pandas-oop-0.9.5.tar.gz

 # Pandas-Oop (Also known as Poop), is a package that uses Pandas dataframes with object oriented programming style Installation: - ```shell script pip install pandas-oop ``` Some examples - ```python from pandas_oop import models from pandas_oop.fields import StringColumn, IntegerColumn, FloatColumn, DateColumn, BoolColumn ``` ```python DB_CONNECTION = models.Connection('sqlite:///pandas_oop.db') # this is the same con_string for sqlalchemy engine ``` ```python @models.sql(table='people', con=DB_CONNECTION) # Use this decorator if you want to connect your class to a database @models.Data 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 ```python people = People() or people = People(from_csv=DATA_FILE, delimiter=";") or people = People(from_sql_query='select * from people') or people = People(from_df=some_dataframe) or people = People(from_iterator=some_function_that_yield_values) ``` example of function that yield values: ```python 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): ```python people.save() ``` You can upsert to the database and this will automatically look at the unique fields that were declared in the class ```python people.save(if_row_exists='update') or people.save(if_row_exists='ignore') ``` 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: ```python people.validate() ``` You can also validate from another class. For example, you can do something like this: ```python 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) ```python from pandas_oop import Base ``` - Add this configuration on the env.py file of your alembic config ```python from your_app import Base target_metadata = Base.metadata ``` - And finaly, update your database url on your alembic.ini file