blocks|key|390562|text|这可以(也应该)在纯Python语言中完成，因为numpy还在内部使用Python字符串操作函数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|390563|>>>+a1+=+['a','b']
>>>+a2+=+['E','F']
>>>+map(''.join,+zip(a1,+a2))
['aE',+'bF']|code-block|syntax|javascript|390564|entityMap^0|O|5|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

This can (and should) be done in pure Python, as <code>numpy</code> also uses the Python string manipulation functions internally:

<pre><code>&gt;&gt;&gt; a1 = ['a','b']
&gt;&gt;&gt; a2 = ['E','F']
&gt;&gt;&gt; map(''.join, zip(a1, a2))
['aE', 'bF']
</code></pre>

blocks|key|390638|text|这可以使用numpy.core.defchararray.add来完成。下面是一个示例：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|390639|>>>+import+numpy+as+np
>>>+a1+=+np.array(['a',+'b'])
>>>+a2+=+np.array(['E',+'F'])
>>>+np.core.defchararray.add(a1,+a2)
array(['aE',+'bF'],+
++++++dtype='<U2')|code-block|syntax|javascript|390640|对于NumPy数据类型，还有其他有用的string+operations可用。|390641|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.scipy.org/doc/numpy/reference/generated/numpy.core.defchararray.add.html|1|https://docs.scipy.org/doc/numpy/reference/routines.char.html^0|5|R|0|0|0|J|H|1|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|K|3|-4|5|6|7|13|8|@]|9|@]|C|$]]]|L|$M|$5|N|O|P|C|$Q|R]]|S|$5|N|O|P|C|$Q|T]]]]

This can be done using <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.core.defchararray.add.html" rel="noreferrer">numpy.core.defchararray.add</a>. Here is an example:

<pre><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; a1 = np.array(['a', 'b'])
&gt;&gt;&gt; a2 = np.array(['E', 'F'])
&gt;&gt;&gt; np.core.defchararray.add(a1, a2)
array(['aE', 'bF'], 
 dtype='&lt;U2')
</code></pre>

There are other useful <a href="https://docs.scipy.org/doc/numpy/reference/routines.char.html" rel="noreferrer">string operations</a> available for NumPy data types.

blocks|key|390745|text|另一种解决方案是将字符串数组转换为对象的python数组，以便调用str.add：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|390746|>>>+import+numpy+as+np
>>>+a+=+np.array(['a',+'b',+'c',+'d'],+dtype=np.object)+++
>>>+print+a%2Ba
array(['aa',+'bb',+'cc',+'dd'],+dtype=object)|code-block|syntax|javascript|390747|这并不是很慢(比添加整数数组慢不到两倍)。|390748|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Another solution is to convert string arrays into arrays of python of objects so that str.add is called:

<pre><code>&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; a = np.array(['a', 'b', 'c', 'd'], dtype=np.object) 
&gt;&gt;&gt; print a+a
array(['aa', 'bb', 'cc', 'dd'], dtype=object)
</code></pre>

This is not that slow (less than twice as slow as adding integer arrays).

blocks|key|606922|text|对字符串执行数组操作的You+can+use+the+chararray+subclass：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|606923|a1+=+np.char.array(['a',+'b'])
a2+=+np.char.array(['E',+'F'])

a1+%2B+a2
#chararray(['aE',+'bF'],+dtype='%7CS2')|code-block|syntax|javascript|606924|另一个很好的例子：|606925|b+=+np.array([2,+4])
a1*b
#chararray(['aa',+'bbbb'],+dtype='%7CS4')|606926|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.chararray.html^0|R|9|B|Y|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|13|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|14|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

<a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.chararray.html" rel="noreferrer">You can use the <code>chararray</code> subclass</a> to perform array operations with strings:

<pre><code>a1 = np.char.array(['a', 'b'])
a2 = np.char.array(['E', 'F'])

a1 + a2
#chararray(['aE', 'bF'], dtype='|S2')
</code></pre>

another nice example:

<pre><code>b = np.array([2, 4])
a1*b
#chararray(['aa', 'bbbb'], dtype='|S4')
</code></pre>

blocks|key|607059|text|一个更基本、更优雅、更快速的解决方案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|607060|In+[11]:+np.array([x1+%2B+x2+for+x1,x2+in+zip(a1,a2)])
Out[11]:+array(['aE',+'bF'],+dtype='<U2')|code-block|syntax|javascript|607061|对于较小的数组，它是非常快的。|607062|In+[12]:+%25timeit+np.array([x1+%2B+x2+for+x1,x2+in+zip(a1,a2)])
3.67+µs+±+136+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+100000+loops+each)

In+[13]:+%25timeit+np.core.defchararray.add(a1,+a2)
6.27+µs+±+28.3+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+100000+loops+each)

In+[14]:+%25timeit+np.char.array(a1)+%2B+np.char.array(a2)
22.1+µs+±+319+ns+per+loop+(mean+±+std.+dev.+of+7+runs,+10000+loops+each)|607063|对于较大的数组，时间差不是很大。|607064|In+[15]:+b1+=+np.full(10000,'a')++++
In+[16]:+b2+=+np.full(10000,'b')++++

In+[189]:+%25timeit+np.array([x1+%2B+x2+for+x1,x2+in+zip(b1,b2)])
6.74+ms+±+66.9+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)

In+[188]:+%25timeit+np.core.defchararray.add(b1,+b2)
7.03+ms+±+419+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)

In+[187]:+%25timeit+np.char.array(b1)+%2B+np.char.array(b2)
6.97+ms+±+284+µs+per+loop+(mean+±+std.+dev.+of+7+runs,+100+loops+each)|607065|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

One more basic, elegant and fast solution:

<pre><code>In [11]: np.array([x1 + x2 for x1,x2 in zip(a1,a2)])
Out[11]: array(['aE', 'bF'], dtype='&lt;U2')
</code></pre>

It is very fast for smaller arrays. 

<pre><code>In [12]: %timeit np.array([x1 + x2 for x1,x2 in zip(a1,a2)])
3.67 µs ± 136 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [13]: %timeit np.core.defchararray.add(a1, a2)
6.27 µs ± 28.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

In [14]: %timeit np.char.array(a1) + np.char.array(a2)
22.1 µs ± 319 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
</code></pre>

For larger arrays, time difference is not much.

<pre><code>In [15]: b1 = np.full(10000,'a') 
In [16]: b2 = np.full(10000,'b') 

In [189]: %timeit np.array([x1 + x2 for x1,x2 in zip(b1,b2)])
6.74 ms ± 66.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [188]: %timeit np.core.defchararray.add(b1, b2)
7.03 ms ± 419 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [187]: %timeit np.char.array(b1) + np.char.array(b2)
6.97 ms ± 284 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
</code></pre>

Is this a bug?

<pre><code>import numpy as np
a1=np.array(['a','b'])
a2=np.array(['E','F'])

In [20]: add(a1,a2)
Out[20]: NotImplemented
</code></pre>

I am trying to do element-wise string concatenation. I thought Add() was the way to do it in numpy but obviously it is not working as expected.

Element-wise string concatenation in numpy

Python

这是一个bug吗？import numpy as npa1=np.array(['a','b'])a2=np.array(['E','F'])In [20]: add(a1,a2)Out[20]: NotImplemented我正在尝试进行逐个元素的字符串连接。我认为Add()是在numpy中实现这一点的方法，但显然它...

问numpy中基于元素的字符串连接
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