Tutorial.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "1. CREATING A DUMMY DATASET"
      ],
      "metadata": {
        "id": "epZBqQoRUn_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas numpy"
      ],
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        },
        "id": "DC8GUW6FUqN5",
        "outputId": "63ef82b2-486a-433e-ffb1-d02b7e09eeec"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.22.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from random import choices"
      ],
      "metadata": {
        "id": "KM6pnTOFUtKP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(10)"
      ],
      "metadata": {
        "id": "B5w7xHcCU2CO"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_records = 1000\n",
        "num_outlets = 10\n",
        "num_products = 5"
      ],
      "metadata": {
        "id": "ZqrDZ0AdU-lm"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#2 quarters (Jan-mar, apr-jun)\n",
        "dates = pd.date_range(start='2023-01-01', end='2023-06-30')\n",
        "dates = choices(dates, k=num_records)"
      ],
      "metadata": {
        "id": "YwtqaL0OVGSv"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "outlets = ['Outlet_'+str(i+1) for i in range(num_outlets)]\n",
        "outlets = choices(outlets, k=num_records)"
      ],
      "metadata": {
        "id": "FRKr51C7VUSq"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "products = ['Product_'+str(i+1) for i in range(num_products)]\n",
        "products = choices(products, k=num_records)"
      ],
      "metadata": {
        "id": "IG-tVyRFVUpS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "units_sold = np.random.randint(1, 200, num_records)\n",
        "price_per_unit = np.random.uniform(10, 500, num_records)"
      ],
      "metadata": {
        "id": "aBmpImBcWDnn"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_sales = units_sold * price_per_unit"
      ],
      "metadata": {
        "id": "gkpy3migWM0z"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\n",
        "    'Date': dates,\n",
        "    'Outlet': outlets,\n",
        "    'Product': products,\n",
        "    'Unit_Sold': units_sold,\n",
        "    'Price_Per_Unit': price_per_unit,\n",
        "    'total_sales': total_sales\n",
        "})"
      ],
      "metadata": {
        "id": "UJBzDjq7Womo"
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      "execution_count": 52,
      "outputs": []
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        "df"
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      "execution_count": 53,
      "outputs": [
        {
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            "text/plain": [
              "          Date    Outlet    Product  Unit_Sold  Price_Per_Unit   total_sales\n",
              "0   2023-05-30  Outlet_6  Product_4        106      277.192861  29382.443271\n",
              "1   2023-02-28  Outlet_7  Product_1         44      119.054276   5238.388145\n",
              "2   2023-01-06  Outlet_2  Product_5          7      120.875668    846.129679\n",
              "3   2023-03-19  Outlet_6  Product_3         45      413.961937  18628.287152\n",
              "4   2023-03-14  Outlet_1  Product_5        178      361.260348  64304.341858\n",
              "..         ...       ...        ...        ...             ...           ...\n",
              "995 2023-01-07  Outlet_1  Product_5        149       54.373431   8101.641167\n",
              "996 2023-04-18  Outlet_9  Product_1         95       85.306864   8104.152035\n",
              "997 2023-01-05  Outlet_3  Product_5         45      329.319618  14819.382809\n",
              "998 2023-02-04  Outlet_5  Product_5         29      325.385231   9436.171703\n",
              "999 2023-01-08  Outlet_3  Product_3         18      440.335371   7926.036682\n",
              "\n",
              "[1000 rows x 6 columns]"
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              "      <th>Date</th>\n",
              "      <th>Outlet</th>\n",
              "      <th>Product</th>\n",
              "      <th>Unit_Sold</th>\n",
              "      <th>Price_Per_Unit</th>\n",
              "      <th>total_sales</th>\n",
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              "      <th>0</th>\n",
              "      <td>2023-05-30</td>\n",
              "      <td>Outlet_6</td>\n",
              "      <td>Product_4</td>\n",
              "      <td>106</td>\n",
              "      <td>277.192861</td>\n",
              "      <td>29382.443271</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-02-28</td>\n",
              "      <td>Outlet_7</td>\n",
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              "      <td>Outlet_2</td>\n",
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              "      <td>7</td>\n",
              "      <td>120.875668</td>\n",
              "      <td>846.129679</td>\n",
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              "      <th>3</th>\n",
              "      <td>2023-03-19</td>\n",
              "      <td>Outlet_6</td>\n",
              "      <td>Product_3</td>\n",
              "      <td>45</td>\n",
              "      <td>413.961937</td>\n",
              "      <td>18628.287152</td>\n",
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              "      <th>995</th>\n",
              "      <td>2023-01-07</td>\n",
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              "      <td>149</td>\n",
              "      <td>54.373431</td>\n",
              "      <td>8101.641167</td>\n",
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              "      <td>Outlet_9</td>\n",
              "      <td>Product_1</td>\n",
              "      <td>95</td>\n",
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              "      <td>8104.152035</td>\n",
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              "      <th>997</th>\n",
              "      <td>2023-01-05</td>\n",
              "      <td>Outlet_3</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>45</td>\n",
              "      <td>329.319618</td>\n",
              "      <td>14819.382809</td>\n",
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              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>2023-02-04</td>\n",
              "      <td>Outlet_5</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>29</td>\n",
              "      <td>325.385231</td>\n",
              "      <td>9436.171703</td>\n",
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              "      <th>999</th>\n",
              "      <td>2023-01-08</td>\n",
              "      <td>Outlet_3</td>\n",
              "      <td>Product_3</td>\n",
              "      <td>18</td>\n",
              "      <td>440.335371</td>\n",
              "      <td>7926.036682</td>\n",
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          "metadata": {},
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        "OPENAI_API_KEY = \"Your key here\""
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      "metadata": {
        "id": "aPVVLnbVXEXq"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandasai"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "kziqmTazXrwC",
        "outputId": "1d786105-de04-4b8c-9c4e-91019d985a4e"
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      "execution_count": 27,
      "outputs": [
        {
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          "text": [
            "Collecting pandasai\n",
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            "\u001b[?25hRequirement already satisfied: matplotlib<4.0.0,>=3.7.1 in /usr/local/lib/python3.10/dist-packages (from pandasai) (3.7.1)\n",
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            "Collecting executing>=1.2.0 (from stack-data->ipython<9.0.0,>=8.13.1->pandasai)\n",
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            "Collecting asttokens>=2.1.0 (from stack-data->ipython<9.0.0,>=8.13.1->pandasai)\n",
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            "  Downloading pure_eval-0.2.2-py3-none-any.whl (11 kB)\n",
            "Installing collected packages: pure-eval, executing, python-dotenv, jedi, asttokens, astor, stack-data, openai, ipython, pandasai\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 7.34.0\n",
            "    Uninstalling ipython-7.34.0:\n",
            "      Successfully uninstalled ipython-7.34.0\n",
            "\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.\n",
            "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.14.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed astor-0.8.1 asttokens-2.2.1 executing-1.2.0 ipython-8.14.0 jedi-0.19.0 openai-0.27.8 pandasai-0.8.1 pure-eval-0.2.2 python-dotenv-1.0.0 stack-data-0.6.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai import PandasAI\n",
        "from pandasai.llm.openai import OpenAI"
      ],
      "metadata": {
        "id": "8_xGZDwiXyZS"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm = OpenAI(api_token=OPENAI_API_KEY)\n",
        "pandas_ai = PandasAI(llm)"
      ],
      "metadata": {
        "id": "wchlI742X7Of"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "flzQl5_PYGBI",
        "outputId": "8953c64e-3270-44d9-decc-3f21ce4fe2e6"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date    Outlet    Product  Unit_Sold  Price_Per_Unit   total_sales\n",
              "0   2023-05-30  Outlet_6  Product_4        106      277.192861  29382.443271\n",
              "1   2023-02-28  Outlet_7  Product_1         44      119.054276   5238.388145\n",
              "2   2023-01-06  Outlet_2  Product_5          7      120.875668    846.129679\n",
              "3   2023-03-19  Outlet_6  Product_3         45      413.961937  18628.287152\n",
              "4   2023-03-14  Outlet_1  Product_5        178      361.260348  64304.341858\n",
              "..         ...       ...        ...        ...             ...           ...\n",
              "995 2023-01-07  Outlet_1  Product_5        149       54.373431   8101.641167\n",
              "996 2023-04-18  Outlet_9  Product_1         95       85.306864   8104.152035\n",
              "997 2023-01-05  Outlet_3  Product_5         45      329.319618  14819.382809\n",
              "998 2023-02-04  Outlet_5  Product_5         29      325.385231   9436.171703\n",
              "999 2023-01-08  Outlet_3  Product_3         18      440.335371   7926.036682\n",
              "\n",
              "[1000 rows x 6 columns]"
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dbe38a14-41cc-45e5-8da6-713ab57ba8aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dbe38a14-41cc-45e5-8da6-713ab57ba8aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, \"Which product has the highest total_sales?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "sHK98DlnYL25",
        "outputId": "47ee7325-900c-4ad1-d5d1-449ec78ef1f6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Product_3'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, \"Format Price_Per_Unit and Total_Sales to 2 decimal places\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "f8dl9bG9ZOON",
        "outputId": "fda88799-7d45-4d14-e906-940b0849074e"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "9uyJUgS_ZhhS",
        "outputId": "f4811e64-95a0-4cbf-8244-51de97b6d62b"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date    Outlet    Product  Unit_Sold  Price_Per_Unit  total_sales\n",
              "0   2023-05-30  Outlet_6  Product_4        106          277.19     29382.44\n",
              "1   2023-02-28  Outlet_7  Product_1         44          119.05      5238.39\n",
              "2   2023-01-06  Outlet_2  Product_5          7          120.88       846.13\n",
              "3   2023-03-19  Outlet_6  Product_3         45          413.96     18628.29\n",
              "4   2023-03-14  Outlet_1  Product_5        178          361.26     64304.34\n",
              "..         ...       ...        ...        ...             ...          ...\n",
              "995 2023-01-07  Outlet_1  Product_5        149           54.37      8101.64\n",
              "996 2023-04-18  Outlet_9  Product_1         95           85.31      8104.15\n",
              "997 2023-01-05  Outlet_3  Product_5         45          329.32     14819.38\n",
              "998 2023-02-04  Outlet_5  Product_5         29          325.39      9436.17\n",
              "999 2023-01-08  Outlet_3  Product_3         18          440.34      7926.04\n",
              "\n",
              "[1000 rows x 6 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Outlet</th>\n",
              "      <th>Product</th>\n",
              "      <th>Unit_Sold</th>\n",
              "      <th>Price_Per_Unit</th>\n",
              "      <th>total_sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2023-05-30</td>\n",
              "      <td>Outlet_6</td>\n",
              "      <td>Product_4</td>\n",
              "      <td>106</td>\n",
              "      <td>277.19</td>\n",
              "      <td>29382.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-02-28</td>\n",
              "      <td>Outlet_7</td>\n",
              "      <td>Product_1</td>\n",
              "      <td>44</td>\n",
              "      <td>119.05</td>\n",
              "      <td>5238.39</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-01-06</td>\n",
              "      <td>Outlet_2</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>7</td>\n",
              "      <td>120.88</td>\n",
              "      <td>846.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-03-19</td>\n",
              "      <td>Outlet_6</td>\n",
              "      <td>Product_3</td>\n",
              "      <td>45</td>\n",
              "      <td>413.96</td>\n",
              "      <td>18628.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-03-14</td>\n",
              "      <td>Outlet_1</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>178</td>\n",
              "      <td>361.26</td>\n",
              "      <td>64304.34</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>2023-01-07</td>\n",
              "      <td>Outlet_1</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>149</td>\n",
              "      <td>54.37</td>\n",
              "      <td>8101.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>2023-04-18</td>\n",
              "      <td>Outlet_9</td>\n",
              "      <td>Product_1</td>\n",
              "      <td>95</td>\n",
              "      <td>85.31</td>\n",
              "      <td>8104.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>2023-01-05</td>\n",
              "      <td>Outlet_3</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>45</td>\n",
              "      <td>329.32</td>\n",
              "      <td>14819.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>2023-02-04</td>\n",
              "      <td>Outlet_5</td>\n",
              "      <td>Product_5</td>\n",
              "      <td>29</td>\n",
              "      <td>325.39</td>\n",
              "      <td>9436.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>2023-01-08</td>\n",
              "      <td>Outlet_3</td>\n",
              "      <td>Product_3</td>\n",
              "      <td>18</td>\n",
              "      <td>440.34</td>\n",
              "      <td>7926.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1000 rows × 6 columns</p>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c31bd7d1-fcc8-4147-aaef-d95f144d56b2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c31bd7d1-fcc8-4147-aaef-d95f144d56b2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43a1e5fc-c23a-496c-925a-e50041a7164e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-43a1e5fc-c23a-496c-925a-e50041a7164e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CHARTING"
      ],
      "metadata": {
        "id": "q6-x3e2VZq4C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, \"Which product has the highest total_sales in the first 3 months?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "7jNygMmyamGi",
        "outputId": "a1071bba-4c79-4221-a408-67add397cd2c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Product_2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, \"Plot the chart of the products based on total_sales for the first 3 months\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "Vq0FXaMvZjLI",
        "outputId": "e0f07b96-6b61-4ef3-99c9-9728507ff70e"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, \"Plot the bar chart of the products based on the monthly sales\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "cyO-fpOWZ7S1",
        "outputId": "ca424f28-e64c-4284-c551-06b4da9c473c"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, 'Plot the pie chart of the overall product sales')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "imRlClDrbMCH",
        "outputId": "18fc801d-8c9d-4572-db9a-a38dcd9b2245"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, 'Plot the pie chart of the overall product sales for the first quarter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "fFahnP6vbal5",
        "outputId": "1a9c8388-2a92-4797-b6c5-59281db38628"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_ai.run(df, 'Plot the pie chart of the overall product sales for the second quarter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "aOJaP2U-bly_",
        "outputId": "3f55e1fd-7259-48fd-e8a2-24f054fe6454"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "VUPA970Mbn_S"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}