Download our e-book of Introduction To Python
Related Blog
Discuss Microsoft Cognitive ToolkitMatplotlib - Subplot2grid() FunctionMatplotlib - MultiplotsMicrosoft Cognitive Toolkit (CNTK) - CPU and GPUMatplotlib - PyLab moduleMatplotlib - Working with ImagesCNTK - Creating First Neural NetworkMatplotlib - Twin AxesPython Forensics - Memory and ForensicsMatplotlib - Setting Ticks and Tick Labels View More
Top Discussion
How can I write Python code to change a date string from "mm/dd/yy hh: mm" format to "YYYY-MM-DD HH: mm" format? Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) Why Sigmoid is a not used in Multi-class classification? Getting this error 'collections.OrderedDict' object has no attribute 'status_code' How do you identify important variables while working on a data set in machine learning? What are metaclasses in Python? How to leave/exit/deactivate a Python virtualenvironment Join Discussion
Top Courses
Webinars
Why You Should Learn Data Science in 2024
Dec 2nd (7:00 PM) 4643 Registered
Python - Processing JSON Data
Neha Kumawat
2 years ago
Table of Contents
- Introduction
- Input Data
- Read the JSON File
Importing Pandas library
Pandas read_json()
Convert the object to a JSON string using dataframe.to_json
Introduction
JSON (JavaScript Object Notation) is the most widely used data format for data exchange on the Web. This data exchange can take place between two computer applications located in different geographical locations or executed on the same machine. The good thing is that JSON is human readable as well as machine readable. So while applications / libraries can parse JSON documents, humans can also examine data and make sense of it.
The JSON dataset analysis using Pandas is much more convenient.Pandas allows you to convert a list of lists to a datafame and specify the names of the columns separately.
Input Data
Create a JSON file by copying the following data into a text editor such as Notepad. Save the file with the .json extension and choose the file type as All Files (*. *).
input.json
{
"employees": [
{
"id": 1,
"name": "Amit",
"dept": "IT"
},
{
"id": 2,
"name": "Asif",
"dept": "HR"
},
{
"id": 3,
"name": "Rohit",
"dept": "IT"
}
]
}Read the JSON File
Importing Pandas library
JSON manipulation is done using the Python data analysis library, called pandas.
import pandas as pdPandas read_json()
To read a JSON file via Pandas, we'll use the read_json () method and pass it the path of the file we want to read. The method returns a Pandas DataFrame that stores the data as columns and rows.
Syntax
pandas.read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None,
convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None,
encoding=None, encoding_errors='strict', lines=False, chunksize=None, compression='infer',
nrows=None, storage_options=None)Parameters
- path_or_buf : a valid JSON str, path object or file-like object
- orient : str
- typ : {‘frame’, ‘series’}, default ‘frame’
- dtype : bool or dict, default None
- convert_axes : bool, default None
- convert_dates : bool or list of str, default True
- keep_default_dates : bool, default True
- numpy : bool, default False
- precise_float : bool, default False
- date_unit : str, default None
- encoding : str, default is ‘utf-8’
- encoding_errors : str, optional, default “strict”
- lines : bool, default False
- chunksize : int, optional
- compression : {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’
- nrows : int, optional
- storage_options : dict, optional
Returns
- Series or DataFrame
Example
import pandas as pd
data = pd.read_json('input.json')
print (data)
Output
employees
0 {'id': 1, 'name': 'Amit', 'dept': 'IT'}
1 {'id': 2, 'name': 'Asif', 'dept': 'HR'}
2 {'id': 3, 'name': 'Rohit', 'dept': 'IT'}Convert the object to a JSON string using dataframe.to_json
Syntax
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)Parameters
- path_or_buf : str or file handle, optional
- orient : str
- date_format : {None, ‘epoch’, ‘iso’}
- double_precision : int, default 10
- force_ascii : bool, default True
- date_unit : str, default ‘ms’ (milliseconds)
- default_handler : callable, default None
- lines : bool, default False
- compression : {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}
- index : bool, default True
- indent : int, optional
- storage_options : dict, optional
Returns
- None or str
Example
import pandas as pd
import json
df = pd.DataFrame(
[["x", "y"], ["w", "z"]],
index=["row_1", "row_2"],
columns=["col_1", "col_2"],
)
result = df.to_json(orient="split")
parsed = json.loads(result)
print(json.dumps(parsed, indent=3) )Output
{
"columns": [
"col_1",
"col_2"
],
"index": [
"row_1",
"row_2"
],
"data": [
[
"x",
"y"
],
[
"w",
"z"
]
]
}Like the Blog, then Share it with your friends and colleagues to make this AI community stronger.
To learn more about nuances of Artificial Intelligence, Python Programming, Deep Learning, Data Science and Machine Learning, visit our insideAIML blog page.
Keep Learning. Keep Growing.