horsql 0.1.22

An experimental SQL ORM that operates with Pandas DataFrames, offering the flexibility of dynamic querying without the need for model or schema definitions.

horsql

An experimental SQL ORM that operates with Pandas DataFrames, offering the flexibility of dynamic querying without the need for model or schema definitions.

Install

$ pip install horsql

Connect

db = horsql.connect(
    database="mydb",
    host="localhost",
    port=5432,
    user="dev",
    password="dev",
    dialect="postgresql", # or "mysql"
    echo=False,
    pool_size=5, # defaults to 5
)

Querying

import horsql.operators as o

# Get all users
df = db.public.users.get()

# Get some columns of the users that are +18
df = db.public.users.get(columns=["user_name", "age"], age=o.EqualsOrGreaterThan(18))

# (Aggregation) get the average age by city from Brazil
df = db.public.users.get(columns=["city"], avg=["age"], country="Brazil")

# Distinct columns
df = db.public.users.get(distinct=["last_name"])

# get users that were born between those dates
df = db.public.users.get(birth_date=o.Between(["1998-01-01", "2000-01-01"]))

# get users that it's last_name is Silva or Machado
df = db.public.users.get(last_name=["Silva", "Machado"])

# get users that it's last_name is not Silva or Machado
df = db.public.users.get(last_name=o.Not(["Silva", "Machado"]))

# get users from Brazil or those that have "Silva" as last_name
df = db.public.users.get(
    where=o.Or(
        country="Brazil",
        last_name="Silva"
    )
)

# get users from United States OR those that have "Smith" as last_name
# OR (first_name is "Wilian" AND last_name is "Silva")
df = db.public.users.get(
    where=o.Or(
        country="United States",
        last_name="Smith",
        o.And(
            first_name="Wilian",
            last_name="Silva"
        )
    )
)

# Ordering
df = db.public.users.order_by("age", ascending=True).get()

df = db.public.users.order_by(["age", "country"], ascending=[True, False]).get()

# Limit
df = db.public.users.limit(limit=10).get()

df = db.public.users.order_by("age", ascending=True).limit(limit=10).get()

# Pagination
df = db.public.users.paginate(page=1, page_size=10).get()

df = db.public.users.order_by("age", ascending=True).paginate(page=1, page_size=10).get()

Creating/Updating records in the database

new_user = pd.DataFrame([
    {
        "user_name": "WilianZilv",
        "first_name": "Wilian",
        "last_name": "Silva"
    }
])

# Create new records based on a dataframe
db.public.users.create(new_user)

# Upsert
db.public.users.create(new_user, on_conflict=["user_name"], update=["city", "country"])

# Updating records
new_user["city"] = "Curitiba"
new_user["country"] = "Brazil"

db.public.users.update(new_user, on_conflict=["user_name"], update=["city", "country"])

# Delete records
db.public.users.delete(user_name="WilianZilv")

Function definitions

Columns = Union[str, List[str]]

# Functions available in the Table object

def get(
    self,
    columns: Optional[Columns] = None,
    distinct: Optional[Columns] = None,
    min: Optional[Columns] = None,
    max: Optional[Columns] = None,
    sum: Optional[Columns] = None,
    avg: Optional[Columns] = None,
    count: Optional[Columns] = None,
    where: Optional[Union[And, Or]] = None,
    **and_where,
):
    ...

def create(
    self,
    df: pd.DataFrame,
    on_conflict: Optional[Columns] = None,
    update: Optional[Columns] = None,
    commit: bool = True,
):
    ...

def update(
    self,
    df: pd.DataFrame,
    on_conflict: Columns,
    update: Columns,
    commit: bool = True,
):
    ...

def limit(self, limit: int):
    ...

def paginate(self, page: int, page_size: int):
    ...

def order_by(
    self,
    columns: Union[List[str], str],
    ascending: Union[List[bool], bool] = []
):
    ...