The Session class is the main entry point for all Fenic DataFrame operations. It manages configuration, execution backends, and resource lifecycle. Similar to PySpark’s SparkSession.
Creating a Session get_or_create Session.get_or_create(config: SessionConfig) -> Session
Gets an existing Session or creates a new one with the configured settings.
Configuration object specifying session settings including:
app_name: Name of the applicationcloud: Whether to use cloud execution backendapi_key: API key for cloud execution (required when cloud=True) A Session instance configured with the provided settings
Local Session
Cloud Session
session = Session.get_or_create( SessionConfig( app_name = "my_app" ) )
Direct construction using Session() is not allowed. Always use Session.get_or_create().
Properties read session.read -> DataFrameReader
Returns a DataFrameReader that can be used to read data as a DataFrame.
A reader interface to read data into DataFrame from various sources (CSV, Parquet, JSON, etc.)
# Read a CSV file df = session.read.csv( "data.csv" ) # Read a Parquet file df = session.read.parquet( "data.parquet" )
catalog session.catalog -> Catalog
Interface for catalog operations on the Session, including listing tables, views, and managing temporary tables.
Catalog interface for managing tables and views
# List all tables tables = session.catalog.list_tables() # Check if table exists exists = session.catalog.table_exists( "my_table" )
Core Methods create_dataframe session.create_dataframe(data: DataLike) -> DataFrame
Create a DataFrame from a variety of Python-native data formats.
Input data. Must be one of:
Polars DataFrame
Pandas DataFrame
dict of column_name → list of values
list of dicts (each dict representing a row)
pyarrow Table
From Dictionary
From List of Dicts
From Polars
From Pandas
From PyArrow
df = session.create_dataframe({ "col1" : [ 1 , 2 , 3 ], "col2" : [ "a" , "b" , "c" ] })
table session.table(table_name: str ) -> DataFrame
Returns the specified table as a DataFrame.
Name of the table to load
# Load an existing table df = session.table( "my_table" )
Raises ValueError if the table does not exist.
view session.view(view_name: str ) -> DataFrame
Returns the specified view as a DataFrame.
# Load an existing view df = session.view( "my_view" )
Raises CatalogError if the view does not exist.
sql session.sql(query: str , / , ** tables: DataFrame) -> DataFrame
Execute a read-only SQL query against one or more DataFrames using named placeholders.
A SQL query string with placeholders like {df}
Keyword arguments mapping placeholder names to DataFrames
A lazy DataFrame representing the result of the SQL query
df1 = session.create_dataframe({ "id" : [ 1 , 2 ]}) df2 = session.create_dataframe({ "id" : [ 2 , 3 ]}) result = session.sql( "SELECT * FROM {df1} JOIN {df2} USING (id)" , df1 = df1, df2 = df2 )
Raises ValidationError if a placeholder is used in the query but not passed as a keyword argument.
stop session.stop(skip_usage_summary: bool = False ) -> None
Stops the session and closes all connections.
Whether to skip printing the usage summary
# Stop session with usage summary session.stop() # Stop without printing summary session.stop( skip_usage_summary = True )
Unless skip_usage_summary is set, a summary of your session’s metrics will print when you stop your session.
Aliases
createDataFrame → create_dataframegetOrCreate → get_or_create 