chain...    Thought: I need to get the average age first    Action: python_repl_ast    Action Input: df.agg({"Age": "mean"}).collect()[0][0]    Observation: 29.69911764705882    Thought: I now have the average age, I need to get the square root    Action: python_repl_ast    Action Input: math.sqrt(29.69911764705882)    Observation: name 'math' is not defined    Thought: I need to import math first    Action: python_repl_ast    Action Input: import math    Observation:     Thought: I now have the math library imported, I can get the square root    Action: python_repl_ast    Action Input: math.sqrt(29.69911764705882)    Observation: 5.449689683556195    Thought: I now know the final answer    Final Answer: 5.449689683556195        > Finished chain.    '5.449689683556195'spark.stop()Spark Connect example‚Äã# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script../sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0from pyspark.sql import SparkSession# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by# creating a remote Spark session on the client where our application runs. Before we can do that, we need# to make sure to stop the existing regular Spark session because it cannot coexist with the remote# Spark Connect session we are about to create.SparkSession.builder.master("local[*]").getOrCreate().stop()    23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.# The command we used above to launch the server configured Spark to run as localhost:15002.# So now we can create a remote Spark session on the client using the following command.spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show()
This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering.
This notebook shows how to use agents to interact with a Spark DataFrame and Spark Connect. It is mostly optimized for question answering. ->: chain...    Thought: I need to get the average age first    Action: python_repl_ast    Action Input: df.agg({"Age": "mean"}).collect()[0][0]    Observation: 29.69911764705882    Thought: I now have the average age, I need to get the square root    Action: python_repl_ast    Action Input: math.sqrt(29.69911764705882)    Observation: name 'math' is not defined    Thought: I need to import math first    Action: python_repl_ast    Action Input: import math    Observation:     Thought: I now have the math library imported, I can get the square root    Action: python_repl_ast    Action Input: math.sqrt(29.69911764705882)    Observation: 5.449689683556195    Thought: I now know the final answer    Final Answer: 5.449689683556195        > Finished chain.    '5.449689683556195'spark.stop()Spark Connect example‚Äã# in apache-spark root directory. (tested here with "spark-3.4.0-bin-hadoop3 and later")# To launch Spark with support for Spark Connect sessions, run the start-connect-server.sh script../sbin/start-connect-server.sh --packages org.apache.spark:spark-connect_2.12:3.4.0from pyspark.sql import SparkSession# Now that the Spark server is running, we can connect to it remotely using Spark Connect. We do this by# creating a remote Spark session on the client where our application runs. Before we can do that, we need# to make sure to stop the existing regular Spark session because it cannot coexist with the remote# Spark Connect session we are about to create.SparkSession.builder.master("local[*]").getOrCreate().stop()    23/05/08 10:06:09 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.# The command we used above to launch the server configured Spark to run as localhost:15002.# So now we can create a remote Spark session on the client using the following command.spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()csv_file_path = "titanic.csv"df = spark.read.csv(csv_file_path, header=True, inferSchema=True)df.show()