blocks|key|4390074|text|与您最初的尝试有些类似，但更具Python风格，是使用Python的标准负索引约定从末尾向后计数：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4390075|df[df.columns[-1]]|code-block|syntax|javascript|4390076|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Somewhat similar to your original attempt, but more Pythonic, is to use Python's standard negative-indexing convention to count backwards from the end:

<pre><code>df[df.columns[-1]]
</code></pre>

blocks|key|4390134|text|使用iloc并针对最后一列(-1)选择所有行(:)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4390135|df.iloc[:,-1:]|code-block|syntax|javascript|4390136|entityMap^0|E|2|N|1|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Use iloc and select all rows (<code>:</code>) against the last column (<code>-1</code>):
<pre><code>df.iloc[:,-1:]
</code></pre>

blocks|key|4389851|text|这是另一种方法。我觉得可能会更笼统一点：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4389852|df.ix[:,-1]|code-block|syntax|javascript|4389853|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is another way to do it. I think maybe a little more general:

<pre><code>df.ix[:,-1]
</code></pre>

blocks|key|4390197|text|问题是:如何选择数据帧的最后一列？Appart+@piRSquared，无人回答问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4390198|获取最后一列数据帧的最简单方法是：|4390199|df.iloc[+:,+-1:]|code-block|syntax|javascript|4390200|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

The question is: how to select the last column of a dataframe ?
Appart @piRSquared, none answer the question. 

the simplest way to get a dataframe with the last column is:

<pre><code>df.iloc[ :, -1:]
</code></pre>

blocks|key|4390253|text|以下是能帮助你理解一切的几件事……使用iloc|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4390254|在iloc中，初始行:结束行，初始列:结束列|4390255|案例1:如果你只想要最后一列-+df.iloc[:,-1]+&+df.iloc[:,-1:]，这意味着你只想要最后一列...|offset|length|style|CODE|4390256|案例2:如果你想要除最后一列之外的所有列和所有行-+df.iloc[:,:-1]这意味着你想要除最后一列之外的所有列和所有行...|4390257|情况3:如果你只想要最后一行-+df.iloc[-1:,:]+&+df.iloc[-1,:]，这意味着你只想要最后一行...|4390258|案例4:如果你想要除最后一行之外的所有列和所有行-+df.iloc[:-1,:]这意味着你想要除最后一列之外的所有列和所有行...|4390259|案例5:如果您想要除最后一行和最后一列之外的所有列和所有行-+df.iloc[:-1,:-1]这意味着您希望除最后一列和最后一行之外的所有列和所有行...|4390260|entityMap^0|0|0|G|U|0|Q|E|0|G|U|0|Q|E|0|V|G|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|V|8|@$F|W|G|X|H|I]]|9|@]|A|$]]|$1|J|3|K|5|6|7|Y|8|@$F|Z|G|10|H|I]]|9|@]|A|$]]|$1|L|3|M|5|6|7|11|8|@$F|12|G|13|H|I]]|9|@]|A|$]]|$1|N|3|O|5|6|7|14|8|@$F|15|G|16|H|I]]|9|@]|A|$]]|$1|P|3|Q|5|6|7|17|8|@$F|18|G|19|H|I]]|9|@]|A|$]]|$1|R|3|-4|5|6|7|1A|8|@]|9|@]|A|$]]]|S|$]]

These are few things which will help you in understanding everything... using iloc

In iloc, [initial row:ending row, initial column:ending column]

case 1: if you want only last column --- <code>df.iloc[:,-1] &amp; df.iloc[:,-1:]</code>
this means that you want only the last column...

case 2: if you want all columns and all rows except the last column --- <code>df.iloc[:,:-1]</code>
this means that you want all columns and all rows except the last column...

case 3: if you want only last row --- <code>df.iloc[-1:,:] &amp; df.iloc[-1,:]</code> 
this means that you want only the last row...

case 4: if you want all columns and all rows except the last row --- <code>df.iloc[:-1,:]</code>
this means that you want all columns and all rows except the last column...

case 5: if you want all columns and all rows except the last row and last column --- <code>df.iloc[:-1,:-1]</code>
this means that you want all columns and all rows except the last column and last row...

blocks|key|4390272|text|只是补充@Anshul+Singh+Suryan的答案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4390273|当我们拆分数据帧以获得最后一列时：|4390274|如果我们像这样分开：|4390275|y+=+df.iloc[:,-1:]+-+y仍然是一个dataframe|offset|length|style|CODE|4390276|但是，如果我们像这样分开|4390277|y+=+df.iloc[:,-1]+-+y变成了Series。|4390278|这是我在这两种方法中发现的一个显著差异。如果您不关心结果类型，则可以使用这两种类型中的任何一种。否则，你需要注意上面的发现。|4390279|这适用于您要提取的任意数量的行，而不仅仅是最后一行。例如，如果您想要数据帧的最后n行数，其中n是小于或等于数据帧中存在的列数的任何整数，则可以轻松地执行以下操作：|4390280|y+=+df.iloc[:,n:]|code-block|syntax|javascript|4390281|用所需的列数替换n。对于行也是如此。|4390282|entityMap^0|0|0|0|0|M|R|9|0|0|0|L|O|6|0|0|14|1|0|0|8|1|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|13|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|14|8|@]|9|@]|A|$]]|$1|F|3|G|5|6|7|15|8|@$H|16|I|17|J|K]|$H|18|I|19|J|K]]|9|@]|A|$]]|$1|L|3|M|5|6|7|1A|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|1B|8|@$H|1C|I|1D|J|K]|$H|1E|I|1F|J|K]]|9|@]|A|$]]|$1|P|3|Q|5|6|7|1G|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|1H|8|@$H|1I|I|1J|J|K]]|9|@]|A|$]]|$1|T|3|U|5|V|7|1K|8|@]|9|@]|A|$W|X]]|$1|Y|3|Z|5|6|7|1L|8|@$H|1M|I|1N|J|K]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1O|8|@]|9|@]|A|$]]]|11|$]]

Just to add to @Anshul Singh Suryan's answer:
When we split the dataframe to just get the last column:
If we split like:
<code>y = df.iloc[:,-1:] - y</code> remains a <code>dataframe</code>
However, if we split like
<code>y = df.iloc[:,-1] - y</code> becomes a <code>Series</code>.
This is a notable difference that I've found in the two approaches. If you don't care about the resultant type, you can use either of the two. Otherwise you need to take care of the above findings.
This is applicable for any number of rows you want to extract and not just the last row.
For example, if you want last <code>n</code> number of rows of a dataframe, where n is any integer less than or equal to the number of columns present in the dataframe, then you can easily do the following:
<pre><code>y = df.iloc[:,n:]
</code></pre>
Replace <code>n</code> by the number of columns you want. Same is true for rows as well.

I have done some searching for the answer to this question, but all I can figure out is this: 

<pre><code>df[df.columns[len(df.columns)-1]]
</code></pre>

which to me seems unweildy, and un-pythonic (and slow?). 

What is the easiest way to select the data for the last column in a pandas dataframe without specifying the name of the column?

How to select the last column of dataframe

我已经做了一些搜索来寻找这个问题的答案，但我所能想到的是：df[df.columns[len(df.columns)-1]]在我看来，这似乎并不奇怪，也不是pythonic式的(而且很慢？)在不指定列名的情况下，为pandas数据框中的最后一列选择数据的最简单方法是什么？

问如何选择数据帧的最后一列
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