in my jupyter notebook I connect to snowflake with an externalbrowser auth like so:
conn = snowflake.connector.connect(
user='<my user>',
authenticator='externalbrowser',
account='<my account>',
warehouse='<the warehouse>')
this opens an external browser to auth and after that works fine with pandas read sql:
pd.read_sql('<a query>', conn)
want to use it with ipython sql, but when I try:
%sql snowflake://conn.user#conn.account
I get:
snowflake.connector.errors.ProgrammingError) Password is empty
well I don't have one :)
any ideas how to pass this?
IPython-sql connection strings are SQLAlchemy URL standard, therefore you can do the following:
%load_ext sql
from sqlalchemy import create_engine
from snowflake.sqlalchemy import URL
engine = create_engine(URL(
account = '<account>',
user = '<user>',
database = 'testdb',
schema = 'public',
warehouse = '<wh>',
role='public',
authenticator='externalbrowser'
))
connection = engine.connect()
This would open the external browser for authentication.
Related
I am attempting to write a Python script that can take Excel sheets and import them into my SQL Server Express (with Windows Authentication) database as tables. To do this, I am using pandas to read the Excel files into a pandas DataFrame, I then hope to use pandas.to_sql() to import the data into my database. To use this function, however, I need to use sqlalchemy.create_engine().
I am able to connect to my database using pyodbc alone, and run test queries. This conection is done with the followng code:
def create_connection(server_name, database_name):
config = dict(server=server_name, database= database_name)
conn_str = ('SERVER={server};DATABASE={database};TRUSTED_CONNECTION=yes')
return pyodbc.connect(r'DRIVER={ODBC Driver 13 for SQL Server};' + conn_str.format(**config))
...
server = '<MY_SERVER_NAME>\SQLEXPRESS'
db = '<MY_DATABASE_NAME>
connection = create_connection(server, db)
cursor = connection.cursor()
cursor.execute('CREATE VIEW test_view AS SELECT * FROM existing_table')
cursor.commit()
However, this isn't much use as I can't use pandas.to_sql() - to do so I need an engine from sqlalchemy.create_engine(), but I am struggling to figure out how to use my same details in my create_connection() function above to successfully create an engine and connect to the database.
I have tried many, many combinations along the lines of:
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?driver={ODBC Driver 13 for SQL Server}?trusted_connection=yes")
conn = engine.connect().connection
or
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?trusted_connection=yes")
conn = engine.connect().connection
A Pass through exact Pyodbc string works for me:
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.engine import URL
connection_string = (
r"Driver=ODBC Driver 17 for SQL Server;"
r"Server=(local)\SQLEXPRESS;"
r"Database=myDb;"
r"Trusted_Connection=yes;"
)
connection_url = URL.create(
"mssql+pyodbc",
query={"odbc_connect": connection_string}
)
engine = create_engine(connection_url)
df = pd.DataFrame([(1, "foo")], columns=["id", "txt"])
pd.to_sql("test_table", engine, if_exists="replace", index=False)
I imported some SQL libraries to be used on jupyter notebook, the SQL Server Management Studio credential is a Window Authentication (i.e i do not need a password to use SQL SSMS).
Below is the code snippet and an image that shows the output.
import sqlalchemy
import pyodbc
SERVER = 'SERVERNAME'
DATABASE = 'DATEBASENAME'
DRIVER = 'SQL Server Native Client 11.0'
USERNAME = 'MyUserName'
PASSWORD = ''
engine = sqlalchemy.create_engine('mssql+pyodbc://#' + SERVER + '/' + DATABASE + '?trusted_connection=yes&driver=ODBC+Driver+13+for+SQL+Server')
connection = engine.connect()
%reload_ext sql
%sql mssql+pyodbc://#SERVERNAME/DATABASENAME?driver=ODBC+Driver+13+for+SQL+Server&trusted_connection=yes
team_query = """
SQL_QUERY
"""
team = %sql $team_query
team = team.DataFrame()
How do i hide the output generated from the image above.
Maybe you could use the cell magic function %%capture --no-display:
I am attempting to write a Python script that can take Excel sheets and import them into my SQL Server Express (with Windows Authentication) database as tables. To do this, I am using pandas to read the Excel files into a pandas DataFrame, I then hope to use pandas.to_sql() to import the data into my database. To use this function, however, I need to use sqlalchemy.create_engine().
I am able to connect to my database using pyodbc alone, and run test queries. This conection is done with the followng code:
def create_connection(server_name, database_name):
config = dict(server=server_name, database= database_name)
conn_str = ('SERVER={server};DATABASE={database};TRUSTED_CONNECTION=yes')
return pyodbc.connect(r'DRIVER={ODBC Driver 13 for SQL Server};' + conn_str.format(**config))
...
server = '<MY_SERVER_NAME>\SQLEXPRESS'
db = '<MY_DATABASE_NAME>
connection = create_connection(server, db)
cursor = connection.cursor()
cursor.execute('CREATE VIEW test_view AS SELECT * FROM existing_table')
cursor.commit()
However, this isn't much use as I can't use pandas.to_sql() - to do so I need an engine from sqlalchemy.create_engine(), but I am struggling to figure out how to use my same details in my create_connection() function above to successfully create an engine and connect to the database.
I have tried many, many combinations along the lines of:
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?driver={ODBC Driver 13 for SQL Server}?trusted_connection=yes")
conn = engine.connect().connection
or
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?trusted_connection=yes")
conn = engine.connect().connection
A Pass through exact Pyodbc string works for me:
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.engine import URL
connection_string = (
r"Driver=ODBC Driver 17 for SQL Server;"
r"Server=(local)\SQLEXPRESS;"
r"Database=myDb;"
r"Trusted_Connection=yes;"
)
connection_url = URL.create(
"mssql+pyodbc",
query={"odbc_connect": connection_string}
)
engine = create_engine(connection_url)
df = pd.DataFrame([(1, "foo")], columns=["id", "txt"])
pd.to_sql("test_table", engine, if_exists="replace", index=False)
I am trying to connect to snowflake using my login credentials. I'm using the following code:
snowflake.connector.connect(
user="<my_user_name>",
password="<my_password>",
account="<my_account_name_with_region_and_cloud>"
)
When I try to run the above code, I'm getting the following error:
OperationalError: 250003: Failed to get the response. Hanging? method: post, url: https://hm53485.us-east-2.aws.snowflakecomputing.com:443/session/v1/login-request?request_id=fcfdd77a-11ff-4956-9ed8-bcc332c5989a&databaseName=S3_DB&schemaName=PUBLIC&warehouse=COMPUTE_WH&request_guid=b9fdb5c9-81cb-4ecb-8d20-abef44249bbf
I'm sure that all my packages are up to date. I'm using python 3.6.4 and the latest snowflake_connector_python.
I'm currently on us-east-2 location in aws.
Can someone please help me out on this????
Just Give your account name in the account .We dont need the region and full URL.
Please check below .
----------------------------------------------------------------------
import snowflake.connector
PASSWORD = '*******'
USER = '<USERNAME>'
ACCOUNT = 'SFCSUPPORT'
WAREHOUSE = '<WHNAME>'
DATABASE = '<DBNAME>'
SCHEMA = 'PUBLIC'
print("Connecting...")
# -- (> ------------------- SECTION=connect_to_snowflake --------------------
con = snowflake.connector.connect(
user=USER,
password=PASSWORD,
account=ACCOUNT,
warehouse=WAREHOUSE,
database=DATABASE,
schema=SCHEMA
)
con.cursor().execute("USE WAREHOUSE " + WAREHOUSE)
con.cursor().execute("USE DATABASE " + DATABASE)
#con.cursor().execute("USE SCHEMA INFORMATION_SCHEMA")
try:
result = con.cursor().execute("Select * from <TABLE>")
result_list = result.fetchall()
print(result_list)
finally:
con.cursor().close()
con.cursor().close()
I'm using sqlalchemy, which you can install via pip:
pip install SQLAlchemy
https://docs.snowflake.net/manuals/user-guide/sqlalchemy.html
Here's what I have at the beginning of my notebook:
import snowflake.connector
import pandas as pd
from sqlalchemy import create_engine
from snowflake.sqlalchemy import URL
url = URL(
account = 'xxxxxxxx.east-us-2.azure',
user = 'xxxxxxxx',
password = 'xxxxxxxx',
database = 'xxxxxxxx',
schema = 'xxxxxxxx',
warehouse = 'xxxxxxxx',
role='xxxxxxxx'
)
engine = create_engine(url)
connection = engine.connect()
query = '''
select 1 AS VAL;
'''
df = pd.read_sql(query, connection)
df
I was getting a similar error. Tried few things like making sure the account name is correct as per https://docs.snowflake.com/en/user-guide/admin-account-identifier.html. The account name depends on the region in which your snowflake account is located. Note that some of the cloud regions need a cloud provider name at the end and some do it.
But it didn't help fix the issue I was facing. For me, it turned out to be a proxy issue. I was trying to connect from a corporate network with a proxy and it was blocking the connection to Snowflake. Whitelisting the snowflake URL in proxy fixed the issue for me.
I am attempting to write a Python script that can take Excel sheets and import them into my SQL Server Express (with Windows Authentication) database as tables. To do this, I am using pandas to read the Excel files into a pandas DataFrame, I then hope to use pandas.to_sql() to import the data into my database. To use this function, however, I need to use sqlalchemy.create_engine().
I am able to connect to my database using pyodbc alone, and run test queries. This conection is done with the followng code:
def create_connection(server_name, database_name):
config = dict(server=server_name, database= database_name)
conn_str = ('SERVER={server};DATABASE={database};TRUSTED_CONNECTION=yes')
return pyodbc.connect(r'DRIVER={ODBC Driver 13 for SQL Server};' + conn_str.format(**config))
...
server = '<MY_SERVER_NAME>\SQLEXPRESS'
db = '<MY_DATABASE_NAME>
connection = create_connection(server, db)
cursor = connection.cursor()
cursor.execute('CREATE VIEW test_view AS SELECT * FROM existing_table')
cursor.commit()
However, this isn't much use as I can't use pandas.to_sql() - to do so I need an engine from sqlalchemy.create_engine(), but I am struggling to figure out how to use my same details in my create_connection() function above to successfully create an engine and connect to the database.
I have tried many, many combinations along the lines of:
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?driver={ODBC Driver 13 for SQL Server}?trusted_connection=yes")
conn = engine.connect().connection
or
engine = create_engine("mssql+pyodbc://#C<MY_SERVER_NAME>\SQLEXPRESS/<MY_DATABASE_NAME>?trusted_connection=yes")
conn = engine.connect().connection
A Pass through exact Pyodbc string works for me:
import pandas as pd
from sqlalchemy import create_engine
from sqlalchemy.engine import URL
connection_string = (
r"Driver=ODBC Driver 17 for SQL Server;"
r"Server=(local)\SQLEXPRESS;"
r"Database=myDb;"
r"Trusted_Connection=yes;"
)
connection_url = URL.create(
"mssql+pyodbc",
query={"odbc_connect": connection_string}
)
engine = create_engine(connection_url)
df = pd.DataFrame([(1, "foo")], columns=["id", "txt"])
pd.to_sql("test_table", engine, if_exists="replace", index=False)