blocks|key|619187|text|错误消息说明了一切:+NumPy数组没有append()方法。但是，有一个免费的函数numpy.append()：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|619188|numpy.append(M,+a)|code-block|syntax|javascript|619189|这将创建一个新的数组，而不是在适当的位置改变M。请注意，使用numpy.append()需要同时复制两个数组。如果使用固定大小的NumPy数组，您将获得更好的代码性能。|619190|entityMap|0|LINK|mutability|MUTABLE|url|http://docs.scipy.org/doc/numpy/reference/generated/numpy.append.html^0|K|8|16|E|16|E|0|0|0|M|1|U|E|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]|$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|@$9|14|A|15|B|C]|$9|16|A|17|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|18|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

Well, the error message says it all: NumPy arrays do not have an <code>append()</code> method. There's a free function <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.append.html"><code>numpy.append()</code></a> however:

<pre><code>numpy.append(M, a)
</code></pre>

This will create a new array instead of mutating <code>M</code> in place. Note that using <code>numpy.append()</code> involves copying both arrays. You will get better performing code if you use fixed-sized NumPy arrays.

blocks|key|619206|text|In+[1]:+import+numpy+as+np

In+[2]:+a+=+np.array([[1,+2,+3],+[4,+5,+6]])

In+[3]:+b+=+np.array([[9,+8,+7],+[6,+5,+4]])

In+[4]:+np.concatenate((a,+b))
Out[4]:+
array([[1,+2,+3],
+++++++[4,+5,+6],
+++++++[9,+8,+7],
+++++++[6,+5,+4]])|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|619207|或者这样：|unstyled|619208|In+[1]:+a+=+np.array([1,+2,+3])

In+[2]:+b+=+np.array([4,+5,+6])

In+[3]:+np.vstack((a,+b))
Out[3]:+
array([[1,+2,+3],
+++++++[4,+5,+6]])|619209|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|L|8|@]|9|@]|A|$]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$B|C]]|$1|I|3|-4|5|F|7|N|8|@]|9|@]|A|$]]]|J|$]]

<pre><code>In [1]: import numpy as np

In [2]: a = np.array([[1, 2, 3], [4, 5, 6]])

In [3]: b = np.array([[9, 8, 7], [6, 5, 4]])

In [4]: np.concatenate((a, b))
Out[4]: 
array([[1, 2, 3],
 [4, 5, 6],
 [9, 8, 7],
 [6, 5, 4]])
</code></pre>

or this:

<pre><code>In [1]: a = np.array([1, 2, 3])

In [2]: b = np.array([4, 5, 6])

In [3]: np.vstack((a, b))
Out[3]: 
array([[1, 2, 3],
 [4, 5, 6]])
</code></pre>

blocks|key|619252|text|Sven说了这一切，只是要非常小心，因为当append被调用时，会自动调整类型。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|619253|In+[2]:+import+numpy+as+np

In+[3]:+a+=+np.array([1,2,3])

In+[4]:+b+=+np.array([1.,2.,3.])

In+[5]:+c+=+np.array(['a','b','c'])

In+[6]:+np.append(a,b)
Out[6]:+array([+1.,++2.,++3.,++1.,++2.,++3.])

In+[7]:+a.dtype
Out[7]:+dtype('int64')

In+[8]:+np.append(a,c)
Out[8]:+
array(['1',+'2',+'3',+'a',+'b',+'c'],+
++++++dtype='%7CS1')|code-block|syntax|javascript|619254|正如您所看到的，根据内容，dtype从int64到float32，再到S1|619255|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|$]]

Sven said it all, just be very cautious because of automatic type adjustments when append is called.

<pre><code>In [2]: import numpy as np

In [3]: a = np.array([1,2,3])

In [4]: b = np.array([1.,2.,3.])

In [5]: c = np.array(['a','b','c'])

In [6]: np.append(a,b)
Out[6]: array([ 1., 2., 3., 1., 2., 3.])

In [7]: a.dtype
Out[7]: dtype('int64')

In [8]: np.append(a,c)
Out[8]: 
array(['1', '2', '3', 'a', 'b', 'c'], 
 dtype='|S1')
</code></pre>

As you see based on the contents the dtype went from int64 to float32, and then to S1

blocks|key|403606|text|如果我理解你的问题，这里有一种方法。假设你有：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|403607|a+=+[4.1,+6.21,+1.0]|code-block|syntax|javascript|403608|这里有一些代码..。|403609|def+array_in_array(scalarlist):
++++return+[(x,)+for+x+in+scalarlist]|403610|这会导致：|403611|In+[72]:+a+=+[4.1,+6.21,+1.0]

In+[73]:+a
Out[73]:+[4.1,+6.21,+1.0]

In+[74]:+def+array_in_array(scalarlist):
+++....:+++++return+[(x,)+for+x+in+scalarlist]
+++....:+

In+[75]:+b+=+array_in_array(a)

In+[76]:+b
Out[76]:+[(4.1,),+(6.21,),+(1.0,)]|403612|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|$]]

If I understand your question, here's one way. Say you have:

<pre><code>a = [4.1, 6.21, 1.0]
</code></pre>

so here's some code... 

<pre><code>def array_in_array(scalarlist):
 return [(x,) for x in scalarlist]
</code></pre>

Which leads to: 

<pre><code>In [72]: a = [4.1, 6.21, 1.0]

In [73]: a
Out[73]: [4.1, 6.21, 1.0]

In [74]: def array_in_array(scalarlist):
 ....: return [(x,) for x in scalarlist]
 ....: 

In [75]: b = array_in_array(a)

In [76]: b
Out[76]: [(4.1,), (6.21,), (1.0,)]
</code></pre>

blocks|key|403648|text|您可以使用numpy.append()...|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|403649|import+numpy

B+=+numpy.array([3])
A+=+numpy.array([1,+2,+2])
B+=+numpy.append(+B+,+A+)

print+B

>+[3+1+2+2]|code-block|syntax|javascript|403650|这不会创建两个单独的数组，但会将两个数组追加到一个一维数组中。|403651|entityMap^0|5|E|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|N|$]]

You may use <code>numpy.append()</code>... 

<pre><code>import numpy

B = numpy.array([3])
A = numpy.array([1, 2, 2])
B = numpy.append( B , A )

print B

&gt; [3 1 2 2]
</code></pre>

This will not create two separate arrays but will append two arrays into a single dimensional array.

blocks|key|403678|text|实际上，人们总是可以创建一个普通的numpy数组列表，并在以后转换它。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|403679|In+[1]:+import+numpy+as+np

In+[2]:+a+=+np.array([[1,2],[3,4]])

In+[3]:+b+=+np.array([[1,2],[3,4]])

In+[4]:+l+=+[a]

In+[5]:+l.append(b)

In+[6]:+l+=+np.array(l)

In+[7]:+l.shape
Out[7]:+(2,+2,+2)

In+[8]:+l
Out[8]:+
array([[[1,+2],
++++++++[3,+4]],

+++++++[[1,+2],
++++++++[3,+4]]])|code-block|syntax|javascript|403680|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|$]]

Actually one can always create an ordinary list of numpy arrays and convert it later.

<pre><code>In [1]: import numpy as np

In [2]: a = np.array([[1,2],[3,4]])

In [3]: b = np.array([[1,2],[3,4]])

In [4]: l = [a]

In [5]: l.append(b)

In [6]: l = np.array(l)

In [7]: l.shape
Out[7]: (2, 2, 2)

In [8]: l
Out[8]: 
array([[[1, 2],
 [3, 4]],

 [[1, 2],
 [3, 4]]])
</code></pre>

blocks|key|403770|text|我也有同样的问题，我不能评论@Sven+Marnach的答案(没有足够的代表，天哪，我记得Stackoverflow第一次开始的时候…)不管怎么说。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|403771|将随机数列表添加到10X10矩阵。|403772|myNpArray+=+np.zeros([1,+10])
for+x+in+range(1,11,1):
++++randomList+=+[list(np.random.randint(99,+size=10))]
++++myNpArray+=+np.vstack((myNpArray,+randomList))
myNpArray+=+myNpArray[1:]|code-block|syntax|javascript|403773|使用np.zeros()创建一个具有1+x+10个零的数组。|403774|array([[0.,+0.,+0.,+0.,+0.,+0.,+0.,+0.,+0.,+0.]])|403775|然后，使用np.random创建一个包含10个随机数的列表，并将其分配给randomList。循环将其堆叠到10个高度。我们只需要记住删除第一个空条目。|403776|myNpArray

array([[31.,+10.,+19.,+78.,+95.,+58.,++3.,+47.,+30.,+56.],
+++++++[51.,+97.,++5.,+80.,+28.,+76.,+92.,+50.,+22.,+93.],
+++++++[64.,+79.,++7.,+12.,+68.,+13.,+59.,+96.,+32.,+34.],
+++++++[44.,+22.,+46.,+56.,+73.,+42.,+62.,++4.,+62.,+83.],
+++++++[91.,+28.,+54.,+69.,+60.,+95.,++5.,+13.,+60.,+88.],
+++++++[71.,+90.,+76.,+53.,+13.,+53.,+31.,++3.,+96.,+57.],
+++++++[33.,+87.,+81.,++7.,+53.,+46.,++5.,++8.,+20.,+71.],
+++++++[46.,+71.,+14.,+66.,+68.,+65.,+68.,+32.,++9.,+30.],
+++++++[+1.,+35.,+96.,+92.,+72.,+52.,+88.,+86.,+94.,+88.],
+++++++[13.,+36.,+43.,+45.,+90.,+17.,+38.,++1.,+41.,+33.]])|403777|所以在一个函数中：|403778|def+array_matrix(random_range,+array_size):
++++myNpArray+=+np.zeros([1,+array_size])
++++for+x+in+range(1,+array_size+%2B+1,+1):
++++++++randomList+=+[list(np.random.randint(random_range,+size=array_size))]
++++++++myNpArray+=+np.vstack((myNpArray,+randomList))
++++return+myNpArray[1:]|403779|使用随机数0-+1000的7+x+7数组|403780|array_matrix(1000,+7)

array([[621.,+377.,+931.,+180.,+964.,+885.,+723.],
+++++++[298.,+382.,+148.,+952.,+430.,+333.,+956.],
+++++++[398.,+596.,+732.,+422.,+656.,+348.,+470.],
+++++++[735.,+251.,+314.,+182.,+966.,+261.,+523.],
+++++++[373.,+616.,+389.,++90.,+884.,+957.,+826.],
+++++++[587.,+963.,++66.,+154.,+111.,+529.,+945.],
+++++++[950.,+413.,+539.,+860.,+634.,+195.,+915.]])|403781|entityMap^0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|14|8|@]|9|@]|A|$G|H]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|16|8|@]|9|@]|A|$G|H]]|$1|Q|3|R|5|6|7|17|8|@]|9|@]|A|$]]|$1|S|3|T|5|F|7|18|8|@]|9|@]|A|$G|H]]|$1|U|3|V|5|6|7|19|8|@]|9|@]|A|$]]|$1|W|3|X|5|F|7|1A|8|@]|9|@]|A|$G|H]]|$1|Y|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|Z|$]]

I had the same issue, and I couldn't comment on @Sven Marnach answer (not enough rep, gosh I remember when Stackoverflow first started...) anyway.

Adding a list of random numbers to a 10 X 10 matrix.

<pre><code>myNpArray = np.zeros([1, 10])
for x in range(1,11,1):
 randomList = [list(np.random.randint(99, size=10))]
 myNpArray = np.vstack((myNpArray, randomList))
myNpArray = myNpArray[1:]
</code></pre>

Using np.zeros() an array is created with 1 x 10 zeros.

<pre><code>array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])
</code></pre>

Then a list of 10 random numbers is created using np.random and assigned to randomList.
The loop stacks it 10 high. We just have to remember to remove the first empty entry.

<pre><code>myNpArray

array([[31., 10., 19., 78., 95., 58., 3., 47., 30., 56.],
 [51., 97., 5., 80., 28., 76., 92., 50., 22., 93.],
 [64., 79., 7., 12., 68., 13., 59., 96., 32., 34.],
 [44., 22., 46., 56., 73., 42., 62., 4., 62., 83.],
 [91., 28., 54., 69., 60., 95., 5., 13., 60., 88.],
 [71., 90., 76., 53., 13., 53., 31., 3., 96., 57.],
 [33., 87., 81., 7., 53., 46., 5., 8., 20., 71.],
 [46., 71., 14., 66., 68., 65., 68., 32., 9., 30.],
 [ 1., 35., 96., 92., 72., 52., 88., 86., 94., 88.],
 [13., 36., 43., 45., 90., 17., 38., 1., 41., 33.]])
</code></pre>

So in a function:

<pre><code>def array_matrix(random_range, array_size):
 myNpArray = np.zeros([1, array_size])
 for x in range(1, array_size + 1, 1):
 randomList = [list(np.random.randint(random_range, size=array_size))]
 myNpArray = np.vstack((myNpArray, randomList))
 return myNpArray[1:]
</code></pre>

a 7 x 7 array using random numbers 0 - 1000

<pre><code>array_matrix(1000, 7)

array([[621., 377., 931., 180., 964., 885., 723.],
 [298., 382., 148., 952., 430., 333., 956.],
 [398., 596., 732., 422., 656., 348., 470.],
 [735., 251., 314., 182., 966., 261., 523.],
 [373., 616., 389., 90., 884., 957., 826.],
 [587., 963., 66., 154., 111., 529., 945.],
 [950., 413., 539., 860., 634., 195., 915.]])
</code></pre>

blocks|key|403825|text|尝试以下代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|403826|import+numpy+as+np

a1+=+np.array([])

n+=+int(input(""))

for+i+in+range(0,n):
++++a+=+int(input(""))
++++a1+=+np.append(a,+a1)
++++a+=+0

print(a1)|code-block|syntax|javascript|403827|你也可以使用数组来代替"a“|403828|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|$]]

Try this code :
<pre><code>import numpy as np

a1 = np.array([])

n = int(input(&quot;&quot;))

for i in range(0,n):
 a = int(input(&quot;&quot;))
 a1 = np.append(a, a1)
 a = 0

print(a1)
</code></pre>
Also you can use array instead of &quot;a&quot;

blocks|key|403842|text|这是为使用numpy's+ndarrays工作的人准备的。函数numpy.concatenate()也可以正常工作。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|403843|>>a+=+np.random.randint(0,9,+size=(10,1,5,4))
>>a.shape
(10,+1,+5,+4)

>>b+=+np.random.randint(0,9,+size=(15,1,5,4))
>>b.shape
(15,+1,+5,+4)

>>X+=+np.concatenate((a,+b))
>>X.shape
(25,+1,+5,+4)|code-block|syntax|javascript|403844|与vstack()的方式大致相同|403845|>>Y+=+np.vstack((a,b))
>>Y.shape
(25,+1,+5,+4)|403846|entityMap^0|5|G|V|J|0|0|1|8|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

This is for people working with <code>numpy's ndarrays</code>. The function <code>numpy.concatenate()</code> does work as well.
<pre><code>&gt;&gt;a = np.random.randint(0,9, size=(10,1,5,4))
&gt;&gt;a.shape
(10, 1, 5, 4)

&gt;&gt;b = np.random.randint(0,9, size=(15,1,5,4))
&gt;&gt;b.shape
(15, 1, 5, 4)

&gt;&gt;X = np.concatenate((a, b))
&gt;&gt;X.shape
(25, 1, 5, 4)
</code></pre>
Much the same way as <code>vstack()</code>
<pre><code>&gt;&gt;Y = np.vstack((a,b))
&gt;&gt;Y.shape
(25, 1, 5, 4)
</code></pre>

blocks|key|403897|text|根据堆栈的方向，有很多方法可以将数组堆叠在一起。例如，您可以考虑np.stack()+(doc)、np.vstack()+(doc)和np.hstack()+(doc)。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|403898|entityMap|0|LINK|mutability|MUTABLE|url|https://numpy.org/doc/stable/reference/generated/numpy.stack.html|1|https://numpy.org/doc/stable/reference/generated/numpy.vstack.html|2|https://numpy.org/doc/stable/reference/generated/numpy.hstack.html^0|18|3|0|1Q|3|1|28|3|2|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]|$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|-4|5|6|7|Z|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]|L|$5|G|H|I|C|$J|M]]|N|$5|G|H|I|C|$J|O]]]]

There is a handfull of method to stack arrays together, depending on the direction of the stack.
You may consider np.stack() (<a href="https://numpy.org/doc/stable/reference/generated/numpy.stack.html" rel="nofollow noreferrer">doc</a>), np.vstack() (<a href="https://numpy.org/doc/stable/reference/generated/numpy.vstack.html" rel="nofollow noreferrer">doc</a>) and np.hstack() (<a href="https://numpy.org/doc/stable/reference/generated/numpy.hstack.html" rel="nofollow noreferrer">doc</a>) for example.

I have a numpy_array. Something like <code>[ a b c ]</code>.
And then I want to concatenate it with another NumPy array (just like we create a list of lists). How do we create a NumPy array containing NumPy arrays?
I tried to do the following without any luck
<pre><code>&gt;&gt;&gt; M = np.array([])
&gt;&gt;&gt; M
array([], dtype=float64)
&gt;&gt;&gt; M.append(a,axis=0)
Traceback (most recent call last):
 File &quot;&lt;stdin&gt;&quot;, line 1, in &lt;module&gt;
AttributeError: 'numpy.ndarray' object has no attribute 'append'
&gt;&gt;&gt; a
array([1, 2, 3])
</code></pre>

Concatenate a NumPy array to another NumPy array

我有一台numpy_array。就像[ a b c ]一样。然后我想把它和另一个NumPy数组连接起来(就像我们创建一个列表列表一样)。如何创建包含NumPy数组的NumPy数组？我试着做了下面的事情，但没有任何运气>>> M = np.array([])>>> Marray([], dtype=float64)>>>...

问将一个NumPy数组连接到另一个NumPy数组
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问将一个NumPy数组连接到另一个NumPy数组
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