How to Make A Dataframe From A Nested Json And Pandas?

12 minutes read

To make a dataframe from a nested JSON using pandas, you can first read the JSON data using the pandas json_normalize() function. This function will flatten the nested JSON data into a tabular format, making it easier to convert it into a dataframe. You can then create a dataframe using the normalized JSON data and manipulate it as needed using pandas functions. This will allow you to analyze and work with the nested JSON data more efficiently within pandas.

Best Python Books to Read in 2024

1
Fluent Python: Clear, Concise, and Effective Programming

Rating is 5 out of 5

Fluent Python: Clear, Concise, and Effective Programming

2
Learning Python, 5th Edition

Rating is 4.9 out of 5

Learning Python, 5th Edition

3
Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

Rating is 4.8 out of 5

Python Crash Course, 3rd Edition: A Hands-On, Project-Based Introduction to Programming

4
Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

Rating is 4.7 out of 5

Automate the Boring Stuff with Python, 2nd Edition: Practical Programming for Total Beginners

  • Language: english
  • Book - automate the boring stuff with python, 2nd edition: practical programming for total beginners
  • It is made up of premium quality material.
5
Python 3: The Comprehensive Guide to Hands-On Python Programming

Rating is 4.6 out of 5

Python 3: The Comprehensive Guide to Hands-On Python Programming

6
Python Programming for Beginners: The Complete Guide to Mastering Python in 7 Days with Hands-On Exercises – Top Secret Coding Tips to Get an Unfair Advantage and Land Your Dream Job!

Rating is 4.5 out of 5

Python Programming for Beginners: The Complete Guide to Mastering Python in 7 Days with Hands-On Exercises – Top Secret Coding Tips to Get an Unfair Advantage and Land Your Dream Job!

7
Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Rating is 4.4 out of 5

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

8
Python All-in-One For Dummies (For Dummies (Computer/Tech))

Rating is 4.3 out of 5

Python All-in-One For Dummies (For Dummies (Computer/Tech))

9
Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

Rating is 4.2 out of 5

Python QuickStart Guide: The Simplified Beginner's Guide to Python Programming Using Hands-On Projects and Real-World Applications (QuickStart Guides™ - Technology)

10
The Big Book of Small Python Projects: 81 Easy Practice Programs

Rating is 4.1 out of 5

The Big Book of Small Python Projects: 81 Easy Practice Programs


How to filter specific elements from a nested json before creating a dataframe in pandas?

To filter specific elements from a nested JSON before creating a DataFrame in Pandas, you can first load the JSON data into a Python dictionary using the json module. Then, you can iterate through the nested elements and filter out the specific elements you are interested in before converting them into a DataFrame.


Here's an example code snippet to demonstrate this process:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
import json
import pandas as pd

# Load the nested JSON data
data = {
    "name": "John",
    "age": 30,
    "details": {
        "address": "123 Main St",
        "city": "New York",
        "phone_number": "555-1234"
    }
}

# Filter out specific elements from the nested JSON
filtered_data = {
    "name": data["name"],
    "age": data["age"],
    "city": data["details"]["city"]
}

# Create a DataFrame from the filtered data
df = pd.DataFrame([filtered_data])

print(df)


In this example, we load a nested JSON data into a dictionary format. We then filter out the "name", "age", and "city" elements from the nested JSON and store them into a new dictionary called filtered_data. Finally, we create a DataFrame from the filtered_data dictionary using pd.DataFrame([filtered_data]) and print the resulting DataFrame.


You can modify the filtering logic based on your specific requirements and the structure of your JSON data.


What is the difference between a regular json and a nested json?

Regular JSON is a simple key-value pair data structure where each key is unique and directly corresponds to a single value. Nested JSON, on the other hand, contains one or more key-value pairs where the value for a key can also be a nested JSON object itself. This allows for more complex and hierarchical data structures to be represented in JSON format. In other words, nested JSON includes JSON objects within JSON objects, creating a tree-like structure with multiple levels of data.


What is the recommended way to load nested json data into pandas?

The recommended way to load nested JSON data into pandas is to use the pd.json_normalize() function. This function can be used to flatten JSON data with nested structures and load it into a pandas DataFrame.


Here is an example of how you can load nested JSON data using pd.json_normalize():

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
import pandas as pd
import json

# Load the JSON data
data = {
    "name": "John Doe",
    "age": 30,
    "address": {
        "street": "123 Main St",
        "city": "New York",
        "zipcode": "10001"
    }
}

# Flatten the nested JSON data and load it into a pandas DataFrame
df = pd.json_normalize(data)

# Print the DataFrame
print(df)


This will output:

1
2
      name  age address.street address.city address.zipcode
0  John Doe   30    123 Main St    New York          10001


Using pd.json_normalize() is a convenient and efficient way to load nested JSON data into pandas and work with it as a tabular data structure.


How to handle duplicate keys in nested json while converting to dataframe in pandas?

When converting a nested JSON to a pandas DataFrame, you may encounter duplicate keys in the nested structure. One way to handle duplicate keys is to flatten the nested JSON structure before converting it to a DataFrame. Here's an example of how you can handle duplicate keys in a nested JSON:

  1. Flatten the nested JSON structure:
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
import pandas as pd
from pandas.io.json import json_normalize

# Sample nested JSON data
data = {
    "id": 1,
    "name": "John",
    "details": {
        "age": 30,
        "city": "New York"
    }
}

# Flatten the nested JSON structure
df = json_normalize(data)
print(df)


  1. Handle duplicate keys manually: If the JSON structure contains duplicate keys that cannot be easily flattened, you may need to handle the duplicates manually. One way to do this is by using a custom function to process the JSON data and then convert it to a DataFrame. Here's an example:
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
import pandas as pd

# Sample nested JSON data with duplicate keys
data = {
    "id": 1,
    "name": "John",
    "details": {
        "age": 30,
        "city": "New York",
        "age": 35
    }
}

# Custom function to handle duplicate keys
def handle_duplicate_keys(data):
    cleaned_data = data.copy()
    if 'details' in cleaned_data:
        details = cleaned_data['details']
        if 'age' in details:
            details['age'] = max(details['age'])
    return cleaned_data

# Process the JSON data and convert it to a DataFrame
processed_data = handle_duplicate_keys(data)
df = pd.DataFrame(processed_data)
print(df)


By flattening the nested JSON structure or handling duplicate keys manually, you can effectively convert the JSON data to a pandas DataFrame without losing information.


What is the best practice for processing nested json efficiently in pandas?

One common approach for processing nested JSON efficiently in pandas is to use the json_normalize function from the pandas library. This function can be used to flatten the nested JSON data into a pandas DataFrame, making it easier to work with.


Here's an example of how to use json_normalize to process nested JSON data in pandas:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
import pandas as pd
import json

# Load the nested JSON data
data = {
  "name": "John",
  "age": 30,
  "address": {
    "street": "123 Main St",
    "city": "New York",
    "zipcode": "10001"
  }
}

# Flatten the nested JSON data into a pandas DataFrame
df = pd.json_normalize(data)

# Print the resulting DataFrame
print(df)


In this example, the data dictionary contains nested JSON data with a nested "address" object. By using json_normalize, we can flatten this nested data into a pandas DataFrame, making it easier to work with and analyze.


Overall, using json_normalize is a best practice for efficiently processing nested JSON data in pandas. It allows you to easily convert nested JSON structures into tabular format for further analysis and manipulation.


What is the structure of a nested json?

A nested JSON structure is one where objects or arrays are nested within other objects or arrays. This structure allows for the grouping of related data together in a hierarchical way.


For example, a nested JSON structure might look like this:


{ "username": "johndoe", "email": "johndoe@example.com", "profile": { "name": "John Doe", "age": 30, "address": { "street": "123 Main St", "city": "Anytown", "state": "CA" } } }


In this example, the "profile" object is nested within the main object, and the "address" object is nested within the "profile" object. This allows for a more organized and structured way of representing data.

Facebook Twitter LinkedIn Whatsapp Pocket

Related Posts:

To parse a CSV (comma-separated values) file into a pandas dataframe, you can follow these steps:Import the pandas library: Begin by importing the pandas library using the following command: import pandas as pd Load the CSV file into a dataframe: Use the read_...
The syntax "dataframe[each]" in pandas represents accessing each element or column in a dataframe.In pandas, a dataframe is a two-dimensional tabular data structure that consists of rows and columns. It is similar to a spreadsheet or a SQL table.By usi...
To get values from a NumPy array into a pandas DataFrame, you can follow these steps:Import the required libraries: import numpy as np import pandas as pd Define a NumPy array: arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) Create a pandas DataFrame from th...