2019-02-05 06:00:00
4 2019-02-05 08:00:00
5 2019-02-05 10:00:00
Name: time, dtype: datetime64[ns]
time year month day hour minute
0 2019-02-05 00:00:00 2019 2 5 0 0
1 2019-02-05 02:00:00 2019 2 5 2 0
2 2019-02-05 04:00:00 2019 2 5 4 0
3 2019-02-05 06:00:00 2019 2 5 6 0
4 2019-02-05 08:00:00 2019 2 5 8 0
5 2019-02-05 10:00:00 2019 2 5 10 0
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将字符串转换为时间戳。
我们使用pd.to_datetime将给定的字符串转换为数据时间格式，然后我们可以使用第一种方法从数据时间提取不同的特征。
# Load libraries
import numpy as np
import pandas as pd
# Create time Strings
dt_strings = np.array(['04-03-2019 12:35 PM',
'22-06-2017 11:01 AM',
'05-09-2009 07:09 PM'])
# Convert to datetime format
timestamps = [pd.to_datetime(date, format ="%d-%m-%Y%I:%M %p",
errors ="coerce") for date in dt_strings]
# Convert to datetimes
timestamps = [pd.to_datetime(date, format ="%d-%m-%Y %I:%M %p",
errors ="coerce") for date in dt_strings]
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输出:
[Timestamp(‘2019-03-04 12:35:00’), Timestamp(‘2017-06-22 11:01:00’), Timestamp(‘2009-09-05 19:09:00’) ]
从给定的日期中提取一周的日期：。
我们使用Series.dt.weekday_name从给定的Date中找到一周中的某一天的名称。
# Load library
import pandas as pd
df = pd.DataFrame()
# Create 6 dates
dates = pd.pd.Series(date_range('2/5/2019', periods = 6, freq ='M'))
print(dates)
# Extract days of week and then print
print(dates.dt.weekday_name)
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输出:
0 2019-02-28
1 2019-03-31
2 2019-04-30
3 2019-05-31
4 2019-06-30
5 2019-07-31
dtype: datetime64[ns]
0 Thursday
1 Sunday
2 Tuesday
3 Friday
4 Sunday
5 Wednesday
dtype: object
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在日期和时间范围内提取数据：
我们可以从给定的数据集中获得位于特定时间范围内的行。
方法#1：如果数据集没有建立时间索引。
# Load library
import pandas as pd
# Create data frame
df = pd.DataFrame()
# Create datetimes
df['date'] = pd.date_range('1/1/2012', periods = 1000, freq ='H')
print(df.head(5))
# Select observations between two datetimes
x = df[(df['date'] > '2012-1-1 01:00:00') &
(df['date'] <= '2012-1-1 11:00:00')]
print(x)
Python
输出:
date
0 2012-01-01 00:00:00
1 2012-01-01 01:00:00 // 5 rows of Timestamps out of 1000
2 2012-01-01 02:00:00
3 2012-01-01 03:00:00
4 2012-01-01 04:00:00
date
2 2012-01-01 02:00:00
3 2012-01-01 03:00:00
4 2012-01-01 04:00:00
5 2012-01-01 05:00:00 //Timestamps in the given range
6 2012-01-01 06:00:00
7 2012-01-01 07:00:00
8 2012-01-01 08:00:00
9 2012-01-01 09:00:00
10 2012-01-01 10:00:00
11 2012-01-01 11:00:00
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方法二：如果数据集是以时间为索引的
# Load library
import pandas as pd
# Create data frame
df = pd.DataFrame()
# Create datetimes
df['date'] = pd.date_range('1/1/2012', periods = 1000, freq ='H')
# Set index
df = df.set_index(df['date'])
print(df.head(5))
# Select observations between two datetimes
x = df.loc['2012-1-1 04:00:00':'2012-1-1 12:00:00']
print(x)
Python
输出:
date
date
2012-01-01 00:00:00 2012-01-01 00:00:00
2012-01-01 01:00:00 2012-01-01 01:00:00
2012-01-01 02:00:00 2012-01-01 02:00:00
2012-01-01 03:00:00 2012-01-01 03:00:00 // 5 rows of Timestamps out of 1000
2012-01-01 04:00:00 2012-01-01 04:00:00
date
date
2012-01-01 04:00:00 2012-01-01 04:00:00
2012-01-01 05:00:00 2012-01-01 05:00:00
2012-01-01 06:00:00 2012-01-01 06:00:00
2012-01-01 07:00:00 2012-01-01 07:00:00
2012-01-01 08:00:00 2012-01-01 08:00:00
2012-01-01 09:00:00 2012-01-01 09:00:00 //Timestamps in the given range
2012-01-01 10:00:00 2012-01-01 10:00:00
2012-01-01 11:00:00 2012-01-01 11:00:00
2012-01-01 12:00:00 2012-01-01 12:00:00
Python