detmold_geodata_analysis.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4cbgwZWWfWpp"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAb77yZ9fzMG"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pafL7Li0jyXW"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "references:\n",
        "\n",
        "*   https://geopandas.org/en/stable/getting_started/introduction.html\n",
        "*   https://pyrosm.readthedocs.io/en/latest/basics.html\n",
        "\n"
      ],
      "metadata": {
        "id": "uQSQDi9h3geT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "important information:\n",
        "\n",
        "\n",
        "*   PBF is a recommended osm file extension mainly because it requires little memory size\n",
        "*   List item\n",
        "\n"
      ],
      "metadata": {
        "id": "3GbV8zoY7qaf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVWRa6Owyjvi",
        "outputId": "15099077-09a2-443a-a9da-827937910e77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyrosm\n",
            "  Downloading pyrosm-0.6.1.tar.gz (2.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1 MB 5.1 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting pygeos\n",
            "  Downloading pygeos-0.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1 MB 47.6 MB/s \n",
            "\u001b[?25hCollecting geopandas>=0.8.0\n",
            "  Downloading geopandas-0.10.2-py2.py3-none-any.whl (1.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0 MB 39.4 MB/s \n",
            "\u001b[?25hCollecting python-rapidjson\n",
            "  Downloading python_rapidjson-1.6-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 60.1 MB/s \n",
            "\u001b[?25hCollecting cykhash\n",
            "  Using cached cykhash-2.0.0-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: setuptools>=18.0 in /usr/local/lib/python3.7/dist-packages (from pyrosm) (57.4.0)\n",
            "Collecting pyrobuf\n",
            "  Using cached pyrobuf-0.9.3-cp37-cp37m-linux_x86_64.whl\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas>=0.8.0->pyrosm) (1.8.2)\n",
            "Collecting fiona>=1.8\n",
            "  Downloading Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl (16.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 16.7 MB 48.5 MB/s \n",
            "\u001b[?25hCollecting pyproj>=2.2.0\n",
            "  Downloading pyproj-3.2.1-cp37-cp37m-manylinux2010_x86_64.whl (6.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 6.3 MB 30.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas>=0.8.0->pyrosm) (1.3.5)\n",
            "Collecting cligj>=0.5\n",
            "  Downloading cligj-0.7.2-py3-none-any.whl (7.1 kB)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.8.0->pyrosm) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.8.0->pyrosm) (21.4.0)\n",
            "Collecting munch\n",
            "  Downloading munch-2.5.0-py2.py3-none-any.whl (10 kB)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.8.0->pyrosm) (7.1.2)\n",
            "Collecting click-plugins>=1.0\n",
            "  Downloading click_plugins-1.1.1-py2.py3-none-any.whl (7.5 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas>=0.8.0->pyrosm) (2022.6.15)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.8.0->pyrosm) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.8.0->pyrosm) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas>=0.8.0->pyrosm) (2022.1)\n",
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            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.8->pyrobuf->pyrosm) (2.0.1)\n",
            "Building wheels for collected packages: pyrosm\n",
            "  Building wheel for pyrosm (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyrosm: filename=pyrosm-0.6.1-cp37-cp37m-linux_x86_64.whl size=5430536 sha256=41acc4b8effbf28062b73515c8764456c3976595fd3f7d98b0e480cf81df1f0f\n",
            "  Stored in directory: /root/.cache/pip/wheels/e8/9e/d8/e424ffc5eaa71ccb122bb79d92ef75802c49b9b0a9366d6da4\n",
            "Successfully built pyrosm\n",
            "Installing collected packages: munch, cligj, click-plugins, pyproj, fiona, python-rapidjson, pyrobuf, pygeos, geopandas, cykhash, pyrosm\n",
            "Successfully installed click-plugins-1.1.1 cligj-0.7.2 cykhash-2.0.0 fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pygeos-0.12.0 pyproj-3.2.1 pyrobuf-0.9.3 pyrosm-0.6.1 python-rapidjson-1.6\n"
          ]
        }
      ],
      "source": [
        "!pip install pyrosm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "PvYqIwrGH6ng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the library\n",
        "import pyrosm\n",
        "\n",
        "# Print information about the basic usage of the `OSM` reader object\n",
        "help(pyrosm.OSM.__init__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHK57qlezlvv",
        "outputId": "5aba5cfd-141d-4ef4-d146-419575e741fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/geopandas/_compat.py:115: UserWarning: The Shapely GEOS version (3.10.2-CAPI-1.16.0) is incompatible with the GEOS version PyGEOS was compiled with (3.10.1-CAPI-1.16.0). Conversions between both will be slow.\n",
            "  shapely_geos_version, geos_capi_version_string\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function __init__ in module pyrosm.pyrosm:\n",
            "\n",
            "__init__(self, filepath, bounding_box=None)\n",
            "    Initialize self.  See help(type(self)) for accurate signature.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyrosm.data import sources\n",
        "\n",
        "# Print available source categories\n",
        "sources.available['subregions']['germany']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IK48EgmaYY0H",
        "outputId": "34e4839b-ee52-4ff7-f73a-62c72b6c6e4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['arnsberg_regbez',\n",
              " 'baden_wuerttemberg',\n",
              " 'bayern',\n",
              " 'berlin',\n",
              " 'brandenburg',\n",
              " 'bremen',\n",
              " 'detmold_regbez',\n",
              " 'duesseldorf_regbez',\n",
              " 'freiburg_regbez',\n",
              " 'hamburg',\n",
              " 'hessen',\n",
              " 'karlsruhe_regbez',\n",
              " 'koeln_regbez',\n",
              " 'mecklenburg_vorpommern',\n",
              " 'mittelfranken',\n",
              " 'muenster_regbez',\n",
              " 'niederbayern',\n",
              " 'niedersachsen',\n",
              " 'nordrhein_westfalen',\n",
              " 'oberbayern',\n",
              " 'oberfranken',\n",
              " 'oberpfalz',\n",
              " 'rheinland_pfalz',\n",
              " 'saarland',\n",
              " 'sachsen',\n",
              " 'sachsen_anhalt',\n",
              " 'schleswig_holstein',\n",
              " 'schwaben',\n",
              " 'stuttgart_regbez',\n",
              " 'thueringen',\n",
              " 'tuebingen_regbez',\n",
              " 'unterfranken']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4"
      ],
      "metadata": {
        "id": "oUWEPMF3EcaS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# read data from Geofabrik\n",
        "area_of_interest='detmold_regbez'#Frieder, please replace 'saarland' with 'nordrhein_westfalen'\n",
        "#tuebingen_regbez\n",
        "fp = pyrosm.get_data(area_of_interest)\n",
        "print(\"Filepath to test data:\", fp)\n",
        "\n",
        "# Initialize the OSM object \n",
        "osm = pyrosm.OSM(fp)\n",
        "\n",
        "# See the type\n",
        "print(\"Type of 'osm' instance: \", type(osm))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trTUbeaSgUnG",
        "outputId": "c8769d47-d06f-4066-ae80-b1e12de73bbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded Protobuf data 'detmold-regbez-latest.osm.pbf' (107.55 MB) to:\n",
            "'/tmp/pyrosm/detmold-regbez-latest.osm.pbf'\n",
            "Filepath to test data: /tmp/pyrosm/detmold-regbez-latest.osm.pbf\n",
            "Type of 'osm' instance:  <class 'pyrosm.pyrosm.OSM'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# read buildings"
      ],
      "metadata": {
        "id": "V6GqB5Uj8P1v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#the session crashed when using NRW,Baden Wurttemberg, Hessen, rheinland_pfalz\n",
        "#colab uses 12 GB RAM\n",
        "buildings = osm.get_buildings()\n"
      ],
      "metadata": {
        "id": "LMUpbYrn28Vn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.plot(figsize=(10,10))\n",
        "#xaxis=longitude, yaxis=latitude"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "fSPfUbyhDIqw",
        "outputId": "58934067-9774-4de0-beef-51be872f7549"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0b3b1e7d0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w50FVXZpDJba"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "ATVA-af6S4cd",
        "outputId": "9d7a92a1-fe77-4421-8628-f2941f9fcfc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   addr:city addr:country addr:full addr:housenumber addr:housename  \\\n",
              "0  Bielefeld           DE      None               26           None   \n",
              "1  Bielefeld           DE      None               50           None   \n",
              "2       None         None      None             None           None   \n",
              "3       None         None      None             None           None   \n",
              "4       None         None      None             None           None   \n",
              "\n",
              "  addr:postcode addr:place         addr:street email        name  ... source  \\\n",
              "0         33609       None        Am Stadtholz  None        None  ...   None   \n",
              "1         33604       None  Teutoburger Straße  None        None  ...   None   \n",
              "2          None       None                None  None  Parkhaus 1  ...   None   \n",
              "3          None       None                None  None  Parkhaus 3  ...   None   \n",
              "4          None       None                None  None  Parkhaus 2  ...   None   \n",
              "\n",
              "  start_date wikipedia       id   timestamp version  \\\n",
              "0       None      None  5037736  1644485463      24   \n",
              "1       None      None  5044976  1624737503      39   \n",
              "2       None      None  5740621  1637154217      24   \n",
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              "      <td>None</td>\n",
              "      <td>38396546436</td>\n",
              "      <td>1654885122</td>\n",
              "      <td>1</td>\n",
              "      <td>{\"leisure\":\"stadium\",\"roof\":\"no\",\"type\":\"multi...</td>\n",
              "      <td>relation</td>\n",
              "      <td>POLYGON ((8.34958 52.06350, 8.34952 52.06357, ...</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 42 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-86f6dc90-2f31-47ff-b5b9-08e3ef1a3f27')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-86f6dc90-2f31-47ff-b5b9-08e3ef1a3f27 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-86f6dc90-2f31-47ff-b5b9-08e3ef1a3f27');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOcZbj_WitxN",
        "outputId": "d2319474-3f3a-4010-9fee-caf8e782d157"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1105055, 42)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pigvTKGoXOh_",
        "outputId": "1679d774-94d9-4c81-e3f6-61fbc88cf734"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'geopandas.geodataframe.GeoDataFrame'>\n",
            "RangeIndex: 1105055 entries, 0 to 1105054\n",
            "Data columns (total 42 columns):\n",
            " #   Column              Non-Null Count    Dtype   \n",
            "---  ------              --------------    -----   \n",
            " 0   addr:city           517165 non-null   object  \n",
            " 1   addr:country        433459 non-null   object  \n",
            " 2   addr:full           2 non-null        object  \n",
            " 3   addr:housenumber    518280 non-null   object  \n",
            " 4   addr:housename      771 non-null      object  \n",
            " 5   addr:postcode       515058 non-null   object  \n",
            " 6   addr:place          429 non-null      object  \n",
            " 7   addr:street         519004 non-null   object  \n",
            " 8   email               424 non-null      object  \n",
            " 9   name                13077 non-null    object  \n",
            " 10  opening_hours       1475 non-null     object  \n",
            " 11  operator            2475 non-null     object  \n",
            " 12  phone               1460 non-null     object  \n",
            " 13  ref                 1130 non-null     object  \n",
            " 14  url                 62 non-null       object  \n",
            " 15  website             2962 non-null     object  \n",
            " 16  building            1105055 non-null  object  \n",
            " 17  amenity             5737 non-null     object  \n",
            " 18  building:flats      479 non-null      object  \n",
            " 19  building:levels     56462 non-null    object  \n",
            " 20  building:material   11006 non-null    object  \n",
            " 21  building:max_level  4 non-null        object  \n",
            " 22  building:min_level  74 non-null       object  \n",
            " 23  building:fireproof  1 non-null        object  \n",
            " 24  building:use        237 non-null      object  \n",
            " 25  craft               315 non-null      object  \n",
            " 26  height              5846 non-null     object  \n",
            " 27  internet_access     113 non-null      object  \n",
            " 28  landuse             45 non-null       object  \n",
            " 29  levels              4 non-null        object  \n",
            " 30  office              396 non-null      object  \n",
            " 31  shop                1909 non-null     object  \n",
            " 32  source              64155 non-null    object  \n",
            " 33  start_date          1119 non-null     object  \n",
            " 34  wikipedia           900 non-null      object  \n",
            " 35  id                  1105055 non-null  int64   \n",
            " 36  timestamp           1105055 non-null  int64   \n",
            " 37  version             1105055 non-null  int8    \n",
            " 38  tags                172495 non-null   object  \n",
            " 39  osm_type            1105055 non-null  object  \n",
            " 40  geometry            1105055 non-null  geometry\n",
            " 41  changeset           466 non-null      float64 \n",
            "dtypes: float64(1), geometry(1), int64(2), int8(1), object(37)\n",
            "memory usage: 346.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mgIkzAXIFnzx",
        "outputId": "f8b6f4a1-f0fd-49e6-9f8e-dba6bb81281f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['addr:city', 'addr:country', 'addr:full', 'addr:housenumber',\n",
              "       'addr:housename', 'addr:postcode', 'addr:place', 'addr:street', 'email',\n",
              "       'name', 'opening_hours', 'operator', 'phone', 'ref', 'url', 'website',\n",
              "       'building', 'amenity', 'building:flats', 'building:levels',\n",
              "       'building:material', 'building:max_level', 'building:min_level',\n",
              "       'building:fireproof', 'building:use', 'craft', 'height',\n",
              "       'internet_access', 'landuse', 'levels', 'office', 'shop', 'source',\n",
              "       'start_date', 'wikipedia', 'id', 'timestamp', 'version', 'tags',\n",
              "       'osm_type', 'geometry', 'changeset'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('columns without similar values:\\n')\n",
        "for col in buildings.columns:\n",
        "  if len(buildings[col].unique())==len(buildings):\n",
        "    print(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CA7X35OOEflG",
        "outputId": "70345af0-e411-4d41-dc38-b851e89d0b65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "columns without similar values:\n",
            "\n",
            "id\n",
            "geometry\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**every building has an identity: id&geometry**"
      ],
      "metadata": {
        "id": "T55V3RBxLhO4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import operator\n",
        "\n",
        "nan_per_column={col:len(buildings.loc[(buildings[col].isnull()) | (buildings[col].isna())])/len(buildings)*100 for col in buildings.columns}\n",
        "sorted(nan_per_column.items(), key=operator.itemgetter(1))\n",
        "#nan_per_column"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWmVbK0AFT1I",
        "outputId": "171df8e5-2a5e-4b10-d093-03e8b9673605"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('building', 0.0),\n",
              " ('id', 0.0),\n",
              " ('timestamp', 0.0),\n",
              " ('version', 0.0),\n",
              " ('osm_type', 0.0),\n",
              " ('geometry', 0.0),\n",
              " ('addr:street', 53.03364990882806),\n",
              " ('addr:housenumber', 53.09916700978684),\n",
              " ('addr:city', 53.200066964992686),\n",
              " ('addr:postcode', 53.39073620770007),\n",
              " ('addr:country', 60.77489355733425),\n",
              " ('tags', 84.39036971010493),\n",
              " ('source', 94.19440661324549),\n",
              " ('building:levels', 94.89057105754917),\n",
              " ('name', 98.81661998724044),\n",
              " ('building:material', 99.00403147354656),\n",
              " ('height', 99.4709765577279),\n",
              " ('amenity', 99.48084032016507),\n",
              " ('website', 99.73195904276257),\n",
              " ('operator', 99.77602924741302),\n",
              " ('shop', 99.82724841749958),\n",
              " ('opening_hours', 99.8665224807815),\n",
              " ('phone', 99.86787987928203),\n",
              " ('ref', 99.89774264629362),\n",
              " ('start_date', 99.89873807186068),\n",
              " ('wikipedia', 99.91855608996836),\n",
              " ('addr:housename', 99.9302297170729),\n",
              " ('building:flats', 99.9566537412165),\n",
              " ('changeset', 99.9578301532503),\n",
              " ('addr:place', 99.96117840288493),\n",
              " ('email', 99.96163086905176),\n",
              " ('office', 99.96416467958609),\n",
              " ('craft', 99.97149463148894),\n",
              " ('building:use', 99.97855310369167),\n",
              " ('internet_access', 99.98977426462936),\n",
              " ('building:min_level', 99.99330350073073),\n",
              " ('url', 99.99438941953116),\n",
              " ('landuse', 99.99592780449842),\n",
              " ('building:max_level', 99.99963802706652),\n",
              " ('levels', 99.99963802706652),\n",
              " ('addr:full', 99.99981901353327),\n",
              " ('building:fireproof', 99.99990950676663)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# columns without missing values"
      ],
      "metadata": {
        "id": "mOHcWmzuM0Ww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.building"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1m_GRFkKtOH",
        "outputId": "27fdefe8-041a-44a9-be61-4647b86711bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                 yes\n",
              "1            hospital\n",
              "2             parking\n",
              "3             parking\n",
              "4             parking\n",
              "              ...    \n",
              "1105050        office\n",
              "1105051    industrial\n",
              "1105052           yes\n",
              "1105053           yes\n",
              "1105054           yes\n",
              "Name: building, Length: 1105055, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['building'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BidTPr4OGdrU",
        "outputId": "32856099-5128-4030-f49c-602faa34d38b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['yes', 'hospital', 'parking', 'retail', 'commercial', 'school',\n",
              "       'civic', 'public', 'farm', 'supermarket', 'sports_hall', 'house',\n",
              "       'sports_centre', 'train_station', 'roof', 'chapel', 'church',\n",
              "       'industrial', 'kindergarten', 'service', 'warehouse',\n",
              "       'manufacture', 'theatre', 'fire_station', 'hotel', 'apartments',\n",
              "       'residential', 'office', 'detached', 'garage', 'cowshed',\n",
              "       'farm_auxiliary', 'university', 'greenhouse', 'community_centre',\n",
              "       'hangar', 'transportation', 'garages', 'carport', 'barn',\n",
              "       'dormitory', 'ruins', 'shed', 'hut', 'casino', 'government',\n",
              "       'auditorium', 'sty', 'riding_hall', 'terrace', 'college',\n",
              "       'semidetached_house', 'pavilion', 'monastery', 'parish_hall',\n",
              "       'transformer_tower', 'construction', 'bridge', 'boathouse',\n",
              "       'police', 'public_building', 'hall', 'cabin', 'stable',\n",
              "       'slurry_tank', 'digester', 'prefabricated', 'agricultural',\n",
              "       'signal_box', 'toilets', 'sedimentation_tank', 'shelter',\n",
              "       'collapsed', 'fermenter', 'shop', 'storage_tank', 'tower',\n",
              "       'glasshouse', 'bungalow', 'mosque', 'religious', 'destroyed',\n",
              "       'kiosk', 'cupola', 'townhall', 'res', 'electricity', 'synagogue',\n",
              "       'mausoleum', 'prison', 'E', 'silo', 'telecommunication',\n",
              "       'Orangerie', 'voliere', 'social_facility', 'allotment_house',\n",
              "       'shopping_centre', 'grandstand', 'kingdom_hall', 'pigsty',\n",
              "       'biogas', 'chimney', 'bakehouse', 'tent', 'container', 'monument',\n",
              "       'fermentation_tank', 'houseboat', 'historic', 'transformer_house',\n",
              "       'g', 'stadium', 'cathedral', 'static_caravan', 'castle', 'mensa',\n",
              "       'yes;apartments', 'house;yes', 'Studentenwohnheim', 'community',\n",
              "       'gasometer', 'gym', 'gs', 'kennel', 'tank', 'water_tower',\n",
              "       'historical', 'bunker', 'chicken_coop', 'guard_house', 'Spenge',\n",
              "       'ossuary', 'pigstow', 'basilica', 'gatehouse', 'conservatory',\n",
              "       'horseshed', 'part', 'pavillon', 'wayside_chapel', 'abandoned',\n",
              "       'wayside_shrine', 'nursing_home', 'new', 'depot', 'proposed',\n",
              "       'office;parking', 'crossing', 'no', 'aviary', 'loo', 'sauna',\n",
              "       'wall', 'fertmenter', 'elevator', 'manor_house', 'hostel', 'greee',\n",
              "       'shet', 'meadow', 'trolley_bay', 'substation'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(buildings.timestamp.unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uecjkGweKN4s",
        "outputId": "304e5683-90a6-4363-ca16-a7e45ceaf144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "238944"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.version.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "htg9WPBSLweF",
        "outputId": "289ef062-dbde-4651-f8c2-f71658740133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([24, 39, 23, 22, 21, 11, 10,  4,  5,  6, 29, 31, 19, 28, 13, 18,  3,\n",
              "       14,  7,  9,  2, 16, 15,  8, 20, 12,  1, 17, 25, 36, 26, 35, 27, 32,\n",
              "       34, 30, 41], dtype=int8)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**\"version\" of a building? what does it mean?**"
      ],
      "metadata": {
        "id": "-xhCg7IfMHVg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "buildings.osm_type.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1brCDegdMCZA",
        "outputId": "ee4d0bb4-587d-4c4c-e805-8ad39137c14c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['way', 'relation'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1Q5WOao_MYnu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# interesting columns with missing values"
      ],
      "metadata": {
        "id": "OjRpYmjLM7nh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['addr:street'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bPJhmx4rNBvN",
        "outputId": "9bbacd81-5be4-44ba-d49b-b217a20eb119"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Am Stadtholz', 'Teutoburger Straße', None, ..., 'Aluminiumstraße',\n",
              "       'Gerhard-Lüking-Straße', 'Brenken Garten'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of streets that exist in Detmold dataset\n",
        "len(buildings['addr:street'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qVIlHq6ONhK",
        "outputId": "4bfe35ed-abe4-44b6-9ebd-f4cdeca3169c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18521"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['addr:city'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ErddLBxwOYlW",
        "outputId": "dbb6b5d6-af38-405d-9e7e-8e182109a821"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bielefeld', None, 'Bad Oeynhausen', 'Paderborn',\n",
              "       'Werther (Westf.)', 'Herford', 'Schlangen', 'Bad Salzuflen',\n",
              "       'Gütersloh', 'Rietberg', 'Lichtenau', 'Lemgo', 'Delbrück',\n",
              "       'Minden', 'Beverungen', 'Preußisch Oldendorf', 'Lage', 'Löhne',\n",
              "       'Altenbeken', 'Espelkamp', 'Schloß Holte-Stukenbrock', 'Bünde',\n",
              "       'Stemwede', 'Detmold', 'Enger', 'Oerlinghausen', 'Hiddenhausen',\n",
              "       'Rheda-Wiedenbrück', 'Verl', 'Petershagen', 'Hüllhorst',\n",
              "       'Bad Driburg', 'Willebadessen', 'Spenge', 'Hövelhof',\n",
              "       'Rödinghausen', 'Warburg', 'Halle (Westf.)', 'Steinhagen',\n",
              "       'Borgholzhausen', 'Herzebrock-Clarholz', 'Leopoldshöhe',\n",
              "       'Schieder-Schwalenberg', 'Blomberg', 'Lügde', 'Brakel',\n",
              "       'Steinheim', 'Horn-Bad Meinberg', 'Marienmünster', 'Extertal',\n",
              "       'Langenberg', 'Bad Pyrmont', 'Rinteln', 'Kalletal', 'Barntrup',\n",
              "       'Dörentrup', 'Lübbecke', 'Harsewinkel', 'Kirchlengern', 'Höxter',\n",
              "       'Hille', 'Bad Lippspringe', 'Marienfeld', 'Porta Westfalica',\n",
              "       'Bad Wünnenberg', 'Büren', 'Vlotho', 'Salzkotten', 'Borchen',\n",
              "       'Wagenfeld', 'Nieheim', 'Bückeburg', 'Melle', 'Rahden', 'Versmold',\n",
              "       'Lippstadt', 'Liebenau', 'Stemshorn', 'Borgentreich', 'Derental',\n",
              "       'Lauenförde', 'Bad Karlshafen', 'Warmsen', 'Sassenberg',\n",
              "       'Lemförde', 'Leese', 'Beelen', 'Wettesingen', 'Diemelstadt',\n",
              "       'Großenheerse', 'Aerzen', 'Bad Laer', 'Dissen am Teutoburger Wald',\n",
              "       'Augustdorf', 'Holzminden', 'Oelde', 'Wiedensahl',\n",
              "       'Preußisch-Oldendorf', 'Brockum', 'Stolzenau', 'Fürstenberg',\n",
              "       'Diepenau', 'Geseke', 'Marsberg', 'Wehrden', 'Breuna',\n",
              "       'Rehburg-Loccum', 'Brilon', 'Polle', 'Vahlbruch', 'Luhden',\n",
              "       'Boffzen', 'Wadersloh', 'Bad Essen', 'Brakel - Bellersen',\n",
              "       'Raddestorf', 'Sennelager', 'Quernheim', 'Meinbrexen',\n",
              "       'Reinerbeck'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['addr:postcode'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFK6tXEqOie1",
        "outputId": "fb500496-9d23-44b3-e6b3-95856a82c7d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['33609', '33604', None, '32547', '33100', '33613', '33824',\n",
              "       '32052', '33602', '33607', '33189', '32108', '33689', '33334',\n",
              "       '33397', '33332', '33330', '32051', '33165', '32657', '33129',\n",
              "       '32049', '32425', '33102', '33647', '37688', '32361', '32791',\n",
              "       '32584', '33184', '32105', '32339', '33605', '33615', '33758',\n",
              "       '32257', '32351', '33739', '32760', '32130', '33813', '33611',\n",
              "       '32120', '33378', '32758', '33415', '32469', '32609', '33014',\n",
              "       '34439', '32139', '33161', '32289', '34414', '33649', '32107',\n",
              "       '33790', '33803', '33699', '33829', '33442', '33818', '33719',\n",
              "       '32545', '33659', '33617', '32816', '32825', '33729', '32676',\n",
              "       '33034', '32839', '32805', '32423', '37696', '32699', '33619',\n",
              "       '33449', '31812', '31737', '32689', '32683', '32694', '32312',\n",
              "       '32756', '33428', '32278', '37671', '33104', '33098', '32479',\n",
              "       '32549', '33175', '32427', '32429', '32457', '33181', '33106',\n",
              "       '33142', '32602', '33154', '33178', '49419', '33039', '31675',\n",
              "       '49328', '49326', '32369', '33775', '59558', '34396', '59556',\n",
              "       '49448', '33335', '34434', '33333', '37691', '37697', '34385',\n",
              "       '31606', '48336', '31633', '48361', '34479', '34474', '31855',\n",
              "       '49196', '49201', '32832', '37603', '59302', '31719', '31592',\n",
              "       '37699', '31603', '34431', '31547', '59929', '37647', '31711',\n",
              "       '59329', '49152', '31604'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['addr:country'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Djenq04OuGk",
        "outputId": "aae4f93d-5776-401d-e0d1-b8808855a3b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['DE', None], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(buildings['tags'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X1JWxosO58w",
        "outputId": "9cc443fc-e997-41f8-e0a7-830f75994b1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13989"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "buildings['tags'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3h0GzgekPEHk",
        "outputId": "cc81aca6-7382-457d-c7d7-9e862e5b572e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([None,\n",
              "       '{\"building:colour\":\"#e0d4c3\",\"roof:colour\":\"#ba9a7e\",\"roof:shape\":\"flat\",\"wheelchair\":\"yes\"}',\n",
              "       '{\"access\":\"yes\",\"building:levels:underground\":\"1\",\"fee\":\"no\",\"level\":\"-1;1\",\"parking\":\"multi-storey\",\"wheelchair\":\"no\"}',\n",
              "       ...,\n",
              "       '{\"access\":\"employees\",\"parking\":\"multi-storey\",\"type\":\"multipolygon\"}',\n",
              "       '{\"bath:indoor\":\"yes\",\"bath:open_air\":\"yes\",\"bath:type\":\"thermal\",\"fee\":\"yes\",\"type\":\"multipolygon\"}',\n",
              "       '{\"leisure\":\"stadium\",\"roof\":\"no\",\"type\":\"multipolygon\"}'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#count the # of available house numbers per building type \n",
        "count_available_house_numbers=buildings.groupby(by='building').count()['addr:housenumber'].reset_index()\n",
        "count_available_house_numbers.rename(columns={'addr:housenumber':'number_of_available_house_numbers'},inplace=True)\n"
      ],
      "metadata": {
        "id": "7s3pZ5v5wy4Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#count the total number of houses per builing type\n",
        "count_available_house_numbers['number_of_houses']=\"\"\n",
        "for i in range(len(count_available_house_numbers)):\n",
        "  count_available_house_numbers['number_of_houses'].iloc[i]=len(buildings.loc[buildings['building']==count_available_house_numbers.building.iloc[i]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-kXIbFaHKN5",
        "outputId": "286af73b-6cea-44a6-9df6-d6931610e35f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n",
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_available_house_numbers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "HJ-NonHdEzPV",
        "outputId": "80960792-6bf3-4520-989c-0a3d9d875d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              building  number_of_available_house_numbers number_of_houses\n",
              "0                    E                                  1                1\n",
              "1            Orangerie                                  1                1\n",
              "2               Spenge                                  4                4\n",
              "3    Studentenwohnheim                                  2                2\n",
              "4            abandoned                                  0                1\n",
              "..                 ...                                ...              ...\n",
              "158        water_tower                                  0                1\n",
              "159     wayside_chapel                                  0                2\n",
              "160     wayside_shrine                                  1               27\n",
              "161                yes                             378443           857326\n",
              "162     yes;apartments                                  1                1\n",
              "\n",
              "[163 rows x 3 columns]"
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            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verify that the # of unique building types is 163 \n",
        "buildings.building.nunique()\n",
        "#done"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF6Loqad1eYe",
        "outputId": "15acbc1e-dbb8-463a-bd13-43b22a44b0db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "163"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def count_occurence_building_type(building_type):\n",
        "  return len(buildings.loc[buildings.building==building_type])\n",
        "\n",
        "#example\n",
        "count_occurence_building_type('yes')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uSu_rEZl31xF",
        "outputId": "b34dc40e-f606-479f-a196-57ac80b770c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "857326"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#build a new column\n",
        "count_available_house_numbers['percentage_available_values']=\"\""
      ],
      "metadata": {
        "id": "2VdRPug55lOh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#computes the % of available house numbers per building type\n",
        "for i in range(len(count_available_house_numbers)):\n",
        "  count_available_house_numbers['percentage_available_values'].iloc[i]=count_available_house_numbers['number_of_available_house_numbers'].iloc[i]/count_available_house_numbers['number_of_houses'].iloc[i]*100\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7-rheyl41PG",
        "outputId": "263a5b41-5534-4905-9626-7a6f2bcd492e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py:1732: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  self._setitem_single_block(indexer, value, name)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_available_house_numbers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ohy8n7ZG7aQK",
        "outputId": "b9cdaeef-655a-4548-8396-50ceddf62fc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              building  number_of_available_house_numbers number_of_houses  \\\n",
              "0                    E                                  1                1   \n",
              "1            Orangerie                                  1                1   \n",
              "2               Spenge                                  4                4   \n",
              "3    Studentenwohnheim                                  2                2   \n",
              "4            abandoned                                  0                1   \n",
              "..                 ...                                ...              ...   \n",
              "158        water_tower                                  0                1   \n",
              "159     wayside_chapel                                  0                2   \n",
              "160     wayside_shrine                                  1               27   \n",
              "161                yes                             378443           857326   \n",
              "162     yes;apartments                                  1                1   \n",
              "\n",
              "    percentage_available_values  \n",
              "0                         100.0  \n",
              "1                         100.0  \n",
              "2                         100.0  \n",
              "3                         100.0  \n",
              "4                           0.0  \n",
              "..                          ...  \n",
              "158                         0.0  \n",
              "159                         0.0  \n",
              "160                    3.703704  \n",
              "161                   44.142252  \n",
              "162                       100.0  \n",
              "\n",
              "[163 rows x 4 columns]"
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              "      <td>yes;apartments</td>\n",
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              "<p>163 rows × 4 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#verify that the total number of available house numbers is the same in both datasets\n",
        "count_available_house_numbers['number_of_available_house_numbers'].sum()==count_available_house_numbers['number_of_houses'].sum()-buildings['addr:housenumber'].isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzCYawfL-fBP",
        "outputId": "8d734e4a-1747-4177-9a43-25ed3df28477"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_available_house_numbers = count_available_house_numbers.astype({'number_of_available_house_numbers':'int',\n",
        "                                                                      'number_of_houses':'int',\n",
        "                                                                      'percentage_available_values':'float'})"
      ],
      "metadata": {
        "id": "5hYNia-8KPN-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "count_available_house_numbers.percentage_available_values.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtkrlhefLONi",
        "outputId": "87cf945d-683c-4667-9253-21e4392dbead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    163.000000\n",
              "mean      36.404933\n",
              "std       40.929385\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%       11.111111\n",
              "75%       80.000000\n",
              "max      100.000000\n",
              "Name: percentage_available_values, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_available_house_numbers.loc[count_available_house_numbers.percentage_available_values==100]\n",
        "#interesting: shopping center, telecom"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "uexAnFtDL-EL",
        "outputId": "131d82a5-0cea-40cb-a6df-221f8cf21762"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              building  number_of_available_house_numbers  number_of_houses  \\\n",
              "0                    E                                  1                 1   \n",
              "1            Orangerie                                  1                 1   \n",
              "2               Spenge                                  4                 4   \n",
              "3    Studentenwohnheim                                  2                 2   \n",
              "20              casino                                  1                 1   \n",
              "21              castle                                  2                 2   \n",
              "22           cathedral                                  1                 1   \n",
              "31           community                                  1                 1   \n",
              "40           destroyed                                  1                 1   \n",
              "65                hall                                  1                 1   \n",
              "68          historical                                  1                 1   \n",
              "71              hostel                                  1                 1   \n",
              "74           house;yes                                  1                 1   \n",
              "80        kingdom_hall                                  1                 1   \n",
              "83         manor_house                                  1                 1   \n",
              "90              mosque                                  7                 7   \n",
              "91                 new                                  4                 4   \n",
              "93        nursing_home                                  1                 1   \n",
              "96             ossuary                                  1                 1   \n",
              "97         parish_hall                                  2                 2   \n",
              "104             police                                  1                 1   \n",
              "109    public_building                                  2                 2   \n",
              "111                res                                  1                 1   \n",
              "126    shopping_centre                                  1                 1   \n",
              "140          synagogue                                  3                 3   \n",
              "142  telecommunication                                  1                 1   \n",
              "148           townhall                                  1                 1   \n",
              "162     yes;apartments                                  1                 1   \n",
              "\n",
              "     percentage_available_values  \n",
              "0                          100.0  \n",
              "1                          100.0  \n",
              "2                          100.0  \n",
              "3                          100.0  \n",
              "20                         100.0  \n",
              "21                         100.0  \n",
              "22                         100.0  \n",
              "31                         100.0  \n",
              "40                         100.0  \n",
              "65                         100.0  \n",
              "68                         100.0  \n",
              "71                         100.0  \n",
              "74                         100.0  \n",
              "80                         100.0  \n",
              "83                         100.0  \n",
              "90                         100.0  \n",
              "91                         100.0  \n",
              "93                         100.0  \n",
              "96                         100.0  \n",
              "97                         100.0  \n",
              "104                        100.0  \n",
              "109                        100.0  \n",
              "111                        100.0  \n",
              "126                        100.0  \n",
              "140                        100.0  \n",
              "142                        100.0  \n",
              "148                        100.0  \n",
              "162                        100.0  "
            ],
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              "      <th>1</th>\n",
              "      <td>Orangerie</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
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              "      <td>100.0</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
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              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>cathedral</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>community</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>destroyed</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>hall</td>\n",
              "      <td>1</td>\n",
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              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>historical</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>hostel</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>house;yes</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>kingdom_hall</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>manor_house</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>mosque</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>new</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>nursing_home</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>ossuary</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>parish_hall</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>police</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>public_building</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>res</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>shopping_centre</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>140</th>\n",
              "      <td>synagogue</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>telecommunication</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>townhall</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# % above the 3rd quartile\n",
        "count_available_house_numbers.loc[count_available_house_numbers.percentage_available_values>80]\n",
        "#interesting: apartments, detached, house, residential, semi detached house, terrace"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TjhWNjgUMQaj",
        "outputId": "46bc1f59-fe0c-4fd3-9b0b-9559e0fc150d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               building  number_of_available_house_numbers  number_of_houses  \\\n",
              "0                     E                                  1                 1   \n",
              "1             Orangerie                                  1                 1   \n",
              "2                Spenge                                  4                 4   \n",
              "3     Studentenwohnheim                                  2                 2   \n",
              "7            apartments                              13696             14672   \n",
              "20               casino                                  1                 1   \n",
              "21               castle                                  2                 2   \n",
              "22            cathedral                                  1                 1   \n",
              "29              college                                 20                24   \n",
              "31            community                                  1                 1   \n",
              "40            destroyed                                  1                 1   \n",
              "41             detached                              16867             18328   \n",
              "43            dormitory                                 98               108   \n",
              "65                 hall                                  1                 1   \n",
              "68           historical                                  1                 1   \n",
              "71               hostel                                  1                 1   \n",
              "73                house                              70324             74769   \n",
              "74            house;yes                                  1                 1   \n",
              "79         kindergarten                                402               498   \n",
              "80         kingdom_hall                                  1                 1   \n",
              "83          manor_house                                  1                 1   \n",
              "90               mosque                                  7                 7   \n",
              "91                  new                                  4                 4   \n",
              "93         nursing_home                                  1                 1   \n",
              "96              ossuary                                  1                 1   \n",
              "97          parish_hall                                  2                 2   \n",
              "104              police                                  1                 1   \n",
              "108              public                                323               394   \n",
              "109     public_building                                  2                 2   \n",
              "111                 res                                  1                 1   \n",
              "112         residential                              25351             26059   \n",
              "120  semidetached_house                               3227              3337   \n",
              "126     shopping_centre                                  1                 1   \n",
              "139         supermarket                                 47                53   \n",
              "140           synagogue                                  3                 3   \n",
              "142   telecommunication                                  1                 1   \n",
              "144             terrace                               2068              2226   \n",
              "148            townhall                                  1                 1   \n",
              "162      yes;apartments                                  1                 1   \n",
              "\n",
              "     percentage_available_values  \n",
              "0                     100.000000  \n",
              "1                     100.000000  \n",
              "2                     100.000000  \n",
              "3                     100.000000  \n",
              "7                      93.347874  \n",
              "20                    100.000000  \n",
              "21                    100.000000  \n",
              "22                    100.000000  \n",
              "29                     83.333333  \n",
              "31                    100.000000  \n",
              "40                    100.000000  \n",
              "41                     92.028590  \n",
              "43                     90.740741  \n",
              "65                    100.000000  \n",
              "68                    100.000000  \n",
              "71                    100.000000  \n",
              "73                     94.055023  \n",
              "74                    100.000000  \n",
              "79                     80.722892  \n",
              "80                    100.000000  \n",
              "83                    100.000000  \n",
              "90                    100.000000  \n",
              "91                    100.000000  \n",
              "93                    100.000000  \n",
              "96                    100.000000  \n",
              "97                    100.000000  \n",
              "104                   100.000000  \n",
              "108                    81.979695  \n",
              "109                   100.000000  \n",
              "111                   100.000000  \n",
              "112                    97.283088  \n",
              "120                    96.703626  \n",
              "126                   100.000000  \n",
              "139                    88.679245  \n",
              "140                   100.000000  \n",
              "142                   100.000000  \n",
              "144                    92.902066  \n",
              "148                   100.000000  \n",
              "162                   100.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22ea65df-25a0-4b84-830b-3fe1d279ce93\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>building</th>\n",
              "      <th>number_of_available_house_numbers</th>\n",
              "      <th>number_of_houses</th>\n",
              "      <th>percentage_available_values</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>E</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Orangerie</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spenge</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Studentenwohnheim</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>apartments</td>\n",
              "      <td>13696</td>\n",
              "      <td>14672</td>\n",
              "      <td>93.347874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>casino</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>castle</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>cathedral</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>college</td>\n",
              "      <td>20</td>\n",
              "      <td>24</td>\n",
              "      <td>83.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>community</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>destroyed</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>detached</td>\n",
              "      <td>16867</td>\n",
              "      <td>18328</td>\n",
              "      <td>92.028590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>dormitory</td>\n",
              "      <td>98</td>\n",
              "      <td>108</td>\n",
              "      <td>90.740741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>hall</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>historical</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>hostel</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>house</td>\n",
              "      <td>70324</td>\n",
              "      <td>74769</td>\n",
              "      <td>94.055023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>house;yes</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>kindergarten</td>\n",
              "      <td>402</td>\n",
              "      <td>498</td>\n",
              "      <td>80.722892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>kingdom_hall</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>manor_house</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>mosque</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
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              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>100.000000</td>\n",
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              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>nursing_home</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>ossuary</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>100.000000</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
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              "      <td>81.979695</td>\n",
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              "      <th>109</th>\n",
              "      <td>public_building</td>\n",
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              "      <td>100.000000</td>\n",
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              "    <tr>\n",
              "      <th>111</th>\n",
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              "      <td>100.000000</td>\n",
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              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>residential</td>\n",
              "      <td>25351</td>\n",
              "      <td>26059</td>\n",
              "      <td>97.283088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>semidetached_house</td>\n",
              "      <td>3227</td>\n",
              "      <td>3337</td>\n",
              "      <td>96.703626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126</th>\n",
              "      <td>shopping_centre</td>\n",
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              "      <td>100.000000</td>\n",
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              "    <tr>\n",
              "      <th>139</th>\n",
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              "      <th>144</th>\n",
              "      <td>terrace</td>\n",
              "      <td>2068</td>\n",
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              "    <tr>\n",
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              "      <th>162</th>\n",
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              "    [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-22ea65df-25a0-4b84-830b-3fe1d279ce93 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-22ea65df-25a0-4b84-830b-3fe1d279ce93');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#"
      ],
      "metadata": {
        "id": "6oHdko5EMe3T"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}