entityMap|blocks|key|24tdp|text|对于一维numpy数组，您可以使用列表：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3jepc|#+serialize+1D+array+x
record['feature1']+=+x.tolist()

#+deserialize+1D+array+x
x+=+np.fromiter(+record['feature1']+)|code-block|syntax|javascript|7brgf|对于多维数据，我认为您将需要使用pickle和pymongo.binary.Binary：|672gp|#+serialize+2D+array+y
record['feature2']+=+pymongo.binary.Binary(+pickle.dumps(+y,+protocol=2)+)+)

#+deserialize+2D+array+y
y+=+pickle.loads(+record['feature2']+)^0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|L|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|M|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|N|9|@]|A|@]|B|$]]|$2|J|4|K|6|E|8|O|9|@]|A|@]|B|$F|G]]]]

For a 1D numpy array, you can use lists:

<pre><code># serialize 1D array x
record['feature1'] = x.tolist()

# deserialize 1D array x
x = np.fromiter( record['feature1'] )
</code></pre>

For multidimensional data, I believe you'll need to use pickle and pymongo.binary.Binary:

<pre><code># serialize 2D array y
record['feature2'] = pymongo.binary.Binary( pickle.dumps( y, protocol=2) ) )

# deserialize 2D array y
y = pickle.loads( record['feature2'] )
</code></pre>

entityMap|blocks|key|3m8h9|text|代码pymongo.binary.Binary(...)对我不起作用，也许我们需要像@tcaswell建议的那样使用bson。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3tr6a|无论如何，这里是多维numpy数组的一种解决方案|dvlmf|>>from+bson.binary+import+Binary
>>import+pickle
#+convert+numpy+array+to+Binary,+store+record+in+mongodb
>>record['feature2']+=+Binary(pickle.dumps(npArray,+protocol=2),+subtype=128+)
#+get+record+from+mongodb,+convert+Binary+to+numpy+array
>>+npArray+=+pickle.loads(record['feature2'])|code-block|syntax|javascript|b0db9|话虽如此，功劳归功于|dqutk|MongoWrapper|1ef75|使用他们写的代码。^0|0|0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|P|9|@]|A|@]|B|$]]|$2|C|4|D|6|7|8|Q|9|@]|A|@]|B|$]]|$2|E|4|F|6|G|8|R|9|@]|A|@]|B|$H|I]]|$2|J|4|K|6|7|8|S|9|@]|A|@]|B|$]]|$2|L|4|M|6|7|8|T|9|@]|A|@]|B|$]]|$2|N|4|O|6|7|8|U|9|@]|A|@]|B|$]]]]

The code pymongo.binary.Binary(...) didnt work for me, may be we need to use bson as
@tcaswell suggested. 

Anyway here is one solution for multi-dimensional numpy array 

<pre><code>&gt;&gt;from bson.binary import Binary
&gt;&gt;import pickle
# convert numpy array to Binary, store record in mongodb
&gt;&gt;record['feature2'] = Binary(pickle.dumps(npArray, protocol=2), subtype=128 )
# get record from mongodb, convert Binary to numpy array
&gt;&gt; npArray = pickle.loads(record['feature2'])
</code></pre>

Having said that, the credit goes to <a href="https://github.com/dattalab/mongowrapper/blob/master/MongoWrapper.py" rel="noreferrer">MongoWrapper</a> used the code written by them.

entityMap|blocks|key|fuif7|text|我们已经构建了一个用于存储数字数据(Pandas、numpy等)的开源库。在MongoDB中：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|enevj|https://github.com/manahl/arctic|3gb4h|最棒的是，它非常容易使用，相当快，并且支持数据版本控制，多个数据库等等。^0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|G|9|@]|A|@]|B|$]]|$2|C|4|D|6|7|8|H|9|@]|A|@]|B|$]]|$2|E|4|F|6|7|8|I|9|@]|A|@]|B|$]]]]

We've built an open source library for storing numeric data (Pandas, numpy, etc.) in MongoDB:

<a href="https://github.com/manahl/arctic" rel="noreferrer">https://github.com/manahl/arctic</a>

Best of all it's really easy to use, pretty fast and supports data versioning, multiple data libraries and more.

entityMap|blocks|key|dc5e5|text|你试过Monary了吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3fj7|他们在网站上有一些例子|2cs0o|http://djcinnovations.com/index.php/archives/103^0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|G|9|@]|A|@]|B|$]]|$2|C|4|D|6|7|8|H|9|@]|A|@]|B|$]]|$2|E|4|F|6|7|8|I|9|@]|A|@]|B|$]]]]

Have you tried Monary? 

They have examples on the site

<a href="http://djcinnovations.com/index.php/archives/103" rel="nofollow">http://djcinnovations.com/index.php/archives/103</a>

entityMap|blocks|key|eb6|text|你试过了吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|din09|MongoWrapper|349vv|，我认为这很简单：|8aul2|声明连接到mongodb服务器和集合以保存您的np。|eqjlm|import+monogowrapper+as+mdb
db+=+mdb.MongoWrapper(dbName='test',
++++++++++++++++++++++collectionName='test_collection',+
++++++++++++++++++++++hostname="localhost",+
++++++++++++++++++++++port="27017")+
my_dict+=+{"name":+"Important+experiment",+
++++++++++++"data":np.random.random((100,100))}|code-block|syntax|javascript|1os22|字典就像你所期望的那样：|b277c|print+my_dict
{'data':+array([[+0.773217,++0.517796,++0.209353,+...,++0.042116,++0.845194,
+++++++++0.733732],
+++++++[+0.281073,++0.182046,++0.453265,+...,++0.873993,++0.361292,
+++++++++0.551493],
+++++++[+0.678787,++0.650591,++0.370826,+...,++0.494303,++0.39029+,
+++++++++0.521739],
+++++++...,+
+++++++[+0.854548,++0.075026,++0.498936,+...,++0.043457,++0.282203,
+++++++++0.359131],
+++++++[+0.099201,++0.211464,++0.739155,+...,++0.796278,++0.645168,
+++++++++0.975352],
+++++++[+0.94907+,++0.363454,++0.912208,+...,++0.480943,++0.810243,
+++++++++0.217947]]),
+'name':+'Important+experiment'}|668g5|将数据保存到mongo：|e3au0|db.save(my_dict)|esddi|要加载回数据，请执行以下操作：|e6qe5|my_loaded_dict+=+db.load({"name":"Important+experiment"})^0|0|0|0|0|0|0|0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|Z|9|@]|A|@]|B|$]]|$2|C|4|D|6|7|8|10|9|@]|A|@]|B|$]]|$2|E|4|F|6|7|8|11|9|@]|A|@]|B|$]]|$2|G|4|H|6|7|8|12|9|@]|A|@]|B|$]]|$2|I|4|J|6|K|8|13|9|@]|A|@]|B|$L|M]]|$2|N|4|O|6|7|8|14|9|@]|A|@]|B|$]]|$2|P|4|Q|6|K|8|15|9|@]|A|@]|B|$L|M]]|$2|R|4|S|6|7|8|16|9|@]|A|@]|B|$]]|$2|T|4|U|6|K|8|17|9|@]|A|@]|B|$L|M]]|$2|V|4|W|6|7|8|18|9|@]|A|@]|B|$]]|$2|X|4|Y|6|K|8|19|9|@]|A|@]|B|$L|M]]]]

Have you try <a href="https://github.com/dattalab/mongowrapper" rel="nofollow noreferrer">MongoWrapper</a>, i think it simple :
<h3>Declare connection to mongodb server and collection to save your np.</h3>
<pre><code>import monogowrapper as mdb
db = mdb.MongoWrapper(dbName='test',
 collectionName='test_collection', 
 hostname=&quot;localhost&quot;, 
 port=&quot;27017&quot;) 
my_dict = {&quot;name&quot;: &quot;Important experiment&quot;, 
 &quot;data&quot;:np.random.random((100,100))}
</code></pre>
The dictionary's just as you'd expect it to be:
<pre><code>print my_dict
{'data': array([[ 0.773217, 0.517796, 0.209353, ..., 0.042116, 0.845194,
 0.733732],
 [ 0.281073, 0.182046, 0.453265, ..., 0.873993, 0.361292,
 0.551493],
 [ 0.678787, 0.650591, 0.370826, ..., 0.494303, 0.39029 ,
 0.521739],
 ..., 
 [ 0.854548, 0.075026, 0.498936, ..., 0.043457, 0.282203,
 0.359131],
 [ 0.099201, 0.211464, 0.739155, ..., 0.796278, 0.645168,
 0.975352],
 [ 0.94907 , 0.363454, 0.912208, ..., 0.480943, 0.810243,
 0.217947]]),
 'name': 'Important experiment'}
</code></pre>
<h3>Save data to mongo :</h3>
<pre><code>db.save(my_dict)
</code></pre>
<h3>To load back data :</h3>
<pre><code>my_loaded_dict = db.load({&quot;name&quot;:&quot;Important experiment&quot;})
</code></pre>

entityMap|blocks|key|1n4lf|text|我知道这是一个古老的问题，但这里有一个优雅的解决方案，可以在新版本的pymongo中工作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3f23r|import+pickle
from+bson.binary+import+Binary,+USER_DEFINED_SUBTYPE
from+bson.codec_options+import+TypeCodec,+TypeRegistry,+CodecOptions
import+numpy+as+np

class+NumpyCodec(TypeCodec):
++++python_type+=+np.ndarray
++++bson_type+=+Binary

++++def+transform_python(self,+value):
++++++++return+Binary(pickle.loads(value),+USER_DEFINED_SUBTYPE)

++++def+transform_bson(self,+value):
++++++++if+value.subtype+==+USER_DEFINED_SUBTYPE:
++++++++++++return+pickle.dumps(value,+protocol=2)
++++++++return+value

def+get_codec_options():
++++numpy_codec+=+NumpyCodec()
++++type_registry+=+TypeRegistry([numpy_codec])
++++codec_options+=+CodecOptions(type_registry=type_registry)
++++return+codec_options

def+get_collection(name,+db):
++++codec_options+=+get_codec_options()
++++return+db.get_collection(name,+codec_options=codec_options)|code-block|syntax|javascript|eej68|然后你可以这样收集：|fbqp0|from+pymongo+import+MongoClient
client+=+MongoClient()
db+=+client['my_db']
my_collection+=+get_collection('my_collection',+db)|cnemm|然后，您只需在数据库中透明地插入和查找with+Numpy数组。^0|0|0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|N|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|O|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|P|9|@]|A|@]|B|$]]|$2|J|4|K|6|E|8|Q|9|@]|A|@]|B|$F|G]]|$2|L|4|M|6|7|8|R|9|@]|A|@]|B|$]]]]

I know this is an old question but here is an elegant solution which works in new versions of pymongo:
<pre class="lang-py prettyprint-override"><code>import pickle
from bson.binary import Binary, USER_DEFINED_SUBTYPE
from bson.codec_options import TypeCodec, TypeRegistry, CodecOptions
import numpy as np

class NumpyCodec(TypeCodec):
 python_type = np.ndarray
 bson_type = Binary

 def transform_python(self, value):
 return Binary(pickle.loads(value), USER_DEFINED_SUBTYPE)

 def transform_bson(self, value):
 if value.subtype == USER_DEFINED_SUBTYPE:
 return pickle.dumps(value, protocol=2)
 return value

def get_codec_options():
 numpy_codec = NumpyCodec()
 type_registry = TypeRegistry([numpy_codec])
 codec_options = CodecOptions(type_registry=type_registry)
 return codec_options

def get_collection(name, db):
 codec_options = get_codec_options()
 return db.get_collection(name, codec_options=codec_options)
</code></pre>
Then you can get you collection this way:
<pre class="lang-py prettyprint-override"><code>from pymongo import MongoClient
client = MongoClient()
db = client['my_db']
my_collection = get_collection('my_collection', db)
</code></pre>
Afterwards, you just insert and find with Numpy arrays in your database transparently.

I have a couple of MongoDB documents wherein one my the fields is best represented as a matrix (numpy array). I would like to save this document to MongoDB, how do I do this?

<pre><code>{
'name' : 'subject1',
'image_name' : 'blah/foo.png',
'feature1' : np.array(...)
}
</code></pre>

Saving numpy array in mongodb

数据库

服务器

MongoDB

 我有两个MongoDB文档，其中一个字段最好用矩阵(numpy数组)表示。我想将此文档保存为MongoDB，该如何操作？ {'name' : 'subject1','image_name' : 'blah/foo.png','feature1' : np.array(...)}

问在mongodb中保存numpy数组
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