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sql server pandas

Innocent Iguana answered on February 11, 2024 Popularity 9/10 Helpfulness 5/10

answer sql server pandas

sql server pandas

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import pymssql
import pandas as pd

# Way 1 : Using pymssql library
def execute_query(sql_query, server='localhost', database='master', username='sa', password='your_password'):
    conn = pymssql.connect(server, username, password, database) # Establish a connection to the SQL Server database
    cursor = conn.cursor() # Create a cursor object to execute SQL queries
    cursor.execute(sql_query) # Execute the SQL query
    rows = cursor.fetchall() # Fetch all rows from the result set
    columns = [column[0] for column in cursor.description] # Get the column names from the cursor description
    df = pd.DataFrame(rows, columns=columns) # Create a DataFrame from the fetched rows and column names
    cursor.close() # Close the cursor
    conn.close() # Close the database connection
    return df # Return the DataFrame

# Way 1 : Using pyodbc library
import pyodbc
import pandas as pd

def execute_query(sql_query, server='localhost', database='master', username='sa', password='your_password'):
    # Create a connection string
    conn_str = f'DRIVER={{SQL Server}};SERVER={server};DATABASE={database};UID={username};PWD={password}'
    conn = pyodbc.connect(conn_str) # Establish a connection to the SQL Server database
    df = pd.read_sql(sql_query, conn) # Execute the query and load the results into a DataFrame
    conn.close() # Close the database connection
    return df # Return the DataFrame

# Execute query
result = execute_query("SELECT * FROM table_name")
result.head(

Popularity 9/10 Helpfulness 5/10 Language sql

Source: Grepper

Tags: pandas sql sql-server

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Contributed on Feb 11 2024

Innocent Iguana

0 Answers Avg Quality 2/10

sql server pandas

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