talking-with-dataset-using-llm.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/faiqueali/talking-with-dataset-using-llm?scriptVersionId=166786092\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"code","source":"!pip install langchain==0.1.12\n!pip install langchain-openai==0.0.8\n!pip install langchain-experimental==0.0.54","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:32:44.619069Z","iopub.execute_input":"2024-03-13T06:32:44.619535Z","iopub.status.idle":"2024-03-13T06:33:34.986497Z","shell.execute_reply.started":"2024-03-13T06:32:44.619503Z","shell.execute_reply":"2024-03-13T06:33:34.985064Z"},"_kg_hide-input":true,"_kg_hide-output":false,"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Requirement already satisfied: langchain==0.1.12 in /opt/conda/lib/python3.10/site-packages (0.1.12)\nRequirement already satisfied: PyYAML>=5.3 in /opt/conda/lib/python3.10/site-packages (from langchain==0.1.12) (6.0.1)\nRequirement already satisfied: SQLAlchemy<3,>=1.4 in /opt/conda/lib/python3.10/site-packages (from langchain==0.1.12) (2.0.25)\nRequirement already satisfied: aiohttp<4.0.0,>=3.8.3 in /opt/conda/lib/python3.10/site-packages (from langchain==0.1.12) (3.9.1)\nRequirement already satisfied: async-timeout<5.0.0,>=4.0.0 in 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/opt/conda/lib/python3.10/site-packages (from SQLAlchemy<3,>=1.4->langchain<0.2.0,>=0.1.12->langchain-experimental==0.0.54) (3.0.3)\nRequirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain<0.2.0,>=0.1.12->langchain-experimental==0.0.54) (1.0.0)\nDownloading langchain_experimental-0.0.54-py3-none-any.whl (177 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.6/177.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hInstalling collected packages: langchain-experimental\nSuccessfully installed langchain-experimental-0.0.54\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import os\nimport warnings\nimport pandas as pd\nimport numpy as np\n\nfrom langchain.agents.agent_types import AgentType\nfrom langchain_experimental.agents.agent_toolkits import create_csv_agent\nfrom langchain_openai import ChatOpenAI, OpenAI\n\nwarnings.simplefilter('ignore')","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:28.334121Z","iopub.execute_input":"2024-03-13T06:41:28.334573Z","iopub.status.idle":"2024-03-13T06:41:28.343428Z","shell.execute_reply.started":"2024-03-13T06:41:28.334542Z","shell.execute_reply":"2024-03-13T06:41:28.34231Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"# Constant","metadata":{}},{"cell_type":"code","source":"DATASET_PATH = '/kaggle/input/stock-exchange-data/indexProcessed.csv'\nos.environ[\"OPENAI_API_KEY\"] = '<KEY WILL GO HERE>' # Remove it later","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:30.860022Z","iopub.execute_input":"2024-03-13T06:41:30.860458Z","iopub.status.idle":"2024-03-13T06:41:30.866255Z","shell.execute_reply.started":"2024-03-13T06:41:30.860427Z","shell.execute_reply":"2024-03-13T06:41:30.865083Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"# Data Exploration Using Pandas","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv(DATASET_PATH)\ndf.head(5)","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:31.255746Z","iopub.execute_input":"2024-03-13T06:41:31.256171Z","iopub.status.idle":"2024-03-13T06:41:31.499254Z","shell.execute_reply.started":"2024-03-13T06:41:31.25614Z","shell.execute_reply":"2024-03-13T06:41:31.49785Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"  Index        Date         Open         High          Low        Close  \\\n0   HSI  1986-12-31  2568.300049  2568.300049  2568.300049  2568.300049   \n1   HSI  1987-01-02  2540.100098  2540.100098  2540.100098  2540.100098   \n2   HSI  1987-01-05  2552.399902  2552.399902  2552.399902  2552.399902   \n3   HSI  1987-01-06  2583.899902  2583.899902  2583.899902  2583.899902   \n4   HSI  1987-01-07  2607.100098  2607.100098  2607.100098  2607.100098   \n\n     Adj Close  Volume    CloseUSD  \n0  2568.300049     0.0  333.879006  \n1  2540.100098     0.0  330.213013  \n2  2552.399902     0.0  331.811987  \n3  2583.899902     0.0  335.906987  \n4  2607.100098     0.0  338.923013  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Index</th>\n      <th>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>CloseUSD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>HSI</td>\n      <td>1986-12-31</td>\n      <td>2568.300049</td>\n      <td>2568.300049</td>\n      <td>2568.300049</td>\n      <td>2568.300049</td>\n      <td>2568.300049</td>\n      <td>0.0</td>\n      <td>333.879006</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>HSI</td>\n      <td>1987-01-02</td>\n      <td>2540.100098</td>\n      <td>2540.100098</td>\n      <td>2540.100098</td>\n      <td>2540.100098</td>\n      <td>2540.100098</td>\n      <td>0.0</td>\n      <td>330.213013</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>HSI</td>\n      <td>1987-01-05</td>\n      <td>2552.399902</td>\n      <td>2552.399902</td>\n      <td>2552.399902</td>\n      <td>2552.399902</td>\n      <td>2552.399902</td>\n      <td>0.0</td>\n      <td>331.811987</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>HSI</td>\n      <td>1987-01-06</td>\n      <td>2583.899902</td>\n      <td>2583.899902</td>\n      <td>2583.899902</td>\n      <td>2583.899902</td>\n      <td>2583.899902</td>\n      <td>0.0</td>\n      <td>335.906987</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>HSI</td>\n      <td>1987-01-07</td>\n      <td>2607.100098</td>\n      <td>2607.100098</td>\n      <td>2607.100098</td>\n      <td>2607.100098</td>\n      <td>2607.100098</td>\n      <td>0.0</td>\n      <td>338.923013</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:31.501337Z","iopub.execute_input":"2024-03-13T06:41:31.501771Z","iopub.status.idle":"2024-03-13T06:41:31.50947Z","shell.execute_reply.started":"2024-03-13T06:41:31.501741Z","shell.execute_reply":"2024-03-13T06:41:31.508365Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"(104224, 9)"},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:46:45.510954Z","iopub.execute_input":"2024-03-13T06:46:45.512053Z","iopub.status.idle":"2024-03-13T06:46:45.586164Z","shell.execute_reply.started":"2024-03-13T06:46:45.511996Z","shell.execute_reply":"2024-03-13T06:46:45.584756Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"                Open           High            Low          Close  \\\ncount  104224.000000  104224.000000  104224.000000  104224.000000   \nmean     8015.353334    8063.324234    7962.581120    8014.366642   \nstd      9140.563404    9196.575802    9082.767802    9140.609758   \nmin        54.869999      54.869999      54.869999      54.869999   \n25%      2046.887756    2057.213990    2037.185943    2047.506470   \n50%      5772.140137    5812.764892    5725.199951    5773.710205   \n75%     10487.377445   10552.179690   10416.092287   10488.622560   \nmax     68775.062500   69403.750000   68516.992190   68775.062500   \n\n           Adj Close        Volume       CloseUSD  \ncount  104224.000000  1.042240e+05  104224.000000  \nmean     8014.161269  1.347646e+09    3046.729177  \nstd      9140.720456  4.427662e+09    3747.865623  \nmin        54.869999  0.000000e+00      10.204900  \n25%      2047.358490  0.000000e+00     320.460898  \n50%      5773.710205  9.529000e+05    1371.598486  \n75%     10488.622560  2.064676e+08    4383.045241  \nmax     68775.062500  9.440374e+10   18934.376173  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>CloseUSD</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>104224.000000</td>\n      <td>104224.000000</td>\n      <td>104224.000000</td>\n      <td>104224.000000</td>\n      <td>104224.000000</td>\n      <td>1.042240e+05</td>\n      <td>104224.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>8015.353334</td>\n      <td>8063.324234</td>\n      <td>7962.581120</td>\n      <td>8014.366642</td>\n      <td>8014.161269</td>\n      <td>1.347646e+09</td>\n      <td>3046.729177</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9140.563404</td>\n      <td>9196.575802</td>\n      <td>9082.767802</td>\n      <td>9140.609758</td>\n      <td>9140.720456</td>\n      <td>4.427662e+09</td>\n      <td>3747.865623</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>54.869999</td>\n      <td>54.869999</td>\n      <td>54.869999</td>\n      <td>54.869999</td>\n      <td>54.869999</td>\n      <td>0.000000e+00</td>\n      <td>10.204900</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2046.887756</td>\n      <td>2057.213990</td>\n      <td>2037.185943</td>\n      <td>2047.506470</td>\n      <td>2047.358490</td>\n      <td>0.000000e+00</td>\n      <td>320.460898</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5772.140137</td>\n      <td>5812.764892</td>\n      <td>5725.199951</td>\n      <td>5773.710205</td>\n      <td>5773.710205</td>\n      <td>9.529000e+05</td>\n      <td>1371.598486</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>10487.377445</td>\n      <td>10552.179690</td>\n      <td>10416.092287</td>\n      <td>10488.622560</td>\n      <td>10488.622560</td>\n      <td>2.064676e+08</td>\n      <td>4383.045241</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>68775.062500</td>\n      <td>69403.750000</td>\n      <td>68516.992190</td>\n      <td>68775.062500</td>\n      <td>68775.062500</td>\n      <td>9.440374e+10</td>\n      <td>18934.376173</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# Data Exploration Using AI CSV Agent","metadata":{}},{"cell_type":"code","source":"agent = create_csv_agent(\n    ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\"),\n    DATASET_PATH,\n    verbose=True,\n    agent_type=AgentType.OPENAI_FUNCTIONS,\n)","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:33.080149Z","iopub.execute_input":"2024-03-13T06:41:33.080721Z","iopub.status.idle":"2024-03-13T06:41:33.325818Z","shell.execute_reply.started":"2024-03-13T06:41:33.080687Z","shell.execute_reply":"2024-03-13T06:41:33.324631Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"agent.run(\"how many rows are there?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:33.327679Z","iopub.execute_input":"2024-03-13T06:41:33.328244Z","iopub.status.idle":"2024-03-13T06:41:35.9587Z","shell.execute_reply.started":"2024-03-13T06:41:33.32819Z","shell.execute_reply":"2024-03-13T06:41:35.957441Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': 'df.shape[0]'}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m104224\u001b[0m\u001b[32;1m\u001b[1;3mThere are 104,224 rows in the dataframe.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"'There are 104,224 rows in the dataframe.'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"how many column are there\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:41:43.729139Z","iopub.execute_input":"2024-03-13T06:41:43.729644Z","iopub.status.idle":"2024-03-13T06:42:26.415523Z","shell.execute_reply.started":"2024-03-13T06:41:43.72961Z","shell.execute_reply":"2024-03-13T06:42:26.414226Z"},"trusted":true},"execution_count":32,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': 'len(df.columns)'}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m9\u001b[0m\u001b[32;1m\u001b[1;3mThere are 9 columns in the dataframe.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"'There are 9 columns in the dataframe.'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"What are the columns types?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:42:26.417344Z","iopub.execute_input":"2024-03-13T06:42:26.417695Z","iopub.status.idle":"2024-03-13T06:42:49.783281Z","shell.execute_reply.started":"2024-03-13T06:42:26.417667Z","shell.execute_reply":"2024-03-13T06:42:49.781856Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': 'df.dtypes'}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3mIndex         object\nDate          object\nOpen         float64\nHigh         float64\nLow          float64\nClose        float64\nAdj Close    float64\nVolume       float64\nCloseUSD     float64\ndtype: object\u001b[0m\u001b[32;1m\u001b[1;3mThe column types in the dataframe are as follows:\n\n- Index: object\n- Date: object\n- Open: float64\n- High: float64\n- Low: float64\n- Close: float64\n- Adj Close: float64\n- Volume: float64\n- CloseUSD: float64\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"agent.run(\"What is the 'High' mean\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:47:13.500197Z","iopub.execute_input":"2024-03-13T06:47:13.500748Z","iopub.status.idle":"2024-03-13T06:47:15.967602Z","shell.execute_reply.started":"2024-03-13T06:47:13.500716Z","shell.execute_reply":"2024-03-13T06:47:15.966078Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"df['High'].mean()\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m8063.324234438219\u001b[0m\u001b[32;1m\u001b[1;3mThe mean value of the 'High' column in the dataframe is approximately 8063.32.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"\"The mean value of the 'High' column in the dataframe is approximately 8063.32.\""},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"What is the 'Close' mean\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T06:53:02.068538Z","iopub.execute_input":"2024-03-13T06:53:02.069029Z","iopub.status.idle":"2024-03-13T06:53:04.668736Z","shell.execute_reply.started":"2024-03-13T06:53:02.069Z","shell.execute_reply":"2024-03-13T06:53:04.667397Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"df['Close'].mean()\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m8014.366642221821\u001b[0m\u001b[32;1m\u001b[1;3mThe mean of the 'Close' column in the dataframe is approximately 8014.37.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"\"The mean of the 'Close' column in the dataframe is approximately 8014.37.\""},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"What is the max closing value?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T07:17:19.300775Z","iopub.execute_input":"2024-03-13T07:17:19.301318Z","iopub.status.idle":"2024-03-13T07:17:21.36014Z","shell.execute_reply.started":"2024-03-13T07:17:19.301286Z","shell.execute_reply":"2024-03-13T07:17:21.358942Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"df['Close'].max()\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m68775.0625\u001b[0m\u001b[32;1m\u001b[1;3mThe maximum closing value is 68775.0625.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"'The maximum closing value is 68775.0625.'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"What is the max closing USD value?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T07:17:40.283629Z","iopub.execute_input":"2024-03-13T07:17:40.284075Z","iopub.status.idle":"2024-03-13T07:17:42.22506Z","shell.execute_reply.started":"2024-03-13T07:17:40.284044Z","shell.execute_reply":"2024-03-13T07:17:42.22394Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"df['CloseUSD'].max()\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m18934.3761734\u001b[0m\u001b[32;1m\u001b[1;3mThe maximum closing USD value is 18934.3761734.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"'The maximum closing USD value is 18934.3761734.'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"Which particular record hai highest USD value?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T07:18:58.631324Z","iopub.execute_input":"2024-03-13T07:18:58.631793Z","iopub.status.idle":"2024-03-13T07:19:03.611033Z","shell.execute_reply.started":"2024-03-13T07:18:58.631763Z","shell.execute_reply":"2024-03-13T07:19:03.609561Z"},"trusted":true},"execution_count":43,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"df[df['CloseUSD'] == df['CloseUSD'].max()]\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3m       Index        Date         Open         High          Low        Close  \\\n88336  GDAXI  2021-05-28  15443.13965  15548.71973  15435.74023  15519.98047   \n\n         Adj Close      Volume      CloseUSD  \n88336  15519.98047  56862000.0  18934.376173  \u001b[0m\u001b[32;1m\u001b[1;3mThe record with the highest USD value is:\n\n| Index | Date       | Open        | High        | Low         | Close       | Adj Close   | Volume     | CloseUSD    |\n|-------|------------|-------------|-------------|-------------|-------------|-------------|------------|-------------|\n| GDAXI | 2021-05-28 | 15443.13965 | 15548.71973 | 15435.74023 | 15519.98047 | 15519.98047 | 56862000.0 | 18934.376173 |\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"'The record with the highest USD value is:\\n\\n| Index | Date       | Open        | High        | Low         | Close       | Adj Close   | Volume     | CloseUSD    |\\n|-------|------------|-------------|-------------|-------------|-------------|-------------|------------|-------------|\\n| GDAXI | 2021-05-28 | 15443.13965 | 15548.71973 | 15435.74023 | 15519.98047 | 15519.98047 | 56862000.0 | 18934.376173 |'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"Does the data follows normal distribution?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T07:19:42.746384Z","iopub.execute_input":"2024-03-13T07:19:42.746979Z","iopub.status.idle":"2024-03-13T07:20:06.818751Z","shell.execute_reply.started":"2024-03-13T07:19:42.746945Z","shell.execute_reply":"2024-03-13T07:20:06.817316Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': \"import pandas as pd\\nimport scipy.stats as stats\\n\\n# Extract the 'Close' column from the dataframe\\nclose_values = df['Close']\\n\\n# Perform normality test\\nresult = stats.normaltest(close_values)\\nresult\"}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3mNormaltestResult(statistic=63401.90411336032, pvalue=0.0)\u001b[0m\u001b[32;1m\u001b[1;3mThe p-value of the normality test is 0.0, which is less than the significance level of 0.05. Therefore, we can conclude that the data does not follow a normal distribution.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"'The p-value of the normality test is 0.0, which is less than the significance level of 0.05. Therefore, we can conclude that the data does not follow a normal distribution.'"},"metadata":{}}]},{"cell_type":"code","source":"agent.run(\"Does the data has any missing values?\")","metadata":{"execution":{"iopub.status.busy":"2024-03-13T07:20:42.436421Z","iopub.execute_input":"2024-03-13T07:20:42.436856Z","iopub.status.idle":"2024-03-13T07:20:44.388173Z","shell.execute_reply.started":"2024-03-13T07:20:42.436818Z","shell.execute_reply":"2024-03-13T07:20:44.387194Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"\n\n\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n\u001b[32;1m\u001b[1;3m\nInvoking: `python_repl_ast` with `{'query': 'df.isnull().sum()'}`\n\n\n\u001b[0m\u001b[36;1m\u001b[1;3mIndex        0\nDate         0\nOpen         0\nHigh         0\nLow          0\nClose        0\nAdj Close    0\nVolume       0\nCloseUSD     0\ndtype: int64\u001b[0m\u001b[32;1m\u001b[1;3mNo, the data does not have any missing values.\u001b[0m\n\n\u001b[1m> Finished chain.\u001b[0m\n","output_type":"stream"},{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"'No, the data does not have any missing values.'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}