========================================= Prompt =========================================
Given a large table regarding "amazon seller order status prediction orders data", I want
to answer a question: "What is the average price for leather wallets?"
Since I cannot view the table directly, please suggest some column names that might contain
the necessary data to answer this question.
Please answer with a list of column names in JSON format without any additional explanation
.
Example:
["column1", "column2", "column3"]
======================================= Completion =======================================
["product_name", "category", "price"]

========================================= Prompt =========================================
Given a large table regarding "amazon seller order status prediction orders data", I want
to answer a question: "What is the average price for leather wallets?"
Please extract some keywords which might appear in the table cells and help answer the
question.
The keywords should be categorical values rather than numerical values.
The keywords should be contained in the question.
Please answer with a list of keywords in JSON format without any additional explanation.
Example:
["keyword1", "keyword2", "keyword3"]
======================================= Completion =======================================
["leather wallets", "average price", "amazon seller", "order status prediction", "orders
data"]

========================================= Prompt =========================================
You are working with a pandas dataframe regarding "amazon seller order status prediction
orders data" in Python. The name of the dataframe is ‘df‘. Your task is to use ‘
python_repl_ast‘ to answer the question: "What is the average price for leather wallets?"
Tool description:
- ‘python_repl_ast‘: A Python interactive shell. Use this to execute python commands. Input
should be a valid single line python command.
Since you cannot view the table directly, here are some schemas and cell values retrieved
from the table.
Schema Retrieval Results:
{"column_name": "item_total", "dtype": "object", "cell_examples": [’$449.00’, ’$399.00’, ’
$549.00’]}
{"column_name": "quantity", "dtype": "int64", "min": 1, "max": 4}
{"column_name": "order_no", "dtype": "object", "cell_examples": [’402-4845680-8041921’,
’405-9763961-5211537’, ’404-3964908-7850720’]}
...
Cell Retrieval Queries: leather wallets, average price, order status, prediction, amazon
seller
Cell Retrieval Results:
{"column_name": "order_status", "cell_value": "Delivered to buyer"}
{"column_name": "description", "cell_value": "Pure Leather Camel Color Gent’s Wallet with
Coin Compartment and Card Holders | Men’s Ultra Slim Money Organiser (1 pc)"}
...
Strictly follow the given format to respond:
Thought: you should always think about what to do
Action: the single line Python command to execute
Observation: the result of the action
... (this Thought/Action/Observation can repeat N times)
Thought: before giving the final answer, you should think about the observations
Final Answer: the final answer to the original input question (Answer1, Answer2, ...)
Notes:
- Do not use markdown or any other formatting in your responses.
- Ensure the last line is only "Final Answer: Answer1, Answer2, ..." form, no other form.
- Directly output the Final Answer rather than outputting by Python.
- Ensure to have a concluding thought that verifies the table, observations and the
question before giving the final answer.
Now, given a table regarding "amazon seller order status prediction orders data", please
use ‘python_repl_ast‘ with the column names and cell values above to answer the question: "
What is the average price for leather wallets?"
Begin!
======================================= Completion =======================================
Thought: I need to remove the comma in the item_total values before converting them to
float.
Action: df[’item_total’] = df[’item_total’].str.replace(’$’,’’).str.replace(’,’,’’).astype(
float)
Observation: success!
Thought: Now, I can filter out the rows with leather wallets and calculate the average
price.
Action: df[df[’description’].str.contains(’wallet’, case=False)][’item_total’].mean()
Observation: 442.7916666666667
Thought: before giving the final answer, I should consider the observations and ensure that
the calculations are accurate.
Final Answer: The average price for leather wallets is $442.79.