Discover how to merge PostgreSQL rows effortlessly with powerful code snippets.

Table of content

  1. Introduction
  2. What is PostgreSQL?
  3. Why merge rows in PostgreSQL?
  4. Code Snippet 1: Using the CONCAT_WS() function
  5. Code Snippet 2: Using the COALESCE() function
  6. Code Snippet 3: Using the jsonb_agg() function
  7. Code Snippet 4: Using the array_agg() function
  8. Conclusion


When working with databases, it is common to encounter duplicate data that needs to be merged in order to maintain data integrity. In PostgreSQL, there are several ways to achieve this, but it can be a daunting task for developers who are not familiar with the various techniques and best practices. Python provides an efficient and easy-to-use solution to merge PostgreSQL rows by leveraging its powerful libraries and built-in functions. In this article, we will explore some powerful code snippets that will help you merge PostgreSQL rows effortlessly while maintaining data integrity. We will cover a variety of merging techniques, including MERGE statements and using pandas DataFrame, which will provide you with a comprehensive understanding of how to merge data in PostgreSQL using Python. Whether you are a beginner or an experienced developer, this article will provide you with the knowledge and skills to merge data in PostgreSQL with ease.

What is PostgreSQL?

PostgreSQL is an open-source relational database management system (RDBMS) that is widely used in web application development. It was developed by a group of developers at the University of California, Berkeley, in the 1980s, and has since become one of the most popular RDBMS systems in the world.

PostgreSQL is known for its stability, reliability, and scalability, which make it a popular choice for enterprise-level applications. It supports a wide range of data types including integers, floating-points, strings, arrays, and JSON, and provides advanced features such as transaction management, concurrency control, and full-text search.

In PostgreSQL, users store data in tables, which are organized into schemas. A schema is a logical container that can hold multiple tables, views, indexes, and other schema objects. Users can interact with this data using SQL (Structured Query Language), which is the standard language for managing relational databases.

Overall, PostgreSQL is a highly flexible and powerful database management system that supports a wide range of use cases ranging from small-scale web applications to large-scale enterprise solutions. Its advanced features make it an attractive choice for developers looking for a reliable and scalable platform for their applications.

Why merge rows in PostgreSQL?

In PostgreSQL, it is sometimes necessary to merge rows in a table. This can be useful for a number of reasons, such as combining related data or removing duplicate records. The ability to merge rows allows you to streamline your database and make it more efficient, which can in turn improve performance and reduce the amount of maintenance required.

One common use case for merging rows is when you have multiple records for a single entity, such as a customer or product. By merging these records, you can consolidate all of the relevant data into a single row, making it easier to analyze and manage. This can be especially valuable in larger databases with many records, where finding and updating individual rows can be time-consuming and error-prone.

Another reason to merge rows in PostgreSQL is to remove duplicate records. This can happen when data is imported from multiple sources or when records are created manually by different users. By identifying and merging duplicate rows, you can eliminate redundant data and ensure that your database remains consistent and accurate.

Overall, merging rows in PostgreSQL is a powerful tool that can help you manage and optimize your database. Whether you are consolidating related data or removing duplicate records, the ability to merge rows can save you time and improve the overall efficiency of your database.

Code Snippet 1: Using the CONCAT_WS() function

The CONCAT_WS() function is a powerful SQL function that allows you to merge multiple PostgreSQL rows with ease. This is especially useful when you have multiple rows of data that you need to combine into one row for analysis or reporting purposes.

To use the CONCAT_WS() function, you simply need to specify the delimiter that you want to use to separate the values in the merged row. For example, if you want to merge three rows that contain the values "John", "Doe", and "25", you could use the following code snippet:

SELECT CONCAT_WS(' ', 'John', 'Doe', '25');

This code would output a single row with the value "John Doe 25", where the values are separated by a space delimiter.

One of the great things about the CONCAT_WS() function is that it automatically handles NULL values. If any of the values in the rows you are merging are NULL, they will be skipped in the final result.

In addition to using space as a delimiter, you can also use other characters such as commas, periods, or dashes. This makes the CONCAT_WS() function extremely versatile and useful for a wide range of tasks.

Overall, the CONCAT_WS() function is a must-have tool for anyone working with PostgreSQL databases. With its ability to easily merge rows of data, it can save you time and effort while also providing you with valuable insights into your data.

Code Snippet 2: Using the COALESCE() function

The COALESCE() function in PostgreSQL is a handy tool to merge rows that contain null values. This function returns the first non-null value in a list of arguments. Code Snippet 2 shows how to use this function in PostgreSQL.

SELECT id, COALESCE(name, 'N/A'), COALESCE(email, 'N/A'), COALESCE(phone, 'N/A')
FROM customers;

In this code snippet, the COALESCE() function is used to merge rows that have null values in the name, email, or phone columns. The function checks each column for a null value and returns the string 'N/A' if it finds one. The result is a table that contains all the columns from the original table but with all null values replaced with 'N/A'.

The COALESCE() function can also be used to merge rows based on specific criteria. For example, consider the following code:

SELECT id, COALESCE(city, state), COALESCE(state, country), country
FROM users;

In this case, the COALESCE() function is used to merge the city and state columns if the city column is null. If both the city and state columns are null, the function returns the country column. The result is a table where each row contains the city or state, a state or country, and the country.

Overall, the COALESCE() function is a powerful tool for merging rows in PostgreSQL. It can be used to replace null values with a default value or to merge rows based on specific criteria.

Code Snippet 3: Using the jsonb_agg() function

The jsonb_agg() function is used to aggregate values as a JSON array. It is a powerful tool that allows you to easily merge rows into a single JSON array. It works by taking the values from the specified column and aggregating them into a single JSON array.

Let's take a look at an example of how to use the jsonb_agg() function in Python to merge rows:

import psycopg2
import json

conn = psycopg2.connect(database="mydatabase", user="myuser", password="mypassword", host="localhost", port="5432")
cur = conn.cursor()

cur.execute("SELECT order_id, jsonb_agg(jsonb_build_object('product', product, 'quantity', quantity)) FROM orders GROUP BY order_id")

rows = cur.fetchall()
for row in rows:
    print(row[0], json.loads(row[1]))


In this example, we are selecting data from the "orders" table and using the jsonb_agg() function to merge rows by the "order_id" column. We are also using the jsonb_build_object() function to create a JSON object containing the "product" and "quantity" columns.

The output of this code will be a list of order IDs and their corresponding products and quantities as a JSON array. This can be extremely useful when working with large datasets that need to be aggregated into a single JSON object.

In summary, the jsonb_agg() function is a powerful tool for merging rows in PostgreSQL. When combined with Python code, it can be used to easily create JSON arrays from database tables. By using this function, you can greatly simplify the process of merging and aggregating data in your PostgreSQL database.

Code Snippet 4: Using the array_agg() function

The array_agg() function is a PostgreSQL-specific function that can be used to aggregate values into an array. This function can be a powerful tool when you want to merge rows and combine their values into an array. Here's an example of how to use it:

SELECT category, array_agg(name)
FROM products
GROUP BY category;

This code snippet selects the category and name columns from the products table and then groups the rows by category. The array_agg() function is used to aggregate the name values for each category into an array. The result is a table with two columns: category and an array of names for each category.

You can customize this code for your own purposes by substituting your own table and column names. Note that the array_agg() function requires PostgreSQL, so it may not work in other SQL environments. However, if you're working with PostgreSQL, this function can be a useful tool for merging rows and consolidating data into an array.


Merging PostgreSQL rows can be a powerful tool when dealing with large volumes of data. With the help of Python code snippets, you can easily merge rows based on specific criteria, such as column values or unique identifiers. By using the UPDATE statement in conjunction with GROUP BY, you can combine multiple rows into a single row with aggregated data.

It's important to note that when merging rows, you should always have a clear understanding of the data you are working with and the criteria for merging. You should also test your code thoroughly to ensure that the resulting data is accurate and free of errors.

By following the code snippets and examples provided in this article, you can gain a deeper understanding of how to merge PostgreSQL rows using Python programming. With this knowledge, you can optimize your data processing and analysis, saving time and increasing efficiency.

Throughout my career, I have held positions ranging from Associate Software Engineer to Principal Engineer and have excelled in high-pressure environments. My passion and enthusiasm for my work drive me to get things done efficiently and effectively. I have a balanced mindset towards software development and testing, with a focus on design and underlying technologies. My experience in software development spans all aspects, including requirements gathering, design, coding, testing, and infrastructure. I specialize in developing distributed systems, web services, high-volume web applications, and ensuring scalability and availability using Amazon Web Services (EC2, ELBs, autoscaling, SimpleDB, SNS, SQS). Currently, I am focused on honing my skills in algorithms, data structures, and fast prototyping to develop and implement proof of concepts. Additionally, I possess good knowledge of analytics and have experience in implementing SiteCatalyst. As an open-source contributor, I am dedicated to contributing to the community and staying up-to-date with the latest technologies and industry trends.
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