From Pandas
When combined with the connector, Pandas can be used to generate data frames that contain your CockroachDB data. Once created, a data frame can be passed to various other Python packages.
Connecting
Pandas relies on an SQLAlchemy engine to execute queries. Before you can use Pandas you must import it:import pandas as pd from sqlalchemy import create_engine engine = create_engine("cdata_cockroachdb:///?User=root;Password=root;Database=system;Server=localhost;Port=26257")
Querying Data
In Pandas, SELECT queries are provided in a call to the read_sql() method, alongside a relevant connection object. Pandas executes the query on that connection, and returns the results in the form of a data frame, which can be used for a variety of purposes.df = pd.read_sql(""" SELECT ShipName, ShipCity, $exNumericCol; FROM "defaultdb"."public".Orders;""", engine) print(df)
Modifying Data
To insert new records into a table, create a new data frame, and define its fields accordingly. When that is done, call to_sql() on the data frame to perform the INSERT operation with the connector, as shown in the example below. You must set the "if _exists" argument to "append" to prevent Pandas from attempting building the table from scratch. To prevent Pandas from writing the data frame index as a column, set index=False.df = pd.DataFrame({"ShipName": ["Raleigh"], "ShipCity": ["New York"]}) df.to_sql("\"defaultdb\".\"public\".Orders", con=engine, if_exists="append", index=False)