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Pandas dataframes with object oriented programming style

Project description



(Also known as Poop), is a package that uses Pandas dataframes with object oriented programming style


  pip install pandas-oop

Some examples

from pandas_oop import models
from pandas_oop.fields import StringColumn, IntegerColumn, FloatColumn, DateColumn, BoolColumn
DB_CONNECTION = models.Connection('sqlite:///pandas_oop.db') # this is the same con_string for sqlalchemy engine
@models.sql(table='people', con=DB_CONNECTION) # Use this decorator if you want to connect your class to a database
class People(models.DataFrame):
    name = StringColumn(unique=True)
    age = IntegerColumn()
    money = FloatColumn(target_name="coins") # target_name if the name in the csv or table is coins and you want to have a different variable name
    insertion_date = DateColumn(format='%d-%m-%Y')
    is_staff = BoolColumn(true='yes', false='no')

Now when instantiating this class, it will return a custom dataframe with all the functionalities of a Pandas dataframe and some others

people = People()
people = People(from_csv=DATA_FILE, delimiter=";")
people = People(from_sql_query='select * from people')
people = People(from_df=some_dataframe)
people = People(from_iterator=some_function_that_yield_values)
for people_chunk in People(from_csv=DATA_FILE, delimiter=";", chunksize=10):

example of function that yield values:

def some_function_that_yield_values():
    while something:
        yield name, age, money, insertion_date, is_staff


You can also save it to the database with the save() method (if the dtypes of the columns change, this will raise a ValidationError):

You can upsert to the database and this will automatically look at the unique fields that were declared in the class'update')

If you want to revalidate your dataframe (convert the columns dtypes to the type that was declared in the class), you can call the validate() method:


You can also validate from another class. For example, you can do something like this:

people = People(from_csv=DATA_FILE)
jobs = Jobs(from_sql_query='select * from jobs')
people_with_jobs = people.merge(jobs, on='name').validate(from_class=PeopleWithJobs)

This is the list of the overriten methods that return a pandas_oop custom dataframe

  • 'isnull'
  • 'head'
  • 'abs'
  • 'merge'
  • 'loc' and dataframe slicing

I will add more and more methods on this list.

New features

Alembic Database migration support added:

  • On your main application package, import Base (this is a declarative_base from sqlalchemy)
from pandas_oop import Base
  • Add this configuration on the file of your alembic config
from your_app import Base
target_metadata = Base.metadata
  • And finaly, update your database url on your alembic.ini file

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