Export Pandas DataFrame to JSON File

April 27, 2024

To export Pandas DataFrame to a JSON file:

df.to_json(r"Path to store the exported JSON file\File Name.json")

Steps

Step 1: Create a DataFrame

Imagine that you have the following DataFrame that contains data about products and prices:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Tablet", "Keyboard"],
    "Price": [1200, 200, 500, 350, 80],
}

df = pd.DataFrame(data)

print(df)

Once you run the code in Python, you’ll get this DataFrame:

    Product  Price
0  Computer   1200
1   Printer    200
2   Monitor    500
3    Tablet    350
4  Keyboard     80

Step 2: Export the DataFrame to a JSON File

To export Pandas DataFrame to a JSON file:

df.to_json(r"Path to store the exported JSON file\File Name.json")

For example, assume that the path where the JSON file will be exported is:

C:\Users\Ron\Desktop\my_data.json

Therefore, the complete code to export the DataFrame to a JSON file is as follows:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Tablet", "Keyboard"],
    "Price": [1200, 200, 500, 350, 80],
}

df = pd.DataFrame(data)

df.to_json(r"C:\Users\Ron\Desktop\my_data.json")

Run the code (adjusted to your path), and the JSON file will be stored at your specified location.

There are few ways to view the JSON content. An easy way is to drag the file created into your web browser. You’ll then get the following result:

{"Product":{"0":"Computer","1":"Printer","2":"Monitor","3":"Tablet","4":"Keyboard"},"Price":{"0":1200,"1":200,"2":500,"3":350,"4":80}}

Different JSON Formats

There are different ways to format the JSON string. You’ll need to set the orient to your desired format. Here are the options:

  • split
  • records
  • index
  • values
  • table
  • columns (the default format)

For example, to get a “split” orientation, set the orient=”split”:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Tablet", "Keyboard"],
    "Price": [1200, 200, 500, 350, 80],
}

df = pd.DataFrame(data)

df.to_json(r"C:\Users\Ron\Desktop\my_data.json", orient="split")

The result:

{"columns":["Product","Price"],"index":[0,1,2,3,4],"data":[["Computer",1200],["Printer",200],["Monitor",500],["Tablet",350],["Keyboard",80]]}

Here are the results that you’ll get for each of the other formats:

orient=”records”

[{"Product":"Computer","Price":1200},{"Product":"Printer","Price":200},{"Product":"Monitor","Price":500},{"Product":"Tablet","Price":350},{"Product":"Keyboard","Price":80}]

orient=”index”

{"0":{"Product":"Computer","Price":1200},"1":{"Product":"Printer","Price":200},"2":{"Product":"Monitor","Price":500},"3":{"Product":"Tablet","Price":350},"4":{"Product":"Keyboard","Price":80}}

orient=”values”

[["Computer",1200],["Printer",200],["Monitor",500],["Tablet",350],["Keyboard",80]]

orient=”table”

{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"Product","type":"string"},{"name":"Price","type":"integer"}],"primaryKey":["index"],"pandas_version":"1.4.0"},"data":[{"index":0,"Product":"Computer","Price":1200},{"index":1,"Product":"Printer","Price":200},{"index":2,"Product":"Monitor","Price":500},{"index":3,"Product":"Tablet","Price":350},{"index":4,"Product":"Keyboard","Price":80}]}

orient=”columns” (default)

{"Product":{"0":"Computer","1":"Printer","2":"Monitor","3":"Tablet","4":"Keyboard"},"Price":{"0":1200,"1":200,"2":500,"3":350,"4":80}}

Refer to the Pandas Documentation to see the different formatting options that you can apply.

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