Working with JSON and Pandas DataFrame is common in data analysis, reporting, and ETL pipelines. While Pandas provides read_json for basic parsing, it can struggle with deeply nested structures, very large files, or Excel-first workflows. This is where Aspose.Cells for Python helps. It provides a rich JSON-to-Excel pipeline, which you can easily integrate with Pandas to get clean DataFrames for analysis. In this blog post, you will learn how to convert JSON to Pandas dataframes in Python.

Python Library to Convert JSON to Pandas DataFrame

Aspose.Cells for Python via .NET is a powerful spreadsheet API that does not require Microsoft Excel. Beyond classic Excel automation, it supports direct JSON import and export, making it ideal when you want to convert JSON into a Pandas DataFrame and later save or process it in Excel.

With Aspose.Cells, you can:

  • Import JSON into worksheets using JsonUtility, with options to handle arrays and nested structures.
  • Convert worksheet ranges to Pandas DataFrames for analysis and visualization.
  • Create, load, and handle JSON inside Excel files, which fits analytics pipelines.
  • Export DataFrames back to Excel (XLSX, CSV, ODS, PDF) for reporting.

In short, the library makes it easy to move data from JSON into Excel for reporting, while you use Pandas for deeper analysis. The JsonUtility imports JSON into a worksheet, and JsonLayoutOptions controls how arrays and nested objects expand.

Convert JSON to DataFrame

Aspose.Cells imports JSON directly into a worksheet. You then read the header row and data rows to build a Pandas DataFrame.

Follow the steps below to convert JSON to a pandas DataFrame:

  1. Create a workbook and get the first worksheet.
  2. Configure JsonLayoutOptions to treat arrays as tables.
  3. Import the JSON string at row 0, column 0.
  4. Use the first row as column headers.
  5. Extract remaining rows as data.
  6. Build a Pandas DataFrame.

The following code example shows how to convert JSON to a pandas DataFrame in Python:

Output

    id   name
0  1.0  Alice
1  2.0    Bob

Convert Nested JSON to Pandas DataFrame

If your JSON contains nested objects, Aspose.Cells imports JSON into a worksheet using JsonUtility, which you can then export to a DataFrame. The JsonLayoutOptions controls how arrays and nested objects are expanded.

Follow the steps below to convert nested JSON to a pandas DataFrame:

  1. Create a workbook and select the first worksheet.
  2. Set required JsonLayoutOptions properties such as array_as_table=True, ignore_array_title=True, ignore_object_title=True, and kept_schema=True.
  3. Import the nested JSON at row 0, column 0.
  4. Detect the used range and read the header row across the full span.
  5. Read all subsequent rows across the same span (fixed width).
  6. Build the DataFrame; optionally cast dtypes (e.g., total to numeric).

The following code example shows how to convert nested JSON to a pandas DataFrame in Python:

Output

     A1  1001  49.90     NYC  10001
0  None  1002  79.00  Boston  02108

Note: If you enable convert_numeric_or_date=True, numeric-looking strings (e.g., totals) may convert to numbers, but ZIP codes like "02108" can lose leading zeros. Keep it False if you need ZIP codes as strings.

Convert Excel to Pandas DataFrame via JSON

Export any Excel range to JSON with Aspose.Cells, then load that JSON into Pandas as a DataFrame. This is helpful when you need a structured JSON handoff for services or pipelines.

Follow the steps below to convert Excel to a pandas DataFrame via JSON:

  1. Create a new workbook, get the first worksheet, and add sample values.
  2. Create JsonSaveOptions with defaults.
  3. Export the used range to a JSON string with the export_range_to_json() method.
  4. Read the JSON string into a DataFrame using the pd.read_json(io.StringIO(json_text)) method.
  5. Inspect or process the DataFrame as needed.

The following code example shows how to convert Excel to a pandas DataFrame via JSON in Python:

Output

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Get a Free License

Evaluate Aspose.Cells for Python via .NET without feature limits by applying a free temporary license. Visit the temporary license page to unlock full functionality, including JSON import (JsonUtility), layout control (JsonLayoutOptions), schema preservation, and numeric/date conversion.

Additional Free Resources

You can use the resources below to go deeper into JSON import, layout options, and other Excel manipulations with Aspose.Cells for Python.

Conclusion

Converting JSON into Pandas DataFrames becomes straightforward with Aspose.Cells for Python. You get reliable handling for nested structures, options for schema stability, and an easy path to Excel export when needed. Combine the flexibility of Pandas with the JSON/Excel pipeline in Aspose.Cells to simplify data processing and unlock powerful analysis in Python.

If you have questions, visit our free support forum. We are happy to help you.

See Also