Converting JSON Nested List of Dictionaries to DataFrame

Introduction

In this article, we will explore how to convert a JSON nested list of dictionaries to a pandas DataFrame. This is a common task in data analysis and manipulation, especially when working with APIs that return JSON data.

JSON Data Format

Before diving into the conversion process, let’s quickly review the format of the JSON data. The provided example JSON output has the following structure:

{
    "PlanCoverages": [
        {
            "PlanId": 65860,
            "FormularyId": 61855,
            "PlanName": "CVS Caremark Performance Standard Control w/Advanced Specialty Control",
            # :
            "OverTheCounter": false
        },
        {
            "PlanId": 69549,
            "FormularyId": 63811,
            "PlanName": "CVS Caremark Performance Standard Opt-Out w/ Advanced Specialty Control ",
            # :
            "OverTheCounter": false
        }
    ]
}

In this example, the PlanCoverages list contains dictionaries that represent individual plans. Each plan has several key-value pairs, including PlanId, FormularyId, PlanName, and others.

Converting JSON to DataFrame

Now, let’s move on to converting this JSON data to a pandas DataFrame. We’ll explore two approaches: using the json_normalize function and manually creating the DataFrame from scratch.

Approach 1: Using json_normalize

The json_normalize function is a convenient way to convert JSON data to a flat table format. Here’s an example:

import pandas as pd
from pandas.io.json import json_normalize

# Load JSON string into a Python variable
json_string = """ {
    "PlanCoverages": [
        {
            "PlanId": 65860,
            "FormularyId": 61855,
            "PlanName": "CVS Caremark Performance Standard Control w/Advanced Specialty Control",
            # :
            "OverTheCounter": false
        },
        {
            "PlanId": 69549,
            "FormularyId": 63811,
            "PlanName": "CVS Caremark Performance Standard Opt-Out w/ Advanced Specialty Control ",
            # :
            "OverTheCounter": false
        }
    ]
} """

# Load JSON string into a Python variable
data = json.loads(json_string)

# Convert to DataFrame using json_normalize
plan_coverages = pd.DataFrame(json_normalize(data['PlanCoverages']))

print(plan_coverages)

This approach is concise and efficient, but it assumes that the JSON data has a simple structure.

Approach 2: Manually Creating the DataFrame

In some cases, you may need more control over the conversion process or want to handle edge cases differently. Here’s an example of manually creating the DataFrame:

import pandas as pd
import json

# Load JSON string into a Python variable
json_string = """ {
    "PlanCoverages": [
        {
            "PlanId": 65860,
            "FormularyId": 61855,
            "PlanName": "CVS Caremark Performance Standard Control w/Advanced Specialty Control",
            # :
            "OverTheCounter": false
        },
        {
            "PlanId": 69549,
            "FormularyId": 63811,
            "PlanName": "CVS Caremark Performance Standard Opt-Out w/ Advanced Specialty Control ",
            # :
            "OverTheCounter": false
        }
    ]
} """

# Load JSON string into a Python variable
data = json.loads(json_string)

# Extract plan details from the data
plan_details = []
for plan in data['PlanCoverages']:
    row = {
        'PlanId': plan['PlanId'],
        'FormularyId': plan['FormularyId'],
        'PlanName': plan['PlanName'],
        # Add other columns as needed
    }
    plan_details.append(row)

# Create the DataFrame from the extracted data
plan_coverages = pd.DataFrame(plan_details)

print(plan_coverages)

This approach provides more control over the conversion process, but it can be more verbose.

Choosing the Right Approach

When deciding between these two approaches, consider the following factors:

• Complexity of JSON structure: If your JSON data has a simple structure like in our example, json_normalize is likely a good choice. For more complex structures, manually creating the DataFrame may be necessary.
• Desired level of control: If you need to customize the conversion process or handle edge cases differently, manually creating the DataFrame provides more flexibility.

Handling Edge Cases

When working with JSON data, it’s essential to consider potential edge cases:

• Nested structures: Some JSON data may have nested structures that require additional handling. For example, if your JSON has a nested list of dictionaries, you’ll need to recursively extract the details.
• Missing values: If your JSON data contains missing values (e.g., null or empty strings), you’ll need to decide how to handle them in your DataFrame.

Conclusion

Converting JSON nested lists of dictionaries to DataFrames is a common task in data analysis and manipulation. By understanding the two approaches outlined above, you can choose the most suitable method for your specific use case and ensure efficient conversion of your JSON data.

Last modified on 2023-10-19