Pipe_Analysis_2.ipynb

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  "metadata": {
    "colab": {
      "name": "Pipe Analysis 2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO8/QfWtVEy9/DHmfpRtXMX",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pabloderen/BIMnferer/blob/master/Pipe_Analysis_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHjuBxluIVMr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "import urllib"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUK3RdE1uwiK",
        "colab_type": "text"
      },
      "source": [
        "We download from de API in Flasgger (http://3.14.88.102:5000/apidocs/) the Pipe list item and it´s parameters\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8evbTkGaURM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "jsonfile = urllib.request.urlopen(\"http://3.14.88.102:5000/api/1/elements/pipes/?limit=6000\").read().decode()\n",
        "j=json.loads(jsonfile)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xZAw2F-Mu2KX",
        "colab_type": "text"
      },
      "source": [
        "Then we make a Data Frame with the json file"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDuejJ1WaoY_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 210
        },
        "outputId": "1345cd54-ef42-46d0-f6b6-d36e59993077"
      },
      "source": [
        "df= pd.json_normalize(j)\n",
        "df.head(3)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>projectId</th>\n",
              "      <th>Category</th>\n",
              "      <th>modelId</th>\n",
              "      <th>_id.$oid</th>\n",
              "      <th>properties_Dimensions_Length</th>\n",
              "      <th>properties_Dimensions_Size</th>\n",
              "      <th>properties_Mechanical_Material</th>\n",
              "      <th>properties_Mechanical_System Classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a82</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a83</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b.c94f85e5-88fb-402c-bc60-72622f5f85b6</td>\n",
              "      <td>Pipes</td>\n",
              "      <td>d4ea3308-4b3f-d382-cacb-9791f772042b</td>\n",
              "      <td>5eee8651cb573f2fc2216a84</td>\n",
              "      <td>0.398 ft</td>\n",
              "      <td>2 1/2\"</td>\n",
              "      <td>Copper</td>\n",
              "      <td>Sanitary</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                projectId  ... properties_Mechanical_System Classification\n",
              "0  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "1  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                         NaN\n",
              "2  b.c94f85e5-88fb-402c-bc60-72622f5f85b6  ...                                    Sanitary\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_u3KTphyvClY",
        "colab_type": "text"
      },
      "source": [
        "We will drop the columns that have unuseful information for our case,\n",
        "like \"projectId\", \"modelId\" and \"_id.$oid\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoiSluyZa3aX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "values=[\"_id.$oid\",\"modelId\",\"projectId\"]\n",
        "df.drop(values, axis=1,inplace=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhPq5G4TvKTg",
        "colab_type": "text"
      },
      "source": [
        "we can see how many different materials we have, so then we will \n",
        "group by PVC, Copper and Steel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgAbL4g_bzz-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "e576c180-63dc-4518-d26c-bc05c17fdb6a"
      },
      "source": [
        "df[\"properties_Mechanical_Material\"].unique()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'Copper', 'Steel, Carbon', '<By Category>',\n",
              "       'Polyvinyl Chloride - Rigid', 'CP_PI - Copper - ASTM B88 H.D.',\n",
              "       'CP_PI - Steel - ASTM A53-B', 'CP_PI - PVC', 'Cast Iron',\n",
              "       'Black Steel', 'Carbon Steel', 'Ductile Iron',\n",
              "       'Polyvinyl Chloride, Rigid', 'Carbon Steel - ASTM A53 B'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgBVVzDvvX9G",
        "colab_type": "text"
      },
      "source": [
        "To make such a grouping, we define a function that search in the \"properties_Mechanical_Material\" and change each row that has another different name for the same material"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTHogQgzb2l2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def search(data):\n",
        "   \n",
        "    if \"A53\" in data.upper():\n",
        "        return \"Carbon Steel - ASTM A53 B\"\n",
        "    if \"CATEGORY\" in data.upper():\n",
        "        return \"NaN\"\n",
        "    if \"POLYVINYL\" in data.upper():\n",
        "        return \"PVC\"\n",
        "    if \"PVC\" in data.upper():\n",
        "        return \"PVC\"\n",
        "    if \"NAN\" in data.upper():\n",
        "        return \"NaN\"\n",
        "    else:\n",
        "        return data"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybOsaokEvXE9",
        "colab_type": "text"
      },
      "source": [
        "For practical purpuses, we will make a copy of the DataFrame, \n",
        "then apply the function in each row of the \"properties_Mechanical_Material\" column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plRdbEgthiWL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2=df\n",
        "df2[\"properties_Mechanical_Material\"]=df2[\"properties_Mechanical_Material\"].apply(lambda x: search(str(x)))\n"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wOwwSG9NvlL6",
        "colab_type": "text"
      },
      "source": [
        "here is the amount of each material kind"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT4kvHHviEZA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "116a184a-2880-4d25-b16f-3ce05641de3d"
      },
      "source": [
        "\n",
        "df2[\"properties_Mechanical_Material\"].value_counts()\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PVC                               1745\n",
              "Carbon Steel - ASTM A53 B         1322\n",
              "Carbon Steel                      1237\n",
              "Copper                            1102\n",
              "CP_PI - Copper - ASTM B88 H.D.     424\n",
              "Steel, Carbon                      121\n",
              "NaN                                 41\n",
              "Black Steel                          4\n",
              "Ductile Iron                         2\n",
              "Cast Iron                            2\n",
              "Name: properties_Mechanical_Material, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWwVmgDth6Z0",
        "colab_type": "text"
      },
      "source": [
        "Here we can see a graphic of the different amount of each materials:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPtZ6q2UZydL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "231a3ded-bca4-4f64-c96a-2c1ce6dc299a"
      },
      "source": [
        "plt.figure(figsize=(20,4))\n",
        "sns.countplot(y=\"properties_Mechanical_Material\",data=df2,order=df2[\"properties_Mechanical_Material\"].value_counts().index)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7002772f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYFuxyS8i3ww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "f83aae20-e8ca-48c8-9e13-d1b689de8f5b"
      },
      "source": [
        "df2[\"properties_Mechanical_System Classification\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sanitary               1499\n",
              "Hydronic Supply        1496\n",
              "Hydronic Return        1209\n",
              "Domestic Cold Water     716\n",
              "Vent                    417\n",
              "Domestic Hot Water      321\n",
              "Other                   187\n",
              "Fire Protection Dry     116\n",
              "Fire Protection Wet       2\n",
              "Name: properties_Mechanical_System Classification, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T3BhD3XMvrpr",
        "colab_type": "text"
      },
      "source": [
        "We drop the rows with properties_Dimensions_Length NaN values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M75QW1_8neCa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df3=df2.dropna(axis=0,subset=[\"properties_Dimensions_Length\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcNUU0BYvwqm",
        "colab_type": "text"
      },
      "source": [
        "Her we start to convert the properties_Dimensions_Length from string to float \n",
        "(we use x.replace(\" ft\",\"\") and x.replace(\"  ft\",\"\"), notice the spaces in each case)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNgt0y4mlQti",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "c7656e53-f359-4d27-a009-b0597bc6e021"
      },
      "source": [
        "df3[\"properties_Dimensions_Length\"]= df3[\"properties_Dimensions_Length\"].apply(lambda x: x.replace(\" ft\",\"\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kHkyOp41o4ai",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "15678848-a83e-4dbc-89b4-2563f398b46b"
      },
      "source": [
        "df3[\"properties_Dimensions_Length\"]= df3[\"properties_Dimensions_Length\"].apply(lambda x: float(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDSSSckRv-Nv",
        "colab_type": "text"
      },
      "source": [
        "we are analazing pipes, so we know that the dimensions Sizes are the diameter sizes. Then we can see that because a Size has the diameter simbol, creates another dimension. So we will take of the diameter simbol of all the column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGjqjizjo7Xx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "ee96a7ad-d55a-4b41-fa46-eee4f593b8b0"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].value_counts().head(5)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-fd65338211e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"properties_Dimensions_Size\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'df3' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPl-4Ya6qSIj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "ec1abf9c-af46-45a7-f7d5-96e9f9ce6c67"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"]= df3[\"properties_Dimensions_Size\"].apply(lambda x: x.replace(\"ø\",\"\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2na5JNpnwHjp",
        "colab_type": "text"
      },
      "source": [
        "Here we eliminate any leading and trailing whitespaces"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5oBxJ0zxsBcc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "4832cd50-9ebf-494a-b0a3-c57f7b2994e3"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"]= df3[\"properties_Dimensions_Size\"].apply(lambda x: x.strip())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cU7zWrMusOSS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "2ec3a6f5-d7fc-4a12-eb6f-d08a6c108ccb"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4\"        1070\n",
              "2\"         835\n",
              "3/4\"       727\n",
              "6\"         636\n",
              "3\"         592\n",
              "1\"         580\n",
              "2 1/2\"     316\n",
              "1 1/4\"     257\n",
              "1/2\"       241\n",
              "1 1/2\"     235\n",
              "16\"        146\n",
              "14\"         96\n",
              "8\"          84\n",
              "12\"         64\n",
              "10\"         60\n",
              "5\"          22\n",
              "1 3/8\"       2\n",
              "Name: properties_Dimensions_Size, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RC1L-PFYwRVk",
        "colab_type": "text"
      },
      "source": [
        "The \"properties_Mechanical_System Classification\" are 9 different types."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3k5FOAuKzDk-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lista=df3[\"properties_Mechanical_System Classification\"].unique()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_J6RFunyEKN",
        "colab_type": "text"
      },
      "source": [
        "Set a number designator to all properties_Mechanical_System Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6Kv2FqT4lPJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dicc={}\n",
        "i=0\n",
        "for i in range(0,9):\n",
        "  dicc[lista[i]]=i"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QiZ6DhR8yJjX",
        "colab_type": "text"
      },
      "source": [
        "we create a column with this designation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrHPPEAY7HLW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "outputId": "7fb79cf4-3ddc-409d-b472-6f72b1debdb4"
      },
      "source": [
        "df3[\"Number_Classification\"]=df3[\"properties_Mechanical_System Classification\"].apply(lambda x: dicc[x])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\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",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t7saSN21yTX-",
        "colab_type": "text"
      },
      "source": [
        "Then we get the dummies properties_Dimensions_Size and properties_Mechanical_Material"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AmyPgZCRAvew",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "379a7822-9240-4386-c86c-bbc338836afc"
      },
      "source": [
        "df3[\"properties_Dimensions_Size\"].nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ycVwcKjCgvR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "material= pd.get_dummies(df3['properties_Mechanical_Material'])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqO2WjXXC68e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Dimensions_Size= pd.get_dummies(df3[\"properties_Dimensions_Size\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDwJ9-vADJn-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df4 = pd.concat([df3,material,Dimensions_Size],axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Km2r9PUNyahz",
        "colab_type": "text"
      },
      "source": [
        "We eliminate some columns that we will not use (also can been seen the \"properties Dimensions Length\", just to try)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZk1FdqEDb71",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "droplist=[\"Category\",\"NaN\",\"properties_Mechanical_System Classification\",\"properties_Mechanical_Material\",\"properties_Dimensions_Size\",\"properties_Dimensions_Length\"]\n",
        "df5=df4.drop(droplist, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stq6cB-oDdfN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "1ee65fb2-b02b-4a8b-d86d-a652755f7ad9"
      },
      "source": [
        "df5.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number_Classification</th>\n",
              "      <th>Black Steel</th>\n",
              "      <th>CP_PI - Copper - ASTM B88 H.D.</th>\n",
              "      <th>Carbon Steel</th>\n",
              "      <th>Carbon Steel - ASTM A53 B</th>\n",
              "      <th>Cast Iron</th>\n",
              "      <th>Copper</th>\n",
              "      <th>Ductile Iron</th>\n",
              "      <th>PVC</th>\n",
              "      <th>Steel, Carbon</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Number_Classification  Black Steel  ...  6\"  8\"\n",
              "2                      0            0  ...   0   0\n",
              "4                      0            0  ...   0   0\n",
              "5                      0            0  ...   0   0\n",
              "6                      0            0  ...   0   0\n",
              "7                      1            0  ...   0   0\n",
              "\n",
              "[5 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W96izbzx0jOk",
        "colab_type": "text"
      },
      "source": [
        "Here we define the size of our test size."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5QHgzA_E6uo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X = df5.drop('Number_Classification',axis=1)\n",
        "y = df5['Number_Classification']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.95, random_state=101)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xInldBn0zXE",
        "colab_type": "text"
      },
      "source": [
        "here we train our Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ByAQB0N0GTun",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "563429b9-0654-4613-9a2e-96ebe6c9af9d"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier(n_estimators=200)\n",
        "rfc.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YN89tZcuGqGP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "5e85b09a-7727-4385-bf21-e478f59d5ff6"
      },
      "source": [
        "predictions = rfc.predict(X_test)\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.99      0.85      1420\n",
            "           1       0.00      0.00      0.00       182\n",
            "           2       0.49      0.54      0.51       682\n",
            "           3       0.85      0.07      0.13       390\n",
            "           4       0.50      0.66      0.57       304\n",
            "           5       0.59      0.58      0.58      1433\n",
            "           6       0.54      0.52      0.53      1138\n",
            "           7       0.00      0.00      0.00         2\n",
            "           8       1.00      1.00      1.00       114\n",
            "\n",
            "    accuracy                           0.62      5665\n",
            "   macro avg       0.52      0.48      0.46      5665\n",
            "weighted avg       0.61      0.62      0.59      5665\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRD3rOERGxMO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "2624c963-09d1-4cf0-f354-b3e019927bdf"
      },
      "source": [
        "print(confusion_matrix(y_test,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1400    0    6    5    0    7    2    0    0]\n",
            " [  24    0  112    0    0   32   14    0    0]\n",
            " [  43    0  369    0  201   67    2    0    0]\n",
            " [ 359    0    3   28    0    0    0    0    0]\n",
            " [   1    0   75    0  202   26    0    0    0]\n",
            " [   8    0  106    0    0  829  490    0    0]\n",
            " [  21    0   85    0    0  444  588    0    0]\n",
            " [   0    0    0    0    0    2    0    0    0]\n",
            " [   0    0    0    0    0    0    0    0  114]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vbJSioNZ1l8v",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbQCE_M6G8yH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "aa57b959-d046-4b25-9e7c-0ca4a004003c"
      },
      "source": [
        "dicc"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Domestic Cold Water': 2,\n",
              " 'Domestic Hot Water': 4,\n",
              " 'Fire Protection Dry': 8,\n",
              " 'Fire Protection Wet': 7,\n",
              " 'Hydronic Return': 6,\n",
              " 'Hydronic Supply': 5,\n",
              " 'Other': 1,\n",
              " 'Sanitary': 0,\n",
              " 'Vent': 3}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kh3hXCHy1Hc3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}