gpt-pandas-matplotlib.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "gpt-pandas-code-generation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "heIf5_ducY8T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "74217a60-6e07-425c-a94a-51387939b7ee"
      },
      "source": [
        "!pip install openai"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting openai\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
            "\r\u001b[K     |██                              | 10kB 15.3MB/s eta 0:00:01\r\u001b[K     |████▏                           | 20kB 6.1MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 30kB 7.7MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 40kB 7.4MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 51kB 6.5MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 61kB 6.9MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 71kB 7.8MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 81kB 7.9MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 92kB 8.5MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 102kB 8.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 112kB 8.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 122kB 8.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 133kB 8.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 143kB 8.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 153kB 8.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 163kB 8.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
            "Building wheels for collected packages: openai\n",
            "  Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170709 sha256=7d8dd1fd8f27ae398961666519a46af67ea9c925b960cd786d619c7f6da3bc54\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
            "Successfully built openai\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIObOT-ybumY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "d44d7576-460f-4af2-b668-9f29cec15b74"
      },
      "source": [
        "import json\n",
        "import openai\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt \n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m42z9jQxqA2b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('GPT_SECRET_KEY.json') as f:\n",
        "    data = json.load(f)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75Yg2gB7p3Q0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "openai.api_key = data[\"API_KEY\"]"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N3sAHxJrhBzK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gpt import GPT\n",
        "from gpt import Example"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XiV0D9PihB2N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt = GPT(engine=\"davinci\",\n",
        "          temperature=0.5,\n",
        "          max_tokens=100)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qLap8Xgvwjt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({\"Gender\": [\"boy\", \"boy\", \"boy\", \"boy\", \"boy\",\n",
        "                         \"girl\", \"girl\", \"girl\", \"girl\"],\n",
        "                   \"Division\": [\"one\", \"one\", \"one\", \"two\", \"two\",\n",
        "                         \"one\", \"one\", \"two\", \"two\"],\n",
        "                   \"Marks\": [50, 55, 67, 85, 44, 84, 65, 56, 87]})"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGQIWZO-AazG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "bdd987fc-929e-450e-d687-6c3b12fa52cf"
      },
      "source": [
        "df"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Gender</th>\n",
              "      <th>Division</th>\n",
              "      <th>Marks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>boy</td>\n",
              "      <td>one</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>boy</td>\n",
              "      <td>one</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>boy</td>\n",
              "      <td>one</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>boy</td>\n",
              "      <td>two</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>boy</td>\n",
              "      <td>two</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>girl</td>\n",
              "      <td>one</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>girl</td>\n",
              "      <td>one</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>girl</td>\n",
              "      <td>two</td>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>girl</td>\n",
              "      <td>two</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Gender Division  Marks\n",
              "0    boy      one     50\n",
              "1    boy      one     55\n",
              "2    boy      one     67\n",
              "3    boy      two     85\n",
              "4    boy      two     44\n",
              "5   girl      one     84\n",
              "6   girl      one     65\n",
              "7   girl      two     56\n",
              "8   girl      two     87"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwVcHYMOvGiU",
        "colab_type": "text"
      },
      "source": [
        "# Adding Examples for GPT Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0iLR1Y6YqTh7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('How many unique values in Division Column?', \n",
        "                        'df[\"Division\"].nunique()'))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x28YlU1-qrCW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Find the Division of boy who scored 55 marks', \n",
        "                        'df.loc[(df.loc[:, \"Gender\"] == \"boy\") & (df.loc[:, \"Marks\"] == 55), \"Division\"]'))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yBQMrnIQx3Uv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Find the average Marks scored by Girls', \n",
        "                        'np.mean(df.loc[(df.loc[:, \"Gender\"] == \"girl\"), \"Marks\"])'))"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sua6IdZgw4tR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3mI7FmwSu9AA",
        "colab_type": "text"
      },
      "source": [
        "# Example 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWSmXABfrdTm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prompt = \"Display Division of girl who scored maximum marks\""
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7RE6DxAyEEy5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8258aafd-bfd6-49fd-a4e0-cd80695bf637"
      },
      "source": [
        "gpt.get_top_reply(prompt)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'output: df.loc[(df.loc[:, \"Gender\"] == \"girl\") & (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \"Division\"]\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FkyPKIYCEin4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "90dcea4c-2f66-451e-baab-fee2b2ba317a"
      },
      "source": [
        "response = gpt.get_top_reply(prompt)\n",
        "print(response)\n",
        "modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
        "eval(modified_response)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output: df.loc[(df.loc[:, \"Gender\"] == \"girl\") & (df.loc[:, \"Marks\"] == max(df.loc[:, \"Marks\"])), \"Division\"]\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8    two\n",
              "Name: Division, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NopaHtl1_9rH",
        "colab_type": "text"
      },
      "source": [
        "# Example 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eO4IW_H_y_5F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prompt = \"Find the median Marks scored by Boys\""
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8ldVXxhyGVD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "d9d5cebf-1a3c-4d3d-e721-04386882d4e2"
      },
      "source": [
        "response = gpt.get_top_reply(prompt)\n",
        "print(response)\n",
        "modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
        "eval(modified_response)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output: np.median(df.loc[(df.loc[:, \"Gender\"] == \"boy\"), \"Marks\"])\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "55.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIftyCylGZOm",
        "colab_type": "text"
      },
      "source": [
        "# Matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUi-UN62zJe_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_LN5yFvGu9h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"iris.csv\")"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JaPeGE21G0OW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "214ede56-9479-429e-da13-2275797187ed"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cf6vxbTADRpf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Plot Scatter Plot between Sepal Length & Sepal Width', \n",
        "                        \"plt.scatter(df['sepal_length'], df['sepal_width'])\"))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kw6K2MGOIqQ0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Plot Bar Plot of Species', \n",
        "                        \"sns.countplot('species',data=df)\"))"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdMy31wCI7IT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Plot a Joint Plot between Sepal Length & Petal Length', \n",
        "                        \"sns.jointplot(x='sepal_length',y='petal_length',data=df)\"))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SvQN_tTgKuHO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "gpt.add_example(Example('Show me the histogram of Petal Length', \n",
        "                        \"plt.hist(df['petal_length'])\"))"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aAVofRjMId_b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ljE2bDJJoY6",
        "colab_type": "text"
      },
      "source": [
        "# Example 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FDd-Cf22Jm2G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prompt = \"Show me the scatter plot between petal length and sepal width\""
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3eVyHEEJ1eH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "de64cded-7157-428f-f8bc-897a1fafa044"
      },
      "source": [
        "response = gpt.get_top_reply(prompt)\n",
        "print(response)\n",
        "modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
        "eval(modified_response)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output: plt.scatter(df['petal_length'], df['sepal_width'])\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f4efbe0e128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Fx9MuHOKZrn",
        "colab_type": "text"
      },
      "source": [
        "# Example 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JQQEa_8J3j_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prompt = \"Show me the Joint Plot between Petal Length & Petal Length\""
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNHuzfNsKhzj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "outputId": "c6d67a08-91a8-4b5f-e916-17514ca70a3b"
      },
      "source": [
        "response = gpt.get_top_reply(prompt)\n",
        "print(response)\n",
        "modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
        "eval(modified_response)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output: sns.jointplot(x='petal_length',y='petal_length',data=df)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7f4efbe583c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "au8eO5gwLOPg",
        "colab_type": "text"
      },
      "source": [
        "# Example 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eHEQ7C9iKjtD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prompt = \"Show me the Distribution of Sepal Length\""
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U93zERQDLYDA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "ee9e923a-369c-4487-97cc-c7e289f3bac3"
      },
      "source": [
        "response = gpt.get_top_reply(prompt)\n",
        "print(response)\n",
        "modified_response = response.split(\"output: \")[-1].strip('\\n')\n",
        "eval(modified_response)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "output: plt.hist(df['sepal_length'],bins=50)\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 1.,  3.,  1.,  0.,  4.,  2.,  5.,  0.,  6., 10.,  0.,  9.,  4.,\n",
              "         1.,  0.,  6.,  7.,  0.,  6.,  8.,  7.,  0.,  3.,  6.,  0.,  6.,\n",
              "         4.,  9.,  0.,  7.,  5.,  2.,  0.,  8.,  3.,  0.,  4.,  1.,  1.,\n",
              "         0.,  3.,  1.,  0.,  1.,  0.,  1.,  0.,  4.,  0.,  1.]),\n",
              " array([4.3  , 4.372, 4.444, 4.516, 4.588, 4.66 , 4.732, 4.804, 4.876,\n",
              "        4.948, 5.02 , 5.092, 5.164, 5.236, 5.308, 5.38 , 5.452, 5.524,\n",
              "        5.596, 5.668, 5.74 , 5.812, 5.884, 5.956, 6.028, 6.1  , 6.172,\n",
              "        6.244, 6.316, 6.388, 6.46 , 6.532, 6.604, 6.676, 6.748, 6.82 ,\n",
              "        6.892, 6.964, 7.036, 7.108, 7.18 , 7.252, 7.324, 7.396, 7.468,\n",
              "        7.54 , 7.612, 7.684, 7.756, 7.828, 7.9  ]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onnSCklcLcJT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}