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！
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import pandas as pd
date=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)
2020 9 27 13 30 0
#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 00:00:00 1 2020-11-25 00:00:00 2 2020-11-26 00:00:00 dtype: object
# 转成时间格式 print(pd.to_datetime(se))
0 2020-11-24 1 2020-11-25 2 2020-11-26 dtype: 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12H pd.Series(pd.date_range(start='2020-09-17',periods=10,freq='12H'))
0 2020-09-17 00:00:00 1 2020-09-17 12:00:00 2 2020-09-18 00:00:00 3 2020-09-18 12:00:00 4 2020-09-19 00:00:00 5 2020-09-19 12:00:00 6 2020-09-20 00:00:00 7 2020-09-20 12:00:00 8 2020-09-21 00:00:00 9 2020-09-21 12:00:00 dtype: 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_348 Time 2009-01-01 00:00:00 2009-01-01 00:00:00 0.137417 0.097500 0.016833 2009-01-01 03:00:00 2009-01-01 03:00:00 0.131250 0.088833 0.016417 2009-01-01 06:00:00 2009-01-01 06:00:00 0.113500 0.091250 0.016750 2009-01-01 09:00:00 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 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 0.129583 2013-01-01 15:00:00 2013-01-01 15:00:00 1.420000 1.420000 0.096333 2013-01-01 18:00:00 2013-01-01 18:00:00 1.178583 1.178583 0.083083 2013-01-01 21:00:00 2013-01-01 21:00:00 0.898250 0.898250 0.077167 2013-01-02 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datetime
df=pd.read_csv('./pandas/data/flowdata.csv',index_col=0,parse_dates=True)
print(df)L06_347 LS06_347 LS06_348 Time 2009-01-01 00:00:00 0.137417 0.097500 0.016833 2009-01-01 03:00:00 0.131250 0.088833 0.016417 2009-01-01 06:00:00 0.113500 0.091250 0.016750 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 12:00:00 0.140917 0.096167 0.017000 ... ... ... ... 2013-01-01 12:00:00 1.710000 1.710000 0.129583 2013-01-01 15:00:00 1.420000 1.420000 0.096333 2013-01-01 18:00:00 1.178583 1.178583 0.083083 2013-01-01 21:00:00 0.898250 0.898250 0.077167 2013-01-02 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_348 Time 2009-01-01 00:00:00 0.137417 0.097500 0.016833 2009-01-01 03:00:00 0.131250 0.088833 0.016417 2009-01-01 06:00:00 0.113500 0.091250 0.016750 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 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_348 Time 2012-12-31 21:00:00 0.846500 0.846500 0.170167 2013-01-01 00:00:00 1.688333 1.688333 0.207333 2013-01-01 03:00:00 2.693333 2.693333 0.201500 2013-01-01 06:00:00 2.220833 2.220833 0.166917 2013-01-01 09:00:00 2.055000 2.055000 0.175667 2013-01-01 12:00:00 1.710000 1.710000 0.129583 2013-01-01 15:00:00 1.420000 1.420000 0.096333 2013-01-01 18:00:00 1.178583 1.178583 0.083083 2013-01-01 21:00:00 0.898250 0.898250 0.077167 2013-01-02 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_348 Time 2012-01-01 00:00:00 0.307167 0.273917 0.028000 2012-01-01 03:00:00 0.302917 0.270833 0.030583 2012-01-01 06:00:00 0.331500 0.284750 0.030917 2012-01-01 09:00:00 0.330750 0.293583 0.029750 2012-01-01 12:00:00 0.295000 0.285167 0.031750 ... ... ... ... 2012-12-31 09:00:00 0.682750 0.682750 0.066583 2012-12-31 12:00:00 0.651250 0.651250 0.063833 2012-12-31 15:00:00 0.629000 0.629000 0.061833 2012-12-31 18:00:00 0.617333 0.617333 0.060583 2012-12-31 21:00:00 0.846500 0.846500 0.170167 [2928 rows x 3 columns] L06_347 LS06_347 LS06_348 Time 2012-01-01 00:00:00 0.307167 0.273917 0.028000 2012-01-01 03:00:00 0.302917 0.270833 0.030583 2012-01-01 06:00:00 0.331500 0.284750 0.030917 2012-01-01 09:00:00 0.330750 0.293583 0.029750 2012-01-01 12:00:00 0.295000 0.285167 0.031750 ... ... ... ... 2012-01-31 09:00:00 0.191000 0.231250 0.025583 2012-01-31 12:00:00 0.183333 0.227167 0.025917 2012-01-31 15:00:00 0.163417 0.221000 0.023750 2012-01-31 18:00:00 0.157083 0.220667 0.023167 2012-01-31 21:00:00 0.160083 0.214750 0.023333 [248 rows x 3 columns] L06_347 LS06_347 LS06_348 Time 2012-01-31 00:00:00 0.191250 0.247417 0.025917 2012-01-31 03:00:00 0.181083 0.241583 0.025833 2012-01-31 06:00:00 0.188750 0.236750 0.026000 2012-01-31 09:00:00 0.191000 0.231250 0.025583 2012-01-31 12:00:00 0.183333 0.227167 0.025917 2012-01-31 15:00:00 0.163417 0.221000 0.023750 2012-01-31 18:00:00 0.157083 0.220667 0.023167 2012-01-31 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_348 Time 2009-01-01 00:00:00 0.137417 0.097500 0.016833 2009-01-01 03:00:00 0.131250 0.088833 0.016417 2009-01-01 06:00:00 0.113500 0.091250 0.016750 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 12:00:00 0.140917 0.096167 0.017000 ... ... ... ... 2012-01-01 09:00:00 0.330750 0.293583 0.029750 2012-01-01 12:00:00 0.295000 0.285167 0.031750 2012-01-01 15:00:00 0.301417 0.287750 0.031417 2012-01-01 18:00:00 0.322083 0.304167 0.038083 2012-01-01 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_348 Time 2009-01-01 00:00:00 0.137417 0.097500 0.016833 2009-01-01 03:00:00 0.131250 0.088833 0.016417 2009-01-01 06:00:00 0.113500 0.091250 0.016750 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 12:00:00 0.140917 0.096167 0.017000 ... ... ... ... 2013-01-01 12:00:00 1.710000 1.710000 0.129583 2013-01-01 15:00:00 1.420000 1.420000 0.096333 2013-01-01 18:00:00 1.178583 1.178583 0.083083 2013-01-01 21:00:00 0.898250 0.898250 0.077167 2013-01-02 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_348 Time 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-02 09:00:00 0.141917 0.097083 0.016417 2009-01-03 09:00:00 0.124583 0.084417 0.015833 2009-01-04 09:00:00 0.109000 0.105167 0.018000 2009-01-05 09:00:00 0.161500 0.114583 0.021583 ... ... ... ... 2012-12-28 09:00:00 0.961500 0.961500 0.092417 2012-12-29 09:00:00 0.786833 0.786833 0.077000 2012-12-30 09:00:00 0.916000 0.916000 0.101583 2012-12-31 09:00:00 0.682750 0.682750 0.066583 2013-01-01 09:00:00 2.055000 2.055000 0.175667 [1462 rows x 3 columns] L06_347 LS06_347 LS06_348 Time 2009-01-01 09:00:00 0.135750 0.091500 0.016250 2009-01-01 12:00:00 0.140917 0.096167 0.017000 2009-01-02 09:00:00 0.141917 0.097083 0.016417 2009-01-02 12:00:00 0.147833 0.101917 0.016417 2009-01-03 09:00:00 0.124583 0.084417 0.015833 ... ... ... ... 2012-12-30 12:00:00 1.465000 1.465000 0.086833 2012-12-31 09:00:00 0.682750 0.682750 0.066583 2012-12-31 12:00:00 0.651250 0.651250 0.063833 2013-01-01 09:00:00 2.055000 2.055000 0.175667 2013-01-01 12:00:00 1.710000 1.710000 0.129583 [2924 rows x 3 columns]
# 按天求平均值
df=df.resample('D').mean()
print(df)L06_347 LS06_347 LS06_348 Time 2009-01-01 0.125010 0.092281 0.016635 2009-01-02 0.124146 0.095781 0.016406 2009-01-03 0.113562 0.085542 0.016094 2009-01-04 0.140198 0.102708 0.017323 2009-01-05 0.128812 0.104490 0.018167 ... ... ... ... 2012-12-29 0.807604 0.807604 0.078031 2012-12-30 1.027240 1.027240 0.088000 2012-12-31 0.748365 0.748365 0.081417 2013-01-01 1.733042 1.733042 0.142198 2013-01-02 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_348 Time 2009-01-01 0.120906 0.091201 0.016378 2009-01-04 0.121594 0.091708 0.016670 2009-01-07 0.097042 0.070740 0.014479 2009-01-10 0.115941 0.086340 0.014545 2009-01-13 0.346962 0.364549 0.034198 ... ... ... ... 2012-12-20 0.996337 0.996337 0.114472 2012-12-23 2.769059 2.769059 0.225542 2012-12-26 1.451583 1.451583 0.140101 2012-12-29 0.861069 0.861069 0.082483 2013-01-01 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_348 Time 2009-01-31 0.517864 0.536660 0.045597 2009-02-28 0.516847 0.529987 0.047238 2009-03-31 0.372536 0.382359 0.037508 2009-04-30 0.163182 0.129354 0.021356 2009-05-31 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_348 Time 2009-01-31 0.078156 0.058438 0.013573 2009-02-28 0.182646 0.135667 0.019073 2009-03-31 0.131385 0.098875 0.016979 2009-04-30 0.078510 0.066375 0.013917 2009-05-31 0.060771 0.047969 0.013656 L06_347 LS06_347 LS06_348 Time 2009-01-31 5.933531 6.199927 0.404708 2009-02-28 4.407604 4.724583 0.231750 2009-03-31 1.337896 1.586833 0.116969 2009-04-30 0.275698 0.247312 0.037375 2009-05-31 2.184250 2.433073 0.168792
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 data 0 a 4 1 a 3 2 a 2 3 b 1 4 b 12 5 b 3 6 c 4 7 c 5 8 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 data 6 c 4 7 c 5 8 c 7 3 b 1 5 b 3 4 b 12 2 a 2 1 a 3 0 a 4
df=pd.DataFrame({
'k1':['one']*3+['tow']*3,
'k2':[1,2,3,4,5,6]
})
print(df)k1 k2 0 one 1 1 one 2 2 one 3 3 tow 4 4 tow 5 5 tow 6
# 排序 print(df.sort_values(by='k2',ascending=False))
k1 k2 5 tow 6 4 tow 5 3 tow 4 2 one 3 1 one 2 0 one 1
# 删除重复数据 print(df.drop_duplicates())
k1 k2 0 one 1 1 one 2 2 one 3 3 tow 4 4 tow 5 5 tow 6
# 按某列去重删除数据 print(df.drop_duplicates(subset='k1'))
k1 k2 0 one 1 3 tow 4
df = pd.DataFrame({'food':['A1','A2','B1','B2','B3','C1','C2'],'data':[1,2,3,4,5,6,7]})
print(df)food data 0 A1 1 1 A2 2 2 B1 3 3 B2 4 4 B3 5 5 C1 6 6 C2 7
# 将A1,A2归类到A, B1,B2,B3归类B,C1,C2归类到C
dict1 = {
'A1':'A',
'A2':'A',
'B1':'B',
'B2':'B',
'B3':'B',
'C1':'C',
'C2':'C'
}
df['Upper']=df['food'].map(dict1)
print(df)food data Upper 0 A1 1 A 1 A2 2 A 2 B1 3 B 3 B2 4 B 4 B3 5 B 5 C1 6 C 6 C2 7 C
import numpy as np
df=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 k2 0 1.977668 -1.136251 1 0.550649 0.010131 2 0.723699 0.304536 3 -0.247529 0.030359 4 -0.351775 0.732785 k1 k2 ration 0 1.977668 -1.136251 -1.740520 1 0.550649 0.010131 54.355131 2 0.723699 0.304536 2.376394 3 -0.247529 0.030359 -8.153389 4 -0.351775 0.732785 -0.480052
# 删除ration列
df2.drop('ration',axis='columns',inplace=True)
print(df2)k1 k2 0 1.977668 -1.136251 1 0.550649 0.010131 2 0.723699 0.304536 3 -0.247529 0.030359 4 -0.351775 0.732785
se=pd.Series([1,2,3,4,5,6,7,8]) print(se)
0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 dtype: int64
se.replace(6,np.nan,inplace=True) print(se)
0 1.0 1 2.0 2 3.0 3 4.0 4 5.0 5 NaN 6 7.0 7 8.0 dtype: float64
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] 4 dtype: int64
df=pd.DataFrame([range(3),[2,np.nan,5],[np.nan,3,np.nan],range(3)]) print(df)
0 1 2 0 0.0 1.0 2.0 1 2.0 NaN 5.0 2 NaN 3.0 NaN 3 0.0 1.0 2.0
print(df.isnull())
0 1 2 0 False False False 1 False True False 2 True False True 3 False False False
# 将NaN填充成10 print(df.fillna(10))
0 1 2 0 0.0 1.0 2.0 1 2.0 10.0 5.0 2 10.0 3.0 10.0 3 0.0 1.0 2.0
# 查看某一行是否存在NaN print(df[df.isnull().any(axis=1)])
0 1 2 1 2.0 NaN 5.0 2 NaN 3.0 NaN
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