Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.9/dist-packages/pandas/core/arrays/floating.py: 91%

1from __future__ import annotations 

2 

3from typing import ClassVar 

4 

5import numpy as np 

6 

7from pandas.core.dtypes.base import register_extension_dtype 

8from pandas.core.dtypes.common import is_float_dtype 

9 

10from pandas.core.arrays.numeric import ( 

11 NumericArray, 

12 NumericDtype, 

13) 

14 

15 

16class FloatingDtype(NumericDtype): 

17 """ 

18 An ExtensionDtype to hold a single size of floating dtype. 

19 

20 These specific implementations are subclasses of the non-public 

21 FloatingDtype. For example we have Float32Dtype to represent float32. 

22 

23 The attributes name & type are set when these subclasses are created. 

24 """ 

25 

26 _default_np_dtype = np.dtype(np.float64) 

27 _checker = is_float_dtype 

28 

29 @classmethod 

30 def construct_array_type(cls) -> type[FloatingArray]: 

31 """ 

32 Return the array type associated with this dtype. 

33 

34 Returns 

35 ------- 

36 type 

37 """ 

38 return FloatingArray 

39 

40 @classmethod 

41 def _get_dtype_mapping(cls) -> dict[np.dtype, FloatingDtype]: 

42 return NUMPY_FLOAT_TO_DTYPE 

43 

44 @classmethod 

45 def _safe_cast(cls, values: np.ndarray, dtype: np.dtype, copy: bool) -> np.ndarray: 

46 """ 

47 Safely cast the values to the given dtype. 

48 

49 "safe" in this context means the casting is lossless. 

50 """ 

51 # This is really only here for compatibility with IntegerDtype 

52 # Here for compat with IntegerDtype 

53 return values.astype(dtype, copy=copy) 

54 

55 

56class FloatingArray(NumericArray): 

57 """ 

58 Array of floating (optional missing) values. 

59 

60 .. warning:: 

61 

62 FloatingArray is currently experimental, and its API or internal 

63 implementation may change without warning. Especially the behaviour 

64 regarding NaN (distinct from NA missing values) is subject to change. 

65 

66 We represent a FloatingArray with 2 numpy arrays: 

67 

68 - data: contains a numpy float array of the appropriate dtype 

69 - mask: a boolean array holding a mask on the data, True is missing 

70 

71 To construct an FloatingArray from generic array-like input, use 

72 :func:`pandas.array` with one of the float dtypes (see examples). 

73 

74 See :ref:`integer_na` for more. 

75 

76 Parameters 

77 ---------- 

78 values : numpy.ndarray 

79 A 1-d float-dtype array. 

80 mask : numpy.ndarray 

81 A 1-d boolean-dtype array indicating missing values. 

82 copy : bool, default False 

83 Whether to copy the `values` and `mask`. 

84 

85 Attributes 

86 ---------- 

87 None 

88 

89 Methods 

90 ------- 

91 None 

92 

93 Returns 

94 ------- 

95 FloatingArray 

96 

97 Examples 

98 -------- 

99 Create an FloatingArray with :func:`pandas.array`: 

100 

101 >>> pd.array([0.1, None, 0.3], dtype=pd.Float32Dtype()) 

102 <FloatingArray> 

103 [0.1, <NA>, 0.3] 

104 Length: 3, dtype: Float32 

105 

106 String aliases for the dtypes are also available. They are capitalized. 

107 

108 >>> pd.array([0.1, None, 0.3], dtype="Float32") 

109 <FloatingArray> 

110 [0.1, <NA>, 0.3] 

111 Length: 3, dtype: Float32 

112 """ 

113 

114 _dtype_cls = FloatingDtype 

115 

116 # The value used to fill '_data' to avoid upcasting 

117 _internal_fill_value = np.nan 

118 # Fill values used for any/all 

119 # Incompatible types in assignment (expression has type "float", base class 

120 # "BaseMaskedArray" defined the type as "<typing special form>") 

121 _truthy_value = 1.0 # type: ignore[assignment] 

122 _falsey_value = 0.0 # type: ignore[assignment] 

123 

124 

125_dtype_docstring = """ 

126An ExtensionDtype for {dtype} data. 

127 

128This dtype uses ``pd.NA`` as missing value indicator. 

129 

130Attributes 

131---------- 

132None 

133 

134Methods 

135------- 

136None 

137 

138Examples 

139-------- 

140For Float32Dtype: 

141 

142>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float32Dtype()) 

143>>> ser.dtype 

144Float32Dtype() 

145 

146For Float64Dtype: 

147 

148>>> ser = pd.Series([2.25, pd.NA], dtype=pd.Float64Dtype()) 

149>>> ser.dtype 

150Float64Dtype() 

151""" 

152 

153# create the Dtype 

154 

155 

156@register_extension_dtype 

157class Float32Dtype(FloatingDtype): 

158 type = np.float32 

159 name: ClassVar[str] = "Float32" 

160 __doc__ = _dtype_docstring.format(dtype="float32") 

161 

162 

163@register_extension_dtype 

164class Float64Dtype(FloatingDtype): 

165 type = np.float64 

166 name: ClassVar[str] = "Float64" 

167 __doc__ = _dtype_docstring.format(dtype="float64") 

168 

169 

170NUMPY_FLOAT_TO_DTYPE: dict[np.dtype, FloatingDtype] = { 

171 np.dtype(np.float32): Float32Dtype(), 

172 np.dtype(np.float64): Float64Dtype(), 

173}