Writ_spl_2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "name": "Exploring factor analysis",
      "authorship_tag": "ABX9TyOFLZ/IdZboHKJVUJR8KG2w",
      "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/Nadezda-Sut/statistics_in_science/blob/main/Writ_spl_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display, HTML\n"
      ],
      "metadata": {
        "id": "tdFbj5d25com"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "pyYPADm441iT",
        "outputId": "aae9b0e3-7375-41c3-8f0e-f9dde5d23873",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "#link google colab and google drive\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# path to file in google drive\n",
        "\n",
        "file_path = \"/content/gdrive/My Drive/Colab Notebooks/writ_sampl.csv\"\n",
        "data = pd.read_csv(file_path)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sOPJ9e7b5MN1",
        "outputId": "0eb1c9c3-8e1e-4844-8cd8-50fe800ad275"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     №  Возраст  Пол  Интернет ответ  Интернет время  Time-test: Среднее 1 1  \\\n",
              "0   63       15    1               1             182                   341.2   \n",
              "1   54       11    0               1             118                   343.3   \n",
              "2   17       13    1               1             298                   275.3   \n",
              "3   57       12    0               0              56                   304.6   \n",
              "4   53       12    0               0              52                   319.5   \n",
              "5   51       11    0               0             552                   254.8   \n",
              "6   60       13    1               1             501                   267.4   \n",
              "7   25       16    1               1              61                   282.6   \n",
              "8   56       12    0               0              55                   295.4   \n",
              "9   26       16    1               1             112                   291.2   \n",
              "10  62       14    1               0              55                   292.3   \n",
              "11  55       16    1               1             120                   292.2   \n",
              "12   6       16    0               1              58                   275.1   \n",
              "13  28        9    1               0             289                   298.2   \n",
              "14  30       11    1               0              60                   280.5   \n",
              "15  18       14    0               0              61                   274.7   \n",
              "16  11       15    1               0              31                   268.4   \n",
              "17  14       14    0               1             177                   275.7   \n",
              "18   9       17    1               1             481                   309.1   \n",
              "19  66       14    1               0              53                   269.7   \n",
              "20  40       16    0               1             182                   299.9   \n",
              "21  52       14    0               0              78                   297.5   \n",
              "22  41       13    0               0              50                   288.1   \n",
              "23  38       15    0               0              88                   284.5   \n",
              "24  43       16    0               1             241                   275.9   \n",
              "25  49       15    1               0              52                   339.9   \n",
              "26  12       12    1               1             254                   315.2   \n",
              "27  50       10    0               0             163                   344.1   \n",
              "28  39       10    1               0             298                   359.1   \n",
              "29  10       11    0               0             395                   299.5   \n",
              "30  21       17    1               0             520                   275.1   \n",
              "31  33       11    0               0             460                   267.1   \n",
              "32  13       13    1               1             119                   346.9   \n",
              "33  32       14    1               0             336                   298.1   \n",
              "34  24       14    0               0             162                   285.9   \n",
              "35  16       14    0               0              56                   342.9   \n",
              "36  42       10    0               0             551                   313.5   \n",
              "37  19       14    0               0             120                   312.8   \n",
              "38  45       10    1               0              55                   366.9   \n",
              "39  36       14    1               1             294                   292.9   \n",
              "40  48       14    1               1              62                   291.2   \n",
              "41  37       10    1               0             550                   320.9   \n",
              "42   7       16    0               0              36                   341.4   \n",
              "43   3       13    0               1             420                   291.8   \n",
              "44   1       13    0               0              30                   280.1   \n",
              "45   8       15    0               1             228                   313.2   \n",
              "46  34       16    1               1              60                   302.7   \n",
              "47  29       11    1               0             516                   335.5   \n",
              "48  31       16    0               0             297                   270.4   \n",
              "49  47       11    1               0             176                   289.2   \n",
              "50  22       13    1               1             182                   258.9   \n",
              "\n",
              "    Time-test: Среднее время реакции, серия 1 (Часть 2)  \\\n",
              "0                                               300.4     \n",
              "1                                               322.4     \n",
              "2                                               279.6     \n",
              "3                                               313.2     \n",
              "4                                               288.1     \n",
              "5                                               280.5     \n",
              "6                                               250.5     \n",
              "7                                               336.0     \n",
              "8                                               310.3     \n",
              "9                                               314.4     \n",
              "10                                              278.4     \n",
              "11                                              297.6     \n",
              "12                                              282.5     \n",
              "13                                              297.4     \n",
              "14                                              340.1     \n",
              "15                                              283.6     \n",
              "16                                              296.4     \n",
              "17                                              282.8     \n",
              "18                                              265.7     \n",
              "19                                              323.1     \n",
              "20                                              281.0     \n",
              "21                                              325.9     \n",
              "22                                              296.9     \n",
              "23                                              321.4     \n",
              "24                                              249.0     \n",
              "25                                              302.0     \n",
              "26                                              290.3     \n",
              "27                                              341.1     \n",
              "28                                              386.9     \n",
              "29                                              314.3     \n",
              "30                                              292.5     \n",
              "31                                              281.7     \n",
              "32                                              360.3     \n",
              "33                                              311.3     \n",
              "34                                              299.8     \n",
              "35                                              347.5     \n",
              "36                                              288.7     \n",
              "37                                              304.2     \n",
              "38                                              335.7     \n",
              "39                                              339.0     \n",
              "40                                              303.1     \n",
              "41                                              311.0     \n",
              "42                                              320.8     \n",
              "43                                              288.0     \n",
              "44                                              288.1     \n",
              "45                                              294.7     \n",
              "46                                              315.7     \n",
              "47                                              319.5     \n",
              "48                                              238.4     \n",
              "49                                              337.0     \n",
              "50                                              265.7     \n",
              "\n",
              "    Time-test: Среднее время реакции, серия 2 (Часть 1)  \\\n",
              "0                                               434.3     \n",
              "1                                               392.0     \n",
              "2                                               360.2     \n",
              "3                                               437.4     \n",
              "4                                               340.5     \n",
              "5                                               395.0     \n",
              "6                                               319.6     \n",
              "7                                               394.0     \n",
              "8                                               388.8     \n",
              "9                                               392.4     \n",
              "10                                              353.9     \n",
              "11                                              361.9     \n",
              "12                                              337.7     \n",
              "13                                              384.4     \n",
              "14                                              395.3     \n",
              "15                                              359.4     \n",
              "16                                              340.0     \n",
              "17                                              357.4     \n",
              "18                                              372.1     \n",
              "19                                              388.6     \n",
              "20                                              414.9     \n",
              "21                                              380.0     \n",
              "22                                              404.1     \n",
              "23                                              360.9     \n",
              "24                                              341.0     \n",
              "25                                              384.8     \n",
              "26                                              395.8     \n",
              "27                                              465.5     \n",
              "28                                              442.7     \n",
              "29                                              406.5     \n",
              "30                                              374.2     \n",
              "31                                              387.7     \n",
              "32                                              390.8     \n",
              "33                                              361.5     \n",
              "34                                              346.2     \n",
              "35                                              397.8     \n",
              "36                                              373.2     \n",
              "37                                              410.7     \n",
              "38                                              369.4     \n",
              "39                                              364.2     \n",
              "40                                              432.3     \n",
              "41                                              391.8     \n",
              "42                                              450.4     \n",
              "43                                              341.4     \n",
              "44                                              363.3     \n",
              "45                                              389.0     \n",
              "46                                              358.9     \n",
              "47                                              371.8     \n",
              "48                                              373.8     \n",
              "49                                              395.3     \n",
              "50                                              348.7     \n",
              "\n",
              "    Time-test: Среднее время реакции, серия 2 (Часть 2)  Time-test: Проп 1 1  \\\n",
              "0                                               492.7                      0   \n",
              "1                                               416.9                      1   \n",
              "2                                               345.7                      1   \n",
              "3                                               423.3                      2   \n",
              "4                                               405.8                      1   \n",
              "5                                               350.1                      3   \n",
              "6                                               335.8                      1   \n",
              "7                                               418.2                      1   \n",
              "8                                               399.6                      2   \n",
              "9                                               392.4                      0   \n",
              "10                                              360.7                      0   \n",
              "11                                              366.9                      0   \n",
              "12                                              349.0                      0   \n",
              "13                                              386.1                      1   \n",
              "14                                              437.3                      2   \n",
              "15                                              429.2                      1   \n",
              "16                                              332.6                      5   \n",
              "17                                              386.3                      0   \n",
              "18                                              381.5                      0   \n",
              "19                                              442.3                      1   \n",
              "20                                              400.4                      0   \n",
              "21                                              393.8                      0   \n",
              "22                                              396.9                      1   \n",
              "23                                              356.0                      2   \n",
              "24                                              337.2                      8   \n",
              "25                                              401.3                      7   \n",
              "26                                              394.0                      1   \n",
              "27                                              445.9                      1   \n",
              "28                                              476.4                      1   \n",
              "29                                              491.7                      1   \n",
              "30                                              387.5                      1   \n",
              "31                                              430.5                      3   \n",
              "32                                              367.8                      0   \n",
              "33                                              345.0                      0   \n",
              "34                                              326.2                      0   \n",
              "35                                              422.1                      3   \n",
              "36                                              396.5                      2   \n",
              "37                                              411.4                      1   \n",
              "38                                              365.1                      4   \n",
              "39                                              394.7                      0   \n",
              "40                                              457.0                      0   \n",
              "41                                              395.8                      0   \n",
              "42                                              471.7                      0   \n",
              "43                                              359.7                      0   \n",
              "44                                              347.0                      0   \n",
              "45                                              411.1                      0   \n",
              "46                                              352.9                      0   \n",
              "47                                              376.5                      0   \n",
              "48                                              380.3                      0   \n",
              "49                                              391.5                      1   \n",
              "50                                              342.4                      1   \n",
              "\n",
              "    ...  WMem: Время серии 3       Время 1       Время 2       Время 3  \\\n",
              "0   ...                31284   5326.000000   4408.857143   4469.142857   \n",
              "1   ...                36989   6579.095238   5890.583333   5284.142857   \n",
              "2   ...                61632   6902.466667   6300.076923   6163.200000   \n",
              "3   ...                17716   4915.954545   4423.333333   4429.000000   \n",
              "4   ...                18581   3669.000000   6115.277778   3716.200000   \n",
              "5   ...                18861   4288.000000   5104.500000   4715.250000   \n",
              "6   ...                93407   4552.222222   5188.500000   6671.928571   \n",
              "7   ...                45979   3216.214286   1976.200000   5108.777778   \n",
              "8   ...               160699   8743.000000   7349.857143   8457.842105   \n",
              "9   ...                45979   3752.250000   3952.400000   4179.909091   \n",
              "10  ...                80240   9215.368421   7821.000000   6686.666667   \n",
              "11  ...                95450   4135.909091   4983.533333   6363.333333   \n",
              "12  ...                50359   5685.066667   5880.000000   5035.900000   \n",
              "13  ...                61632   6902.466667   6300.076923   6163.200000   \n",
              "14  ...                33851   8654.250000   6942.777778   4231.375000   \n",
              "15  ...                56298   3856.125000   4460.444444   4691.500000   \n",
              "16  ...                65466  11102.846150   7411.500000   8183.250000   \n",
              "17  ...                50416   8274.240000   7730.619048   7202.285714   \n",
              "18  ...               183638  11131.333330   8513.000000  10202.111110   \n",
              "19  ...                22167   7491.478261   5087.500000   5541.750000   \n",
              "20  ...                99965   6700.842105   5109.500000   5261.315789   \n",
              "21  ...                39374   6866.760000   6858.222222   4921.750000   \n",
              "22  ...               104511   7304.071429   5791.428571   6967.400000   \n",
              "23  ...                29641   4330.142857   4619.666667   4940.166667   \n",
              "24  ...                22387  13185.950000   7946.363636   5596.750000   \n",
              "25  ...                45067   5522.411765   5845.500000   4506.700000   \n",
              "26  ...                46739   5446.315789   4901.571429   4249.000000   \n",
              "27  ...                10551   6496.214286  10956.750000   5275.500000   \n",
              "28  ...                97494   7193.800000   5550.166667   5734.941176   \n",
              "29  ...                52607   8184.380952   6469.416667   4383.916667   \n",
              "30  ...                27766   4511.461538   4745.636364   4627.666667   \n",
              "31  ...                12290   3708.000000   3658.714286   4096.666667   \n",
              "32  ...                97467   5966.066667   5955.000000   5733.352941   \n",
              "33  ...                25222   8317.666667   6660.727273   5044.400000   \n",
              "34  ...                34482   5070.772727   5212.500000   4310.250000   \n",
              "35  ...                29850   7401.384615   5646.000000   4975.000000   \n",
              "36  ...                16529   4864.545455   4643.666667   4132.250000   \n",
              "37  ...                34885   4160.666667   4832.153846   4360.625000   \n",
              "38  ...                17318   5979.500000   5867.333333   4329.500000   \n",
              "39  ...                28678   6074.333333   5709.750000   4096.857143   \n",
              "40  ...                62238   6366.700000   5021.250000   4445.571429   \n",
              "41  ...               164476   6059.600000   6122.650000   8223.800000   \n",
              "42  ...                54087   5730.187500   5083.666667   5408.700000   \n",
              "43  ...                48939   5595.782609   5302.600000   4449.000000   \n",
              "44  ...                53384   7195.785714   8909.058824   5931.555556   \n",
              "45  ...               102944   8222.444444   5701.000000   6434.000000   \n",
              "46  ...                32293   5324.000000   4459.214286   5382.166667   \n",
              "47  ...                40060   7250.153846   5735.294118   5007.500000   \n",
              "48  ...                32214   4460.600000   4644.500000   4602.000000   \n",
              "49  ...               186743   7140.464286   5475.250000   9828.578947   \n",
              "50  ...                21372   9243.750000   1699.333333   2671.500000   \n",
              "\n",
              "    Время фиксации на области интереса (тексте)  \\\n",
              "0                                      8.346596   \n",
              "1                                     16.781460   \n",
              "2                                      6.289856   \n",
              "3                                     13.191900   \n",
              "4                                      9.284821   \n",
              "5                                     19.902344   \n",
              "6                                      5.271957   \n",
              "7                                      9.506622   \n",
              "8                                     26.452972   \n",
              "9                                      9.977242   \n",
              "10                                     8.545059   \n",
              "11                                    11.304489   \n",
              "12                                     8.492905   \n",
              "13                                    20.834183   \n",
              "14                                     8.697250   \n",
              "15                                     6.333145   \n",
              "16                                     5.803558   \n",
              "17                                    12.769135   \n",
              "18                                    14.793701   \n",
              "19                                     7.601181   \n",
              "20                                     5.536896   \n",
              "21                                    55.655716   \n",
              "22                                     8.185715   \n",
              "23                                    10.389240   \n",
              "24                                     8.334137   \n",
              "25                                     8.953888   \n",
              "26                                    21.430270   \n",
              "27                                    54.240600   \n",
              "28                                    13.064018   \n",
              "29                                    21.592926   \n",
              "30                                     9.656479   \n",
              "31                                    23.346298   \n",
              "32                                    16.367905   \n",
              "33                                    11.869629   \n",
              "34                                    13.906189   \n",
              "35                                    10.576263   \n",
              "36                                    13.661346   \n",
              "37                                    15.231049   \n",
              "38                                    15.156464   \n",
              "39                                     6.117889   \n",
              "40                                    15.652939   \n",
              "41                                     8.818848   \n",
              "42                                    18.403534   \n",
              "43                                    22.460450   \n",
              "44                                    53.394531   \n",
              "45                                    24.798706   \n",
              "46                                    23.009750   \n",
              "47                                    16.366974   \n",
              "48                                    25.062480   \n",
              "49                                    31.878273   \n",
              "50                                    18.903427   \n",
              "\n",
              "    Длительность первой фиксации на области интереса  Среднее время фиксации  \\\n",
              "0                                           0.367599                0.439295   \n",
              "1                                           0.615524                0.762794   \n",
              "2                                           0.343597                0.251594   \n",
              "3                                           0.899536                0.507381   \n",
              "4                                           0.581558                0.343882   \n",
              "5                                           1.550644                0.710798   \n",
              "6                                           0.180954                0.159756   \n",
              "7                                           0.330429                0.288079   \n",
              "8                                           0.049255                0.755799   \n",
              "9                                           0.278290                0.255827   \n",
              "10                                          0.049561                0.213626   \n",
              "11                                          0.509140                0.282612   \n",
              "12                                          0.475067                0.207144   \n",
              "13                                          0.557816                0.452917   \n",
              "14                                          0.034973                0.189071   \n",
              "15                                          0.016731                0.131941   \n",
              "16                                          0.909882                0.120907   \n",
              "17                                          0.180832                0.260595   \n",
              "18                                          0.589203                0.301912   \n",
              "19                                          0.179642                0.152024   \n",
              "20                                          0.065712                0.110738   \n",
              "21                                          0.163300                1.011922   \n",
              "22                                          0.306320                0.143609   \n",
              "23                                          0.102676                0.182267   \n",
              "24                                          0.229706                0.143692   \n",
              "25                                          0.181198                0.151761   \n",
              "26                                          0.024933                0.345649   \n",
              "27                                          0.026123                0.753342   \n",
              "28                                          0.241508                0.167487   \n",
              "29                                          0.014328                0.276832   \n",
              "30                                          0.277649                0.122234   \n",
              "31                                          0.148346                0.291829   \n",
              "32                                          0.132645                0.204599   \n",
              "33                                          0.210205                0.143008   \n",
              "34                                          0.573273                0.161700   \n",
              "35                                          0.341553                0.117514   \n",
              "36                                          0.171021                0.145333   \n",
              "37                                          0.139160                0.162032   \n",
              "38                                          0.015427                0.156252   \n",
              "39                                          0.082001                0.061797   \n",
              "40                                          0.525757                0.158110   \n",
              "41                                          0.019638                0.088188   \n",
              "42                                          0.166321                0.184035   \n",
              "43                                          0.016846                0.184102   \n",
              "44                                          0.033203                0.434102   \n",
              "45                                          0.098602                0.201615   \n",
              "46                                          0.034027                0.170443   \n",
              "47                                          0.049225                0.104248   \n",
              "48                                          0.025635                0.147426   \n",
              "49                                          0.374008                0.184267   \n",
              "50                                          0.081818                0.109268   \n",
              "\n",
              "    Все фиксации  Амплитуда саккады  Количество саккад  \n",
              "0             19           8.624043                 18  \n",
              "1             22           8.681620                 20  \n",
              "2             25           5.778367                 24  \n",
              "3             26           6.292604                 23  \n",
              "4             27           5.897567                 22  \n",
              "5             28          10.093942                 27  \n",
              "6             33           5.816387                 19  \n",
              "7             33           7.225397                 32  \n",
              "8             35           7.600752                 32  \n",
              "9             39           6.825592                 38  \n",
              "10            40           6.909260                 39  \n",
              "11            40           5.912111                 39  \n",
              "12            41           6.800113                 38  \n",
              "13            46           4.733608                 42  \n",
              "14            46           3.807731                 43  \n",
              "15            48           4.212930                 35  \n",
              "16            48           5.808091                 42  \n",
              "17            49           6.755057                 46  \n",
              "18            49           3.736357                 45  \n",
              "19            50           5.345787                 49  \n",
              "20            50           6.016181                 38  \n",
              "21            55           8.825168                 49  \n",
              "22            57           5.136739                 53  \n",
              "23            57           3.805939                 54  \n",
              "24            58           4.871253                 57  \n",
              "25            59           3.557558                 53  \n",
              "26            62           6.680077                 59  \n",
              "27            72           6.565492                 69  \n",
              "28            78           3.315215                 76  \n",
              "29            78           4.409905                 76  \n",
              "30            79           3.864580                 75  \n",
              "31            80           5.587113                 73  \n",
              "32            80           7.469327                 77  \n",
              "33            83           3.488810                 79  \n",
              "34            86           4.406625                 82  \n",
              "35            90           3.129224                 77  \n",
              "36            94           2.687866                 91  \n",
              "37            94           3.043699                 92  \n",
              "38            97           4.585595                 85  \n",
              "39            99           6.390610                 81  \n",
              "40            99           3.562151                 96  \n",
              "41           100           7.940523                 80  \n",
              "42           100           4.074020                 96  \n",
              "43           122           3.190559                100  \n",
              "44           123           4.645144                 97  \n",
              "45           123           3.204071                120  \n",
              "46           135           4.405387                128  \n",
              "47           157           3.187945                147  \n",
              "48           170           4.598891                136  \n",
              "49           173           2.314006                165  \n",
              "50           173           2.736056                166  \n",
              "\n",
              "[51 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f96365a8-2b29-45b1-b306-46d288f0bf5b\" 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>№</th>\n",
              "      <th>Возраст</th>\n",
              "      <th>Пол</th>\n",
              "      <th>Интернет ответ</th>\n",
              "      <th>Интернет время</th>\n",
              "      <th>Time-test: Среднее 1 1</th>\n",
              "      <th>Time-test: Среднее время реакции, серия 1 (Часть 2)</th>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 1)</th>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 2)</th>\n",
              "      <th>Time-test: Проп 1 1</th>\n",
              "      <th>...</th>\n",
              "      <th>WMem: Время серии 3</th>\n",
              "      <th>Время 1</th>\n",
              "      <th>Время 2</th>\n",
              "      <th>Время 3</th>\n",
              "      <th>Время фиксации на области интереса (тексте)</th>\n",
              "      <th>Длительность первой фиксации на области интереса</th>\n",
              "      <th>Среднее время фиксации</th>\n",
              "      <th>Все фиксации</th>\n",
              "      <th>Амплитуда саккады</th>\n",
              "      <th>Количество саккад</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>182</td>\n",
              "      <td>341.2</td>\n",
              "      <td>300.4</td>\n",
              "      <td>434.3</td>\n",
              "      <td>492.7</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>31284</td>\n",
              "      <td>5326.000000</td>\n",
              "      <td>4408.857143</td>\n",
              "      <td>4469.142857</td>\n",
              "      <td>8.346596</td>\n",
              "      <td>0.367599</td>\n",
              "      <td>0.439295</td>\n",
              "      <td>19</td>\n",
              "      <td>8.624043</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>54</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>118</td>\n",
              "      <td>343.3</td>\n",
              "      <td>322.4</td>\n",
              "      <td>392.0</td>\n",
              "      <td>416.9</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>36989</td>\n",
              "      <td>6579.095238</td>\n",
              "      <td>5890.583333</td>\n",
              "      <td>5284.142857</td>\n",
              "      <td>16.781460</td>\n",
              "      <td>0.615524</td>\n",
              "      <td>0.762794</td>\n",
              "      <td>22</td>\n",
              "      <td>8.681620</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>298</td>\n",
              "      <td>275.3</td>\n",
              "      <td>279.6</td>\n",
              "      <td>360.2</td>\n",
              "      <td>345.7</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>61632</td>\n",
              "      <td>6902.466667</td>\n",
              "      <td>6300.076923</td>\n",
              "      <td>6163.200000</td>\n",
              "      <td>6.289856</td>\n",
              "      <td>0.343597</td>\n",
              "      <td>0.251594</td>\n",
              "      <td>25</td>\n",
              "      <td>5.778367</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>57</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>56</td>\n",
              "      <td>304.6</td>\n",
              "      <td>313.2</td>\n",
              "      <td>437.4</td>\n",
              "      <td>423.3</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>17716</td>\n",
              "      <td>4915.954545</td>\n",
              "      <td>4423.333333</td>\n",
              "      <td>4429.000000</td>\n",
              "      <td>13.191900</td>\n",
              "      <td>0.899536</td>\n",
              "      <td>0.507381</td>\n",
              "      <td>26</td>\n",
              "      <td>6.292604</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>319.5</td>\n",
              "      <td>288.1</td>\n",
              "      <td>340.5</td>\n",
              "      <td>405.8</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>18581</td>\n",
              "      <td>3669.000000</td>\n",
              "      <td>6115.277778</td>\n",
              "      <td>3716.200000</td>\n",
              "      <td>9.284821</td>\n",
              "      <td>0.581558</td>\n",
              "      <td>0.343882</td>\n",
              "      <td>27</td>\n",
              "      <td>5.897567</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>51</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>552</td>\n",
              "      <td>254.8</td>\n",
              "      <td>280.5</td>\n",
              "      <td>395.0</td>\n",
              "      <td>350.1</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>18861</td>\n",
              "      <td>4288.000000</td>\n",
              "      <td>5104.500000</td>\n",
              "      <td>4715.250000</td>\n",
              "      <td>19.902344</td>\n",
              "      <td>1.550644</td>\n",
              "      <td>0.710798</td>\n",
              "      <td>28</td>\n",
              "      <td>10.093942</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>501</td>\n",
              "      <td>267.4</td>\n",
              "      <td>250.5</td>\n",
              "      <td>319.6</td>\n",
              "      <td>335.8</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>93407</td>\n",
              "      <td>4552.222222</td>\n",
              "      <td>5188.500000</td>\n",
              "      <td>6671.928571</td>\n",
              "      <td>5.271957</td>\n",
              "      <td>0.180954</td>\n",
              "      <td>0.159756</td>\n",
              "      <td>33</td>\n",
              "      <td>5.816387</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>25</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>61</td>\n",
              "      <td>282.6</td>\n",
              "      <td>336.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>418.2</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>45979</td>\n",
              "      <td>3216.214286</td>\n",
              "      <td>1976.200000</td>\n",
              "      <td>5108.777778</td>\n",
              "      <td>9.506622</td>\n",
              "      <td>0.330429</td>\n",
              "      <td>0.288079</td>\n",
              "      <td>33</td>\n",
              "      <td>7.225397</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>56</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>295.4</td>\n",
              "      <td>310.3</td>\n",
              "      <td>388.8</td>\n",
              "      <td>399.6</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>160699</td>\n",
              "      <td>8743.000000</td>\n",
              "      <td>7349.857143</td>\n",
              "      <td>8457.842105</td>\n",
              "      <td>26.452972</td>\n",
              "      <td>0.049255</td>\n",
              "      <td>0.755799</td>\n",
              "      <td>35</td>\n",
              "      <td>7.600752</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>26</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>112</td>\n",
              "      <td>291.2</td>\n",
              "      <td>314.4</td>\n",
              "      <td>392.4</td>\n",
              "      <td>392.4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>45979</td>\n",
              "      <td>3752.250000</td>\n",
              "      <td>3952.400000</td>\n",
              "      <td>4179.909091</td>\n",
              "      <td>9.977242</td>\n",
              "      <td>0.278290</td>\n",
              "      <td>0.255827</td>\n",
              "      <td>39</td>\n",
              "      <td>6.825592</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>62</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>292.3</td>\n",
              "      <td>278.4</td>\n",
              "      <td>353.9</td>\n",
              "      <td>360.7</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>80240</td>\n",
              "      <td>9215.368421</td>\n",
              "      <td>7821.000000</td>\n",
              "      <td>6686.666667</td>\n",
              "      <td>8.545059</td>\n",
              "      <td>0.049561</td>\n",
              "      <td>0.213626</td>\n",
              "      <td>40</td>\n",
              "      <td>6.909260</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>55</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>292.2</td>\n",
              "      <td>297.6</td>\n",
              "      <td>361.9</td>\n",
              "      <td>366.9</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>95450</td>\n",
              "      <td>4135.909091</td>\n",
              "      <td>4983.533333</td>\n",
              "      <td>6363.333333</td>\n",
              "      <td>11.304489</td>\n",
              "      <td>0.509140</td>\n",
              "      <td>0.282612</td>\n",
              "      <td>40</td>\n",
              "      <td>5.912111</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>6</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>58</td>\n",
              "      <td>275.1</td>\n",
              "      <td>282.5</td>\n",
              "      <td>337.7</td>\n",
              "      <td>349.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>50359</td>\n",
              "      <td>5685.066667</td>\n",
              "      <td>5880.000000</td>\n",
              "      <td>5035.900000</td>\n",
              "      <td>8.492905</td>\n",
              "      <td>0.475067</td>\n",
              "      <td>0.207144</td>\n",
              "      <td>41</td>\n",
              "      <td>6.800113</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>28</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>289</td>\n",
              "      <td>298.2</td>\n",
              "      <td>297.4</td>\n",
              "      <td>384.4</td>\n",
              "      <td>386.1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>61632</td>\n",
              "      <td>6902.466667</td>\n",
              "      <td>6300.076923</td>\n",
              "      <td>6163.200000</td>\n",
              "      <td>20.834183</td>\n",
              "      <td>0.557816</td>\n",
              "      <td>0.452917</td>\n",
              "      <td>46</td>\n",
              "      <td>4.733608</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>280.5</td>\n",
              "      <td>340.1</td>\n",
              "      <td>395.3</td>\n",
              "      <td>437.3</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>33851</td>\n",
              "      <td>8654.250000</td>\n",
              "      <td>6942.777778</td>\n",
              "      <td>4231.375000</td>\n",
              "      <td>8.697250</td>\n",
              "      <td>0.034973</td>\n",
              "      <td>0.189071</td>\n",
              "      <td>46</td>\n",
              "      <td>3.807731</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>18</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61</td>\n",
              "      <td>274.7</td>\n",
              "      <td>283.6</td>\n",
              "      <td>359.4</td>\n",
              "      <td>429.2</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>56298</td>\n",
              "      <td>3856.125000</td>\n",
              "      <td>4460.444444</td>\n",
              "      <td>4691.500000</td>\n",
              "      <td>6.333145</td>\n",
              "      <td>0.016731</td>\n",
              "      <td>0.131941</td>\n",
              "      <td>48</td>\n",
              "      <td>4.212930</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>31</td>\n",
              "      <td>268.4</td>\n",
              "      <td>296.4</td>\n",
              "      <td>340.0</td>\n",
              "      <td>332.6</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>65466</td>\n",
              "      <td>11102.846150</td>\n",
              "      <td>7411.500000</td>\n",
              "      <td>8183.250000</td>\n",
              "      <td>5.803558</td>\n",
              "      <td>0.909882</td>\n",
              "      <td>0.120907</td>\n",
              "      <td>48</td>\n",
              "      <td>5.808091</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>177</td>\n",
              "      <td>275.7</td>\n",
              "      <td>282.8</td>\n",
              "      <td>357.4</td>\n",
              "      <td>386.3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>50416</td>\n",
              "      <td>8274.240000</td>\n",
              "      <td>7730.619048</td>\n",
              "      <td>7202.285714</td>\n",
              "      <td>12.769135</td>\n",
              "      <td>0.180832</td>\n",
              "      <td>0.260595</td>\n",
              "      <td>49</td>\n",
              "      <td>6.755057</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>481</td>\n",
              "      <td>309.1</td>\n",
              "      <td>265.7</td>\n",
              "      <td>372.1</td>\n",
              "      <td>381.5</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>183638</td>\n",
              "      <td>11131.333330</td>\n",
              "      <td>8513.000000</td>\n",
              "      <td>10202.111110</td>\n",
              "      <td>14.793701</td>\n",
              "      <td>0.589203</td>\n",
              "      <td>0.301912</td>\n",
              "      <td>49</td>\n",
              "      <td>3.736357</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>66</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53</td>\n",
              "      <td>269.7</td>\n",
              "      <td>323.1</td>\n",
              "      <td>388.6</td>\n",
              "      <td>442.3</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>22167</td>\n",
              "      <td>7491.478261</td>\n",
              "      <td>5087.500000</td>\n",
              "      <td>5541.750000</td>\n",
              "      <td>7.601181</td>\n",
              "      <td>0.179642</td>\n",
              "      <td>0.152024</td>\n",
              "      <td>50</td>\n",
              "      <td>5.345787</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>40</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>182</td>\n",
              "      <td>299.9</td>\n",
              "      <td>281.0</td>\n",
              "      <td>414.9</td>\n",
              "      <td>400.4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99965</td>\n",
              "      <td>6700.842105</td>\n",
              "      <td>5109.500000</td>\n",
              "      <td>5261.315789</td>\n",
              "      <td>5.536896</td>\n",
              "      <td>0.065712</td>\n",
              "      <td>0.110738</td>\n",
              "      <td>50</td>\n",
              "      <td>6.016181</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>52</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>297.5</td>\n",
              "      <td>325.9</td>\n",
              "      <td>380.0</td>\n",
              "      <td>393.8</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>39374</td>\n",
              "      <td>6866.760000</td>\n",
              "      <td>6858.222222</td>\n",
              "      <td>4921.750000</td>\n",
              "      <td>55.655716</td>\n",
              "      <td>0.163300</td>\n",
              "      <td>1.011922</td>\n",
              "      <td>55</td>\n",
              "      <td>8.825168</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>41</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>288.1</td>\n",
              "      <td>296.9</td>\n",
              "      <td>404.1</td>\n",
              "      <td>396.9</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>104511</td>\n",
              "      <td>7304.071429</td>\n",
              "      <td>5791.428571</td>\n",
              "      <td>6967.400000</td>\n",
              "      <td>8.185715</td>\n",
              "      <td>0.306320</td>\n",
              "      <td>0.143609</td>\n",
              "      <td>57</td>\n",
              "      <td>5.136739</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>38</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>88</td>\n",
              "      <td>284.5</td>\n",
              "      <td>321.4</td>\n",
              "      <td>360.9</td>\n",
              "      <td>356.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>29641</td>\n",
              "      <td>4330.142857</td>\n",
              "      <td>4619.666667</td>\n",
              "      <td>4940.166667</td>\n",
              "      <td>10.389240</td>\n",
              "      <td>0.102676</td>\n",
              "      <td>0.182267</td>\n",
              "      <td>57</td>\n",
              "      <td>3.805939</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>43</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>241</td>\n",
              "      <td>275.9</td>\n",
              "      <td>249.0</td>\n",
              "      <td>341.0</td>\n",
              "      <td>337.2</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>22387</td>\n",
              "      <td>13185.950000</td>\n",
              "      <td>7946.363636</td>\n",
              "      <td>5596.750000</td>\n",
              "      <td>8.334137</td>\n",
              "      <td>0.229706</td>\n",
              "      <td>0.143692</td>\n",
              "      <td>58</td>\n",
              "      <td>4.871253</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>49</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>52</td>\n",
              "      <td>339.9</td>\n",
              "      <td>302.0</td>\n",
              "      <td>384.8</td>\n",
              "      <td>401.3</td>\n",
              "      <td>7</td>\n",
              "      <td>...</td>\n",
              "      <td>45067</td>\n",
              "      <td>5522.411765</td>\n",
              "      <td>5845.500000</td>\n",
              "      <td>4506.700000</td>\n",
              "      <td>8.953888</td>\n",
              "      <td>0.181198</td>\n",
              "      <td>0.151761</td>\n",
              "      <td>59</td>\n",
              "      <td>3.557558</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>254</td>\n",
              "      <td>315.2</td>\n",
              "      <td>290.3</td>\n",
              "      <td>395.8</td>\n",
              "      <td>394.0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>46739</td>\n",
              "      <td>5446.315789</td>\n",
              "      <td>4901.571429</td>\n",
              "      <td>4249.000000</td>\n",
              "      <td>21.430270</td>\n",
              "      <td>0.024933</td>\n",
              "      <td>0.345649</td>\n",
              "      <td>62</td>\n",
              "      <td>6.680077</td>\n",
              "      <td>59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>50</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>344.1</td>\n",
              "      <td>341.1</td>\n",
              "      <td>465.5</td>\n",
              "      <td>445.9</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>10551</td>\n",
              "      <td>6496.214286</td>\n",
              "      <td>10956.750000</td>\n",
              "      <td>5275.500000</td>\n",
              "      <td>54.240600</td>\n",
              "      <td>0.026123</td>\n",
              "      <td>0.753342</td>\n",
              "      <td>72</td>\n",
              "      <td>6.565492</td>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>39</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>298</td>\n",
              "      <td>359.1</td>\n",
              "      <td>386.9</td>\n",
              "      <td>442.7</td>\n",
              "      <td>476.4</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>97494</td>\n",
              "      <td>7193.800000</td>\n",
              "      <td>5550.166667</td>\n",
              "      <td>5734.941176</td>\n",
              "      <td>13.064018</td>\n",
              "      <td>0.241508</td>\n",
              "      <td>0.167487</td>\n",
              "      <td>78</td>\n",
              "      <td>3.315215</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>395</td>\n",
              "      <td>299.5</td>\n",
              "      <td>314.3</td>\n",
              "      <td>406.5</td>\n",
              "      <td>491.7</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>52607</td>\n",
              "      <td>8184.380952</td>\n",
              "      <td>6469.416667</td>\n",
              "      <td>4383.916667</td>\n",
              "      <td>21.592926</td>\n",
              "      <td>0.014328</td>\n",
              "      <td>0.276832</td>\n",
              "      <td>78</td>\n",
              "      <td>4.409905</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>21</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>520</td>\n",
              "      <td>275.1</td>\n",
              "      <td>292.5</td>\n",
              "      <td>374.2</td>\n",
              "      <td>387.5</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>27766</td>\n",
              "      <td>4511.461538</td>\n",
              "      <td>4745.636364</td>\n",
              "      <td>4627.666667</td>\n",
              "      <td>9.656479</td>\n",
              "      <td>0.277649</td>\n",
              "      <td>0.122234</td>\n",
              "      <td>79</td>\n",
              "      <td>3.864580</td>\n",
              "      <td>75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>33</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>460</td>\n",
              "      <td>267.1</td>\n",
              "      <td>281.7</td>\n",
              "      <td>387.7</td>\n",
              "      <td>430.5</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>12290</td>\n",
              "      <td>3708.000000</td>\n",
              "      <td>3658.714286</td>\n",
              "      <td>4096.666667</td>\n",
              "      <td>23.346298</td>\n",
              "      <td>0.148346</td>\n",
              "      <td>0.291829</td>\n",
              "      <td>80</td>\n",
              "      <td>5.587113</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>13</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>119</td>\n",
              "      <td>346.9</td>\n",
              "      <td>360.3</td>\n",
              "      <td>390.8</td>\n",
              "      <td>367.8</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>97467</td>\n",
              "      <td>5966.066667</td>\n",
              "      <td>5955.000000</td>\n",
              "      <td>5733.352941</td>\n",
              "      <td>16.367905</td>\n",
              "      <td>0.132645</td>\n",
              "      <td>0.204599</td>\n",
              "      <td>80</td>\n",
              "      <td>7.469327</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>32</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>336</td>\n",
              "      <td>298.1</td>\n",
              "      <td>311.3</td>\n",
              "      <td>361.5</td>\n",
              "      <td>345.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>25222</td>\n",
              "      <td>8317.666667</td>\n",
              "      <td>6660.727273</td>\n",
              "      <td>5044.400000</td>\n",
              "      <td>11.869629</td>\n",
              "      <td>0.210205</td>\n",
              "      <td>0.143008</td>\n",
              "      <td>83</td>\n",
              "      <td>3.488810</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>162</td>\n",
              "      <td>285.9</td>\n",
              "      <td>299.8</td>\n",
              "      <td>346.2</td>\n",
              "      <td>326.2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>34482</td>\n",
              "      <td>5070.772727</td>\n",
              "      <td>5212.500000</td>\n",
              "      <td>4310.250000</td>\n",
              "      <td>13.906189</td>\n",
              "      <td>0.573273</td>\n",
              "      <td>0.161700</td>\n",
              "      <td>86</td>\n",
              "      <td>4.406625</td>\n",
              "      <td>82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>16</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>56</td>\n",
              "      <td>342.9</td>\n",
              "      <td>347.5</td>\n",
              "      <td>397.8</td>\n",
              "      <td>422.1</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>29850</td>\n",
              "      <td>7401.384615</td>\n",
              "      <td>5646.000000</td>\n",
              "      <td>4975.000000</td>\n",
              "      <td>10.576263</td>\n",
              "      <td>0.341553</td>\n",
              "      <td>0.117514</td>\n",
              "      <td>90</td>\n",
              "      <td>3.129224</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>42</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>551</td>\n",
              "      <td>313.5</td>\n",
              "      <td>288.7</td>\n",
              "      <td>373.2</td>\n",
              "      <td>396.5</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>16529</td>\n",
              "      <td>4864.545455</td>\n",
              "      <td>4643.666667</td>\n",
              "      <td>4132.250000</td>\n",
              "      <td>13.661346</td>\n",
              "      <td>0.171021</td>\n",
              "      <td>0.145333</td>\n",
              "      <td>94</td>\n",
              "      <td>2.687866</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>312.8</td>\n",
              "      <td>304.2</td>\n",
              "      <td>410.7</td>\n",
              "      <td>411.4</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>34885</td>\n",
              "      <td>4160.666667</td>\n",
              "      <td>4832.153846</td>\n",
              "      <td>4360.625000</td>\n",
              "      <td>15.231049</td>\n",
              "      <td>0.139160</td>\n",
              "      <td>0.162032</td>\n",
              "      <td>94</td>\n",
              "      <td>3.043699</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>45</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>366.9</td>\n",
              "      <td>335.7</td>\n",
              "      <td>369.4</td>\n",
              "      <td>365.1</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>17318</td>\n",
              "      <td>5979.500000</td>\n",
              "      <td>5867.333333</td>\n",
              "      <td>4329.500000</td>\n",
              "      <td>15.156464</td>\n",
              "      <td>0.015427</td>\n",
              "      <td>0.156252</td>\n",
              "      <td>97</td>\n",
              "      <td>4.585595</td>\n",
              "      <td>85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>36</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>294</td>\n",
              "      <td>292.9</td>\n",
              "      <td>339.0</td>\n",
              "      <td>364.2</td>\n",
              "      <td>394.7</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>28678</td>\n",
              "      <td>6074.333333</td>\n",
              "      <td>5709.750000</td>\n",
              "      <td>4096.857143</td>\n",
              "      <td>6.117889</td>\n",
              "      <td>0.082001</td>\n",
              "      <td>0.061797</td>\n",
              "      <td>99</td>\n",
              "      <td>6.390610</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>48</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>62</td>\n",
              "      <td>291.2</td>\n",
              "      <td>303.1</td>\n",
              "      <td>432.3</td>\n",
              "      <td>457.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>62238</td>\n",
              "      <td>6366.700000</td>\n",
              "      <td>5021.250000</td>\n",
              "      <td>4445.571429</td>\n",
              "      <td>15.652939</td>\n",
              "      <td>0.525757</td>\n",
              "      <td>0.158110</td>\n",
              "      <td>99</td>\n",
              "      <td>3.562151</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>37</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>550</td>\n",
              "      <td>320.9</td>\n",
              "      <td>311.0</td>\n",
              "      <td>391.8</td>\n",
              "      <td>395.8</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>164476</td>\n",
              "      <td>6059.600000</td>\n",
              "      <td>6122.650000</td>\n",
              "      <td>8223.800000</td>\n",
              "      <td>8.818848</td>\n",
              "      <td>0.019638</td>\n",
              "      <td>0.088188</td>\n",
              "      <td>100</td>\n",
              "      <td>7.940523</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>7</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>341.4</td>\n",
              "      <td>320.8</td>\n",
              "      <td>450.4</td>\n",
              "      <td>471.7</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>54087</td>\n",
              "      <td>5730.187500</td>\n",
              "      <td>5083.666667</td>\n",
              "      <td>5408.700000</td>\n",
              "      <td>18.403534</td>\n",
              "      <td>0.166321</td>\n",
              "      <td>0.184035</td>\n",
              "      <td>100</td>\n",
              "      <td>4.074020</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>420</td>\n",
              "      <td>291.8</td>\n",
              "      <td>288.0</td>\n",
              "      <td>341.4</td>\n",
              "      <td>359.7</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>48939</td>\n",
              "      <td>5595.782609</td>\n",
              "      <td>5302.600000</td>\n",
              "      <td>4449.000000</td>\n",
              "      <td>22.460450</td>\n",
              "      <td>0.016846</td>\n",
              "      <td>0.184102</td>\n",
              "      <td>122</td>\n",
              "      <td>3.190559</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>280.1</td>\n",
              "      <td>288.1</td>\n",
              "      <td>363.3</td>\n",
              "      <td>347.0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>53384</td>\n",
              "      <td>7195.785714</td>\n",
              "      <td>8909.058824</td>\n",
              "      <td>5931.555556</td>\n",
              "      <td>53.394531</td>\n",
              "      <td>0.033203</td>\n",
              "      <td>0.434102</td>\n",
              "      <td>123</td>\n",
              "      <td>4.645144</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>8</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>228</td>\n",
              "      <td>313.2</td>\n",
              "      <td>294.7</td>\n",
              "      <td>389.0</td>\n",
              "      <td>411.1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>102944</td>\n",
              "      <td>8222.444444</td>\n",
              "      <td>5701.000000</td>\n",
              "      <td>6434.000000</td>\n",
              "      <td>24.798706</td>\n",
              "      <td>0.098602</td>\n",
              "      <td>0.201615</td>\n",
              "      <td>123</td>\n",
              "      <td>3.204071</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>34</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>60</td>\n",
              "      <td>302.7</td>\n",
              "      <td>315.7</td>\n",
              "      <td>358.9</td>\n",
              "      <td>352.9</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>32293</td>\n",
              "      <td>5324.000000</td>\n",
              "      <td>4459.214286</td>\n",
              "      <td>5382.166667</td>\n",
              "      <td>23.009750</td>\n",
              "      <td>0.034027</td>\n",
              "      <td>0.170443</td>\n",
              "      <td>135</td>\n",
              "      <td>4.405387</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>29</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>516</td>\n",
              "      <td>335.5</td>\n",
              "      <td>319.5</td>\n",
              "      <td>371.8</td>\n",
              "      <td>376.5</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>40060</td>\n",
              "      <td>7250.153846</td>\n",
              "      <td>5735.294118</td>\n",
              "      <td>5007.500000</td>\n",
              "      <td>16.366974</td>\n",
              "      <td>0.049225</td>\n",
              "      <td>0.104248</td>\n",
              "      <td>157</td>\n",
              "      <td>3.187945</td>\n",
              "      <td>147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>31</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>297</td>\n",
              "      <td>270.4</td>\n",
              "      <td>238.4</td>\n",
              "      <td>373.8</td>\n",
              "      <td>380.3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>32214</td>\n",
              "      <td>4460.600000</td>\n",
              "      <td>4644.500000</td>\n",
              "      <td>4602.000000</td>\n",
              "      <td>25.062480</td>\n",
              "      <td>0.025635</td>\n",
              "      <td>0.147426</td>\n",
              "      <td>170</td>\n",
              "      <td>4.598891</td>\n",
              "      <td>136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>47</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>176</td>\n",
              "      <td>289.2</td>\n",
              "      <td>337.0</td>\n",
              "      <td>395.3</td>\n",
              "      <td>391.5</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>186743</td>\n",
              "      <td>7140.464286</td>\n",
              "      <td>5475.250000</td>\n",
              "      <td>9828.578947</td>\n",
              "      <td>31.878273</td>\n",
              "      <td>0.374008</td>\n",
              "      <td>0.184267</td>\n",
              "      <td>173</td>\n",
              "      <td>2.314006</td>\n",
              "      <td>165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>22</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>182</td>\n",
              "      <td>258.9</td>\n",
              "      <td>265.7</td>\n",
              "      <td>348.7</td>\n",
              "      <td>342.4</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>21372</td>\n",
              "      <td>9243.750000</td>\n",
              "      <td>1699.333333</td>\n",
              "      <td>2671.500000</td>\n",
              "      <td>18.903427</td>\n",
              "      <td>0.081818</td>\n",
              "      <td>0.109268</td>\n",
              "      <td>173</td>\n",
              "      <td>2.736056</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>51 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f96365a8-2b29-45b1-b306-46d288f0bf5b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-f96365a8-2b29-45b1-b306-46d288f0bf5b 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-f96365a8-2b29-45b1-b306-46d288f0bf5b');\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-75fd7cfb-ca70-4996-b8d8-a13b396116a6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-75fd7cfb-ca70-4996-b8d8-a13b396116a6')\"\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",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-75fd7cfb-ca70-4996-b8d8-a13b396116a6 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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.keys()"
      ],
      "metadata": {
        "id": "0YAIAbZ_MLQa",
        "outputId": "a3eecb3d-b2eb-40bd-adab-ea90cc1a31bf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['№', 'Возраст', 'Пол', 'Интернет ответ', 'Интернет время',\n",
              "       'Time-test: Среднее 1 1',\n",
              "       'Time-test: Среднее время реакции, серия 1 (Часть 2)',\n",
              "       'Time-test: Среднее время реакции, серия 2 (Часть 1)',\n",
              "       'Time-test: Среднее время реакции, серия 2 (Часть 2)',\n",
              "       'Time-test: Проп 1 1',\n",
              "       'Time-test: Количество пропусков, серия 1 (Часть 2)',\n",
              "       'Time-test: Количество пропусков, серия 2 (Часть 1)',\n",
              "       'Time-test: Количество пропусков, серия 2 (Часть 2)',\n",
              "       'Time-test: Ош 2 1', 'Time-test: Количество ошибок, серия 2 (Часть 2) ',\n",
              "       'WMem: Количество объектов, серия 1',\n",
              "       'WMem: Количество объектов, серия 2',\n",
              "       'WMem: Количество объектов, серия 3', 'WMem: Время серии 1',\n",
              "       'WMem: Время серии 2', 'WMem: Время серии 3', 'Время 1', 'Время 2',\n",
              "       'Время 3', 'Время фиксации на области интереса (тексте)',\n",
              "       'Длительность первой фиксации на области интереса',\n",
              "       'Среднее время фиксации', 'Все фиксации', 'Амплитуда саккады',\n",
              "       'Количество саккад'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax = plt.subplots(1,figsize=(60, 20))\n",
        "\n",
        "ax = sns.boxplot(data=data)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation=45)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Wt_IfK2Lnbv8",
        "outputId": "2e844e2e-6555-4697-ee71-39f353efbf46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 6000x2000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create an empty DataFrame to store the outliers\n",
        "outliers_df = pd.DataFrame(columns=['Variable', 'Outlier Values'])\n",
        "\n",
        "# Calculate and store the outliers for each variable\n",
        "for column in data.columns:\n",
        "    q1 = data[column].quantile(0.25)\n",
        "    q3 = data[column].quantile(0.75)\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - 1.5 * iqr\n",
        "    upper_bound = q3 + 1.5 * iqr\n",
        "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)][column]\n",
        "    if not outliers.empty:\n",
        "        outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
        "\n",
        "# Display the table with outliers\n",
        "print(\"Outliers Table:\")\n",
        "display(HTML(outliers_df.to_html()))\n"
      ],
      "metadata": {
        "id": "-lpqKI5p8bls",
        "outputId": "358d8f13-5c8f-4557-e835-bb83f3dbf6c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n",
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers Table:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-2443add9f9b2>:15: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  outliers_df = outliers_df.append({'Variable': column, 'Outlier Values': outliers.tolist()}, ignore_index=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Variable</th>\n",
              "      <th>Outlier Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Time-test: Среднее время реакции, серия 1 (Часть 2)</td>\n",
              "      <td>[386.9]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Time-test: Среднее время реакции, серия 2 (Часть 1)</td>\n",
              "      <td>[465.5, 450.4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Time-test: Проп 1 1</td>\n",
              "      <td>[3, 5, 8, 7, 3, 3, 4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Time-test: Количество пропусков, серия 2 (Часть 1)</td>\n",
              "      <td>[10, 10]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Time-test: Количество пропусков, серия 2 (Часть 2)</td>\n",
              "      <td>[15, 9, 8]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Time-test: Ош 2 1</td>\n",
              "      <td>[18, 19]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Time-test: Количество ошибок, серия 2 (Часть 2)</td>\n",
              "      <td>[19]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>WMem: Время серии 2</td>\n",
              "      <td>[162343]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>WMem: Время серии 3</td>\n",
              "      <td>[160699, 183638, 164476, 186743]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Время 1</td>\n",
              "      <td>[11102.84615, 11131.33333, 13185.95]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Время 2</td>\n",
              "      <td>[1976.2, 8513.0, 10956.75, 8909.058824, 1699.333333]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Время 3</td>\n",
              "      <td>[10202.11111, 9828.578947]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Время фиксации на области интереса (тексте)</td>\n",
              "      <td>[55.655716, 54.2406, 53.394531]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Длительность первой фиксации на области интереса</td>\n",
              "      <td>[0.899536, 1.550644, 0.909882]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Среднее время фиксации</td>\n",
              "      <td>[0.762794, 0.507381, 0.710798, 0.755799, 1.011922, 0.753342]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Количество саккад</td>\n",
              "      <td>[165, 166]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create an empty dictionary to store the count of outliers for each variable\n",
        "outlier_counts = {}\n",
        "\n",
        "# Calculate and store the count of outliers for each variable\n",
        "for column in data.columns:\n",
        "    q1 = data[column].quantile(0.25)\n",
        "    q3 = data[column].quantile(0.75)\n",
        "    iqr = q3 - q1\n",
        "    lower_bound = q1 - 1.5 * iqr\n",
        "    upper_bound = q3 + 1.5 * iqr\n",
        "    outliers = data[(data[column] < lower_bound) | (data[column] > upper_bound)][column]\n",
        "    outlier_counts[column] = len(outliers)\n",
        "\n",
        "# Create a DataFrame with variable names and outlier counts\n",
        "outliers_df = pd.DataFrame(list(outlier_counts.items()), columns=['Variable', 'Outlier Count'])\n",
        "\n",
        "# Sort the DataFrame by outlier count in descending order\n",
        "outliers_df = outliers_df.sort_values(by='Outlier Count', ascending=False)\n",
        "\n",
        "# Display the sorted table\n",
        "print(\"Variables with Outlier Counts (Sorted):\")\n",
        "display(outliers_df)\n"
      ],
      "metadata": {
        "id": "N5skyPaYBeNL",
        "outputId": "1868669f-0215-4797-ede0-477e7534d98e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-166-1de549a310b5>\u001b[0m in \u001b[0;36m<cell line: 7>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Calculate and store the count of outliers for each variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mq1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mq3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0miqr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mq3\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mq1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, interpolation)\u001b[0m\n\u001b[1;32m   2681\u001b[0m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2682\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2683\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2684\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2685\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[1;32m  11272\u001b[0m             \u001b[0;31m# ExtensionArray, ndarray[Any, Any]], Index, Series], Sequence[float]]\";\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11273\u001b[0m             \u001b[0;31m# expected \"float\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 11274\u001b[0;31m             res_df = self.quantile(  # type: ignore[call-overload]\n\u001b[0m\u001b[1;32m  11275\u001b[0m                 \u001b[0;34m[\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11276\u001b[0m                 \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, q, axis, numeric_only, interpolation, method)\u001b[0m\n\u001b[1;32m  11319\u001b[0m             \u001b[0;31m# error: Argument \"qs\" to \"quantile\" of \"BlockManager\" has incompatible type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11320\u001b[0m             \u001b[0;31m# \"Index\"; expected \"Float64Index\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 11321\u001b[0;31m             res = data._mgr.quantile(\n\u001b[0m\u001b[1;32m  11322\u001b[0m                 \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m  \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11323\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, qs, axis, interpolation)\u001b[0m\n\u001b[1;32m   1629\u001b[0m         \u001b[0mnew_axes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mFloat64Index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1631\u001b[0;31m         blocks = [\n\u001b[0m\u001b[1;32m   1632\u001b[0m             \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1633\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1630\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1631\u001b[0m         blocks = [\n\u001b[0;32m-> 1632\u001b[0;31m             \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1633\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mblk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1634\u001b[0m         ]\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mquantile\u001b[0;34m(self, qs, interpolation, axis)\u001b[0m\n\u001b[1;32m   1333\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# caller is responsible for this\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1335\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mquantile_compat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1336\u001b[0m         \u001b[0;31m# ensure_block_shape needed for cases where we start with EA and result\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1337\u001b[0m         \u001b[0;31m#  is ndarray, e.g. IntegerArray, SparseArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36mquantile_compat\u001b[0;34m(values, qs, interpolation)\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mfill_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mna_value_for_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m         \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mquantile_with_mask\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36mquantile_with_mask\u001b[0;34m(values, mask, fill_value, qs, interpolation)\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m         result = _nanpercentile(\n\u001b[0m\u001b[1;32m     96\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m             \u001b[0mqs\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/array_algos/quantile.py\u001b[0m in \u001b[0;36m_nanpercentile\u001b[0;34m(values, qs, na_value, mask, interpolation)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         return np.percentile(\n\u001b[0m\u001b[1;32m    217\u001b[0m             \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m             \u001b[0mqs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mpercentile\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims, interpolation)\u001b[0m\n\u001b[1;32m   4164\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_quantile_is_valid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4165\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Percentiles must be in the range [0, 100]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4166\u001b[0;31m     return _quantile_unchecked(\n\u001b[0m\u001b[1;32m   4167\u001b[0m         a, q, axis, out, overwrite_input, method, keepdims)\n\u001b[1;32m   4168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_unchecked\u001b[0;34m(a, q, axis, out, overwrite_input, method, keepdims)\u001b[0m\n\u001b[1;32m   4422\u001b[0m                         keepdims=False):\n\u001b[1;32m   4423\u001b[0m     \u001b[0;34m\"\"\"Assumes that q is in [0, 1], and is an ndarray\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4424\u001b[0;31m     r, k = _ureduce(a,\n\u001b[0m\u001b[1;32m   4425\u001b[0m                     \u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_quantile_ureduce_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4426\u001b[0m                     \u001b[0mq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[0;34m(a, func, **kwargs)\u001b[0m\n\u001b[1;32m   3723\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3725\u001b[0;31m     \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3726\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3727\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile_ureduce_func\u001b[0;34m(a, q, axis, out, overwrite_input, method)\u001b[0m\n\u001b[1;32m   4591\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4592\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4593\u001b[0;31m     result = _quantile(arr,\n\u001b[0m\u001b[1;32m   4594\u001b[0m                        \u001b[0mquantiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4595\u001b[0m                        \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_quantile\u001b[0;34m(arr, quantiles, axis, method, out)\u001b[0m\n\u001b[1;32m   4708\u001b[0m         \u001b[0mresult_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvirtual_indexes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4709\u001b[0m         \u001b[0mgamma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgamma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4710\u001b[0;31m         result = _lerp(previous,\n\u001b[0m\u001b[1;32m   4711\u001b[0m                        \u001b[0mnext\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4712\u001b[0m                        \u001b[0mgamma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_lerp\u001b[0;34m(a, b, t, out)\u001b[0m\n\u001b[1;32m   4525\u001b[0m         \u001b[0mOutput\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4526\u001b[0m     \"\"\"\n\u001b[0;32m-> 4527\u001b[0;31m     \u001b[0mdiff_b_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msubtract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4528\u001b[0m     \u001b[0;31m# asanyarray is a stop-gap until gh-13105\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4529\u001b[0m     \u001b[0mlerp_interpolation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdiff_b_a\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install factor-analyzer\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggo6W0h8-yTG",
        "outputId": "57c4473b-5ced-4216-82c3-605e48bf4449"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting factor-analyzer\n",
            "  Downloading factor_analyzer-0.5.0.tar.gz (42 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.5/42.5 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.5.3)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.11.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.23.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from factor-analyzer) (1.2.2)\n",
            "Collecting pre-commit (from factor-analyzer)\n",
            "  Downloading pre_commit-3.4.0-py2.py3-none-any.whl (203 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m203.7/203.7 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->factor-analyzer) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->factor-analyzer) (2023.3.post1)\n",
            "Collecting cfgv>=2.0.0 (from pre-commit->factor-analyzer)\n",
            "  Downloading cfgv-3.4.0-py2.py3-none-any.whl (7.2 kB)\n",
            "Collecting identify>=1.0.0 (from pre-commit->factor-analyzer)\n",
            "  Downloading identify-2.5.29-py2.py3-none-any.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.9/98.9 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting nodeenv>=0.11.1 (from pre-commit->factor-analyzer)\n",
            "  Downloading nodeenv-1.8.0-py2.py3-none-any.whl (22 kB)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from pre-commit->factor-analyzer) (6.0.1)\n",
            "Collecting virtualenv>=20.10.0 (from pre-commit->factor-analyzer)\n",
            "  Downloading virtualenv-20.24.5-py3-none-any.whl (3.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.7/3.7 MB\u001b[0m \u001b[31m57.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->factor-analyzer) (1.3.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->factor-analyzer) (3.2.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nodeenv>=0.11.1->pre-commit->factor-analyzer) (67.7.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->factor-analyzer) (1.16.0)\n",
            "Collecting distlib<1,>=0.3.7 (from virtualenv>=20.10.0->pre-commit->factor-analyzer)\n",
            "  Downloading distlib-0.3.7-py2.py3-none-any.whl (468 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.9/468.9 kB\u001b[0m \u001b[31m22.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock<4,>=3.12.2 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit->factor-analyzer) (3.12.2)\n",
            "Requirement already satisfied: platformdirs<4,>=3.9.1 in /usr/local/lib/python3.10/dist-packages (from virtualenv>=20.10.0->pre-commit->factor-analyzer) (3.10.0)\n",
            "Building wheels for collected packages: factor-analyzer\n",
            "  Building wheel for factor-analyzer (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for factor-analyzer: filename=factor_analyzer-0.5.0-py2.py3-none-any.whl size=42487 sha256=cf82df4e5e1d7be29901a7a78708365cdb263aeae527bc3cf84ca77b37351f36\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/a2/6c/26fb1addf1ce6c60a8cef8397f2999f0a1e6e2fcddc8abf33e\n",
            "Successfully built factor-analyzer\n",
            "Installing collected packages: distlib, virtualenv, nodeenv, identify, cfgv, pre-commit, factor-analyzer\n",
            "Successfully installed cfgv-3.4.0 distlib-0.3.7 factor-analyzer-0.5.0 identify-2.5.29 nodeenv-1.8.0 pre-commit-3.4.0 virtualenv-20.24.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from factor_analyzer import FactorAnalyzer\n",
        "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "# Assume you have loaded your data into the 'data' DataFrame\n",
        "\n",
        "\n",
        "#Specify the variables you want to include in the factor analysis\n",
        "selected_variables = [\n",
        "       'Возраст',\n",
        "       #'Пол',\n",
        "       'Интернет ответ',\n",
        "       'Интернет время',\n",
        "       'Time-test: Среднее 1 1',\n",
        "       #\"Time-test: Среднее время реакции, серия 1 (Часть 2)\",\n",
        "       \"Time-test: Среднее время реакции, серия 2 (Часть 1)\",\n",
        "       \"Time-test: Среднее время реакции, серия 2 (Часть 2)\",\n",
        "       #'Time-test: Проп 1 1',\n",
        "       #\"Time-test: Количество пропусков, серия 1 (Часть 2)\",\n",
        "       #\"Time-test: Количество пропусков, серия 2 (Часть 1)\",\n",
        "       #\"Time-test: Количество пропусков, серия 2 (Часть 2)\",\n",
        "       'Time-test: Ош 2 1',\n",
        "       \"Time-test: Количество ошибок, серия 2 (Часть 2) \",\n",
        "       \"WMem: Количество объектов, серия 1\",\n",
        "       #\"WMem: Количество объектов, серия 2\",\n",
        "       #\"WMem: Количество объектов, серия 3\",\n",
        "       'WMem: Время серии 1',\n",
        "       #'WMem: Время серии 2',\n",
        "       #'WMem: Время серии 3',\n",
        "       #'Время 1',\n",
        "       #'Время 2',\n",
        "       #'Время 3',\n",
        "       #'Время фиксации на области интереса (тексте)',\n",
        "       #'Длительность первой фиксации на области интереса',\n",
        "       #'Среднее время фиксации',\n",
        "       #'Все фиксации',\n",
        "       'Амплитуда саккады',\n",
        "       'Количество саккад',\n",
        "]  # Replace with the actual variable names you want to include\n",
        "\n",
        "\n",
        "selected_data = data[selected_variables]\n",
        "\n",
        "# Create a new DataFrame with only the selected variables\n",
        "#selected_data = data[selected_variables]\n",
        "\n",
        "# Specify the number of factors you want to extract\n",
        "num_factors = 4\n",
        "\n",
        "# Calculate the cumulative variance explained by factors\n",
        "cumulative_variance_explained = []\n",
        "for i in range(1, len(selected_variables) + 1):\n",
        "    factor_analyzer = FactorAnalyzer(rotation='geomin_obl', method='uls', n_factors=i)\n",
        "    factor_analyzer.fit(selected_data)\n",
        "    fa_var = factor_analyzer.get_factor_variance()\n",
        "    total_cum_variance = fa_var[-1][-1]  # Access the last element of the array\n",
        "    cumulative_variance_explained.append(total_cum_variance)\n",
        "\n",
        "# Print the cumulative variance explained by factors\n",
        "for i, variance_explained in enumerate(cumulative_variance_explained):\n",
        "    print(f\"Total {variance_explained * 100:.2f}% Cumulative Variance explained by the {i + 1} factors\")\n",
        "\n",
        "# Initialize the factor analyzer with the desired number of factors\n",
        "factor_analyzer = FactorAnalyzer(rotation='geomin_obl', n_factors=num_factors, method='uls')\n",
        "\n",
        "# Fit the model to your selected data\n",
        "factor_analyzer.fit(selected_data)\n",
        "\n",
        "# Get the factor loadings\n",
        "factor_loadings = factor_analyzer.loadings_\n",
        "\n",
        "# Create a pandas DataFrame from the factor loadings\n",
        "factor_loading_df = pd.DataFrame(factor_loadings, columns=[f'Factor {i+1}' for i in range(num_factors)], index=selected_data.columns)\n",
        "\n",
        "# Print the factor loadings table with grid lines using HTML\n",
        "print(\"Factor Loadings Table:\")\n",
        "display(HTML(factor_loading_df.to_html()))\n",
        "\n",
        "# Bartlett’s Test\n",
        "chi_square_value, p_value = calculate_bartlett_sphericity(selected_data)\n",
        "print(\"Bartlett’s Test:\")\n",
        "print(\"Chi-Square Value:\", chi_square_value)\n",
        "print(\"P-value:\", p_value)\n",
        "\n",
        "# Kaiser-Meyer-Olkin (KMO) Test\n",
        "kmo_all, kmo_model = calculate_kmo(selected_data)\n",
        "print(\"\\nKaiser-Meyer-Olkin (KMO) Test:\")\n",
        "print(\"KMO Overall:\", kmo_all)\n",
        "print(\"KMO Model:\", kmo_model)\n",
        "\n",
        "# Scree Plot\n",
        "ev, v = factor_analyzer.get_eigenvalues()\n",
        "plt.plot(range(1, len(ev) + 1), ev, marker='o')\n",
        "plt.title('Scree Plot')\n",
        "plt.xlabel('Factor')\n",
        "plt.ylabel('Eigenvalue')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "# Calculate basic descriptive statistics for the selected data\n",
        "descriptive_stats = selected_data.describe()\n",
        "\n",
        "# Calculate skewness for each variable\n",
        "skewness = selected_data.skew()\n",
        "\n",
        "# Calculate kurtosis for each variable\n",
        "kurtosis_value = selected_data.kurtosis()\n",
        "\n",
        "# Add skewness and kurtosis to the descriptive statistics DataFrame\n",
        "descriptive_stats.loc['Skewness'] = skewness\n",
        "descriptive_stats.loc['Kurtosis'] = kurtosis_value\n",
        "\n",
        "# Print the descriptive statistics\n",
        "print(\"Descriptive Statistics:\")\n",
        "display(HTML(descriptive_stats.to_html()))"
      ],
      "metadata": {
        "id": "OcOekVIS9rP2",
        "outputId": "8dad6e33-42ba-4421-954f-bcf074a62e33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/factor_analyzer/factor_analyzer.py:663: UserWarning: No rotation will be performed when the number of factors equals 1.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total 19.10% Cumulative Variance explained by the 1 factors\n",
            "Total 35.39% Cumulative Variance explained by the 2 factors\n",
            "Total 51.00% Cumulative Variance explained by the 3 factors\n",
            "Total 61.10% Cumulative Variance explained by the 4 factors\n",
            "Total 65.56% Cumulative Variance explained by the 5 factors\n",
            "Total 73.88% Cumulative Variance explained by the 6 factors\n",
            "Total 80.88% Cumulative Variance explained by the 7 factors\n",
            "Total 77.26% Cumulative Variance explained by the 8 factors\n",
            "Total 80.92% Cumulative Variance explained by the 9 factors\n",
            "Total 82.25% Cumulative Variance explained by the 10 factors\n",
            "Total 78.80% Cumulative Variance explained by the 11 factors\n",
            "Total 78.80% Cumulative Variance explained by the 12 factors\n",
            "Factor Loadings Table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Factor 1</th>\n",
              "      <th>Factor 2</th>\n",
              "      <th>Factor 3</th>\n",
              "      <th>Factor 4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Возраст</th>\n",
              "      <td>-0.582439</td>\n",
              "      <td>-0.644043</td>\n",
              "      <td>0.005252</td>\n",
              "      <td>0.014356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет ответ</th>\n",
              "      <td>-0.375109</td>\n",
              "      <td>-0.302193</td>\n",
              "      <td>0.044708</td>\n",
              "      <td>-0.137417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет время</th>\n",
              "      <td>0.036769</td>\n",
              "      <td>0.266536</td>\n",
              "      <td>-0.295140</td>\n",
              "      <td>0.174754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее 1 1</th>\n",
              "      <td>0.571161</td>\n",
              "      <td>0.163545</td>\n",
              "      <td>0.044291</td>\n",
              "      <td>0.031191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 1)</th>\n",
              "      <td>0.898346</td>\n",
              "      <td>-0.093132</td>\n",
              "      <td>0.008542</td>\n",
              "      <td>-0.012278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 2)</th>\n",
              "      <td>0.774424</td>\n",
              "      <td>-0.078790</td>\n",
              "      <td>0.009610</td>\n",
              "      <td>-0.038112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Ош 2 1</th>\n",
              "      <td>-0.069597</td>\n",
              "      <td>0.787396</td>\n",
              "      <td>-0.021793</td>\n",
              "      <td>-0.067370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество ошибок, серия 2 (Часть 2)</th>\n",
              "      <td>-0.034221</td>\n",
              "      <td>0.845673</td>\n",
              "      <td>0.178059</td>\n",
              "      <td>0.023725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Количество объектов, серия 1</th>\n",
              "      <td>0.083295</td>\n",
              "      <td>-0.020092</td>\n",
              "      <td>0.918415</td>\n",
              "      <td>0.053611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Время серии 1</th>\n",
              "      <td>-0.028791</td>\n",
              "      <td>0.042229</td>\n",
              "      <td>0.910341</td>\n",
              "      <td>-0.022679</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Амплитуда саккады</th>\n",
              "      <td>0.027424</td>\n",
              "      <td>-0.015964</td>\n",
              "      <td>-0.045832</td>\n",
              "      <td>-0.692638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Количество саккад</th>\n",
              "      <td>-0.003620</td>\n",
              "      <td>-0.021442</td>\n",
              "      <td>-0.001174</td>\n",
              "      <td>0.898775</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bartlett’s Test:\n",
            "Chi-Square Value: 248.711973221361\n",
            "P-value: 5.366167779299572e-23\n",
            "\n",
            "Kaiser-Meyer-Olkin (KMO) Test:\n",
            "KMO Overall: [0.55282764 0.47273222 0.55476803 0.72705816 0.62933491 0.59931682\n",
            " 0.61495735 0.60236613 0.57291131 0.54017368 0.44628443 0.50950324]\n",
            "KMO Model: 0.5737285144473036\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Descriptive Statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Возраст</th>\n",
              "      <th>Интернет ответ</th>\n",
              "      <th>Интернет время</th>\n",
              "      <th>Time-test: Среднее 1 1</th>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 1)</th>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 2)</th>\n",
              "      <th>Time-test: Ош 2 1</th>\n",
              "      <th>Time-test: Количество ошибок, серия 2 (Часть 2)</th>\n",
              "      <th>WMem: Количество объектов, серия 1</th>\n",
              "      <th>WMem: Время серии 1</th>\n",
              "      <th>Амплитуда саккады</th>\n",
              "      <th>Количество саккад</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>51.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.372549</td>\n",
              "      <td>0.392157</td>\n",
              "      <td>203.980392</td>\n",
              "      <td>300.241176</td>\n",
              "      <td>382.225490</td>\n",
              "      <td>393.700000</td>\n",
              "      <td>9.313725</td>\n",
              "      <td>8.470588</td>\n",
              "      <td>16.823529</td>\n",
              "      <td>111544.607843</td>\n",
              "      <td>5.265746</td>\n",
              "      <td>66.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.144395</td>\n",
              "      <td>0.493089</td>\n",
              "      <td>168.754791</td>\n",
              "      <td>27.678065</td>\n",
              "      <td>31.246919</td>\n",
              "      <td>41.526681</td>\n",
              "      <td>3.921684</td>\n",
              "      <td>4.428783</td>\n",
              "      <td>6.710308</td>\n",
              "      <td>60923.535099</td>\n",
              "      <td>1.845311</td>\n",
              "      <td>37.282525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>254.800000</td>\n",
              "      <td>319.600000</td>\n",
              "      <td>326.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>17152.000000</td>\n",
              "      <td>2.314006</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>11.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>278.000000</td>\n",
              "      <td>360.550000</td>\n",
              "      <td>360.200000</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>5.500000</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>70369.000000</td>\n",
              "      <td>3.771148</td>\n",
              "      <td>38.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>162.000000</td>\n",
              "      <td>292.900000</td>\n",
              "      <td>384.400000</td>\n",
              "      <td>393.800000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>103480.000000</td>\n",
              "      <td>4.871253</td>\n",
              "      <td>57.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>297.500000</td>\n",
              "      <td>314.350000</td>\n",
              "      <td>395.300000</td>\n",
              "      <td>417.550000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>10.500000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>158726.500000</td>\n",
              "      <td>6.622784</td>\n",
              "      <td>83.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>552.000000</td>\n",
              "      <td>366.900000</td>\n",
              "      <td>465.500000</td>\n",
              "      <td>492.700000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>263719.000000</td>\n",
              "      <td>10.093942</td>\n",
              "      <td>166.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skewness</th>\n",
              "      <td>-0.203941</td>\n",
              "      <td>0.455272</td>\n",
              "      <td>0.885478</td>\n",
              "      <td>0.650838</td>\n",
              "      <td>0.565332</td>\n",
              "      <td>0.557238</td>\n",
              "      <td>0.401279</td>\n",
              "      <td>0.444801</td>\n",
              "      <td>-0.201636</td>\n",
              "      <td>0.455515</td>\n",
              "      <td>0.536155</td>\n",
              "      <td>0.958137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>-0.991309</td>\n",
              "      <td>-1.867594</td>\n",
              "      <td>-0.531489</td>\n",
              "      <td>-0.412404</td>\n",
              "      <td>0.250981</td>\n",
              "      <td>-0.110622</td>\n",
              "      <td>-0.112308</td>\n",
              "      <td>-0.447082</td>\n",
              "      <td>-0.919614</td>\n",
              "      <td>-0.553959</td>\n",
              "      <td>-0.373633</td>\n",
              "      <td>0.549311</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Communalities from factor analysis results\n",
        "communalities = factor_analyzer.get_communalities()\n",
        "communalities_df = pd.DataFrame(communalities, columns=[\"Communalities\"], index=selected_data.columns)\n",
        "\n",
        "# Print the Communalities table with grid lines using HTML\n",
        "print(\"Communalities Table:\")\n",
        "display(HTML(communalities_df.to_html()))"
      ],
      "metadata": {
        "id": "sYZkfYIVuXeT",
        "outputId": "6ce76519-4322-4e4d-96fc-77056aba95ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Communalities Table:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Communalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Возраст</th>\n",
              "      <td>0.754778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет ответ</th>\n",
              "      <td>0.251979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет время</th>\n",
              "      <td>0.194003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее 1 1</th>\n",
              "      <td>0.360601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 1)</th>\n",
              "      <td>0.818766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 2)</th>\n",
              "      <td>0.616977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Ош 2 1</th>\n",
              "      <td>0.634938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество ошибок, серия 2 (Часть 2)</th>\n",
              "      <td>0.748996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Количество объектов, серия 1</th>\n",
              "      <td>0.859979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Время серии 1</th>\n",
              "      <td>0.827213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Все фиксации</th>\n",
              "      <td>0.985820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Амплитуда саккады</th>\n",
              "      <td>0.366147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Количество саккад</th>\n",
              "      <td>0.999833</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Define a function to calculate Cronbach's Alpha\n",
        "def cronbach_alpha(items):\n",
        "    num_items = items.shape[1]\n",
        "\n",
        "    # Calculate the covariance matrix\n",
        "    cov_matrix = np.cov(items, rowvar=False)\n",
        "\n",
        "    # Calculate the mean for each item\n",
        "    item_means = np.mean(items, axis=0)\n",
        "\n",
        "    # Calculate the overall variance of the scale\n",
        "    scale_variance = np.var(items)\n",
        "\n",
        "    # Calculate Cronbach's Alpha\n",
        "    alpha = (num_items / (num_items - 1)) * (1 - (scale_variance / cov_matrix.sum()))\n",
        "\n",
        "    return alpha\n",
        "\n",
        "# Calculate Cronbach's Alpha for all selected variables\n",
        "cronbach_alpha_value = cronbach_alpha(selected_data.to_numpy())\n",
        "\n",
        "# Print the Cronbach's Alpha value for all selected variables\n",
        "print(\"Cronbach's Alpha for Selected Variables (as a scale):\", cronbach_alpha_value)"
      ],
      "metadata": {
        "id": "53vC-oJa14f8",
        "outputId": "feb3009b-d7ee-45b6-cafa-733b2c672be3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cronbach's Alpha for Selected Variables (as a scale): 0.9575298161193287\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Define a function to calculate Cronbach's Alpha\n",
        "def cronbach_alpha(items):\n",
        "    num_items = items.shape[1]\n",
        "\n",
        "    # Calculate the covariance matrix\n",
        "    cov_matrix = np.cov(items, rowvar=False)\n",
        "\n",
        "    # Calculate the mean for each item\n",
        "    item_means = np.mean(items, axis=0)\n",
        "\n",
        "    # Calculate the overall variance of the scale\n",
        "    scale_variance = np.var(items)\n",
        "\n",
        "    # Calculate Cronbach's Alpha\n",
        "    alpha = (num_items / (num_items - 1)) * (1 - (scale_variance / cov_matrix.sum()))\n",
        "\n",
        "    return alpha\n",
        "\n",
        "# Initialize a dictionary to store results\n",
        "alpha_values = {}\n",
        "\n",
        "# Calculate Cronbach's Alpha for all selected variables\n",
        "cronbach_alpha_all = cronbach_alpha(selected_data.to_numpy())\n",
        "\n",
        "# Iterate over each variable, temporarily removing it and calculating Cronbach's Alpha\n",
        "for column in selected_data.columns:\n",
        "    # Create a copy of the data with the current variable removed\n",
        "    data_without_variable = selected_data.drop(columns=[column]).to_numpy()\n",
        "\n",
        "    # Calculate Cronbach's Alpha without the current variable\n",
        "    cronbach_alpha_without_variable = cronbach_alpha(data_without_variable)\n",
        "\n",
        "    # Store the result in the dictionary\n",
        "    alpha_values[column] = cronbach_alpha_without_variable\n",
        "\n",
        "# Create a DataFrame to display the Cronbach's Alpha values if each item is deleted\n",
        "alpha_table = pd.DataFrame.from_dict(alpha_values, orient='index', columns=['Cronbach Alpha if Item Deleted'])\n",
        "\n",
        "# Print the table\n",
        "print(\"Cronbach's Alpha if Item Deleted:\")\n",
        "display(alpha_table)"
      ],
      "metadata": {
        "id": "zELStqQR-ab5",
        "outputId": "ae78ee8b-e0f9-4db7-d76b-0efc5c5eef67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cronbach's Alpha if Item Deleted:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                    Cronbach Alpha if Item Deleted\n",
              "Возраст                                                                   0.957709\n",
              "Пол                                                                       0.957710\n",
              "Интернет ответ                                                            0.957710\n",
              "Интернет время                                                            0.957718\n",
              "Time-test: Среднее 1 1                                                    0.957693\n",
              "Time-test: Среднее время реакции, серия 1 (Част...                        0.957693\n",
              "Time-test: Среднее время реакции, серия 2 (Част...                        0.957696\n",
              "Time-test: Среднее время реакции, серия 2 (Част...                        0.957694\n",
              "Time-test: Проп 1 1                                                       0.957710\n",
              "Time-test: Количество пропусков, серия 1 (Часть 2)                        0.957710\n",
              "Time-test: Количество пропусков, серия 2 (Часть 1)                        0.957710\n",
              "Time-test: Количество пропусков, серия 2 (Часть 2)                        0.957709\n",
              "Time-test: Ош 2 1                                                         0.957709\n",
              "Time-test: Количество ошибок, серия 2 (Часть 2)                           0.957709\n",
              "WMem: Количество объектов, серия 1                                        0.957706\n",
              "WMem: Количество объектов, серия 2                                        0.957705\n",
              "WMem: Количество объектов, серия 3                                        0.957704\n",
              "WMem: Время серии 1                                                       0.980652\n",
              "WMem: Время серии 2                                                       0.931082\n",
              "WMem: Время серии 3                                                       0.930707\n",
              "Время 1                                                                   0.955849\n",
              "Время 2                                                                   0.955776\n",
              "Время 3                                                                   0.955782\n",
              "Время фиксации на области интереса (тексте)                               0.957709\n",
              "Длительность первой фиксации на области интереса                          0.957710\n",
              "Среднее время фиксации                                                    0.957710\n",
              "Все фиксации                                                              0.957720\n",
              "Амплитуда саккады                                                         0.957710\n",
              "Количество саккад                                                         0.957720"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-574374ea-3503-438c-afc9-0fdf7beed009\" 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>Cronbach Alpha if Item Deleted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Возраст</th>\n",
              "      <td>0.957709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Пол</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет ответ</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Интернет время</th>\n",
              "      <td>0.957718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее 1 1</th>\n",
              "      <td>0.957693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 1 (Часть 2)</th>\n",
              "      <td>0.957693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 1)</th>\n",
              "      <td>0.957696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Среднее время реакции, серия 2 (Часть 2)</th>\n",
              "      <td>0.957694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Проп 1 1</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество пропусков, серия 1 (Часть 2)</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество пропусков, серия 2 (Часть 1)</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество пропусков, серия 2 (Часть 2)</th>\n",
              "      <td>0.957709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Ош 2 1</th>\n",
              "      <td>0.957709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time-test: Количество ошибок, серия 2 (Часть 2)</th>\n",
              "      <td>0.957709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Количество объектов, серия 1</th>\n",
              "      <td>0.957706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Количество объектов, серия 2</th>\n",
              "      <td>0.957705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Количество объектов, серия 3</th>\n",
              "      <td>0.957704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Время серии 1</th>\n",
              "      <td>0.980652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Время серии 2</th>\n",
              "      <td>0.931082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WMem: Время серии 3</th>\n",
              "      <td>0.930707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Время 1</th>\n",
              "      <td>0.955849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Время 2</th>\n",
              "      <td>0.955776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Время 3</th>\n",
              "      <td>0.955782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Время фиксации на области интереса (тексте)</th>\n",
              "      <td>0.957709</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Длительность первой фиксации на области интереса</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Среднее время фиксации</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Все фиксации</th>\n",
              "      <td>0.957720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Амплитуда саккады</th>\n",
              "      <td>0.957710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Количество саккад</th>\n",
              "      <td>0.957720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-574374ea-3503-438c-afc9-0fdf7beed009')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-574374ea-3503-438c-afc9-0fdf7beed009 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-574374ea-3503-438c-afc9-0fdf7beed009');\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-f3b4cdc7-4627-420f-b5e0-245931048b35\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f3b4cdc7-4627-420f-b5e0-245931048b35')\"\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",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f3b4cdc7-4627-420f-b5e0-245931048b35 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}