pandas 时间对象处理¶
时间序列类型
时间戳：特定时刻
固定时期：如2017年7月
时间间隔：起始时间-结束时间
Python标准库处理时间对象：datetime
灵活处理时间对象：dateutil
dateutil.parser.parse()
成组处理时间对象：pandas
pd.to_datetime()In [11]:
import datetime
import pandas as pd
import numpy as np
In [2]:
datetime.datetime.strptime('2010-01-01','%Y-%m-%d')
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In [3]:
datetime.datetime.strptime('2010/01/01','%Y/%m/%d')
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In [4]:
import dateutil
In [6]:
dateutil.parser.parse('03/08/2020 14:35')
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In [7]:
dateutil.parser.parse('2020-Mar-8')
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In [13]:
pd.to_datetime(['2001-01-01','2020/Mar/08'])
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pandas-时间对象处理
| 产生时间对象数组 | pd.date_range |
|---|---|
| start | 开始时间 |
| end | 结束时间 |
| periods | 时间长度 |
| freq | 时间频率，默认为D，可选Hour,Week,Business,Sem,Month,（min）T（es）,S（econd）,A（year） |
In [15]:
pd.date_range('2019/7/23','2021/7/23')
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In [16]:
pd.date_range('2019-7-23',periods=720)
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In [17]:
pd.date_range('2019/7/23',periods=30,freq='M')
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In [18]:
pd.date_range('2019-7-23',periods=30,freq='W-MON')
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B business day frequency
C custom business day frequency (experimental)
D calendar day frequency
W weekly frequency
M month end frequency
SM semi-month end frequency (15th and end of month)
BM business month end frequency
CBM custom business month end frequency
MS month start frequency
SMS semi-month start frequency (1st and 15th)
BMS business month start frequency
CBMS custom business month start frequency
Q quarter end frequency
BQ business quarter endfrequency
QS quarter start frequency
BQS business quarter start frequency
A year end frequency
BA business year end frequency
AS year start frequency
BAS business year start frequency
BH business hour frequency
H hourly frequency
T, min minutely frequency
S secondly frequency
L, ms milliseconds
U, us microseconds
N nanosecondsIn [23]:
pd.date_range('2019-7-23',periods=60,freq='B') #B Business Day
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In [24]:
dt = _
dt[0]
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In [27]:
dt[0].to_pydatetime()
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pandas 时间序列¶
时间序列就是以时间对象为索引的 Series 或 Dataframe。
datetime对象作为索引时是存储在 DatetimeIndex对象中的。
时间序列特殊功能
传入“年”或“年月”作为切片方式
传入日期范围作为切片方式
丰富的函数支持：resample, truncate，In [29]:
sr = pd.Series(np.arange(100),index=pd.date_range('2020-3-8',periods=100))
In [30]:
sr
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In [31]:
sr.index
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In [32]:
sr['2020-3']
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In [33]:
sr['2020-3':'2020-4']
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In [34]:
sr.resample('W').sum()
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In [36]:
sr.resample('M').sum()
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In [37]:
sr.resample('M').mean()
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In [38]:
sr.truncate(before='2020-4-1')
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pandas 文件处理¶
数据文件常用格式：csv（以某间隔符分割数据）
pandas读取文件：从文件名、URL、文件对象中加载数据
read_csv 默认分隔符为逗号
read_table 默认分隔符为制表符
| read_csv、read_table | 函数主要参数: |
|---|---|
| sep | 指定分隔符，可用正则表达式如's+' |
| header=None | 指定文件无列名 |
| name | 指定列名 |
| index_col | 指定某列作为索引 |
| skip_row | 指定跳过某些行 |
| na_values | 指定某些字符串表示缺失值 |
| parse_dates | 指定某些列是否被解析为日期，类型为布尔值或列表 |
In [39]:
pd.read_csv('600519.csv')
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In [40]:
pd.read_csv('600519.csv',index_col=0)
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In [41]:
pd.read_csv('600519.csv',index_col='date')
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In [42]:
df = pd.read_csv('600519.csv',index_col='date')
In [43]:
df.index[0]
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In [44]:
df.index
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In [46]:
pd.read_csv('600519.csv',index_col='date',parse_dates=True).index
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In [52]:
pd.read_csv('600519.csv',index_col='date',parse_dates=['date']).index
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In [55]:
pd.read_csv('600519.csv',header=None,names=list('abcdefgh'))
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In [56]:
pd.read_csv('600519.csv',header=None,skiprows=[1,2,3])
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In [58]:
pd.read_csv('600519.csv',header=None,skiprows=[1,2,3],na_values=['None'])
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