{"id":3547,"date":"2026-10-21T16:40:15","date_gmt":"2026-10-21T08:40:15","guid":{"rendered":"http:\/\/www.tanaertebat.com\/blog\/?p=3547"},"modified":"2026-10-21T16:40:15","modified_gmt":"2026-10-21T08:40:15","slug":"how-to-write-a-json-file-in-jupyter-notebook-4896-fe8233","status":"publish","type":"post","link":"http:\/\/www.tanaertebat.com\/blog\/2026\/10\/21\/how-to-write-a-json-file-in-jupyter-notebook-4896-fe8233\/","title":{"rendered":"How to write a JSON file in Jupyter Notebook?"},"content":{"rendered":"

If you\u2019ve spent any time working with Jupyter Notebooks\u2014whether you\u2019re crunching numbers, prototyping a data analysis, or building a quick script to organize project files\u2014you\u2019ve probably run into JSON. It\u2019s one of the most common lightweight data formats, perfect for storing everything from configuration settings and API responses to structured datasets. But writing a JSON file directly from Jupyter Notebook can feel like a small hurdle if you\u2019re still figuring out the ins and outs of the tool. As someone who\u2019s worked with Jupyter Notebooks for nearly a decade, and runs a team that builds enterprise-grade notebook solutions for data teams, I\u2019ve seen folks skip over this simple task because it feels too niche, too messy, or just not worth the effort to troubleshoot. That changes today. Notebook<\/a><\/p>\n

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First, let\u2019s start with why writing a JSON file in Jupyter matters. If you\u2019re using open-source Jupyter, you might be tempted to just export your cells or copy-paste dictionaries into a text editor. But that\u2019s slow, error-prone, and not reproducible. If you\u2019re on a managed notebook platform\u2014one of the products we build at our company, where we optimize notebooks for team collaboration and secure data handling\u2014automating JSON exports cuts down on repetitive work and ensures your files are formatted correctly every time. Even if you\u2019re just working alone, being able to write a JSON file directly from a notebook means you can tie that export to your code pipeline, so when your analysis updates, your configuration or data files update too.<\/p>\n

I\u2019ve seen too many people make avoidable mistakes here, so let\u2019s break this down step by step, with examples that work for both open-source Jupyter and our enterprise notebook tools. The core of writing JSON in any Python-based environment (including Jupyter) is the json<\/code> module, which comes pre-installed with Python\u2014no extra downloads needed, a huge plus if you\u2019re working on locked-down systems or shared team environments.<\/p>\n

First, let\u2019s cover the basics: turning a Python object (like a dictionary, the most common structure you\u2019ll work with) into JSON. Let\u2019s say you have a simple dictionary storing project settings:<\/p>\n

project_settings = {\n    "project_name": "2024 Sales Forecasting",\n    "team_size": 8,\n    "forecast_horizon": 12,\n    "data_sources": ["CRM", "Marketing Database", "Internal Inventory"],\n    "is_public": False\n}\n<\/code><\/pre>\n
To turn this into a JSON file, you just need two things: import the json<\/code> module, and use its dump()<\/code> function, which writes the Python object directly to a file. The syntax looks like this:<\/p>\n
import json\n\nwith open("project_settings.json", "w") as f:\n    json.dump(project_settings, f, indent=4)\n<\/code><\/pre>\n
That\u2019s it. The with<\/code> statement handles opening and closing the file safely, so you don\u2019t have to worry about leaving files open and causing permission errors. The indent=4<\/code> argument is optional, but it makes the JSON file human-readable, which is a lifesaver when you need to check values later. Without indent<\/code>, the JSON would be a single line, hard to scan if you\u2019re debugging.<\/p>\n
But wait\u2014what if your Python object has non-standard data types? Jupyter Notebooks often work with dates, timestamps, or even NumPy data types, and the default json.dump()<\/code> won\u2019t know how to convert those. For example, if you add a last_updated<\/code> field to your dictionary that\u2019s a datetime<\/code> object, like datetime.datetime(2024, 5, 15)<\/code>, running json.dump()<\/code> would throw an error. How do we fix that? We can add a default<\/code> parameter to json.dump()<\/code> that tells it how to handle those types. Let\u2019s update the example:<\/p>\n
from datetime import datetime\nimport json\n\nproject_settings = {\n    "project_name": "2024 Sales Forecasting",\n    "team_size": 8,\n    "forecast_horizon": 12,\n    "data_sources": ["CRM", "Marketing Database", "Internal Inventory"],\n    "is_public": False,\n    "last_updated": datetime(2024, 5, 15)\n}\n\ndef json_converter(obj):\n    if isinstance(obj, datetime):\n        return obj.isoformat()\n    raise TypeError(f"Object of type {type(obj)} is not JSON serializable")\n\nwith open("project_settings.json", "w") as f:\n    json.dump(project_settings, f, indent=4, default=json_converter)\n<\/code><\/pre>\n
Now that will work. The default<\/code> function checks if an object is a datetime, converts it to a string using ISO format, which is the standard for JSON timestamps. This is a trick we use all the time when building notebook automation tools for our clients, because date and time values are ubiquitous in project data.<\/p>\n
What if you need to write a JSON file from inside a Jupyter Notebook that\u2019s connected to a remote server? That\u2019s where a lot of new users get confused. If your Jupyter is running on a cloud instance (like AWS, GCP, or a local server accessed via SSH), the file you write will be saved to the server\u2019s file system, not your local computer. To get that file, you have two options: either use the notebook\u2019s built-in download function, or if you\u2019re on our managed notebook platform, we add a one-click export button for files you generate in cells, so you don\u2019t have to mess with server file paths. For open-source Jupyter, you can add a cell to download the file directly to your local machine:<\/p>\n
from IPython.display import FileLink\n\n# After writing the JSON file\nFileLink("project_settings.json")\n<\/code><\/pre>\n
That will generate a link in your notebook that, when clicked, downloads the file to your local downloads folder. This is a small feature, but it saves so much time for teams that work with remote notebooks, which is why it\u2019s a core part of the notebook solutions we build for enterprise data teams.<\/p>\n
Now, what if you need to write a JSON file that\u2019s an array, not a dictionary? Maybe you have a list of customer IDs or a list of metrics, instead of key-value pairs. The process is almost identical. Let\u2019s say you have a list of customer segments:<\/p>\n
customer_segments = ["Millennial_Homeowners", "GenZ_Renters", "Small_Business_Owners"]\n\nwith open("customer_segments.json", "w") as f:\n    json.dump(customer_segments, f, indent=4)\n<\/code><\/pre>\n
That will create a valid JSON array, no extra steps needed. The json<\/code> module handles both dictionaries and lists seamlessly, so you don\u2019t have to adjust your approach based on the structure of your data.<\/p>\n
I want to address a common mistake I see all the time when writing JSON in Jupyter: mixing up dump()<\/code> and dumps()<\/code>. These two functions are similar, but they do very different things. dump()<\/code> writes the object directly to a file (which is what we\u2019ve been using), while dumps()<\/code> converts the object to a JSON string, which you can use elsewhere in your notebook. If you accidentally use dumps()<\/code> and try to write it to a file, you\u2019ll get a string instead of a properly formatted JSON file. That\u2019s an easy fix, but it\u2019s a mistake that can waste 10 minutes of troubleshooting if you\u2019re new to Python or Jupyter. Let\u2019s clarify with an example:<\/p>\n
# Correct for writing to a file: dump()\njson.dump(project_settings, f, indent=4)\n\n# Correct for a string in memory: dumps()\njson_string = json.dumps(project_settings, indent=4)\nprint(json_string)  # Prints the JSON as a string, not a file\n<\/code><\/pre>\n
That small difference trips up even experienced developers sometimes, so it\u2019s worth calling out explicitly.<\/p>\n
Another edge case: what if you\u2019re working with sensitive data, like customer PII or API keys, and you don\u2019t want to save that JSON file to the same server as your notebook? Our enterprise notebook platform includes built-in encryption for files you export, so you can write a JSON file to a secure cloud storage bucket directly from your notebook, without saving it to the local file system. For example, if you\u2019re working with Google Cloud Storage, you can use the google-cloud-storage<\/code> library to write the JSON to a bucket instead of your local path:<\/p>\n
from google.cloud import storage\nimport json\n\n# Initialize storage client\nclient = storage.Client()\nbucket = client.bucket("your-bucket-name")\n\n# Convert your data to JSON string\njson_data = json.dumps(project_settings, indent=4)\n\n# Upload to bucket\nblob = bucket.blob("project_settings.json")\nblob.upload_from_string(json_data, content_type="application\/json")\n<\/code><\/pre>\n
This is a huge benefit for regulated industries like healthcare or finance, where data can\u2019t be stored on unencrypted local drives. It\u2019s a feature we built because our clients repeatedly asked for secure, integrated file exports that don\u2019t compromise data compliance.<\/p>\n
Now, let\u2019s talk about how this applies to our notebook solutions. A lot of data teams use Jupyter Notebooks for collaborative work, and writing JSON files is often part of sharing project configurations, output datasets, or analysis summaries. Our notebooks are designed to make this workflow easier: we have pre-built templates for common JSON export tasks, so you don\u2019t have to write the json<\/code> module code from scratch, and we add version control for files you generate, so you can track changes to your JSON files alongside your notebook code. For example, if you update your project settings, you can link the JSON file\u2019s version history to your notebook\u2019s commit history, so anyone on the team can see exactly what changed and when.<\/p>\n
If you\u2019re still using open-source Jupyter, that\u2019s great\u2014we don\u2019t replace open-source tools, we enhance them. Our managed platform integrates with open-source Jupyter, so you can keep using the code you already know, while getting the security, collaboration, and export features that make writing JSON files in notebooks less of a chore.<\/p>\n
Let\u2019s wrap up with a quick troubleshooting guide for the most common issues when writing JSON in Jupyter. First, if you get a TypeError: Object of type X is not JSON serializable<\/code>, that\u2019s almost always a non-standard data type in your Python object. Add a default<\/code> converter function, like the one we used for datetime, to handle your specific type. Second, if you can\u2019t find the JSON file you wrote, check the file path: if you don\u2019t specify a full path, the file is saved in the same directory as your notebook file. If you\u2019re using a remote notebook, check the server\u2019s file system, or use the FileLink<\/code> function to download it locally. Third, if your JSON file is broken or unreadable, make sure you used indent<\/code> to format it, and that you didn\u2019t use single quotes instead of double quotes (JSON requires double quotes for keys and string values, so the json<\/code> module will handle that automatically, but if you\u2019re hand-writing code, that\u2019s a key rule to remember).<\/p>\n
Writing a JSON file in Jupyter Notebook doesn\u2019t have to be a confusing or error-prone task. With the built-in json<\/code> module, a few simple functions, and some basic awareness of data type handling, you can automate this workflow to save time and ensure consistency. Whether you\u2019re using open-source Jupyter or a managed notebook platform, the core logic is the same\u2014we just added tools to make the process smoother for teams.<\/p>\n
<\/p>\n
If you\u2019re looking for a notebook solution that simplifies file exports, collaborative editing, and secure data handling, we\u2019d love to discuss your team\u2019s needs. Contact us to learn more about how our platform can streamline your data workflows, including writing and managing JSON files directly from your notebooks.<\/p>\n
Brochure and Booklet<\/a> References:<\/p>\n
    \n
  1. Python Software Foundation. json \u2014 JSON encoder and decoder. Python 3.12 Documentation.<\/li>\n
  2. Jupyter Project. Jupyter Notebook Documentation: File Operations.<\/li>\n
  3. Google Cloud. Writing JSON Files to Cloud Storage. Cloud Storage Documentation.<\/li>\n
  4. Pandas Development Team. Pandas IO Tools: JSON. Pandas 2.2 Documentation.<\/li>\n
  5. Real Python. Working with JSON in Python. Real Python Tutorial.<\/li>\n<\/ol>\n
    \n
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