COMPAT: create UInt64Block #15145
Description
xref #14937 (comment)
a number of indexing / conversion issues arise because we are treating uint as a direct int, rather than a sub-class. (e.g. if we make UIntBlock a sub-class of IntBlock), I think can easily handle some small overrides to, for instance check for negative values when indexing.
In [1]: df = pd.DataFrame({'A' : np.array([1,2,3],dtype='uint64'), 'B': range(3)}) In [2]: df Out[2]: A B 0 1 0 1 2 1 2 3 2 In [4]: df.dtypes Out[4]: A uint64 B int64 dtype: object Buggy
In [5]: df.iloc[1] = -1 In [6]: df Out[6]: A B 0 1 0 1 18446744073709551615 -1 2 3 2 In [7]: df.iloc[1] = np.nan In [8]: df Out[8]: A B 0 1.0 0.0 1 NaN NaN 2 3.0 2.0 This is correct
In [9]: df.A.astype('uint64') --------------------------------------------------------------------------- ValueError: Cannot convert non-finite values (NA or inf) to integer However, this is not
In [10]: df.iloc[1] = -1 In [11]: df Out[11]: A B 0 1.0 0.0 1 -1.0 -1.0 2 3.0 2.0 In [12]: df.dtypes Out[12]: A float64 B float64 dtype: object In [13]: df.A.astype('uint64') Out[13]: 0 1 1 18446744073709551615 2 3 Name: A, dtype: uint64 Construction with invalid values
In [1]: Series([-1], dtype='uint64') Out[1]: 0 18446744073709551615 dtype: uint64