Python Dictionary Append With Examples

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While working on a project that involved managing customer data for a retail analytics dashboard, I needed to append multiple dictionaries together in Python.

The task seemed simple at first, but as I explored different ways of doing it, I realized there are multiple methods, each with its own advantages depending on the use case.

In this tutorial, I’ll share the exact methods I’ve used in my 10+ years of Python development experience to append dictionaries in Python efficiently and safely.

Table of Contents

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• What Does It Mean to Append a Dictionary in Python?
• Method 1: Append Dictionary Using the update() Method
• Method 2: Append Dictionary Using the | (Pipe) Operator (Python 3.9+)
• Method 3: Append Dictionary Using Dictionary Unpacking (** Operator)
• Method 4: Append Dictionary Items Using a Loop
• Method 5: Append a List of Dictionaries in Python
• Method 6: Append Nested Dictionaries in Python
• Pro Tip: Avoid Overwrite Keys When Appending Dictionaries
• Common Use Cases for Appending Dictionaries in Python

What Does It Mean to Append a Dictionary in Python?

Appending a dictionary in Python simply means adding new key-value pairs to an existing dictionary or merging two or more dictionaries.

Unlike lists, dictionaries don’t have an append() method. However, Python provides multiple ways to achieve the same result.

Method 1: Append Dictionary Using the update() Method

The update() method is one of the most common and straightforward ways to append one dictionary to another in Python. It directly modifies the original dictionary by adding key-value pairs from another dictionary.

Here’s an example:

customer_data = { "name": "John Doe", "city": "New York" } additional_info = { "email": "[email protected]", "membership": "Gold" } # Append additional_info to customer_data customer_data.update(additional_info) print("Updated Dictionary:") print(customer_data)

When I run this code, Python merges the two dictionaries and prints:

Updated Dictionary: {'name': 'John Doe', 'city': 'New York', 'email': '[email protected]', 'membership': 'Gold'}

I executed the above example code and added the screenshot below.

The update() method is ideal when you want to modify the original dictionary in place. However, if you need to keep the original dictionary unchanged, there’s a better way.

Method 2: Append Dictionary Using the | (Pipe) Operator (Python 3.9+)

Starting from Python 3.9, we can use the pipe operator (|) to merge or append dictionaries in a clean and modern way. This method returns a new dictionary without modifying the original ones.

Let’s see how it works:

customer_data = { "name": "Emma Johnson", "city": "Los Angeles" } additional_info = { "email": "[email protected]", "membership": "Platinum" } # Create a new merged dictionary merged_data = customer_data | additional_info print("Merged Dictionary:") print(merged_data)

Output:

Merged Dictionary: {'name': 'Emma Johnson', 'city': 'Los Angeles', 'email': '[email protected]', 'membership': 'Platinum'}

I executed the above example code and added the screenshot below.

I love this approach because it’s clean, readable, and doesn’t alter the original data, perfect for production-level Python code.

Method 3: Append Dictionary Using Dictionary Unpacking (** Operator)

Before Python 3.9 introduced the pipe operator, developers often used dictionary unpacking with ** to merge or append dictionaries. This method works in Python 3.5 and above and returns a new dictionary.

Here’s how you can use it:

customer_data = { "name": "Michael Brown", "city": "Chicago" } additional_info = { "email": "[email protected]", "membership": "Silver" } # Merge using dictionary unpacking merged_data = {**customer_data, **additional_info} print("Merged Dictionary:") print(merged_data)

Output:

Merged Dictionary: {'name': 'Michael Brown', 'city': 'Chicago', 'email': '[email protected]', 'membership': 'Silver'}

I executed the above example code and added the screenshot below.

This method is widely used because it’s backward-compatible and works even if you’re running Python 3.6 or 3.7 on legacy systems.

Method 4: Append Dictionary Items Using a Loop

Sometimes, you might want to append dictionary items conditionally, such as adding only specific keys or checking for duplicates before merging.

In such cases, using a for loop gives you full control.

Let’s look at an example:

customer_data = { "name": "Sarah Miller", "city": "Houston" } additional_info = { "email": "[email protected]", "membership": "Gold", "city": "Dallas" # Duplicate key to demonstrate control } # Append only non-existing keys for key, value in additional_info.items(): if key not in customer_data: customer_data[key] = value print("Dictionary after conditional append:") print(customer_data)

Output:

Dictionary after conditional append: {'name': 'Sarah Miller', 'city': 'Houston', 'email': '[email protected]', 'membership': 'Gold'}

I executed the above example code and added the screenshot below.

This approach is useful when you want to preserve existing data and avoid overwriting keys that already exist.

Method 5: Append a List of Dictionaries in Python

In real-world Python projects, it’s common to have a list of dictionaries, for example, customer records, sales transactions, or API responses. You can easily append new dictionaries to such a list using the append() method.

Here’s a quick example:

customers = [ {"name": "Alice", "city": "Boston"}, {"name": "Bob", "city": "Seattle"} ] new_customer = {"name": "Charlie", "city": "San Francisco"} # Append new dictionary customers.append(new_customer) print("Updated List of Dictionaries:") for customer in customers: print(customer)

Output:

Updated List of Dictionaries: {'name': 'Alice', 'city': 'Boston'} {'name': 'Bob', 'city': 'Seattle'} {'name': 'Charlie', 'city': 'San Francisco'}

This is a great way to manage structured data like customer lists, product catalogs, or employee records in Python.

Method 6: Append Nested Dictionaries in Python

Sometimes, dictionaries contain other dictionaries, also known as nested dictionaries. Appending nested dictionaries requires a slightly different approach, especially if you want to merge them deeply.

Here’s how I handle it:

store_data = { "store_1": {"city": "New York", "revenue": 150000}, "store_2": {"city": "Miami", "revenue": 120000} } new_store = { "store_3": {"city": "San Diego", "revenue": 135000} } # Append new nested dictionary store_data.update(new_store) print("Updated Store Data:") print(store_data)

Output:

Updated Store Data: { 'store_1': {'city': 'New York', 'revenue': 150000}, 'store_2': {'city': 'Miami', 'revenue': 120000}, 'store_3': {'city': 'San Diego', 'revenue': 135000} }

This method is great for data aggregation tasks, such as combining multiple store reports or merging API responses in Python.

Pro Tip: Avoid Overwrite Keys When Appending Dictionaries

When appending dictionaries in Python, always be aware that if both dictionaries contain the same key, the value from the second dictionary will overwrite the first.

If you need to preserve both values, consider storing them in a list or using the loop method shown earlier.

Example:

data1 = {"city": "Austin", "sales": 200} data2 = {"city": "Austin", "sales": 250} merged = {} for key in data1.keys() | data2.keys(): merged[key] = [data1.get(key), data2.get(key)] print("Merged with preserved values:") print(merged)

Output:

Merged with preserved values: {'city': ['Austin', 'Austin'], 'sales': [200, 250]}

This approach ensures that no data is lost when appending dictionaries in Python.

Common Use Cases for Appending Dictionaries in Python

Here are a few real-world examples where I often use dictionary appending in Python projects:

• Combining API responses from multiple endpoints
• Aggregating sales or customer data from different sources
• Merging configuration files in automation scripts
• Appending JSON data before writing to a file

Each of these scenarios benefits from one of the methods we discussed above.

Appending dictionaries in Python is a simple yet powerful operation that can save you a lot of time when managing structured data.

Whether you use update(), the | operator, dictionary unpacking, or loops, each method has its own place depending on your project’s needs.

Personally, I prefer using the | operator for modern Python versions because it’s concise and doesn’t modify the original dictionary. But in older systems, update() remains a reliable choice.

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Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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