Datasets
Models
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"\n",
"\n",
"# PandasAI\n",
"\n",
"PandasAI is a Python library that adds Generative AI capabilities to pandas, the popular data analysis and manipulation tool. It is designed to be used in conjunction with pandas, and is not a replacement for it.\n",
"\n",
"PandasAI makes pandas (and all the most used data analyst libraries) conversational, allowing you to ask questions to your data in natural language. For example, you can ask PandasAI to find all the rows in a DataFrame where the value of a column is greater than 5, and it will return a DataFrame containing only those rows.\n",
"\n",
"You can also ask PandasAI to draw graphs, clean data, impute missing values, and generate features.\n",
"\n",
"\n",
"\n",
"# Installing PandasAI\n",
"We can directly install PandasAI with pip command."
]
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"text": [
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
"beatrix-jupyterlab 2023.128.151533 requires jupyterlab~=3.6.0, but you have jupyterlab 4.0.11 which is incompatible.\r\n",
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"tensorflowjs 4.16.0 requires packaging~=23.1, but you have packaging 21.3 which is incompatible.\r\n",
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"ydata-profiling 4.6.4 requires pydantic>=2, but you have pydantic 1.10.14 which is incompatible.\u001b[0m\u001b[31m\r\n",
"\u001b[0m"
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],
"source": [
"!pip install --quiet pandasai"
]
},
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"source": [
"# Imports\n",
"\n",
"I am using **PandasAI** with **GooglePalm** LLM model.\n",
"\n",
"<div class=\"alert alert-block alert-info\">\n",
"<b>Note that: </b> PandasAI gives the best results with OpenAI's GPT LLM models. I don't have access to OpenAI's API key because it is paid. It is also possible that some of the operations that do not work with Palm can work with OpenAI. \n",
"</div>"
]
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{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'kaggle_secrets'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandasai\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SmartDataframe\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandasai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mllm\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GooglePalm\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkaggle_secrets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m UserSecretsClient\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mseaborn\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msns\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'kaggle_secrets'"
]
}
],
"source": [
"from pandasai import SmartDataframe\n",
"from pandasai.llm import GooglePalm\n",
"from kaggle_secrets import UserSecretsClient\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import Markdown\n",
"import textwrap\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"def to_markdown(text):\n",
" return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))"
]
},
{
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"rc = {\n",
" \"axes.facecolor\": \"#F8F8F8\",\n",
" \"figure.facecolor\": \"#F8F8F8\",\n",
" \"axes.edgecolor\": \"#000000\",\n",
" \"grid.color\": \"#EBEBE7\" + \"30\",\n",
" \"font.family\": \"serif\",\n",
" \"axes.labelcolor\": \"#000000\",\n",
" \"xtick.color\": \"#000000\",\n",
" \"ytick.color\": \"#000000\",\n",
" \"grid.alpha\": 0.4,\n",
"}\n",
"\n",
"sns.set(rc=rc)\n",
"palette = ['#302c36', '#037d97', '#E4591E', '#C09741',\n",
" '#EC5B6D', '#90A6B1', '#6ca957', '#D8E3E2']\n",
"\n",
"from colorama import Style, Fore\n",
"blk = Style.BRIGHT + Fore.BLACK\n",
"mgt = Style.BRIGHT + Fore.MAGENTA\n",
"red = Style.BRIGHT + Fore.RED\n",
"blu = Style.BRIGHT + Fore.BLUE\n",
"res = Style.RESET_ALL\n",
"\n",
"plt.style.use('fivethirtyeight')"
]
},
{
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"source": [
"# Setup\n",
"1. First of all we have to setup the API key for using Google Palm model. You can get one from https://ai.google.dev/tutorials/setup\n",
"2. and then create a new secret called \"YOUR_NAME\" via Add-ons -> Secrets in the top menu, and attach it to this notebook.\n",
"3. After that define `llm` variable and pass the GooglePalm model.\n",
"4. Now we can define `SmartDataframe` in which we just pass the `.csv` file."
]
},
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"source": [
"user_secrets = UserSecretsClient()\n",
"apiKey = user_secrets.get_secret(\"Gemini_API\")"
]
},
{
"cell_type": "code",
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"source": [
"llm = GooglePalm(api_key=apiKey)\n",
"df = SmartDataframe(\"/kaggle/input/playground-series-s4e2/train.csv\", config={\"llm\": llm})"
]
},
{
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"source": [
"# 1. Basic Data Exploration:\n",
"Now that we have defined the Dataframe we can directly ask the questions or give the instructions.\n",
"\n",
"### head() function:"
]
},
{
"cell_type": "code",
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{
"name": "stdout",
"output_type": "stream",
"text": [
" id Gender Age Height Weight family_history_with_overweight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 yes \n",
"1 1 Female 18.000000 1.560000 57.000000 yes \n",
"2 2 Female 18.000000 1.711460 50.165754 yes \n",
"3 3 Female 20.952737 1.710730 131.274851 yes \n",
"4 4 Male 31.641081 1.914186 93.798055 yes \n",
"\n",
" FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF \\\n",
"0 yes 2.000000 2.983297 Sometimes no 2.763573 no 0.000000 \n",
"1 yes 2.000000 3.000000 Frequently no 2.000000 no 1.000000 \n",
"2 yes 1.880534 1.411685 Sometimes no 1.910378 no 0.866045 \n",
"3 yes 3.000000 3.000000 Sometimes no 1.674061 no 1.467863 \n",
"4 yes 2.679664 1.971472 Sometimes no 1.979848 no 1.967973 \n",
"\n",
" TUE CALC MTRANS NObeyesdad \n",
"0 0.976473 Sometimes Public_Transportation Overweight_Level_II \n",
"1 1.000000 no Automobile Normal_Weight \n",
"2 1.673584 no Public_Transportation Insufficient_Weight \n",
"3 0.780199 Sometimes Public_Transportation Obesity_Type_III \n",
"4 0.931721 Sometimes Public_Transportation Overweight_Level_II \n"
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],
"source": [
"print(df.chat('Show me the first 5 rows of data in tabular form.'))"
]
},
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"source": [
"As we can see it's giving us the first 5 rows if we put the response into `dislay` function then it'll show us it in tabular formate."
]
},
{
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"text/plain": [
" id Gender Age Height Weight family_history_with_overweight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 yes \n",
"1 1 Female 18.000000 1.560000 57.000000 yes \n",
"2 2 Female 18.000000 1.711460 50.165754 yes \n",
"3 3 Female 20.952737 1.710730 131.274851 yes \n",
"4 4 Male 31.641081 1.914186 93.798055 yes \n",
"\n",
" FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF \\\n",
"0 yes 2.000000 2.983297 Sometimes no 2.763573 no 0.000000 \n",
"1 yes 2.000000 3.000000 Frequently no 2.000000 no 1.000000 \n",
"2 yes 1.880534 1.411685 Sometimes no 1.910378 no 0.866045 \n",
"3 yes 3.000000 3.000000 Sometimes no 1.674061 no 1.467863 \n",
"4 yes 2.679664 1.971472 Sometimes no 1.979848 no 1.967973 \n",
"\n",
" TUE CALC MTRANS NObeyesdad \n",
"0 0.976473 Sometimes Public_Transportation Overweight_Level_II \n",
"1 1.000000 no Automobile Normal_Weight \n",
"2 1.673584 no Public_Transportation Insufficient_Weight \n",
"3 0.780199 Sometimes Public_Transportation Obesity_Type_III \n",
"4 0.931721 Sometimes Public_Transportation Overweight_Level_II "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Show me the first 5 rows of data in tabular form.'))"
]
},
{
"cell_type": "markdown",
"id": "b1690fb4",
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"tags": []
},
"source": [
"Printing last 5 rows."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "66928894",
"metadata": {
"execution": {
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"outputs": [
{
"data": {
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" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
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" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.407817</td>\n",
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" <td>Obesity_Type_II</td>\n",
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" <th>20756</th>\n",
" <td>20756</td>\n",
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" <td>83.520113</td>\n",
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" <td>yes</td>\n",
" <td>2.671238</td>\n",
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" <td>2.144838</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.973834</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20757</th>\n",
" <td>20757</td>\n",
" <td>Male</td>\n",
" <td>26.680376</td>\n",
" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
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" <td>Obesity_Type_II</td>\n",
" </tr>\n",
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],
"text/plain": [
" id Gender Age Height Weight \\\n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20754 Public_Transportation Insufficient_Weight \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20756 Automobile Overweight_Level_II \n",
"20757 Public_Transportation Obesity_Type_II "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Show me the last 5 rows of data in tabular form.'))"
]
},
{
"cell_type": "markdown",
"id": "e7662636",
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},
"tags": []
},
"source": [
"Printing all the columns."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bcc290b4",
"metadata": {
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},
"tags": []
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>column_name</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>id</td>\n",
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" <td>Gender</td>\n",
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" <th>3</th>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>family_history_with_overweight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>FAVC</td>\n",
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" <tr>\n",
" <th>7</th>\n",
" <td>FCVC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>NCP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>CAEC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>SMOKE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>CH2O</td>\n",
" </tr>\n",
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" </tr>\n",
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" <th>13</th>\n",
" <td>FAF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>TUE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>CALC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>MTRANS</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>NObeyesdad</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" column_name\n",
"0 id\n",
"1 Gender\n",
"2 Age\n",
"3 Height\n",
"4 Weight\n",
"5 family_history_with_overweight\n",
"6 FAVC\n",
"7 FCVC\n",
"8 NCP\n",
"9 CAEC\n",
"10 SMOKE\n",
"11 CH2O\n",
"12 SCC\n",
"13 FAF\n",
"14 TUE\n",
"15 CALC\n",
"16 MTRANS\n",
"17 NObeyesdad"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('List all the column names'))"
]
},
{
"cell_type": "markdown",
"id": "ca2996ed",
"metadata": {
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"duration": 0.01804,
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"start_time": "2024-02-26T04:10:04.902804",
"status": "completed"
},
"tags": []
},
"source": [
"Checking if there is any duplicate values. Same can be done with NULL values too!!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7cede9ca",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:04.958316Z",
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},
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/markdown": [
"> No duplicate values found."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_markdown(df.chat('Are there any duplicate values?'))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fa2053d2",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:06.904399Z",
"iopub.status.busy": "2024-02-26T04:10:06.903271Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/markdown": [
"> There are no NULL values in the data."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_markdown(df.chat('Are there any NULL values?'))"
]
},
{
"cell_type": "markdown",
"id": "0eb918d2",
"metadata": {
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"duration": 0.017681,
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"status": "completed"
},
"tags": []
},
"source": [
"# 2. Basic Oprations on Data.\n",
"Now that we have explored the dataset let's do some baseic operations on the data.\n",
"\n",
"Finding `mean` of the columns."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "575dd665",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"23.841804418681953"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('What is the mean of Age'))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6b832f7f",
"metadata": {
"execution": {
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"87.88776840264958"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('What is the mean of Weight'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "c1072bd3",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:14.220705Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"1.7002449351575297"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('What is the mean of Height'))"
]
},
{
"cell_type": "markdown",
"id": "0d9fc052",
"metadata": {
"papermill": {
"duration": 0.018173,
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"status": "completed"
},
"tags": []
},
"source": [
"Finding `value_counts` for some categorical columns."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f08f3867",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:16.998771Z",
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},
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"start_time": "2024-02-26T04:10:16.978277",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Female</th>\n",
" <td>10422</td>\n",
" </tr>\n",
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"text/plain": [
" Gender\n",
"Female 10422\n",
"Male 10336"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat(\"What are the value counts for the column 'Gender'\"))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a6ed6f81",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:26.994064Z",
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},
"tags": []
},
"outputs": [
{
"data": {
"text/markdown": [
"> The most common gender is Female."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_markdown(df.chat('Which is the most common gender?'))"
]
},
{
"cell_type": "markdown",
"id": "1e9b1d3d",
"metadata": {
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},
"tags": []
},
"source": [
"Now let's print all data in which age is equal to 18 basically using pandas `where` function."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d7720306",
"metadata": {
"execution": {
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{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>family_history_with_overweight</th>\n",
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" <td>1.880534</td>\n",
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" <th>24</th>\n",
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" <td>18.0</td>\n",
" <td>1.753321</td>\n",
" <td>52.058335</td>\n",
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" <th>20720</th>\n",
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" <td>1.000000</td>\n",
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" <th>20754</th>\n",
" <td>20754</td>\n",
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" <td>18.0</td>\n",
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" <td>4.000000</td>\n",
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" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
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],
"text/plain": [
" id Gender Age Height Weight \\\n",
"1 1 Female 18.0 1.560000 57.000000 \n",
"2 2 Female 18.0 1.711460 50.165754 \n",
"12 12 Male 18.0 1.811189 108.251044 \n",
"17 17 Female 18.0 1.560000 50.000000 \n",
"24 24 Male 18.0 1.753321 52.058335 \n",
"... ... ... ... ... ... \n",
"20717 20717 Female 18.0 1.720000 50.000000 \n",
"20720 20720 Male 18.0 1.717432 108.897324 \n",
"20722 20722 Female 18.0 1.650000 56.000000 \n",
"20746 20746 Female 18.0 1.610000 64.000000 \n",
"20754 20754 Male 18.0 1.710000 50.000000 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"12 yes yes 2.000000 2.164839 Sometimes \n",
"17 no yes 3.000000 3.000000 Sometimes \n",
"24 yes yes 2.000000 3.000000 Sometimes \n",
"... ... ... ... ... ... \n",
"20717 yes no 3.000000 3.000000 Always \n",
"20720 yes yes 2.000000 1.255350 Sometimes \n",
"20722 no yes 3.000000 3.000000 Sometimes \n",
"20746 yes yes 3.000000 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
"2 no 1.910378 no 0.866045 1.673584 no \n",
"12 no 2.530157 no 1.000000 0.553311 no \n",
"17 no 1.000000 no 1.000000 0.000000 Sometimes \n",
"24 no 2.072194 no 0.680464 1.258881 no \n",
"... ... ... ... ... ... ... \n",
"20717 no 2.000000 yes 3.000000 2.000000 Sometimes \n",
"20720 no 2.000000 no 0.000000 1.967259 Sometimes \n",
"20722 no 2.000000 no 1.000000 1.000000 no \n",
"20746 no 2.000000 no 1.000000 1.000000 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"1 Automobile Normal_Weight \n",
"2 Public_Transportation Insufficient_Weight \n",
"12 Public_Transportation Obesity_Type_I \n",
"17 Public_Transportation Normal_Weight \n",
"24 Public_Transportation Insufficient_Weight \n",
"... ... ... \n",
"20717 Public_Transportation Insufficient_Weight \n",
"20720 Public_Transportation Obesity_Type_I \n",
"20722 Public_Transportation Normal_Weight \n",
"20746 Public_Transportation Normal_Weight \n",
"20754 Public_Transportation Insufficient_Weight \n",
"\n",
"[1916 rows x 18 columns]"
]
},
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}
],
"source": [
"display(df.chat(\"Show the data in the row where 'Age'='18'\"))"
]
},
{
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"id": "01b5c0f8",
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},
"source": [
"As we can see all the rows have age=18. now, we can also try printing all the data in which weight is between 57 and 60."
]
},
{
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{
"data": {
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" <th>1</th>\n",
" <td>1</td>\n",
" <td>Female</td>\n",
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" <th>100</th>\n",
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" <th>112</th>\n",
" <td>112</td>\n",
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" <th>114</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>119</td>\n",
" <td>Male</td>\n",
" <td>23.0</td>\n",
" <td>1.82</td>\n",
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" <tr>\n",
" <th>20546</th>\n",
" <td>20546</td>\n",
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" <td>23.0</td>\n",
" <td>1.64</td>\n",
" <td>59.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>20692</th>\n",
" <td>20692</td>\n",
" <td>Female</td>\n",
" <td>19.0</td>\n",
" <td>1.65</td>\n",
" <td>58.0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>1.0</td>\n",
" <td>no</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Normal_Weight</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1028 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" id Gender Age Height Weight family_history_with_overweight \\\n",
"1 1 Female 18.0 1.56 57.0 yes \n",
"100 100 Female 38.0 1.50 60.0 no \n",
"112 112 Female 18.0 1.63 59.0 no \n",
"114 114 Male 18.0 1.76 57.0 yes \n",
"119 119 Male 23.0 1.82 58.0 no \n",
"... ... ... ... ... ... ... \n",
"20546 20546 Female 23.0 1.64 59.0 no \n",
"20565 20565 Female 20.0 1.56 58.0 no \n",
"20575 20575 Male 22.0 1.70 60.0 yes \n",
"20643 20643 Female 18.0 1.65 58.0 no \n",
"20692 20692 Female 19.0 1.65 58.0 no \n",
"\n",
" FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE CALC \\\n",
"1 yes 2.0 3.0 Frequently no 2.0 no 1.0 1.0 no \n",
"100 yes 2.0 3.0 Sometimes no 1.0 no 0.0 1.0 Sometimes \n",
"112 yes 3.0 3.0 Sometimes no 2.0 no 2.0 0.0 no \n",
"114 yes 3.0 3.0 Frequently no 2.0 no 0.0 2.0 no \n",
"119 yes 2.0 3.0 Sometimes no 1.0 no 1.0 1.0 no \n",
"... ... ... ... ... ... ... .. ... ... ... \n",
"20546 yes 2.0 3.0 Sometimes no 2.0 no 2.0 0.0 no \n",
"20565 yes 3.0 1.0 Sometimes no 2.0 no 1.0 0.0 Sometimes \n",
"20575 yes 3.0 3.0 Frequently no 2.0 no 3.0 0.0 no \n",
"20643 yes 3.0 3.0 Sometimes no 2.0 no 2.0 0.0 no \n",
"20692 yes 2.0 1.0 no no 1.0 no 1.0 0.0 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"1 Automobile Normal_Weight \n",
"100 Automobile Overweight_Level_I \n",
"112 Automobile Normal_Weight \n",
"114 Public_Transportation Insufficient_Weight \n",
"119 Public_Transportation Normal_Weight \n",
"... ... ... \n",
"20546 Public_Transportation Normal_Weight \n",
"20565 Public_Transportation Normal_Weight \n",
"20575 Walking Normal_Weight \n",
"20643 Public_Transportation Normal_Weight \n",
"20692 Public_Transportation Normal_Weight \n",
"\n",
"[1028 rows x 18 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat(\"Show the rows where 'Weight' is between 57 and 60\"))"
]
},
{
"cell_type": "markdown",
"id": "ef380538",
"metadata": {
"papermill": {
"duration": 0.019804,
"end_time": "2024-02-26T04:10:43.479000",
"exception": false,
"start_time": "2024-02-26T04:10:43.459196",
"status": "completed"
},
"tags": []
},
"source": [
"As we can see all the results have weight between 57 and 60. Now, let's try to rename a column."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "64efde1b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:43.521568Z",
"iopub.status.busy": "2024-02-26T04:10:43.520919Z",
"iopub.status.idle": "2024-02-26T04:10:45.697680Z",
"shell.execute_reply": "2024-02-26T04:10:45.696553Z"
},
"papermill": {
"duration": 2.202521,
"end_time": "2024-02-26T04:10:45.701532",
"exception": false,
"start_time": "2024-02-26T04:10:43.499011",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>family_history_with_overweight</th>\n",
" <th>FAVC</th>\n",
" <th>FCVC</th>\n",
" <th>NCP</th>\n",
" <th>CAEC</th>\n",
" <th>Smoke</th>\n",
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" <th>FAF</th>\n",
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" <th>MTRANS</th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>1.699998</td>\n",
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" <td>0.976473</td>\n",
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" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.560000</td>\n",
" <td>57.000000</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
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" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Normal_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.711460</td>\n",
" <td>50.165754</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>1.880534</td>\n",
" <td>1.411685</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.910378</td>\n",
" <td>no</td>\n",
" <td>0.866045</td>\n",
" <td>1.673584</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Female</td>\n",
" <td>20.952737</td>\n",
" <td>1.710730</td>\n",
" <td>131.274851</td>\n",
" <td>yes</td>\n",
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" <td>3.000000</td>\n",
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" <td>no</td>\n",
" <td>1.674061</td>\n",
" <td>no</td>\n",
" <td>1.467863</td>\n",
" <td>0.780199</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Male</td>\n",
" <td>31.641081</td>\n",
" <td>1.914186</td>\n",
" <td>93.798055</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.679664</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.979848</td>\n",
" <td>no</td>\n",
" <td>1.967973</td>\n",
" <td>0.931721</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20753</th>\n",
" <td>20753</td>\n",
" <td>Male</td>\n",
" <td>25.137087</td>\n",
" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.919584</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.151809</td>\n",
" <td>no</td>\n",
" <td>1.330519</td>\n",
" <td>0.196680</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20754</th>\n",
" <td>20754</td>\n",
" <td>Male</td>\n",
" <td>18.000000</td>\n",
" <td>1.710000</td>\n",
" <td>50.000000</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>Frequently</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.407817</td>\n",
" <td>3.000000</td>\n",
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" <td>2.000000</td>\n",
" <td>no</td>\n",
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" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20756</th>\n",
" <td>20756</td>\n",
" <td>Male</td>\n",
" <td>33.852953</td>\n",
" <td>1.700000</td>\n",
" <td>83.520113</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.671238</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.144838</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.973834</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20757</th>\n",
" <td>20757</td>\n",
" <td>Male</td>\n",
" <td>26.680376</td>\n",
" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.003563</td>\n",
" <td>no</td>\n",
" <td>0.684487</td>\n",
" <td>0.713823</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20758 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" id Gender Age Height Weight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 \n",
"1 1 Female 18.000000 1.560000 57.000000 \n",
"2 2 Female 18.000000 1.711460 50.165754 \n",
"3 3 Female 20.952737 1.710730 131.274851 \n",
"4 4 Male 31.641081 1.914186 93.798055 \n",
"... ... ... ... ... ... \n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"0 yes yes 2.000000 2.983297 Sometimes \n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"4 yes yes 2.679664 1.971472 Sometimes \n",
"... ... ... ... ... ... \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" Smoke CH2O SCC FAF TUE CALC \\\n",
"0 no 2.763573 no 0.000000 0.976473 Sometimes \n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
"2 no 1.910378 no 0.866045 1.673584 no \n",
"3 no 1.674061 no 1.467863 0.780199 Sometimes \n",
"4 no 1.979848 no 1.967973 0.931721 Sometimes \n",
"... ... ... .. ... ... ... \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"0 Public_Transportation Overweight_Level_II \n",
"1 Automobile Normal_Weight \n",
"2 Public_Transportation Insufficient_Weight \n",
"3 Public_Transportation Obesity_Type_III \n",
"4 Public_Transportation Overweight_Level_II \n",
"... ... ... \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20754 Public_Transportation Insufficient_Weight \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20756 Automobile Overweight_Level_II \n",
"20757 Public_Transportation Obesity_Type_II \n",
"\n",
"[20758 rows x 18 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat(\"Rename column 'SMOKE' as 'Smoke'\"))"
]
},
{
"cell_type": "markdown",
"id": "5da7e57b",
"metadata": {
"papermill": {
"duration": 0.021589,
"end_time": "2024-02-26T04:10:45.745004",
"exception": false,
"start_time": "2024-02-26T04:10:45.723415",
"status": "completed"
},
"tags": []
},
"source": [
"Creating a new column `BMI` also the important feature for this competition.\n",
"\n",
"$BMI=Weight / Height*2$"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "5842f258",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:45.787746Z",
"iopub.status.busy": "2024-02-26T04:10:45.787377Z",
"iopub.status.idle": "2024-02-26T04:10:51.083685Z",
"shell.execute_reply": "2024-02-26T04:10:51.082517Z"
},
"papermill": {
"duration": 5.32126,
"end_time": "2024-02-26T04:10:51.086652",
"exception": false,
"start_time": "2024-02-26T04:10:45.765392",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>2</th>\n",
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" <td>Insufficient_Weight</td>\n",
" <td>17.126706</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>3</td>\n",
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" <td>20.952737</td>\n",
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" <td>1.674061</td>\n",
" <td>no</td>\n",
" <td>1.467863</td>\n",
" <td>0.780199</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" <td>44.855798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Male</td>\n",
" <td>31.641081</td>\n",
" <td>1.914186</td>\n",
" <td>93.798055</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.679664</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.979848</td>\n",
" <td>no</td>\n",
" <td>1.967973</td>\n",
" <td>0.931721</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" <td>25.599151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20753</th>\n",
" <td>20753</td>\n",
" <td>Male</td>\n",
" <td>25.137087</td>\n",
" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.919584</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.151809</td>\n",
" <td>no</td>\n",
" <td>1.330519</td>\n",
" <td>0.196680</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" <td>36.587084</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20754</th>\n",
" <td>20754</td>\n",
" <td>Male</td>\n",
" <td>18.000000</td>\n",
" <td>1.710000</td>\n",
" <td>50.000000</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>Frequently</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" <td>17.099278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.407817</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.158040</td>\n",
" <td>1.198439</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" <td>31.889841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20756</th>\n",
" <td>20756</td>\n",
" <td>Male</td>\n",
" <td>33.852953</td>\n",
" <td>1.700000</td>\n",
" <td>83.520113</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.671238</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.144838</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.973834</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Overweight_Level_II</td>\n",
" <td>28.899693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20757</th>\n",
" <td>20757</td>\n",
" <td>Male</td>\n",
" <td>26.680376</td>\n",
" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.003563</td>\n",
" <td>no</td>\n",
" <td>0.684487</td>\n",
" <td>0.713823</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" <td>35.800157</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20758 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" id Gender Age Height Weight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 \n",
"1 1 Female 18.000000 1.560000 57.000000 \n",
"2 2 Female 18.000000 1.711460 50.165754 \n",
"3 3 Female 20.952737 1.710730 131.274851 \n",
"4 4 Male 31.641081 1.914186 93.798055 \n",
"... ... ... ... ... ... \n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"0 yes yes 2.000000 2.983297 Sometimes \n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"4 yes yes 2.679664 1.971472 Sometimes \n",
"... ... ... ... ... ... \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"0 no 2.763573 no 0.000000 0.976473 Sometimes \n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
"2 no 1.910378 no 0.866045 1.673584 no \n",
"3 no 1.674061 no 1.467863 0.780199 Sometimes \n",
"4 no 1.979848 no 1.967973 0.931721 Sometimes \n",
"... ... ... .. ... ... ... \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad BMI \n",
"0 Public_Transportation Overweight_Level_II 28.259565 \n",
"1 Automobile Normal_Weight 23.422091 \n",
"2 Public_Transportation Insufficient_Weight 17.126706 \n",
"3 Public_Transportation Obesity_Type_III 44.855798 \n",
"4 Public_Transportation Overweight_Level_II 25.599151 \n",
"... ... ... ... \n",
"20753 Public_Transportation Obesity_Type_II 36.587084 \n",
"20754 Public_Transportation Insufficient_Weight 17.099278 \n",
"20755 Public_Transportation Obesity_Type_II 31.889841 \n",
"20756 Automobile Overweight_Level_II 28.899693 \n",
"20757 Public_Transportation Obesity_Type_II 35.800157 \n",
"\n",
"[20758 rows x 19 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat(\"Create a new column 'BMI=Weight / Height**2'\"))"
]
},
{
"cell_type": "markdown",
"id": "ebdc9842",
"metadata": {
"papermill": {
"duration": 0.021236,
"end_time": "2024-02-26T04:10:51.131266",
"exception": false,
"start_time": "2024-02-26T04:10:51.110030",
"status": "completed"
},
"tags": []
},
"source": [
"Creating a coreraltion matrix."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "7b3401cf",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:10:51.176285Z",
"iopub.status.busy": "2024-02-26T04:10:51.175908Z",
"iopub.status.idle": "2024-02-26T04:11:00.026378Z",
"shell.execute_reply": "2024-02-26T04:11:00.025108Z"
},
"papermill": {
"duration": 8.876003,
"end_time": "2024-02-26T04:11:00.029168",
"exception": false,
"start_time": "2024-02-26T04:10:51.153165",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"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>Weight</th>\n",
" <th>Age</th>\n",
" <th>Height</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>0.014020</td>\n",
" <td>0.007634</td>\n",
" <td>0.012041</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>0.283381</td>\n",
" <td>1.000000</td>\n",
" <td>-0.011713</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Height</th>\n",
" <td>0.416677</td>\n",
" <td>-0.011713</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weight</th>\n",
" <td>1.000000</td>\n",
" <td>0.283381</td>\n",
" <td>0.416677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FCVC</th>\n",
" <td>0.245682</td>\n",
" <td>0.034414</td>\n",
" <td>-0.071546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NCP</th>\n",
" <td>0.095947</td>\n",
" <td>-0.048479</td>\n",
" <td>0.191383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CH2O</th>\n",
" <td>0.317914</td>\n",
" <td>-0.016325</td>\n",
" <td>0.183706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FAF</th>\n",
" <td>-0.084845</td>\n",
" <td>-0.192259</td>\n",
" <td>0.295278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TUE</th>\n",
" <td>-0.086471</td>\n",
" <td>-0.296154</td>\n",
" <td>0.076433</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Weight Age Height\n",
"id 0.014020 0.007634 0.012041\n",
"Age 0.283381 1.000000 -0.011713\n",
"Height 0.416677 -0.011713 1.000000\n",
"Weight 1.000000 0.283381 0.416677\n",
"FCVC 0.245682 0.034414 -0.071546\n",
"NCP 0.095947 -0.048479 0.191383\n",
"CH2O 0.317914 -0.016325 0.183706\n",
"FAF -0.084845 -0.192259 0.295278\n",
"TUE -0.086471 -0.296154 0.076433"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Print the correlation matrix for weight, age, height.'))"
]
},
{
"cell_type": "markdown",
"id": "e71e9a77",
"metadata": {
"papermill": {
"duration": 0.02157,
"end_time": "2024-02-26T04:11:00.073101",
"exception": false,
"start_time": "2024-02-26T04:11:00.051531",
"status": "completed"
},
"tags": []
},
"source": [
"# 3. Graph Plotting\n",
"PandasAI is also capable of plotting grahs too. You don't even have to import `matplotlib` or `seaborn` for that but I have imported them to use styling in graphs.\n",
"\n",
"## Bar-Plot"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "3de1b39b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:00.118228Z",
"iopub.status.busy": "2024-02-26T04:11:00.117824Z",
"iopub.status.idle": "2024-02-26T04:11:20.522479Z",
"shell.execute_reply": "2024-02-26T04:11:20.521398Z"
},
"papermill": {
"duration": 20.430414,
"end_time": "2024-02-26T04:11:20.525235",
"exception": false,
"start_time": "2024-02-26T04:11:00.094821",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nexpected str, bytes or os.PathLike object, not Figure\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Plot the bar plot of gender'))"
]
},
{
"cell_type": "markdown",
"id": "20e8ee22",
"metadata": {
"papermill": {
"duration": 0.0217,
"end_time": "2024-02-26T04:11:20.568876",
"exception": false,
"start_time": "2024-02-26T04:11:20.547176",
"status": "completed"
},
"tags": []
},
"source": [
"Although it is saying that I am not able to give answer to your question it is also giving us the right plot for gender."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "f9a3c52b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:20.615176Z",
"iopub.status.busy": "2024-02-26T04:11:20.614094Z",
"iopub.status.idle": "2024-02-26T04:11:27.498971Z",
"shell.execute_reply": "2024-02-26T04:11:27.497836Z"
},
"papermill": {
"duration": 6.910501,
"end_time": "2024-02-26T04:11:27.501498",
"exception": false,
"start_time": "2024-02-26T04:11:20.590997",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nexpected str, bytes or os.PathLike object, not Figure\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Plot the bar plot of NObeyesdad'))"
]
},
{
"cell_type": "markdown",
"id": "85bedb81",
"metadata": {
"papermill": {
"duration": 0.022692,
"end_time": "2024-02-26T04:11:27.547479",
"exception": false,
"start_time": "2024-02-26T04:11:27.524787",
"status": "completed"
},
"tags": []
},
"source": [
"## Pie-Plot"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "dd11752a",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:27.595150Z",
"iopub.status.busy": "2024-02-26T04:11:27.594764Z",
"iopub.status.idle": "2024-02-26T04:11:35.684278Z",
"shell.execute_reply": "2024-02-26T04:11:35.683272Z"
},
"papermill": {
"duration": 8.117166,
"end_time": "2024-02-26T04:11:35.687396",
"exception": false,
"start_time": "2024-02-26T04:11:27.570230",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'/kaggle/working/exports/charts/temp_chart.png'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('Plot the pie plot of gender'))"
]
},
{
"cell_type": "markdown",
"id": "401d5e5d",
"metadata": {
"papermill": {
"duration": 0.033156,
"end_time": "2024-02-26T04:11:35.755800",
"exception": false,
"start_time": "2024-02-26T04:11:35.722644",
"status": "completed"
},
"tags": []
},
"source": [
"## Histogram"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "97964bba",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:35.817594Z",
"iopub.status.busy": "2024-02-26T04:11:35.816859Z",
"iopub.status.idle": "2024-02-26T04:11:39.436630Z",
"shell.execute_reply": "2024-02-26T04:11:39.435391Z"
},
"papermill": {
"duration": 3.649664,
"end_time": "2024-02-26T04:11:39.439277",
"exception": false,
"start_time": "2024-02-26T04:11:35.789613",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Error: no \"view\" mailcap rules found for type \"image/png\"\n",
"/usr/bin/xdg-open: 869: www-browser: not found\n",
"/usr/bin/xdg-open: 869: links2: not found\n",
"/usr/bin/xdg-open: 869: elinks: not found\n",
"/usr/bin/xdg-open: 869: links: not found\n",
"/usr/bin/xdg-open: 869: lynx: not found\n",
"/usr/bin/xdg-open: 869: w3m: not found\n",
"xdg-open: no method available for opening '/tmp/tmplat3u3z3.PNG'\n"
]
},
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nexpected str, bytes or os.PathLike object, not Figure\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('plot the histogram of height'))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "3416cb71",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:39.489892Z",
"iopub.status.busy": "2024-02-26T04:11:39.489506Z",
"iopub.status.idle": "2024-02-26T04:11:43.498105Z",
"shell.execute_reply": "2024-02-26T04:11:43.497028Z"
},
"papermill": {
"duration": 4.036484,
"end_time": "2024-02-26T04:11:43.500379",
"exception": false,
"start_time": "2024-02-26T04:11:39.463895",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nexpected str, bytes or os.PathLike object, not Figure\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('plot the histogram of weight'))"
]
},
{
"cell_type": "markdown",
"id": "9856762d",
"metadata": {
"papermill": {
"duration": 0.024854,
"end_time": "2024-02-26T04:11:43.550262",
"exception": false,
"start_time": "2024-02-26T04:11:43.525408",
"status": "completed"
},
"tags": []
},
"source": [
"## Box-Plot"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "01377fb7",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:43.602781Z",
"iopub.status.busy": "2024-02-26T04:11:43.602373Z",
"iopub.status.idle": "2024-02-26T04:11:52.018760Z",
"shell.execute_reply": "2024-02-26T04:11:52.017501Z"
},
"papermill": {
"duration": 8.445448,
"end_time": "2024-02-26T04:11:52.021197",
"exception": false,
"start_time": "2024-02-26T04:11:43.575749",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nThe number of FixedLocator locations (3), usually from a call to set_ticks, does not match the number of labels (1).\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('plot the box-plot of weight'))"
]
},
{
"cell_type": "markdown",
"id": "85b32ea3",
"metadata": {
"papermill": {
"duration": 0.025154,
"end_time": "2024-02-26T04:11:52.072046",
"exception": false,
"start_time": "2024-02-26T04:11:52.046892",
"status": "completed"
},
"tags": []
},
"source": [
"Finding outliers for weight."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "c6bc6bd9",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:52.124273Z",
"iopub.status.busy": "2024-02-26T04:11:52.123840Z",
"iopub.status.idle": "2024-02-26T04:11:54.662180Z",
"shell.execute_reply": "2024-02-26T04:11:54.661076Z"
},
"papermill": {
"duration": 2.567288,
"end_time": "2024-02-26T04:11:54.664459",
"exception": false,
"start_time": "2024-02-26T04:11:52.097171",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"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>id</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>family_history_with_overweight</th>\n",
" <th>FAVC</th>\n",
" <th>FCVC</th>\n",
" <th>NCP</th>\n",
" <th>CAEC</th>\n",
" <th>SMOKE</th>\n",
" <th>CH2O</th>\n",
" <th>SCC</th>\n",
" <th>FAF</th>\n",
" <th>TUE</th>\n",
" <th>CALC</th>\n",
" <th>MTRANS</th>\n",
" <th>NObeyesdad</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Female</td>\n",
" <td>20.952737</td>\n",
" <td>1.710730</td>\n",
" <td>131.274851</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.674061</td>\n",
" <td>no</td>\n",
" <td>1.467863</td>\n",
" <td>0.780199</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>Male</td>\n",
" <td>29.883021</td>\n",
" <td>1.754711</td>\n",
" <td>112.725005</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>1.991240</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.696948</td>\n",
" <td>Sometimes</td>\n",
" <td>Automobile</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>Male</td>\n",
" <td>29.891473</td>\n",
" <td>1.750150</td>\n",
" <td>118.206565</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>1.397468</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>0.598655</td>\n",
" <td>0.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>Automobile</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>Female</td>\n",
" <td>26.000000</td>\n",
" <td>1.638836</td>\n",
" <td>111.275646</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.632253</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.218645</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>Male</td>\n",
" <td>18.000000</td>\n",
" <td>1.811189</td>\n",
" <td>108.251044</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>2.164839</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.530157</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>0.553311</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_I</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20749</th>\n",
" <td>20749</td>\n",
" <td>Female</td>\n",
" <td>25.783865</td>\n",
" <td>1.646390</td>\n",
" <td>104.835346</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.530992</td>\n",
" <td>no</td>\n",
" <td>0.015860</td>\n",
" <td>0.445495</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20751</th>\n",
" <td>20751</td>\n",
" <td>Female</td>\n",
" <td>21.030909</td>\n",
" <td>1.605495</td>\n",
" <td>133.466763</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.839069</td>\n",
" <td>no</td>\n",
" <td>1.683497</td>\n",
" <td>0.143675</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20753</th>\n",
" <td>20753</td>\n",
" <td>Male</td>\n",
" <td>25.137087</td>\n",
" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.919584</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.151809</td>\n",
" <td>no</td>\n",
" <td>1.330519</td>\n",
" <td>0.196680</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.407817</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.158040</td>\n",
" <td>1.198439</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20757</th>\n",
" <td>20757</td>\n",
" <td>Male</td>\n",
" <td>26.680376</td>\n",
" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.003563</td>\n",
" <td>no</td>\n",
" <td>0.684487</td>\n",
" <td>0.713823</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>7976 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" id Gender Age Height Weight \\\n",
"3 3 Female 20.952737 1.710730 131.274851 \n",
"6 6 Male 29.883021 1.754711 112.725005 \n",
"7 7 Male 29.891473 1.750150 118.206565 \n",
"9 9 Female 26.000000 1.638836 111.275646 \n",
"12 12 Male 18.000000 1.811189 108.251044 \n",
"... ... ... ... ... ... \n",
"20749 20749 Female 25.783865 1.646390 104.835346 \n",
"20751 20751 Female 21.030909 1.605495 133.466763 \n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"6 yes yes 1.991240 3.000000 Sometimes \n",
"7 yes yes 1.397468 3.000000 Sometimes \n",
"9 yes yes 3.000000 3.000000 Sometimes \n",
"12 yes yes 2.000000 2.164839 Sometimes \n",
"... ... ... ... ... ... \n",
"20749 yes yes 3.000000 3.000000 Sometimes \n",
"20751 yes yes 3.000000 3.000000 Sometimes \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"3 no 1.674061 no 1.467863 0.780199 Sometimes \n",
"6 no 2.000000 no 0.000000 0.696948 Sometimes \n",
"7 no 2.000000 no 0.598655 0.000000 Sometimes \n",
"9 no 2.632253 no 0.000000 0.218645 Sometimes \n",
"12 no 2.530157 no 1.000000 0.553311 no \n",
"... ... ... .. ... ... ... \n",
"20749 no 1.530992 no 0.015860 0.445495 Sometimes \n",
"20751 no 2.839069 no 1.683497 0.143675 Sometimes \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"3 Public_Transportation Obesity_Type_III \n",
"6 Automobile Obesity_Type_II \n",
"7 Automobile Obesity_Type_II \n",
"9 Public_Transportation Obesity_Type_III \n",
"12 Public_Transportation Obesity_Type_I \n",
"... ... ... \n",
"20749 Public_Transportation Obesity_Type_III \n",
"20751 Public_Transportation Obesity_Type_III \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20757 Public_Transportation Obesity_Type_II \n",
"\n",
"[7976 rows x 18 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('are there any ouliers for weight?'))"
]
},
{
"cell_type": "markdown",
"id": "1bbe8e6d",
"metadata": {
"papermill": {
"duration": 0.026083,
"end_time": "2024-02-26T04:11:54.717068",
"exception": false,
"start_time": "2024-02-26T04:11:54.690985",
"status": "completed"
},
"tags": []
},
"source": [
"### Saving Dataframe into `.csv` file.\n",
"It is also capable of saving the dataframe into csv file."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "daedfc6e",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:54.771219Z",
"iopub.status.busy": "2024-02-26T04:11:54.770817Z",
"iopub.status.idle": "2024-02-26T04:11:56.932341Z",
"shell.execute_reply": "2024-02-26T04:11:56.931320Z"
},
"papermill": {
"duration": 2.192083,
"end_time": "2024-02-26T04:11:56.935481",
"exception": false,
"start_time": "2024-02-26T04:11:54.743398",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/markdown": [
"> The dataframe has been saved to train.csv."
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"to_markdown(df.chat(\"Save the dataframe to 'train.csv'\"))"
]
},
{
"cell_type": "markdown",
"id": "0f20bcf4",
"metadata": {
"papermill": {
"duration": 0.027153,
"end_time": "2024-02-26T04:11:56.988819",
"exception": false,
"start_time": "2024-02-26T04:11:56.961666",
"status": "completed"
},
"tags": []
},
"source": [
"# 4. Limitations\n",
"Here are some of the limitation I found during the coding. It is possible that it'll work with another LLM type.\n",
"\n",
"\n",
"## Scatter-Plot"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "4cb30d37",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:11:57.044471Z",
"iopub.status.busy": "2024-02-26T04:11:57.043158Z",
"iopub.status.idle": "2024-02-26T04:12:07.913386Z",
"shell.execute_reply": "2024-02-26T04:12:07.912402Z"
},
"papermill": {
"duration": 10.899987,
"end_time": "2024-02-26T04:12:07.915726",
"exception": false,
"start_time": "2024-02-26T04:11:57.015739",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'Unfortunately, I was not able to answer your question, because of the following error:\\n\\nexpected str, bytes or os.PathLike object, not Figure\\n'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(df.chat('plot the scatter plot of weight and age'))"
]
},
{
"cell_type": "markdown",
"id": "d1e4cf83",
"metadata": {
"papermill": {
"duration": 0.029203,
"end_time": "2024-02-26T04:12:07.974334",
"exception": false,
"start_time": "2024-02-26T04:12:07.945131",
"status": "completed"
},
"tags": []
},
"source": [
"Plots scatter plot 3 times."
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "ec7cd413",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:12:08.036095Z",
"iopub.status.busy": "2024-02-26T04:12:08.035096Z",
"iopub.status.idle": "2024-02-26T04:12:20.814338Z",
"shell.execute_reply": "2024-02-26T04:12:20.813135Z"
},
"papermill": {
"duration": 12.813186,
"end_time": "2024-02-26T04:12:20.817088",
"exception": false,
"start_time": "2024-02-26T04:12:08.003902",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id Gender\n",
"0 0 Male\n",
"1 1 Female\n",
"2 2 Female\n"
]
}
],
"source": [
"print(df.chat(\"Show first 3 rows of columns 'Weight' and 'Gender'\"))"
]
},
{
"cell_type": "markdown",
"id": "3ccaadad",
"metadata": {
"papermill": {
"duration": 0.029751,
"end_time": "2024-02-26T04:12:20.877093",
"exception": false,
"start_time": "2024-02-26T04:12:20.847342",
"status": "completed"
},
"tags": []
},
"source": [
"Shows only `gender` rows."
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "2f9601a3",
"metadata": {
"execution": {
"iopub.execute_input": "2024-02-26T04:12:20.939031Z",
"iopub.status.busy": "2024-02-26T04:12:20.938425Z",
"iopub.status.idle": "2024-02-26T04:12:22.333756Z",
"shell.execute_reply": "2024-02-26T04:12:22.332952Z"
},
"papermill": {
"duration": 1.42897,
"end_time": "2024-02-26T04:12:22.335951",
"exception": false,
"start_time": "2024-02-26T04:12:20.906981",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>id</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Height</th>\n",
" <th>Weight</th>\n",
" <th>family_history_with_overweight</th>\n",
" <th>FAVC</th>\n",
" <th>FCVC</th>\n",
" <th>NCP</th>\n",
" <th>CAEC</th>\n",
" <th>SMOKE</th>\n",
" <th>CH2O</th>\n",
" <th>SCC</th>\n",
" <th>FAF</th>\n",
" <th>TUE</th>\n",
" <th>CALC</th>\n",
" <th>MTRANS</th>\n",
" <th>NObeyesdad</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Male</td>\n",
" <td>24.443011</td>\n",
" <td>1.699998</td>\n",
" <td>81.669950</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>2.983297</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.763573</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.976473</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.560000</td>\n",
" <td>57.000000</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Frequently</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Normal_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.711460</td>\n",
" <td>50.165754</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>1.880534</td>\n",
" <td>1.411685</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.910378</td>\n",
" <td>no</td>\n",
" <td>0.866045</td>\n",
" <td>1.673584</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Female</td>\n",
" <td>20.952737</td>\n",
" <td>1.710730</td>\n",
" <td>131.274851</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.674061</td>\n",
" <td>no</td>\n",
" <td>1.467863</td>\n",
" <td>0.780199</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Male</td>\n",
" <td>31.641081</td>\n",
" <td>1.914186</td>\n",
" <td>93.798055</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.679664</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.979848</td>\n",
" <td>no</td>\n",
" <td>1.967973</td>\n",
" <td>0.931721</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
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" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20753</th>\n",
" <td>20753</td>\n",
" <td>Male</td>\n",
" <td>25.137087</td>\n",
" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.919584</td>\n",
" <td>3.000000</td>\n",
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" <td>no</td>\n",
" <td>2.151809</td>\n",
" <td>no</td>\n",
" <td>1.330519</td>\n",
" <td>0.196680</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
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" <th>20754</th>\n",
" <td>20754</td>\n",
" <td>Male</td>\n",
" <td>18.000000</td>\n",
" <td>1.710000</td>\n",
" <td>50.000000</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
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" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
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" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
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" <td>1.700000</td>\n",
" <td>83.520113</td>\n",
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" <td>2.671238</td>\n",
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" <th>20757</th>\n",
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" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
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" id Gender Age Height Weight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 \n",
"1 1 Female 18.000000 1.560000 57.000000 \n",
"2 2 Female 18.000000 1.711460 50.165754 \n",
"3 3 Female 20.952737 1.710730 131.274851 \n",
"4 4 Male 31.641081 1.914186 93.798055 \n",
"... ... ... ... ... ... \n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"0 yes yes 2.000000 2.983297 Sometimes \n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"4 yes yes 2.679664 1.971472 Sometimes \n",
"... ... ... ... ... ... \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
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"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"0 no 2.763573 no 0.000000 0.976473 Sometimes \n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
"2 no 1.910378 no 0.866045 1.673584 no \n",
"3 no 1.674061 no 1.467863 0.780199 Sometimes \n",
"4 no 1.979848 no 1.967973 0.931721 Sometimes \n",
"... ... ... .. ... ... ... \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"0 Public_Transportation Overweight_Level_II \n",
"1 Automobile Normal_Weight \n",
"2 Public_Transportation Insufficient_Weight \n",
"3 Public_Transportation Obesity_Type_III \n",
"4 Public_Transportation Overweight_Level_II \n",
"... ... ... \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20754 Public_Transportation Insufficient_Weight \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20756 Automobile Overweight_Level_II \n",
"20757 Public_Transportation Obesity_Type_II \n",
"\n",
"[20758 rows x 18 columns]"
]
},
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}
],
"source": [
"display(df.chat('Can you give me the Description of the Data.'))"
]
},
{
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"source": [
"doesn't show the description which we get from `describe()` function."
]
},
{
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{
"data": {
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"text/plain": [
" id Gender Age Height Weight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 \n",
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"2 2 Female 18.000000 1.711460 50.165754 \n",
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"... ... ... ... ... ... \n",
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"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"0 yes yes 2.000000 2.983297 Sometimes \n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"4 yes yes 2.679664 1.971472 Sometimes \n",
"... ... ... ... ... ... \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"0 no 2.763573 no 0.000000 0.976473 Sometimes \n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
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"4 no 1.979848 no 1.967973 0.931721 Sometimes \n",
"... ... ... .. ... ... ... \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"0 Public_Transportation Overweight_Level_II \n",
"1 Automobile Normal_Weight \n",
"2 Public_Transportation Insufficient_Weight \n",
"3 Public_Transportation Obesity_Type_III \n",
"4 Public_Transportation Overweight_Level_II \n",
"... ... ... \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20754 Public_Transportation Insufficient_Weight \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20756 Automobile Overweight_Level_II \n",
"20757 Public_Transportation Obesity_Type_II \n",
"\n",
"[20758 rows x 18 columns]"
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},
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}
],
"source": [
"display(df.chat('Can you give me the information of the Data.'))"
]
},
{
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"id": "519efd6b",
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{
"data": {
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" <th>family_history_with_overweight</th>\n",
" <th>FAVC</th>\n",
" <th>FCVC</th>\n",
" <th>NCP</th>\n",
" <th>CAEC</th>\n",
" <th>SMOKE</th>\n",
" <th>CH2O</th>\n",
" <th>SCC</th>\n",
" <th>FAF</th>\n",
" <th>TUE</th>\n",
" <th>CALC</th>\n",
" <th>MTRANS</th>\n",
" <th>NObeyesdad</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Male</td>\n",
" <td>24.443011</td>\n",
" <td>1.699998</td>\n",
" <td>81.669950</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>2.983297</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.763573</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.976473</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.560000</td>\n",
" <td>57.000000</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Frequently</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Normal_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Female</td>\n",
" <td>18.000000</td>\n",
" <td>1.711460</td>\n",
" <td>50.165754</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>1.880534</td>\n",
" <td>1.411685</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.910378</td>\n",
" <td>no</td>\n",
" <td>0.866045</td>\n",
" <td>1.673584</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>Female</td>\n",
" <td>20.952737</td>\n",
" <td>1.710730</td>\n",
" <td>131.274851</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.674061</td>\n",
" <td>no</td>\n",
" <td>1.467863</td>\n",
" <td>0.780199</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_III</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>Male</td>\n",
" <td>31.641081</td>\n",
" <td>1.914186</td>\n",
" <td>93.798055</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.679664</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>1.979848</td>\n",
" <td>no</td>\n",
" <td>1.967973</td>\n",
" <td>0.931721</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20753</th>\n",
" <td>20753</td>\n",
" <td>Male</td>\n",
" <td>25.137087</td>\n",
" <td>1.766626</td>\n",
" <td>114.187096</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.919584</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.151809</td>\n",
" <td>no</td>\n",
" <td>1.330519</td>\n",
" <td>0.196680</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20754</th>\n",
" <td>20754</td>\n",
" <td>Male</td>\n",
" <td>18.000000</td>\n",
" <td>1.710000</td>\n",
" <td>50.000000</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>Frequently</td>\n",
" <td>no</td>\n",
" <td>1.000000</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Insufficient_Weight</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20755</th>\n",
" <td>20755</td>\n",
" <td>Male</td>\n",
" <td>20.101026</td>\n",
" <td>1.819557</td>\n",
" <td>105.580491</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.407817</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.000000</td>\n",
" <td>no</td>\n",
" <td>1.158040</td>\n",
" <td>1.198439</td>\n",
" <td>no</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20756</th>\n",
" <td>20756</td>\n",
" <td>Male</td>\n",
" <td>33.852953</td>\n",
" <td>1.700000</td>\n",
" <td>83.520113</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>2.671238</td>\n",
" <td>1.971472</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.144838</td>\n",
" <td>no</td>\n",
" <td>0.000000</td>\n",
" <td>0.973834</td>\n",
" <td>no</td>\n",
" <td>Automobile</td>\n",
" <td>Overweight_Level_II</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20757</th>\n",
" <td>20757</td>\n",
" <td>Male</td>\n",
" <td>26.680376</td>\n",
" <td>1.816547</td>\n",
" <td>118.134898</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>Sometimes</td>\n",
" <td>no</td>\n",
" <td>2.003563</td>\n",
" <td>no</td>\n",
" <td>0.684487</td>\n",
" <td>0.713823</td>\n",
" <td>Sometimes</td>\n",
" <td>Public_Transportation</td>\n",
" <td>Obesity_Type_II</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20758 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" id Gender Age Height Weight \\\n",
"0 0 Male 24.443011 1.699998 81.669950 \n",
"1 1 Female 18.000000 1.560000 57.000000 \n",
"2 2 Female 18.000000 1.711460 50.165754 \n",
"3 3 Female 20.952737 1.710730 131.274851 \n",
"4 4 Male 31.641081 1.914186 93.798055 \n",
"... ... ... ... ... ... \n",
"20753 20753 Male 25.137087 1.766626 114.187096 \n",
"20754 20754 Male 18.000000 1.710000 50.000000 \n",
"20755 20755 Male 20.101026 1.819557 105.580491 \n",
"20756 20756 Male 33.852953 1.700000 83.520113 \n",
"20757 20757 Male 26.680376 1.816547 118.134898 \n",
"\n",
" family_history_with_overweight FAVC FCVC NCP CAEC \\\n",
"0 yes yes 2.000000 2.983297 Sometimes \n",
"1 yes yes 2.000000 3.000000 Frequently \n",
"2 yes yes 1.880534 1.411685 Sometimes \n",
"3 yes yes 3.000000 3.000000 Sometimes \n",
"4 yes yes 2.679664 1.971472 Sometimes \n",
"... ... ... ... ... ... \n",
"20753 yes yes 2.919584 3.000000 Sometimes \n",
"20754 no yes 3.000000 4.000000 Frequently \n",
"20755 yes yes 2.407817 3.000000 Sometimes \n",
"20756 yes yes 2.671238 1.971472 Sometimes \n",
"20757 yes yes 3.000000 3.000000 Sometimes \n",
"\n",
" SMOKE CH2O SCC FAF TUE CALC \\\n",
"0 no 2.763573 no 0.000000 0.976473 Sometimes \n",
"1 no 2.000000 no 1.000000 1.000000 no \n",
"2 no 1.910378 no 0.866045 1.673584 no \n",
"3 no 1.674061 no 1.467863 0.780199 Sometimes \n",
"4 no 1.979848 no 1.967973 0.931721 Sometimes \n",
"... ... ... .. ... ... ... \n",
"20753 no 2.151809 no 1.330519 0.196680 Sometimes \n",
"20754 no 1.000000 no 2.000000 1.000000 Sometimes \n",
"20755 no 2.000000 no 1.158040 1.198439 no \n",
"20756 no 2.144838 no 0.000000 0.973834 no \n",
"20757 no 2.003563 no 0.684487 0.713823 Sometimes \n",
"\n",
" MTRANS NObeyesdad \n",
"0 Public_Transportation Overweight_Level_II \n",
"1 Automobile Normal_Weight \n",
"2 Public_Transportation Insufficient_Weight \n",
"3 Public_Transportation Obesity_Type_III \n",
"4 Public_Transportation Overweight_Level_II \n",
"... ... ... \n",
"20753 Public_Transportation Obesity_Type_II \n",
"20754 Public_Transportation Insufficient_Weight \n",
"20755 Public_Transportation Obesity_Type_II \n",
"20756 Automobile Overweight_Level_II \n",
"20757 Public_Transportation Obesity_Type_II \n",
"\n",
"[20758 rows x 18 columns]"
]
},
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],
"source": [
"display(df.chat('Can you give me the shape of the Data.'))"
]
},
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"tags": []
},
"source": [
"Nope!!!\n",
"\n",
"# 5. Conclusion\n",
"\n",
"### Pros:\n",
"\n",
"- Can easily perform simple tasks without having to remember any complex syntax\n",
"- Capable of giving conversational replies\n",
"- Easy report generation for quick analysis or data manipulation\n",
"\n",
"### Cons:\n",
"\n",
"- Cannot perform complex tasks\n",
"- Cannot create or interact with variables other than the passed dataframe\n",
"\n",
"In conclusion, Pandas AI is not meant to replace Pandas. Though Pandas AI can easily perform simple tasks, it still faces difficulty performing some complex tasks like saving the dataframe, making a correlation matrix and many more.\n"
]
}
],
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