In this quick tutorial, we'll show how to export DataFrame to JSON format in Pandas. We will cover different export options.

(1) save DataFrame to a JSON file

df.to_json('file.json')

(2) change JSON format and data

df.to_json('file.json', orient='split')

There are multiple options for parameter orient of method - to_json:

  • split - dict
    • {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}
  • records - list
    • [{column -> value}, … , {column -> value}]
  • index - dict
    • {column -> {index -> value}}
  • columns - dict (default for DataFrame)
    • {column -> {index -> value}}
  • values
    • just the values array
  • table - dict
    • {‘schema’: {schema}, ‘data’: {data}}
Pro Tip 1
For Pandas Series several options are available for export to JSON
{‘split’, ‘records’, ‘index’, ‘table’}

Setup

Let's create a sample DataFrame which will be exported to a JSON file:

import pandas as pd

data = {'day': [1, 2, 3, 4, 5, 6, 7, 8],
        'temp': [9, 8, 6, 13, 10, 15, 9, 10],
        'humidity': [0.89, 0.86, 0.54, 0.73, 0.45, 0.63, 0.95, 0.67]}

df = pd.DataFrame(data=data)

data looks like:

day temp humidity
0 1 9 0.89
1 2 8 0.86
2 3 6 0.54
3 4 13 0.73
4 5 10 0.45

1: Export DataFrame as JSON file

So let's start with the most simple example - exporting DataFrame as JSON string or a JSON file:

df.to_json('file.json')

If we provide the file name we will get a new JSON file created.

The output file path can be provided as relative path:

df.to_json(r'/home/json/file.json')
Pro Tip 1
NaN’s
and
None
will be converted to
null
and datetime objects will be converted to UNIX timestamps.

2: Export DataFrame as JSON string

Without file parameter - path_or_buf we will get JSON string:

df.to_json()

which will result into:

'{"day":{"0":1,"1":2,"2":3,"3":4,"4":5,"5":6,"6":7,"7":8},"temp":{"0":9,"1":8,"2":6,"3":13,"4":10,"5":15,"6":9,"7":10},"humidity":{"0":0.89,"1":0.86,"2":0.54,"3":0.73,"4":0.45,"5":0.63,"6":0.95,"7":0.67}}'

3: Export DataFrame as pretty JSON

Using parameter indent=True will prettify the JSON output of Pandas method to_json():

print(df.to_json(indent=True))

result:

{
 "day":{
  "0":1,
  "1":2,
  "2":3,
  "3":4,
  "4":5,
  "5":6,
  "6":7,
  "7":8
 },
 "temp":{
  "0":9,
  "1":8,
  "2":6,
  "3":13,
  "4":10,
  "5":15,
  "6":9,
  "7":10
 },
 "humidity":{
  "0":0.89,
  "1":0.86,
  "2":0.54,
  "3":0.73,
  "4":0.45,
  "5":0.63,
  "6":0.95,
  "7":0.67
 }
}

4: Export DataFrame JSON formats - orient

In this section we can see different JSON formats and data outputs.

4.1: columns - {column -> {index -> value}}

Let's start by the default export option for DataFrame - columns:

df.to_json()

is equivalent to df.to_json(orient='columns')

{"day":{"0":1,"1":2,"2":3,"3":4,"4":5,"5":6,"6":7,"7":8},
"temp":{"0":9,"1":8,"2":6,"3":13,"4":10,"5":15,"6":9,"7":10},
"humidity":{"0":0.89,"1":0.86,"2":0.54,"3":0.73,"4":0.45,"5":0.63,"6":0.95,"7":0.67}}

4.2: split - {‘index’ -> [index], ‘columns’ -> [columns], ‘data’ -> [values]}

Using orient with option split will give us:

df.to_json(orient='split')

JSON with keys - columns, index and data:

{"columns":["day","temp","humidity"],
"index":[0,1,2,3,4,5,6,7],
"data":[[1,9,0.89],[2,8,0.86],[3,6,0.54],[4,13,0.73]..]}

4.3: records - [{column -> value}, … , {column -> value}]

For records we get list of dictionaries - each row as a new entry in the output JSON:

df.to_json(orient='records')

result:

[
{"day":1,"temp":9,"humidity":0.89},
{"day":2,"temp":8,"humidity":0.86},
{"day":3,"temp":6,"humidity":0.54},
{"day":4,"temp":13,"humidity":0.73},
{"day":5,"temp":10,"humidity":0.45}..]

4.4: index - {index -> {column -> value}}

With option index we got JSON file formatted as - index/data pairs:

df.to_json(orient='index')

result:

{"0":{"day":1,"temp":9,"humidity":0.89},
"1":{"day":2,"temp":8,"humidity":0.86},
"2":{"day":3,"temp":6,"humidity":0.54},
"3":{"day":4,"temp":13,"humidity":0.73},
"4":{"day":5,"temp":10,"humidity":0.45}..}

4.5: orient='values' <-> df.values

The option orient='values' is similar to df.values:

df.to_json(orient='values')

resulted JSON data is:

[[1,9,0.89],[2,8,0.86],[3,6,0.54],[4,13,0.73],[5,10,0.45],[6,15,0.63],[7,9,0.95],[8,10,0.67]]

4.6: table - {‘schema’: {schema}, ‘data’: {data}}

Finally we can use the option table. This give us DB like and JSON schema like JSON export of a DataFrame:

df.to_json(orient='table')

which results in next JSON schema:

{
 "schema":
 {
  "fields":
  [
   {
    "name": "index",
    "type": "integer"
   },
   {
    "name": "day",
    "type": "integer"
   },
   {
    "name": "temp",
    "type": "integer"
   },
   {
    "name": "humidity",
    "type": "number"
   }
  ],
  "primaryKey":
  [
   "index"
  ],
  "pandas_version": "1.4.0"
 },
 "data":
 [
  {
   "index": 0,
   "day": 1,
   "temp": 9,
   "humidity": 0.89
  },
  {
   "index": 1,
   "day": 2,
   "temp": 8,
   "humidity": 0.86
  },
  {
   "index": 2,
   "day": 3,
   "temp": 6,
   "humidity": 0.54
  },
  {
   "index": 3,
   "day": 4,
   "temp": 13,
   "humidity": 0.73
  }
 ]
}

5: Export DataFrame as JSON lines

To save Pandas DataFrame as JSON lines we can use two parameters:

  • orient='records'
  • lines=True
df.to_json(orient='records', lines=True)

this gives JSON line output:

{"day":1,"temp":9,"humidity":0.89}
{"day":2,"temp":8,"humidity":0.86}
{"day":3,"temp":6,"humidity":0.54}
{"day":4,"temp":13,"humidity":0.73}

Conclusion

In this article we saw how to export DataFrame as a JSON string of files. We show how to pretty print the JSON output.

We show different JSON formats and export to JSON lines.