10_minutes_to_PandasAI.ipynb

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      "source": [
        "# PandasAI\n",
        "PandasAI is a library that makes data analysis conversational and fun again. It leverages the power of pandas dataframes combined to the most advanced LLMs to let users to data analysis in a conversational way.\n",
        "\n",
        "Similarly to what `pandas` has done (10 minutes to pandas -> https://pandas.pydata.org/docs/user_guide/10min.html), we wanted to create the most straightforward way to learn how to master PandasAI.\n",
        "\n",
        "Let's start!\n"
      ],
      "metadata": {
        "id": "5m9QRh2ofb0M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "To get started, we need to install the last version of PandasAI."
      ],
      "metadata": {
        "id": "8duxIdoChr2g"
      }
    },
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          "metadata": {}
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      ],
      "source": [
        "!pip install pandasai"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SmartDataframe\n",
        "\n",
        "A SmartDataframe is a pandas (or polars) dataframe that inherits all the properties and methods from the `pd.DataFrame`, but also adds conversational features to it."
      ],
      "metadata": {
        "id": "EkSwZhHqfIzg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai import SmartDataframe"
      ],
      "metadata": {
        "id": "DKTLrefOgGYj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can instantiate a dataframe importing from several different sources (pandas or polars dataframe, csv, xlsx or google sheets)."
      ],
      "metadata": {
        "id": "7NkkotuPf1Tw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing from a pandas dataframe"
      ],
      "metadata": {
        "id": "LJdaiLU9gA4g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To import from a pandas dataframe, you need to import the pandas libraries and create a dataframe first."
      ],
      "metadata": {
        "id": "viepQXfkglJY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "LOj6QLWGLMlD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\n",
        "    \"country\": [\n",
        "        \"United States\",\n",
        "        \"United Kingdom\",\n",
        "        \"France\",\n",
        "        \"Germany\",\n",
        "        \"Italy\",\n",
        "        \"Spain\",\n",
        "        \"Canada\",\n",
        "        \"Australia\",\n",
        "        \"Japan\",\n",
        "        \"China\",\n",
        "    ],\n",
        "    \"gdp\": [\n",
        "        19294482071552,\n",
        "        2891615567872,\n",
        "        2411255037952,\n",
        "        3435817336832,\n",
        "        1745433788416,\n",
        "        1181205135360,\n",
        "        1607402389504,\n",
        "        1490967855104,\n",
        "        4380756541440,\n",
        "        14631844184064,\n",
        "    ],\n",
        "    \"happiness_index\": [6.94, 7.16, 6.66, 7.07, 6.38, 6.4, 7.23, 7.22, 5.87, 5.12],\n",
        "})"
      ],
      "metadata": {
        "id": "ugogHbQxf0Y6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since PandasAI is powered by a LLM, you should import the LLM you'd like to use for your use case. In thic case we're gonna use OpenAI.\n",
        "\n",
        "To use OpenAI, you'll need a API token. Follow below simple steps to generate your API_TOKEN with\n",
        "[openai](https://platform.openai.com/overview).\n",
        "\n",
        "1. Go to  https://openai.com/api/ and signup with your email address or connect your Google Account.\n",
        "2. Go to View API Keys on left side of your Personal Account Settings\n",
        "3. Select Create new Secret key\n",
        "\n",
        "> The API access to openai is a paid service. You have to set up billing.\n",
        ">Read the [Pricing](https://platform.openai.com/docs/quickstart/pricing) information before experimenting."
      ],
      "metadata": {
        "id": "2bD7r0EtgsdD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai.llm import OpenAI\n",
        "\n",
        "llm = OpenAI(api_token=\"YOUR TOKEN\")"
      ],
      "metadata": {
        "id": "rB64X01YgrmH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that we have instantiated the LLM, we can finally instantiate the `SmartDataframe`"
      ],
      "metadata": {
        "id": "TlhiAynHiQ0y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf = SmartDataframe(df, config={\"llm\": llm})"
      ],
      "metadata": {
        "id": "hXFeO02JiYRC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A `SmartDataframe` inherits all the methods and properties from the original dataframe. For example:"
      ],
      "metadata": {
        "id": "_-6qOIbHidtm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf[sdf['country'] == 'United States']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "39NiBGZWim2q",
        "outputId": "f06148ed-602e-40ff-c9fa-2185ddc6bee3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "         country             gdp  happiness_index\n",
              "0  United States  19294482071552             6.94"
            ],
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4e204252-fa43-464f-9d2c-ed03e9e5629b 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-4e204252-fa43-464f-9d2c-ed03e9e5629b');\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",
              "\n",
              "\n",
              "<div id=\"df-f4742ab5-1f6c-4047-8ca2-b34d818d455c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4742ab5-1f6c-4047-8ca2-b34d818d455c')\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\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 charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f4742ab5-1f6c-4047-8ca2-b34d818d455c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "But you can also query it in natural language"
      ],
      "metadata": {
        "id": "SEsxIYyTjHm1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.chat(\"Return the top 5 countries by GDP\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0pVAthBSjEpx",
        "outputId": "a0e4b6a7-9abd-45b9-f88f-47c935a27fef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country             gdp  happiness_index\n",
              "0   United States  19294482071552             6.94\n",
              "9           China  14631844184064             5.12\n",
              "8           Japan   4380756541440             5.87\n",
              "3         Germany   3435817336832             7.07\n",
              "1  United Kingdom   2891615567872             7.16"
            ],
            "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>country</th>\n",
              "      <th>gdp</th>\n",
              "      <th>happiness_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>19294482071552</td>\n",
              "      <td>6.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>China</td>\n",
              "      <td>14631844184064</td>\n",
              "      <td>5.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Japan</td>\n",
              "      <td>4380756541440</td>\n",
              "      <td>5.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Germany</td>\n",
              "      <td>3435817336832</td>\n",
              "      <td>7.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2891615567872</td>\n",
              "      <td>7.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.chat(\"What's the sum of the gdp of the 2 unhappiest countries?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "us1r15KQjZlu",
        "outputId": "efa8a617-e197-4f7f-9fed-366ad5948479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19012600725504"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sdf.last_code_generated)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uzOEjV3LjqDF",
        "outputId": "78c4fe2d-7bd8-4569-b058-34ac5ff2d29b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def analyze_data(dfs: list[pd.DataFrame]) ->dict:\n",
            "    df_combined = pd.concat(dfs)\n",
            "    df_sorted = df_combined.sort_values('happiness_index')\n",
            "    sum_gdp = df_sorted.head(2)['gdp'].sum()\n",
            "    return {'type': 'number', 'value': sum_gdp}\n",
            "\n",
            "\n",
            "result = analyze_data(dfs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting a chart\n",
        "\n",
        "You can also use PandasAI to easily plot a chart"
      ],
      "metadata": {
        "id": "Pl_PQflF4_1u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.chat(\"Plot a chart of the gdp by country\")"
      ],
      "metadata": {
        "id": "OeAXD-9pzlSP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "a2d2bf83-6ee6-4dc9-940c-bedc0982e770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also provide additional instructions. For example, imagine you want to use different colors for each bar. You just need to ask to PandasAI:"
      ],
      "metadata": {
        "id": "J0lqXjcq5QyN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.chat(\"Plot a histogram of the gdp by country, using a different color for each bar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "U5pJRgyY5QR2",
        "outputId": "ba021037-b019-4c31-8ea0-e00db13c299c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As an alternative, you can use `shortcuts`. Shortcuts are functions that avoid you to write a prompt and \"do the magic\" under the hood for you.\n",
        "\n",
        "For example, you can use `.plot_bar_chart()` to generate the same chart, providing the field:"
      ],
      "metadata": {
        "id": "ypcZrF9L6N-T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.plot_bar_chart(x=\"country\", y=\"gdp\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "QuheJpPq52X-",
        "outputId": "5e466610-514d-40cc-f3d5-ec9b48d381e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So for example if we want to visualize as a pie chart, you can instead call the `plot_pie_chart` shortcut, passing the field we want ot use as labels and the one we want to use as values."
      ],
      "metadata": {
        "id": "IU4WMVP_K4zP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sdf.plot_pie_chart(labels=\"country\", values=\"gdp\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "0HUY4PkV9w-O",
        "outputId": "e60db880-3e3e-4dfe-9ed4-be2d71c9d26b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SmartDatalake\n",
        "\n",
        "Sometimes, you might want to work with multiple dataframes at a time, letting the LLM orchestrate which one(s) to use to answer your queries. In such cases, instead of using a `SmartDataframe` you should rather use a `SmartDatalake`.\n",
        "\n",
        "The concept is very similar to the `SmartDataframe`, but instead of accepting only 1 df as input, it can accept multiple ones."
      ],
      "metadata": {
        "id": "JvFf3OQPJ2T5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai import SmartDatalake"
      ],
      "metadata": {
        "id": "Xf-q8r35KYL1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "For example, in this example, we are provided with 2 different dfs.\n",
        "In the first one, it's reported for each employee, an employee id, a name and a department.\n",
        "In the second one, instad, it's provided the employee id and the salary for each employee.\n",
        "\n",
        "Asking PandasAI, it will join the 2 different dataframes by id and figure out the name of the one that is paid the most."
      ],
      "metadata": {
        "id": "yX3Ph1yYKfst"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "employees_df = pd.DataFrame(\n",
        "    {\n",
        "        \"EmployeeID\": [1, 2, 3, 4, 5],\n",
        "        \"Name\": [\"John\", \"Emma\", \"Liam\", \"Olivia\", \"William\"],\n",
        "        \"Department\": [\"HR\", \"Sales\", \"IT\", \"Marketing\", \"Finance\"],\n",
        "    }\n",
        ")\n",
        "\n",
        "salaries_df = pd.DataFrame(\n",
        "    {\n",
        "        \"EmployeeID\": [1, 2, 3, 4, 5],\n",
        "        \"Salary\": [5000, 6000, 4500, 7000, 5500],\n",
        "    }\n",
        ")\n",
        "\n",
        "lake = SmartDatalake(\n",
        "    [employees_df, salaries_df],\n",
        "    config={\"llm\": llm}\n",
        ")\n",
        "lake.chat(\"Who gets paid the most?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "3GIglds_KbHo",
        "outputId": "a6b8e9d8-5931-41eb-d484-8b1caabc9ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The employee who gets paid the most is Olivia.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here's an example of the code that is generated:"
      ],
      "metadata": {
        "id": "FVElosbbK-_i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(lake.last_code_executed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyaxD4N4LEl2",
        "outputId": "af79f932-811d-486c-a69d-13c932e05867"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def analyze_data(dfs: list[pd.DataFrame]) ->dict:\n",
            "    merged_df = pd.merge(dfs[0], dfs[1], on='EmployeeID')\n",
            "    max_salary_employee = merged_df.loc[merged_df['Salary'].idxmax()]\n",
            "    employee_name = max_salary_employee['Name']\n",
            "    return {'type': 'text', 'value': f'The employee who gets paid the most is {employee_name}.'}\n",
            "\n",
            "\n",
            "result = analyze_data(dfs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ok in this case it was easy: both the tables are share a common value called `EmployeeID`, right?\n",
        "\n",
        "Let's try with something more complex"
      ],
      "metadata": {
        "id": "wr-xjtHvMf_q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "users_df = pd.DataFrame(\n",
        "    {\n",
        "        \"id\": [1, 2, 3, 4, 5],\n",
        "        \"name\": [\"John\", \"Emma\", \"Liam\", \"Olivia\", \"William\"]\n",
        "    }\n",
        ")\n",
        "users = SmartDataframe(users_df, name=\"users\")\n",
        "\n",
        "photos_df = pd.DataFrame(\n",
        "    {\n",
        "        \"id\": [31, 32, 33, 34, 35],\n",
        "        \"user_id\": [1, 1, 2, 4, 5]\n",
        "    }\n",
        ")\n",
        "photos = SmartDataframe(photos_df, name=\"photos\")\n",
        "\n",
        "lake = SmartDatalake([users, photos], config={\"llm\": llm})\n",
        "lake.chat(\"How many photos has been uploaded by John?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VjJc8HH9MfNH",
        "outputId": "d976c99e-94f6-4dd3-e6b6-fc1eee1efabc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this case we provided for each df a table name, so that the LLM has some context and can better perform the joins. As you can see on the example below, it succeeded at figuring out the right join to do. In fact, the user \"John\" has actually 2 photos."
      ],
      "metadata": {
        "id": "pstO0KLvNjK9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(lake.last_code_executed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KNnAKWyNenA",
        "outputId": "5056fd38-1438-420f-8ca7-602d200a0d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def analyze_data(dfs: list[pd.DataFrame]) ->dict:\n",
            "    users = dfs[0]\n",
            "    photos = dfs[1]\n",
            "    merged_df = pd.merge(users, photos, left_on='id', right_on='user_id')\n",
            "    john_photos = merged_df[merged_df['name'] == 'John']\n",
            "    num_photos = john_photos.shape[0]\n",
            "    return {'type': 'number', 'value': num_photos}\n",
            "\n",
            "\n",
            "result = analyze_data(dfs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Different LLMs\n",
        "\n",
        "Although at the moment OpenAI GPT3.5 and GPT4 are the recommended models, we also support other models, like Starcoder and Falcon.\n",
        "\n",
        "You can use them as if follows:"
      ],
      "metadata": {
        "id": "AKaXv9VApKF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai import SmartDataframe\n",
        "from pandasai.llm import Starcoder, Falcon\n",
        "\n",
        "starcoder_llm = Starcoder(api_token=\"YOUR TOKEN\")\n",
        "falcon_llm = Falcon(api_token=\"YOUR TOKEN\")\n",
        "\n",
        "df1 = SmartDataframe(df, config={\"llm\": starcoder_llm})\n",
        "df2 = SmartDataframe(df, config={\"llm\": falcon_llm})\n",
        "\n",
        "print(df1.chat(\"Which country has the highest GDP?\"))\n",
        "print(df2.chat(\"Which one is the unhappiest country?\"))"
      ],
      "metadata": {
        "id": "qMYya2bZpJCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LangChain LLMs"
      ],
      "metadata": {
        "id": "cPweiIeKqrXA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In some cases, you might want to use LangChain LLMs instead."
      ],
      "metadata": {
        "id": "VC8obpOBq1eG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandasai[langchain]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqWtvULErC73",
        "outputId": "b0a984cf-258b-4c98-ea85-698f96557683"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, openapi-schema-pydantic, langchainplus-sdk, dataclasses-json, langchain\n",
            "Successfully installed dataclasses-json-0.5.14 langchain-0.0.199 langchainplus-sdk-0.0.20 marshmallow-3.20.1 mypy-extensions-1.0.0 openapi-schema-pydantic-1.2.4 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then you can use them as if they were PandasAI LLMs."
      ],
      "metadata": {
        "id": "_gMX50dXrFET"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pandasai import SmartDataframe\n",
        "from langchain.llms import OpenAI\n",
        "# from langchain.llms import Anthropic\n",
        "# from langchain.llms import LlamaCpp\n",
        "\n",
        "langchain_llm = OpenAI(openai_api_key=\"YOUR TOKEN\", max_tokens=1000)\n",
        "langchain_sdf = SmartDataframe(df, config={\"llm\": langchain_llm})\n",
        "langchain_sdf.chat(\"Which are the top 5 countries by GPD?\")"
      ],
      "metadata": {
        "id": "kJzSEArCqtkZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cab00d4e-f260-49b9-bf86-4b7f6815800a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          country             gdp  happiness_index\n",
              "0   United States  19294482071552             6.94\n",
              "9           China  14631844184064             5.12\n",
              "8           Japan   4380756541440             5.87\n",
              "3         Germany   3435817336832             7.07\n",
              "1  United Kingdom   2891615567872             7.16"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>country</th>\n",
              "      <th>gdp</th>\n",
              "      <th>happiness_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>United States</td>\n",
              "      <td>19294482071552</td>\n",
              "      <td>6.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>China</td>\n",
              "      <td>14631844184064</td>\n",
              "      <td>5.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Japan</td>\n",
              "      <td>4380756541440</td>\n",
              "      <td>5.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Germany</td>\n",
              "      <td>3435817336832</td>\n",
              "      <td>7.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2891615567872</td>\n",
              "      <td>7.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    }
  ]
}