Adil_UN_DATATHON_FAO.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Install Antigranular Package**"
      ],
      "metadata": {
        "id": "9mnSmJtg1A1T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install antigranular"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qz28g2LqU03F",
        "outputId": "dced4fdb-d835-4acc-83d7-3f0eae070f92"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting antigranular\n",
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            "\u001b[?25hCollecting onnx<2.0.0,>=1.14.0 (from antigranular)\n",
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            "Collecting jedi>=0.16 (from ipython<8.0.0,>=7.34.0->antigranular)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.31.0->antigranular) (2023.7.22)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython<8.0.0,>=7.34.0->antigranular) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython<8.0.0,>=7.34.0->antigranular) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython<8.0.0,>=7.34.0->antigranular) (0.2.8)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas<2.0.0,>=1.5.3->antigranular) (1.16.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24.2->diffprivlib<0.7.0,>=0.6.2->antigranular) (3.2.0)\n",
            "Installing collected packages: onnx, oblv-client, jedi, diffprivlib, antigranular\n",
            "Successfully installed antigranular-0.2.12 diffprivlib-0.6.3 jedi-0.19.1 oblv-client-0.1.15 onnx-1.15.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Antigranular for Secure Computations"
      ],
      "metadata": {
        "id": "1ws7gbJa1VFn"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "QvcurAP6UvhV",
        "outputId": "67060c40-b017-4b36-8d9b-b757517dcb65"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Connected to Antigranular server session id: 16950eaa-17d1-4d31-bef7-fa18bf5c8071, the session will time out if idle for 60 minutes\n",
            "Cell magic '%%ag' registered successfully, use `%%ag` in a notebook cell to execute your python code on Antigranular private python server\n",
            "🚀 Everything's set up and ready to roll!\n"
          ]
        }
      ],
      "source": [
        "# copy login code snippet from the \"UN Datathon PETs Track\" section and paste it.\n",
        "# https://www.antigranular.com/datasets\n",
        "import antigranular as ag\n",
        "session = ag.login(\"uGb9/7aO47ROFevycrNYvrmRuqRhDCfe\", \"1zpBT+kf5mUCseV2ofyPzB1tUB0Pe0Qvxclx+jFNgb89jrHv9kc/IlckhAwm5soS\", competition = \"UN Datathon PETs Track\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Checking Epsilon Budget"
      ],
      "metadata": {
        "id": "4QACyV6b19Fx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Loading the Data"
      ],
      "metadata": {
        "id": "3VjhUcBT1aCi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "df = load_dataset(\"undata_income\")"
      ],
      "metadata": {
        "id": "5GwzP3HMSmRl"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Question asked: 'What is the gender of the household member(s) generating income...?'**"
      ],
      "metadata": {
        "id": "tfkoCpj_O_z3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "#df[\"income_main\"]\n",
        "income_main = [1, 2, 3]"
      ],
      "metadata": {
        "id": "JclEqTFf3nzY"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "\n",
        "def is_Male (x: int) -> int:\n",
        "  if x == 1:\n",
        "    return 1\n",
        "  return 0\n",
        "\n",
        "def is_Female (x: int) -> int:\n",
        "  if x == 2:\n",
        "    return 1\n",
        "  return 0\n",
        "\n",
        "def is_Joint (x: int) -> int:\n",
        "  if x == 3:\n",
        "    return 1\n",
        "  return 0\n",
        "\n",
        "def is_Others (x: int) -> int:\n",
        "  if not ('x == 1' and 'x == 2' and 'x == 3'):\n",
        "    return 1\n",
        "  return 0"
      ],
      "metadata": {
        "id": "FfzO_dI04RGK"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "# \"Male\",  \"Female\",  \"Joint\"\n",
        "df[\"Male\"] = df[\"income_main\"].map(is_Male, output_bounds = (0 , 1))\n",
        "df[\"Female\"] = df[\"income_main\"].map(is_Female, output_bounds = (0 , 1))\n",
        "df[\"Joint\"] = df[\"income_main\"].map(is_Joint, output_bounds = (0 , 1))\n",
        "df[\"Others\"] = df[\"income_main\"].map(is_Others, output_bounds = (0 , 1))"
      ],
      "metadata": {
        "id": "StSoLQC75hBH"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data Visualization"
      ],
      "metadata": {
        "id": "aGmqJ1q2SLD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "from ag_utils import export\n",
        "ret = [\n",
        "    df[\"Male\"].mean(eps=0.01),\n",
        "    df[\"Female\"].mean(eps=0.01),\n",
        "    df[\"Joint\"].mean(eps=0.01),\n",
        "    df[\"Others\"].mean(eps=0.01),\n",
        "         ]\n",
        "export(\n",
        "    ret,\n",
        "    \"data_plot\"\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZ0Afbwi7wOg",
        "outputId": "591f33eb-42d9-4c86-ff84-9f4287d14ea4"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting up exported variable in local environment: data_plot\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_plot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4jsA05tBbvZ",
        "outputId": "bcb321fa-e939-4400-c02c-de6cab00d59c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.1793170104101114, 0.030906718857999973, 0.028050335460994023, 0.0]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as plt\n",
        "%pylab inline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrqAGjUKBfXN",
        "outputId": "3e47217d-e208-475c-c104-ab7b2f73b22b"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar([1, 2, 3, 4], data_plot)\n",
        "plt.xlabel([\"Male\", \"Female\", \"Joint\", \"Others\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "0pSTkN5ZBjFG",
        "outputId": "68d8ce04-200d-4218-bd1a-ff83fc9e0185"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, \"['Male', 'Female', 'Joint', 'Others']\")"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "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": [
        "**Pie Chart**"
      ],
      "metadata": {
        "id": "8vmKL706PxHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "session.privacy_odometer()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ElE8qdT1u8L",
        "outputId": "90c22e77-3de3-4833-b832-590f6e75fd31"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'total_epsilon_used': 19.999100000000034,\n",
              " 'total_delta_used': 0.0,\n",
              " 'library_costs': {'op_pandas': {'total_delta': 0,\n",
              "   'total_epsilon': 19.999100000000034,\n",
              "   'total_requests': 89}},\n",
              " 'dataset_costs': {'undata_ls': {'delta': 0, 'eps': 2.999999999999995},\n",
              "  'undata_ls_dif': {'delta': 0, 'eps': 2.999999999999995},\n",
              "  'undata_crp': {'delta': 0, 'eps': 11.499099999999993},\n",
              "  'undata_income': {'delta': 0, 'eps': 2.500000000000001}}}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data for the pie chart\n",
        "labels = ['Male', 'Female', 'Joint', 'Others']\n",
        "sizes = [0.1793170104101114, 0.030906718857999973, 0.028050335460994023, 0.0]  # These values should add up to 100 for a proper pie chart\n",
        "colors = ['blue', 'green', 'red']\n",
        "explode = (0.1, 0, 0, 0)  # Adjust the explosion of the first slice\n",
        "\n",
        "# Create a pie chart\n",
        "plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%', explode=explode, shadow=True, startangle=140)\n",
        "\n",
        "# Title of the pie chart\n",
        "plt.title('Gender generating Income')\n",
        "\n",
        "# Display the chart\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that the pie is drawn as a circle.\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "IpbxTbcPCZGZ",
        "outputId": "9f6f703b-a97d-4b28-c939-401ec5bc00e3"
      },
      "execution_count": 2,
      "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": [
        "###Viewing Columns, Metadata, and Data Types"
      ],
      "metadata": {
        "id": "KQLHDm_l2DGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "\n",
        "ag_print(\"undata_income Details: \\n\")\n",
        "ag_print(\"Columns: \\n\", undata_income.columns)\n",
        "ag_print(\"Metadata: \\n\", undata_income.metadata)\n",
        "ag_print(\"Dtypes: \\n\", undata_income.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAuoij7luf0h",
        "outputId": "076f15e3-db03-45f5-8925-0f9e6f29798a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "undata_income Details: \n",
            "\n",
            "Columns: \n",
            " Index(['objectid', 'income_main', 'income_main_amount', 'income_main_gender',\n",
            "       'income_main_control', 'income_main_comp', 'income_sec',\n",
            "       'income_sec_amount', 'income_sec_gender', 'income_sec_control',\n",
            "       'income_sec_comp', 'income_third', 'income_third_amount',\n",
            "       'income_third_gender', 'income_third_control', 'income_third_comp'],\n",
            "      dtype='object')\n",
            "Metadata: \n",
            " {'objectid': (3496, 239871), 'income_main': (1.0, 999.0), 'income_main_amount': (0.0, 1480000000.0), 'income_main_gender': (0.0, 999.0), 'income_main_control': (0.0, 999.0), 'income_main_comp': (1.0, 999.0), 'income_sec': (1.0, 999.0), 'income_sec_amount': (0.0, 15000000.0), 'income_sec_gender': (0.0, 999.0), 'income_sec_control': (0.0, 999.0), 'income_sec_comp': (1.0, 999.0), 'income_third': (1.0, 999.0), 'income_third_amount': (0.0, 10000000.0), 'income_third_gender': (0.0, 999.0), 'income_third_control': (0.0, 999.0), 'income_third_comp': (1.0, 999.0)}\n",
            "Dtypes: \n",
            " objectid                  int64\n",
            "income_main             float64\n",
            "income_main_amount      float64\n",
            "income_main_gender      float64\n",
            "income_main_control     float64\n",
            "income_main_comp        float64\n",
            "income_sec              float64\n",
            "income_sec_amount       float64\n",
            "income_sec_gender       float64\n",
            "income_sec_control      float64\n",
            "income_sec_comp         float64\n",
            "income_third            float64\n",
            "income_third_amount     float64\n",
            "income_third_gender     float64\n",
            "income_third_control    float64\n",
            "income_third_comp       float64\n",
            "dtype: object\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Conducting Some Basic Descriptive Statistics"
      ],
      "metadata": {
        "id": "Djo2mS762kpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "\n",
        "undata_income_describe = undata_income.describe(eps=0.0001)\n",
        "ag_print(\"undata_income Describe:\\n\", undata_income_describe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvITwnib02GQ",
        "outputId": "107bc0bf-6206-4adf-9ded-6863d321ea4c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "undata_income Describe:\n",
            "            objectid   income_main  ...  income_third_control  income_third_comp\n",
            "count  2.008634e+06  2.008634e+06  ...          2.008634e+06       2.008634e+06\n",
            "mean   3.496000e+03  1.000000e+00  ...          0.000000e+00       1.000000e+00\n",
            "std    9.111164e+04  3.502424e+02  ...          4.858827e+02       4.672423e+02\n",
            "min    2.135885e+05  9.707999e+02  ...          6.952567e+02       3.334568e+02\n",
            "25%    1.109806e+05  6.360951e+02  ...          7.377155e+02       8.039225e+02\n",
            "50%    6.020434e+04  2.185438e+01  ...          5.184699e+02       2.007312e+02\n",
            "75%    1.774041e+04  7.684181e+02  ...          3.255990e+02       8.674634e+02\n",
            "max    1.179397e+04  2.236145e+01  ...          7.297822e+02       7.448825e+02\n",
            "\n",
            "[8 rows x 16 columns]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Exporting the non-private result to the local Jupyter server using the `export` method"
      ],
      "metadata": {
        "id": "wksztHA48xOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "\n",
        "export(undata_income_describe, name='undata_income_describe')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0SpwLxN828T",
        "outputId": "192cb62d-2ff7-4af2-d479-a367102fce6e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting up exported variable in local environment: undata_income_describe\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Accessing exported in local jupyter environment"
      ],
      "metadata": {
        "id": "dhbx8uvI-Oua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(undata_income_describe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzSSpAQB9PtD",
        "outputId": "9b7b86fc-1811-4e1b-c7ea-c6f364d8eef5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           objectid   income_main  income_main_amount  income_main_gender  \\\n",
            "count  2.008634e+06  2.008634e+06        2.008634e+06        2.008634e+06   \n",
            "mean   3.496000e+03  1.000000e+00        0.000000e+00        0.000000e+00   \n",
            "std    9.111164e+04  3.502424e+02        5.462272e+08        4.421978e+02   \n",
            "min    2.135885e+05  9.707999e+02        8.219567e+08        9.036968e+01   \n",
            "25%    1.109806e+05  6.360951e+02        1.149541e+09        5.082307e+02   \n",
            "50%    6.020434e+04  2.185438e+01        4.579240e+08        4.161680e+02   \n",
            "75%    1.774041e+04  7.684181e+02        1.412926e+09        8.738279e+02   \n",
            "max    1.179397e+04  2.236145e+01        2.465306e+08        8.980744e+02   \n",
            "\n",
            "       income_main_control  income_main_comp    income_sec  income_sec_amount  \\\n",
            "count         2.008634e+06      2.008634e+06  2.008634e+06       2.008634e+06   \n",
            "mean          9.990000e+02      9.990000e+02  1.000000e+00       1.500000e+07   \n",
            "std           2.422850e+02      3.217768e+02  3.406139e+02       5.462463e+06   \n",
            "min           1.754492e+02      2.082637e+02  8.755505e+02       1.409070e+07   \n",
            "25%           1.608230e+02      8.411968e+02  7.174845e+02       1.054655e+07   \n",
            "50%           5.834841e+02      8.538472e+02  1.025396e+02       1.233126e+07   \n",
            "75%           5.956341e+02      1.478319e+01  3.860990e+02       3.300582e+04   \n",
            "max           4.928372e+02      8.746913e+02  7.722475e+02       5.312563e+06   \n",
            "\n",
            "       income_sec_gender  income_sec_control  income_sec_comp  income_third  \\\n",
            "count       2.008634e+06        2.008634e+06     2.008634e+06  2.008634e+06   \n",
            "mean        0.000000e+00        0.000000e+00     9.990000e+02  9.990000e+02   \n",
            "std         2.689501e+02        4.644807e+02     2.999467e+02  3.378873e+02   \n",
            "min         8.388074e+02        2.226372e+02     6.807410e+02  1.047898e+02   \n",
            "25%         6.396672e+02        7.672852e+02     3.862261e+02  1.995824e+02   \n",
            "50%         1.771741e+02        5.392254e+01     8.016159e+02  4.217173e+02   \n",
            "75%         7.499958e+02        3.855539e+02     4.737836e+02  3.070692e+02   \n",
            "max         6.142593e+02        3.785297e+02     5.564048e+02  6.849797e+02   \n",
            "\n",
            "       income_third_amount  income_third_gender  income_third_control  \\\n",
            "count         2.008634e+06         2.008634e+06          2.008634e+06   \n",
            "mean          0.000000e+00         0.000000e+00          0.000000e+00   \n",
            "std           3.782059e+06         1.705495e+02          4.858827e+02   \n",
            "min           2.191803e+06         4.832623e+02          6.952567e+02   \n",
            "25%           9.044110e+06         5.096505e+00          7.377155e+02   \n",
            "50%           5.097126e+06         8.527803e+02          5.184699e+02   \n",
            "75%           8.879898e+06         1.796598e+02          3.255990e+02   \n",
            "max           5.219886e+06         2.222996e+02          7.297822e+02   \n",
            "\n",
            "       income_third_comp  \n",
            "count       2.008634e+06  \n",
            "mean        1.000000e+00  \n",
            "std         4.672423e+02  \n",
            "min         3.334568e+02  \n",
            "25%         8.039225e+02  \n",
            "50%         2.007312e+02  \n",
            "75%         8.674634e+02  \n",
            "max         7.448825e+02  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(undata_income_describe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F1WdnggV9kw_",
        "outputId": "d35167be-3c3b-4dfb-ffb8-b9cf876d4c77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            objectid    income_main  income_main_amount  income_main_gender  \\\n",
            "count  101255.000000  101255.000000        1.012550e+05       101255.000000   \n",
            "mean    83897.759393       1.000000        1.967504e+07            0.000000   \n",
            "std     79579.891159      40.226836        2.579784e+08          246.828252   \n",
            "min      4865.500351       1.758440        3.886139e+03            0.427428   \n",
            "25%     28705.702787       3.686861        1.220279e+04            0.380800   \n",
            "50%     60891.794946      10.501695        5.100025e+04            1.318700   \n",
            "75%    133777.931898      12.316866        1.409739e+09          320.396328   \n",
            "max    234249.436189     660.151549        1.182468e+09          259.796627   \n",
            "\n",
            "       income_main_control  income_main_comp     income_sec  \\\n",
            "count        101255.000000     101255.000000  101255.000000   \n",
            "mean              4.991941         39.554300      33.097410   \n",
            "std              58.224872        164.042221     189.183157   \n",
            "min               0.179317          1.113347       1.563442   \n",
            "25%               0.702686          2.470420       7.301146   \n",
            "50%               1.643774          3.356604      12.222418   \n",
            "75%             278.298563          4.499693     424.331259   \n",
            "max             742.192634        968.336004     944.245056   \n",
            "\n",
            "       income_sec_amount  income_sec_gender  income_sec_control  \\\n",
            "count       1.012550e+05      101255.000000       101255.000000   \n",
            "mean        4.243215e+04           0.000000            2.410264   \n",
            "std         5.074974e+06         231.335141          108.147121   \n",
            "min         1.301345e+07         643.112207          922.763403   \n",
            "25%         1.660179e+06           0.944881          988.796445   \n",
            "50%         9.672594e+06         673.725350          874.322750   \n",
            "75%         6.553515e+06         553.693400          783.354234   \n",
            "max         1.481601e+07         885.771140           99.457348   \n",
            "\n",
            "       income_sec_comp   income_third  income_third_amount  \\\n",
            "count    101255.000000  101255.000000         1.012550e+05   \n",
            "mean         43.963711      12.793806         6.100738e+05   \n",
            "std         285.119391     227.506326         4.384638e+05   \n",
            "min           1.926248       3.694110         1.891527e+06   \n",
            "25%           2.448448       3.242148         7.120162e+06   \n",
            "50%         432.348069     249.142347         1.817694e+06   \n",
            "75%         512.813611     620.406415         9.878406e+06   \n",
            "max         131.214908     444.335450         1.931227e+06   \n",
            "\n",
            "       income_third_gender  income_third_control  income_third_comp  \n",
            "count        101255.000000         101255.000000      101255.000000  \n",
            "mean              0.000000              4.195610          15.956547  \n",
            "std             138.307390             62.900344         131.111009  \n",
            "min             973.698076            976.636766         715.472906  \n",
            "25%             457.315456            630.840869         322.049139  \n",
            "50%             754.421372            212.292503         831.889107  \n",
            "75%              59.426267            582.162351         142.304769  \n",
            "max             373.867852            172.954531         889.750570  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing Data"
      ],
      "metadata": {
        "id": "WXNGMTPb__7x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing External Data\n"
      ],
      "metadata": {
        "id": "ovws4Q0CKUlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Creating mock data\n",
        "\n",
        "Mock data will just be 100 rows of age and salary information\n",
        "'''\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "n_num = 100\n",
        "df = pd.DataFrame({'age': np.random.randint(0, 80, n_num), 'salary': np.random.randint(100, 100000, n_num)})\n",
        "session.private_import(data = df, name= 'imported_df')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Gsrn0FpAA2h",
        "outputId": "48a020d6-270a-4434-a5e2-77d0901c464d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dataframe cached to server, loading to kernel...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "# Creating a PrivateDataFrame out of the DataFrame imported.\n",
        "import op_pandas\n",
        "\n",
        "metadata = {\n",
        "    'age': (0, 80),\n",
        "    'salary': (1, 200000)\n",
        "}\n",
        "\n",
        "priv_df = op_pandas.PrivateDataFrame(imported_df ,metadata = metadata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZnQVtQ4J2N3",
        "outputId": "ac651f31-e35d-4d92-ff6b-95a3df129d90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe loaded successfully to the kernel\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%ag\n",
        "\n",
        "ag_print(\"Private DataFrame Describe:\\n\", priv_df.describe(eps=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lITWDJEuJ-8O",
        "outputId": "920b078c-4046-443f-c383-aa15622471f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Private DataFrame Describe:\n",
            "              age        salary\n",
            "count  43.000000     43.000000\n",
            "mean   34.094782  66924.689613\n",
            "std    38.359294  67381.699878\n",
            "min     0.000000  15756.119113\n",
            "25%     6.869826  23625.161981\n",
            "50%    52.620382  90038.318080\n",
            "75%    55.635158  80062.742815\n",
            "max    50.583837  52523.835833\n",
            "\n"
          ]
        }
      ]
    }
  ]
}