2ndPriceAuctionStatistics.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNUkHUWLSYEssgUvsUyloVR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tinsir888/algorithms-incentives-and-data/blob/main/2ndPriceAuctionStatistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "FadbIVuP1fdB"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "\n",
        "data = []\n",
        "\n",
        "with open('ad3-23.txt', 'r') as file:\n",
        "    for line in file:\n",
        "        # Use a regular expression to match both integer and floating-point numbers with a comma as the decimal separator\n",
        "        match = re.search(r'\\d+(,\\d+)?', line)\n",
        "\n",
        "        if match:\n",
        "            data.append(match.group(0))\n",
        "\n",
        "# Create a DataFrame\n",
        "data_df = pd.DataFrame({'payment': data})\n",
        "\n",
        "# Convert the 'payment' column to numeric\n",
        "data_df['payment'] = data_df['payment'].str.replace(',', '.').astype(float)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Create a histogram to visualize the data\n",
        "plt.hist(data_df['payment'], bins=100, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Payment Amount')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Payments')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7I11c5JMlgzw",
        "outputId": "8c12e095-781a-4a4b-bad9-d15d01a32097"
      },
      "execution_count": 4,
      "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": [
        "def cnt_val(target_value):\n",
        "  return (data_df['payment'] == target_value).sum()\n",
        "\n",
        "def cnt(target_value):\n",
        "  # use value_counts() to count times of occurrance of certain value\n",
        "  count = (data_df['payment'] == target_value).sum()\n",
        "\n",
        "  print(f\"value {target_value}, occurs: {count} times\")\n",
        "\n",
        "print(f\"size of data: {len(data_df)}\")\n",
        "\n",
        "cnt(0)\n",
        "cnt(390)\n",
        "\n",
        "print(f\"value > 390 occurs: {len(data_df)-cnt_val(0)-cnt_val(390)} times\")\n",
        "print(f\"maximum of payment is: {data_df.max()}\")\n",
        "print(f\"value > 500 occurs: {(data_df['payment']>=500).sum()} times\")\n",
        "print(f\"value > 600 occurs: {(data_df['payment']>=600).sum()} times\")\n",
        "print(f\"average revenue: {data_df['payment'].sum()/len(data_df)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-09A0OXlwLh",
        "outputId": "f9edcc6a-e738-4357-d254-fc4bee031599"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "size of data: 10000\n",
            "value 0, occurs: 1456 times\n",
            "value 390, occurs: 4675 times\n",
            "value > 390 occurs: 3869 times\n",
            "maximum of payment is: payment    686.060145\n",
            "dtype: float64\n",
            "value > 500 occurs: 1535 times\n",
            "value > 600 occurs: 355 times\n",
            "average revenue: 371.74492910665\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=210\n",
        "b=690\n",
        "print((390-a)/(b-a))\n",
        "print((1-(500-a)/(b-a))**2)\n",
        "print((1-(600-a)/(b-a))**2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PvIMNwpzyIay",
        "outputId": "1b2e603b-69f0-4766-d076-f018eed83fd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.375\n",
            "0.15668402777777782\n",
            "0.03515625\n"
          ]
        }
      ]
    }
  ]
}