Transforming Excel to Nested JSON Data: A Deep Dive

As data becomes increasingly complex and interconnected, the need for efficient and effective data processing has never been more pressing. In this article, we’ll explore how to transform Excel data into a nested JSON structure using Python’s Pandas library.

Understanding the Challenge

Let’s take a closer look at the JSON structure in question:

{
  "name": "person name",
  "food": {
    "fruit": "apple",
    "meal": {
      "lunch": "burger",
      "dinner": "pizza"
    }
  }
}

We’re given a nested JSON object with multiple levels of hierarchy. The challenge is to transform this data into an Excel (or CSV) file and then convert that data back into the original nested JSON structure.

Writing Data to CSV or Excel

The first question we need to answer is: how do I write this data into an CSV or Excel file? There are several libraries available in Python, such as pandas and openpyxl, that make it easy to work with spreadsheets. We’ll focus on using pandas for this example.

To create a DataFrame from our JSON data, we can use the json_normalize() function:

import pandas as pd

jsn = {
  "name": "person name",
  "food": {
    "fruit": "apple",
    "meal": {
      "lunch": "burger",
      "dinner": "pizza"
    }
  }
}

# create the flat structure
df = pd.json_normalize(jsn, errors='ignore')

In this example, we pass in our JSON data as a dictionary to json_normalize(). The resulting DataFrame will have a flat structure with no nested columns.

Writing to CSV or Excel

Once we have our flat DataFrame, we can write it to an Excel file using the to_excel() method:

df.to_excel('file_name.xlsx', index=False)

Alternatively, we can use the to_csv() method to write the data to a CSV file:

df.to_csv('file_name.csv', index=False)

Converting Flat Data Back into Nested JSON

Now that we have our data written to an Excel or CSV file, we need to convert it back into the original nested JSON structure. This is where json_normalize() comes in handy again.

The json_normalize() function can take multiple arguments to specify how to handle nested columns:

df = pd.json_normalize(df, 'food')

In this example, we pass in our flat DataFrame as an argument to json_normalize(). We also specify the key 'food' to indicate that we want to normalize the data based on this column.

The resulting DataFrame will have a nested structure with columns from each level of hierarchy:

| name  | food.fruit | food.meal.lunch | food.meal.dinner |
| --- | --- | --- | --- |
| person name | apple | burger | pizza |

We can now use the to_json() method to write this data back into a JSON file:

df.to_json('file_name.json', orient='index')

Note that we’re using the 'orient' argument to specify how to handle the nesting. In this case, we want to nest the data based on the column names.

Example Code

Here’s an example code snippet that demonstrates the entire process:

import pandas as pd

# create the JSON data
jsn = {
  "name": "person name",
  "food": {
    "fruit": "apple",
    "meal": {
      "lunch": "burger",
      "dinner": "pizza"
    }
  }
}

# create the flat structure
df = pd.json_normalize(jsn, errors='ignore')

# write to a CSV file
df.to_csv('file_name.csv', index=False)

# convert back into nested JSON
df = pd.json_normalize(df, 'food')

# write to a JSON file
df.to_json('file_name.json', orient='index')

Conclusion

Transforming Excel data into a nested JSON structure using Python’s Pandas library is a powerful and efficient way to work with complex data. By leveraging the json_normalize() function and combining it with other Pandas methods, we can easily write data to an Excel or CSV file and then convert it back into its original nested format.

Whether you’re working with large datasets or simply need to process some JSON data for a project, this technique is definitely worth exploring. With practice, you’ll become proficient in using json_normalize() and other Pandas methods to transform your data into the desired structure.

Last modified on 2024-05-09