mysqlSaver 0.2.7

This Is MySQL Package That You Can Save DataFrame As Table, Partition, Update , Primarykey And Unique In MySQL

MySqlSaver

Effortlessly save your pandas DataFrames into MySQL with automatic table creation and support for primary keys and partitioning.

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Installation

pip install mysqlsaver

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Connection

Create a connection to your MySQL database:

from mysqlSaver import Connection

conn = Connection.connect(
    host="your_host",
    port="your_port",
    username="your_username",
    password="your_password",
    database="your_database"
)

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Keys Manager

from mysqlSaver import KeyManager

key_mgr = KeyManager(conn)


key_mgr.add_primary_key('my_table', ['id'])

key_mgr.drop_primary_key('my_table')


key_mgr.add_unique_key('my_table', ['email' , 'name'])

key_mgr.drop_unique_key('my_table', 'unique_email_name')

• Create the primarykey or unique fields
• Drop the primarykey or unique fields

Save a DataFrame

from mysqlSaver import Saver
import pandas as pd

df = pd.DataFrame({...})
saver = Saver(conn)
saver.sql_saver(df, "table_name")

This function:

• Creates the table if it does not exist
• Inserts the DataFrame data

Save with Primary Key

saver.sql_saver_with_primarykey(df, "table_name", primary_key_list=["id"])

Save with Primary Key and Auto-Update

saver.sql_saver_with_primarykey_and_update(df, "table_name", primary_key_list=["id"])

This performs UPSERT using the primary key.

Save with Unique Key (No Duplicate Insertions)

saver.sql_saver_with_unique_key(df, "table_name")

Update Table Based on Primary Key

saver.sql_updater_with_primarykey(df, "table_name", primary_key_list=["id"])

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Checker and Reader

from mysqlSaver import CheckerAndReceiver

checker = CheckerAndReceiver(conn)
exists = checker.table_exist("table_name")
df = checker.read_table("table_name")

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Table and Database Creation

from mysqlSaver import Creator

creator = Creator(conn)
creator.database_creator("new_database")
creator.create_table(df, "new_table")

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Partitioning Tables

from mysqlSaver import Partition

partitioner = Partition(conn)
partitioner.create_partition_table(
    df,
    "partitioned_table",
    range_key="your_date_column",
    primary_key_list=["id"],
    start_year_partition=2020,
    end_year_partition=2025
)