SAS
libname mylib mssql server=myserver user=myusername password=mypassword database=mydatabase;
PROC SQL;
CREATE TABLE output_table AS
SELECT *
FROM mylib.mytable
WHERE column1 > 0 AND column2 < 10;
QUIT;
*
This will create a SAS library called mylib that is connected to the Microsoft SQL Server database specified by the server, database, user, and password options. You can then use this library to access the tables and views in the database using SAS data step and SQL statements.
;
PROC SQL;
CREATE TABLE output_table AS
SELECT *
FROM mylib.mytable
WHERE column1 > 0 AND column2 < 10;
QUIT;
*
This will create a SAS library called mylib that is connected to the Microsoft SQL Server database specified by the server, database, user, and password options. You can then use this library to access the tables and views in the database using SAS data step and SQL statements.
;
Python
"""
You can use the pandas.read_sql() function to execute a SQL query and return the results as a pandas DataFrame. This can be useful if you need to work with data stored in a SQL database in your Python code.
Here's an example of how to use pandas.read_sql() to execute a SELECT statement and return the results:
"""
import pandas as pd
import sqlite3
# Connect to the database
conn = sqlite3.connect("mydatabase.db")
# Execute a SELECT statement and store the results in a DataFrame
df = pd.read_sql("SELECT * FROM mytable", conn)
# Close the connection
conn.close()
"""
You can also use the pandas.read_sql_query() function, which is similar to pandas.read_sql() but takes an additional sqlalchemy.sql.expression.Select object as the first argument.
If you want to execute a different type of SQL statement, such as an INSERT or UPDATE, you can use the cursor.execute() method of a SQLite connection object. For example:
"""
import sqlite3
# Connect to the database
conn = sqlite3.connect("mydatabase.db")
# Create a cursor
cursor = conn.cursor()
# Execute an INSERT statement
cursor.execute("INSERT INTO mytable (column1, column2) VALUES (?, ?)", (value1, value2))
# Commit the changes
conn.commit()
# Close the connection
conn.close()
You can use the pandas.read_sql() function to execute a SQL query and return the results as a pandas DataFrame. This can be useful if you need to work with data stored in a SQL database in your Python code.
Here's an example of how to use pandas.read_sql() to execute a SELECT statement and return the results:
"""
import pandas as pd
import sqlite3
# Connect to the database
conn = sqlite3.connect("mydatabase.db")
# Execute a SELECT statement and store the results in a DataFrame
df = pd.read_sql("SELECT * FROM mytable", conn)
# Close the connection
conn.close()
"""
You can also use the pandas.read_sql_query() function, which is similar to pandas.read_sql() but takes an additional sqlalchemy.sql.expression.Select object as the first argument.
If you want to execute a different type of SQL statement, such as an INSERT or UPDATE, you can use the cursor.execute() method of a SQLite connection object. For example:
"""
import sqlite3
# Connect to the database
conn = sqlite3.connect("mydatabase.db")
# Create a cursor
cursor = conn.cursor()
# Execute an INSERT statement
cursor.execute("INSERT INTO mytable (column1, column2) VALUES (?, ?)", (value1, value2))
# Commit the changes
conn.commit()
# Close the connection
conn.close()