Python - TypeError Object of type int64 is not JSON serializable

Encountering the TypeError: Object of type ‘int64’ is not JSON serializable in Python can be a frustrating experience, especially when you’re working with data from libraries like NumPy or Pandas. This error arises because the standard json module in Python doesn’t inherently know how to convert NumPy’s int64 or Pandas’ int64 data types directly into JSON format. These data types, while efficient for numerical computations within Python, require explicit conversion to a standard Python integer (int) for successful JSON serialization. Understanding the root cause and implementing the correct solutions is crucial for smooth data handling and API development. We will explore the common causes of this error and provide practical solutions to resolve it, ensuring your Python applications can seamlessly handle numerical data when working with JSON.

Understanding the “TypeError: Object of type ‘int64’ is not JSON serializable”

The core issue stems from the way Python’s built-in json module handles data types. JSON, being a universal data format for web applications and APIs, defines a limited set of basic data types: strings, numbers (integers and floating-point), booleans, arrays, and objects. When you attempt to serialize a Python object containing NumPy’s int64 (or similar types like Pandas’ int64) directly using json.dumps(), the serialization process fails because the json module doesn’t recognize int64 as a valid JSON number. This discrepancy is a common pitfall when integrating data science workflows, which heavily rely on NumPy and Pandas, with web services that communicate via JSON. Proper data type conversion is essential to bridge this gap and ensure compatibility.

Essentially, the int64 type is a 64-bit integer representation provided by libraries like NumPy to handle large numerical datasets efficiently. While Python’s built-in int type can often handle these values, the json library needs explicit instruction on how to convert int64 objects. Without this instruction, the serialization process halts, resulting in the dreaded TypeError. Recognizing this fundamental incompatibility is the first step towards resolving the issue effectively. Failing to address this can lead to unpredictable errors in data pipelines and API responses, causing significant disruption.

To illustrate, consider a scenario where you’re building an API endpoint that returns data processed using Pandas. The Pandas DataFrame might contain columns with int64 data types. If you naively attempt to serialize this DataFrame directly into JSON, you’ll encounter this error. The solution involves explicitly converting these int64 values to standard Python int objects before serialization. This ensures that the json module can correctly encode the data into a valid JSON format, enabling seamless communication between your Python application and other systems.

Common Scenarios and Causes

This TypeError typically arises in a few key scenarios. One frequent case is when you’re working with data retrieved from a database using libraries like SQLAlchemy, where numerical columns might be represented as int64 or other NumPy-specific types. Another common scenario involves data analysis workflows using Pandas, where DataFrame columns often default to int64 when containing integer data. When you try to serialize the resulting DataFrame or its components directly into JSON for API responses or data storage, the error surfaces. It’s critical to identify these potential sources of int64 data to proactively address the serialization issue.

Another subtle cause can be the use of third-party libraries that internally rely on NumPy or Pandas for data manipulation. Even if your code doesn’t explicitly use these libraries, a dependency might introduce int64 data types into your data structures. Therefore, it’s important to be aware of the underlying data types when dealing with numerical data, especially when serialization is involved. Debugging can become challenging if the source of the int64 values is hidden within a dependency’s implementation.

Furthermore, data cleaning and transformation processes can inadvertently introduce int64 types. For example, filling missing values in a Pandas DataFrame with integer values might automatically convert the column to an int64 data type. This unexpected type conversion can then lead to serialization errors down the line. Therefore, it’s essential to carefully inspect the data types of your variables throughout the data processing pipeline to identify and address potential int64 issues before serialization.

Solutions and Code Examples

Several approaches can be used to resolve the “TypeError: Object of type ‘int64’ is not JSON serializable” error. The most straightforward solution is to explicitly convert the int64 values to standard Python int objects before serialization. This can be achieved using Python’s built-in int() function or by leveraging Pandas’ astype() method. Here’s a breakdown of common techniques:

Explicit Type Conversion: Convert int64 values to int using int(value) or numpy.asscalar(value).
Pandas astype() Method: Use df[‘column’].astype(int) to convert an entire Pandas Series to int.
Custom JSON Encoder: Create a custom JSON encoder that handles int64 types automatically.

Here’s an example using Pandas:

python import pandas as pd import json data = {‘col1’: [1, 2, 3], ‘col2’: [4, 5, 6]} df = pd.DataFrame(data) Convert all int64 columns to int for col in df.columns: if df[col].dtype == ‘int64’: df[col] = df[col].astype(int) json_data = df.to_json(orient=‘records’) print(json_data) Another approach involves creating a custom JSON encoder. This is particularly useful when you need to handle int64 values consistently across your application. A custom encoder extends the default json.JSONEncoder class and overrides the default() method to handle int64 types. By using this encoder, you can seamlessly serialize complex objects containing int64 values without manual conversion.

Here’s an example of a custom JSON encoder:

python import json import numpy as np class CustomJSONEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.int64): return int(obj) return super().default(obj) data = {‘value’: np.int64(12345)} json_data = json.dumps(data, cls=CustomJSONEncoder) print(json_data) ### Choosing the Right Approach

The best approach depends on your specific use case and the structure of your data. For simple cases where you only need to convert a few int64 values, explicit type conversion using int() might be sufficient. However, if you’re working with Pandas DataFrames or need to handle int64 values consistently throughout your application, using astype() or a custom JSON encoder is generally a better option. A custom JSON encoder offers the most flexibility and ensures that int64 values are always handled correctly during serialization.

Identify the columns or variables containing int64 data types.
Choose the appropriate conversion method (explicit conversion, astype(), or custom JSON encoder).
Implement the chosen method to convert int64 values to standard Python int objects.
Verify that the serialization process now works correctly without raising a TypeError.

Best Practices and Prevention

Preventing the “TypeError: Object of type ‘int64’ is not JSON serializable” error requires a proactive approach. One key best practice is to be mindful of data types throughout your data processing pipeline. Explicitly specify the desired data types when reading data from external sources, such as databases or CSV files. For example, when reading a CSV file into a Pandas DataFrame, you can use the dtype parameter in pd.read_csv() to specify the data types of each column. This proactive approach can prevent unexpected int64 conversions and reduce the likelihood of serialization errors.

Another important practice is to use data validation techniques to identify and correct data type issues early on. Libraries like Cerberus or Voluptuous can be used to define schemas for your data and validate that the data conforms to these schemas. By validating data types before serialization, you can catch potential int64 errors and take corrective action before they cause problems. According to a study by IBM, data validation can reduce data quality issues by up to 80% [^1^].

Furthermore, consider using libraries that provide built-in JSON serialization capabilities that handle int64 types automatically. For example, the orjson library is a fast and correct JSON library that natively supports NumPy data types, including int64 [^2^]. By using such libraries, you can avoid the need for manual type conversions and simplify your code. This approach aligns with the principle of using the right tool for the job and can significantly improve the efficiency and reliability of your data serialization process.

Here are some additional tips:

Always check the data types of your variables before serialization.
Use data validation libraries to enforce data type constraints.
Consider using libraries with built-in int64 support for JSON serialization.

FAQ

Why does the json module not support int64 directly?: The json module is designed to be a lightweight and portable library that adheres to the JSON standard. The JSON standard only defines a limited set of basic data types, and int64 is not one of them. To maintain compatibility across different platforms and programming languages, the json module sticks to these basic types.
Is converting int64 to int always safe?: In most cases, converting int64 to int is safe, as Python's int type can handle arbitrarily large integers. However, if you're working with extremely large int64 values that exceed the maximum representable value for a Python int, you might encounter data loss or unexpected behavior. Be mindful of the range of your data and consider using alternative representations, such as strings, if necessary.
Are there performance implications to converting int64 to int?: Converting int64 to int does introduce a small performance overhead, as it involves creating a new Python int object for each int64 value. However, this overhead is usually negligible unless you're performing this conversion on a very large dataset. In most cases, the benefits of ensuring correct JSON serialization outweigh the minor performance cost.

Featured snippet paragraph: A common issue when working with Python and JSON is the "TypeError: Object of type 'int64' is not JSON serializable". This occurs because Python's built-in json module cannot directly serialize NumPy's int64 data type. To resolve this, explicitly convert int64 values to standard Python int objects before serialization using methods like int(value) or df\['column'\].astype(int). Alternatively, create a custom JSON encoder to handle int64 types automatically, ensuring seamless data serialization.

By understanding the nature of the TypeError: Object of type ‘int64’ is not JSON serializable and implementing the appropriate solutions, you can ensure that your Python applications handle numerical data effectively when working with JSON. Remember to proactively manage data types, validate your data, and consider using libraries that provide built-in int64 support. These strategies will help you avoid serialization errors and build robust and reliable data pipelines.

Don’t let this error slow you down. Implement these solutions in your own projects, explore the linked resources for deeper understanding, and share your experiences with other developers. By tackling these challenges head-on, you contribute to a more robust and efficient Python ecosystem. Start converting those int64 values today and keep your JSON flowing smoothly!

[^1^]: IBM. (n.d.). The Four Cornerstones of Data Quality. Retrieved from [https://www.ibm.com/downloads/cas/W0M1762Y](https://www.ibm.com/downloads/cas/W0M1762Y)

[^2^]: Igushkin, S. (n.d.). orjson. Retrieved from [https://github.com/ijl/orjson](https://github.com/ijl/orjson)

[^3^]: Python Documentation. (n.d.). json — JSON encoder and decoder. Retrieved from [https://docs.python.org/3/library/json.html](https://docs.python.org/3/library/json.html)

Question & Answer :
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:

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

Below, there is the view of the data frame.

Storename,Count Store A,10 Store B,12 Store C,5

I use the following code to insert it to Salesforce.

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)

I get the error when the last line above gets executed.

How do I fix this?

You can define your own encoder to solve this problem.

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)