- Mapping pandas data types to redshift data types install#
- Mapping pandas data types to redshift data types password#
Redshift_connector uses the guideline for exception handling specified in the Python DB-API. paramstyle = 'named' sql = 'insert into foo(bar, jar) VALUES(:p1, :p2)' cursor. execute ( sql, ( 1, "hello world" )) # named redshift_connector. paramstyle = 'numeric' sql = 'insert into foo(bar, jar) VALUES(:1, :2)' cursor. execute ( sql, ( 1, "hello world" )) # numeric redshift_connector. paramstyle = 'qmark' sql = 'insert into foo(bar, jar) VALUES(?, ?)' cursor. Valid values for paramstyle include qmark, numeric, named, format, pyformat. The paramstyle for a cursor can be modified via cursor.paramstyle. autocommit = False Configuring cursor paramstyle # Make sure we're not in a transaction con. It can be turned on by using the autocommit property of the connection. fetchall () print ( result ) > (, ) Enabling autocommitįollowing the DB-API specification, autocommit is off by default. execute ( "select * from book" ) result : tuple = cursor. executemany ( "insert into book (bookname, author) values ( %s, %s )", ) cursor. execute ( "create Temp table book(bookname varchar,author varchar)" ) cursor.
Mapping pandas data types to redshift data types password#
connect ( host = '.', database = 'dev', user = 'awsuser', password = 'my_password' ) cursor : redshift_connector. Please open an issue with our project to request new integrations or get support for a redshift_connector issue seen in an existing integration.īasic Example import redshift_connector # Connects to Redshift cluster using AWS credentials conn = redshift_connector. Redshift_connector integrates with various open source projects to provide an interface to Amazon Redshift. Please reach out to the team by opening an issue or starting a discussion to help us fill in the gaps in our documentation. We are working to add more documentation and would love your feedback.
Mapping pandas data types to redshift data types install#
You may install from source by cloning this repository. Getting Started Install from BinaryĬonda install -c conda-forge redshift_connector This pure Python connector implements Python Database API Specification 2.0. Supported Amazon Redshift features include: Easy integration with pandas and numpy, as well as support for numerous Amazon Redshift specific features help you get the most out of your data To set the date column as the index df = df.Redshift_connector is the Amazon Redshift connector for Let take a look at an example dataset city_sales.csv, which has 1,795,144 rows data df = pd.read_csv('data/city_sales.csv',parse_dates=) df.info() RangeIndex: 1795144 entries, 0 to 1795143 Data columns (total 3 columns): # Column Dtype - 0 date datetime64 1 num int64 2 city object dtypes: datetime64(1), int64(1), object(1) memory usage: 41.1+ MB Then, you can select data by date using df.loc.
If you are going to do a lot of selections by date, it would be faster to set date column as the index first so you take advantage of the Pandas built-in optimization. And in fact, this solution is slow when you are doing a lot of selections by date in a large dataset. This solution normally requires start_date, end_date and date column to be datetime format. For example condition = (df > start_date) & (df <= end_date) df.loc
Improve performance by setting date column as the indexĪ common solution to select data by date is using a boolean maks.
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