pysql-manager 0.0.3

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 IntegerType, StringType


class User:
    id = IntegerType()
    name = StringType(length=25)
    age = 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 showing all the data from table User

FETCH ALL DATA FROM SQL TABLE

from pysql_manager.types import IntegerType, StringType


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


from pysql_manager import PySql

users = PySql("localhost", "root", "passowrd", "DB", User)
users.fetch_all  # Return PySqlConnection

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

.show() - To show data in table form

users.fetch_all.show()  # Return 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.first() # Return single meta_class object

.last() - To get last row

users.fetch_all.last() # Return single meta_class object

.is_empty() - To get last row

users.fetch_all.is_empty() # Return Boolean

.count() - To get total count of rows

users.fetch_all.count() # Return Integer

.to_df() - Create pandas DataFrame

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

users.fetch_all.to_df() # Return 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.to_list_dict() # Return List[dict]

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

users.fetch_all.save_as_csv("path", delimiter="|") # Return None

.select() - To select specific columns from PySqlCollection

users.fetch_all.select(["age", "id"]) # Return 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"]).first()
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") # Return 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").fetch_filtered # Return PySqlCollection

.update() - To update filtered data

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

.delete() - To delete filtered data

users.filter("age > 10").delete() # Return None

INSERT DATA TO SQL TABLE

Insert is done using .insert() method, The data should be List of python dictionaries.

from pysql_manager.types import IntegerType, StringType


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


from pysql_manager import PySql

users = PySql("localhost", "root", "passowrd", "DB", User)
sql_data = [{"id": 1, "name": "user1", "age": 22}, {"id": 2, "name": "user2", "age": 12}]
users.insert(sql_data)  # Return PySql self

If there is duplicate entry for primary key (In this case id column, it will raise PRIMARY KEY ERROR). To avoid this and update on duplicate key you can use update_on_duplicate argument and pass list columns you need to update when there is a duplicate entry.

from pysql_manager.types import IntegerType, StringType


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


from pysql_manager import PySql

users = PySql("localhost", "root", "passowrd", "DB", User)
sql_data = [{"id": 1, "name": "user1", "age": 22}, {"id": 2, "name": "user2", "age": 12}]
users.insert(sql_data, update_on_duplicate=["age"])  # Return PySql self