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dbactor-0.0.3
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DBActor: ORM helper and alternative
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dbactor-0.0.3 |
| نام | dbactor |
| نسخه کتابخانه | 0.0.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jack Schultz |
| ایمیل نویسنده | jackschultz23@gmail.com |
| آدرس صفحه اصلی | http://github.com/jackschultz/dbactor |
| آدرس اینترنتی | https://pypi.org/project/dbactor/ |
| مجوز | MIT |
# DBActor
Simple class that interacts with Postgres to avoid ORM specifics.
### Instillation
DBActor supports multiple types of integrations. In many cases, you're looking to use it for a specific purpose. Sepcific extras installations allows for this.
Examples of what the different extras allows you to do are below.
```bash
$ pip install dbactor
$ pip install dbactor['jinjasql']
$ pip install dbactor['sqlalchemy']
$ pip install dbactor['pandas']
$ pip install dbactor['all']
```
### Initialize
Preference for DBActor location is in `db/__init__.py`.
```python
import os
from dbactor import DBActor
db_name = os.environ.get('DB_NAME', 'dbname')
db_user = os.environ.get('DB_USER', 'dbuser')
db_password = os.environ.get('DB_PASSWORD', 'dbpassword')
db_host = os.environ.get('DB_HOST', 'localhost')
db_port = os.environ.get('DB_PORT', '5432')
actor = DBActor(database=db_name, user=db_user, password=db_password, host=db_host, port=db_port)
# Or initialize with the url
db_url = f'postgresql://{db_user}:{db_password}@{db_host}:{db_port}/{db_name}'
actor = DBActor(url=db_url)
```
Then, to use in any file import with,
```python
from db import actor
```
## Examples
In examples below, the following table.
**Product**
`__tablename__ == products`
| column_name | data_type |
| ----------- | --------- |
| id | int4 |
| name | varchar |
| cost | numeric
Statements for initial creation without using DBActor.
```sql
-- products.sql
-- Sequence and defined type
CREATE SEQUENCE IF NOT EXISTS products_id_seq;
-- Table Definition
CREATE TABLE "public"."products" (
"id" int4 NOT NULL DEFAULT nextval('products_id_seq'::regclass),
"name" varchar,
"cost" numeric,
PRIMARY KEY ("id")
);
INSERT INTO "public"."products" ("id", "name", "cost") VALUES
('1', 'mug', '4'),
('2', 'fork', '1'),
('3', 'spoon', '1'),
('4', 'knife', '1'),
('5', 'pan', '8');
```
## Actions
```python
from db import actor
qstr = 'select count(*) from products'
res = actor.call_one(qstr)
print(res)
# RealDictRow([('count', 5)])
qstr = 'select * from products;'
# If wanting RealDict
res = actor.call_all(qstr)
print(res)
# [RealDictRow([('id', 1), ('name', 'mug'), ('cost', Decimal('4'))]), RealDictRow([('id', 2), ('name', 'fork'), ('cost', Decimal('1'))]), RealDictRow([('id', 3), ('name', 'spoon'), ('cost', Decimal('1'))]), RealDictRow([('id', 4), ('name', 'knife'), ('cost', Decimal('1'))]), RealDictRow([('id', 5), ('name', 'pan'), ('cost', Decimal('8'))])]
# If wanting python dict rather than RealDict:
res = actor.call_all_dict(qstr)
print(res)
# [{'id': 1, 'name': 'mug', 'cost': Decimal('4')}, {'id': 2, 'name': 'fork', 'cost': Decimal('1')}, {'id': 3, 'name': 'spoon', 'cost': Decimal('1')}, {'id': 4, 'name': 'knife', 'cost': Decimal('1')}, {'id': 5, 'name': 'pan', 'cost': Decimal('8')}]
```
## JinjaSql
In cases where you may or may not have paramaters, JinjaSql is a great way to design queries, and DBActor allows for those in all cases.
```python
from dbactor import DBJinjaSqlActor
from db import db_url
actor = DBJinjaSqlActor(url=db_url)
qstr = '''
SELECT
*
FROM
products
WHERE
1 = 1
{% if min_cost %}
AND asdf >= min_cost
{% endif %}
{% if max_cost %}
AND asdf <= max_cost
{% endif %}
;
'''
qparams = dict(min_cost=3, max_cost=4)
res = actor.call_all_dict(qstr, qparams=qparams)
print(res)
# [{'id': 1, 'name': 'mug', 'cost': Decimal('4')}]
```
## SqlAlchemy
DBActor is made to avoid using ORMs for the most part. However, when inserting or updating rows, ORMs prove to be simpler. with `DBSqlalchemyActor`, you can either create or update with sql strings alone, or also with SQLAlchemy models.
```python
# db/models.py
from sqlalchemy import Column, Integer, Float, String
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
class Product(Base):
__tablename__ = 'products'
id = Column(Integer, primary_key=True)
name = Column(Integer)
price = Column(Float)
```
A perfect use case for this might be importing csvs.
Imagine a case where every day, someone creates a new csv with updated prices for the Things. In this case, there might be a daily job of looping through the csv and wanting to create or update an object for each row.
products.csv
```csv
name,price
mug, 10
cup, 5
plate, 3
bowl, 6
```
```python
import csv
from dbactor import DBSqlAlchemyActor
from db import db_url
from db.models import Product
actor = DBSqlAlchemyActor(url=db_url)
with open('products.csv', 'r') as csvfile:
csvreader = csv.DictReader(csvfile)
for row in csvreader:
keys = dict(name=row['name'])
values = dict(price=row['price'])
actor.create_or_update_model(Product, keys=keys, values=values)
qstr = 'select * from products'
res = actor.call_all_dict(qstr)
print(res)
# [{'id': 2, 'name': 'fork', 'cost': Decimal('1')}, {'id': 3, 'name': 'spoon', 'cost': Decimal('1')}, {'id': 4, 'name': 'knife', 'cost': Decimal('1')}, {'id': 5, 'name': 'pan', 'cost': Decimal('8')}, {'id': 1, 'name': 'mug', 'cost': Decimal('10')}, {'id': 6, 'name': 'cup', 'cost': Decimal('5')}, {'id': 7, 'name': 'plate', 'cost': Decimal('3')}, {'id': 8, 'name': 'bowl', 'cost': Decimal('6')}]
qstr = 'select count(*) from products'
res = actor.call_one(qstr)
print(res)
# RealDictRow([('count', 8)])
```
### Pandas
When using `DBPandasActor`, you'll have the ability to `call_df` using the same connection. This is done with JinjaSQL templates.
```python
from dbactor import DBPandasActor
from db import db_url
actor = DBPandasActor(url=db_url)
qstr = '''
select * from products where cost > {{min_cost}};
'''
qparams = dict(min_cost=4)
df = actor.call_df(qstr, qparams=qparams)
print(type(df))
# <class 'pandas.core.frame.DataFrame'>
print(df)
# id name cost
# 0 2 fork 1.0
# 1 3 spoon 1.0
# 2 4 knife 1.0
# 3 5 pan 8.0
# 4 1 mug 10.0
# 5 6 cup 5.0
# 6 7 plate 3.0
# 7 8 bowl 6.0
```
نحوه نصب
نصب پکیج whl dbactor-0.0.3:
pip install dbactor-0.0.3.whl
نصب پکیج tar.gz dbactor-0.0.3:
pip install dbactor-0.0.3.tar.gz