What is pandas read_json() function in Python?

Overview

Python uses an open-source package Pandas. With it, we can easily read and analyze large datasets.

The read_json() function

By using the read_json() function and importing the Pandas package, we can easily read a JSON file and display its data in a grid formatDataFrame.

Syntax

read_json(path)

Parameter

This method takes the parameter, path, which represents the file path or directory.

Return value

It returns JSON data as DataFrame or series.

Example

main.py
data.json
{   

    "Names":{

    "0":"Amelia Jacob",

    "1":"Samantha Kyle",

    "2":"Isabella Elizabeth",

    "3":"Rhys Amelia",

    "4":"James Sarah",

    "5":"Olivia Poppy"

  },

  "Hobbies":{

    "0":"NovelReading",

    "1":"Cricket",

    "2":"Painting",

    "3":"Football",

    "4":"Cooking",

    "5":"Gaming"

  },

  "Age":{

    "0":25,

    "1":20,

    "2":23,

    "3":19,

    "4":23,

    "5":12

  }

}

Explanation

The main.py file

  • Line 1: We import the Pandas library as pd alias.
  • Line 2: We return data of the file in a DataFrame. We assign a variable name, mydataframe, in which the data.json file is loaded with the help of the read_json() function.
  • Line 3: We print the resultant DataFrame on the screen. Here, we use to_string() to display all the data present in the JSON file.

The data.json file

We can store any type of data we want to store in this file. In this case, we are storing demo data.

"Names" represents the columns of the data frame. Similarly, "Hobbies" and "Age" are also representing the other columns of the resultant data frame.

The numbers included inside the brackets {"0", "1", and so on} represent the index number. By convention, DataFrame always starts at the index 0. Therefore, the first entry will be at the 0 index.

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