blocks|key|712769|text|我假设您想要列1和9|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|712770|要一次选择多个列，请使用|712771|X+=+data[:,+[1,+9]]|code-block|syntax|javascript|712772|若要一次选择一个，请使用|712773|x,+y+=+data[:,+1],+data[:,+9]|712774|712775|使用名称：|712776|data[:,+['Column+Name1','Column+Name2']]|712777|您可以从data.dtype.names…获取名称|712778|entityMap^0|7|1|9|1|0|0|0|0|0|0|0|0|4|G|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]|$9|12|A|13|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|14|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|15|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|16|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|17|8|@]|D|@]|E|$K|L]]|$1|Q|3|-4|5|6|7|18|8|@]|D|@]|E|$]]|$1|R|3|S|5|6|7|19|8|@]|D|@]|E|$]]|$1|T|3|U|5|J|7|1A|8|@]|D|@]|E|$K|L]]|$1|V|3|W|5|6|7|1B|8|@$9|1C|A|1D|B|C]]|D|@]|E|$]]|$1|X|3|-4|5|6|7|1E|8|@]|D|@]|E|$]]]|Y|$]]

I assume you wanted columns <code>1</code> and <code>9</code>?
To select multiple columns at once, use
<pre><code>X = data[:, [1, 9]]
</code></pre>
To select one at a time, use
<pre><code>x, y = data[:, 1], data[:, 9]
</code></pre>
<hr />
With names:
<pre><code>data[:, ['Column Name1','Column Name2']]
</code></pre>
You can get the names from <code>data.dtype.names</code>…

blocks|key|497261|text|假设您想使用该代码片段获取第1列和第9列，它应该是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|497262|extractedData+=+data[:,[1,9]]|code-block|syntax|javascript|497263|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|$]]

Assuming you want to get columns 1 and 9 with that code snippet, it should be:

<pre><code>extractedData = data[:,[1,9]]
</code></pre>

blocks|key|712863|text|如果只想提取一些列：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|712864|idx_IN_columns+=+[1,+9]
extractedData+=+data[:,idx_IN_columns]|code-block|syntax|javascript|712865|如果要排除特定列：|712866|idx_OUT_columns+=+[1,+9]
idx_IN_columns+=+[i+for+i+in+xrange(np.shape(data)[1])+if+i+not+in+idx_OUT_columns]
extractedData+=+data[:,idx_IN_columns]|712867|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

if you want to extract only some columns:

<pre><code>idx_IN_columns = [1, 9]
extractedData = data[:,idx_IN_columns]
</code></pre>

if you want to exclude specific columns:

<pre><code>idx_OUT_columns = [1, 9]
idx_IN_columns = [i for i in xrange(np.shape(data)[1]) if i not in idx_OUT_columns]
extractedData = data[:,idx_IN_columns]
</code></pre>

blocks|key|712912|text|您也可以使用extractedData=data(:,1，:,9)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|712913|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

you can also use extractedData=data([:,1],[:,9])

blocks|key|497330|text|您可以使用以下内容：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|497331|extracted_data+=+data.ix[:,['Column1','Column2']]|code-block|syntax|javascript|497332|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|$]]

You can use the following:
<pre><code>extracted_data = data.ix[:,['Column1','Column2']]
</code></pre>

blocks|key|497451|text|只要：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|497452|>>>+m+=+np.matrix(np.random.random((5,+5)))
>>>+m
matrix([[0.91074101,+0.65999332,+0.69774588,+0.007355++,+0.33025395],
++++++++[0.11078742,+0.67463754,+0.43158254,+0.95367876,+0.85926405],
++++++++[0.98665185,+0.86431513,+0.12153138,+0.73006437,+0.13404811],
++++++++[0.24602225,+0.66139215,+0.08400288,+0.56769924,+0.47974697],
++++++++[0.25345299,+0.76385882,+0.11002419,+0.2509888+,+0.06312359]])
>>>+m[:,[1,+2]]
matrix([[0.65999332,+0.69774588],
++++++++[0.67463754,+0.43158254],
++++++++[0.86431513,+0.12153138],
++++++++[0.66139215,+0.08400288],
++++++++[0.76385882,+0.11002419]])|code-block|syntax|javascript|497453|列不需要按顺序排列：|497454|>>>+m[:,[2,+1,+3]]
matrix([[0.69774588,+0.65999332,+0.007355++],
++++++++[0.43158254,+0.67463754,+0.95367876],
++++++++[0.12153138,+0.86431513,+0.73006437],
++++++++[0.08400288,+0.66139215,+0.56769924],
++++++++[0.11002419,+0.76385882,+0.2509888+]])|497455|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Just:

<pre><code>&gt;&gt;&gt; m = np.matrix(np.random.random((5, 5)))
&gt;&gt;&gt; m
matrix([[0.91074101, 0.65999332, 0.69774588, 0.007355 , 0.33025395],
 [0.11078742, 0.67463754, 0.43158254, 0.95367876, 0.85926405],
 [0.98665185, 0.86431513, 0.12153138, 0.73006437, 0.13404811],
 [0.24602225, 0.66139215, 0.08400288, 0.56769924, 0.47974697],
 [0.25345299, 0.76385882, 0.11002419, 0.2509888 , 0.06312359]])
&gt;&gt;&gt; m[:,[1, 2]]
matrix([[0.65999332, 0.69774588],
 [0.67463754, 0.43158254],
 [0.86431513, 0.12153138],
 [0.66139215, 0.08400288],
 [0.76385882, 0.11002419]])
</code></pre>

The columns need not to be in order:

<pre><code>&gt;&gt;&gt; m[:,[2, 1, 3]]
matrix([[0.69774588, 0.65999332, 0.007355 ],
 [0.43158254, 0.67463754, 0.95367876],
 [0.12153138, 0.86431513, 0.73006437],
 [0.08400288, 0.66139215, 0.56769924],
 [0.11002419, 0.76385882, 0.2509888 ]])
</code></pre>

blocks|key|497501|text|我认为这里的解决方案不再是python版本的更新，一种方法是使用一个新的python函数来完成这项工作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|497502|extracted_data+=+data[['Column+Name1','Column+Name2']].to_numpy()|code-block|syntax|javascript|497503|这就给了你想要的结果。|497504|您可以在此处找到文档：https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy|offset|length|497505|entityMap|0|LINK|mutability|MUTABLE|url|https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy^0|0|0|0|B|37|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|X|8|@]|9|@$K|Y|L|Z|1|10]]|A|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|T]]]]

I think the solution here is not working with an update of the python version anymore, one way to do it with a new python function for it is:

<pre><code>extracted_data = data[['Column Name1','Column Name2']].to_numpy()
</code></pre>

which gives you the desired outcome.

The documentation you can find here: <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy" rel="nofollow noreferrer">https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html#pandas.DataFrame.to_numpy</a>

This is an easy question but say I have an MxN matrix. All I want to do is extract specific columns and store them in another numpy array but I get invalid syntax errors.
Here is the code:

<pre><code>extractedData = data[[:,1],[:,9]]. 
</code></pre>

It seems like the above line should suffice but I guess not. I looked around but couldn't find anything syntax wise regarding this specific scenario.

Extracting specific columns in numpy array

这是一个简单的问题，但是假设我有一个MxN矩阵。我所要做的就是提取特定的列，并将它们存储在另一个numpy数组中，但是我得到了无效的语法错误。代码如下：extractedData = data[[:,1],[:,9]]. 上面这行看起来应该足够了，但我猜不够。我环顾四周，但找不到关于这个特定场景的任何语法方面的东西。

问提取numpy数组中的特定列
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