example-car-viz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize cars dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import libraries and Setup the API keys:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import evadb\n",
    "os.environ['OPENAI_API_KEY'] = 'sk-xxx'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Script generated by GPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connected to EvaDB\n"
     ]
    }
   ],
   "source": [
    "cursor = evadb.connect().cursor()\n",
    "print(\"Connected to EvaDB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "11-25-2023 11:57:22 WARNING[drop_object_executor:drop_object_executor.py:_handle_drop_function:0086] Function Visualize does not exist, therefore cannot be dropped.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created Function\n"
     ]
    }
   ],
   "source": [
    "create_function_query = f\"\"\"CREATE FUNCTION IF NOT EXISTS Visualize\n",
    "            IMPL  './functions/visualize.py';\n",
    "            \"\"\"\n",
    "cursor.query(\"DROP FUNCTION IF EXISTS Visualize;\").execute()\n",
    "cursor.query(create_function_query).execute()\n",
    "print(\"Created Function\")\n",
    "\n",
    "create_table_query = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS CARS (\n",
    "name TEXT(255),\n",
    "type TEXT(255),\n",
    "awd TEXT(255),\n",
    "rwd TEXT(255),\n",
    "retail_price INTEGER,\n",
    "dealer_cost INTEGER,\n",
    "engine_size FLOAT(64,64),\n",
    "cyl INTEGER,\n",
    "horsepower INTEGER,\n",
    "city_mpg INTEGER,\n",
    "highway_mpg INTEGER,\n",
    "weight INTEGER,\n",
    "wheel_base INTEGER,\n",
    "len INTEGER,\n",
    "width INTEGER\n",
    ");\n",
    "\"\"\"\n",
    "\n",
    "load_data_query = f\"\"\" LOAD CSV 'viz-cars-data/data/cars.csv' INTO CARS;\"\"\" \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create table and load data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<evadb.models.storage.batch.Batch at 0x7ff4cd550990>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cursor.query(create_table_query).execute()\n",
    "cursor.query(load_data_query).execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check scripts to Visualize data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/preethi/projects/evadb-viz-generation/dataframe/goal_gen.py:10: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  self.df = df\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SUMMARY PROMPT: [{'role': 'system', 'content': '\\n        You are an experienced data analyst that can annotate datasets. Your instructions are as follows:\\n        i) ALWAYS generate the name of the dataset and the dataset_description\\n        ii) ALWAYS generate a field description.\\n        iii.) ALWAYS generate a semantic_type (a single word) for each field given its values e.g. company, city, number, supplier, location, gender, longitude, latitude, url, ip address, zip code, email, etc\\n        You must return an updated JSON dictionary without any preamble or explanation.\\n        '}, {'role': 'assistant', 'content': \"\\n        Annotate the dictionary below. Only return a JSON object.\\n        {'name': 'cars.csv', 'file_name': 'cars.csv', 'dataset_description': '', 'fields': [{'column': 'name', 'properties': {'dtype': 'string', 'samples': ['Chevrolet Astro', 'BMW M3 convertible 2dr'], 'num_unique_values': 99, 'semantic_type': '', 'description': ''}}, {'column': 'type', 'properties': {'dtype': 'category', 'samples': ['SUV', 'Minivan'], 'num_unique_values': 5, 'semantic_type': '', 'description': ''}}, {'column': 'awd', 'properties': {'dtype': 'category', 'samples': ['1', '0'], 'num_unique_values': 2, 'semantic_type': '', 'description': ''}}, {'column': 'rwd', 'properties': {'dtype': 'category', 'samples': ['1', '0'], 'num_unique_values': 2, 'semantic_type': '', 'description': ''}}, {'column': 'retail_price', 'properties': {'dtype': 'number', 'std': 14705, 'min': 11690, 'max': 89765, 'samples': [11690, 48195], 'num_unique_values': 98, 'semantic_type': '', 'description': ''}}, {'column': 'dealer_cost', 'properties': {'dtype': 'number', 'std': 13260, 'min': 10965, 'max': 79978, 'samples': [23954, 51815], 'num_unique_values': 99, 'semantic_type': '', 'description': ''}}, {'column': 'engine_size', 'properties': {'dtype': 'number', 'std': 0.96617179664426, 'min': 0.0, 'max': 5.7, 'samples': [3.5, 3.4], 'num_unique_values': 22, 'semantic_type': '', 'description': ''}}, {'column': 'cyl', 'properties': {'dtype': 'number', 'std': 1, 'min': 0, 'max': 8, 'samples': [4, 8], 'num_unique_values': 4, 'semantic_type': '', 'description': ''}}, {'column': 'horsepower', 'properties': {'dtype': 'number', 'std': 61, 'min': 103, 'max': 450, 'samples': [215, 333], 'num_unique_values': 33, 'semantic_type': '', 'description': ''}}, {'column': 'city_mpg', 'properties': {'dtype': 'number', 'std': 2, 'min': 14, 'max': 28, 'samples': [28, 19], 'num_unique_values': 13, 'semantic_type': '', 'description': ''}}, {'column': 'highway_mpg', 'properties': {'dtype': 'number', 'std': 3, 'min': 17, 'max': 37, 'samples': [24, 23], 'num_unique_values': 17, 'semantic_type': '', 'description': ''}}, {'column': 'weight', 'properties': {'dtype': 'number', 'std': 558, 'min': 2348, 'max': 5367, 'samples': [2932, 2921], 'num_unique_values': 92, 'semantic_type': '', 'description': ''}}, {'column': 'wheel_base', 'properties': {'dtype': 'number', 'std': 12, 'min': 0, 'max': 130, 'samples': [121, 111], 'num_unique_values': 25, 'semantic_type': '', 'description': ''}}, {'column': 'len', 'properties': {'dtype': 'number', 'std': 13, 'min': 153, 'max': 219, 'samples': [194, 177], 'num_unique_values': 36, 'semantic_type': '', 'description': ''}}, {'column': 'width', 'properties': {'dtype': 'number', 'std': 7, 'min': 2, 'max': 79, 'samples': [73, 79], 'num_unique_values': 15, 'semantic_type': '', 'description': ''}}]}\\n        \"}]\n",
      "SUMMARY:  {'name': 'cars.csv', 'file_name': 'cars.csv', 'dataset_description': 'Dataset containing information about cars.', 'fields': [{'column': 'name', 'properties': {'dtype': 'string', 'samples': ['Chevrolet Astro', 'BMW M3 convertible 2dr'], 'num_unique_values': 99, 'semantic_type': 'car_model', 'description': 'The name of the car model.'}}, {'column': 'type', 'properties': {'dtype': 'category', 'samples': ['SUV', 'Minivan'], 'num_unique_values': 5, 'semantic_type': 'car_type', 'description': 'The type of car.'}}, {'column': 'awd', 'properties': {'dtype': 'category', 'samples': ['1', '0'], 'num_unique_values': 2, 'semantic_type': 'boolean', 'description': 'Whether the car has all-wheel drive (1) or not (0).'}}, {'column': 'rwd', 'properties': {'dtype': 'category', 'samples': ['1', '0'], 'num_unique_values': 2, 'semantic_type': 'boolean', 'description': 'Whether the car has rear-wheel drive (1) or not (0).'}}, {'column': 'retail_price', 'properties': {'dtype': 'number', 'std': 14705, 'min': 11690, 'max': 89765, 'samples': [11690, 48195], 'num_unique_values': 98, 'semantic_type': 'price', 'description': 'The retail price of the car.'}}, {'column': 'dealer_cost', 'properties': {'dtype': 'number', 'std': 13260, 'min': 10965, 'max': 79978, 'samples': [23954, 51815], 'num_unique_values': 99, 'semantic_type': 'price', 'description': 'The cost of the car for the dealer.'}}, {'column': 'engine_size', 'properties': {'dtype': 'number', 'std': 0.96617179664426, 'min': 0.0, 'max': 5.7, 'samples': [3.5, 3.4], 'num_unique_values': 22, 'semantic_type': 'engine_size', 'description': \"The size of the car's engine.\"}}, {'column': 'cyl', 'properties': {'dtype': 'number', 'std': 1, 'min': 0, 'max': 8, 'samples': [4, 8], 'num_unique_values': 4, 'semantic_type': 'number_of_cylinders', 'description': \"The number of cylinders in the car's engine.\"}}, {'column': 'horsepower', 'properties': {'dtype': 'number', 'std': 61, 'min': 103, 'max': 450, 'samples': [215, 333], 'num_unique_values': 33, 'semantic_type': 'horsepower', 'description': 'The horsepower of the car.'}}, {'column': 'city_mpg', 'properties': {'dtype': 'number', 'std': 2, 'min': 14, 'max': 28, 'samples': [28, 19], 'num_unique_values': 13, 'semantic_type': 'fuel_efficiency', 'description': 'The city fuel efficiency of the car in miles per gallon.'}}, {'column': 'highway_mpg', 'properties': {'dtype': 'number', 'std': 3, 'min': 17, 'max': 37, 'samples': [24, 23], 'num_unique_values': 17, 'semantic_type': 'fuel_efficiency', 'description': 'The highway fuel efficiency of the car in miles per gallon.'}}, {'column': 'weight', 'properties': {'dtype': 'number', 'std': 558, 'min': 2348, 'max': 5367, 'samples': [2932, 2921], 'num_unique_values': 92, 'semantic_type': 'weight', 'description': 'The weight of the car in pounds.'}}, {'column': 'wheel_base', 'properties': {'dtype': 'number', 'std': 12, 'min': 0, 'max': 130, 'samples': [121, 111], 'num_unique_values': 25, 'semantic_type': 'length', 'description': 'The wheel base of the car in inches.'}}, {'column': 'len', 'properties': {'dtype': 'number', 'std': 13, 'min': 153, 'max': 219, 'samples': [194, 177], 'num_unique_values': 36, 'semantic_type': 'length', 'description': 'The length of the car in inches.'}}, {'column': 'width', 'properties': {'dtype': 'number', 'std': 7, 'min': 2, 'max': 79, 'samples': [73, 79], 'num_unique_values': 15, 'semantic_type': 'width', 'description': 'The width of the car in inches.'}}]}\n",
      "GEN GOAL PROMPT: [{'role': 'system', 'content': '\\n        You are a an experienced data analyst who can generate a given number of insightful GOALS about data, when given a summary of the data, and a specified persona. The VISUALIZATIONS YOU RECOMMEND MUST FOLLOW VISUALIZATION BEST PRACTICES (e.g., must use bar charts instead of pie charts for comparing quantities) AND BE MEANINGFUL (e.g., plot longitude and latitude on maps where appropriate). They must also be relevant to the specified persona. Each goal must include a question, a visualization (THE VISUALIZATION MUST REFERENCE THE EXACT COLUMN FIELDS FROM THE SUMMARY), and a rationale (JUSTIFICATION FOR WHICH dataset FIELDS ARE USED and what we will learn from the visualization). Each goal MUST mention the exact fields from the dataset summary above\\n        '}, {'role': 'assistant', 'content': 'The number of GOALS to generate is 3. The goals should be based on the data summary below, \\n\\n .\\n        {\\'name\\': \\'cars.csv\\', \\'file_name\\': \\'cars.csv\\', \\'dataset_description\\': \\'Dataset containing information about cars.\\', \\'fields\\': [{\\'column\\': \\'name\\', \\'properties\\': {\\'dtype\\': \\'string\\', \\'samples\\': [\\'Chevrolet Astro\\', \\'BMW M3 convertible 2dr\\'], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'car_model\\', \\'description\\': \\'The name of the car model.\\'}}, {\\'column\\': \\'type\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'SUV\\', \\'Minivan\\'], \\'num_unique_values\\': 5, \\'semantic_type\\': \\'car_type\\', \\'description\\': \\'The type of car.\\'}}, {\\'column\\': \\'awd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has all-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'rwd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has rear-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'retail_price\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 14705, \\'min\\': 11690, \\'max\\': 89765, \\'samples\\': [11690, 48195], \\'num_unique_values\\': 98, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The retail price of the car.\\'}}, {\\'column\\': \\'dealer_cost\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13260, \\'min\\': 10965, \\'max\\': 79978, \\'samples\\': [23954, 51815], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The cost of the car for the dealer.\\'}}, {\\'column\\': \\'engine_size\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 0.96617179664426, \\'min\\': 0.0, \\'max\\': 5.7, \\'samples\\': [3.5, 3.4], \\'num_unique_values\\': 22, \\'semantic_type\\': \\'engine_size\\', \\'description\\': \"The size of the car\\'s engine.\"}}, {\\'column\\': \\'cyl\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 1, \\'min\\': 0, \\'max\\': 8, \\'samples\\': [4, 8], \\'num_unique_values\\': 4, \\'semantic_type\\': \\'number_of_cylinders\\', \\'description\\': \"The number of cylinders in the car\\'s engine.\"}}, {\\'column\\': \\'horsepower\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 61, \\'min\\': 103, \\'max\\': 450, \\'samples\\': [215, 333], \\'num_unique_values\\': 33, \\'semantic_type\\': \\'horsepower\\', \\'description\\': \\'The horsepower of the car.\\'}}, {\\'column\\': \\'city_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 2, \\'min\\': 14, \\'max\\': 28, \\'samples\\': [28, 19], \\'num_unique_values\\': 13, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The city fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'highway_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 3, \\'min\\': 17, \\'max\\': 37, \\'samples\\': [24, 23], \\'num_unique_values\\': 17, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The highway fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'weight\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 558, \\'min\\': 2348, \\'max\\': 5367, \\'samples\\': [2932, 2921], \\'num_unique_values\\': 92, \\'semantic_type\\': \\'weight\\', \\'description\\': \\'The weight of the car in pounds.\\'}}, {\\'column\\': \\'wheel_base\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 12, \\'min\\': 0, \\'max\\': 130, \\'samples\\': [121, 111], \\'num_unique_values\\': 25, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The wheel base of the car in inches.\\'}}, {\\'column\\': \\'len\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13, \\'min\\': 153, \\'max\\': 219, \\'samples\\': [194, 177], \\'num_unique_values\\': 36, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The length of the car in inches.\\'}}, {\\'column\\': \\'width\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 7, \\'min\\': 2, \\'max\\': 79, \\'samples\\': [73, 79], \\'num_unique_values\\': 15, \\'semantic_type\\': \\'width\\', \\'description\\': \\'The width of the car in inches.\\'}}]} \\n\\n\\n The generated goals SHOULD BE FOCUSED ON THE INTERESTS AND PERSPECTIVE of a \\'A highly skilled data analyst who can come up with complex, insightful goals about data persona, who is insterested in complex, insightful goals about the data. \\n\\n\\n \\n        THE OUTPUT MUST BE A CODE SNIPPET OF A VALID LIST OF JSON OBJECTS. IT MUST USE THE FOLLOWING FORMAT:\\n\\n        ```[\\n            { \"index\": 0,  \"question\": \"What is the distribution of X\", \"visualization\": \"histogram of X\", \"rationale\": \"This tells about \"} ..\\n            ]\\n        ```\\n        THE OUTPUT SHOULD ONLY USE THE JSON FORMAT ABOVE.\\n         \\n\\n. The generated 3 goals are: \\n '}]\n",
      "GOALS:  [{'index': 0, 'question': 'What is the distribution of car types in the dataset?', 'visualization': 'bar chart of car types', 'rationale': 'By visualizing the distribution of car types, we can understand the proportion of each car type in the dataset. This information can be useful for analyzing market trends and customer preferences.'}, {'index': 1, 'question': \"How does the retail price vary based on the number of cylinders in the car's engine?\", 'visualization': 'box plot of retail price by number of cylinders', 'rationale': \"A box plot can show the distribution of retail prices for different numbers of cylinders in the car's engine. This visualization can help identify any patterns or trends in pricing based on the number of cylinders, which can be valuable for pricing strategies and market analysis.\"}, {'index': 2, 'question': 'What is the relationship between engine size and horsepower?', 'visualization': 'scatter plot of engine size vs horsepower', 'rationale': 'A scatter plot can show the relationship between engine size and horsepower. This visualization can help identify any correlation or patterns between these two variables, which can be useful for understanding the performance characteristics of different car models.'}]\n",
      "PLOT PROMPT: [{'role': 'system', 'content': 'You are a helpful assistant highly skilled in writing PERFECT code for visualizations. Given some \\n            code template, you complete the template to generate a visualization given the dataset and the goal described. \\n            The code you write MUST FOLLOW VISUALIZATION BEST PRACTICES ie. meet the specified goal, apply the right \\n            transformation, use the right visualization type, use the right data encoding, and use the right aesthetics \\n            (e.g., ensure axis are legible). The transformations you apply MUST be correct and the fields you use MUST be \\n            correct. The visualization CODE MUST BE CORRECT and MUST NOT CONTAIN ANY SYNTAX OR LOGIC ERRORS (e.g., it must \\n            consider the field types and use them correctly). You MUST first generate a brief plan for how you would solve \\n            the task e.g. what transformations you would apply e.g. if you need to construct a new column, what fields you would \\n            use, what visualization type you would use, what aesthetics you would use, etc.'}, {'role': 'system', 'content': 'The dataset summary is : {\\'name\\': \\'cars.csv\\', \\'file_name\\': \\'cars.csv\\', \\'dataset_description\\': \\'Dataset containing information about cars.\\', \\'fields\\': [{\\'column\\': \\'name\\', \\'properties\\': {\\'dtype\\': \\'string\\', \\'samples\\': [\\'Chevrolet Astro\\', \\'BMW M3 convertible 2dr\\'], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'car_model\\', \\'description\\': \\'The name of the car model.\\'}}, {\\'column\\': \\'type\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'SUV\\', \\'Minivan\\'], \\'num_unique_values\\': 5, \\'semantic_type\\': \\'car_type\\', \\'description\\': \\'The type of car.\\'}}, {\\'column\\': \\'awd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has all-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'rwd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has rear-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'retail_price\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 14705, \\'min\\': 11690, \\'max\\': 89765, \\'samples\\': [11690, 48195], \\'num_unique_values\\': 98, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The retail price of the car.\\'}}, {\\'column\\': \\'dealer_cost\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13260, \\'min\\': 10965, \\'max\\': 79978, \\'samples\\': [23954, 51815], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The cost of the car for the dealer.\\'}}, {\\'column\\': \\'engine_size\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 0.96617179664426, \\'min\\': 0.0, \\'max\\': 5.7, \\'samples\\': [3.5, 3.4], \\'num_unique_values\\': 22, \\'semantic_type\\': \\'engine_size\\', \\'description\\': \"The size of the car\\'s engine.\"}}, {\\'column\\': \\'cyl\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 1, \\'min\\': 0, \\'max\\': 8, \\'samples\\': [4, 8], \\'num_unique_values\\': 4, \\'semantic_type\\': \\'number_of_cylinders\\', \\'description\\': \"The number of cylinders in the car\\'s engine.\"}}, {\\'column\\': \\'horsepower\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 61, \\'min\\': 103, \\'max\\': 450, \\'samples\\': [215, 333], \\'num_unique_values\\': 33, \\'semantic_type\\': \\'horsepower\\', \\'description\\': \\'The horsepower of the car.\\'}}, {\\'column\\': \\'city_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 2, \\'min\\': 14, \\'max\\': 28, \\'samples\\': [28, 19], \\'num_unique_values\\': 13, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The city fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'highway_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 3, \\'min\\': 17, \\'max\\': 37, \\'samples\\': [24, 23], \\'num_unique_values\\': 17, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The highway fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'weight\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 558, \\'min\\': 2348, \\'max\\': 5367, \\'samples\\': [2932, 2921], \\'num_unique_values\\': 92, \\'semantic_type\\': \\'weight\\', \\'description\\': \\'The weight of the car in pounds.\\'}}, {\\'column\\': \\'wheel_base\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 12, \\'min\\': 0, \\'max\\': 130, \\'samples\\': [121, 111], \\'num_unique_values\\': 25, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The wheel base of the car in inches.\\'}}, {\\'column\\': \\'len\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13, \\'min\\': 153, \\'max\\': 219, \\'samples\\': [194, 177], \\'num_unique_values\\': 36, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The length of the car in inches.\\'}}, {\\'column\\': \\'width\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 7, \\'min\\': 2, \\'max\\': 79, \\'samples\\': [73, 79], \\'num_unique_values\\': 15, \\'semantic_type\\': \\'width\\', \\'description\\': \\'The width of the car in inches.\\'}}]} \\n\\n'}, {'role': 'assistant', 'content': \"   If the solution requires a single value (e.g. max, min, median, first, last etc), ALWAYS \\n            add a line (axvline or axhline) to the chart, ALWAYS with a legend containing the single value (formatted with 0.2F).\\n            If using a <field> where semantic_type=date, YOU MUST APPLY the following transform before using that column i) \\n            convert date fields to date types using data[''] = pd.to_datetime(data[<field>], errors='coerce'), ALWAYS use  \\n            errors='coerce' ii) drop the rows with NaT values data = data[pd.notna(data[<field>])] iii) convert field to right \\n            time format for plotting.  ALWAYS make sure the x-axis labels are legible (e.g., rotate when needed). Solve the\\n            task  carefully by completing ONLY the <imports> AND <stub> section. DO NOT WRITE  ANY CODE TO LOAD THE DATA. \\n            The data is already loaded and available in the variable data. Given the dataset summary, the plot(data) \\n            method should generate a matplotlib chart (bar chart of car types) that addresses this goal: What is the distribution of car types in the dataset?.  DONOT include plt.show(). The plot method must return a matplotlib object (plt). Think step by step. \\n. Use BaseMap for charts that require a map. \"}, {'role': 'user', 'content': \"Always add a legend with various colors where appropriate. The visualization code MUST only use data fields \\n                that exist in the dataset (field_names) or fields that are transformations based on existing field_names). \\n                Only use variables that have been defined in the code or are in the dataset summary. The response should have only the python code and no additional text.                 I repeat, give the python code only for the function. NO ADDITIONAL CODE.  \\n\\n \\n                \\\\THE GENERATED CODE SOLUTION SHOULD BE CREATED BY MODIFYING THE SPECIFIED PARTS OF THE TEMPLATE BELOW \\n\\n \\n                    import matplotlib.pyplot as plt\\n                    import pandas as pd\\n                    <imports>\\n                    # plan -\\n                    def plot(data: pd.DataFrame):\\n                        <stub> # only modify this section\\n                        plt.title('What is the distribution of car types in the dataset?', wrap=True)\\n                        plt.savefig(/home/preethi/projects/evadb-viz-generation/ + 'viz-cars-data/data/plot.png')\\n                        return plt;\\n                        # No additional code beyond this line. \\n\\n.\\n                The FINAL COMPLETED CODE BASED ON THE TEMPLATE above is ... \\n\\n\"}] \n",
      "\n",
      "\n",
      "python viz script:  import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "def plot(data: pd.DataFrame):\n",
      "    # Count the number of cars for each car type\n",
      "    car_type_counts = data['type'].value_counts()\n",
      "\n",
      "    # Create a bar chart of car types\n",
      "    plt.bar(car_type_counts.index, car_type_counts.values)\n",
      "\n",
      "    # Add a title and labels\n",
      "    plt.title('Distribution of Car Types')\n",
      "    plt.xlabel('Car Type')\n",
      "    plt.ylabel('Count')\n",
      "\n",
      "    # Save the plot as an image\n",
      "    plt.savefig('/home/preethi/projects/evadb-viz-generation/viz-cars-data/data/plot.png')\n",
      "\n",
      "    return plt\n",
      "Plots saved in viz-cars-data/data directory\n",
      "PLOT PROMPT: [{'role': 'system', 'content': 'You are a helpful assistant highly skilled in writing PERFECT code for visualizations. Given some \\n            code template, you complete the template to generate a visualization given the dataset and the goal described. \\n            The code you write MUST FOLLOW VISUALIZATION BEST PRACTICES ie. meet the specified goal, apply the right \\n            transformation, use the right visualization type, use the right data encoding, and use the right aesthetics \\n            (e.g., ensure axis are legible). The transformations you apply MUST be correct and the fields you use MUST be \\n            correct. The visualization CODE MUST BE CORRECT and MUST NOT CONTAIN ANY SYNTAX OR LOGIC ERRORS (e.g., it must \\n            consider the field types and use them correctly). You MUST first generate a brief plan for how you would solve \\n            the task e.g. what transformations you would apply e.g. if you need to construct a new column, what fields you would \\n            use, what visualization type you would use, what aesthetics you would use, etc.'}, {'role': 'system', 'content': 'The dataset summary is : {\\'name\\': \\'cars.csv\\', \\'file_name\\': \\'cars.csv\\', \\'dataset_description\\': \\'Dataset containing information about cars.\\', \\'fields\\': [{\\'column\\': \\'name\\', \\'properties\\': {\\'dtype\\': \\'string\\', \\'samples\\': [\\'Chevrolet Astro\\', \\'BMW M3 convertible 2dr\\'], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'car_model\\', \\'description\\': \\'The name of the car model.\\'}}, {\\'column\\': \\'type\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'SUV\\', \\'Minivan\\'], \\'num_unique_values\\': 5, \\'semantic_type\\': \\'car_type\\', \\'description\\': \\'The type of car.\\'}}, {\\'column\\': \\'awd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has all-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'rwd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has rear-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'retail_price\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 14705, \\'min\\': 11690, \\'max\\': 89765, \\'samples\\': [11690, 48195], \\'num_unique_values\\': 98, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The retail price of the car.\\'}}, {\\'column\\': \\'dealer_cost\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13260, \\'min\\': 10965, \\'max\\': 79978, \\'samples\\': [23954, 51815], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The cost of the car for the dealer.\\'}}, {\\'column\\': \\'engine_size\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 0.96617179664426, \\'min\\': 0.0, \\'max\\': 5.7, \\'samples\\': [3.5, 3.4], \\'num_unique_values\\': 22, \\'semantic_type\\': \\'engine_size\\', \\'description\\': \"The size of the car\\'s engine.\"}}, {\\'column\\': \\'cyl\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 1, \\'min\\': 0, \\'max\\': 8, \\'samples\\': [4, 8], \\'num_unique_values\\': 4, \\'semantic_type\\': \\'number_of_cylinders\\', \\'description\\': \"The number of cylinders in the car\\'s engine.\"}}, {\\'column\\': \\'horsepower\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 61, \\'min\\': 103, \\'max\\': 450, \\'samples\\': [215, 333], \\'num_unique_values\\': 33, \\'semantic_type\\': \\'horsepower\\', \\'description\\': \\'The horsepower of the car.\\'}}, {\\'column\\': \\'city_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 2, \\'min\\': 14, \\'max\\': 28, \\'samples\\': [28, 19], \\'num_unique_values\\': 13, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The city fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'highway_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 3, \\'min\\': 17, \\'max\\': 37, \\'samples\\': [24, 23], \\'num_unique_values\\': 17, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The highway fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'weight\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 558, \\'min\\': 2348, \\'max\\': 5367, \\'samples\\': [2932, 2921], \\'num_unique_values\\': 92, \\'semantic_type\\': \\'weight\\', \\'description\\': \\'The weight of the car in pounds.\\'}}, {\\'column\\': \\'wheel_base\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 12, \\'min\\': 0, \\'max\\': 130, \\'samples\\': [121, 111], \\'num_unique_values\\': 25, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The wheel base of the car in inches.\\'}}, {\\'column\\': \\'len\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13, \\'min\\': 153, \\'max\\': 219, \\'samples\\': [194, 177], \\'num_unique_values\\': 36, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The length of the car in inches.\\'}}, {\\'column\\': \\'width\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 7, \\'min\\': 2, \\'max\\': 79, \\'samples\\': [73, 79], \\'num_unique_values\\': 15, \\'semantic_type\\': \\'width\\', \\'description\\': \\'The width of the car in inches.\\'}}]} \\n\\n'}, {'role': 'assistant', 'content': \"   If the solution requires a single value (e.g. max, min, median, first, last etc), ALWAYS \\n            add a line (axvline or axhline) to the chart, ALWAYS with a legend containing the single value (formatted with 0.2F).\\n            If using a <field> where semantic_type=date, YOU MUST APPLY the following transform before using that column i) \\n            convert date fields to date types using data[''] = pd.to_datetime(data[<field>], errors='coerce'), ALWAYS use  \\n            errors='coerce' ii) drop the rows with NaT values data = data[pd.notna(data[<field>])] iii) convert field to right \\n            time format for plotting.  ALWAYS make sure the x-axis labels are legible (e.g., rotate when needed). Solve the\\n            task  carefully by completing ONLY the <imports> AND <stub> section. DO NOT WRITE  ANY CODE TO LOAD THE DATA. \\n            The data is already loaded and available in the variable data. Given the dataset summary, the plot(data) \\n            method should generate a matplotlib chart (box plot of retail price by number of cylinders) that addresses this goal: How does the retail price vary based on the number of cylinders in the car's engine?.  DONOT include plt.show(). The plot method must return a matplotlib object (plt). Think step by step. \\n. Use BaseMap for charts that require a map. \"}, {'role': 'user', 'content': \"Always add a legend with various colors where appropriate. The visualization code MUST only use data fields \\n                that exist in the dataset (field_names) or fields that are transformations based on existing field_names). \\n                Only use variables that have been defined in the code or are in the dataset summary. The response should have only the python code and no additional text.                 I repeat, give the python code only for the function. NO ADDITIONAL CODE.  \\n\\n \\n                \\\\THE GENERATED CODE SOLUTION SHOULD BE CREATED BY MODIFYING THE SPECIFIED PARTS OF THE TEMPLATE BELOW \\n\\n \\n                    import matplotlib.pyplot as plt\\n                    import pandas as pd\\n                    <imports>\\n                    # plan -\\n                    def plot(data: pd.DataFrame):\\n                        <stub> # only modify this section\\n                        plt.title('How does the retail price vary based on the number of cylinders in the car's engine?', wrap=True)\\n                        plt.savefig(/home/preethi/projects/evadb-viz-generation/ + 'viz-cars-data/data/plot.png')\\n                        return plt;\\n                        # No additional code beyond this line. \\n\\n.\\n                The FINAL COMPLETED CODE BASED ON THE TEMPLATE above is ... \\n\\n\"}] \n",
      "\n",
      "\n",
      "python viz script:  import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "def plot(data: pd.DataFrame):\n",
      "    # Group the data by the number of cylinders and calculate the median retail price for each group\n",
      "    median_price = data.groupby('cyl')['retail_price'].median()\n",
      "\n",
      "    # Create a box plot of the retail price by number of cylinders\n",
      "    plt.boxplot(data.groupby('cyl')['retail_price'].apply(list), labels=median_price.index)\n",
      "\n",
      "    # Add a title and labels to the plot\n",
      "    plt.title(\"How does the retail price vary based on the number of cylinders in the car's engine?\")\n",
      "    plt.xlabel('Number of Cylinders')\n",
      "    plt.ylabel('Retail Price')\n",
      "\n",
      "    # Save the plot as a file\n",
      "    plt.savefig('/home/preethi/projects/evadb-viz-generation/viz-cars-data/data/plot.png')\n",
      "\n",
      "    return plt\n",
      "Plots saved in viz-cars-data/data directory\n",
      "PLOT PROMPT: [{'role': 'system', 'content': 'You are a helpful assistant highly skilled in writing PERFECT code for visualizations. Given some \\n            code template, you complete the template to generate a visualization given the dataset and the goal described. \\n            The code you write MUST FOLLOW VISUALIZATION BEST PRACTICES ie. meet the specified goal, apply the right \\n            transformation, use the right visualization type, use the right data encoding, and use the right aesthetics \\n            (e.g., ensure axis are legible). The transformations you apply MUST be correct and the fields you use MUST be \\n            correct. The visualization CODE MUST BE CORRECT and MUST NOT CONTAIN ANY SYNTAX OR LOGIC ERRORS (e.g., it must \\n            consider the field types and use them correctly). You MUST first generate a brief plan for how you would solve \\n            the task e.g. what transformations you would apply e.g. if you need to construct a new column, what fields you would \\n            use, what visualization type you would use, what aesthetics you would use, etc.'}, {'role': 'system', 'content': 'The dataset summary is : {\\'name\\': \\'cars.csv\\', \\'file_name\\': \\'cars.csv\\', \\'dataset_description\\': \\'Dataset containing information about cars.\\', \\'fields\\': [{\\'column\\': \\'name\\', \\'properties\\': {\\'dtype\\': \\'string\\', \\'samples\\': [\\'Chevrolet Astro\\', \\'BMW M3 convertible 2dr\\'], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'car_model\\', \\'description\\': \\'The name of the car model.\\'}}, {\\'column\\': \\'type\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'SUV\\', \\'Minivan\\'], \\'num_unique_values\\': 5, \\'semantic_type\\': \\'car_type\\', \\'description\\': \\'The type of car.\\'}}, {\\'column\\': \\'awd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has all-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'rwd\\', \\'properties\\': {\\'dtype\\': \\'category\\', \\'samples\\': [\\'1\\', \\'0\\'], \\'num_unique_values\\': 2, \\'semantic_type\\': \\'boolean\\', \\'description\\': \\'Whether the car has rear-wheel drive (1) or not (0).\\'}}, {\\'column\\': \\'retail_price\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 14705, \\'min\\': 11690, \\'max\\': 89765, \\'samples\\': [11690, 48195], \\'num_unique_values\\': 98, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The retail price of the car.\\'}}, {\\'column\\': \\'dealer_cost\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13260, \\'min\\': 10965, \\'max\\': 79978, \\'samples\\': [23954, 51815], \\'num_unique_values\\': 99, \\'semantic_type\\': \\'price\\', \\'description\\': \\'The cost of the car for the dealer.\\'}}, {\\'column\\': \\'engine_size\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 0.96617179664426, \\'min\\': 0.0, \\'max\\': 5.7, \\'samples\\': [3.5, 3.4], \\'num_unique_values\\': 22, \\'semantic_type\\': \\'engine_size\\', \\'description\\': \"The size of the car\\'s engine.\"}}, {\\'column\\': \\'cyl\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 1, \\'min\\': 0, \\'max\\': 8, \\'samples\\': [4, 8], \\'num_unique_values\\': 4, \\'semantic_type\\': \\'number_of_cylinders\\', \\'description\\': \"The number of cylinders in the car\\'s engine.\"}}, {\\'column\\': \\'horsepower\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 61, \\'min\\': 103, \\'max\\': 450, \\'samples\\': [215, 333], \\'num_unique_values\\': 33, \\'semantic_type\\': \\'horsepower\\', \\'description\\': \\'The horsepower of the car.\\'}}, {\\'column\\': \\'city_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 2, \\'min\\': 14, \\'max\\': 28, \\'samples\\': [28, 19], \\'num_unique_values\\': 13, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The city fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'highway_mpg\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 3, \\'min\\': 17, \\'max\\': 37, \\'samples\\': [24, 23], \\'num_unique_values\\': 17, \\'semantic_type\\': \\'fuel_efficiency\\', \\'description\\': \\'The highway fuel efficiency of the car in miles per gallon.\\'}}, {\\'column\\': \\'weight\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 558, \\'min\\': 2348, \\'max\\': 5367, \\'samples\\': [2932, 2921], \\'num_unique_values\\': 92, \\'semantic_type\\': \\'weight\\', \\'description\\': \\'The weight of the car in pounds.\\'}}, {\\'column\\': \\'wheel_base\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 12, \\'min\\': 0, \\'max\\': 130, \\'samples\\': [121, 111], \\'num_unique_values\\': 25, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The wheel base of the car in inches.\\'}}, {\\'column\\': \\'len\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 13, \\'min\\': 153, \\'max\\': 219, \\'samples\\': [194, 177], \\'num_unique_values\\': 36, \\'semantic_type\\': \\'length\\', \\'description\\': \\'The length of the car in inches.\\'}}, {\\'column\\': \\'width\\', \\'properties\\': {\\'dtype\\': \\'number\\', \\'std\\': 7, \\'min\\': 2, \\'max\\': 79, \\'samples\\': [73, 79], \\'num_unique_values\\': 15, \\'semantic_type\\': \\'width\\', \\'description\\': \\'The width of the car in inches.\\'}}]} \\n\\n'}, {'role': 'assistant', 'content': \"   If the solution requires a single value (e.g. max, min, median, first, last etc), ALWAYS \\n            add a line (axvline or axhline) to the chart, ALWAYS with a legend containing the single value (formatted with 0.2F).\\n            If using a <field> where semantic_type=date, YOU MUST APPLY the following transform before using that column i) \\n            convert date fields to date types using data[''] = pd.to_datetime(data[<field>], errors='coerce'), ALWAYS use  \\n            errors='coerce' ii) drop the rows with NaT values data = data[pd.notna(data[<field>])] iii) convert field to right \\n            time format for plotting.  ALWAYS make sure the x-axis labels are legible (e.g., rotate when needed). Solve the\\n            task  carefully by completing ONLY the <imports> AND <stub> section. DO NOT WRITE  ANY CODE TO LOAD THE DATA. \\n            The data is already loaded and available in the variable data. Given the dataset summary, the plot(data) \\n            method should generate a matplotlib chart (scatter plot of engine size vs horsepower) that addresses this goal: What is the relationship between engine size and horsepower?.  DONOT include plt.show(). The plot method must return a matplotlib object (plt). Think step by step. \\n. Use BaseMap for charts that require a map. \"}, {'role': 'user', 'content': \"Always add a legend with various colors where appropriate. The visualization code MUST only use data fields \\n                that exist in the dataset (field_names) or fields that are transformations based on existing field_names). \\n                Only use variables that have been defined in the code or are in the dataset summary. The response should have only the python code and no additional text.                 I repeat, give the python code only for the function. NO ADDITIONAL CODE.  \\n\\n \\n                \\\\THE GENERATED CODE SOLUTION SHOULD BE CREATED BY MODIFYING THE SPECIFIED PARTS OF THE TEMPLATE BELOW \\n\\n \\n                    import matplotlib.pyplot as plt\\n                    import pandas as pd\\n                    <imports>\\n                    # plan -\\n                    def plot(data: pd.DataFrame):\\n                        <stub> # only modify this section\\n                        plt.title('What is the relationship between engine size and horsepower?', wrap=True)\\n                        plt.savefig(/home/preethi/projects/evadb-viz-generation/ + 'viz-cars-data/data/plot.png')\\n                        return plt;\\n                        # No additional code beyond this line. \\n\\n.\\n                The FINAL COMPLETED CODE BASED ON THE TEMPLATE above is ... \\n\\n\"}] \n",
      "\n",
      "\n",
      "python viz script:  import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "def plot(data: pd.DataFrame):\n",
      "    plt.scatter(data['engine_size'], data['horsepower'])\n",
      "    plt.xlabel('Engine Size')\n",
      "    plt.ylabel('Horsepower')\n",
      "    plt.title('What is the relationship between engine size and horsepower?')\n",
      "    plt.savefig('/home/preethi/projects/evadb-viz-generation/viz-cars-data/data/plot.png')\n",
      "    return plt\n",
      "Plots saved in viz-cars-data/data directory\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "query= f\"\"\" SELECT Visualize(name,type,awd,rwd,retail_price,dealer_cost,engine_size,cyl,horsepower,city_mpg,highway_mpg,weight,wheel_base,len,width) FROM CARS;\"\"\"\n",
    "result = cursor.query(query).execute()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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