In this article, we are going to see how to convert nested JSON structures to Pandas DataFrames.
JSON with multiple levels
In this case, the nested JSON data contains another JSON object as the value for some of its attributes. This makes the data multi-level and we need to flatten it as per the project requirements for better readability, as explained below.
Python3
# importing the libraries usedimport pandas as pd # initializing the datadata = { 'company': 'XYZ pvt ltd', 'location': 'London', 'info': { 'president': 'Rakesh Kapoor', 'contacts': { 'email': 'contact@xyz.com', 'tel': '9876543210' } }} |
Here, the data contains multiple levels. To convert it to a dataframe we will use the json_normalize() function of the pandas library.
Python3
pd.json_normalize(data) |
Output:
json data converted to pandas dataframe
Here, we see that the data is flattened and converted to columns. If we do not wish to completely flatten the data, we can use the max_level attribute as shown below.
Python3
pd.json_normalize(data,max_level=0) |
Output:
json data converted to pandas dataframe
Here, we see that the info column is not flattened further.
Python3
pd.json_normalize(data,max_level=1) |
Output:
json data converted to pandas dataframe
Here, we see that the contacts column is not flattened further.
List of nested JSON
Now, if the data is a list of nested JSONs, we will get multiple records in our dataframe.
Python3
data = [ { 'id': '001', 'company': 'XYZ pvt ltd', 'location': 'London', 'info': { 'president': 'Rakesh Kapoor', 'contacts': { 'email': 'contact@xyz.com', 'tel': '9876543210' } } }, { 'id': '002', 'company': 'PQR Associates', 'location': 'Abu Dhabi', 'info': { 'president': 'Neelam Subramaniyam', 'contacts': { 'email': 'contact@pqr.com', 'tel': '8876443210' } } }] pd.json_normalize(data) |
Output:
json data converted to pandas dataframe
So, in the case of multiple levels of JSON, we can try out different values of max_level attribute.
JSON with nested lists
In this case, the nested JSON has a list of JSON objects as the value for some of its attributes. In such a case, we can choose the inner list items to be the records/rows of our dataframe using the record_path attribute.
Python3
# initialising the datadata = { 'company': 'XYZ pvt ltd', 'location': 'London', 'info': { 'president': 'Rakesh Kapoor', 'contacts': { 'email': 'contact@xyz.com', 'tel': '9876543210' } }, 'employees': [ {'name': 'A'}, {'name': 'B'}, {'name': 'C'} ]} # converting the data to dataframedf = pd.json_normalize(data) |
Output:
json data converted to pandas dataframe
Here, the nested list is not flattened. We need to use record_path attribute to flatten the nested list.
Python3
pd.json_normalize(data,record_path=['employees']) |
Output:
nested list is not flattened
Now, we observe that it does not include ‘info’ and other features. To include them we use another attribute, meta. Note that, in the below code, to include an attribute of an inner JSON we have specified the path as “[‘info’, ‘president’]”.
Python3
pd.json_normalize(data, record_path=['employees'], meta=[ 'company', 'location', ['info', 'president']]) |
Output:
json data converted to pandas dataframe
Now in the case of multiple nested JSON objects, we will get a dataframe with multiple records as shown below.
Python3
data = [ { 'id': '001', 'company': 'XYZ pvt ltd', 'location': 'London', 'info': { 'president': 'Rakesh Kapoor', 'contacts': { 'email': 'contact@xyz.com', 'tel': '9876543210' } }, 'employees': [ {'name': 'A'}, {'name': 'B'}, {'name': 'C'} ] }, { 'id': '002', 'company': 'PQR Associates', 'location': 'Abu Dhabi', 'info': { 'president': 'Neelam Subramaniyam', 'contacts': { 'email': 'contact@pqr.com', 'tel': '8876443210' } }, 'employees': [ {'name': 'L'}, {'name': 'M'}, {'name': 'N'} ] }] df = pd.json_normalize(data, record_path=['employees'], meta=[ 'company', 'location', ['info', 'president']])print(df) |
Output :
json data converted to pandas dataframe
