Package: lisql
LiSQLSimplifying MySQL Database Management with PythonOverviewLiSQL is a feature-rich Python package designed to streamline interactions with multiple MySQL databases across different servers. Whether you're a developer or a data scientist, LiSQL offers an intuitive interface that simplifies database management, data manipulation, and aggregation tasks.With LiSQL, connecting to both local and remote MySQL databases becomes effortless. Its seamless integration with Python projects, including web development, data analytics, and business intelligence, makes it a versatile tool for various applications.Key Features of LiSQLEfficient ConnectionManagement: LiSQL facilitates establishing connections to different servers, enabling seamless data access and manipulation.Simplified Database and Table Operations: Create, drop, and describe databases and tables with ease, effectively organizing and structuring your data.Easy Data Manipulation: Perform data manipulations effortlessly with LiSQL's functions for selecting, inserting, updating, and deleting data.Insightful Aggregate Functions: Quickly gain valuable insights with LiSQL's aggregate functions, including COUNT, SUM, AVG, MIN, and MAX.Transaction Support: Execute multiple database operations as a single unit with transaction support, ensuring data integrity and consistency.Requirements1. Python 3.6 or higher:LiSQL requires Python 3.6 or higher.2. Python MySQL Connector:LiSQL depends on the MySQL Connector/Python library to establish connections with MySQL databases. Make sure you have this library installed.You can install the mysql-connector-python library using pip:pip install mysql-connector-python3. MySQL Server:You need access to a MySQL server, either on your local machine or a remote server, to use LiSQL for database operations.4. MySQL Database:Ensure that you have the necessary privileges to create, drop, and modify databases on the MySQL server you are connecting to.5. Network Connectivity:If you plan to connect to a remote MySQL server, make sure you have network connectivity to the server and the required permissions to access it.6. Operating System:LiSQL is compatible with Windows, macOS, and Linux operating systems.7. Python Packages:LiSQL itself does not have additional dependencies beyond the MySQL Connector/Python library. However, depending on your specific use case, you may need additional Python packages for data processing, web development, or other functionalities.Installation:To install thelisqlpackage, you can follow the steps below:1: Open a terminal or command prompt on your machine.python --version2: Make sure you havePython 3.6or higher installed on your machine. You can check your Python version by running the above command.3: Install thelisqlpackage using pip:pip install lisqlThis will automatically install the requiredmysql-connector-pythonlibrary as a dependency.4: You can now start using thelisqlpackage in your Python scripts by importing it:import lisql5: You can now start executing SQL queries using thelisqlpackage.
Note: If you encounter any errors during installation, make sure you have the necessary permissions to install Python packages on your machine. You can also try running the installation command with administrative privileges (e.g., using sudo on Linux or macOS).Usage:LiSQL provides a range of functions for seamless MySQL database interaction:1: Creating a ConnectionTo create a connection to a MySQL server, you can use the create_connection() function:import lisql

mydb = lisql.create_connection()You can also create a connection to a remote server by passing in the host, username, and password:mydb = lisql.create_remote_connection(host, username, password)2: Connecting to a DatabaseTo connect to a database, you can use the connect_database() function:mydb = lisql.connect_database(mydb, 'mydatabase')3: Connecting to a remote DatabaseTo creates a connection to a remote MySQL database using the specified host, user, and password.mydb = lisql.connect_remote_database(mydb, 'mydatabase')4: Creating a DatabaseTo create a new database, you can use the create_database() function:mydb = lisql.create_database(mydb, 'mydatabase')5: Showing Tables and DatabasesTo show the tables in the current database, you can use the show_tables() function:tables = lisql.show_tables(mydb)To show the databases on the MySQL server, you can use the show_databases() function:databases = lisql.show_databases(mydb)6: Selecting DataTo select data from a table, you can use the select_data() function:data = lisql.select_data(mydb, 'mytable', ['column1', 'column2'])
for row in data:
    print(row)7: Inserting DataTo insert data into a table, you can use the insert_data() function:lisql.insert_data(mydb, 'mytable', ['value1', 'value2'])8: Updating DataTo update data in a table, you can use the update_data() function:lisql.update_data(mydb, 'mytable', {'column1': 'value1'}, 'column2 = "value2"')9: Deleting DataTo delete data from a table, you can use the delete_data() function:lisql.delete_data(mydb, 'mytable', 'column1 = "value1"')10: Dropping a Table or DatabaseTo drop a table, you can use the drop_table() function:lisql.drop_table(mydb, 'mytable')To drop a database, you can use the drop_database() function:lisql.drop_database(mydb, 'mydatabase')11: Executing a QueryTo execute a custom SQL query, you can use the execute_query() function:lisql.execute_query(mydb, 'SELECT * FROM mytable', fetch=True)12: Describing a TableTo describe the structure of a table, you can use the describe_table() function:lisql.describe_table(mydb, 'mytable')12: Describing a DatabaseTo describe the structure of a database, you can use the describe_database() function:lisql.describe_database(mydb)Here are some usage examples:1: Creating a connection to a local MySQL server:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Select data from a table
data = lisql.select_data(mydb, 'mytable', ['column1', 'column2'])
for row in data:
    print(row)2: Creating a connection to a remote MySQL server:import lisql

# Create a connection to a remote server
mydb = lisql.create_remote_connection('remote_host', 'remote_user', 'remote_password')

# Connect to a database
mydb = lisql.connect_remote_database(mydb, 'mydatabase')

# Select data from a table
data = lisql.select_data(mydb, 'mytable', ['column1', 'column2'])
for row in data:
    print(row)3: Inserting data into a table:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Insert data into a table
lisql.insert_data(mydb, 'mytable', ['value1', 'value2'])4: Updating data in a table:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Update data in a table
lisql.update_data(mydb, 'mytable', {'column1': 'new_value'}, 'column2 = "value2"')5: Deleting data from a table:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Delete data from a table
lisql.delete_data(mydb, 'mytable', 'column1 = "value1"')6: Creating a new database:import lisql

# Create a connection
mydb = lisql.create_connection()

# Create a new database
mydb = lisql.create_database(mydb, 'mydatabase')7: Creating a new table:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Create a new table
lisql.create_table(mydb, 'mytable', {'column1': 'VARCHAR(255)', 'column2': 'INT'})8: Dropping a table:import lisql

# Create a connection
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Drop a table
lisql.drop_table(mydb, 'mytable')9: Dropping a database:import lisql

# Create a connection
mydb = lisql.create_connection()

# Drop a database
lisql.drop_database(mydb, 'mydatabase')10: For help:import lisql

lisql.helpExamplesHere's an example program that demonstrates connecting to multiple servers and databases:import lisql

# Create a connection to a local MySQL server with default user and password values
local_db = lisql.create_connection()

# Create a connection to a remote MySQL server with specified host, user, and password values
remote_db = lisql.create_remote_connection(host="example.com", user="myusername", password="mypassword")

# Connect to a database on the local server
lisql.connect_database(local_db, "my_local_database")

# Connect to a database on the remote server
lisql.connect_remote_database(remote_db, host="example.com", username="myusername", password="mypassword", database="my_remote_database")

# List all tables in the current database on the local server
local_tables = lisql.show_tables(local_db)
print("Tables in local database:")
for table in local_tables:
    print(table)

# List all tables in the current database on the remote server
remote_tables = lisql.show_tables(remote_db)
print("Tables in remote database:")
for table in remote_tables:
    print(table)

# Select data from a table in the local database
local_data = lisql.select_data(local_db, "my_table", ["column1", "column2"])
for row in local_data:
    print(row)

# Insert data into a table in the remote database
remote_data = [("value1", "value2"), ("value3", "value4")]
lisql.insert_data(remote_db, "my_table", remote_data)

# Delete data from a table in the remote database where a condition is true
lisql.delete_data(remote_db, "my_table", "column1 = 'value1'")

# Update data in a table in the local database where a condition is true
lisql.update_data(local_db, "my_table", {"column2": "new_value"}, "column1 = 'value1'")

# Execute a SQL query on the remote database and fetch all rows
remote_query = "SELECT * FROM my_table"
remote_result = lisql.execute_query(remote_db, remote_query, fetch=True)
for row in remote_result:
    print(row)

# Describe a table in the local database
local_description = lisql.describe_table(local_db, "my_table")
print("Columns in local table:")
for column in local_description:
    print(column)

# Describe the remote database
remote_description = lisql.describe_database(remote_db)
print("Tables in remote database:")
for table in remote_description:
    print(table)This program connects to a local and remote MySQL server, connects to databases on each server, performs various database operations, and retrieves information about tables and databases.Here's an example of using Transaction Support in the lisql package to create a table and insert data into it as part of a single transaction:import lisql

# Create a connection to a local MySQL server
mydb = lisql.create_connection()

# Connect to a database
mydb = lisql.connect_database(mydb, 'mydatabase')

# Enable transaction support
mydb.autocommit = False

# Define the table columns
columns = ['id INT PRIMARY KEY', 'name VARCHAR(255)', 'age INT']

# Create a table with transaction support
lisql.create_table(mydb, 'students', columns, transaction=True)

# Insert data into the table as part of the same transaction
values = [(1, 'John Doe', 25), (2, 'Jane Smith', 30)]
lisql.insert_data(mydb, 'students', values, transaction=True)

# Commit the transaction to save changes
mydb.commit()

# Display the contents of the table after the transaction
data = lisql.select_data(mydb, 'students', ['id', 'name', 'age'])
for row in data:
    print(row)Here's an example program that demonstrates how to use lisql with pandas and numpy on two servers:import lisql
import pandas as pd
import numpy as np

# create connections to two MySQL servers
conn1 = lisql.create_remote_connection('server1.example.com', 'user1', 'password1')
conn2 = lisql.create_remote_connection('server2.example.com', 'user2', 'password2')

# create databases on each server
lisql.create_database(conn1, 'mydb1')
lisql.create_database(conn2, 'mydb2')

# connect to databases on each server
db1 = lisql.connect_database(conn1, 'mydb1')
db2 = lisql.connect_database(conn2, 'mydb2')

# create tables on each database
lisql.execute_query(db1, 'CREATE TABLE mytable1 (id INT, value FLOAT)')
lisql.execute_query(db2, 'CREATE TABLE mytable2 (id INT, value FLOAT)')

# insert data into each table
data1 = np.random.rand(100, 2)
data2 = np.random.rand(100, 2)
df1 = pd.DataFrame(data1, columns=['id', 'value'])
df2 = pd.DataFrame(data2, columns=['id', 'value'])
lisql.insert_data(db1, 'mytable1', df1)
lisql.insert_data(db2, 'mytable2', df2)

# select data from each table and combine with pandas
query1 = 'SELECT * FROM mytable1'
query2 = 'SELECT * FROM mytable2'
result1 = lisql.select_data(db1, query1)
result2 = lisql.select_data(db2, query2)
df3 = pd.concat([pd.DataFrame(result1), pd.DataFrame(result2)], axis=1)

# print results
print(df3.head())Here's an example of using lisql to train a machine learning model using scikit-learn on more than two servers:import lisql
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Create connections to multiple MySQL servers
server1 = lisql.create_remote_connection('server1.com', 'user1', 'pass1')
server2 = lisql.create_remote_connection('server2.com', 'user2', 'pass2')
server3 = lisql.create_remote_connection('server3.com', 'user3', 'pass3')

# Connect to multiple databases on different servers
db1 = lisql.connect_remote_database(server1, 'database1')
db2 = lisql.connect_remote_database(server2, 'database2')
db3 = lisql.connect_remote_database(server3, 'database3')

# Query data from multiple tables on different databases and servers
data1 = lisql.select_data(db1, 'table1', ['col1', 'col2', 'col3'])
data2 = lisql.select_data(db2, 'table2', ['col4', 'col5', 'col6'])
data3 = lisql.select_data(db3, 'table3', ['col7', 'col8', 'col9'])

# Merge the data into a single DataFrame using pandas and numpy
df1 = pd.DataFrame(list(data1), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(list(data2), columns=['col4', 'col5', 'col6'])
df3 = pd.DataFrame(list(data3), columns=['col7', 'col8', 'col9'])
df = pd.concat([df1, df2, df3], axis=1)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.iloc[:, :-1], df.iloc[:, -1], test_size=0.2, random_state=42)

# Train a machine learning model using scikit-learn on the combined data
model = LinearRegression()
model.fit(X_train, y_train)

# Evaluate the model performance on the test set
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('Mean squared error:', mse)In this example, we create connections to three different MySQL servers and connect to three different databases on these servers. We then query data from three different tables on these databases and merge the data into a single DataFrame using pandas and numpy. We split the data into training and testing sets and train a machine learning model using scikit-learn on the combined data. Finally, we evaluate the model performance on the test set.Here's a sample code snippet that demonstrates how you can use lisql with TensorFlow to train a machine learning model on data stored in multiple databases and servers:import lisql
import tensorflow as tf
import pandas as pd

# Create connections to databases and servers
conn1 = lisql.create_remote_connection('host1', 'user1', 'password1')
conn2 = lisql.create_remote_connection('host2', 'user2', 'password2')

# Extract data from databases and servers
data1 = pd.read_sql_query("SELECT * FROM table1", conn1)
data2 = pd.read_sql_query("SELECT * FROM table2", conn2)

# Preprocess data
# ...

# Train machine learning model using TensorFlow
model = tf.keras.Sequential([...])
model.compile(...)
model.fit(data, labels, epochs=10, validation_data=(val_data, val_labels))

# Evaluate model on test data
test_data = pd.read_sql_query("SELECT * FROM test_table", conn1)
test_labels = pd.read_sql_query("SELECT label FROM test_table", conn2)
test_loss, test_acc = model.evaluate(test_data, test_labels, verbose=2)
print('Test accuracy:', test_acc)In this example, we first create connections to two remote MySQL servers using create_remote_connection(). We then use Pandas and SQL queries to extract data from the servers and preprocess the data. We then train a machine learning model using TensorFlow and evaluate the model on test data extracted from one of the servers.Note that this is just an example, and you can use other data science technologies such as Apache Spark or PyTorch with lisql in a similar way.LicenseLiSQL is distributed under the MIT License. See theLICENSEfile for more details.Contactstions, bug reports, or feedback, please feel free to contact the developers via:Email:aliahammad0812@outlook.comInstagram:https://www.instagram.com/the_raptor_rider_/