Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/pandas/io/clipboards.py: 91%

1""" io on the clipboard """ 

2from __future__ import annotations 

3 

4from io import StringIO 

5from typing import TYPE_CHECKING 

6import warnings 

7 

8from pandas._libs import lib 

9from pandas.util._exceptions import find_stack_level 

10from pandas.util._validators import check_dtype_backend 

11 

12from pandas.core.dtypes.generic import ABCDataFrame 

13 

14from pandas import ( 

15 get_option, 

16 option_context, 

17) 

18 

19if TYPE_CHECKING: 

20 from pandas._typing import DtypeBackend 

21 

22 

23def read_clipboard( 

24 sep: str = r"\s+", 

25 dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default, 

26 **kwargs, 

27): # pragma: no cover 

28 r""" 

29 Read text from clipboard and pass to read_csv. 

30 

31 Parameters 

32 ---------- 

33 sep : str, default '\s+' 

34 A string or regex delimiter. The default of '\s+' denotes 

35 one or more whitespace characters. 

36 

37 dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames 

38 Which dtype_backend to use, e.g. whether a DataFrame should have NumPy 

39 arrays, nullable dtypes are used for all dtypes that have a nullable 

40 implementation when "numpy_nullable" is set, pyarrow is used for all 

41 dtypes if "pyarrow" is set. 

42 

43 The dtype_backends are still experimential. 

44 

45 .. versionadded:: 2.0 

46 

47 **kwargs 

48 See read_csv for the full argument list. 

49 

50 Returns 

51 ------- 

52 DataFrame 

53 A parsed DataFrame object. 

54 """ 

55 encoding = kwargs.pop("encoding", "utf-8") 

56 

57 # only utf-8 is valid for passed value because that's what clipboard 

58 # supports 

59 if encoding is not None and encoding.lower().replace("-", "") != "utf8": 

60 raise NotImplementedError("reading from clipboard only supports utf-8 encoding") 

61 

62 check_dtype_backend(dtype_backend) 

63 

64 from pandas.io.clipboard import clipboard_get 

65 from pandas.io.parsers import read_csv 

66 

67 text = clipboard_get() 

68 

69 # Try to decode (if needed, as "text" might already be a string here). 

70 try: 

71 text = text.decode(kwargs.get("encoding") or get_option("display.encoding")) 

72 except AttributeError: 

73 pass 

74 

75 # Excel copies into clipboard with \t separation 

76 # inspect no more then the 10 first lines, if they 

77 # all contain an equal number (>0) of tabs, infer 

78 # that this came from excel and set 'sep' accordingly 

79 lines = text[:10000].split("\n")[:-1][:10] 

80 

81 # Need to remove leading white space, since read_csv 

82 # accepts: 

83 # a b 

84 # 0 1 2 

85 # 1 3 4 

86 

87 counts = {x.lstrip(" ").count("\t") for x in lines} 

88 if len(lines) > 1 and len(counts) == 1 and counts.pop() != 0: 

89 sep = "\t" 

90 # check the number of leading tabs in the first line 

91 # to account for index columns 

92 index_length = len(lines[0]) - len(lines[0].lstrip(" \t")) 

93 if index_length != 0: 

94 kwargs.setdefault("index_col", list(range(index_length))) 

95 

96 # Edge case where sep is specified to be None, return to default 

97 if sep is None and kwargs.get("delim_whitespace") is None: 

98 sep = r"\s+" 

99 

100 # Regex separator currently only works with python engine. 

101 # Default to python if separator is multi-character (regex) 

102 if len(sep) > 1 and kwargs.get("engine") is None: 

103 kwargs["engine"] = "python" 

104 elif len(sep) > 1 and kwargs.get("engine") == "c": 

105 warnings.warn( 

106 "read_clipboard with regex separator does not work properly with c engine.", 

107 stacklevel=find_stack_level(), 

108 ) 

109 

110 return read_csv(StringIO(text), sep=sep, dtype_backend=dtype_backend, **kwargs) 

111 

112 

113def to_clipboard( 

114 obj, excel: bool | None = True, sep: str | None = None, **kwargs 

115) -> None: # pragma: no cover 

116 """ 

117 Attempt to write text representation of object to the system clipboard 

118 The clipboard can be then pasted into Excel for example. 

119 

120 Parameters 

121 ---------- 

122 obj : the object to write to the clipboard 

123 excel : bool, defaults to True 

124 if True, use the provided separator, writing in a csv 

125 format for allowing easy pasting into excel. 

126 if False, write a string representation of the object 

127 to the clipboard 

128 sep : optional, defaults to tab 

129 other keywords are passed to to_csv 

130 

131 Notes 

132 ----- 

133 Requirements for your platform 

134 - Linux: xclip, or xsel (with PyQt4 modules) 

135 - Windows: 

136 - OS X: 

137 """ 

138 encoding = kwargs.pop("encoding", "utf-8") 

139 

140 # testing if an invalid encoding is passed to clipboard 

141 if encoding is not None and encoding.lower().replace("-", "") != "utf8": 

142 raise ValueError("clipboard only supports utf-8 encoding") 

143 

144 from pandas.io.clipboard import clipboard_set 

145 

146 if excel is None: 

147 excel = True 

148 

149 if excel: 

150 try: 

151 if sep is None: 

152 sep = "\t" 

153 buf = StringIO() 

154 

155 # clipboard_set (pyperclip) expects unicode 

156 obj.to_csv(buf, sep=sep, encoding="utf-8", **kwargs) 

157 text = buf.getvalue() 

158 

159 clipboard_set(text) 

160 return 

161 except TypeError: 

162 warnings.warn( 

163 "to_clipboard in excel mode requires a single character separator.", 

164 stacklevel=find_stack_level(), 

165 ) 

166 elif sep is not None: 

167 warnings.warn( 

168 "to_clipboard with excel=False ignores the sep argument.", 

169 stacklevel=find_stack_level(), 

170 ) 

171 

172 if isinstance(obj, ABCDataFrame): 

173 # str(df) has various unhelpful defaults, like truncation 

174 with option_context("display.max_colwidth", None): 

175 objstr = obj.to_string(**kwargs) 

176 else: 

177 objstr = str(obj) 

178 clipboard_set(objstr)