Pandas DataFrame to JSON

In this post, you will learn how to convert Pandas DataFrame to JSON.

A DataFrame is a tabular-like data structure containing an ordered collection of columns. The each column can be of different data types, like numeric, boolean, string, etc. It has both a row and a column index. It is basically used for analytical purposes.

JSON (JavaScript Object Notation) is popular among developers for data serialization. It is a data format consisting of key-value pairs. It is widely used to transmit and receive data in JSON format between a server and a web applicationormat consisting of key-value pairs. It is so popular that every modern programming language has methods to generate and parse JSON formatted data.

Convert pandas DataFrame into JSON

We can use the function DataFrame.to_json() from the pandas library in Python to convert pandas DataFrames to JSON format.

DataFrame.to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression='infer', index=True, indent=None, storage_options=None)

Parameter	Value	Uses
path_or_buf	str, path object, file-like object, default None	File path or object. If None, the result is returned as a string.
orient	split, records, index, columns, values, table, default='index'	Indication of expected JSON string format.
date_format	None, 'epoch', 'iso', default='epoch'	The default depends on the orient. For orient='table', the default is 'iso'. For all other orients, the default is 'epoch'.
double_precision	integer value, default=10	The number of decimal places to use when encoding floating point values.
force_ascii	boolean value, default=True	Force encoded string to be ASCII.
date_unit	's', 'ms', 'us', 'ns', default='ms'	The values represent second, millisecond, microsecond, and nanosecond respectively.
default_handler	callable, default None	Handler to call if object cannot otherwise be converted to a suitable format for JSON.
lines	bool, default False	If orient is 'records' write out line delimited json format. Will throw ValueError if incorrect orient since others are not list like.
compression	str or dict, default 'infer'	A string representing the compression to use in the output file, only used when the first argument is a filename.
index	bool, default True	Whether to include the index values in the JSON string.
indent	integer value	Length of whitespace used to indent each record.

Create a DataFrame

Suppose, we have the following data -

Name	Subject	Marks
Smith	PHY	90
Priska	CHM	88
Andy	BIO	89
Paul	SST	82

STEP1: Create a dataframe

import pandas as pd

stu_dict = {'Name': ["Smith", "Priska", "Kashyap", "Andy"]
        ,'Subject': ["Phy", "Maths", "Chm", "Bio"],
        'Marks': [90, 99, 92, 89]}  

df = pd.DataFrame(stu_dict)

print(df)

Name Subject  Marks
0    Smith     Phy     90
1   Priska   Maths     99
2  Kashyap     Chm     92
3     Andy     Bio     89

Convert pandas DataFrame into JSON

Here is the simple Python code to convert pandas Dataframe to JSON.

import pandas as pd
  
  
data = stu_dict = {'Name': ["Smith", "Priska", "Kashyap", "Andy"]
        ,'Subject': ["Phy", "Maths", "Chm", "Bio"],
        'Marks': [90, 99, 92, 89]}  
  
dataFrame = pd.DataFrame(data, columns = ['col1', 'col2', 'col3'])
json = dataFrame.to_json()
print(json)

Output of the above code:

{"Name":{"0":"Smith","1":"Priska","2":"Kashyap","3":"Andy"},
"Subject":{"0":"Phy","1":"Maths","2":"Chm","3":"Bio"},
"Marks":{"0":90,"1":99,"2":92,"3":89}}

Use DataFrame.to_json() to orient = 'records'

Here, we have used to orient='records' to convert DataFrame to JSON in format.

dataFrame = pd.DataFrame(data, columns = ['Name', 'Subject', 'Marks'])

json = dataFrame.to_json( orient = 'records')
print(json)

Output of the above code:

[{"Name":"Smith","Subject":"Phy","Marks":90},
{"Name":"Priska","Subject":"Maths","Marks":99},
{"Name":"Kashyap","Subject":"Chm","Marks":92},
{"Name":"Andy","Subject":"Bio","Marks":89}]

Use DataFrame.to_json() to orient = 'index'

Here, we have used to orient='index' to convert DataFrame to JSON in format.

dataFrame = pd.DataFrame(data, columns = ['Name', 'Subject', 'Marks'])

json = dataFrame.to_json( orient = 'index')
print(json)

Output of the above code:

{"0":{"Name":"Smith","Subject":"Phy","Marks":90},
"1":{"Name":"Priska","Subject":"Maths","Marks":99},
"2":{"Name":"Kashyap","Subject":"Chm","Marks":92},
"3":{"Name":"Andy","Subject":"Bio","Marks":89}}

Use DataFrame.to_json() to orient = 'split'

Here, we have used to orient='split' to convert DataFrame to JSON in format.

dataFrame = pd.DataFrame(data, columns = ['Name', 'Subject', 'Marks'])

json = dataFrame.to_json( orient = 'split')
print(json)

Output of the above code:

{"columns":["Name","Subject","Marks"],
"index":[0,1,2,3],
"data":[["Smith","Phy",90],["Priska","Maths",99],["Kashyap","Chm",92],["Andy","Bio",89]]}

Use DataFrame.to_json() to orient = 'table'

Here, we have used to orient='table' to convert DataFrame to JSON in format.

dataFrame = pd.DataFrame(data, columns = ['Name', 'Subject', 'Marks'])

json = dataFrame.to_json( orient = 'table')
print(json)

Output of the above code:

{"schema":{"fields":[{"name":"index","type":"integer"},{"name":"Name","type":"string"},{"name":"Subject","type":"string"},{"name":"Marks","type":"integer"}],"primaryKey":["index"],"pandas_version":"0.20.0"},"data":[{"index":0,"Name":"Smith","Subject":"Phy","Marks":90},{"index":1,"Name":"Priska","Subject":"Maths","Marks":99},{"index":2,"Name":"Kashyap","Subject":"Chm","Marks":92},{"index":3,"Name":"Andy","Subject":"Bio","Marks":89}]}

Use DataFrame.to_json() to orient = 'values'

Here, we have used to orient='values' to convert DataFrame to JSON in format.

dataFrame = pd.DataFrame(data, columns = ['Name', 'Subject', 'Marks'])

json = dataFrame.to_json( orient = 'values')
print(json)

Output of the above code:

[["Smith","Phy",90],["Priska","Maths",99],["Kashyap","Chm",92],["Andy","Bio",89]]

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