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This tutorial shows how to pandas.read_json()load a JSON file into a Pandas DataFrame using the method.

Loading JSON File into Pandas DataFrame

We can load the JSON file into a Pandas DataFrame using pandas.read_json()the load_data function by passing the path of the JSON file as a parameter to the load_data function.pandas.read_json()

{
    "Name": {"1": "Anil", "2": "Biraj", "3": "Apil", "4": "Kapil"},
    "Age": {"1": 23, "2": 25, "3": 28, "4": 30},
}

The content of the example data.jsonfile is shown above. We will create a DataFrame from the above JSON file.

import pandas as pd

df = pd.read_json("data.json")

print("DataFrame generated using JSON file:")
print(df)

Output:

DataFrame generated using JSON file:
    Name  Age
1   Anil   23
2  Biraj   25
3   Apil   28
4  Kapil   30

It shows data.jsonthe DataFrame generated from the data in the file. We must ensure that the file is available in the current working directory data.jsonto generate the DataFrame, otherwise we need to provide the full path to the JSON file as pandas.read_json()a parameter to the method.

The DataFrame formed from a JSON file depends on the orientation of the JSON file. We generally have three different orientations of JSON files.

• Index-oriented
• Value-oriented
• Column-oriented

Loading an index-oriented JSON file into a Pandas DataFrame

{
    "0": {"Name": "Anil", "Age": 23},
    "1": {"Name": "Biraj", "Age": 25},
    "2": {"Name": "Apil", "Age": 26},
}

This is an example of an index-oriented JSON file, where the top-level keys represent the index of the data.

import pandas as pd

df = pd.read_json("data.json")

print("DataFrame generated from Index Oriented JSON file:")
print(df)

Output:

DataFrame generated from Index Oriented JSON file:
         0      1     2
Name  Anil  Biraj  Apil
Age     23     25    26

It will data.jsoncreate a DataFrame from the file, with the top-level keys represented as columns in the DataFrame.

Loading a value-oriented JSON file into a Pandas DataFrame

[["Anil", 23], ["Biraj", 25], ["Apil", 26]]

This is an example of a value-oriented JSON file, where each element in the array represents the value of each row.

import pandas as pd

df = pd.read_json("data.json")

print("DataFrame generated from Value Oriented JSON file:")
print(df)

Output:

DataFrame generated from Value Oriented JSON file:
       0   1
0   Anil  23
1  Biraj  25
2   Apil  26

It will data.jsoncreate a DataFrame from the file, and each element of the array in the JSON file will be represented as a row in the DataFrame.

Loading a column-oriented JSON file into a Pandas DataFrame

{"Name": {"1": "Anil", "2": "Biraj", "3": "Apil"}, "Age": {"1": 23, "2": 25, "3": 28}}

It is an example of a top-level index for a column-oriented JSON file, representing the column names of the data.

import pandas as pd

df = pd.read_json("data.json")

print("DataFrame generated from  Column Oriented JSON file:")
print(df)

Output:

DataFrame generated from Column Oriented JSON file:
    Name  Age
1   Anil   23
2  Biraj   25
3   Apil   28

It will data.jsoncreate a DataFrame from the file, with the top-level index of the JSON file as the column names in the DataFrame.