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Showing content from https://www.geeksforgeeks.org/pandas/how-to-convert-pandas-dataframe-into-json-in-python/ below:

How to convert pandas DataFrame into JSON in Python?

We are given a pandas DataFrame, and our task is to convert it into JSON format using different orientations and custom options. JSON (JavaScript Object Notation) is a lightweight, human-readable format used for data exchange. With Pandas, this can be done easily using the to_json() method. For example:

Python


 import pandas as pd

df = pd.DataFrame({
    'Name': ['John', 'Anna', 'Peter'],
    'Age': [28, 24, 35],
    'City': ['New York', 'Paris', 'Berlin']
})

a = df.to_json()
print(a)

Output:

{"Name":{"0":"John","1":"Anna","2":"Peter"},"Age":{"0":28,"1":24,"2":35},"City":{"0":"New York","1":"Paris","2":"Berlin"}}

This example shows how easy it is to convert a DataFrame into JSON format using the to_json() method where both columns and rows are represented as key-value pairs. Now, let’s dive deeper into the process and explore different options available for customization.

Using the to_json() Method

The to_json() function converts a DataFrame into a JSON string or file. Key parameters include:

path_or_buf: File path or buffer. If not specified, JSON string is returned.

orient: Defines JSON structure (e.g., 'records', 'index', 'columns', etc.).

date_format: 'iso' or 'epoch' format for datetime values.

double_precision: Controls decimal places for floats.

force_ascii: Escapes non-ASCII characters if True.

date_unit: Sets time unit for datetime ('ms', 's', 'us', 'ns').

indent: Pretty prints JSON with indentation.

Customizing The JSON Method with orient

The orient parameter allows you to control how the DataFrame is structured in the resulting JSON. Pandas to_json() provides multiple format options through the orient parameter. Here are some common orientations:

1. 'records' (Row-wise Objects)

Converts each row into a dictionary, making it ideal for JSON structures that prioritize row-based data. Each row is a JSON object in an array.

Python


 df.to_json(orient='records')

Output:

[{"Name":"John","Age":28,"City":"New York"}, {"Name":"Anna","Age":24,"City":"Paris"}, {"Name":"Peter","Age":35,"City":"Berlin"}]

2. 'index' – Index as Keys

Uses the DataFrame index as JSON keys, with each index mapping to a dictionary representing a row. This structure is useful for indexed data.

Python


 df.to_json(orient='index')

Output:

{"0":{"Name":"John","Age":28,"City":"New York"}, "1":{"Name":"Anna","Age":24,"City":"Paris"}, "2":{"Name":"Peter","Age":35,"City":"Berlin"}}

3. 'columns' – Default Format

Converts each column into a key with an array of values, creating a dictionary that maps column names to lists of their values.

Python


 df.to_json(orient='columns')

Output:

{"Name":{"0":"John","1":"Anna","2":"Peter"}, "Age":{"0":28,"1":24,"2":35}, "City":{"0":"New York","1":"Paris","2":"Berlin"}}

4. 'split' – Structured Parts

Organizes the output into three distinct parts—index, columns, and data—which helps reconstruct the DataFrame more easily.

Python


 df.to_json(orient='split')

Output:

{"columns":["Name","Age","City"], "index":[0,1,2], "data":[["John",28,"New York"],["Anna",24,"Paris"],["Peter",35,"Berlin"]]}

5. 'values' – Values Only Python


 df.to_json(orient='values')

Output:

[["John",28,"New York"],["Anna",24,"Paris"],["Peter",35,"Berlin"]]

6. 'table' – Table Schema

Follows a table schema with metadata, which includes schema details and the data. This format is suitable for preserving DataFrame structure.

Python


 df.to_json(orient='table')

Output:

{
"schema": {
"fields": [...],
"primaryKey": ["index"],
"pandas_version": "1.4.0"
},
"data": [
{"index": 0, "Name": "John", "Age": 28, "City": "New York"},
...
]
}

Customizing Output with Parameters

Pandas gives several powerful options for customizing the JSON output using to_json() parameters. These options let you choose how precise, readable, compact, or compatible your JSON output should be:

orient: Controls the JSON structure. Options include 'records', 'index', 'columns', 'split', 'values', 'table'.
date_format: Convert datetime values to either ISO ('iso') or Unix timestamp ('epoch').
double_precision: Set how many decimal points to include for float values.
force_ascii: If True, non-ASCII characters are escaped (e.g., for ASCII-only systems).
date_unit: Sets the unit for datetime output—milliseconds ('ms'), seconds ('s'), microseconds ('us'), or nanoseconds ('ns').
indent: Pretty-prints the output with indentation, useful for logging or readability.
path_or_buf: If a file path is passed here, the output is saved directly to that file instead of returned as a string.

These parameters work together to make the output more suitable for your specific use case—whether for APIs, config files, storage, or human review.

Example: All orient types on a simple DataFrame Python


 import numpy as np
import pandas as pd

data = np.array([["1", "2"], ["3", "4"]])
df = pd.DataFrame(data, columns=['col1', 'col2'])

print(df.to_json())                      # Default (columns)
print(df.to_json(orient='split'))
print(df.to_json(orient='records'))
print(df.to_json(orient='index'))
print(df.to_json(orient='columns'))
print(df.to_json(orient='values'))
print(df.to_json(orient='table'))

Output

{"col1":{"0":"1","1":"3"},"col2":{"0":"2","1":"4"}}
{"columns":["col1","col2"],"index":[0,1],"data":[["1","2"],["3","4"]]}
[{"col1":"1","col2":"2"},{"col1":"3","col2":"4"}]
{"0":{"col1":"1","col2":"2"...

Example: Using Other Parameters Python


 import pandas as pd

data = {
    'Name': ['John', 'Jane', 'Bob'],
    'Age': [30, 25, 40],
    'Salary': [50000.0, 60000.0, 70000.0],
    'Join_date': ['2022-01-01', '2021-06-15', '2020-11-30']
}
df = pd.DataFrame(data)

print(df.to_json())                                      # Basic
print(df.to_json(orient='records'))                      # Row-wise
print(df.to_json(date_format='iso'))                     # ISO date format
print(df.to_json(double_precision=2))                    # Limit float decimals
print(df.to_json(force_ascii=False))                     # Allow Unicode
print(df.to_json(date_unit='ms'))                        # Milliseconds unit
print(df.to_json(indent=4))                              # Pretty print
df.to_json(path_or_buf='output.json')                    # Save to file

Output

{"Name":{"0":"John","1":"Jane","2":"Bob"},"Age":{"0":30,"1":25,"2":40},"Salary":{"0":50000.0,"1":60000.0,"2":70000.0},"Join_date":{"0":"2022-01-01","1":"2021-06-15","2":"2020-11-30"}}
[{"Name":"John",...

Key Takeaways:

The to_json() method in Pandas provides a flexible way to convert a DataFrame into different JSON formats.
The orient parameter allows you to customize how rows and columns are represented in the output.
Special data types like missing values (NaN, None) are handled gracefully by converting them to null.
Depending on your use case (e.g., web APIs or data storage), you can choose from various orientations like 'records', 'index', 'columns', etc.

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