Python JSON Convert Pandas DataFrame into JSON














































Python JSON Convert Pandas DataFrame into JSON



Convert Pandas DataFrame into JSON

To convert pandas DataFrames to JSON format we use the function DataFrame.to_json() from the pandas library in Python. There are multiple customizations available in the to_json function to achieve the desired formats of JSON.

We now look at a  example to understand the usage of the function DataFrame.to_json.
Example 1: Basic usage
import numpy as np 
import pandas as pd 


data = np.array([['1', '2'], ['3', '4']]) 

dataFrame = pd.DataFrame(data, columns = ['col1', 'col2']) 
json = dataFrame.to_json() 
print(json)

output:
{"col1":{"0":"1","1":"3"},"col2":{"0":"2","1":"4"}}

Example 2: Exploring the %u2018orient%u2019 attribute of DataFrame.to_json function
import numpy as np 
import pandas as pd 


data = np.array([['1', '2'], ['3', '4']]) 

dataFrame = pd.DataFrame(data, columns = ['col1', 'col2']) 
json = dataFrame.to_json() 
print(json) 

json_split = dataFrame.to_json(orient ='split') 
print("json_split = ", json_split, "\n") 

json_records = dataFrame.to_json(orient ='records') 
print("json_records = ", json_records, "\n") 

json_index = dataFrame.to_json(orient ='index') 
print("json_index = ", json_index, "\n") 

json_columns = dataFrame.to_json(orient ='columns') 
print("json_columns = ", json_columns, "\n") 

json_values = dataFrame.to_json(orient ='values') 
print("json_values = ", json_values, "\n") 

json_table = dataFrame.to_json(orient ='table') 
print("json_table = ", json_table, "\n")

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

json_records =  [{"col1":"1","col2":"2"},{"col1":"3","col2":"4"}] 

json_index =  {"0":{"col1":"1","col2":"2"},"1":{"col1":"3","col2":"4"}} 

json_columns =  {"col1":{"0":"1","1":"3"},"col2":{"0":"2","1":"4"}} 

json_values =  [["1","2"],["3","4"]] 

json_table =  {"schema": {"fields":[{"name":"index","type":"integer"},{"name":"col1","type":"string"},{"name":"col2","type":"string"}],"primaryKey":["index"],"pandas_version":"0.20.0"}, "data": [{"index":0,"col1":"1","col2":"2"},{"index":1,"col1":"3","col2":"4"}]}


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