Comments (6)
@Enaunimes the absolute value of the diff would fit in Uint64, but diffs can be negative & Uint64 is unsigned
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Because it doesn't fit into i64
. Until we have support for i128
, there isn't a good fit.
Note that the pandas strategy of converting to float is also wrong:
>>> import pandas as pd
>>> s = [0, 18446743990181366216, 0]
>>> pd.Series(s).diff()
0 NaN
1 1.844674e+19
2 8.352819e+10
dtype: float64
Index 2 is 9 decimals off.
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Because it doesn't fit into
i64
. Until we have support fori128
, there isn't a good fit.Note that the pandas strategy of converting to float is also wrong:
>>> import pandas as pd >>> s = [0, 18446743990181366216, 0] >>> pd.Series(s).diff() 0 NaN 1 1.844674e+19 2 8.352819e+10 dtype: float64Index 2 is 9 decimals off.
I suppose specifying dtype=polars.UInt64 when creating the series should have made the data fit into u64?
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Sorry, what do you mean?
from polars.
Sorry, what do you mean?
I'm not from a CS background, so please correct me if I'm wrong.
As in the code sample of the issue, I specified the dtype=polars.UInt64 when creating the series. The number does not fit in the polars.Int64, but should have fit into the polars.Uint64, isn't it?
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@Enaunimes the absolute value of the diff would fit in Uint64, but diffs can be negative & Uint64 is unsigned
Got it. Thanks.
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