blocks|key|4175526|text|json不识别NumPy数据类型。在序列化对象之前，将数字转换为Python+int：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4175527|'count__c':+int(store['count'].iloc[i])|code-block|syntax|javascript|4175528|entityMap^0|0|4|13|3|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|$]]

<code>json</code> does not recognize NumPy data types. Convert the number to a Python <code>int</code> before serializing the object:

<pre><code>'count__c': int(store['count'].iloc[i])
</code></pre>

blocks|key|4175562|text|另一种选择是在创建数据帧时使用dtype=str|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4175563|例如，如果从csv文件加载store：|4175564|import+pandas+as+pd
store+=+pd.read_csv('store.csv',+dtype=str)|code-block|syntax|javascript|4175565|然后，所有东西都有一种可以序列化为json的str类型。|4175566|entityMap^0|F|9|0|D|5|0|0|M|3|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|T|8|@$9|U|A|V|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|W|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|X|8|@$9|Y|A|Z|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

Another option is that when you create the dataframe use <code>dtype=str</code>

For example, if you loaded <code>store</code> from a csv file:

<pre><code>import pandas as pd
store = pd.read_csv('store.csv', dtype=str)
</code></pre>

Then everything has a type of <code>str</code> which can be serialized to json.

blocks|key|4174995|text|你可以定义你自己的编码器来解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4174996|import+json
import+numpy+as+np

class+NpEncoder(json.JSONEncoder):
++++def+default(self,+obj):
++++++++if+isinstance(obj,+np.integer):
++++++++++++return+int(obj)
++++++++if+isinstance(obj,+np.floating):
++++++++++++return+float(obj)
++++++++if+isinstance(obj,+np.ndarray):
++++++++++++return+obj.tolist()
++++++++return+super(NpEncoder,+self).default(obj)

#+Your+codes+....+
json.dumps(data,+cls=NpEncoder)|code-block|syntax|javascript|4174997|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 define your own encoder to solve this problem.
<pre><code>import json
import numpy as np

class NpEncoder(json.JSONEncoder):
 def default(self, obj):
 if isinstance(obj, np.integer):
 return int(obj)
 if isinstance(obj, np.floating):
 return float(obj)
 if isinstance(obj, np.ndarray):
 return obj.tolist()
 return super(NpEncoder, self).default(obj)

# Your codes .... 
json.dumps(data, cls=NpEncoder)
</code></pre>

blocks|key|4175641|text|这可能是较晚的响应，但最近我收到了相同的错误。在大量的冲浪之后，这个解决方案对我很有帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4175642|def+myconverter(obj):
++++++++if+isinstance(obj,+np.integer):
++++++++++++return+int(obj)
++++++++elif+isinstance(obj,+np.floating):
++++++++++++return+float(obj)
++++++++elif+isinstance(obj,+np.ndarray):
++++++++++++return+obj.tolist()
++++++++elif+isinstance(obj,+datetime.datetime):
++++++++++++return+obj.__str__()|code-block|syntax|javascript|4175643|在json.dumps()中调用myconverter，如下所示。json.dumps('message',+default=myconverter)|offset|length|style|CODE|4175644|entityMap^0|0|0|1|C|G|B|X|16|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]|$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|N|$]]

This might be the late response, but recently i got the same error. After lot of surfing this solution helped me.

<pre><code>def myconverter(obj):
 if isinstance(obj, np.integer):
 return int(obj)
 elif isinstance(obj, np.floating):
 return float(obj)
 elif isinstance(obj, np.ndarray):
 return obj.tolist()
 elif isinstance(obj, datetime.datetime):
 return obj.__str__()
</code></pre>

Call <code>myconverter</code> in <code>json.dumps()</code> like below.
<code>json.dumps('message', default=myconverter)</code>

blocks|key|4175092|text|我将把我对ring的回答作为@Jie+Yang的excellent+solution的一个更稳定的版本。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4175093|我的解决方案|4175094|numpyencoder和its+repository。|style|CODE|4175095|from+numpyencoder+import+NumpyEncoder

numpy_data+=+np.array([0,+1,+2,+3])

with+open(json_file,+'w')+as+file:
++++json.dump(numpy_data,+file,+indent=4,+sort_keys=True,
++++++++++++++separators=(',+',+':+'),+ensure_ascii=False,
++++++++++++++cls=NumpyEncoder)|code-block|syntax|javascript|4175096|细目|4175097|如果你深入研究numpyencoder/numpyencoder.py文件中的hmallen's代码，你会发现它与@Jie+Yang的答案非常相似：|4175098|class+NumpyEncoder(json.JSONEncoder):
++++"""+Custom+encoder+for+numpy+data+types+"""
++++def+default(self,+obj):
++++++++if+isinstance(obj,+(np.int_,+np.intc,+np.intp,+np.int8,
++++++++++++++++++++++++++++np.int16,+np.int32,+np.int64,+np.uint8,
++++++++++++++++++++++++++++np.uint16,+np.uint32,+np.uint64)):

++++++++++++return+int(obj)

++++++++elif+isinstance(obj,+(np.float_,+np.float16,+np.float32,+np.float64)):
++++++++++++return+float(obj)

++++++++elif+isinstance(obj,+(np.complex_,+np.complex64,+np.complex128)):
++++++++++++return+{'real':+obj.real,+'imag':+obj.imag}

++++++++elif+isinstance(obj,+(np.ndarray,)):
++++++++++++return+obj.tolist()

++++++++elif+isinstance(obj,+(np.bool_)):
++++++++++++return+bool(obj)

++++++++elif+isinstance(obj,+(np.void)):+
++++++++++++return+None

++++++++return+json.JSONEncoder.default(self,+obj)|4175099|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/57915246/5369706|1|https://pypi.org/project/numpyencoder/|2|https://github.com/hmallen/numpyencoder|3|https://github.com/hmallen/numpyencoder/blob/f8199a61ccde25f829444a9df4b21bcb2d1de8f2/numpyencoder/numpyencoder.py|4|https://github.com/hmallen^0|O|I|0|0|0|0|C|0|C|1|D|E|2|0|0|0|7|S|7|S|3|13|9|4|0|0^^$0|@$1|2|3|4|5|6|7|1A|8|@]|9|@$A|1B|B|1C|1|1D]]|C|$]]|$1|D|3|E|5|6|7|1E|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|1F|8|@$A|1G|B|1H|H|I]]|9|@$A|1I|B|1J|1|1K]|$A|1L|B|1M|1|1N]]|C|$]]|$1|J|3|K|5|L|7|1O|8|@]|9|@]|C|$M|N]]|$1|O|3|P|5|6|7|1P|8|@]|9|@]|C|$]]|$1|Q|3|R|5|6|7|1Q|8|@$A|1R|B|1S|H|I]]|9|@$A|1T|B|1U|1|1V]|$A|1W|B|1X|1|1Y]]|C|$]]|$1|S|3|T|5|L|7|1Z|8|@]|9|@]|C|$M|N]]|$1|U|3|-4|5|6|7|20|8|@]|9|@]|C|$]]]|V|$W|$5|X|Y|Z|C|$10|11]]|12|$5|X|Y|Z|C|$10|13]]|14|$5|X|Y|Z|C|$10|15]]|16|$5|X|Y|Z|C|$10|17]]|18|$5|X|Y|Z|C|$10|19]]]]

I'll throw in my answer to the ring as a bit more stable version of @Jie Yang's <a href="https://stackoverflow.com/a/57915246/5369706">excellent solution</a>.

<h2>My solution</h2>

<a href="https://pypi.org/project/numpyencoder/" rel="noreferrer"><code>numpyencoder</code></a> and <a href="https://github.com/hmallen/numpyencoder" rel="noreferrer">its repository</a>.

<pre class="lang-py prettyprint-override"><code>from numpyencoder import NumpyEncoder

numpy_data = np.array([0, 1, 2, 3])

with open(json_file, 'w') as file:
 json.dump(numpy_data, file, indent=4, sort_keys=True,
 separators=(', ', ': '), ensure_ascii=False,
 cls=NumpyEncoder)
</code></pre>

<h3>The breakdown</h3>

If you dig into <a href="https://github.com/hmallen" rel="noreferrer">hmallen's</a> code in the <a href="https://github.com/hmallen/numpyencoder/blob/f8199a61ccde25f829444a9df4b21bcb2d1de8f2/numpyencoder/numpyencoder.py" rel="noreferrer"><code>numpyencoder/numpyencoder.py</code></a> file you'll see that it's very similar to @Jie Yang's answer:

<pre class="lang-py prettyprint-override"><code>
class NumpyEncoder(json.JSONEncoder):
 """ Custom encoder for numpy data types """
 def default(self, obj):
 if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,
 np.int16, np.int32, np.int64, np.uint8,
 np.uint16, np.uint32, np.uint64)):

 return int(obj)

 elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
 return float(obj)

 elif isinstance(obj, (np.complex_, np.complex64, np.complex128)):
 return {'real': obj.real, 'imag': obj.imag}

 elif isinstance(obj, (np.ndarray,)):
 return obj.tolist()

 elif isinstance(obj, (np.bool_)):
 return bool(obj)

 elif isinstance(obj, (np.void)): 
 return None

 return json.JSONEncoder.default(self, obj)
</code></pre>

blocks|key|4175732|text|一个非常简单的numpy编码器可以实现更一般的类似结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4175733|注意，它使用np.generic类(大多数np类都继承自该类)并使用a.item()方法。|offset|length|style|CODE|4175734|如果要编码的对象不是numpy实例，则json序列化程序将照常继续。这是包含一些numpy对象和其他类对象的字典的理想选择。|4175735|import+json
import+numpy+as+np

def+np_encoder(object):
++++if+isinstance(object,+np.generic):
++++++++return+object.item()

json.dumps(obj,+default=np_encoder)|code-block|syntax|javascript|4175736|entityMap^0|0|6|A|Y|8|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|R|8|@$D|S|E|T|F|G]|$D|U|E|V|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|W|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|X|8|@]|9|@]|A|$M|N]]|$1|O|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|P|$]]

A very simple numpy encoder can achieve similar results more generically.
Note this uses the <code>np.generic</code> class (which most np classes inherit from) and uses the <code>a.item()</code> method.
If the object to encode is not a numpy instance, then the json serializer will continue as normal. This is ideal for dictionaries with some numpy objects and some other class objects.
<pre><code>import json
import numpy as np

def np_encoder(object):
 if isinstance(object, np.generic):
 return object.item()

json.dumps(obj, default=np_encoder)
</code></pre>

blocks|key|4175173|text|如果您可以控制DataFrame的创建，则可以通过将dtype设置为object来强制其使用标准Python值类型(例如，int而不是numpy.int64|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4175174|df+=+pd.DataFrame(data=some_your_data,+dtype=object)|code-block|syntax|javascript|4175175|明显的缺点是，与原始数据类型相比，您获得的性能较差。但我喜欢这个解决方案，它真的很简单，并且消除了所有可能的类型问题。不需要给ORM或json任何提示。|4175176|entityMap^0|7|9|Q|5|Y|6|1P|3|1V|B|0|0|1V|4|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|13|8|@]|D|@]|E|$]]]|N|$]]

If you have control over the creation of <code>DataFrame</code>, you can force it to use standard Python types for values (e.g. <code>int</code> instead of <code>numpy.int64</code>) by setting <code>dtype</code> to <code>object</code>:
<pre><code>df = pd.DataFrame(data=some_your_data, dtype=object)
</code></pre>
The obvious downside is that you get less performance than with primitive datatypes. But I like this solution tbh, it's really simple and eliminates all possible type problems. No need to give any hints to the ORM or <code>json</code>.

blocks|key|4175202|text|update_data+=+{
++++'name':+str(store['entity_name'].iloc[i]),
++++'count__c':+str(store['count'].iloc[i])+
}|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4175203|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>update_data = {
 'name': str(store['entity_name'].iloc[i]),
 'count__c': str(store['count'].iloc[i]) 
}
</code></pre>

I have a data frame that stores store name and daily sales count. I am trying to insert this to Salesforce using the Python script below.
However, I get the following error:
<pre><code>TypeError: Object of type 'int64' is not JSON serializable
</code></pre>
Below, there is the view of the data frame.
<pre><code>Storename,Count
Store A,10
Store B,12
Store C,5
</code></pre>
I use the following code to insert it to Salesforce.
<pre><code>update_list = []
for i in range(len(store)):
 update_data = {
 'name': store['entity_name'].iloc[i],
 'count__c': store['count'].iloc[i] 
 }
 update_list.append(update_data)

sf_data_cursor = sf_datapull.salesforce_login()
sf_data_cursor.bulk.Account.update(update_list)
</code></pre>
I get the error when the last line above gets executed.
How do I fix this?

Python - TypeError: Object of type 'int64' is not JSON serializable

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个存储商店名称和每日销售计数的数据框架。我正在尝试使用下面的Python脚本将其插入到Salesforce中。但是，我得到以下错误：TypeError: Object of type 'int64' is not JSON serializable下面是数据框的视图。Storename,CountStore A,...

问Python - JSON：'int64‘类型的对象不可序列化
EN

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问Python - JSON：'int64‘类型的对象不可序列化EN