pandas-to-pydantic 0.1.3

Library for converting pandas dataframes to pydantic models

pandas-to-pydantic

WARNING: Library is currently unstable and in beta.

This library provides functions for converting Pandas Dataframes to Pydantic Models. This allows you to easily transform data in a table-like format into a json-like format. Pydantic Model annotations are matched with Pandas Dataframe columns. Supports models nested in lists.

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Table of Contents

• Installation
• License
• Example 1
• dataframe_to_pydantic
• Example 2

Installation

pip install pandas-to-pydantic

Example 1

This example will show how to convert data from a flat structure (.csv file, pandas dataframe) to a hierarchical structure (json file, pydantic models)

Example Book Data

BookID	Title	AuthorName	Genre	PublishedYear
1	Harry Potter and the Philosopher's Stone	J.K. Rowling	Fantasy	1997
2	Harry Potter and the Chamber of Secrets	J.K. Rowling	Fantasy	1998
3	1984	George Orwell	Dystopian Fiction	1949
4	Animal Farm	George Orwell	Political Satire	1945
5	Pride and Prejudice	Jane Austen	Romance	1813
7	Murder on the Orient Express	Agatha Christie	Mystery	1934
9	Adventures of Huckleberry Finn	Mark Twain	Adventure	1884
10	The Adventures of Tom Sawyer	Mark Twain	Adventure	1876
11	The Hobbit	J.R.R. Tolkien	Fantasy	1937
12	The Lord of the Rings	J.R.R. Tolkien	Fantasy	1954

import pandas as pd
from pydantic import BaseModel
from pandas_to_pydantic import dataframe_to_pydantic

# Declare pydantic models
class Book(BaseModel):
    BookID: int
    Title: str
    AuthorName: str
    Genre: str
    PublishedYear: int

# Update this to your your file path
book_data = pd.read_csv(FILE_PATH)

# Convert pandas dataframe to a pydantic root model
book_list_root = dataframe_to_pydantic(book_data, Book)

dataframe_to_pydantic returns a pydantic RootModel. Data can be accessed using its attributes and methods. https://docs.pydantic.dev/latest/api/root_model/

For example:

# Access data as a list of pydantic models
book_list_root.root

Returns (output shortened):

[Book(BookID=1, Title="Harry Potter and the Philosopher's Stone", AuthorName='J.K. Rowling', Genre='Fantasy', PublishedYear=1997),
Book(BookID=2, Title='Harry Potter and the Chamber of Secrets', AuthorName='J.K. Rowling', Genre='Fantasy', PublishedYear=1998),
Book(BookID=3, Title='1984', AuthorName='George Orwell', Genre='Dystopian Fiction', PublishedYear=1949),
...]

For example:

# Access data as a list of dict
book_list_root.model_dump()

Returns (output shortened):

[{'BookID': 1,
  'Title': "Harry Potter and the Philosopher's Stone",
  'AuthorName': 'J.K. Rowling',
  'Genre': 'Fantasy',
  'PublishedYear': 1997},
 {'BookID': 2,
  'Title': 'Harry Potter and the Chamber of Secrets',
  'AuthorName': 'J.K. Rowling',
  'Genre': 'Fantasy',
  'PublishedYear': 1998},
 {'BookID': 3,
  'Title': '1984',
  'AuthorName': 'George Orwell',
  'Genre': 'Dystopian Fiction',
  'PublishedYear': 1949},
...]

Example 2

In this example, Pydantic models are nested using the list type annotation. When there are multiple layers of nesting, unique id fields should be provided for each list field with a child model using id_column_map.

Here, the unique id column for the Genre model is Genre, and the unique id column for the Author model is AuthorName. Keys in id_column_map can be the model name or field name. Values in id_column_map are the unique column name.

For example:

class Book(BaseModel):
    BookID: int
    Title: str
    PublishedYear: int

class Author(BaseModel):
    AuthorName: str
    BookList: list[Book]

class Genre(BaseModel):
    Genre: str
    AuthorList: list[Author]

dataframe_to_pydantic(
    data=bookData,
    model=Genre,
    id_column_map={"Genre": "Genre", "AuthorList": "AuthorName"},
).model_dump()

Returns (output shortened)

[{'Genre': 'Fantasy',
  'AuthorList': [{'AuthorName': 'J.K. Rowling',
    'BookList': [{'BookID': 1,
      'Title': "Harry Potter and the Philosopher's Stone",
      'PublishedYear': 1997},
     {'BookID': 2,
      'Title': 'Harry Potter and the Chamber of Secrets',
      'PublishedYear': 1998}]},
   {'AuthorName': 'J.R.R. Tolkien',
    'BookList': [{'BookID': 11, 'Title': 'The Hobbit', 'PublishedYear': 1937},
     {'BookID': 12,
      'Title': 'The Lord of the Rings',
      'PublishedYear': 1954}]}]},
 {'Genre': 'Dystopian Fiction',
  'AuthorList': [{'AuthorName': 'George Orwell',
    'BookList': [{'BookID': 3, 'Title': '1984', 'PublishedYear': 1949}]}]},
...]

dataframe_to_pydantic

Args

• data (pandas.DataFrame)
  • Dataframe with columns matching fields in the pydantic model
  • When the pydantic model includes nested models, it is assumed that the first column is unique. See Example 2
• model (pydantic._internal._model_construction.ModelMetaClass)
  • Accepts classes created with pydantic.BaseModel
  • Supports nested models in lists
  • Annotation names must match columns in the dataframe
• id_column_map(dict[str,str])
  • Required when nesting Pydantic models
  • Each key corresponds with field name or model name
  • Each value corresponds with the unique id column for the nested Pydantic model
  • For the parent level model, use the model name as key

Returns

• model_list (pydantic.RootModel)
  • Pydantic root model created as a list of the input model
  • https://docs.pydantic.dev/latest/api/root_model/

Advanced Example

This example uses a larger data set with additional nesting.

Example Library Data

import pandas as pd
from pydantic import BaseModel
from pandas_to_pydantic import dataframe_to_pydantic

# Declare pydantic models
class LibaryDetail(BaseModel):
    LibraryName: str
    Location: str
    EstablishedYear: int
    BookCollectionSize: int

class Author(BaseModel):
    AuthorID: int
    AuthorName: str
    AuthorBirthdate: str

class Book(BaseModel):
    BookID: int
    Title: str
    Genre: str
    PublishedYear: int

class Library(BaseModel):
    LibraryID: int
    Detail: LibaryDetail
    AuthorList: list[Author]
    BookList: list[Book]

# Input data is a pandas dataframe
data = pd.read_csv(FILE_PATH)

# Convert pandas dataframe to a pydantic root model
library_list_root = dataframe_to_pydantic(
    data,
    Library,
    {
        "Library": "LibraryID",
        "BookList": "BookID",
        "AuthorList": "AuthorID",
    },
)

# Access data as a list of pydantic models
library_list_root.root

# Access data as a list of dict
library_list_root.model_dump()