Python Pandas 简明教程

Python Pandas - Caveats & Gotchas

警告是指警示而意外问题是指不可预见的问题。

Caveats means warning and gotcha means an unseen problem.

Using If/Truth Statement with Pandas

当您尝试将某个内容转换成 bool 时，Pandas 遵循 numpy 惯例发出错误。这发生在 if 中或 when 中，使用布尔运算以及 or 或 not 。结果应该是什么还不清楚。它应该是 True 因为它不是零长度吗？还是 False 因为存在 False 值？还不清楚，因此，Pandas 会发出 ValueError −

Pandas follows the numpy convention of raising an error when you try to convert something to a bool. This happens in an if or when using the Boolean operations, and, or, or not. It is not clear what the result should be. Should it be True because it is not zerolength? False because there are False values? It is unclear, so instead, Pandas raises a ValueError −

import pandas as pd

if pd.Series([False, True, False]):
   print 'I am True'

它的 output 如下所示 −

Its output is as follows −

ValueError: The truth value of a Series is ambiguous.
Use a.empty, a.bool() a.item(),a.any() or a.all().

在 if 条件中，不清楚该对其做什么。错误暗示应使用 None 还是 any of those 。

In if condition, it is unclear what to do with it. The error is suggestive of whether to use a None or any of those.

import pandas as pd

if pd.Series([False, True, False]).any():
   print("I am any")

它的 output 如下所示 −

Its output is as follows −

I am any

若要在布尔上下文中评估单元素 pandas 对象，使用 .bool() 方法 −

To evaluate single-element pandas objects in a Boolean context, use the method .bool() −

import pandas as pd

print pd.Series([True]).bool()

它的 output 如下所示 −

Its output is as follows −

True

Bitwise Boolean

Bitwise 布尔运算符（如 == 和 ! ）将返回一个布尔序列，这几乎总是所需。

Bitwise Boolean operators like == and != will return a Boolean series, which is almost always what is required anyways.

import pandas as pd

s = pd.Series(range(5))
print s==4

它的 output 如下所示 −

Its output is as follows −

0 False
1 False
2 False
3 False
4 True
dtype: bool

isin Operation

这返回一个布尔序列，显示 Series 中的每个元素是否恰好包含在传递的值序列中。

This returns a Boolean series showing whether each element in the Series is exactly contained in the passed sequence of values.

import pandas as pd

s = pd.Series(list('abc'))
s = s.isin(['a', 'c', 'e'])
print s

它的 output 如下所示 −

Its output is as follows −

0 True
1 False
2 True
dtype: bool

Reindexing vs ix Gotcha

许多用户会发现自己使用 ix indexing capabilities 作为从 Pandas 对象选择数据的一种简洁方式 −

Many users will find themselves using the ix indexing capabilities as a concise means of selecting data from a Pandas object −

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 df.ix[['b', 'c', 'e']]

它的 output 如下所示 −

Its output is as follows −

          one        two      three       four
a   -1.582025   1.335773   0.961417  -1.272084
b    1.461512   0.111372  -0.072225   0.553058
c   -1.240671   0.762185   1.511936  -0.630920
d   -2.380648  -0.029981   0.196489   0.531714
e    1.846746   0.148149   0.275398  -0.244559
f   -1.842662  -0.933195   2.303949   0.677641

          one        two      three       four
b    1.461512   0.111372  -0.072225   0.553058
c   -1.240671   0.762185   1.511936  -0.630920
e    1.846746   0.148149   0.275398  -0.244559

这当然与使用 reindex 方法在此情况下完全等效 −

This is, of course, completely equivalent in this case to using the reindex method −

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 df.reindex(['b', 'c', 'e'])

它的 output 如下所示 −

Its output is as follows −

          one        two      three       four
a    1.639081   1.369838   0.261287  -1.662003
b   -0.173359   0.242447  -0.494384   0.346882
c   -0.106411   0.623568   0.282401  -0.916361
d   -1.078791  -0.612607  -0.897289  -1.146893
e    0.465215   1.552873  -1.841959   0.329404
f    0.966022  -0.190077   1.324247   0.678064

          one        two      three       four
b   -0.173359   0.242447  -0.494384   0.346882
c   -0.106411   0.623568   0.282401  -0.916361
e    0.465215   1.552873  -1.841959   0.329404

有人可能由此得出 ix 和 reindex 100% 等效的结论。除整型索引的情形外，这确实成立。例如，上述操作也可以表述为 −

Some might conclude that ix and reindex are 100% equivalent based on this. This is true except in the case of integer indexing. For example, the above operation can alternatively be expressed as −

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 df.ix[[1, 2, 4]]
print df.reindex([1, 2, 4])

它的 output 如下所示 −

Its output is as follows −

          one        two      three       four
a   -1.015695  -0.553847   1.106235  -0.784460
b   -0.527398  -0.518198  -0.710546  -0.512036
c   -0.842803  -1.050374   0.787146   0.205147
d   -1.238016  -0.749554  -0.547470  -0.029045
e   -0.056788   1.063999  -0.767220   0.212476
f    1.139714   0.036159   0.201912   0.710119

          one        two      three       four
b   -0.527398  -0.518198  -0.710546  -0.512036
c   -0.842803  -1.050374   0.787146   0.205147
e   -0.056788   1.063999  -0.767220   0.212476

    one  two  three  four
1   NaN  NaN    NaN   NaN
2   NaN  NaN    NaN   NaN
4   NaN  NaN    NaN   NaN

记住 reindex is strict label indexing only 非常重要。在病理病例中，其中索引同时包含（例如）整型和字符串时，这会产生一些可能令人惊讶的结果。

It is important to remember that reindex is strict label indexing only. This can lead to some potentially surprising results in pathological cases where an index contains, say, both integers and strings.