How to Convert Python JSON List to DataFrame Columns Without Looping

Learn the efficient way to convert JSON lists in a pandas DataFrame to separate columns, utilizing list comprehensions for optimal performance.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to convert python JSON list to dataframe columns without looping

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Convert Python JSON List to DataFrame Columns Without Looping

When working with data in Python, especially using pandas, it's not uncommon to encounter JSON objects nested within DataFrames. You may find yourself needing to extract this data and convert it into a more accessible format, specifically into separate columns, without the cumbersome process of looping through each element. In this post, we will explore a clean and efficient method for achieving this.

Understanding the Problem

Let's say we have a pandas DataFrame that contains several columns, including one that holds a list of JSON objects. For instance, consider the following Table:

nameagegroupJohn35[{"testid": "001", "marks": 67}, {"testid": "002", "marks": 70}]Ann20[{"testid": "001", "marks": 75}, {"testid": "002", "marks": 80}, {"testid": "003", "marks": 87}]Emma25[{"testid": "001", "marks": 90}, {"testid": "002", "marks": 99}]Our goal is to convert the group column's JSON data into separate columns, specifically extracting marks for testid = 001 and testid = 002, resulting in a DataFrame like this:

nameagetest_id1test_id2John356770Ann207580Emma259099The Solution: Using List Comprehensions

Step 1: Extracting the Marks

We can use a list comprehension to efficiently pull the marks for testid values 001 and 002 from the group column:

[[See Video to Reveal this Text or Code Snippet]]

In this code, we:

Iterate over each entry in the group column.

For each entry of JSON objects, we check if the testid matches 001 or 002.

We then collect the corresponding marks.

Step 2: Assigning Values to New Columns

Once we have the list of marks, the next step is to unpack these values and assign them to new columns in our DataFrame:

[[See Video to Reveal this Text or Code Snippet]]

This code does the following:

zip is used to unpack our outcome list into two separate variables, test_id1 and test_id2.

We then use the filter method to select the original name and age columns, and assign to create our new columns test_id1 and test_id2.

Final Result

After executing the above code, our DataFrame now appears as follows:

nameagetest_id1test_id2John356770Ann207580Emma259099Conclusion

Converting JSON lists stored in pandas DataFrame columns into separate DataFrame columns can be done effectively without explicit looping through each element. By utilizing list comprehensions in combination with functions like zip and assign, we streamline the process and maintain code efficiency.

This approach not only saves time but also enhances your code readability and performance. Give it a try in your data manipulation tasks!