stdx::interval, a library for intervals on totally ordered sets Eric Hughes, Meadhbh Hamrick stdx::interval, a library for intervals on totally ordered sets Eric Hughes, Meadhbh Hamrick In brief stdx::interval stdx::interval implements the mathematical sense of an interval on a totally ordered set. The library reasons about intervals as sets, not as interval expressions. The library is header-only and targets as a module for import. The library needs a working accommodation with the concept std::totally_ordered. The unfortunate fact is that this concept does not enforce its stated semantic requirements (so

public+virtual or protected+nonvirtual ordered A totally ordered type with operator<=> that implements strong_ordering. Also: weakly_ordered, partially_ordered copyable A type that has copy/move c functions value An ordered basic_value. Also: weakly_ordered_value, partially_ordered_value struct A basic_value with all public members, no virtuals, no custom assignment enum An ordered basic_value with with all public values flag_enum An ordered basic_value with all public values, and bitwise sets/tests union A safe (tagged) union with names (unlike std::variant)51 interface An abstract class having

pandas 0.25.x In [1]: pd.Categorical([1, 2, np.nan], ordered=True).min() Out[1]: nan pandas 1.0.0 In [47]: pd.Categorical([1, 2, np.nan], ordered=True).min() Out[47]: 1 1.5.9 Default dtype of empty 0.25.x >>> df.resample("2D").agg(lambda x: 'a').A.dtype CategoricalDtype(categories=['a', 'b'], ordered=False) pandas 1.0.0 In [50]: df.resample("2D").agg(lambda x: 'a').A.dtype Out[50]: dtype('O') tz_localize(), DatetimeIndex.tz_localize(), and Series.tz_localize() (GH22644) • Changed the default “ordered” argument in CategoricalDtype from None to False (GH26336) 1.7. Removal of prior version deprecations/changes

will preserve these dtypes. (GH18502) In [29]: cat = pd.Categorical(["foo", "bar", "bar", "qux"], ordered=True) In [30]: df = pd.DataFrame({'payload': [-1, -2, -1, -2], 'col': cat}) In [31]: df Out[31]: groupby('payload').first().col.dtype Out[33]: CategoricalDtype(categories=['bar', 'foo', 'qux'], ordered=True) 1.2.7 Incompatible Index type unions When performing Index.union() operations between objects Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) Previous behavior In [2]: cat = pd.Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) In [3]: cat.argsort() Out[3]: array([1

will preserve these dtypes. (GH18502) In [29]: cat = pd.Categorical(["foo", "bar", "bar", "qux"], ordered=True) In [30]: df = pd.DataFrame({'payload': [-1, -2, -1, -2], 'col': cat}) In [31]: df Out[31]: groupby('payload').first().col.dtype Out[33]: CategoricalDtype(categories=['bar', 'foo', 'qux'], ordered=True) 1.2.7 Incompatible Index type unions When performing Index.union() operations between objects Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) Previous behavior In [2]: cat = pd.Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) In [3]: cat.argsort() Out[3]: array([1

data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously dtype: int64 Note: When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23. will be the lexically ordered list of dict keys. In the example above, if you were on a Python version lower than 3.6 or a Pandas version lower than 0.23, the Series would be ordered by the lexical order

data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously dtype: int64 Note: When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23. will be the lexically ordered list of dict keys. In the example above, if you were on a Python version lower than 3.6 or a Pandas version lower than 0.23, the Series would be ordered by the lexical order

data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 733639 2000-01-10 0.733639 Freq: D, dtype: float64 These methods require that the indexes are ordered increasing or decreasing. Note that the same result could have been achieved using fillna (except dtype: int64 Note: When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23.

data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 733639 2000-01-10 0.733639 Freq: D, dtype: float64 These methods require that the indexes are ordered increasing or decreasing. Note that the same result could have been achieved using fillna (except dtype: int64 Note: When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23.

data: • Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet • Ordered and unordered (not necessarily fixed-frequency) time series data. • Arbitrary matrix data (homogeneously 733639 2000-01-10 0.733639 Freq: D, dtype: float64 These methods require that the indexes are ordered increasing or decreasing. Note that the same result could have been achieved using fillna (except dtype: int64 Note: When the data is a dict, and an index is not passed, the Series index will be ordered by the dict’s insertion order, if you’re using Python version >= 3.6 and Pandas version >= 0.23.