[英]What does the subset argument do in pandas.io.formats.style.Styler.format?
pandas.io.formats.style.Styler.format
的公共文檔說
子集:索引切片
DataFrame.loc
一個參數,用於限制formatter
應用於哪些元素。
但是看看代碼,這不太正確……這是什么_non_reducing_slice
東西?
if subset is None:
row_locs = range(len(self.data))
col_locs = range(len(self.data.columns))
else:
subset = _non_reducing_slice(subset)
if len(subset) == 1:
subset = subset, self.data.columns
sub_df = self.data.loc[subset]
用例:我想格式化一個特定的行,但是當我天真地按照文檔使用.loc[]
可以正常工作的內容時出現錯誤:
>>> import pandas as pd
>>>
>>> df = pd.DataFrame([dict(a=1,b=2,c=3),dict(a=3,b=5,c=4)])
>>> df = df.set_index('a')
>>> print df
b c
a
1 2 3
3 5 4
>>> def J(x):
... return '!!!%s!!!' % x
...
>>> df.style.format(J, subset=[3])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\app\python\anaconda\2\lib\site-packages\pandas\io\formats\style.py", line 372, in format
sub_df = self.data.loc[subset]
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1325, in __getitem__
return self._getitem_tuple(key)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 841, in _getitem_tuple
self._has_valid_tuple(tup)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 189, in _has_valid_tuple
if not self._has_valid_type(k, i):
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1418, in _has_valid_type
(key, self.obj._get_axis_name(axis)))
KeyError: 'None of [[3]] are in the [columns]'
>>> df.loc[3]
b 5
c 4
Name: 3, dtype: int64
>>> df.loc[[3]]
b c
a
3 5 4
好的,我嘗試使用IndexSlice
並且它看起來很IndexSlice
——在某些情況下有效,在其他情況下不起作用,至少在 Pandas 0.20.3 中:
Python 2.7.14 |Anaconda custom (64-bit)| (default, Oct 15 2017, 03:34:40) [MSC v.1500 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas as pd
>>> import numpy as np
>>> idx = pd.IndexSlice
>>> r = np.arange(16).astype(int)
>>> colors = 'red green blue yellow'.split()
>>> df = pd.DataFrame(dict(a=[colors[i] for i in r//4], b=r%4, c=r*100)).set_index(['a','b'])
>>> print df
c
a b
red 0 0
1 100
2 200
3 300
green 0 400
1 500
2 600
3 700
blue 0 800
1 900
2 1000
3 1100
yellow 0 1200
1 1300
2 1400
3 1500
>>> df.loc[idx['yellow']]
c
b
0 1200
1 1300
2 1400
3 1500
>>> def J(x):
... return '!!!%s!!!' % x
...
>>> df.style.format(J,idx['yellow'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\app\python\anaconda\2\lib\site-packages\pandas\io\formats\style.py", line 372, in format
sub_df = self.data.loc[subset]
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1325, in __getitem__
return self._getitem_tuple(key)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 836, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 948, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1023, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1541, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1081, in _getitem_iterable
self._has_valid_type(key, axis)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1418, in _has_valid_type
(key, self.obj._get_axis_name(axis)))
KeyError: "None of [['yellow']] are in the [columns]"
>>> pd.__version__
u'0.20.3'
在熊貓 0.24.2 中,我得到了類似的錯誤,但略有不同:
>>> df.style.format(J,idx['yellow'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "c:\app\python\anaconda\2\lib\site-packages\pandas\io\formats\style.py", line 401, in format
sub_df = self.data.loc[subset]
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1494, in __getitem__
return self._getitem_tuple(key)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 868, in _getitem_tuple
return self._getitem_lowerdim(tup)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 969, in _getitem_lowerdim
return self._getitem_nested_tuple(tup)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1048, in _getitem_nested_tuple
obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1902, in _getitem_axis
return self._getitem_iterable(key, axis=axis)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1205, in _getitem_iterable
raise_missing=False)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1161, in _get_listlike_indexer
raise_missing=raise_missing)
File "c:\app\python\anaconda\2\lib\site-packages\pandas\core\indexing.py", line 1246, in _validate_read_indexer
key=key, axis=self.obj._get_axis_name(axis)))
KeyError: u"None of [Index([u'yellow'], dtype='object')] are in the [columns]"
>>> pd.__version__
u'0.24.2'
哦等等——我沒有指定足夠的索引信息; 這有效:
df.style.format(J,idx['yellow',:])
我同意你表現出的行為並不理想。
>>> df = (pandas.DataFrame([dict(a=1,b=2,c=3),
dict(a=3,b=5,c=4)])
.set_index('a'))
>>> df.loc[[3]]
b c
a
3 5 4
>>> df.style.format('{:.2f}', subset=[3])
Traceback (most recent call last)
...
KeyError: "None of [Int64Index([3], dtype='int64')] are in the [columns]"
您可以通過將完整pandas.IndexSlice
作為子集參數傳遞來解決此問題:
>>> df.style.format('{:.2f}', subset=pandas.IndexSlice[[3], :])
由於您詢問_non_reducing_slice()
正在做什么,它的目標是合理的(確保子集不會將維度降低到系列)。 它的實現將列表視為一系列列名:
def _non_reducing_slice(slice_): """ Ensurse that a slice doesn't reduce to a Series or Scalar. Any user-paseed `subset` should have this called on it to make sure we're always working with DataFrames. """ # default to column slice, like DataFrame # ['A', 'B'] -> IndexSlices[:, ['A', 'B']] kinds = (ABCSeries, np.ndarray, Index, list, str) if isinstance(slice_, kinds): slice_ = IndexSlice[:, slice_] ...
我想知道是否可以改進文檔:在這種情況下,使用subset=[3]
引發的異常與df[[3]]
而不是df.loc[[3]]
的行為相匹配。
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