pandas out of bounds nanosecond timestamp after of

问题:

I am confused how pandas blew out of bounds for datetime objects with these lines:

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 INDATUMA is date information in the format 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:

pandas.tslib.OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 2262-05-01 00:00:00

回答1:

Since pandas represents timestamps in nanosecond resolution, the timespan that can be represented using a 64-bit integer is limited to approximately 584 years

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

Straight out of: http://pandas-docs.github.io/pandas-docs-travis/timeseries.html#timestamp-limitations

回答2:

Setting the errors parameter in pd.to_datetime to 'coerce' causes replacement of out of bounds values with NaT. Quoting the docs:

If ‘coerce’, then invalid parsing will be set as NaT

E.g.:

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.