Very nice walkthrough. Now my problem is to find a data set for my own demo project that has an plausible chance of producing even a small profit. Perhaps an analysis of popular children's books, fed into a coloring book generator. The book might not actually sell, but it seems reasonable to start thinking early about how to use AI to make stuff that people will want to buy.
Great video. My question is can we use it in Power Bi with questions embedded in the form of tubs, or as a list of questions with data sources on Azure?
Thanks a lot it was great leanring, I just wanted to mention that chatgpt already knows this dataset so it's able to understand where to find the specific things. If you try something on custom data set it does not provide such in depth answers
Thnaks a lot.. I am getting this error OutputParserException: "Could not parse LLM output: `I need to use the `python_repl_ast` tool to execute the command.`" Can you please help? If I do this it works query="What is the average sepal_length?" query=query+ " using tool python_repl_ast" agent.run( query)
specifically what are you wanting? It is not good practice to read every row in a giant dataframe because it will use RAM. If you want specific rows ask for those.
import pandas as pd fruits_df = pd.read_csv('fruits.csv') with pd.option_context('display.max_rows', None,): print(fruits_df) option context is temporary change, so the next time code is executed in another cell, you will see ... again. For permanent option change use: import pandas as pd fruits_df = pd.read_csv('fruits.csv') pd.set_option('display.max_rows', None) print(fruits_df)
This was awesome - clear, concise and relatable.
Thanks
Excellent tutorial and to the point, thank you for this hands on session, looking forward to checking it out :)
Excellent tutorial, easy to understand.
Glad it was helpful!
enjoyable session :)
Great video! I'm doing some data analysis with pandas right now and this could come in handy.
Very nice walkthrough. Now my problem is to find a data set for my own demo project that has an plausible chance of producing even a small profit. Perhaps an analysis of popular children's books, fed into a coloring book generator. The book might not actually sell, but it seems reasonable to start thinking early about how to use AI to make stuff that people will want to buy.
any chance you could demonstrate using a model running locally (e.g. LLama2) please?
Do you have some recommendations about tuning up the prompt to get better outputs? Like passing dataframe schema, some explanation, etc
Great video thank you. I use the Falcon model to save money by the way. Can you summarize text that is within the pandas data frame? How?
Great video. My question is can we use it in Power Bi with questions embedded in the form of tubs, or as a list of questions with data sources on Azure?
Good one, thank you!
You're welcome!
Thanks a lot it was great leanring,
I just wanted to mention that chatgpt already knows this dataset so it's able to understand where to find the specific things. If you try something on custom data set it does not provide such in depth answers
Thats not how it works. It works by generating code and executing it behind the scene
is it possible to add conversational memory to the pandas agent?
Thnaks a lot.. I am getting this error OutputParserException: "Could not parse LLM output: `I need to use the `python_repl_ast` tool to execute the command.`" Can you please help?
If I do this it works
query="What is the average sepal_length?"
query=query+ " using tool python_repl_ast"
agent.run( query)
The video is outdated. Is there a more up-to-date version available?
how to get the action input formula as the output
for those wondering, the agent supports openai only
When the list of rows in response is very large, ... is returned in the response instead of the actual values. How can we get the actual values?
specifically what are you wanting? It is not good practice to read every row in a giant dataframe because it will use RAM. If you want specific rows ask for those.
import pandas as pd
fruits_df = pd.read_csv('fruits.csv')
with pd.option_context('display.max_rows', None,):
print(fruits_df)
option context is temporary change, so the next time code is executed in another cell, you will see ... again.
For permanent option change use:
import pandas as pd
fruits_df = pd.read_csv('fruits.csv')
pd.set_option('display.max_rows', None)
print(fruits_df)
Nice video ! AI agents have a lot of potential, very cool to see.
Can it work for pyspark? Great
I don't know you're name but I love you a lot.
It doesn't work because it is already all deprecated
Did you get any way around for this problem, I'm also getting import error and all.
I referred through new documentation still didn't helped.