blocks|key|2933716|text|您可以跟踪pandas/python环境正在消耗的内存，并假设一个副本将比一个视图使用更多的内存，从而能够以某种方式做出决定。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2933717|我相信有一些库可以显示python环境本身的内存使用情况--例如，Heapy/Guppy。|2933718|应该有一个可以应用的度量标准，在创建被检查的对象之前获取内存使用情况的基线图片，然后再拍摄另一张图片。两个内存映射的比较(假设没有创建任何其他内容，我们可以确定更改是由新对象引起的)应该提供了一个视图或副本是否已经生成的想法。|2933719|我们需要了解每种实现类型的不同内存配置文件，但一些实验应该会产生结果。|2933720|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|J|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|K|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|L|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|M|8|@]|9|@]|A|$]]|$1|H|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|I|$]]

You might trace the memory your pandas/python environment is consuming, and, on the assumption that a copy will utilise more memory than a view, be able to decide one way or another. 

I believe there are libraries out there that will present the memory usage within the python environment itself - e.g. Heapy/Guppy.

There ought to be a metric you can apply that takes a baseline picture of memory usage prior to creating the object under inspection, then another picture afterwards. Comparison of the two memory maps (assuming nothing else has been created and we can isolate the change is due to the new object) should provide an idea of whether a view or copy has been produced.

We'd need to get an idea of the different memory profiles of each type of implementation, but some experimentation should yield results.

blocks|key|2933820|text|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2933821|可以通过以下两种方式进行检查：|2933822|2933823|测试values.base属性而不是values属性的等价性，如下所示：|unordered-list-item|offset|length|style|CODE|2933824|2933825|用df.values+is+df2.values.代替df.values.base+is+df2.values.base|2933826|2933827|或使用(诚然是内部的)+_is_view属性(df2._is_view为True).|2933828|2933829|谢谢大家！|2933830|entityMap^0|0|0|0|2|B|I|6|0|0|1|N|R|X|0|0|C|8|N|C|10|4|0|0|0^^$0|@$1|2|3|-4|4|5|6|V|7|@]|8|@]|9|$]]|$1|A|3|B|4|5|6|W|7|@]|8|@]|9|$]]|$1|C|3|-4|4|5|6|X|7|@]|8|@]|9|$]]|$1|D|3|E|4|F|6|Y|7|@$G|Z|H|10|I|J]|$G|11|H|12|I|J]]|8|@]|9|$]]|$1|K|3|-4|4|5|6|13|7|@]|8|@]|9|$]]|$1|L|3|M|4|5|6|14|7|@$G|15|H|16|I|J]|$G|17|H|18|I|J]]|8|@]|9|$]]|$1|N|3|-4|4|5|6|19|7|@]|8|@]|9|$]]|$1|O|3|P|4|F|6|1A|7|@$G|1B|H|1C|I|J]|$G|1D|H|1E|I|J]|$G|1F|H|1G|I|J]]|8|@]|9|$]]|$1|Q|3|-4|4|5|6|1H|7|@]|8|@]|9|$]]|$1|R|3|S|4|5|6|1I|7|@]|8|@]|9|$]]|$1|T|3|-4|4|5|6|1J|7|@]|8|@]|9|$]]]|U|$]]

Answers from HYRY and Marius in comments!
One can check either by:
<ul>
<li>testing equivalence of the <code>values.base</code> attribute rather than the <code>values</code> attribute, as in:
<code>df.values.base is df2.values.base</code> instead of <code>df.values is df2.values</code>.
</li>
<li>or using the (admittedly internal) <code>_is_view</code> attribute (<code>df2._is_view</code> is <code>True</code>).
</li>
</ul>
Thanks everyone!

blocks|key|145540|text|我已经用pandas+1.0.1详细阐述了这个例子。这里不仅有一个布尔型_is_view属性，还有_is_copy，它可以是None，也可以是对原始DataFrame的引用：|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|CODE|entityRanges|data|145541|df+=+pd.DataFrame([[1,2,3,4],[5,6,7,8]],+index+=+['row1','row2'],+
++++++++columns+=+['a','b','c','d'])
df2+=+df.iloc[0:2,+:]
df3+=+df.loc[df['a']+==+1,+:]

#+df+is+neither+copy+nor+view
df._is_view,+df._is_copy
Out[1]:+(False,+None)

#+df2+is+a+view+AND+a+copy
df2._is_view,+df2._is_copy
Out[2]:+(True,+<weakref+at+0x00000236635C2228;+to+'DataFrame'+at+0x00000236635DAA58>)

#+df3+is+not+a+view,+but+a+copy
df3._is_view,+df3._is_copy
Out[3]:+(False,+<weakref+at+0x00000236635C2228;+to+'DataFrame'+at+0x00000236635DAA58>)|code-block|syntax|javascript|145542|因此，检查这两个属性不仅应该告诉您是否正在处理视图，还应该告诉您是否有副本或“原始”DataFrame。|145543|另请参阅this+thread进行讨论，解释为什么不能总是预测代码是否会返回视图。|145544|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/39101933/pandas-views-vs-copy-the-docs-says-nobody-knows^0|4|C|10|8|1D|8|1Q|4|0|0|0|4|B|0|0^^$0|@$1|2|3|4|5|6|7|X|8|@$9|Y|A|Z|B|C]|$9|10|A|11|B|D]|$9|12|A|13|B|D]|$9|14|A|15|B|D]]|E|@]|F|$]]|$1|G|3|H|5|I|7|16|8|@]|E|@]|F|$J|K]]|$1|L|3|M|5|6|7|17|8|@]|E|@]|F|$]]|$1|N|3|O|5|6|7|18|8|@]|E|@$9|19|A|1A|1|1B]]|F|$]]|$1|P|3|-4|5|6|7|1C|8|@]|E|@]|F|$]]]|Q|$R|$5|S|T|U|F|$V|W]]]]

I've elaborated on this example with pandas 1.0.1. There's not only a boolean <code>_is_view</code> attribute, but also <code>_is_copy</code> which can be <code>None</code> or a reference to the original DataFrame:

<pre><code>df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index = ['row1','row2'], 
 columns = ['a','b','c','d'])
df2 = df.iloc[0:2, :]
df3 = df.loc[df['a'] == 1, :]

# df is neither copy nor view
df._is_view, df._is_copy
Out[1]: (False, None)

# df2 is a view AND a copy
df2._is_view, df2._is_copy
Out[2]: (True, &lt;weakref at 0x00000236635C2228; to 'DataFrame' at 0x00000236635DAA58&gt;)

# df3 is not a view, but a copy
df3._is_view, df3._is_copy
Out[3]: (False, &lt;weakref at 0x00000236635C2228; to 'DataFrame' at 0x00000236635DAA58&gt;)
</code></pre>

So checking these two attributes should tell you not only if you're dealing with a view or not, but also if you have a copy or an "original" DataFrame.

See also <a href="https://stackoverflow.com/questions/39101933/pandas-views-vs-copy-the-docs-says-nobody-knows">this thread</a> for a discussion explaining why you can't always predict whether your code will return a view or not.

Is there an easy way to check whether two data frames are different copies or views of the same underlying data that doesn't involve manipulations? I'm trying to get a grip on when each is generated, and given how idiosyncratic the rules seem to be, I'd like an easy way to test. 

For example, I thought "id(df.values)" would be stable across views, but they don't seem to be:

<pre><code># Make two data frames that are views of same data.
df = pd.DataFrame([[1,2,3,4],[5,6,7,8]], index = ['row1','row2'], 
 columns = ['a','b','c','d'])
df2 = df.iloc[0:2,:]

# Demonstrate they are views:
df.iloc[0,0] = 99
df2.iloc[0,0]
Out[70]: 99

# Now try and compare the id on values attribute
# Different despite being views! 

id(df.values)
Out[71]: 4753564496

id(df2.values)
Out[72]: 4753603728

# And we can of course compare df and df2
df is df2
Out[73]: False
</code></pre>

Other answers I've looked up that try to give rules, but don't seem consistent, and also don't answer this question of how to test:

<ul>
<li><a href="https://stackoverflow.com/questions/23296282/what-rules-does-pandas-use-to-generate-a-view-vs-a-copy">What rules does Pandas use to generate a view vs a copy?</a></li>
<li><a href="https://stackoverflow.com/questions/17960511/pandas-subindexing-dataframes-copies-vs-views">Pandas: Subindexing dataframes: Copies vs views</a></li>
<li><a href="https://stackoverflow.com/questions/14192741/understanding-pandas-dataframe-indexing">Understanding pandas dataframe indexing</a></li>
<li><a href="https://stackoverflow.com/questions/22537112/re-assignment-in-pandas-copy-or-view">Re-assignment in Pandas: Copy or view?</a></li>
</ul>

And of course: 
 - <a href="http://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy" rel="noreferrer">http://pandas.pydata.org/pandas-docs/stable/indexing.html#returning-a-view-versus-a-copy</a>

UPDATE: Comments below seem to answer the question -- looking at the <code>df.values.base</code> attribute rather than <code>df.values</code> attribute does it, as does a reference to the <code>df._is_copy</code> attribute (though the latter is probably very bad form since it's an internal).

Checking whether data frame is copy or view in Pandas

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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有没有一种简单的方法来检查两个数据帧是否是相同底层数据的不同副本或视图，而不涉及操作？我正在试图掌握每个规则是什么时候生成的，并且考虑到这些规则看起来是多么独特，我希望有一种简单的方法来测试。例如，我认为"id(df.values)“在视图之间应该是稳定的，但它们似乎不是：# Make two data frames ...

问在Pandas中检查数据帧是复制的还是查看的
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