将Timestamp转为datetime类型
在Pandas中我们在处理时间序列的时候常用的方法有：
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pd.to_datetime() -
pd.date_range()
pandas生成时间索引
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# pd.date_range()index = pd.date_range("20210101",periods=20)indexOut[29]: DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08', '2021-01-09', '2021-01-10', '2021-01-11', '2021-01-12', '2021-01-13', '2021-01-14', '2021-01-15', '2021-01-16', '2021-01-17', '2021-01-18', '2021-01-19', '2021-01-20'], dtype='datetime64[ns]', freq='D')# pd.to_datetime()df = pd.DataFrame(data=range(20210101,20210128),columns=["period"])df["aa"] = pd.to_datetime(df["period"],format="%Y%m%d")dfOut[24]: period aa0 20210101 2021-01-011 20210102 2021-01-022 20210103 2021-01-033 20210104 2021-01-044 20210105 2021-01-055 20210106 2021-01-066 20210107 2021-01-077 20210108 2021-01-088 20210109 2021-01-099 20210110 2021-01-1010 20210111 2021-01-1111 20210112 2021-01-1212 20210113 2021-01-1313 20210114 2021-01-1414 20210115 2021-01-1515 20210116 2021-01-1616 20210117 2021-01-1717 20210118 2021-01-1818 20210119 2021-01-1919 20210120 2021-01-2020 20210121 2021-01-2121 20210122 2021-01-2222 20210123 2021-01-2323 20210124 2021-01-2424 20210125 2021-01-2525 20210126 2021-01-2626 20210127 2021-01-27index[1]Out[30]: Timestamp('2021-01-02 00:00:00', freq='D')df["aa"][1]Out[31]: Timestamp('2021-01-02 00:00:00')df["aa"][1] == index[1]Out[32]: Truetype(df["aa"][1])Out[33]: pandas._libs.tslibs.timestamps.Timestamptype(index[1])Out[34]: pandas._libs.tslibs.timestamps.Timestamp |
Timestamp与datetime
从上面代码可以看出，pandas中的时间格式是pandas._libs.tslibs.timestamps.Timestamp
但是python中常用的时间格式是datetime.datetime
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to_pydatetime()
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t = datetime(2021,1,2)type(t)Out[54]: datetime.datetimetOut[55]: datetime.datetime(2021, 1, 2, 0, 0)r = (index[1].to_pydatetime())type(r)Out[57]: datetime.datetimet == rOut[58]: True |
将pandas Timestamp 转为 datetime 类型
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In [11]: ts = pd.Timestamp('2014-01-23 00:00:00', tz=None)In [12]: ts.to_pydatetime()Out[12]: datetime.datetime(2014, 1, 23, 0, 0) |
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It's also available on a DatetimeIndexrng = pd.date_range('1/10/2011', periods=3, freq='D')rng.to_pydatetime()Out[60]: array([datetime.datetime(2011, 1, 10, 0, 0), datetime.datetime(2011, 1, 11, 0, 0), datetime.datetime(2011, 1, 12, 0, 0)], dtype=object) |
pandas从Timestamp中提取小时分钟等
官方文档： https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#from-timestamps-to-epoch
最近需要提取某一天的时刻距离0:00的分钟数，找了文档之后想到这样一个办法：
假设数据为
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In [64]: stamps = pd.date_range('2012-10-08 18:15:05', periods=4, freq='h')In [65]: stampsOut[65]: DatetimeIndex(['2012-10-08 18:15:05', '2012-10-08 19:15:05', '2012-10-08 20:15:05', '2012-10-08 21:15:05'], dtype='datetime64[ns]', freq='D') |
先得到距离1970-01-01的秒数
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In [66]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')Out[66]: Int64Index([1349720105, 1349723705, 1349727305, 1349730905], dtype='int64') |
对天取余，得到距离0:00的秒数
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In [67]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') % 86400Out[67]: Int64Index([65705, 69305, 72905, 76505], dtype='int64') |
取距离0:00的分钟数
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In [68]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') % 86400 /60Out[68]: Int64Index([1095.0833333333333, 1155.0833333333333, 1215.0833333333333, 1275.0833333333333], dtype='float64') |
同样的，也可以取小时数
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In [69]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') % 86400 /3600Out[68]: Int64Index([18.25138888888889, 19.25138888888889, 20.25138888888889, 21.25138888888889], dtype='float64') |
取小时整数–当然取小时整数也有别的方法。
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In [70]: (stamps - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s') % 86400 //3600Out[70]: Int64Index([18, 19, 20, 21], dtype='int64') |
以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/qq_34184505/article/details/124380393
