mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
7652d2abb0
Add Multi-CSV/DF support in CSV and DataFrame Toolkits * CSV and DataFrame toolkits now accept list of CSVs/DFs * Add default prompts for many dataframes in `pandas_dataframe` toolkit Fixes #1958 Potentially fixes #4423 ## Testing * Add single and multi-dataframe integration tests for `pandas_dataframe` toolkit with permutations of `include_df_in_prompt` * Add single and multi-CSV integration tests for csv toolkit --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
266 lines
7.0 KiB
Plaintext
266 lines
7.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c81da886",
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"metadata": {},
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"source": [
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"# Pandas Dataframe Agent\n",
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"\n",
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"This notebook shows how to use agents to interact with a pandas dataframe. It is mostly optimized for question answering.\n",
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"\n",
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"**NOTE: this agent calls the Python agent under the hood, which executes LLM generated Python code - this can be bad if the LLM generated Python code is harmful. Use cautiously.**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0cdd9bf5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import create_pandas_dataframe_agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "051ebe84",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv('titanic.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "4185ff46",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a9207a2e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to count the number of rows\n",
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"Action: python_repl_ast\n",
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"Action Input: df.shape[0]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m891\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: There are 891 rows.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'There are 891 rows.'"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"how many rows are there?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "bd43617c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to count the number of people with more than 3 siblings\n",
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"Action: python_repl_ast\n",
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"Action Input: df[df['SibSp'] > 3].shape[0]\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m30\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 30 people have more than 3 siblings.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'30 people have more than 3 siblings.'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"how many people have more than 3 siblings\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "94e64b58",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to calculate the average age first\n",
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"Action: python_repl_ast\n",
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"Action Input: df['Age'].mean()\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m29.69911764705882\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate the square root of the average age\n",
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"Action: python_repl_ast\n",
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"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mNameError(\"name 'math' is not defined\")\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to import the math library\n",
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"Action: python_repl_ast\n",
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"Action Input: import math\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now need to calculate the square root of the average age\n",
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"Action: python_repl_ast\n",
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"Action Input: math.sqrt(df['Age'].mean())\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m5.449689683556195\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: The square root of the average age is 5.449689683556195.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'The square root of the average age is 5.449689683556195.'"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"whats the square root of the average age?\")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "c4bc0584",
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"metadata": {},
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"source": [
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"### Multi DataFrame Example\n",
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"\n",
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"This next part shows how the agent can interact with multiple dataframes passed in as a list."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "42a15bd9",
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"metadata": {},
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"outputs": [],
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"source": [
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"df1 = df.copy()\n",
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"df1[\"Age\"] = df1[\"Age\"].fillna(df1[\"Age\"].mean())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "eba13b4d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mThought: I need to compare the age columns in both dataframes\n",
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"Action: python_repl_ast\n",
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"Action Input: len(df1[df1['Age'] != df2['Age']])\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m177\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: 177 rows in the age column are different.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'177 rows in the age column are different.'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent = create_pandas_dataframe_agent(OpenAI(temperature=0), [df, df1], verbose=True)\n",
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"agent.run(\"how many rows in the age column are different?\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "60d08a56",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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