""" io on the clipboard """
from __future__ import annotations
from io import StringIO
from typing import TYPE_CHECKING
import warnings
from pandas._libs import lib
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import check_dtype_backend
from pandas.core.dtypes.generic import ABCDataFrame
from pandas import (
get_option,
option_context,
)
if TYPE_CHECKING:
from pandas._typing import DtypeBackend
[docs]def read_clipboard(
sep: str = r"\s+",
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
**kwargs,
): # pragma: no cover
r"""
Read text from clipboard and pass to :func:`~pandas.read_csv`.
Parses clipboard contents similar to how CSV files are parsed
using :func:`~pandas.read_csv`.
Parameters
----------
sep : str, default '\\s+'
A string or regex delimiter. The default of ``'\\s+'`` denotes
one or more whitespace characters.
dtype_backend : {'numpy_nullable', 'pyarrow'}, default 'numpy_nullable'
Back-end data type applied to the resultant :class:`DataFrame`
(still experimental). Behaviour is as follows:
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
(default).
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
DataFrame.
.. versionadded:: 2.0
**kwargs
See :func:`~pandas.read_csv` for the full argument list.
Returns
-------
DataFrame
A parsed :class:`~pandas.DataFrame` object.
See Also
--------
DataFrame.to_clipboard : Copy object to the system clipboard.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_fwf : Read a table of fixed-width formatted lines into DataFrame.
Examples
--------
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard() # doctest: +SKIP
>>> pd.read_clipboard() # doctest: +SKIP
A B C
0 1 2 3
1 4 5 6
"""
encoding = kwargs.pop("encoding", "utf-8")
# only utf-8 is valid for passed value because that's what clipboard
# supports
if encoding is not None and encoding.lower().replace("-", "") != "utf8":
raise NotImplementedError("reading from clipboard only supports utf-8 encoding")
check_dtype_backend(dtype_backend)
from pandas.io.clipboard import clipboard_get
from pandas.io.parsers import read_csv
text = clipboard_get()
# Try to decode (if needed, as "text" might already be a string here).
try:
text = text.decode(kwargs.get("encoding") or get_option("display.encoding"))
except AttributeError:
pass
# Excel copies into clipboard with \t separation
# inspect no more then the 10 first lines, if they
# all contain an equal number (>0) of tabs, infer
# that this came from excel and set 'sep' accordingly
lines = text[:10000].split("\n")[:-1][:10]
# Need to remove leading white space, since read_csv
# accepts:
# a b
# 0 1 2
# 1 3 4
counts = {x.lstrip(" ").count("\t") for x in lines}
if len(lines) > 1 and len(counts) == 1 and counts.pop() != 0:
sep = "\t"
# check the number of leading tabs in the first line
# to account for index columns
index_length = len(lines[0]) - len(lines[0].lstrip(" \t"))
if index_length != 0:
kwargs.setdefault("index_col", list(range(index_length)))
# Edge case where sep is specified to be None, return to default
if sep is None and kwargs.get("delim_whitespace") is None:
sep = r"\s+"
# Regex separator currently only works with python engine.
# Default to python if separator is multi-character (regex)
if len(sep) > 1 and kwargs.get("engine") is None:
kwargs["engine"] = "python"
elif len(sep) > 1 and kwargs.get("engine") == "c":
warnings.warn(
"read_clipboard with regex separator does not work properly with c engine.",
stacklevel=find_stack_level(),
)
return read_csv(StringIO(text), sep=sep, dtype_backend=dtype_backend, **kwargs)
def to_clipboard(
obj, excel: bool | None = True, sep: str | None = None, **kwargs
) -> None: # pragma: no cover
"""
Attempt to write text representation of object to the system clipboard
The clipboard can be then pasted into Excel for example.
Parameters
----------
obj : the object to write to the clipboard
excel : bool, defaults to True
if True, use the provided separator, writing in a csv
format for allowing easy pasting into excel.
if False, write a string representation of the object
to the clipboard
sep : optional, defaults to tab
other keywords are passed to to_csv
Notes
-----
Requirements for your platform
- Linux: xclip, or xsel (with PyQt4 modules)
- Windows:
- OS X:
"""
encoding = kwargs.pop("encoding", "utf-8")
# testing if an invalid encoding is passed to clipboard
if encoding is not None and encoding.lower().replace("-", "") != "utf8":
raise ValueError("clipboard only supports utf-8 encoding")
from pandas.io.clipboard import clipboard_set
if excel is None:
excel = True
if excel:
try:
if sep is None:
sep = "\t"
buf = StringIO()
# clipboard_set (pyperclip) expects unicode
obj.to_csv(buf, sep=sep, encoding="utf-8", **kwargs)
text = buf.getvalue()
clipboard_set(text)
return
except TypeError:
warnings.warn(
"to_clipboard in excel mode requires a single character separator.",
stacklevel=find_stack_level(),
)
elif sep is not None:
warnings.warn(
"to_clipboard with excel=False ignores the sep argument.",
stacklevel=find_stack_level(),
)
if isinstance(obj, ABCDataFrame):
# str(df) has various unhelpful defaults, like truncation
with option_context("display.max_colwidth", None):
objstr = obj.to_string(**kwargs)
else:
objstr = str(obj)
clipboard_set(objstr)