Python 偏移前滚加上一个月的偏移后，熊猫越界纳秒时间戳
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原文地址: http://stackoverflow.com/questions/32888124/
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pandas out of bounds nanosecond timestamp after offset rollforward plus adding a month offset
提问by László
I am confused how pandas blew out of bounds for datetime objects with these lines:
我很困惑熊猫如何用这些行超出日期时间对象的范围：
import pandas as pd
BOMoffset = pd.tseries.offsets.MonthBegin()
# here some code sets the all_treatments dataframe and the newrowix, micolix, mocolix counters
all_treatments.iloc[newrowix,micolix] = BOMoffset.rollforward(all_treatments.iloc[i,micolix] + pd.tseries.offsets.DateOffset(months = x))
all_treatments.iloc[newrowix,mocolix] = BOMoffset.rollforward(all_treatments.iloc[newrowix,micolix]+ pd.tseries.offsets.DateOffset(months = 1))
Here all_treatments.iloc[i,micolix]is a datetime set by pd.to_datetime(all_treatments['INDATUMA'], errors='coerce',format='%Y%m%d'), and INDATUMAis date information in the format 20070125.
这里all_treatments.iloc[i,micolix]是设置日期时间pd.to_datetime(all_treatments['INDATUMA'], errors='coerce',format='%Y%m%d')，并INDATUMA在格式的最新信息20070125。
This logic seems to work on mock data (no errors, dates make sense), so at the moment I cannot reproduce while it fails in my entire data with the following error:
这个逻辑似乎适用于模拟数据（没有错误，日期有意义），所以目前我无法重现，而它在我的整个数据中失败并出现以下错误：
pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-05-01 00:00:00
采纳答案by Shankar ARUL - jupyterdata.com
Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years
由于 pandas 以纳秒分辨率表示时间戳，因此可以使用 64 位整数表示的时间跨度被限制为大约 584 年
pd.Timestamp.min
Out[54]: Timestamp('1677-09-22 00:12:43.145225')
In [55]: pd.Timestamp.max
Out[55]: Timestamp('2262-04-11 23:47:16.854775807')
And your value is out of this range 2262-05-01 00:00:00 and hence the outofbounds error
并且您的值超出此范围 2262-05-01 00:00:00 因此出现越界错误
Straight out of: http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#timestamp-limitations
直接出自：http: //pandas-docs.github.io/pandas-docs-travis/timeseries.html#timestamp-limitations
回答by Pawel Kranzberg
Setting the errorsparameter in pd.to_datetimeto 'coerce'causes replacement of out of bounds values with NaT. Quoting the docs:
将errors参数设置pd.to_datetime为'coerce'会导致用 替换越界值NaT。引用文档：
If ‘coerce', then invalid parsing will be set as NaT
如果'coerce'，则无效解析将被设置为NaT
E.g.:
例如：
datetime_variable = pd.to_datetime(datetime_variable, errors = 'coerce')
This does not fix the data (obviously), but still allows processing the non-NaT data points.
这不会修复数据（显然），但仍然允许处理非 NaT 数据点。
回答by EdivaldoJunior
None of above are so good, because it will delete your data. But, you can only mantain and edit your conversion:
以上都不是很好，因为它会删除您的数据。但是，您只能维护和编辑您的转换：
# convertin from epoch to datatime mantainig the nanoseconds timestamp
xbarout= pd.to_datetime(xbarout.iloc[:,0],unit='ns')
