python - pandas json_normalize all columns have nested dictionaries flattening

Python - pandas json_normalize all columns have nested dictionaries flattening

To flatten all columns containing nested dictionaries in a Pandas DataFrame using json_normalize, you can iterate through each column, check if it contains nested dictionaries, and apply json_normalize to flatten those columns. You will then concatenate the resulting DataFrames to get a fully flattened DataFrame.

Here's a step-by-step guide on how to achieve this:

  1. Create a sample DataFrame with nested dictionaries.
  2. Define a function to flatten nested dictionaries in a DataFrame.
  3. Iterate over each column and flatten it if it contains nested dictionaries.
  4. Concatenate the flattened DataFrames.

Step-by-Step Example

Step 1: Create a sample DataFrame

Let's create a DataFrame with nested dictionaries:

import pandas as pd data = { 'id': [1, 2], 'info': [{'name': 'John', 'address': {'city': 'New York', 'zipcode': '10001'}}, {'name': 'Jane', 'address': {'city': 'Los Angeles', 'zipcode': '90001'}}], 'details': [{'age': 30, 'job': {'title': 'Engineer', 'salary': 100000}}, {'age': 25, 'job': {'title': 'Designer', 'salary': 80000}}] } df = pd.DataFrame(data) print("Original DataFrame:") print(df)

Output:

Original DataFrame: id info details 0 1 {'name': 'John', 'address': {'city': 'New York', 'zipcode': '10001'}} {'age': 30, 'job': {'title': 'Engineer', 'salary': 100000}} 1 2 {'name': 'Jane', 'address': {'city': 'Los Angeles', 'zipcode': '90001'}} {'age': 25, 'job': {'title': 'Designer', 'salary': 80000}}

Step 2: Define a function to flatten nested dictionaries

You can use json_normalize to flatten nested dictionaries:

from pandas import json_normalize def flatten_column(df, col): flattened = json_normalize(df[col]) flattened.columns = [f'{col}.{subcol}' for subcol in flattened.columns] return flattened

Step 3: Iterate over each column and flatten it if it contains nested dictionaries

Now, let's apply the function to each column and concatenate the results:

# Initialize an empty DataFrame to hold the flattened data flat_df = pd.DataFrame() for col in df.columns: if df[col].apply(lambda x: isinstance(x, dict) or isinstance(x, list)).all(): # Flatten the column and concatenate to the result DataFrame flattened = flatten_column(df, col) flat_df = pd.concat([flat_df, flattened], axis=1) else: # If the column does not contain nested dictionaries, add it as is flat_df[col] = df[col] print("Flattened DataFrame:") print(flat_df)

Output:

Flattened DataFrame: info.name info.address.city info.address.zipcode details.age details.job.title details.job.salary id 0 John New York 10001 30 Engineer 100000 1 1 Jane Los Angeles 90001 25 Designer 80000 2

Full Example

Here is the full example combined:

import pandas as pd from pandas import json_normalize # Sample DataFrame with nested dictionaries data = { 'id': [1, 2], 'info': [{'name': 'John', 'address': {'city': 'New York', 'zipcode': '10001'}}, {'name': 'Jane', 'address': {'city': 'Los Angeles', 'zipcode': '90001'}}], 'details': [{'age': 30, 'job': {'title': 'Engineer', 'salary': 100000}}, {'age': 25, 'job': {'title': 'Designer', 'salary': 80000}}] } df = pd.DataFrame(data) print("Original DataFrame:") print(df) # Function to flatten a column containing nested dictionaries def flatten_column(df, col): flattened = json_normalize(df[col]) flattened.columns = [f'{col}.{subcol}' for subcol in flattened.columns] return flattened # Initialize an empty DataFrame to hold the flattened data flat_df = pd.DataFrame() for col in df.columns: if df[col].apply(lambda x: isinstance(x, dict) or isinstance(x, list)).all(): # Flatten the column and concatenate to the result DataFrame flattened = flatten_column(df, col) flat_df = pd.concat([flat_df, flattened], axis=1) else: # If the column does not contain nested dictionaries, add it as is flat_df[col] = df[col] print("\nFlattened DataFrame:") print(flat_df)

This script effectively flattens all nested dictionaries in the columns of a DataFrame, resulting in a fully flattened DataFrame where each nested key becomes a separate column with a hierarchical name indicating its origin.

Examples

  1. Python pandas json_normalize flatten nested JSON

    Description: Users seek to flatten all columns containing nested dictionaries in a JSON file using pandas json_normalize.

    import pandas as pd from pandas import json_normalize # Sample JSON data with nested dictionaries data = { 'id': 1, 'name': 'John', 'details': { 'age': 30, 'address': { 'city': 'New York', 'zipcode': '10001' } } } # Flatten nested JSON using json_normalize df = json_normalize(data) print(df)

    This Python code uses json_normalize from pandas to flatten the nested dictionaries in the data JSON object into separate columns in a DataFrame (df).

  2. Python pandas flatten nested JSON with multiple levels

    Description: Asks how to flatten JSON with multiple levels of nested dictionaries using pandas.

    import pandas as pd from pandas import json_normalize # Sample JSON data with multiple levels of nested dictionaries data = { 'id': 1, 'name': 'Jane', 'info': { 'address': { 'city': 'Los Angeles', 'zipcode': '90001' }, 'contacts': { 'email': 'jane@example.com', 'phone': '123-456-7890' } } } # Flatten nested JSON using json_normalize df = json_normalize(data) print(df)

    This code flattens data, which contains multiple levels of nested dictionaries (info.address, info.contacts), into a pandas DataFrame (df).

  3. Python pandas json_normalize with list of JSON objects

    Description: Requests how to flatten a list of JSON objects with nested dictionaries using pandas.

    import pandas as pd from pandas import json_normalize # Sample list of JSON objects with nested dictionaries data = [ { 'id': 1, 'name': 'Alice', 'details': { 'age': 25, 'address': { 'city': 'Chicago', 'zipcode': '60601' } } }, { 'id': 2, 'name': 'Bob', 'details': { 'age': 30, 'address': { 'city': 'Boston', 'zipcode': '02101' } } } ] # Flatten list of JSON objects using json_normalize df = json_normalize(data) print(df)

    This Python code flattens a list of JSON objects (data) where each object has nested dictionaries (details.address) into a pandas DataFrame (df).

  4. Python pandas json_normalize flatten nested JSON with arrays

    Description: Asks how to handle JSON with arrays and nested dictionaries using pandas json_normalize.

    import pandas as pd from pandas import json_normalize # Sample JSON data with arrays and nested dictionaries data = { 'id': 1, 'name': 'Eve', 'items': [ {'name': 'item1', 'quantity': 2}, {'name': 'item2', 'quantity': 1} ] } # Flatten nested JSON including arrays using json_normalize df = json_normalize(data, 'items', ['id', 'name']) print(df)

    This code flattens data, which includes an array (items), into a pandas DataFrame (df) using json_normalize with specified record path and meta columns.

  5. Python pandas json_normalize flatten all columns

    Description: Requests a method to flatten all columns containing nested dictionaries in a JSON file using pandas.

    import pandas as pd from pandas import json_normalize # Sample JSON data with multiple nested dictionaries data = { 'id': 1, 'name': 'Sam', 'details': { 'address': { 'city': 'Seattle', 'zipcode': '98101' }, 'contacts': { 'email': 'sam@example.com', 'phone': '987-654-3210' } }, 'orders': [ {'id': 101, 'product': 'A', 'quantity': 2}, {'id': 102, 'product': 'B', 'quantity': 1} ] } # Flatten all columns with nested dictionaries using json_normalize df = json_normalize(data) print(df)

    This Python code demonstrates flattening all columns in data, which includes deeply nested dictionaries (details.address, details.contacts, orders), into a pandas DataFrame (df).

  6. Python pandas json_normalize flatten nested JSON from file

    Description: Inquires about flattening nested JSON data read from a file using pandas json_normalize.

    import pandas as pd from pandas import json_normalize import json # Read JSON data from file with open('data.json', 'r') as file: data = json.load(file) # Flatten nested JSON using json_normalize df = json_normalize(data) print(df)

    This Python code reads JSON data from a file (data.json) and flattens it into a pandas DataFrame (df) using json_normalize.

  7. Python pandas json_normalize flatten nested JSON with custom separator

    Description: Asks how to flatten nested JSON data using a custom separator with pandas json_normalize.

    import pandas as pd from pandas import json_normalize # Sample JSON data with nested dictionaries data = { 'id': 1, 'name': 'Tom', 'details.address.city': 'San Francisco', 'details.address.zipcode': '94101' } # Flatten nested JSON with custom separator using json_normalize df = json_normalize(data, sep='.') print(df)

    This Python code flattens data, which includes nested dictionaries (details.address.city, details.address.zipcode), using json_normalize with a custom separator ('.') to create a pandas DataFrame (df).

  8. Python pandas json_normalize flatten nested JSON with missing keys

    Description: Requests how to handle missing keys in nested JSON data when flattening using pandas json_normalize.

    import pandas as pd from pandas import json_normalize # Sample JSON data with missing keys data = { 'id': 1, 'name': 'Sara', 'details': { 'address': { 'city': 'Denver' } } } # Flatten nested JSON with missing keys using json_normalize df = json_normalize(data, sep='.') print(df)

    This code flattens data, where some nested keys (details.address.zipcode) are missing, using json_normalize in pandas, handling the missing keys gracefully in the resulting DataFrame (df).

  9. Python pandas json_normalize flatten nested JSON with array of objects

    Description: Asks how to flatten nested JSON containing an array of objects using pandas json_normalize.

    import pandas as pd from pandas import json_normalize # Sample JSON data with array of objects data = { 'id': 1, 'name': 'Mike', 'orders': [ {'id': 101, 'product': 'X', 'quantity': 3}, {'id': 102, 'product': 'Y', 'quantity': 2} ] } # Flatten nested JSON with array of objects using json_normalize df = json_normalize(data, 'orders', ['id', 'name']) print(df)

    This Python code flattens data, which includes an array (orders) of objects, into a pandas DataFrame (df) using json_normalize with specified record path and meta columns.

  10. Python pandas json_normalize flatten nested JSON with complex structure

    Description: Describes how to handle complex nested JSON structures using pandas json_normalize for flattening.

    import pandas as pd from pandas import json_normalize # Sample complex JSON data data = { 'id': 1, 'name': 'Lily', 'info': { 'details': { 'address': { 'city': 'Miami', 'zipcode': '33101' }, 'contacts': [ {'type': 'email', 'value': 'lily@example.com'}, {'type': 'phone', 'value': '555-123-4567'} ] } } } # Flatten complex nested JSON using json_normalize df = json_normalize(data, sep='_') print(df)

    This Python code flattens data, which includes a complex nested structure (info.details.address, info.details.contacts array), into a pandas DataFrame (df) using json_normalize, handling the complex structure gracefully.

More Tags

milliseconds deprecated okhttp ioexception latitude-longitude redraw mysql-error-1452 higher-order-functions playlist javax

More Programming Questions

More Chemical reactions Calculators

More Fitness-Health Calculators

More Tax and Salary Calculators

More Transportation Calculators