NFT_rarity_algorithm_based_on_jaccard_distance.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "RTtN3cmY37hk"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\Anaconda3\\lib\\site-packages\\numpy\\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:\n",
            "D:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.QVLO2T66WEPI7JZ63PS3HMOHFEY472BC.gfortran-win_amd64.dll\n",
            "D:\\Anaconda3\\lib\\site-packages\\numpy\\.libs\\libopenblas.XWYDX2IKJW2NMTWSFYNGFUWKQU3LYTCZ.gfortran-win_amd64.dll\n",
            "  warnings.warn(\"loaded more than 1 DLL from .libs:\"\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "dt=pd.read_csv('./mint_data - Sheet1.csv',encoding= 'unicode_escape',index_col='Number')\n",
        "#dt = dt.drop(columns='rarity')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "9SGs3Nw48vOv",
        "outputId": "c66bb42e-ccfe-4e63-fb0d-23028cc7b02e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f31e389f-3acc-4e49-b6e4-b92885a68243\">\n",
              "    <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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Background</th>\n",
              "      <th>Face color</th>\n",
              "      <th>Occasions</th>\n",
              "      <th>Eyes</th>\n",
              "      <th>Accs on face</th>\n",
              "      <th>Facial Expressions</th>\n",
              "      <th>Accs on head</th>\n",
              "      <th>Clothes</th>\n",
              "      <th>Little Accs</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>18</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f31e389f-3acc-4e49-b6e4-b92885a68243')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \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-f31e389f-3acc-4e49-b6e4-b92885a68243 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-f31e389f-3acc-4e49-b6e4-b92885a68243');\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",
              "  "
            ],
            "text/plain": [
              "        Gender  Background  Face color  Occasions  Eyes  Accs on face  \\\n",
              "Number                                                                  \n",
              "1            1           4           1          2     1             4   \n",
              "2            1           2           2          2     1             3   \n",
              "3            1           3           3          2     1             0   \n",
              "4            2          10           5          2     2             0   \n",
              "5            1           1           4          1     1             0   \n",
              "\n",
              "        Facial Expressions  Accs on head  Clothes  Little Accs  \n",
              "Number                                                          \n",
              "1                        1             5        0            0  \n",
              "2                        2             5       18            2  \n",
              "3                        1             5       19            1  \n",
              "4                        1             5       10            2  \n",
              "5                        1             5        4            0  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uUVOt1m_XOyV"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xC60FwHsPKHA",
        "outputId": "510332b5-2125-4797-ba04-364775494266"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 889 entries, 1 to 888\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype\n",
            "---  ------              --------------  -----\n",
            " 0   Gender              889 non-null    int64\n",
            " 1   Background          889 non-null    int64\n",
            " 2   Face color          889 non-null    int64\n",
            " 3   Occasions           889 non-null    int64\n",
            " 4   Eyes                889 non-null    int64\n",
            " 5   Accs on face        889 non-null    int64\n",
            " 6   Facial Expressions  889 non-null    int64\n",
            " 7   Accs on head        889 non-null    int64\n",
            " 8   Clothes             889 non-null    int64\n",
            " 9   Little Accs         889 non-null    int64\n",
            "dtypes: int64(10)\n",
            "memory usage: 76.4 KB\n"
          ]
        }
      ],
      "source": [
        "dt.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax9_lEBqGgXh",
        "outputId": "83b3579b-2d65-4c5a-a81d-1b1f0054d987"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Gender', 'Background', 'Face color', 'Occasions', 'Eyes',\n",
              "       'Accs on face', 'Facial Expressions', 'Accs on head', 'Clothes',\n",
              "       'Little Accs'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "TLnkwk0rGK_D"
      },
      "outputs": [],
      "source": [
        "traits_count = {}\n",
        "for col in dt.columns:\n",
        "  traits_count[col]=dt[col].value_counts().to_dict()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AoSKGh3FHKAN",
        "outputId": "99ce01ab-085f-4321-b756-80d53790595d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[2, 10, 5, 2, 3, 8, 2, 11, 21, 6]\n",
            "[0.5        0.1        0.2        0.5        0.33333333 0.125\n",
            " 0.5        0.09090909 0.04761905 0.16666667]\n"
          ]
        }
      ],
      "source": [
        "len(dt.Gender.value_counts())\n",
        "value_count = [len(dt[c].value_counts()) for c in dt.columns]\n",
        "print(value_count)\n",
        "print(1./np.array(value_count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "KNOv_iuh5GGU"
      },
      "outputs": [],
      "source": [
        "\n",
        "# data:\n",
        "# col:features of NFT\n",
        "# row:one NFT\n",
        "\n",
        "\n",
        "def jaccard_distance(df):\n",
        "  all_mean = [] # 存储所有NFT之间的平均距离\n",
        "  value_count = [len(df[c].value_counts()) for c in df.columns] #求得每个特征的类别数目\n",
        "  value_count = np.array(value_count)\n",
        "  weight = 1./value_count\n",
        "  for i in range(len(df)): #对于每一个NFT\n",
        "    nft = df.iloc[i].to_numpy() #提取特征\n",
        "    jaccard_all = [] #存储这个NFT到其他NFT的距离\n",
        "    for j in range(len(df)): #对于每一个其他（包括自己）的NFT\n",
        "      other_nft = df.iloc[j].to_numpy() #提取特征\n",
        "      k = nft==other_nft #找到交集\n",
        "      k = np.array(k)\n",
        "      k = k * weight #增加类别权重，某特征类别越多，越不容易相同，相同的时候权重越小\n",
        "      intersection = k.sum() #得到交集总权重\n",
        "      union = weight.sum()*2 - intersection #得到并集总权重\n",
        "      jaccard_index = intersection/union #求得两NFT之间相似度\n",
        "      jaccard_dis = 1 - jaccard_index #求得两NFT之间差异度\n",
        "      jaccard_all.append(jaccard_dis) \n",
        "    jaccard_all=np.array(jaccard_all)\n",
        "    mean_distance = jaccard_all.mean() #计算此NFT与其他NFT的平均差异\n",
        "    all_mean.append(mean_distance) #将平均差异放入数组\n",
        "  all_mean = np.array(jaccard_all) \n",
        "  all_mean = (all_mean - all_mean.min())*100/(all_mean.max()-all_mean.min()) #归一化\n",
        "  df['rarity'] = all_mean \n",
        "    \n",
        "  \n",
        "jaccard_distance(dt)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9I0Td_buJdxU",
        "outputId": "bf32479b-4966-49a0-d31d-ec03c980b26c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        Gender  Background  Face color  Occasions  Eyes  Accs on face  \\\n",
            "Number                                                                  \n",
            "1            1           4           1          2     1             4   \n",
            "2            1           2           2          2     1             3   \n",
            "3            1           3           3          2     1             0   \n",
            "4            2          10           5          2     2             0   \n",
            "5            1           1           4          1     1             0   \n",
            "...        ...         ...         ...        ...   ...           ...   \n",
            "884          1           3           4          2     1             4   \n",
            "885          1           6           3          2     1             0   \n",
            "886          1           1           1          2     2             0   \n",
            "887          1           6           1          2     1             0   \n",
            "888          1           9           1          2     1             0   \n",
            "\n",
            "        Facial Expressions  Accs on head  Clothes  Little Accs     rarity  \n",
            "Number                                                                     \n",
            "1                        1             5        0            0  42.529773  \n",
            "2                        2             5       18            2  80.472463  \n",
            "3                        1             5       19            1  36.250805  \n",
            "4                        1             5       10            2  89.201916  \n",
            "5                        1             5        4            0  74.758905  \n",
            "...                    ...           ...      ...          ...        ...  \n",
            "884                      1             0       16            0  55.016212  \n",
            "885                      1             9       16            2  47.396900  \n",
            "886                      1             5       16            0  55.503656  \n",
            "887                      1             4       15            0  33.871405  \n",
            "888                      1             6       13            1   0.000000  \n",
            "\n",
            "[889 rows x 11 columns]\n"
          ]
        }
      ],
      "source": [
        "print(dt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pTtCWa4SFl0F",
        "outputId": "4bdc2dc8-5cbb-4e75-9ad5-6843470ff1ac"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/series.py:1056: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  cacher_needs_updating = self._check_is_chained_assignment_possible()\n"
          ]
        }
      ],
      "source": [
        "avg_rar = dt.copy(deep=True).astype(float)\n",
        "\n",
        "\n",
        "def avg_rarity(df):\n",
        "  \n",
        "  for i in range(len(df)): #对于每一个NFT\n",
        "    nft = df.iloc[i] #提取特征\n",
        "    for k,v in traits_count.items():\n",
        "      # print(v[nft[k]]/float(df.shape[0]))\n",
        "      avg_rar.iloc[i][k] = float(v[nft[k]])/float(df.shape[0])\n",
        " \n",
        "\n",
        "avg_rarity(dt)\n",
        "avg_rar['average_rarity'] = avg_rar.iloc[:,0:avg_rar.shape[1]-1].sum(axis=1)/(avg_rar.shape[1]-1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn4jnzTRTMAy",
        "outputId": "01d25208-cc31-44fc-f408-0485e7b4f6d8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Number\n",
              "1      1.9\n",
              "2      3.8\n",
              "3      3.6\n",
              "4      3.9\n",
              "5      1.8\n",
              "      ... \n",
              "884    3.2\n",
              "885    4.1\n",
              "886    2.9\n",
              "887    3.1\n",
              "888    3.5\n",
              "Name: average_rarity, Length: 889, dtype: float64"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_rar['average_rarity']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpM8aWoaOYST",
        "outputId": "91ab10ed-61c3-49de-9498-d786cf7faab6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8     1\n",
              "0.9     1\n",
              "1.0     1\n",
              "1.1     1\n",
              "1.2     4\n",
              "1.3     5\n",
              "1.4     3\n",
              "1.5     7\n",
              "1.6    12\n",
              "1.7    19\n",
              "1.8    13\n",
              "1.9    21\n",
              "2.0    20\n",
              "2.1    15\n",
              "2.2    27\n",
              "2.3    10\n",
              "2.4    24\n",
              "2.5    16\n",
              "2.6    25\n",
              "2.7    29\n",
              "2.8    47\n",
              "2.9    29\n",
              "3.0    44\n",
              "3.1    49\n",
              "3.2    47\n",
              "3.3    57\n",
              "3.4    47\n",
              "3.5    40\n",
              "3.6    52\n",
              "3.7    41\n",
              "3.8    37\n",
              "3.9    32\n",
              "4.0    28\n",
              "4.1    26\n",
              "4.2    13\n",
              "4.3    13\n",
              "4.4    13\n",
              "4.5     6\n",
              "4.6     5\n",
              "4.7     3\n",
              "4.8     1\n",
              "4.9     3\n",
              "5.1     1\n",
              "5.4     1\n",
              "Name: average_rarity, dtype: int64"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "avg_rar['average_rarity'].value_counts().sort_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "tMMQIIHeHYLV",
        "outputId": "9287b9bb-7948-464f-94cf-6244ac3cc22d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78dca46b10>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "avg_rar['average_rarity'].value_counts().sort_index().plot(kind='bar')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "B4ROlERG_bOl"
      },
      "outputs": [],
      "source": [
        "pd.options.display.max_columns = None\n",
        "pd.options.display.max_rows = None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5S5W9kEZmjJ",
        "outputId": "bea71eec-4cfe-46af-c696-41043f5cefd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 889 entries, 1 to 888\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Gender              889 non-null    int64  \n",
            " 1   Background          889 non-null    int64  \n",
            " 2   Face color          889 non-null    int64  \n",
            " 3   Occasions           889 non-null    int64  \n",
            " 4   Eyes                889 non-null    int64  \n",
            " 5   Accs on face        889 non-null    int64  \n",
            " 6   Facial Expressions  889 non-null    int64  \n",
            " 7   Accs on head        889 non-null    int64  \n",
            " 8   Clothes             889 non-null    int64  \n",
            " 9   Little Accs         889 non-null    int64  \n",
            " 10  rarity              889 non-null    float64\n",
            "dtypes: float64(1), int64(10)\n",
            "memory usage: 83.3 KB\n"
          ]
        }
      ],
      "source": [
        "dt.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "9wyGpmHbL3d1",
        "outputId": "26d73ac7-5508-4178-8fdd-5da6eeeba850"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f78dc25bc50>"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dt['rarity'].value_counts().sort_index().plot(kind='bar')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FawXpGLsC89S"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOvE5GD2DmPX",
        "outputId": "9d0c9b01-53cf-42db-c898-0e2f7ef5e882"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count    889.000000\n",
              "mean      55.076727\n",
              "std       19.313968\n",
              "min        0.000000\n",
              "25%       39.315138\n",
              "50%       55.016212\n",
              "75%       72.766872\n",
              "max      100.000000\n",
              "Name: rarity, dtype: float64"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt['rarity'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ZF7YgIGMcF8",
        "outputId": "805054b0-961e-421b-cfd8-d3d6e3614804"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33.871405     106\n",
              "42.529773     105\n",
              "74.758905      77\n",
              "80.472463      68\n",
              "64.760688      61\n",
              "55.016212      49\n",
              "80.840009      45\n",
              "47.396900      41\n",
              "71.139986      41\n",
              "21.125246      25\n",
              "66.506362      24\n",
              "55.503656      20\n",
              "59.824643      15\n",
              "39.315138      14\n",
              "30.378063      13\n",
              "35.660922      12\n",
              "70.352832      12\n",
              "26.401436      11\n",
              "27.101976      10\n",
              "69.899878      10\n",
              "89.201916       9\n",
              "66.545650       8\n",
              "36.304268       8\n",
              "52.182810       6\n",
              "32.226148       6\n",
              "72.766872       5\n",
              "95.557269       5\n",
              "36.250805       5\n",
              "44.333771       5\n",
              "48.968477       4\n",
              "61.665075       3\n",
              "49.486882       3\n",
              "22.658175       3\n",
              "34.525615       3\n",
              "72.478553       3\n",
              "40.854529       3\n",
              "75.932553       3\n",
              "76.741199       3\n",
              "12.722798       3\n",
              "76.355405       3\n",
              "41.466858       3\n",
              "29.005837       2\n",
              "100.000000      2\n",
              "52.684641       2\n",
              "13.512270       2\n",
              "17.200170       1\n",
              "23.382526       1\n",
              "38.218641       1\n",
              "9.317121        1\n",
              "61.166716       1\n",
              "28.948626       1\n",
              "69.899878       1\n",
              "37.586887       1\n",
              "56.951336       1\n",
              "90.188755       1\n",
              "44.721764       1\n",
              "78.340830       1\n",
              "62.530841       1\n",
              "92.155114       1\n",
              "15.149354       1\n",
              "49.533876       1\n",
              "32.835227       1\n",
              "45.953475       1\n",
              "21.859462       1\n",
              "71.586668       1\n",
              "50.049347       1\n",
              "29.633191       1\n",
              "58.512131       1\n",
              "8.499118        1\n",
              "32.890427       1\n",
              "64.002229       1\n",
              "76.320247       1\n",
              "0.000000        1\n",
              "Name: rarity, dtype: int64"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dt['rarity'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wmSJCs_tMkgH"
      },
      "outputs": [],
      "source": [
        "dt.to_csv('./calculated_rarity_sheet.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zk588cbkMl8O"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "NFT rarity algorithm based on jaccard distance.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}