>>> from env_helper import info; info()
页面更新时间： 2023-12-16 22:02:25
运行环境：
Linux发行版本: Debian GNU/Linux 12 (bookworm)
操作系统内核: Linux-6.1.0-15-amd64-x86_64-with-glibc2.36
Python版本: 3.11.2
8.7. Pandas注意事项＆窍门¶
警告和疑难意味着一个看不见的问题。在使用Pandas过程中，需要特别注意的地方。 与Pandas一起使用If/Truth语句
当尝试将某些东西转换成布尔值时，Pandas遵循了一个错误的惯例。 这种情况发生在使用布尔运算的。 目前还不清楚结果是什么。 如果它是真的，因为它不是zerolength？ 错误，因为有错误的值？ 目前还不清楚，Pandas提出了一个ValueError -
import pandas as pd
if pd.Series([False, True, False]):
print ('I am True')
执行上面示例代码，得到以下结果 -
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
在if条件，它不清楚如何处理它。错误提示是否使用None或任何这些。
>>> import pandas as pd
>>> if pd.Series([False, True, False]).any():
>>> print("I am any")
I am any
要在布尔上下文中评估单元素Pandas对象，请使用方法.bool() -
>>> import pandas as pd
>>> print (pd.Series([True]).bool())
True
8.7.1. 按位布尔值¶
按位布尔运算符(如 == 和 !=
)将返回一个布尔系列，这几乎总是需要的。
>>> import pandas as pd
>>>
>>> s = pd.Series(range(5))
>>> print (s==4)
0 False
1 False
2 False
3 False
4 True
dtype: bool
isin操作符¶
这将返回一个布尔序列，显示系列中的每个元素是否完全包含在传递的值序列中。
>>> import pandas as pd
>>>
>>> s = pd.Series(list('abc'))
>>> s = s.isin(['a', 'c', 'e'])
>>> print (s)
0 True
1 False
2 True
dtype: bool
重构索引与ix陷阱¶
许多用户会发现自己使用ix索引功能作为从Pandas对象中选择数据的简洁方法 -
>>> import pandas as pd
>>> import numpy as np
>>>
>>> df = pd.DataFrame(np.random.randn(6, 4),
>>> columns=['one', 'two', 'three','four'],
>>> index=list('abcdef'))
>>>
>>> print (df)
>>> print ("=============================================")
>>> print (df.loc[['b', 'c', 'e']])
one two three four
a -0.109092 -1.049401 1.471157 -1.488999
b -0.620283 0.333117 0.265730 0.756193
c -0.151341 -0.088314 -2.208548 -0.361831
d 0.519641 0.547256 -1.531098 1.087439
e -0.235775 -0.563923 -0.491280 0.634726
f 1.049348 0.243605 0.549927 -1.430398
=============================================
one two three four
b -0.620283 0.333117 0.265730 0.756193
c -0.151341 -0.088314 -2.208548 -0.361831
e -0.235775 -0.563923 -0.491280 0.634726
这当然在这种情况下完全等同于使用reindex方法 -
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame(np.random.randn(6, 4),
>>> columns=['one', 'two', 'three','four'],
>>> index=list('abcdef'))
>>> print (df)
>>> print("=============================================")
>>> print (df.reindex(['b', 'c', 'e']))
one two three four
a -1.898824 -1.353821 -0.626713 0.189775
b 0.896474 -0.652357 -0.128021 1.419379
c 0.410777 0.730201 -0.270423 -0.548112
d -0.899703 0.866182 -0.676110 -1.597479
e 0.445195 1.646908 1.608295 0.746869
f -1.680827 0.102113 0.478425 -0.172722
=============================================
one two three four
b 0.896474 -0.652357 -0.128021 1.419379
c 0.410777 0.730201 -0.270423 -0.548112
e 0.445195 1.646908 1.608295 0.746869
有人可能会得出这样的结论，ix和reindex是基于这个100％的等价物。 除了整数索引的情况，它是true。例如，上述操作可选地表示为 -
>>> import pandas as pd
>>> import numpy as np
>>>
>>> df = pd.DataFrame(np.random.randn(6, 4),
>>> columns=['one', 'two', 'three','four'],
>>> index=list('abcdef'))
>>>
>>> print (df)
>>> print("=====================================")
>>> print (df.loc[[1, 2, 3]])
>>> print("=====================================")
>>> print (df.reindex([1, 2, 3]))
one two three four
a 0.405851 0.612873 -0.392511 -0.579213
b 0.580977 -0.772240 0.470936 0.737463
c -0.169078 1.548112 1.513963 0.235278
d -0.535708 -1.116789 0.833861 0.285282
e -0.494875 0.108672 -0.229865 -0.501433
f -0.522854 0.787164 -0.225666 0.509507
=====================================
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Cell In [19], line 10
8 print (df)
9 print("=====================================")
---> 10 print (df.loc[[1, 2, 3]])
11 print("=====================================")
12 print (df.reindex([1, 2, 3]))
File /usr/lib/python3/dist-packages/pandas/core/indexing.py:1073, in _LocationIndexer.__getitem__(self, key)
1070 axis = self.axis or 0
1072 maybe_callable = com.apply_if_callable(key, self.obj)
-> 1073 return self._getitem_axis(maybe_callable, axis=axis)
File /usr/lib/python3/dist-packages/pandas/core/indexing.py:1301, in _LocIndexer._getitem_axis(self, key, axis)
1298 if hasattr(key, "ndim") and key.ndim > 1:
1299 raise ValueError("Cannot index with multidimensional key")
-> 1301 return self._getitem_iterable(key, axis=axis)
1303 # nested tuple slicing
1304 if is_nested_tuple(key, labels):
File /usr/lib/python3/dist-packages/pandas/core/indexing.py:1239, in _LocIndexer._getitem_iterable(self, key, axis)
1236 self._validate_key(key, axis)
1238 # A collection of keys
-> 1239 keyarr, indexer = self._get_listlike_indexer(key, axis)
1240 return self.obj._reindex_with_indexers(
1241 {axis: [keyarr, indexer]}, copy=True, allow_dups=True
1242 )
File /usr/lib/python3/dist-packages/pandas/core/indexing.py:1432, in _LocIndexer._get_listlike_indexer(self, key, axis)
1429 ax = self.obj._get_axis(axis)
1430 axis_name = self.obj._get_axis_name(axis)
-> 1432 keyarr, indexer = ax._get_indexer_strict(key, axis_name)
1434 return keyarr, indexer
File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:6070, in Index._get_indexer_strict(self, key, axis_name)
6067 else:
6068 keyarr, indexer, new_indexer = self._reindex_non_unique(keyarr)
-> 6070 self._raise_if_missing(keyarr, indexer, axis_name)
6072 keyarr = self.take(indexer)
6073 if isinstance(key, Index):
6074 # GH 42790 - Preserve name from an Index
File /usr/lib/python3/dist-packages/pandas/core/indexes/base.py:6130, in Index._raise_if_missing(self, key, indexer, axis_name)
6128 if use_interval_msg:
6129 key = list(key)
-> 6130 raise KeyError(f"None of [{key}] are in the [{axis_name}]")
6132 not_found = list(ensure_index(key)[missing_mask.nonzero()[0]].unique())
6133 raise KeyError(f"{not_found} not in index")
KeyError: "None of [Int64Index([1, 2, 3], dtype='int64')] are in the [index]"
重要的是要记住，reindex只是严格的标签索引。这可能会导致一些潜在的令人惊讶的结果，例如索引包含整数和字符串的病态情况。