[Pandas技巧] 时间类型转换与处理

在平时的需求开发中,经常涉及到利用Pandas处理日期相关类型字段的转换和操作,为此特地记录以下练习案例,帮助大家的同时,也便于日后的学习和复盘

案例1

问题: 提取'W1|2022/7/28'字段中的年月日信息,取名为week_start,即一周开始的日期,并根据week_start计算出该周结束的具体日期week_end

import pandas as pd
import datetime
df1 = pd.DataFrame([[6,3],[6,3]], columns = ['Working day','W1|2022/7/28'])
# 一周开始的日期
# '2022/7/28'——>str类型
week_start = df1.columns[1].split('|')[1]
# 将start_day类型转换成date类型(2022-07-28)
week_start = datetime.datetime.strptime(week_start, '%Y/%m/%d').date()
# 一周结束的日期(2022-08-03)
week_end = week_start + datetime.timedelta(days=6)

df1

案例2

问题: 根据'Date'字段生成'Date – 2'字段

import pandas as pd
from datetime import timedelta
from datetime import datetime
 
df2 = pd.DataFrame([[1,'20191031'],
                   [2,'20191106'],
                   [3,'20191106']],columns=['Id','Date'])
# 'Date'字段中的值减去2天,生成'Date - 2'字段
df2['Date - 2'] = df2['Date'].apply(lambda x:(datetime.strptime(x,'%Y%m%d') - timedelta(days=datetime.strptime(x,'%Y%m%d').weekday())).strftime("%Y%m%d"))

df2

案例3

问题:从字符串表示的日期时间中仅获取“年/月/日” 

import pandas as pd
from datetime import datetime

df3 = pd.DataFrame([[1,'2017-01-02 00:00:00'],
                   [2,'2017-01-09 00:00:00']
                   ],columns = ['Id','Wk'])

df3

错误写法

# 运行以下代码会报错'str' object has no attribute 'strftime'
df3['new_wk'] = df3['Wk'].apply(lambda x:x.strftime("%Y%m%d"))

正确写法

# 先利用.strptime()将str格式的变量转化成datetime下的时间格式
# 然后再利用.strftime()获取“年/月/日”
df3['Wk'] = df3['Wk'].apply(lambda x:datetime.strptime(x,"%Y-%m-%d %H:%M:%S"))
df3['new_Wk'] = df3['Wk'].apply(lambda x:x.strftime("%Y/%m/%d"))

处理过后的df3

案例4

问题:将'月/日/年 时间'格式的值转换为'年月日'(10/11/19 05:28:27 => 20191011)

import pandas as pd
 
df4 = pd.DataFrame([['A','10/11/19 05:28:27','08/04/20 08:38:59'],
                   ['B','10/11/19 05:28:27',None],
                   ['C','10/11/19 05:28:27',None]
                  ],columns = ['site','creation_date','closure_date'])

df4

# 将'creation_date'栏位的值变形
# 10/11/19 05:28:27 => 20191011
df4['creation_date'] = df4['creation_date'].apply(lambda x:pd.to_datetime(x).strftime("%Y%m%d"))

# 将'closure_date'字段中nan值填充为0
df4['closure_date'] = df4['closure_date'].fillna(0)
# 筛选closure_date'字段中值为0的数据记录,取名为df4_na
df4_na = df4[df4['closure_date'].isin([0])]
# 筛选closure_date'字段中值不为0的数据记录,取名为df4
df4 = df4[~df4['closure_date'].isin([0])]

# 将'closure_date'栏位的值变形
# 08/04/20 08:38:59 => 20200804
df4['closure_date'] = df4['closure_date'].apply(lambda x:pd.to_datetime(x).strftime("%Y%m%d"))

df4 = pd.concat([df4, df4_na], ignore_index = True)

 处理过后的df4

补充知识

我们通常使用pd.to_datetime()和s.astype('datetime64[ns]')来做时间类型转换

import pandas as pd

t = pd.Series(['20220720','20220724'])
# dtype: datetime64[ns]
new_t1 = pd.to_datetime(t)
new_t2 = t.astype('datetime64[ns]')

t

new_t1

new_t2 

案例5

问题: 添加字段'Week'，逐行递增

import pandas as pd
 
df5 = pd.DataFrame(columns=['Week','Materials'])
all_material = ['A32456','B78495']
 
for row in range(0,3):
    week = row + 1
    datas = [week, all_material]
    df5.loc[row] = datas
'''
df5:
 
  Week         Materials
0    1  [A32456, B78495]
1    2  [A32456, B78495]
2    3  [A32456, B78495]
'''
print(df5)

案例6

问题:日期型转换为字符型

import datetime
today = datetime.date.today() # date类型 2022-07-28
today.strftime('%Y-%m-%d') # '2022-07-28'

import datetime
dt = datetime.datetime.now() # datetime类型 2022-07-28 22:46:20.528813
dt.strftime('%Y-%m-%d') # '2022-07-28'

import datetime
today = str(datetime.date.today()) # str类型 2022-07-28
today.replace("-","") # '20220728'

案例7

问题:文本型转日期型

#文本型日期转为日期型日期
import pandas as pd
from datetime import datetime
df7=pd.DataFrame({'销售日期':['2022-05-01','2022-05-02','2022-05-03','2022-05-04','2022-05-05','2022-05-06','2022-05-07','2022-05-08','2022-05-09','2022-05-10'],
                '城市':['兰州','白银','天水','武威','金昌','陇南','嘉峪关','酒泉','敦煌','甘南']})

df7

文本型转为日期型可用datetime.strptime函数 

# "%Y-%m-%d"表示将文本日期解析为年月日的日期格式
df7['日期'] = df7['销售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

文本型转为日期型也可用pd.to_datetime函数

# "%Y-%m-%d"表示将文本日期解析为年月日的日期格式
df7['日期'] = pd.to_datetime(df7['销售日期'],format='%Y-%m-%d')

处理过后的df7

案例8

问题:提取日期字段的年份、月份、日份和周数

import pandas as pd
from datetime import datetime
df8=pd.DataFrame({'销售日期':['2022-05-01','2022-05-02','2022-05-03','2022-05-04','2022-05-05','2022-05-06','2022-05-07','2022-05-08','2022-05-09','2022-05-10'],
                '城市':['兰州','白银','天水','武威','金昌','陇南','嘉峪关','酒泉','敦煌','甘南']})

df8['日期'] = df8['销售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

df8 

#由日期数据提取年
df8['年份'] = df8['日期'].apply(lambda x: x.year)
df8['年份'] =df8['年份'].astype(str)+'年'

#由日期数据提取月
df8['月份'] = df8['日期'].apply(lambda x: x.month)
df8['月份'] =df8['月份'].astype(str)+'月'

#由日期数据提取日
df8['日份'] = df8['日期'].apply(lambda x: x.day)
df8['日份'] =df8['日份'].astype(str)+'日'

# 日期中的周使用date.isocalendar()[1]提取
#根据日期返回周数，以周一为第一天开始
df8['周数'] = [date.isocalendar()[1] for date in df8['日期'].tolist()]
df8['周数'] = df8['周数'].astype(str)+'周'

处理后的df8

案例9

问题:借助offset时间偏移函数将日期加3天 

import pandas as pd
from datetime import datetime
df9=pd.DataFrame({'销售日期':['2022-05-01','2022-05-02','2022-05-03','2022-05-04','2022-05-05','2022-05-06','2022-05-07','2022-05-08','2022-05-09','2022-05-10'],
                '城市':['兰州','白银','天水','武威','金昌','陇南','嘉峪关','酒泉','敦煌','甘南']})

df9['日期'] = df9['销售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))

df9

#借助offset时间偏移函数将日期加3天
from pandas.tseries.offsets import Day
df9['日期_3']=df9['日期']+Day(3)

处理后的df9

案例10

问题:将文本型日期转换为日期型日期

#文本型日期转为日期型日期
import pandas as pd
import datetime as dt
from datetime import datetime
df1=pd.DataFrame({'销售时间':['2022-05-01 00:00:00','2022-05-02 00:00:00','2022-05-03 00:00:00','2022-05-04 00:00:00','2022-05-05 00:00:00',
                         '2022-05-06 00:00:00','2022-05-07 00:00:00','2022-05-08 00:00:00','2022-05-09 00:00:00','2022-05-10 00:00:00',]})
#df['日期']=df['销售日期'].map(lambda x:datetime.strptime(x,"%Y-%m-%d"))
df1['日期_x']=df1['销售时间'].str.split(' ',expand=True)[0]
df1['日期_y']=pd.to_datetime(df1['销售时间'],format='%Y-%m-%d')
df1

df10

日期中带有时分秒'00:00:00',有如下方法将其处理为'%Y-%m-%d'形式

df10['日期']=df10['销售时间'].str.split(' ',expand=True)[0]

处理后的df10