pandas 处理日期时间变量

浅浅的花香味﹌ 2023-05-31 06:38 6阅读 0赞

## 创建Timestamp类和Period类 ##

### Timestamp对象 ###

import pandas as pd
    from datetime import datetime
    
    print(datetime(2019,10,21))
    print(pd.Timestamp(datetime(2019,10,21)))
    print(pd.Timestamp(datetime(2019,10,21,1,2,3)))
    print(pd.Timestamp('2012-09-09 1:3:3'))
    print(pd.Timestamp(2012,4,4))

### 转换为Timestamp对象 ###

df1 = pd.read_csv('Beijing_2008.csv',header=2)
    print(pd.Timestamp(df1['Date (LST)'][0]))
    print(df1['Date (LST)'].apply(pd.Timestamp))

### 利用to\_datetime进行批量转换 ###

‘’’  
pd.to\_datetime(  
arg,errors,dayfirst =‘False’,yeatfirst = ‘False’,box,format  
)  
‘’’

print(pd.to_datetime(datetime(2019,10,21,1,2,3)))
    print(pd.to_datetime('2019-10-21 1:2:3'))
    print(pd.to_datetime(['2015/2/2','2010.3.3']))
    print(pd.to_datetime(df1['Date (LST)'],format = '%Y-%m-%d %H:%M'))

### 多列数据合成Timestamp类 ###

print(pd.to_datetime(df1[['Year','Month','Day','Hour']]))

## DatetimeIndex类 ##

### 创建DatetimeIndex对象 ###

#### 创建索引时自动转换 ####

bj08_datetimeindex = df1.set_index(pd.to_datetime(df1['Date (LST)']))
    print(bj08_datetimeindex)

#### 使用date\_range创建DatetimeIndex(主要和reindex配合使用以快速完成对时间序列中缺失值的填充) ####

print(pd.date_range('2019/1/1',periods=12,freq = '2M'))
    print(bj08_datetimeindex['2008-11'])
    print(bj08_datetimeindex['2008-11-1':'2008-11-6 9:00:00'])

## 对时间做分组汇总 ##

### 序列的分组汇总 ###

#### 直接取出索引的相应层级 ####

print(bj08_datetimeindex.index.hour)
    print(bj08_datetimeindex.index.quarter)
    print(bj08_datetimeindex.index.week)

#### 直接利用groupby方法进行汇总 ####

print(bj08_datetimeindex.groupby(bj08_datetimeindex.index.month).Value.max())

#### 使用更强大的resample函数 ####

print(bj08_datetimeindex.resample('3D').mean())

### 序列的缺失值处理 ###

‘’’  
时间序列要求记录的时间点连贯无缺失 ，因此需要：  
首先建立针对整个时间范围的完整序列框架  
随后针对无数据的时间点进行缺失值处理、  
df.reindex()是完成该任务的强有力工具  
‘’’

bjpm09 = pd.read_csv('Beijing_2009.csv',encoding='utf-8',header = 0)
    bjpm09idx = bjpm09.set_index(pd.to_datetime(bjpm09['Date (LST)']))
    bjpm09idx = bjpm09idx[bjpm09idx['Value'] > 0]
    print(bjpm09idx)
    idx = pd.date_range(start = '2009-02-01 00:00',
                        end = '2009-12-31 23:00',freq = 'H')
    print(idx)
    
    bj09new = bjpm09idx[~bjpm09idx.index.duplicated()].reindex(idx,method = 'bfill')
    
    print(bj09new)

### 序列数值平移 ###

print(bj09new.shift(3))