pysql-manager 0.0.1.1

A python package for managing Mysql

PysqlManager

A python package to manage sql

GETTING STARTED !

Creating a PySql object is the first step. All function are defined in PySql Class (Which is base class for PysqlManager Module)

For creating PySql object we need a meta_class / reference class (meta_class is nothing but a class structure for SQL table)

User(id varchar(25), name varchar(20), Age INT)

For above table , the reference class will be

from pysql_manager.types import Column, IntegerType, StringType

class User:
    id = Column(col_type=IntegerType())
    name = Column(col_type=StringType(25))
    age = Column(col_type=IntegerType())
    __table__ = "User"

Now we can use this meta_class to create actual PySql objcet

from pysql_manager import PySql

users = PySql("localhost", "root", "passowrd", "DB", User)
users.fetch_all.show() #sample method for fetching and shoing all the data from table User

FETCH ALL DATA FROM SQL TABLE

from pysql_manager.types import Column, IntegerType, StringType

class User:
    id = Column(col_type=IntegerType())
    name = Column(col_type=StringType(25))
    age = Column(col_type=IntegerType())
    __table__ = "User"

    from pysql_manager import PySql

users = PySql("localhost", "root", "passowrd", "DB", User)
users.fetch_all.show()

fetch_all method will return a PySqlCollection object , which contain rich functionalities().

.show() - To show data in table form

users.fetch_all.show() -> None

.first() - Return first row

A single Row is nothing but an object of base class. For above example , each row will be an object of class User means, we can access row.column (In this case row.age, row.id, row.name etc)

users.fetch_all.fisrt() -> single meta_class object

.last() - To get last row

users.fetch_all.last() -> single meta_class object

.is_empty() - To get last row

users.fetch_all.is_empty() -> Boolean

.count() - To get total count of rows

users.fetch_all.count() -> Integer

.count() - To get total count of rows

users.fetch_all.count() -> Integer

.to_df() - Create pandas DataFrame

Column name defined in meta_class will be taken for Pandas DataFrame creation

users.fetch_all.to_df() -> Pandas DataFrame

.to_list_dict() - Creates List of python dictionaries

List of python dictionaries. Where each dictionary will be a SQL record

users.fetch_all.count() -> List[dict]

.save_as_csv() - To save PySqlCollection object as CSV file.

users.fetch_all.save_as_csv(path, delimiter="|") -> None

.select() - To select specific columns from PySqlCollection

users.fetch_all.select(["age", "id]) -> PySqlCollection

Since this is also returning a PySqlCollection, this can be again chained with all above methods.

Eg

users.fetch_all.select(["age", "id]).count()
users.fetch_all.select(["age", "id]).fisrt()
users.fetch_all.select(["age", "id]).last()
users.fetch_all.select(["age", "id]).show()

FILTER DATA FROM SQL

For filtering data from SQL using PySql-Manager just use the inbuilt filter() method

users.filter("age > 10") -> PySqlFilterObj

filter is a special method which will return a PySqlFilterObj which can be then used to fetch filtered data (which will return same PySqlCollection when using fetch_all() - fetch_all will return all data from SQL, but filter().fetch_filtered will return filtered data) or can be used to update, or delete filtered data

.fetch_filtered - To get PySqlCollection of filtered SQL data

    users.filter("age > 10").fecth_filtered -> PySqlCollection

.update() - To update filtered data

    users.filter("age > 10").update(nam="newName", age="12") -> None

.delete() - To delete filtered data

    users.filter("age > 10").delete() -> None