From Pandas
When combined with the connector, Pandas can be used to generate data frames that contain your Apache Impala 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("apacheimpala:///?Server=127.0.0.1;Port=21050;")
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 City, CompanyName, $exNumericCol; FROM [CData].[Default].Customers;""", 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({"City": ["Jon Deere"], "CompanyName": ["RSSBus Inc."]}) df.to_sql("[CData].[Default].Customers", con=engine, if_exists="append", index=False)