How to convert a list of Pydantic BaseModels to Pandas Dataframe

How to convert a list of Pydantic BaseModels to Pandas Dataframe

To convert a list of Pydantic BaseModels to a Pandas DataFrame, you can use the dict() method of the Pydantic models to get the model attributes as dictionaries and then use the pd.DataFrame() constructor to create the DataFrame. Here's a step-by-step guide on how to do it:

Let's assume you have a Pydantic BaseModel called MyModel as follows:

from pydantic import BaseModel class MyModel(BaseModel): name: str age: int email: str

And you have a list of instances of MyModel:

my_model_list = [ MyModel(name="Alice", age=30, email="alice@example.com"), MyModel(name="Bob", age=25, email="bob@example.com"), MyModel(name="Charlie", age=35, email="charlie@example.com"), ]

To convert this list of Pydantic BaseModels to a Pandas DataFrame, you can follow these steps:

import pandas as pd # Get the attributes of each model as dictionaries data_dict_list = [model.dict() for model in my_model_list] # Create a DataFrame from the list of dictionaries df = pd.DataFrame(data_dict_list) print(df)

Output:

 name age email 0 Alice 30 alice@example.com 1 Bob 25 bob@example.com 2 Charlie 35 charlie@example.com

In this example, we use a list comprehension to get the attribute values of each Pydantic BaseModel in the my_model_list as dictionaries using the dict() method. Then, we use pd.DataFrame() to convert the list of dictionaries into a Pandas DataFrame.

Now, df is a Pandas DataFrame containing the data from the list of Pydantic BaseModels, and you can use it for further analysis or data manipulation tasks.

Examples

  1. Convert a list of Pydantic BaseModels to Pandas DataFrame using list comprehension:

    • Description: This query involves iterating over the list of Pydantic BaseModels and extracting their attributes using dictionary comprehension. Then, using the pd.DataFrame() constructor, a Pandas DataFrame is created from the list of dictionaries.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.dict() for model in BaseModelList] df = pd.DataFrame(data)

  2. Python convert list of Pydantic BaseModels to Pandas DataFrame with to_dict():

    • Description: This query suggests using the to_dict() method provided by Pydantic BaseModels to convert each model into a dictionary representation. Then, these dictionaries are passed to the pd.DataFrame() constructor to create a Pandas DataFrame.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame(data)

  3. Convert list of Pydantic BaseModels to Pandas DataFrame using pd.json_normalize():

    • Description: This query involves converting the list of Pydantic BaseModels into a JSON-like structure using the to_dict() method and then normalizing it using pd.json_normalize(). This function converts semi-structured JSON data into a flat table.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.json_normalize(data)

  4. Python convert list of Pydantic BaseModels to Pandas DataFrame with DataFrame.from_records():

    • Description: This query suggests using the DataFrame.from_records() method provided by Pandas to directly create a DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame.from_records(data)

  5. Convert list of Pydantic BaseModels to Pandas DataFrame with explicit column names:

    • Description: This query involves explicitly specifying column names while creating the DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame(data, columns=['Column1', 'Column2', ...])

  6. Python convert list of Pydantic BaseModels to Pandas DataFrame with DataFrame constructor and orient parameter:

    • Description: This query involves using the DataFrame constructor with the orient parameter set to 'records' to directly create a DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame(data, orient='records')

  7. Convert list of Pydantic BaseModels to Pandas DataFrame with explicit index:

    • Description: This query involves explicitly specifying an index while creating the DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame(data, index=range(len(BaseModelList)))

  8. Python convert list of Pydantic BaseModels to Pandas DataFrame with DataFrame constructor and dtype parameter:

    • Description: This query involves using the DataFrame constructor with the dtype parameter to explicitly specify data types for columns while creating a DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [model.to_dict() for model in BaseModelList] df = pd.DataFrame(data, dtype={'Column1': 'int', 'Column2': 'float', ...})

  9. Convert list of Pydantic BaseModels to Pandas DataFrame with nested data handling:

    • Description: This query involves handling nested data structures within Pydantic BaseModels by flattening them before creating a Pandas DataFrame from a list of dictionaries obtained from Pydantic BaseModels using the to_dict() method.
    • Code:
      import pandas as pd from pandas.json_normalize import json_normalize # Assuming BaseModelList is a list of Pydantic BaseModels data = [json_normalize(model.dict()) for model in BaseModelList] df = pd.concat(data, ignore_index=True)

  10. Python convert list of Pydantic BaseModels to Pandas DataFrame with specific columns:

    • Description: This query involves selecting specific columns from the dictionaries obtained from Pydantic BaseModels using the to_dict() method and then creating a Pandas DataFrame from these selected columns.
    • Code:
      import pandas as pd # Assuming BaseModelList is a list of Pydantic BaseModels data = [{key: model.dict()[key] for key in ['Column1', 'Column2', ...]} for model in BaseModelList] df = pd.DataFrame(data)

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