Comments (1)
mroeschke
commented on June 26, 2024
Thanks for the report, but this a limitation of converting floating point precision back to integers. Numpy exhibits the same behavior
In [1]: import numpy as np
In [2]: np.array([995279394541916024+i for i in range(128)]).astype('int64')
Out[2]:
array([995279394541916024, 995279394541916025, 995279394541916026,
995279394541916027, 995279394541916028, 995279394541916029,
995279394541916030, 995279394541916031, 995279394541916032,
995279394541916033, 995279394541916034, 995279394541916035,
995279394541916036, 995279394541916037, 995279394541916038,
995279394541916039, 995279394541916040, 995279394541916041,
995279394541916042, 995279394541916043, 995279394541916044,
995279394541916045, 995279394541916046, 995279394541916047,
995279394541916048, 995279394541916049, 995279394541916050,
995279394541916051, 995279394541916052, 995279394541916053,
995279394541916054, 995279394541916055, 995279394541916056,
995279394541916057, 995279394541916058, 995279394541916059,
995279394541916060, 995279394541916061, 995279394541916062,
995279394541916063, 995279394541916064, 995279394541916065,
995279394541916066, 995279394541916067, 995279394541916068,
995279394541916069, 995279394541916070, 995279394541916071,
995279394541916072, 995279394541916073, 995279394541916074,
995279394541916075, 995279394541916076, 995279394541916077,
995279394541916078, 995279394541916079, 995279394541916080,
995279394541916081, 995279394541916082, 995279394541916083,
995279394541916084, 995279394541916085, 995279394541916086,
995279394541916087, 995279394541916088, 995279394541916089,
995279394541916090, 995279394541916091, 995279394541916092,
995279394541916093, 995279394541916094, 995279394541916095,
995279394541916096, 995279394541916097, 995279394541916098,
995279394541916099, 995279394541916100, 995279394541916101,
995279394541916102, 995279394541916103, 995279394541916104,
995279394541916105, 995279394541916106, 995279394541916107,
995279394541916108, 995279394541916109, 995279394541916110,
995279394541916111, 995279394541916112, 995279394541916113,
995279394541916114, 995279394541916115, 995279394541916116,
995279394541916117, 995279394541916118, 995279394541916119,
995279394541916120, 995279394541916121, 995279394541916122,
995279394541916123, 995279394541916124, 995279394541916125,
995279394541916126, 995279394541916127, 995279394541916128,
995279394541916129, 995279394541916130, 995279394541916131,
995279394541916132, 995279394541916133, 995279394541916134,
995279394541916135, 995279394541916136, 995279394541916137,
995279394541916138, 995279394541916139, 995279394541916140,
995279394541916141, 995279394541916142, 995279394541916143,
995279394541916144, 995279394541916145, 995279394541916146,
995279394541916147, 995279394541916148, 995279394541916149,
995279394541916150, 995279394541916151])
In [3]: np.array([995279394541916024+i for i in range(128)]).astype('float64').astype('int64')
Out[3]:
array([995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916032, 995279394541916032, 995279394541916032,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160, 995279394541916160,
995279394541916160, 995279394541916160])Closing as the expected behavior
from pandas.
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from pandas.