Pandas中如何实现时间格式化和时间查询
发表于：2026-11-02 作者：千家信息网编辑
千家信息网最后更新 2026年11月02日，这篇文章主要介绍Pandas中如何实现时间格式化和时间查询，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！时间格式化和时间查询import pandas as pddate=pd
千家信息网最后更新 2026年11月02日Pandas中如何实现时间格式化和时间查询
这篇文章主要介绍Pandas中如何实现时间格式化和时间查询，文中介绍的非常详细，具有一定的参考价值，感兴趣的小伙伴们一定要看完！
时间格式化和时间查询
import pandas as pddate=pd.Timestamp('2020/09/27 13:30:00')print(date)2020-09-27 13:30:00
#年print(date.year)#月print(date.month)#日print(date.day)#时print(date.hour)#分print(date.minute)#秒print(date.second)
202092713300
#加5天date+pd.Timedelta('5 days')Timestamp('2020-10-02 13:30:00')#时间转换res=pd.to_datetime('2020-09-10 13:20:00')print(res.year)2020
#生成一列数据se=pd.Series(['2020-11-24 00:00:00','2020-11-25 00:00:00','2020-11-26 00:00:00'])print(se)
0 2020-11-24 00:00:001 2020-11-25 00:00:002 2020-11-26 00:00:00dtype: object
# 转成时间格式print(pd.to_datetime(se))
0 2020-11-241 2020-11-252 2020-11-26dtype: datetime64[ns]
# 生成一个等差的时间序列,从2020-09-17开始,长度为periods,间隔为12Hpd.Series(pd.date_range(start='2020-09-17',periods=10,freq='12H'))
0 2020-09-17 00:00:001 2020-09-17 12:00:002 2020-09-18 00:00:003 2020-09-18 12:00:004 2020-09-19 00:00:005 2020-09-19 12:00:006 2020-09-20 00:00:007 2020-09-20 12:00:008 2020-09-21 00:00:009 2020-09-21 12:00:00dtype: datetime64[ns]
df=pd.read_csv('./pandas/data/flowdata.csv')# 将Time列转换成时间格式df['Time']=pd.to_datetime(df['Time'])df=df.set_index(df['Time'])print(df)Time L06_347 LS06_347 LS06_348Time 2009-01-01 00:00:00 2009-01-01 00:00:00 0.137417 0.097500 0.0168332009-01-01 03:00:00 2009-01-01 03:00:00 0.131250 0.088833 0.0164172009-01-01 06:00:00 2009-01-01 06:00:00 0.113500 0.091250 0.0167502009-01-01 09:00:00 2009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 2009-01-01 12:00:00 0.140917 0.096167 0.017000... ... ... ... ...2013-01-01 12:00:00 2013-01-01 12:00:00 1.710000 1.710000 0.1295832013-01-01 15:00:00 2013-01-01 15:00:00 1.420000 1.420000 0.0963332013-01-01 18:00:00 2013-01-01 18:00:00 1.178583 1.178583 0.0830832013-01-01 21:00:00 2013-01-01 21:00:00 0.898250 0.898250 0.0771672013-01-02 00:00:00 2013-01-02 00:00:00 0.860000 0.860000 0.075000[11697 rows x 4 columns]
# 打印索引print(df.index)
DatetimeIndex(['2009-01-01 00:00:00', '2009-01-01 03:00:00', '2009-01-01 06:00:00', '2009-01-01 09:00:00', '2009-01-01 12:00:00', '2009-01-01 15:00:00', '2009-01-01 18:00:00', '2009-01-01 21:00:00', '2009-01-02 00:00:00', '2009-01-02 03:00:00', ... '2012-12-31 21:00:00', '2013-01-01 00:00:00', '2013-01-01 03:00:00', '2013-01-01 06:00:00', '2013-01-01 09:00:00', '2013-01-01 12:00:00', '2013-01-01 15:00:00', '2013-01-01 18:00:00', '2013-01-01 21:00:00', '2013-01-02 00:00:00'], dtype='datetime64[ns]', name='Time', length=11697, freq=None)
# 读取时间的时候自动将时间转换成datetimedf=pd.read_csv('./pandas/data/flowdata.csv',index_col=0,parse_dates=True)print(df)L06_347 LS06_347 LS06_348Time 2009-01-01 00:00:00 0.137417 0.097500 0.0168332009-01-01 03:00:00 0.131250 0.088833 0.0164172009-01-01 06:00:00 0.113500 0.091250 0.0167502009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 0.140917 0.096167 0.017000... ... ... ...2013-01-01 12:00:00 1.710000 1.710000 0.1295832013-01-01 15:00:00 1.420000 1.420000 0.0963332013-01-01 18:00:00 1.178583 1.178583 0.0830832013-01-01 21:00:00 0.898250 0.898250 0.0771672013-01-02 00:00:00 0.860000 0.860000 0.075000[11697 rows x 3 columns]
# 通过时间索引筛选数据print(df[('2009-01-01 00:00:00'):('2009-01-01 12:00:00')])L06_347 LS06_347 LS06_348Time 2009-01-01 00:00:00 0.137417 0.097500 0.0168332009-01-01 03:00:00 0.131250 0.088833 0.0164172009-01-01 06:00:00 0.113500 0.091250 0.0167502009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 0.140917 0.096167 0.017000
# 查看数据的最后10条数据print(df.tail(10))
L06_347 LS06_347 LS06_348Time 2012-12-31 21:00:00 0.846500 0.846500 0.1701672013-01-01 00:00:00 1.688333 1.688333 0.2073332013-01-01 03:00:00 2.693333 2.693333 0.2015002013-01-01 06:00:00 2.220833 2.220833 0.1669172013-01-01 09:00:00 2.055000 2.055000 0.1756672013-01-01 12:00:00 1.710000 1.710000 0.1295832013-01-01 15:00:00 1.420000 1.420000 0.0963332013-01-01 18:00:00 1.178583 1.178583 0.0830832013-01-01 21:00:00 0.898250 0.898250 0.0771672013-01-02 00:00:00 0.860000 0.860000 0.075000
# 通过年，月，日筛选数据print(df['2012'])print(df['2012-01'])print(df['2012-01-31'])
L06_347 LS06_347 LS06_348Time 2012-01-01 00:00:00 0.307167 0.273917 0.0280002012-01-01 03:00:00 0.302917 0.270833 0.0305832012-01-01 06:00:00 0.331500 0.284750 0.0309172012-01-01 09:00:00 0.330750 0.293583 0.0297502012-01-01 12:00:00 0.295000 0.285167 0.031750... ... ... ...2012-12-31 09:00:00 0.682750 0.682750 0.0665832012-12-31 12:00:00 0.651250 0.651250 0.0638332012-12-31 15:00:00 0.629000 0.629000 0.0618332012-12-31 18:00:00 0.617333 0.617333 0.0605832012-12-31 21:00:00 0.846500 0.846500 0.170167[2928 rows x 3 columns] L06_347 LS06_347 LS06_348Time 2012-01-01 00:00:00 0.307167 0.273917 0.0280002012-01-01 03:00:00 0.302917 0.270833 0.0305832012-01-01 06:00:00 0.331500 0.284750 0.0309172012-01-01 09:00:00 0.330750 0.293583 0.0297502012-01-01 12:00:00 0.295000 0.285167 0.031750... ... ... ...2012-01-31 09:00:00 0.191000 0.231250 0.0255832012-01-31 12:00:00 0.183333 0.227167 0.0259172012-01-31 15:00:00 0.163417 0.221000 0.0237502012-01-31 18:00:00 0.157083 0.220667 0.0231672012-01-31 21:00:00 0.160083 0.214750 0.023333[248 rows x 3 columns] L06_347 LS06_347 LS06_348Time 2012-01-31 00:00:00 0.191250 0.247417 0.0259172012-01-31 03:00:00 0.181083 0.241583 0.0258332012-01-31 06:00:00 0.188750 0.236750 0.0260002012-01-31 09:00:00 0.191000 0.231250 0.0255832012-01-31 12:00:00 0.183333 0.227167 0.0259172012-01-31 15:00:00 0.163417 0.221000 0.0237502012-01-31 18:00:00 0.157083 0.220667 0.0231672012-01-31 21:00:00 0.160083 0.214750 0.023333
# 选择一段时间的数据print(df['2009-01-01':'2012-01-01'])
L06_347 LS06_347 LS06_348Time 2009-01-01 00:00:00 0.137417 0.097500 0.0168332009-01-01 03:00:00 0.131250 0.088833 0.0164172009-01-01 06:00:00 0.113500 0.091250 0.0167502009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 0.140917 0.096167 0.017000... ... ... ...2012-01-01 09:00:00 0.330750 0.293583 0.0297502012-01-01 12:00:00 0.295000 0.285167 0.0317502012-01-01 15:00:00 0.301417 0.287750 0.0314172012-01-01 18:00:00 0.322083 0.304167 0.0380832012-01-01 21:00:00 0.355417 0.346500 0.080917[8768 rows x 3 columns]
# 筛选所有1月份的数据print(df[df.index.month==1])
L06_347 LS06_347 LS06_348Time 2009-01-01 00:00:00 0.137417 0.097500 0.0168332009-01-01 03:00:00 0.131250 0.088833 0.0164172009-01-01 06:00:00 0.113500 0.091250 0.0167502009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 0.140917 0.096167 0.017000... ... ... ...2013-01-01 12:00:00 1.710000 1.710000 0.1295832013-01-01 15:00:00 1.420000 1.420000 0.0963332013-01-01 18:00:00 1.178583 1.178583 0.0830832013-01-01 21:00:00 0.898250 0.898250 0.0771672013-01-02 00:00:00 0.860000 0.860000 0.075000[1001 rows x 3 columns]
# 筛选所有8点到12点的数据print(df[(df.index.hour>8) & (df.index.hour<12)])# 或者通过between_time筛选8点到12点的数据print(df.between_time('8:00','12:00'))L06_347 LS06_347 LS06_348Time 2009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-02 09:00:00 0.141917 0.097083 0.0164172009-01-03 09:00:00 0.124583 0.084417 0.0158332009-01-04 09:00:00 0.109000 0.105167 0.0180002009-01-05 09:00:00 0.161500 0.114583 0.021583... ... ... ...2012-12-28 09:00:00 0.961500 0.961500 0.0924172012-12-29 09:00:00 0.786833 0.786833 0.0770002012-12-30 09:00:00 0.916000 0.916000 0.1015832012-12-31 09:00:00 0.682750 0.682750 0.0665832013-01-01 09:00:00 2.055000 2.055000 0.175667[1462 rows x 3 columns] L06_347 LS06_347 LS06_348Time 2009-01-01 09:00:00 0.135750 0.091500 0.0162502009-01-01 12:00:00 0.140917 0.096167 0.0170002009-01-02 09:00:00 0.141917 0.097083 0.0164172009-01-02 12:00:00 0.147833 0.101917 0.0164172009-01-03 09:00:00 0.124583 0.084417 0.015833... ... ... ...2012-12-30 12:00:00 1.465000 1.465000 0.0868332012-12-31 09:00:00 0.682750 0.682750 0.0665832012-12-31 12:00:00 0.651250 0.651250 0.0638332013-01-01 09:00:00 2.055000 2.055000 0.1756672013-01-01 12:00:00 1.710000 1.710000 0.129583[2924 rows x 3 columns]
resample重采样
# 按天求平均值df=df.resample('D').mean()print(df)L06_347 LS06_347 LS06_348Time 2009-01-01 0.125010 0.092281 0.0166352009-01-02 0.124146 0.095781 0.0164062009-01-03 0.113562 0.085542 0.0160942009-01-04 0.140198 0.102708 0.0173232009-01-05 0.128812 0.104490 0.018167... ... ... ...2012-12-29 0.807604 0.807604 0.0780312012-12-30 1.027240 1.027240 0.0880002012-12-31 0.748365 0.748365 0.0814172013-01-01 1.733042 1.733042 0.1421982013-01-02 0.860000 0.860000 0.075000[1463 rows x 3 columns]
# 求3天的平均值print(df.resample('3D').mean())L06_347 LS06_347 LS06_348Time 2009-01-01 0.120906 0.091201 0.0163782009-01-04 0.121594 0.091708 0.0166702009-01-07 0.097042 0.070740 0.0144792009-01-10 0.115941 0.086340 0.0145452009-01-13 0.346962 0.364549 0.034198... ... ... ...2012-12-20 0.996337 0.996337 0.1144722012-12-23 2.769059 2.769059 0.2255422012-12-26 1.451583 1.451583 0.1401012012-12-29 0.861069 0.861069 0.0824832013-01-01 1.296521 1.296521 0.108599[488 rows x 3 columns]
# 求1个月的平均值print(df.resample('M').mean().head())L06_347 LS06_347 LS06_348Time 2009-01-31 0.517864 0.536660 0.0455972009-02-28 0.516847 0.529987 0.0472382009-03-31 0.372536 0.382359 0.0375082009-04-30 0.163182 0.129354 0.0213562009-05-31 0.178588 0.160616 0.020744
# 求某个时间单位的平均值，最大值，最小值print(df.resample('M').min().head())print(df.resample('M').max().head())L06_347 LS06_347 LS06_348Time 2009-01-31 0.078156 0.058438 0.0135732009-02-28 0.182646 0.135667 0.0190732009-03-31 0.131385 0.098875 0.0169792009-04-30 0.078510 0.066375 0.0139172009-05-31 0.060771 0.047969 0.013656 L06_347 LS06_347 LS06_348Time 2009-01-31 5.933531 6.199927 0.4047082009-02-28 4.407604 4.724583 0.2317502009-03-31 1.337896 1.586833 0.1169692009-04-30 0.275698 0.247312 0.0373752009-05-31 2.184250 2.433073 0.168792
列排序
df = pd.DataFrame({'group':['a','a','a','b','b','b','c','c','c'], 'data':[4,3,2,1,12,3,4,5,7]})print(df)group data0 a 41 a 32 a 23 b 14 b 125 b 36 c 47 c 58 c 7
# 默认(ascending=True)从小到大排列. 从大到小(ascending=False)df.sort_values(by=['group','data'],ascending=[False,True],inplace=True)print(df)
group data6 c 47 c 58 c 73 b 15 b 34 b 122 a 21 a 30 a 4
df=pd.DataFrame({ 'k1':['one']*3+['tow']*3, 'k2':[1,2,3,4,5,6] })print(df)k1 k20 one 11 one 22 one 33 tow 44 tow 55 tow 6
# 排序print(df.sort_values(by='k2',ascending=False))
k1 k25 tow 64 tow 53 tow 42 one 31 one 20 one 1
# 删除重复数据print(df.drop_duplicates())
k1 k20 one 11 one 22 one 33 tow 44 tow 55 tow 6
# 按某列去重删除数据print(df.drop_duplicates(subset='k1'))
k1 k20 one 13 tow 4
df = pd.DataFrame({'food':['A1','A2','B1','B2','B3','C1','C2'],'data':[1,2,3,4,5,6,7]})print(df)food data0 A1 11 A2 22 B1 33 B2 44 B3 55 C1 66 C2 7
# 将A1,A2归类到A, B1,B2,B3归类B,C1,C2归类到Cdict1 = { 'A1':'A', 'A2':'A', 'B1':'B', 'B2':'B', 'B3':'B', 'C1':'C', 'C2':'C'}df['Upper']=df['food'].map(dict1)print(df)food data Upper0 A1 1 A1 A2 2 A2 B1 3 B3 B2 4 B4 B3 5 B5 C1 6 C6 C2 7 C
import numpy as npdf=pd.DataFrame({'k1':np.random.randn(5),'k2':np.random.randn(5)})print(df)df2=df.assign(ration=df['k1']/df['k2'])print(df2)k1 k20 1.977668 -1.1362511 0.550649 0.0101312 0.723699 0.3045363 -0.247529 0.0303594 -0.351775 0.732785 k1 k2 ration0 1.977668 -1.136251 -1.7405201 0.550649 0.010131 54.3551312 0.723699 0.304536 2.3763943 -0.247529 0.030359 -8.1533894 -0.351775 0.732785 -0.480052
# 删除ration列df2.drop('ration',axis='columns',inplace=True)print(df2)k1 k20 1.977668 -1.1362511 0.550649 0.0101312 0.723699 0.3045363 -0.247529 0.0303594 -0.351775 0.732785
数据替换
se=pd.Series([1,2,3,4,5,6,7,8])print(se)
0 11 22 33 44 55 66 77 8dtype: int64
se.replace(6,np.nan,inplace=True)print(se)
0 1.01 2.02 3.03 4.04 5.05 NaN6 7.07 8.0dtype: float64
Pandas.cut计算每个值在给定的哪个范围
ages = [15,18,20,21,22,34,41,52,63,79]bins = [10,40,80]bins_res = pd.cut(ages,bins)print(bins_res)
[(10, 40], (10, 40], (10, 40], (10, 40], (10, 40], (10, 40], (40, 80], (40, 80], (40, 80], (40, 80]]Categories (2, interval[int64]): [(10, 40] < (40, 80]]
# 统计各个范围的数量print(pd.value_counts(bins_res))
(10, 40] 6(40, 80] 4dtype: int64
np.nan
df=pd.DataFrame([range(3),[2,np.nan,5],[np.nan,3,np.nan],range(3)])print(df)
0 1 20 0.0 1.0 2.01 2.0 NaN 5.02 NaN 3.0 NaN3 0.0 1.0 2.0
print(df.isnull())
0 1 20 False False False1 False True False2 True False True3 False False False
# 将NaN填充成10print(df.fillna(10))
0 1 20 0.0 1.0 2.01 2.0 10.0 5.02 10.0 3.0 10.03 0.0 1.0 2.0
# 查看某一行是否存在NaNprint(df[df.isnull().any(axis=1)])
0 1 21 2.0 NaN 5.02 NaN 3.0 NaN
以上是"Pandas中如何实现时间格式化和时间查询"这篇文章的所有内容，感谢各位的阅读！希望分享的内容对大家有帮助，更多相关知识，欢迎关注行业资讯频道！
时间
数据
格式
平均值
查询
归类
内容
点到
篇文章
索引
范围
排序
生成
最大
最小
从小到大
一行
从小
价值
兴趣
数据库的安全要保护哪些东西
数据库安全各自的含义是什么
生产安全数据库录入
数据库的安全性及管理
数据库安全策略包含哪些
海淀数据库安全审计系统
建立农村房屋安全信息数据库
易用的数据库客户端支持安全管理
连接数据库失败ssl安全错误
数据库的锁怎样保障安全
互联网与科技
新华互联网科技
湖南永感互联网科技有限公司
中关村互联网科技集团
新华互联网科技学校
互联网科技财经
广州凡科互联网科技
互联网科技的未来发展
互联网科技汛
互联网养老 桔豆科技
重庆科技学院互联网
湖南洣水互联网科技有限公司扣款
团飞科技与伯堂互联网服务平台
上海宁丞科技互联网
科技大学互联网
互联网科技公司营业执照怎么办理
上海钢联属于互联网科技股吗
广州梦谷互联网科技有限公司领导
互联网科技手机排行榜
青岛易易互联网科技
咖啡网
精品咖啡豆
互联网科技公司简介
江苏根荣互联网科技
以房互联网科技
互联网科技咨询
晓花互联网科技有限公司网址
知域互联网科技有限公司大骗子
互联网科技企业文化
互联网科技快速发展优美句子
