sklearn-KMeans.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "KR_UYRMQSxEb"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import io"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Task 1: Extract the dataset using panda / read the dataset\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "QVs16BUFTJZo",
        "outputId": "889f10d2-be4f-44c1-d9ed-5e7280bfc39d"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-02528e0f-82c8-4f47-b99c-eb40171efe48\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-02528e0f-82c8-4f47-b99c-eb40171efe48\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving College_K_Means.csv to College_K_Means.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('College_K_Means.csv')"
      ],
      "metadata": {
        "id": "U_KOHt-6Te5w"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Label Encoding\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        "df['private']= label_encoder.fit_transform(df['private'])\n",
        "df['private'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8GtwPsOU1cQ",
        "outputId": "6eb1017a-0dbe-41ad-8397-25649935a27f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Pre-processing\n",
        "# Check for missing values\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h29T8HCmVuPI",
        "outputId": "cc59f94f-ae28-4d91-c295-911a6b9a4688"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "private        0\n",
            "apps           0\n",
            "accept         0\n",
            "enroll         0\n",
            "top10perc      0\n",
            "top25perc      0\n",
            "f_undergrad    0\n",
            "p_undergrad    0\n",
            "outstate       0\n",
            "room_board     0\n",
            "books          0\n",
            "personal       0\n",
            "phd            0\n",
            "terminal       0\n",
            "s_f_ratio      0\n",
            "perc_alumni    0\n",
            "expend         0\n",
            "grad_rate      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss = []\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state=0)\n",
        "    kmeans.fit(df)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(range(1, 11), wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ntkYmx5wTnDG",
        "outputId": "49a6224e-845c-40cf-b3c8-2773e87690b7"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Training the K-means algorithm on the training dataset\n",
        "kmeans = KMeans(n_clusters=3, init='k-means++', random_state=0)\n",
        "y_kmeans = kmeans.fit_predict(df)"
      ],
      "metadata": {
        "id": "dzGTVpSmWSOB"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Visualizing the clusters\n",
        "plt.scatter(df[y_kmeans == 0, 0], df[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(df[y_kmeans == 1, 0], df[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(df[y_kmeans == 2, 0], df[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
        "plt.title('Clusters of colleges')\n",
        "plt.xlabel('Private')\n",
        "plt.ylabel('Apps')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hjabqIOTWa4f",
        "outputId": "29450504-a403-41d4-f7fd-70f1579ff6b0"
      },
      "execution_count": 21,
      "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-21-9cb4ccc4faed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 4. Visualizing the clusters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 1'\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      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'blue'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_kmeans\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'green'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cluster 3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_centers_\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[0mkmeans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcluster_centers_\u001b[0m\u001b[0;34m[\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[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'yellow'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Centroids'\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/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3456\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\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   3457\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3459\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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   3460\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\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/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3359\u001b[0m             \u001b[0mcasted_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3360\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3362\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3363\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: '(array([False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False,  True, False,  True, False, False, False,\n        True, False, False, False, False, False, False, False, False,\n       False, False, False,  True, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True, False,  True, False,\n       False, False, False, False, False, False,  True, False, False,\n       False, False, False, False, False, False,  True,  True, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False,  True, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False,  True, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False,  True, False, False, False,  True, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False,  True,\n       False, False, False, False,  True, False, False,  True, False,\n        True, False, False, False, False, False, False, False, False,\n        True, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n        True, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True,  True, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False,  True, False,\n       False, False, False, False,  True,  True,  True, False, False,\n       False, False, False, False, False, False, False, False, False,\n        True, False, False, False,  True, False, False, False, False,\n       False, False, False, False,  True, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False,  True, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False,  True, False, False,\n       False, False, False,  True, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False,  True, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False,  True,  True,  True,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n        True, False, False, False, False,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False,  True,  True,  True, False, False,  True,  True,\n       False, False,  True, False, False, False, False,  True,  True,\n       False,  True,  True,  True, False,  True, False,  True, False,\n       False, False, False,  True,  True, False, False,  True, False,\n       False,  True, False,  True, False, False, False, False,  True,\n       False,  True, False,  True,  True, False, False, False, False,\n        True, False, False, False,  True,  True, False,  True, False,\n       False,  True, False, False, False, False, False, False, False,\n        True,  True,  True, False, False, False, False, False,  True,\n        True,  True, False, False, False, False, False,  True, False,\n        True,  True, False, False, False, False, False,  True,  True,\n       False, False, False, False, False, False, False, False, False,\n        True, False,  True, False, False, False, False, False, False,\n       False, False, False, False, False, False, False,  True, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False,  True, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False, False, False, False, False, False, False,\n       False, False, False]), 0)' is an invalid key"
          ]
        }
      ]
    }
  ]
}