Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/pandas/core/array_algos/datetimelike_accumulations.py: 35%

1""" 

2datetimelke_accumulations.py is for accumulations of datetimelike extension arrays 

3""" 

4 

5from __future__ import annotations 

6 

7from typing import Callable 

8 

9import numpy as np 

10 

11from pandas._libs import iNaT 

12 

13from pandas.core.dtypes.missing import isna 

14 

15 

16def _cum_func( 

17 func: Callable, 

18 values: np.ndarray, 

19 *, 

20 skipna: bool = True, 

21): 

22 """ 

23 Accumulations for 1D datetimelike arrays. 

24 

25 Parameters 

26 ---------- 

27 func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate 

28 values : np.ndarray 

29 Numpy array with the values (can be of any dtype that support the 

30 operation). Values is changed is modified inplace. 

31 skipna : bool, default True 

32 Whether to skip NA. 

33 """ 

34 try: 

35 fill_value = { 

36 np.maximum.accumulate: np.iinfo(np.int64).min, 

37 np.cumsum: 0, 

38 np.minimum.accumulate: np.iinfo(np.int64).max, 

39 }[func] 

40 except KeyError: 

41 raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray") 

42 

43 mask = isna(values) 

44 y = values.view("i8") 

45 y[mask] = fill_value 

46 

47 if not skipna: 

48 mask = np.maximum.accumulate(mask) 

49 

50 result = func(y) 

51 result[mask] = iNaT 

52 

53 if values.dtype.kind in ["m", "M"]: 

54 return result.view(values.dtype.base) 

55 return result 

56 

57 

58def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray: 

59 return _cum_func(np.cumsum, values, skipna=skipna) 

60 

61 

62def cummin(values: np.ndarray, *, skipna: bool = True): 

63 return _cum_func(np.minimum.accumulate, values, skipna=skipna) 

64 

65 

66def cummax(values: np.ndarray, *, skipna: bool = True): 

67 return _cum_func(np.maximum.accumulate, values, skipna=skipna)