How To Work Around Python Pandas DataFrame's "Out Of Bounds Nanosecond Timestamp" Error?

The following code throws an 'Out of bounds nanosecond timestamp: 1452-04-15 00:00:00 ' error. The same code works if I replace the date strings to some recent dates such as 2017-0

Solution 1:

You need period_range:

r = pd.period_range('1452-04-15', '1519-05-02')
print (r)
PeriodIndex(['1452-04-15', '1452-04-16', '1452-04-17', '1452-04-18',
             '1452-04-19', '1452-04-20', '1452-04-21', '1452-04-22',
             '1452-04-23', '1452-04-24',
             ...
             '1519-04-23', '1519-04-24', '1519-04-25', '1519-04-26',
             '1519-04-27', '1519-04-28', '1519-04-29', '1519-04-30',
             '1519-05-01', '1519-05-02'],
            dtype='period[D]', length=24488, freq='D')

df = pd.DataFrame({'Date' : r})
print (df.head())
        Date
0 1452-04-15
1 1452-04-16
2 1452-04-17
3 1452-04-18
4 1452-04-19

because timestamp limitations:

In [66]: pd.Timestamp.min
Out[66]: Timestamp('1677-09-21 00:12:43.145225')

In [67]: pd.Timestamp.max
Out[67]: Timestamp('2262-04-11 23:47:16.854775807')