waterpipe_breakage_data_analysis.ipynb

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      "source": [
        "## Data Analysis of potential factors affecting water pipe breakage"
      ],
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        "id": "LbVYdd3JTlPF"
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      "source": [
        "Using the dataset of water pipe and breakage history over the years,\n",
        "\n",
        "I have analyzed the dataset and made insights report on potential factors affecing the water pipes breakage.\n",
        "\n",
        "I have documented all processes and achievements in this notebook with following steps:\n",
        "\n",
        "- EDA\n",
        "- Data cleaning\n",
        "- Analysis findings #1\n",
        "- Data Analysis of numerical variables Part 1\n",
        "- Analysis findings #2\n",
        "- Data Analysis of numerical variables Part 2\n",
        "- Data Analysis of categorial variable\n",
        "- Analysis findings #3\n",
        "- Correlation\n",
        "- Conclusion\n"
      ],
      "metadata": {
        "id": "n768Q43fTurm"
      }
    },
    {
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      "source": [
        "Loading libraries"
      ],
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
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      "cell_type": "markdown",
      "source": [
        "Loading the dataset"
      ],
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        "id": "EiSsa1pxVM8h"
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      "source": [
        "df = pd.read_csv('pipe_data.csv')\n",
        "df.head()"
      ],
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          "height": 256
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              "       pipe_id  install_year material  diameter  break_year   env_dat_0  \\\n",
              "0  P0000000000          1985        D     20.32         NaN  623.542175   \n",
              "1  P0000000001          1934        C     15.24         NaN  644.830017   \n",
              "2  P0000000002          1904        C     20.32         NaN  604.570007   \n",
              "3  P0000000003          1979        D     15.24         NaN  573.424011   \n",
              "4  P0000000004          1987        D     20.32         NaN  583.429993   \n",
              "\n",
              "   env_dat_1  env_dat_2  env_dat_3   env_dat_4  ...  env_dat_135  env_dat_136  \\\n",
              "0  18.052843  14.161422  10.268579  159.662827  ...   554.479675    20.000000   \n",
              "1  17.790001  13.830000   9.870000  257.001221  ...   276.357605     9.200573   \n",
              "2  18.219999  14.350000  10.470000  225.165039  ...   236.882950     4.872639   \n",
              "3  17.671381  14.132636  10.586646  170.244339  ...   321.886658    12.353504   \n",
              "4  18.280001  14.350000  10.420000  171.034073  ...   758.841431    25.052542   \n",
              "\n",
              "   env_dat_137  env_dat_138  env_dat_139  env_dat_140  env_dat_141  \\\n",
              "0     0.000038   438.628143    27.000000  3085.786865     0.002728   \n",
              "1     0.304329   239.719299    32.571018  3188.292236     0.003291   \n",
              "2     0.182168   500.638367    28.978306  4542.257324     0.002928   \n",
              "3     0.480517   295.554504    41.983772  4521.720215     0.004242   \n",
              "4     0.021532   778.687256    25.842377  4978.718262     0.002611   \n",
              "\n",
              "   env_dat_142  env_dat_143  env_dat_144  \n",
              "0     0.311803          0.0          0.0  \n",
              "1     0.322161          0.0          0.0  \n",
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              "      <th>pipe_id</th>\n",
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        "df.shape"
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              "(25115, 150)"
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      "source": [
        "df['pipe_id'].nunique()"
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      "execution_count": 4,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24581"
            ]
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "markdown",
      "source": [
        "**Column Description**\n",
        "\n",
        "*  pipe_id - Unique ID for each pipe\n",
        "\n",
        "*  install_year - Year when the pipe was installed\n",
        "*  break_year - Year when the pipe was broken\n",
        "*  material - Material of the pipe\n",
        "*  diameter - Diameter of the pipe\n",
        "\n",
        "\n",
        "*  env_dat_xxx - Environments around the pipe; utilization of these columns are excluded from this report as column details were not found with the dataset"
      ],
      "metadata": {
        "id": "Buh8-GNlWCVq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "DAlOA7OPYXKw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First of all, I will start with removing unnecessary columns for analysis as:\n",
        "\n",
        "- env_dat_xxx columns are not utilized in this data anaysis"
      ],
      "metadata": {
        "id": "1RmxXvdTYbdO"
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      "source": [
        "df = df[df.columns[:5]]\n",
        "df.head()"
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        "outputId": "26055f47-5450-45c0-a8e3-b5dff7cf7733"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       pipe_id  install_year material  diameter  break_year\n",
              "0  P0000000000          1985        D     20.32         NaN\n",
              "1  P0000000001          1934        C     15.24         NaN\n",
              "2  P0000000002          1904        C     20.32         NaN\n",
              "3  P0000000003          1979        D     15.24         NaN\n",
              "4  P0000000004          1987        D     20.32         NaN"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pipe_id</th>\n",
              "      <th>install_year</th>\n",
              "      <th>material</th>\n",
              "      <th>diameter</th>\n",
              "      <th>break_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>P0000000000</td>\n",
              "      <td>1985</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
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              "      <td>15.24</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>P0000000002</td>\n",
              "      <td>1904</td>\n",
              "      <td>C</td>\n",
              "      <td>20.32</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>P0000000003</td>\n",
              "      <td>1979</td>\n",
              "      <td>D</td>\n",
              "      <td>15.24</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P0000000004</td>\n",
              "      <td>1987</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5BetTwXZRe2",
        "outputId": "81010065-63e3-4b66-d322-316c9e2dc0be"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(25115, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T6J4UmrHZcae",
        "outputId": "2e47ac80-51e3-4f55-c9d9-ce748a6b8f7b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 25115 entries, 0 to 25114\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   pipe_id       25115 non-null  object \n",
            " 1   install_year  25115 non-null  int64  \n",
            " 2   material      25115 non-null  object \n",
            " 3   diameter      24985 non-null  float64\n",
            " 4   break_year    1479 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(2)\n",
            "memory usage: 981.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quick basic EDA shows that dataset has data of 56774 records,\n",
        "\n",
        "after removing unnecessary columns for analysis there are 5 variables\n",
        "\n",
        "and they are all numerical variables except pipe_id and material"
      ],
      "metadata": {
        "id": "qh1B6iT5aFGU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Target variable**"
      ],
      "metadata": {
        "id": "JAUvMNF6bWIc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['break_year'].value_counts(dropna=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8HC5f29aCzM",
        "outputId": "d39257dc-3640-4967-ba4d-323d9aea493f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NaN       23636\n",
              "2015.0       75\n",
              "1990.0       66\n",
              "1988.0       62\n",
              "1993.0       57\n",
              "1982.0       57\n",
              "2008.0       53\n",
              "1984.0       48\n",
              "2016.0       48\n",
              "2004.0       47\n",
              "2013.0       45\n",
              "2009.0       45\n",
              "1983.0       44\n",
              "1989.0       43\n",
              "1987.0       43\n",
              "1986.0       42\n",
              "2003.0       39\n",
              "2014.0       38\n",
              "2011.0       38\n",
              "1981.0       37\n",
              "2007.0       37\n",
              "1992.0       37\n",
              "1991.0       36\n",
              "1980.0       36\n",
              "2010.0       35\n",
              "2012.0       35\n",
              "1985.0       34\n",
              "1995.0       34\n",
              "2001.0       34\n",
              "2006.0       32\n",
              "1994.0       30\n",
              "2005.0       30\n",
              "2002.0       26\n",
              "1998.0       26\n",
              "1997.0       24\n",
              "1996.0       22\n",
              "1999.0       22\n",
              "2000.0       22\n",
              "Name: break_year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['break_year'].agg(['max', 'min'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQvoRkhicIrF",
        "outputId": "375b5dfd-0628-42cc-9f07-8db9baf5cc99"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "max    2016.0\n",
              "min    1980.0\n",
              "Name: break_year, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this analysis the target variable is break_year, which has the year the pipe broke if the pipe has broken\n",
        "\n",
        "and the oldest year in the variable is 1980, the most recent is 2016\n",
        "\n",
        "If the break_year is not recorded, the pipe has no record of breakage\n",
        "\n",
        "and I will compare the number of pipe breakage with no breakage record in the dataset"
      ],
      "metadata": {
        "id": "5-8IZEObb8RF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.copy()\n",
        "data['break_year'] = data['break_year'].fillna(0)\n",
        "data['break_year'] = data['break_year'].apply(lambda x: 1 if x != 0 else 0)"
      ],
      "metadata": {
        "id": "ypDlwEH4job7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "break_year = data['break_year'].value_counts()\n",
        "break_year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_IM1XQ7ni2DK",
        "outputId": "faf481dd-96c9-4ac0-fa8d-3d145e66b8cb"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    23636\n",
              "1     1479\n",
              "Name: break_year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('NO breakage record', round((break_year[0]/len(data)*100),2), 'percent')\n",
        "print('record of breakage', round((break_year[1]/len(data)*100),2), 'percent')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YC8v5GC_m6da",
        "outputId": "ba3f93eb-fac6-49e1-d086-707ccff49c04"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NO breakage record 94.11 percent\n",
            "record of breakage 5.89 percent\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['break_year'].value_counts().plot.bar()\n",
        "plt.title('break_year')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "sRnyRB1tkYFt",
        "outputId": "5a372c68-1ed3-410e-9d4d-1c848ec5be97"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To look into the balance of break_year variable, meaning the ratio of pipe breakage and no breakage record,\n",
        "\n",
        "I made the variable categorial - 0 if no breakage record, 1 if record of pipe breakage\n",
        "\n",
        "As visualized in the bar plot, nearly 95% of the dataset is the data of no breakage and only about 5% is data of breakage\n",
        "\n",
        "This tells that the dataset is significantly imbalance as class 1, breakage records in this case have significantly low proportions compared to class 0"
      ],
      "metadata": {
        "id": "mHVGHaIyk0oh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Input variables**"
      ],
      "metadata": {
        "id": "wp_rXPH0uH3s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "pipe_id - categorical"
      ],
      "metadata": {
        "id": "ECdxUxW9uQA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['pipe_id'].nunique()"
      ],
      "metadata": {
        "id": "e4pkhYf9uTSc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "587874b3-fe80-45de-f360-444da14bd84a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24581"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "install_year - numerical and temporal"
      ],
      "metadata": {
        "id": "w3xmDmuiuxac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['install_year'].nunique()"
      ],
      "metadata": {
        "id": "AsLnValYvdT9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef4d4049-4d85-4fb4-90cb-9720bb954119"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "156"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "material - categorical"
      ],
      "metadata": {
        "id": "lfMDOb3UvfIj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['material'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kb4kcRQc0vVa",
        "outputId": "420ed209-83ed-4f02-b2da-ee7791caeec1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(x=df['material'], data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jOQvY-hSx_LX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "344e40d4-6923-44c9-bf07-c1543b252798"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RM3DgQCMwSVJsbKwKCwt18uRJo+bc/dTX1O/nQqqqqmS3250WAADQcrltaLLZbJKkoKAgp/GgoCBjzmazqWPHjk7znp6eCgwMdKq50DbO3cfFaurnL2TevHny9/c3lpCQkMs9RAAA0Iy4bWhydzNnzlRFRYWxFBcXu7olAABwBbltaAoODpYklZSUOI2XlJQYc8HBwSotLXWar62tVVlZmVPNhbZx7j4uVlM/fyHe3t7y8/NzWgAAQMvltqEpLCxMwcHBysrKMsbsdru2b9+u6OhoSVJ0dLTKy8uVl5dn1GRnZ6uurk79+/c3anJyclRTU2PUZGZmKjw8XO3atTNqzt1PfU39fgAAAFwamiorK5Wfn6/8/HxJ3z/8nZ+fr6KiIlksFk2bNk2/+c1vtG7dOu3bt08PPvigrFar8Q27yMhIDRs2TJMmTdKOHTu0detWJSUlaezYsbJarZKkcePGycvLSwkJCTpw4IDWrFmjJUuWaPr06UYfU6dOVUZGhhYsWKCDBw9qzpw52rVrl5KSkpr6lAAAADfl6cqd79q1S4MHDzbW64PMxIkTtXLlSj399NM6deqUJk+erPLyct1+++3KyMiQj4+P8Zm0tDQlJSVpyJAh8vDw0OjRo7V06VJj3t/fX5s2bVJiYqKioqLUoUMHJScnO73LacCAAVq9erVeeOEFPffcc7rpppuUnp6u7t27N8FZAAAAzYHbvKepueM9Tc0f72lqGrynCYA7aRHvaQIAAHAnhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATPB0dQMwp6ioSCdOnHB1Gy1aQUGBq1sAALgxQlMzUFRUpIiISJ0+/Z2rW7kq1FRVu7oFAIAbIjQ1AydOnNDp09+p/yOz5depi6vbabGO78vV/nXLVVtb6+pWAABuiNDUjPh16qLA0HBXt9Fi2Y8fcXULAAA3xoPgAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwIQGhaaf/vSn+uabb84bLy8v109/+tMf3RQAAIC7aVBoOnLkiM6ePXveeFVVlY4ePfqjmwIAAHA3l/Vyy3Xr1hn/3rhxo/z9/Y31s2fPKisrS126dGm05gAAANzFZYWmuLg4SZLFYtHEiROd5lq3bq0uXbpowYIFjdYcAACAu7is0FRXVydJCgsL086dO9WhQ4cr0hQAAIC7adDfnjt8+HBj9wEAAODWGvwHe7OyspSVlaXS0lLjClS9FStW/OjGAAAA3EmDQtOLL76ouXPnql+/furUqZMsFktj9wUAAOBWGhSaUlNTtXLlSk2YMKGx+wEAAHBLDXpPU3V1tQYMGNDYvQAAALitBoWmRx99VKtXr27sXgAAANxWg27PnTlzRsuXL9eHH36onj17qnXr1k7zCxcubJTmAAAA3EWDQtPevXvVu3dvSdL+/fud5ngoHAAAtEQNCk3//Oc/G7sPAAAAt9agZ5qaytmzZzVr1iyFhYXJ19dXN9xwg1566SU5HA6jxuFwKDk5WZ06dZKvr69iYmJ06NAhp+2UlZUpPj5efn5+CggIUEJCgiorK51q9u7dqzvuuEM+Pj4KCQnR/Pnzm+QYAQBA89CgK02DBw++5G247OzsBjd0rldeeUXLli3TqlWr1K1bN+3atUsPP/yw/P399cQTT0iS5s+fr6VLl2rVqlUKCwvTrFmzFBsbq88++0w+Pj6SpPj4eB0/flyZmZmqqanRww8/rMmTJxsPs9vtdg0dOlQxMTFKTU3Vvn379MgjjyggIECTJ09ulGMBAADNW4NCU/3zTPVqamqUn5+v/fv3n/eHfH+Mbdu2adSoURoxYoQkqUuXLvrzn/+sHTt2SPr+KtPixYv1wgsvaNSoUZKkt99+W0FBQUpPT9fYsWNVUFCgjIwM7dy5U/369ZMkvfbaaxo+fLh+97vfyWq1Ki0tTdXV1VqxYoW8vLzUrVs35efna+HChRcNTVVVVaqqqjLW7XZ7ox03AABwPw0KTYsWLbrg+Jw5c8677fVjDBgwQMuXL9fnn3+un/3sZ/r000/18ccfG9/OO3z4sGw2m2JiYozP+Pv7q3///srNzdXYsWOVm5urgIAAIzBJUkxMjDw8PLR9+3b993//t3JzczVw4EB5eXkZNbGxsXrllVd08uRJtWvX7rze5s2bpxdffLHRjhUAALi3Rn2mafz48Y36d+eeffZZjR07VhEREWrdurX69OmjadOmKT4+XpJks9kkSUFBQU6fCwoKMuZsNps6duzoNO/p6anAwECnmgtt49x9/KeZM2eqoqLCWIqLi3/k0QIAAHfW4D/YeyG5ubnGc0SN4d1331VaWppWr15t3DKbNm2arFZro94GbAhvb295e3u7tAcAANB0GhSa7rvvPqd1h8Oh48ePa9euXZo1a1ajNCZJM2bMMK42SVKPHj305Zdfat68eZo4caKCg4MlSSUlJerUqZPxuZKSEuO5q+DgYJWWljptt7a2VmVlZcbng4ODVVJS4lRTv15fAwAArm4Nuj3n7+/vtAQGBmrQoEHasGGDZs+e3WjNfffdd/LwcG6xVatWqqurkySFhYUpODhYWVlZxrzdbtf27dsVHR0tSYqOjlZ5ebny8vKMmuzsbNXV1al///5GTU5OjmpqaoyazMxMhYeHX/B5JgAAcPVp0JWmt956q7H7uKB7771XL7/8skJDQ9WtWzft2bNHCxcu1COPPCLp+7ePT5s2Tb/5zW900003Ga8csFqtiouLkyRFRkZq2LBhmjRpklJTU1VTU6OkpCSNHTtWVqtVkjRu3Di9+OKLSkhI0DPPPKP9+/dryZIlF33gHQAAXH1+1DNNeXl5KigokCR169ZNffr0aZSm6r322muaNWuWHn/8cZWWlspqteqXv/ylkpOTjZqnn35ap06d0uTJk1VeXq7bb79dGRkZTs9WpaWlKSkpSUOGDJGHh4dGjx6tpUuXGvP+/v7atGmTEhMTFRUVpQ4dOig5OZl3NAEAAEODQlNpaanGjh2rzZs3KyAgQJJUXl6uwYMH65133tF1113XKM21bdtWixcv1uLFiy9aY7FYNHfuXM2dO/eiNYGBgcaLLC+mZ8+e2rJlS0NbBQAALVyDnmmaMmWKvv32Wx04cEBlZWUqKyvT/v37ZbfbjTd1AwAAtCQNutKUkZGhDz/8UJGRkcZY165dlZKSoqFDhzZacwAAAO6iQVea6urq1Lp16/PGW7dubXyzDQAAoCVpUGi66667NHXqVB07dswYO3r0qJ588kkNGTKk0ZoDAABwFw0KTa+//rrsdru6dOmiG264QTfccIPCwsJkt9v12muvNXaPAAAALtegZ5pCQkK0e/duffjhhzp48KCk79+HdO4fzgUAAGhJLutKU3Z2trp27Sq73S6LxaK7775bU6ZM0ZQpU3TzzTerW7dufG0fAAC0SJcVmhYvXqxJkybJz8/vvDl/f3/98pe/1MKFCxutOQAAAHdxWaHp008/1bBhwy46P3ToUKe/8QYAANBSXFZoKikpueCrBup5enrq66+//tFNAQAAuJvLCk0/+clPtH///ovO7927V506dfrRTQEAALibywpNw4cP16xZs3TmzJnz5k6fPq3Zs2dr5MiRjdYcAACAu7isVw688MILeu+99/Szn/1MSUlJCg8PlyQdPHhQKSkpOnv2rJ5//vkr0igAAIArXVZoCgoK0rZt2/TYY49p5syZcjgckiSLxaLY2FilpKQoKCjoijQKAADgSpf9csvOnTtrw4YNOnnypL744gs5HA7ddNNNateu3ZXoDwAAwC006I3gktSuXTvdfPPNjdkLAACA22rQ354DAAC42hCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACY4Pah6ejRoxo/frzat28vX19f9ejRQ7t27TLmHQ6HkpOT1alTJ/n6+iomJkaHDh1y2kZZWZni4+Pl5+engIAAJSQkqLKy0qlm7969uuOOO+Tj46OQkBDNnz+/SY4PAAA0D24dmk6ePKnbbrtNrVu31j/+8Q999tlnWrBggdq1a2fUzJ8/X0uXLlVqaqq2b9+uNm3aKDY2VmfOnDFq4uPjdeDAAWVmZmr9+vXKycnR5MmTjXm73a6hQ4eqc+fOysvL06uvvqo5c+Zo+fLlTXq8AADAfXm6uoFLeeWVVxQSEqK33nrLGAsLCzP+7XA4tHjxYr3wwgsaNWqUJOntt99WUFCQ0tPTNXbsWBUUFCgjI0M7d+5Uv379JEmvvfaahg8frt/97neyWq1KS0tTdXW1VqxYIS8vL3Xr1k35+flauHChU7gCAABXL7e+0rRu3Tr169dPY8aMUceOHdWnTx+98cYbxvzhw4dls9kUExNjjPn7+6t///7Kzc2VJOXm5iogIMAITJIUExMjDw8Pbd++3agZOHCgvLy8jJrY2FgVFhbq5MmTF+ytqqpKdrvdaQEAAC2XW4emf//731q2bJluuukmbdy4UY899pieeOIJrVq1SpJks9kkSUFBQU6fCwoKMuZsNps6duzoNO/p6anAwECnmgtt49x9/Kd58+bJ39/fWEJCQn7k0QIAAHfm1qGprq5Offv21W9/+1v16dNHkydP1qRJk5Samurq1jRz5kxVVFQYS3FxsatbAgAAV5Bbh6ZOnTqpa9euTmORkZEqKiqSJAUHB0uSSkpKnGpKSkqMueDgYJWWljrN19bWqqyszKnmQts4dx//ydvbW35+fk4LAABoudw6NN12220qLCx0Gvv888/VuXNnSd8/FB4cHKysrCxj3m63a/v27YqOjpYkRUdHq7y8XHl5eUZNdna26urq1L9/f6MmJydHNTU1Rk1mZqbCw8OdvqkHAACuXm4dmp588kl98skn+u1vf6svvvhCq1ev1vLly5WYmChJslgsmjZtmn7zm99o3bp12rdvnx588EFZrVbFxcVJ+v7K1LBhwzRp0iTt2LFDW7duVVJSksaOHSur1SpJGjdunLy8vJSQkKADBw5ozZo1WrJkiaZPn+6qQwcAAG7GrV85cPPNN+v999/XzJkzNXfuXIWFhWnx4sWKj483ap5++mmdOnVKkydPVnl5uW6//XZlZGTIx8fHqElLS1NSUpKGDBkiDw8PjR49WkuXLjXm/f39tWnTJiUmJioqKkodOnRQcnIyrxsAAAAGtw5NkjRy5EiNHDnyovMWi0Vz587V3LlzL1oTGBio1atXX3I/PXv21JYtWxrcJwAAaNnc+vYcAACAuyA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADChWYWm//mf/5HFYtG0adOMsTNnzigxMVHt27fXtddeq9GjR6ukpMTpc0VFRRoxYoSuueYadezYUTNmzFBtba1TzebNm9W3b195e3vrxhtv1MqVK5vgiAAAQHPRbELTzp079b//+7/q2bOn0/iTTz6pv//971q7dq0++ugjHTt2TPfdd58xf/bsWY0YMULV1dXatm2bVq1apZUrVyo5OdmoOXz4sEaMGKHBgwcrPz9f06ZN06OPPqqNGzc22fEBAAD31ixCU2VlpeLj4/XGG2+oXbt2xnhFRYXefPNNLVy4UHfddZeioqL01ltvadu2bfrkk08kSZs2bdJnn32mP/3pT+rdu7fuuecevfTSS0pJSVF1dbUkKTU1VWFhYVqwYIEiIyOVlJSkn//851q0aJFLjhcAALifZhGaEhMTNWLECMXExDiN5+Xlqaamxmk8IiJCoaGhys3NlSTl5uaqR48eCgoKMmpiY2Nlt9t14MABo+Y/tx0bG2ts40Kqqqpkt9udFgAA0HJ5urqBH/LOO+9o9+7d2rlz53lzNptNXl5eCggIcBoPCgqSzWYzas4NTPXz9XOXqrHb7Tp9+rR8fX3P2/e8efP04osvNvi4AABA8+LWV5qKi4s1depUpaWlycfHx9XtOJk5c6YqKiqMpbi42NUtAQCAK8itQ1NeXp5KS0vVt29feXp6ytPTUx999JGWLl0qT09PBQUFqbq6WuXl5U6fKykpUXBwsCQpODj4vG/T1a//UI2fn98FrzJJkre3t/z8/JwWAADQcrl1aBoyZIj27dun/Px8Y+nXr5/i4+ONf7du3VpZWVnGZwoLC1VUVKTo6GhJUnR0tPbt26fS0lKjJjMzU35+furatatRc+426mvqtwEAAODWzzS1bdtW3bt3dxpr06aN2rdvb4wnJCRo+vTpCgwMlJ+fn6ZMmaLo6GjdeuutkqShQ4eqa9eumjBhgubPny+bzaYXXnhBiYmJ8vb2liT96le/0uuvv66nn35ajzzyiLKzs/Xuu+/qgw8+aNoDBgAAbsutQ5MZixYtkoeHh0aPHq2qqirFxsbq97//vTHfqlUrrV+/Xo899piio6PVpk0bTZw4UXPnzjVqwsLC9MEHH+jJJ5/UkiVLdP311+sPf/iDYmNjXXFIAADADTW70LR582andR8fH6WkpCglJeWin+ncubM2bNhwye0OGjRIe/bsaYwWAQBAC+TWzzQBAAC4C0ITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABgAqEJAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACa4dWiaN2+ebr75ZrVt21YdO3ZUXFycCgsLnWrOnDmjxMREtW/fXtdee61Gjx6tkpISp5qioiKNGDFC11xzjTp27KgZM2aotrbWqWbz5s3q27evvL29deONN2rlypVX+vAAAEAz4tah6aOPPlJiYqI++eQTZWZmqqamRkOHDtWpU6eMmieffFJ///vftXbtWn300Uc6duyY7rvvPmP+7NmzGjFihKqrq7Vt2zatWrVKK1euVHJyslFz+PBhjRgxQoMHD1Z+fr6mTZumRx99VBs3bmzS4wUAAO7L09UNXEpGRobT+sqVK9WxY0fl5eVp4MCBqqio0JtvvqnVq1frrrvukiS99dZbioyM1CeffKJbb71VmzZt0meffaYPP/xQQUFB6t27t1566SU988wzmjNnjry8vJSamqqwsDAtWLBAkhQZGamPP/5YixYtUmxs7AV7q6qqUlVVlbFut9uv0FkAAADuwK2vNP2niooKSVJgYKAkKS8vTzU1NYqJiTFqIiIiFBoaqtzcXElSbm6uevTooaCgIKMmNjZWdrtdBw4cMGrO3UZ9Tf02LmTevHny9/c3lpCQkMY5SAAA4JaaTWiqq6vTtGnTdNttt6l79+6SJJvNJi8vLwUEBDjVBgUFyWazGTXnBqb6+fq5S9XY7XadPn36gv3MnDlTFRUVxlJcXPyjjxEAALgvt749d67ExETt379fH3/8satbkSR5e3vL29vb1W0AAIAm0iyuNCUlJWn9+vX65z//qeuvv94YDw4OVnV1tcrLy53qS0pKFBwcbNT857fp6td/qMbPz0++vr6NfTgAAKAZcuvQ5HA4lJSUpPfff1/Z2dkKCwtzmo+KilLr1q2VlZVljBUWFqqoqEjR0dGSpOjoaO3bt0+lpaVGTWZmpvz8/NS1a1ej5txt1NfUbwMAAMCtb88lJiZq9erV+tvf/qa2bdsazyD5+/vL19dX/v7+SkhI0PTp0xUYGCg/Pz9NmTJF0dHRuvXWWyVJQ4cOVdeuXTVhwgTNnz9fNptNL7zwghITE43ba7/61a/0+uuv6+mnn9Yjjzyi7Oxsvfvuu/rggw9cduwAAMC9uPWVpmXLlqmiokKDBg1Sp06djGXNmjVGzaJFizRy5EiNHj1aAwcOVHBwsN577z1jvlWrVlq/fr1atWql6OhojR8/Xg8++KDmzp1r1ISFhemDDz5QZmamevXqpQULFugPf/jDRV83AAAArj5ufaXJ4XD8YI2Pj49SUlKUkpJy0ZrOnTtrw4YNl9zOoEGDtGfPnsvuEQAAXB3c+koTAACAuyA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGgCAAAwgdAEAABggqerGwBwdSooKHB1Cy1ehw4dFBoa6uo2gBaD0ASgSZ2u+EaSRePHj3d1Ky2er+81OniwgOAENBJCE4AmVfPdt5Ic6j3uGV0XFuHqdlos+/Ej2r7iRZ04cYLQBDQSQhMAl7i2Y6gCQ8Nd3QYAmMaD4AAAACYQmgAAAEwgNAEAAJhAaAIAADCB0AQAAGACoQkAAMAEQhMAAIAJhCYAAAATCE0AAAAmEJoAAABMIDQBAACYQGj6DykpKerSpYt8fHzUv39/7dixw9UtAQAAN0BoOseaNWs0ffp0zZ49W7t371avXr0UGxur0tJSV7cGAABczNPVDbiThQsXatKkSXr44YclSampqfrggw+0YsUKPfvssy7uDgAuX0FBgatbaPE6dOig0NBQV7eBJkBo+j/V1dXKy8vTzJkzjTEPDw/FxMQoNzf3vPqqqipVVVUZ6xUVFZIku93e6L1VVlZKksq+LFRt1elG3z6+Zz/+pSSp4ughtfa0uLiblovz3DRO/GufJGn8+PEu7qTl8/b20R//+LaCgoJc3UqLFxwcrODg4EbdZv3PbYfD8cPFDjgcDofj6NGjDkmObdu2OY3PmDHDccstt5xXP3v2bIckFhYWFhYWlhawFBcX/2BW4EpTA82cOVPTp0831uvq6lRWVqb27dvLYuG3Z7vdrpCQEBUXF8vPz8/V7bRYnOemwXluGpznpsO5/v8cDoe+/fZbWa3WH6wlNP2fDh06qFWrViopKXEaLykpueClQG9vb3l7ezuNBQQEXMkWmyU/P7+r/n/IpsB5bhqc56bBeW46nOvv+fv7m6rj23P/x8vLS1FRUcrKyjLG6urqlJWVpejoaBd2BgAA3AFXms4xffp0TZw4Uf369dMtt9yixYsX69SpU8a36QAAwNWL0HSO+++/X19//bWSk5Nls9nUu3dvZWRk8I2IBvD29tbs2bPPu4WJxsV5bhqc56bBeW46nOuGsTgcZr5jBwAAcHXjmSYAAAATCE0AAAAmEJoAAABMIDQBAACYQGhCo3nooYdksVhksVjUunVrBQUF6e6779aKFStUV1fn6vZaHJvNpilTpuinP/2pvL29FRISonvvvdfpXWP48b7++ms99thjCg0Nlbe3t4KDgxUbG6utW7e6urUWpbi4WI888oisVqu8vLzUuXNnTZ06Vd98842rW2txcnNz1apVK40YMcLVrTQ7hCY0qmHDhun48eM6cuSI/vGPf2jw4MGaOnWqRo4cqdraWle312IcOXJEUVFRys7O1quvvqp9+/YpIyNDgwcPVmJioqvba1FGjx6tPXv2aNWqVfr888+1bt06DRo0iB/mjejf//63+vXrp0OHDunPf/6zvvjiC6WmphovFy4rK3N1iy3Km2++qSlTpignJ0fHjh1zdTvNCq8cQKN56KGHVF5ervT0dKfx7OxsDRkyRG+88YYeffRR1zTXwgwfPlx79+5VYWGh2rRp4zRXXl7On/RpJOXl5WrXrp02b96sO++809XttFj33HOP9u/fr88//1y+vr7GuM1m0w033KAHH3xQy5Ytc2GHLUdlZaU6deqkXbt2afbs2erZs6eee+45V7fVbHClCVfcXXfdpV69eum9995zdSstQllZmTIyMpSYmHheYJL4G4iN6dprr9W1116r9PR0VVVVubqdFqmsrEwbN27U448/7hSYJCk4OFjx8fFas2aN+P2+cbz77ruKiIhQeHi4xo8frxUrVnBuLwOhCU0iIiJCR44ccXUbLcIXX3whh8OhiIgIV7fS4nl6emrlypVatWqVAgICdNttt+m5557T3r17Xd1ai3Ho0CE5HA5FRkZecD4yMlInT57U119/3cSdtUxvvvmmxo8fL+n7xykqKir00Ucfubir5oPQhCbhcDhksVhc3UaLwG+FTWv06NE6duyY1q1bp2HDhmnz5s3q27evVq5c6erWWhT+u77yCgsLtWPHDj3wwAOSvv+l4P7779ebb77p4s6aD0ITmkRBQYHCwsJc3UaLcNNNN8lisejgwYOubuWq4ePjo7vvvluzZs3Stm3b9NBDD2n27NmubqtFuPHGG2WxWFRQUHDB+YKCArVr107XXXddE3fW8rz55puqra2V1WqVp6enPD09tWzZMv31r39VRUWFq9trFghNuOKys7O1b98+jR492tWttAiBgYGKjY1VSkqKTp06dd58eXl50zd1lenatesFzz0uX/v27XX33Xfr97//vU6fPu00Z7PZlJaWpvvvv58r1T9SbW2t3n77bS1YsED5+fnG8umnn8pqterPf/6zq1tsFghNaFRVVVWy2Ww6evSodu/erd/+9rcaNWqURo4cqQcffNDV7bUYKSkpOnv2rG655Rb99a9/1aFDh1RQUKClS5cqOjra1e21GN98843uuusu/elPf9LevXt1+PBhrV27VvPnz9eoUaNc3V6L8frrr6uqqkqxsbHKyclRcXGxMjIydPfdd+snP/mJXn75ZVe32OytX79eJ0+eVEJCgrp37+60jB49mlt0JvHKATSahx56SKtWrZL0/b3ydu3aqVevXho3bpwmTpwoDw8yemM6fvy4Xn75Za1fv17Hjx/Xddddp6ioKD355JMaNGiQq9trEaqqqjRnzhxt2rRJ//rXv1RTU6OQkBCNGTNGzz333Hnf9kLDffnll5o9e7YyMjJUVlam4OBgxcXFafbs2Wrfvr2r22v27r33XtXV1emDDz44b27Hjh3q37+/Pv30U/Xs2dMF3TUfhCYAAAAT+NUfAADABEITAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQCukC5dumjx4sWm61euXKmAgIAr1g+AH8fT1Q0AgLt46KGHVF5ervT09EbZ3s6dO9WmTZtG2RYA1yM0AUAjq66ulpeXl6677jpXtwKgEXF7DkCzNGjQIE2ZMkXTpk1Tu3btFBQUpDfeeEOnTp3Sww8/rLZt2+rGG2/UP/7xD0nS2bNnlZCQoLCwMPn6+io8PFxLliwxtjdnzhytWrVKf/vb32SxWGSxWLR582ZJUnFxsX7xi18oICBAgYGBGjVqlI4cOWJ89qGHHlJcXJxefvllWa1WhYeHSzr/9tzChQvVo0cPtWnTRiEhIXr88cdVWVl5xc8VgMZBaALQbK1atUodOnTQjh07NGXKFD322GMaM2aMBgwYoN27d2vo0KGaMGGCvvvuO9XV1en666/X2rVr9dlnnyk5OVnPPfec3n33XUnSr3/9a/3iF7/QsGHDdPz4cR0/flwDBgxQTU2NYmNj1bZtW23ZskVbt27Vtddeq2HDhqm6utroJSsrS4WFhcrMzNT69esv2K+Hh4eWLl2qAwcOaNWqVcrOztbTTz/dJOcKQCNwAEAzdOeddzpuv/12Y722ttbRpk0bx4QJE4yx48ePOyQ5cnNzL7iNxMREx+jRo431iRMnOkaNGuVU88c//tERHh7uqKurM8aqqqocvr6+jo0bNxqfCwoKclRVVTl9tnPnzo5FixZd9BjWrl3raN++vbH+1ltvOfz9/S9aD8C1eKYJQLPVs2dP49+tWrVS+/bt1aNHD2MsKChIklRaWipJSklJ0YoVK1RUVKTTp0+rurpavXv3vuQ+Pv30U33xxRdq27at0/iZM2f0r3/9y1jv0aOHvLy8LrmtDz/8UPPmzdPBgwdlt9tVW1urM2fO6LvvvtM111xj6pgBuA6hCUCz1bp1a6d1i8XiNGaxWCRJdXV1euedd/TrX/9aCxYsUHR0tNq2batXX31V27dvv+Q+KisrFRUVpbS0tPPmzn3Q+4e+JXfkyBGNHDlSjz32mF5++WUFBgbq448/VkJCgqqrqwlNQDNAaAJwVdi6dasGDBigxx9/3Bg790qRJHl5eens2bNOY3379tWaNWvUsWNH+fn5NXj/eXl5qqur04IFC+Th8f3jpPXPUwFoHngQHMBV4aabbtKuXbu0ceNGff7555o1a5Z27tzpVNOlSxft3btXhYWFOnHihGpqahQfH68OHTpo1KhR2rJliw4fPqzNmzfriSee0FdffWV6/zfeeKNqamr02muv6d///rf++Mc/KjU1tbEPE8AVRGgCcFX45S9/qfvuu0/333+/+vfvr2+++cbpqpMkTZo0SeHh4erXr5+uu+46bd26Vddcc41ycnIUGhqq++67T5GRkUpISNCZM2cu68pTr169tHDhQr3yyivq3r270tLSNG/evMY+TABXkMXhcDhc3QQAAIC740oTAACACYQmAAAAEwhNAAAAJhCaAAAATCA0AQAAmEBoAgAAMIHQBAAAYAKhCQAAwARCEwAAgAmEJgAAABMITQAAACb8PzxF7LjdrtNEAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "diameter - numerical"
      ],
      "metadata": {
        "id": "RAQwyxzDvjEi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['diameter'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IS32N4Lm1APw",
        "outputId": "6a87c435-01d4-43ac-c8f0-80e67005de64"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df\n",
        "df['diameter'].hist(bins=10)\n",
        "plt.xlabel('diameter')\n",
        "plt.ylabel('count')\n",
        "plt.title('diameter')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "QnmtTdfy1SwS",
        "outputId": "4d1cd1ee-cd56-4bc3-c153-1eedbccb2393"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "break_year - numerical and temporal"
      ],
      "metadata": {
        "id": "MgJYs-fNvakm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['break_year'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SYbSel471uIL",
        "outputId": "f2940ba3-a108-4940-a1a9-14d31b4b31db"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning\n",
        "\n",
        "Now that very quick and basic EDA on the raw dataset is done,\n",
        "\n",
        "I will clean the data starting with handling missing values and duplicates"
      ],
      "metadata": {
        "id": "TmeEhUVvqUMG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Missing values**"
      ],
      "metadata": {
        "id": "Wrpqv-KQpKGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBBliFmbpLHg",
        "outputId": "6efb28aa-309f-438b-cd32-cf2fcbba01df"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pipe_id             0\n",
              "install_year        0\n",
              "material            0\n",
              "diameter          130\n",
              "break_year      23636\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "diameter variable has 314 missing values,\n",
        "\n",
        "break_year also has 53630 missing values, however as shown in the section earlier,\n",
        "\n",
        "break_year is target variable and NaN values mean NOT breakage record so we treat these values as 'NO record' rather than values missing"
      ],
      "metadata": {
        "id": "GZFzmCn0rPSZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Duplicates**\n",
        "\n"
      ],
      "metadata": {
        "id": "7_wyO-noqp02"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking if pipe_id variable is all unique values,\n",
        "\n",
        "if so, it is simply unique ID assigned to each pipe and pipes have no duplicate/multipe records and the variable can be removed as unique IDs will not affect the analysis and the target variable,\n",
        "\n",
        "if not, it needs to be looked into for duplicates and data cleaning"
      ],
      "metadata": {
        "id": "xIiVKnFZsS1J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['pipe_id'].duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4OBev3ar2Fg",
        "outputId": "aaff8073-b7e9-48c1-a4ed-3eea1500da7f"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "534"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It tells that not all values in the variable are unique,\n",
        "\n",
        "most likely it is because some pipes have multiple rows/records\n",
        "\n",
        "so this needs to be looked into before dealing with missing values"
      ],
      "metadata": {
        "id": "8Ja44oWfsyES"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['pipe_id'].nunique()"
      ],
      "metadata": {
        "id": "edQkLCCTtX5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "729ebcab-24d7-4db5-fe21-cc127115deab"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24581"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset has records of 55559 unique pipes,\n",
        "\n",
        "now I will look into pipes with multiple records"
      ],
      "metadata": {
        "id": "62tYgkNses_U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "multiple_records = df[df['pipe_id'].duplicated(keep=False)]\n",
        "multiple_records['pipe_id'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E6iKQ6ZffBVj",
        "outputId": "860a913a-8e56-4911-bad3-2e339bc3c779"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "P0000008827    9\n",
              "P0000024104    9\n",
              "P0000012123    7\n",
              "P0000023285    6\n",
              "P0000023522    6\n",
              "              ..\n",
              "P0000000501    3\n",
              "P0000015424    3\n",
              "P0000015466    3\n",
              "P0000004616    3\n",
              "P0000000114    3\n",
              "Name: pipe_id, Length: 219, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Out of 55559 unique pipes in the dataset,\n",
        "498 pipes have multiple records.\n",
        "\n",
        "And I will look into the pipe with ID 'P0000031371', this is the pipe with maximum number of multiple records of 10"
      ],
      "metadata": {
        "id": "KZRhyRfFhPj_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['pipe_id'] == 'P0000031371']"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "T6C7sQTjh8RC",
        "outputId": "b837aee4-0b7e-4171-97fa-49336ed5fdbb"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [pipe_id, install_year, material, diameter, break_year]\n",
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          },
          "metadata": {},
          "execution_count": 25
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So this particular pipe, has 10 records in the dataset\n",
        "\n",
        "Since it was installed in 1949, it has broken 9 times,\n",
        "\n",
        "there is one row/record with no breakage,\n",
        "\n",
        "assuming this particular pipe was not broken at a certain inspection/maintenance period, or perhaps this particular no breakage record was recorded between 1949 (when it was first installed) and 1982, when its first breakage was recorded\n",
        "\n",
        "Now 2 steps are needed to clean multiple_records data for analysis:\n",
        "\n",
        "1. Any pipes with multiple records must have its no breakage records removed\n",
        "so that I can only associate its breakage records\n",
        "\n",
        "2. After Step 1, I will create new column that ranks each pipe's breakage record from oldest to recent so that each breakage record can tell whether the same pipe has broken in the past"
      ],
      "metadata": {
        "id": "KdAML8R0iF51"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KSRk3_2vh8ay",
        "outputId": "aa11b084-5a58-4652-f2c0-800a1a57af9c"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before performing the 2 steps, checked that there is no duplicated records of the same pipe with same break_year or duplicated no records\n",
        "\n",
        "Another thing I will check before removing records is diameter variable missing values in pipes with multiple records"
      ],
      "metadata": {
        "id": "palOQMkkmXwU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vg1G1iEfgzyO",
        "outputId": "c5a563af-9537-41e4-d51f-189f87b5c79d"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pipe_id             0\n",
              "install_year        0\n",
              "material            0\n",
              "diameter          130\n",
              "break_year      23636\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "If pipes are missing their diameter values but have multiple records, the diameter value of exact same pipe can be extracted and copied for the same pipe"
      ],
      "metadata": {
        "id": "FAwjl9I7g6th"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['pipe_id'].duplicated(keep=False)&df['diameter'].isnull()]"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
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        "id": "J62k0tCChqh8",
        "outputId": "5577241a-f0e6-40fb-cca2-6a47919ec734"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [pipe_id, install_year, material, diameter, break_year]\n",
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              "          'suggestCharts', [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unfortunately pipes with multiple records with missing diameter values all have the values missing so I will have to deal with missing values after the 2 steps data cleaning."
      ],
      "metadata": {
        "id": "wch1bRNKiEqn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 1. Any pipes with multiple records must have its no breakage records removed\n",
        "so that I can only associate its breakage records"
      ],
      "metadata": {
        "id": "rUG1xKFhgOpZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.copy()"
      ],
      "metadata": {
        "id": "r671R_ZQm1u8"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_index = df2[df2['pipe_id'].duplicated(keep=False)&df2['break_year'].isnull()].index\n",
        "drop_index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "joWx3V1MQNg7",
        "outputId": "04eeb170-cf74-4fd7-e54c-b13c93d72e75"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([  114,   504,   634,   746,   771,  1372,  1468,  1561,  1659,\n",
              "             1748,\n",
              "            ...\n",
              "            24300, 24616, 24653, 24716, 24720, 24734, 24740, 24810, 24900,\n",
              "            24925],\n",
              "           dtype='int64', length=219)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2.drop(drop_index)\n",
        "len(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPodd_AWKPrb",
        "outputId": "7c9af43f-2418-43cb-93f8-12b47f8c286c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24896"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Step 2. I will create new column that ranks each pipe's breakage record from oldest to recent so that each breakage record can tell whether the same pipe has broken in the past"
      ],
      "metadata": {
        "id": "0h20e16CXTm0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['history'] = df2.groupby('pipe_id')['break_year'].rank().fillna(0)\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3QYVrv-NQuQg",
        "outputId": "65929f74-e45d-410d-a288-d2da4c3dc30d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       pipe_id  install_year material  diameter  break_year  history\n",
              "0  P0000000000          1985        D     20.32         NaN      0.0\n",
              "1  P0000000001          1934        C     15.24         NaN      0.0\n",
              "2  P0000000002          1904        C     20.32         NaN      0.0\n",
              "3  P0000000003          1979        D     15.24         NaN      0.0\n",
              "4  P0000000004          1987        D     20.32         NaN      0.0"
            ],
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now 2 steps of data cleaning is done,\n",
        "\n",
        "new 'history' variable is added to the dataset and value is 0 if the record is not a breakage record, and if the record is of breakage, it can tell the nth breakage of the particular pipe in order or break_year,\n",
        "\n",
        "I will look into the same pipe I looked into previously with most number of recrods again to see how the data looks after data cleaning"
      ],
      "metadata": {
        "id": "PKyEUI9TXskw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_id'] == 'P0000031371']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "mS91ar8QQ6op",
        "outputId": "910dc7f1-454d-46e0-afbd-ec7bdce80db4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [pipe_id, install_year, material, diameter, break_year, history]\n",
              "Index: []"
            ],
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              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5f42aa17-d6f3-491a-929c-aa83ea7facc3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that data had some cleaning done and history variable of breakage has been added, I can add following insights at this stage:"
      ],
      "metadata": {
        "id": "-rxD0-K7ZMH_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis findings #1"
      ],
      "metadata": {
        "id": "w_flkUUr26z0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of unique pipes in the dataset:', df2['pipe_id'].nunique())\n",
        "\n",
        "broken_pipe = df2[df2['history'] > 0]['pipe_id'].nunique()\n",
        "print('Number of pipes that have breakeage record:', broken_pipe,'-',  round(broken_pipe/df2['pipe_id'].nunique()*100, 2), '%')\n",
        "\n",
        "no_breakage_pipe = len(df2[df2['history'] == 0])\n",
        "print('Number of pipes that have NO breakeage record:', no_breakage_pipe,'-',  round(no_breakage_pipe/df2['pipe_id'].nunique()*100, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KeKPBsmoVE-I",
        "outputId": "3ac7330a-2890-4ce4-f97e-279eb43193ba"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique pipes in the dataset: 24581\n",
            "Number of pipes that have breakeage record: 1164 - 4.74 %\n",
            "Number of pipes that have NO breakeage record: 23417 - 95.26 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "temp = df2.copy()\n",
        "temp['history'] = temp['history'].apply(lambda x: 1 if x != 0 else 0)"
      ],
      "metadata": {
        "id": "kFGcyNQ0cR0E"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp['history'].value_counts().plot.bar()\n",
        "plt.title('history (Pipes with no breakage vs. Pipes broken)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "Rmg-Qw7Hb-h1",
        "outputId": "68a2490b-8a8a-474f-ecda-c18efb9ac489"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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+0GldqIULF6pSpUqqVKmS6tatq5kzZ6pr16567bXXzjtu7969nZ/zTpFmZ2dr8eLFkoq+fYOCgiT9eS3fqVOnLngZwsPDdf/99zvvAwIC9NBDD2nTpk357kjq2bOn2/VFixcvVnZ2tvr06eO2j/Ts2VMBAQGaN29evvkVtm9dzH7w6KOPun2fxsXFKScnR7/++mu++ZYrV65IjyQpqbi7qoS6/vrr3d7n/fI/fPiwAgICChxn+PDhuu+++1SjRg3ddNNNat26tbp27aqbb775vPP79ddfFR4e7hYGJKlWrVrO8DNVqVKlwOk89NBD6t27t3799VdFRkZq5syZOnXqlLp27XrePkg65wVyn376qQICAuTl5aXrrruuyNfu3HDDDW7vXS6Xqlevrl9++UWSnFCYnJxc6DQyMjJUrlw5vf7660pOTlZERIRiYmLUpk0bPfTQQ26/8AsSFxen06dPKzU1VREREdq3b5/i4uK0detWt5ATHR2t8uXLF2m5CnLdddflC5PlypXT999/X6Txz97v8sbPuxZi//79OnHihG688cZ8dbVq1VJubq52796t2rVrn3M+Z28T6c+Q/Mknn7i1lSpVyu0aCOnP7ZWRkaHg4OACp71v3z7n561bt+rFF1/U0qVL84WvjIwMt/e7du3Sgw8+qL/97W8aN25cgdN+//33NWrUKG3bts3tF+uZn4dff/1VYWFh+cJ39erV3d7v2LFDxhj9/e9/19///vdCl+XMMHimUqVKqV27dpo+fbqysrLk4+Oj2bNn69SpU24h57nnntPixYvVsGFDVa9eXa1atVLnzp11++23FzjdomjUqJFefvll55EOtWrVckLHuXh4eOT7rNSoUUOS3D6PRdm+zZo1U7t27TRs2DCNHj1ad9xxh5KSktS5c2f5+Picty/Vq1fP91k5sy+hoaFO+9nfd3nfh2d/Dry9vVW1atV835fn2rcuZj841++HsxljzvkHZklHyCmhCrsr4VwhoGnTptq5c6c+//xzLVy4UO+8845Gjx6tyZMn65FHHrms/TvzL+QzderUSX379tW0adP0/PPP66OPPlKDBg0K/KV4pvLly8vlchX4Ic3TtGlTVaxY8ZL6XZC8ozQjR44s9LbsMmXKSJI6dOjgPBNk4cKFGjlypF577TXNnj1bd911V6HzaNCggXx9fbVy5Updf/31Cg4OVo0aNRQXF6eJEycqKytL33zzjdtflhfjYvabyzn+5ebj45PvMQm5ubnnfGBkpUqVJP158X6zZs0UEBCg4cOHq1q1avL19dV3332n5557ztnuecLCwpwjfBs2bFCDBg3chn/00Ufq1q2bkpKS1L9/fwUHB8vT01MjRozQzp07L3jZ8ub/7LPPKiEhocCas4PR2Tp16qQpU6bo66+/VlJSkj755BPVrFlTdevWdWpq1aql7du3a+7cuZo/f74+/fRTTZw4UYMHD9awYcMuuN+SVLFiRcXHx1/UuOdT1O3rcrk0a9YsrVmzRl9++aUWLFighx9+WKNGjdKaNWucz+zlUNj3XVGda9+6mP3gQj6nR44cuSLfm38VhJxrTN4dF927d9exY8fUtGlTDR061Ak5hSX6yMhILV68WEePHnU7mpN3SD8yMrLI809MTNS0adPUpUsXrVq1SmPGjDnveKVKlVK1atWcuzMup7NP3xljtGPHDucIV94RoYCAgCJ9cYeFhenJJ5/Uk08+qX379umWW27RK6+8cs6Qk3fa6JtvvtH111+vuLg4SX8e4cnKytK0adOUnp6upk2bnnPexf0XWaVKleTn56ft27fnG7Zt2zZ5eHgoIiLivNMp6JTq//3f/ykqKuq841arVk2LFy/W7bfffs5fPsuXL9fBgwc1e/Zst/Va2D7m6+uruXPnqkWLFmrdurVWrFjhdkRq1qxZqlq1qmbPnu22HYYMGeI2ncjISC1btkwnTpxwO5qzY8cOt7q8IxpeXl4XHRiaNm2qsLAwzZgxQ02aNNHSpUv1wgsv5Kvz9/dXx44d1bFjR2VnZ6tt27Z65ZVXNGjQIPn6+l7UvC9Gbm6ufv75Z+eIifTndpfkbPuibt88jRs3VuPGjfXKK69o+vTp6tKliz7++OPz/mGXdwTlzG15dl8Kk/d9uH37drcjU9nZ2dq1a1e+7Xmufety7AeF+d///qfs7GzniLyNuCbnGnLw4EG392XKlFH16tWVlZXltPn7+0tSvtsc27Rpo5ycHI0fP96tffTo0XK5XOf8BX62rl276scff1T//v3l6enp9vTic4mNjdWGDRuKPJ+i+uCDD3T06FHn/axZs/T77787yxQTE6Nq1arpjTfe0LFjx/KNn3drZk5OTr5THMHBwQoPD3dbx4WJi4vT2rVrtWzZMifkVKxYUbVq1XKuZchrL0xh2+9q8fT0VKtWrfT55587pxckKT09XdOnT1eTJk0KPZ16pjlz5rhdZ7Bu3TqtXbu2SPtZhw4dlJOTo5deeinfsNOnTzvrJu+v3TP/us3OztbEiRMLnXZgYKAWLFjgPBrgzCM0BU1v7dq1Sk1NdZtGQkKCTp06pbfffttpy83NdR7fkCc4OFh33HGHpkyZot9//z1fXwq6Jfhsec9C+fLLL/Xhhx/q9OnTbqeqpPzfC97e3oqOjpYxxjnllneN0tW4duPM7xhjjMaPHy8vLy+1bNlSUtG37+HDh/Mducg7EluUz+PevXvdntKcmZmpDz74QPXq1XM7VVWQ+Ph4eXt7680333Trw7/+9S9lZGQoMTEx3ziF7VuXYz8ozMaNGyVJt91220VP46+OIznXkOjoaN1xxx2KiYlR+fLltWHDBs2aNcvtQr+YmBhJ0tNPP62EhAQnhNxzzz1q3ry5XnjhBf3yyy+qW7euFi5cqM8//1x9+vQp8vUvkpSYmKgKFSpo5syZuuuuuwo9t362++67Tx9++KH+7//+z+0vvUtVvnx5NWnSRN27d1d6errGjBmj6tWrOxeGenh46J133tFdd92l2rVrq3v37qpcubL+97//admyZQoICNCXX36po0eP6rrrrlP79u1Vt25dlSlTRosXL9b69es1atSo8/YjLi5Or7zyinbv3u0WZpo2baopU6YoKioq3/UnZ6tWrZqCgoI0efJklS1bVv7+/mrUqFGh10hdCS+//LIWLVqkJk2a6Mknn1SpUqU0ZcoUZWVlFfmZPNWrV1eTJk30xBNPKCsrS2PGjFGFChWK9L92mjVrpscee0wjRozQ5s2b1apVK3l5eemnn37SzJkzNXbsWLVv31633XabypUrp+TkZD399NNyuVz68MMPz3vqrWLFis7yxcfH69tvv1XlypV19913a/bs2br//vuVmJioXbt2afLkyYqOjnYLx0lJSWrYsKH69eunHTt2qGbNmvriiy906NAhSe5H4yZMmKAmTZqoTp066tmzp6pWrar09HSlpqZqz5492rJly3nXR8eOHTVu3DgNGTJEderUyfdXe6tWrRQaGqrbb79dISEh+u9//6vx48crMTHROWq7bt06NW/eXEOGDLmiT0P39fXV/PnzlZycrEaNGunrr7/WvHnz9PzzzzunoYq6fd9//31NnDhR999/v6pVq6ajR4/q7bffVkBAgNq0aXPevtSoUUM9evTQ+vXrFRISonfffVfp6el67733zjtupUqVNGjQIA0bNkytW7fWvffeq+3bt2vixIm69dZb3S7+P1Nh+9bl2A8KsmjRIl1//fWqX7/+RY1fIlzlu7lwiQp7JkzerYO7du1y2s6+hfzll182DRs2NEFBQaZ06dKmZs2a5pVXXnF7ZsPp06fNU089ZSpVqmRcLpfb7eRHjx41ffv2NeHh4cbLy8vccMMNZuTIkflu/ZZ03tuon3zySSPJTJ8+vcjLnpWVZSpWrGheeuklt/bC1snZCruF/N///rcZNGiQCQ4ONqVLlzaJiYnm119/zTf+pk2bTNu2bU2FChWMj4+PiYyMNB06dDBLlixx+te/f39Tt25dU7ZsWePv72/q1q1rJk6cWKTly8zMNJ6enqZs2bJut6N+9NFHRpLp2rXreZfJGGM+//xzEx0dbUqVKuV2m+7Zz+PIk5ycbCIjI8/bv8K269n7mTF/3u6ekJBgypQpY/z8/Ezz5s3N6tWrzzuPvFuLR44caUaNGmUiIiKMj4+PiYuLM1u2bMnXb39//0Kn9dZbb5mYmBhTunRpU7ZsWVOnTh0zYMAAs3fvXqdm1apVpnHjxqZ06dImPDzcDBgwwLn1etmyZU5dQetux44dJiwszNSqVcvs37/f5Obmmn/84x8mMjLS+Pj4mPr165u5c+cWuH73799vOnfubMqWLWsCAwNNt27dzKpVq4wk8/HHH7vV7ty50zz00EMmNDTUeHl5mcqVK5u7777bzJo167zr05g/bzWOiIgo8BEQxhgzZcoU07RpU2e/rlatmunfv7/zfCZj/v9nZciQIeedX0HPyTlbYbeQ+/v7m507d5pWrVoZPz8/ExISYoYMGeJ2O3We823f7777zjzwwAPm+uuvNz4+PiY4ONjcfffdZsOGDUVehgULFpibb77Z+Pj4mJo1a5qZM2e61eV97xZ2m/r48eNNzZo1jZeXlwkJCTFPPPGEOXz4sFtNUfYtY4q2HxTWn7ztd+Y+nZOTY8LCwsyLL7543vVRkrmMsfh5zvjL6tu3r/71r38pLS3tvLd4n+mll17Se++9p59++umSHwm/fPlyNW/eXDNnzlT79u0vaVrApZozZ47uv/9+ffvtt5d0Z1NJ1a1bN82aNavAU8K4/ObMmaPOnTtr586dCgsLK+7uXDFck4Or7uTJk/roo4/Url27Cwo40p/h6NixY/r444+vUO+AK++PP/5we5+Tk6Nx48YpICAg37/yAK6E1157Tb1797Y64Ehck4OraN++fVq8eLFmzZqlgwcP6plnnrngaZQpU8btOSdASfTUU0/pjz/+UGxsrLKysjR79mytXr1a//jHPy75dmSgKM6+IN5WhBxcNT/++KO6dOmi4OBgvfnmm4U+cwawXYsWLTRq1CjNnTtXJ0+eVPXq1TVu3Di3mwAAXDquyQEAAFbimhwAAGAlQg4AALDSNX1NTm5urvbu3auyZcsW++PwAQBA0RhjdPToUYWHh+f7/3VnuqZDzt69e4v0f3QAAMBfz+7du8/5JPhrOuTkPbJ89+7dRfp/OgAAoPhlZmYqIiLC7R9GF+SaDjl5p6gCAgIIOQAAlDDnu9SEC48BAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArFSquDuA4hE1cF5xdwFX0S+vJhZ3FwDgquNIDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEoXFHJGjBihW2+9VWXLllVwcLCSkpK0fft2t5qTJ0+qV69eqlChgsqUKaN27dopPT3drea3335TYmKi/Pz8FBwcrP79++v06dNuNcuXL9ctt9wiHx8fVa9eXVOnTs3XnwkTJigqKkq+vr5q1KiR1q1bdyGLAwAALHZBIWfFihXq1auX1qxZo0WLFunUqVNq1aqVjh8/7tT07dtXX375pWbOnKkVK1Zo7969atu2rTM8JydHiYmJys7O1urVq/X+++9r6tSpGjx4sFOza9cuJSYmqnnz5tq8ebP69OmjRx55RAsWLHBqZsyYoZSUFA0ZMkTfffed6tatq4SEBO3bt+9S1gcAALCEyxhjLnbk/fv3Kzg4WCtWrFDTpk2VkZGhSpUqafr06Wrfvr0kadu2bapVq5ZSU1PVuHFjff3117r77ru1d+9ehYSESJImT56s5557Tvv375e3t7eee+45zZs3Tz/88IMzr06dOunIkSOaP3++JKlRo0a69dZbNX78eElSbm6uIiIi9NRTT2ngwIFF6n9mZqYCAwOVkZGhgICAi10NJVLUwHnF3QVcRb+8mljcXQCAy6aov78v6ZqcjIwMSVL58uUlSRs3btSpU6cUHx/v1NSsWVPXX3+9UlNTJUmpqamqU6eOE3AkKSEhQZmZmdq6datTc+Y08mryppGdna2NGze61Xh4eCg+Pt6pKUhWVpYyMzPdXgAAwE4XHXJyc3PVp08f3X777brpppskSWlpafL29lZQUJBbbUhIiNLS0pyaMwNO3vC8YeeqyczM1B9//KEDBw4oJyenwJq8aRRkxIgRCgwMdF4REREXvuAAAKBEuOiQ06tXL/3www/6+OOPL2d/rqhBgwYpIyPDee3evbu4uwQAAK6QUhczUu/evTV37lytXLlS1113ndMeGhqq7OxsHTlyxO1oTnp6ukJDQ52as++Cyrv76syas+/ISk9PV0BAgEqXLi1PT095enoWWJM3jYL4+PjIx8fnwhcYAACUOBd0JMcYo969e+uzzz7T0qVLVaVKFbfhMTEx8vLy0pIlS5y27du367ffflNsbKwkKTY2Vv/5z3/c7oJatGiRAgICFB0d7dScOY28mrxpeHt7KyYmxq0mNzdXS5YscWoAAMC17YKO5PTq1UvTp0/X559/rrJlyzrXvwQGBqp06dIKDAxUjx49lJKSovLlyysgIEBPPfWUYmNj1bhxY0lSq1atFB0dra5du+r1119XWlqaXnzxRfXq1cs5yvL4449r/PjxGjBggB5++GEtXbpUn3zyiebN+/93BKWkpCg5OVkNGjRQw4YNNWbMGB0/flzdu3e/XOsGAACUYBcUciZNmiRJuuOOO9za33vvPXXr1k2SNHr0aHl4eKhdu3bKyspSQkKCJk6c6NR6enpq7ty5euKJJxQbGyt/f38lJydr+PDhTk2VKlU0b9489e3bV2PHjtV1112nd955RwkJCU5Nx44dtX//fg0ePFhpaWmqV6+e5s+fn+9iZAAAcG26pOfklHQ8JwfXCp6TA8AmV+U5OQAAAH9VhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlS445KxcuVL33HOPwsPD5XK5NGfOHLfh3bp1k8vlcnu1bt3arebQoUPq0qWLAgICFBQUpB49eujYsWNuNd9//73i4uLk6+uriIgIvf766/n6MnPmTNWsWVO+vr6qU6eOvvrqqwtdHAAAYKkLDjnHjx9X3bp1NWHChEJrWrdurd9//915/fvf/3Yb3qVLF23dulWLFi3S3LlztXLlSj366KPO8MzMTLVq1UqRkZHauHGjRo4cqaFDh+qtt95yalavXq0HHnhAPXr00KZNm5SUlKSkpCT98MMPF7pIAADAQi5jjLnokV0uffbZZ0pKSnLaunXrpiNHjuQ7wpPnv//9r6Kjo7V+/Xo1aNBAkjR//ny1adNGe/bsUXh4uCZNmqQXXnhBaWlp8vb2liQNHDhQc+bM0bZt2yRJHTt21PHjxzV37lxn2o0bN1a9evU0efLkIvU/MzNTgYGBysjIUEBAwEWsgZIrauC84u4CrqJfXk0s7i4AwGVT1N/fV+SanOXLlys4OFg33nijnnjiCR08eNAZlpqaqqCgICfgSFJ8fLw8PDy0du1ap6Zp06ZOwJGkhIQEbd++XYcPH3Zq4uPj3eabkJCg1NTUQvuVlZWlzMxMtxcAALDTZQ85rVu31gcffKAlS5botdde04oVK3TXXXcpJydHkpSWlqbg4GC3cUqVKqXy5csrLS3NqQkJCXGryXt/vpq84QUZMWKEAgMDnVdERMSlLSwAAPjLKnW5J9ipUyfn5zp16ujmm29WtWrVtHz5crVs2fJyz+6CDBo0SCkpKc77zMxMgg4AAJa64reQV61aVRUrVtSOHTskSaGhodq3b59bzenTp3Xo0CGFhoY6Nenp6W41ee/PV5M3vCA+Pj4KCAhwewEAADtd8ZCzZ88eHTx4UGFhYZKk2NhYHTlyRBs3bnRqli5dqtzcXDVq1MipWblypU6dOuXULFq0SDfeeKPKlSvn1CxZssRtXosWLVJsbOyVXiQAAFACXHDIOXbsmDZv3qzNmzdLknbt2qXNmzfrt99+07Fjx9S/f3+tWbNGv/zyi5YsWaL77rtP1atXV0JCgiSpVq1aat26tXr27Kl169Zp1apV6t27tzp16qTw8HBJUufOneXt7a0ePXpo69atmjFjhsaOHet2qumZZ57R/PnzNWrUKG3btk1Dhw7Vhg0b1Lt378uwWgAAQEl3wSFnw4YNql+/vurXry9JSklJUf369TV48GB5enrq+++/17333qsaNWqoR48eiomJ0TfffCMfHx9nGtOmTVPNmjXVsmVLtWnTRk2aNHF7Bk5gYKAWLlyoXbt2KSYmRv369dPgwYPdnqVz2223afr06XrrrbdUt25dzZo1S3PmzNFNN910KesDAABY4pKek1PS8ZwcXCt4Tg4AmxTrc3IAAACKGyEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYKULDjkrV67UPffco/DwcLlcLs2ZM8dtuDFGgwcPVlhYmEqXLq34+Hj99NNPbjWHDh1Sly5dFBAQoKCgIPXo0UPHjh1zq/n+++8VFxcnX19fRURE6PXXX8/Xl5kzZ6pmzZry9fVVnTp19NVXX13o4gAAAEtdcMg5fvy46tatqwkTJhQ4/PXXX9ebb76pyZMna+3atfL391dCQoJOnjzp1HTp0kVbt27VokWLNHfuXK1cuVKPPvqoMzwzM1OtWrVSZGSkNm7cqJEjR2ro0KF66623nJrVq1frgQceUI8ePbRp0yYlJSUpKSlJP/zww4UuEgAAsJDLGGMuemSXS5999pmSkpIk/XkUJzw8XP369dOzzz4rScrIyFBISIimTp2qTp066b///a+io6O1fv16NWjQQJI0f/58tWnTRnv27FF4eLgmTZqkF154QWlpafL29pYkDRw4UHPmzNG2bdskSR07dtTx48c1d+5cpz+NGzdWvXr1NHny5CL1PzMzU4GBgcrIyFBAQMDFroYSKWrgvOLuAq6iX15NLO4uAMBlU9Tf35f1mpxdu3YpLS1N8fHxTltgYKAaNWqk1NRUSVJqaqqCgoKcgCNJ8fHx8vDw0Nq1a52apk2bOgFHkhISErR9+3YdPnzYqTlzPnk1efMpSFZWljIzM91eAADATpc15KSlpUmSQkJC3NpDQkKcYWlpaQoODnYbXqpUKZUvX96tpqBpnDmPwmryhhdkxIgRCgwMdF4REREXuogAAKCEuKburho0aJAyMjKc1+7du4u7SwAA4Aq5rCEnNDRUkpSenu7Wnp6e7gwLDQ3Vvn373IafPn1ahw4dcqspaBpnzqOwmrzhBfHx8VFAQIDbCwAA2OmyhpwqVaooNDRUS5YscdoyMzO1du1axcbGSpJiY2N15MgRbdy40alZunSpcnNz1ahRI6dm5cqVOnXqlFOzaNEi3XjjjSpXrpxTc+Z88mry5gMAAK5tFxxyjh07ps2bN2vz5s2S/rzYePPmzfrtt9/kcrnUp08fvfzyy/riiy/0n//8Rw899JDCw8OdO7Bq1aql1q1bq2fPnlq3bp1WrVql3r17q1OnTgoPD5ckde7cWd7e3urRo4e2bt2qGTNmaOzYsUpJSXH68cwzz2j+/PkaNWqUtm3bpqFDh2rDhg3q3bv3pa8VAABQ4pW60BE2bNig5s2bO+/zgkdycrKmTp2qAQMG6Pjx43r00Ud15MgRNWnSRPPnz5evr68zzrRp09S7d2+1bNlSHh4eateund58801neGBgoBYuXKhevXopJiZGFStW1ODBg92epXPbbbdp+vTpevHFF/X888/rhhtu0Jw5c3TTTTdd1IoAAAB2uaTn5JR0PCcH1wqekwPAJsXynBwAAIC/CkIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqXPeQMHTpULpfL7VWzZk1n+MmTJ9WrVy9VqFBBZcqUUbt27ZSenu42jd9++02JiYny8/NTcHCw+vfvr9OnT7vVLF++XLfccot8fHxUvXp1TZ069XIvCgAAKMGuyJGc2rVr6/fff3de3377rTOsb9+++vLLLzVz5kytWLFCe/fuVdu2bZ3hOTk5SkxMVHZ2tlavXq33339fU6dO1eDBg52aXbt2KTExUc2bN9fmzZvVp08fPfLII1qwYMGVWBwAAFAClboiEy1VSqGhofnaMzIy9K9//UvTp09XixYtJEnvvfeeatWqpTVr1qhx48ZauHChfvzxRy1evFghISGqV6+eXnrpJT333HMaOnSovL29NXnyZFWpUkWjRo2SJNWqVUvffvutRo8erYSEhCuxSAAAoIS5IkdyfvrpJ4WHh6tq1arq0qWLfvvtN0nSxo0bderUKcXHxzu1NWvW1PXXX6/U1FRJUmpqqurUqaOQkBCnJiEhQZmZmdq6datTc+Y08mryplGYrKwsZWZmur0AAICdLnvIadSokaZOnar58+dr0qRJ2rVrl+Li4nT06FGlpaXJ29tbQUFBbuOEhIQoLS1NkpSWluYWcPKG5w07V01mZqb++OOPQvs2YsQIBQYGOq+IiIhLXVwAAPAXddlPV911113OzzfffLMaNWqkyMhIffLJJypduvTlnt0FGTRokFJSUpz3mZmZBB0AACx1xW8hDwoKUo0aNbRjxw6FhoYqOztbR44ccatJT093ruEJDQ3Nd7dV3vvz1QQEBJwzSPn4+CggIMDtBQAA7HTFQ86xY8e0c+dOhYWFKSYmRl5eXlqyZIkzfPv27frtt98UGxsrSYqNjdV//vMf7du3z6lZtGiRAgICFB0d7dScOY28mrxpAAAAXPaQ8+yzz2rFihX65ZdftHr1at1///3y9PTUAw88oMDAQPXo0UMpKSlatmyZNm7cqO7duys2NlaNGzeWJLVq1UrR0dHq2rWrtmzZogULFujFF19Ur1695OPjI0l6/PHH9fPPP2vAgAHatm2bJk6cqE8++UR9+/a93IsDAABKqMt+Tc6ePXv0wAMP6ODBg6pUqZKaNGmiNWvWqFKlSpKk0aNHy8PDQ+3atVNWVpYSEhI0ceJEZ3xPT0/NnTtXTzzxhGJjY+Xv76/k5GQNHz7cqalSpYrmzZunvn37auzYsbruuuv0zjvvcPs4AABwuIwxprg7UVwyMzMVGBiojIyMa+76nKiB84q7C7iKfnk1sbi7AACXTVF/f/O/qwAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlQg5AADASoQcAABgJUIOAACwEiEHAABYiZADAACsRMgBAABWIuQAAAArEXIAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxUqrg7AAC4vKIGzivuLuAq+uXVxOLuwl8WR3IAAICVCDkAAMBKhBwAAGAlQg4AALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEqEHAAAYCVCDgAAsBIhBwAAWImQAwAArETIAQAAViLkAAAAKxFyAACAlUp8yJkwYYKioqLk6+urRo0aad26dcXdJQAA8BdQokPOjBkzlJKSoiFDhui7775T3bp1lZCQoH379hV31wAAQDEr0SHnn//8p3r27Knu3bsrOjpakydPlp+fn959993i7hoAAChmpYq7AxcrOztbGzdu1KBBg5w2Dw8PxcfHKzU1tcBxsrKylJWV5bzPyMiQJGVmZl7Zzv4F5WadKO4u4Cq6Fvfxaxmf72vLtfj5zltmY8w560psyDlw4IBycnIUEhLi1h4SEqJt27YVOM6IESM0bNiwfO0RERFXpI/AX0XgmOLuAYAr5Vr+fB89elSBgYGFDi+xIediDBo0SCkpKc773NxcHTp0SBUqVJDL5SrGnuFqyMzMVEREhHbv3q2AgIDi7g6Ay4jP97XFGKOjR48qPDz8nHUlNuRUrFhRnp6eSk9Pd2tPT09XaGhogeP4+PjIx8fHrS0oKOhKdRF/UQEBAXwJApbi833tONcRnDwl9sJjb29vxcTEaMmSJU5bbm6ulixZotjY2GLsGQAA+CsosUdyJCklJUXJyclq0KCBGjZsqDFjxuj48ePq3r17cXcNAAAUsxIdcjp27Kj9+/dr8ODBSktLU7169TR//vx8FyMD0p+nK4cMGZLvlCWAko/PNwriMue7/woAAKAEKrHX5AAAAJwLIQcAAFiJkAMAAKxEyAEAAFYi5AAAACuV6FvIgcIcOHBA7777rlJTU5WWliZJCg0N1W233aZu3bqpUqVKxdxDAMCVxi3ksM769euVkJAgPz8/xcfHO89NSk9P15IlS3TixAktWLBADRo0KOaeAgCuJEIOrNO4cWPVrVtXkydPzvePV40xevzxx/X9998rNTW1mHoI4EravXu3hgwZonfffbe4u4JiRsiBdUqXLq1NmzapZs2aBQ7ftm2b6tevrz/++OMq9wzA1bBlyxbdcsstysnJKe6uoJhxTQ6sExoaqnXr1hUactatW8e//gBKsC+++OKcw3/++eer1BP81RFyYJ1nn31Wjz76qDZu3KiWLVvmuybn7bff1htvvFHMvQRwsZKSkuRyuXSuExFnn6rGtYnTVbDSjBkzNHr0aG3cuNE5ZO3p6amYmBilpKSoQ4cOxdxDABercuXKmjhxou67774Ch2/evFkxMTGcrgIhB3Y7deqUDhw4IEmqWLGivLy8irlHAC7Vvffeq3r16mn48OEFDt+yZYvq16+v3Nzcq9wz/NVwugpW8/LyUlhYWHF3A8Bl1L9/fx0/frzQ4dWrV9eyZcuuYo/wV8WRHAAAYCX+rQMAALASIQcAAFiJkAMAAKxEyAEAAFYi5AAAACsRcgAAgJUIOQAAwEr/DyImqTy/tsEyAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The ratio/balance of no breakage data against breakage data is still significantly imbalance"
      ],
      "metadata": {
        "id": "ICsAn4vbdOCq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missing values\n",
        "\n",
        "Data has been cleaned and taking a look at missing values for diameter variable again"
      ],
      "metadata": {
        "id": "XtVy6Axdiys4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3oKZfQ2nd2N2",
        "outputId": "c67b421d-065b-466d-b755-c39f1999d115"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pipe_id             0\n",
              "install_year        0\n",
              "material            0\n",
              "diameter          130\n",
              "break_year      23417\n",
              "history             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].isnull().sum() / len(df2) * 100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nf24cUqKQ7CW",
        "outputId": "33a14f88-226e-4120-dc99-6f21dd753c78"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5221722365038561"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since number of records missing diameter values is 311 and accounts for only 0.55% of the dataset, I will go with removing the records for analysis this time\n",
        "\n",
        "unless the 311 records show significantly different imbalance or ratio on their break_year or history values"
      ],
      "metadata": {
        "id": "HQDTaD-VzGui"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diameter_missing = df2.copy()\n",
        "diameter_missing['break_year'] = diameter_missing['break_year'].fillna(0)\n",
        "diameter_missing['break_year'] = diameter_missing['break_year'].apply(lambda x: 1 if x != 0 else 0)"
      ],
      "metadata": {
        "id": "bcbPKQAH0EDO"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_diameter = diameter_missing[diameter_missing['diameter'].isnull()]"
      ],
      "metadata": {
        "id": "YdU4u16-0EM7"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(no_diameter)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5hSq-Y30EV-",
        "outputId": "57dcf1fc-43ec-4f5e-b758-fc577fc7d47c"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "130"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of unique pipes with no diameter values:', no_diameter['pipe_id'].nunique())\n",
        "\n",
        "broken_pipe = no_diameter[no_diameter['history'] > 0]['pipe_id'].nunique()\n",
        "print('Number of pipes with no diameter values that have breakeage record:', broken_pipe,'-',  round(broken_pipe/no_diameter['pipe_id'].nunique()*100, 2), '%')\n",
        "\n",
        "no_breakage_pipe = len(no_diameter[no_diameter['history'] == 0])\n",
        "print('Number of pipes with no diamter values that have NO breakeage record:', no_breakage_pipe,'-',  round(no_breakage_pipe/no_diameter['pipe_id'].nunique()*100, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdw8RYCy0Edr",
        "outputId": "1008ef0d-b991-48e5-a4b1-c71125cf7a4c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique pipes with no diameter values: 130\n",
            "Number of pipes with no diameter values that have breakeage record: 3 - 2.31 %\n",
            "Number of pipes with no diamter values that have NO breakeage record: 127 - 97.69 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As checked that the ratio of no breakage against breakage of pipes with no diameter values are similar to the ratio by the whole dataset,\n",
        "\n",
        "I will drop the records with no diameter values as it will not affect the anaysis"
      ],
      "metadata": {
        "id": "_v9EHqGY3A4t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2[df2['diameter'].notnull()]\n",
        "len(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_utlPmEH30jk",
        "outputId": "bb310297-09ee-45fd-a830-d2475975e83e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24766"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Update on the overview of the dataset after records removed"
      ],
      "metadata": {
        "id": "mMSgAety4oJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Number of unique pipes in the dataset:', df2['pipe_id'].nunique())\n",
        "\n",
        "broken_pipe = df2[df2['history'] > 0]['pipe_id'].nunique()\n",
        "print('Number of pipes that have breakeage record:', broken_pipe,'-',  round(broken_pipe/df2['pipe_id'].nunique()*100, 2), '%')\n",
        "\n",
        "no_breakage_pipe = len(df2[df2['history'] == 0])\n",
        "print('Number of pipes that have NO breakeage record:', no_breakage_pipe,'-',  round(no_breakage_pipe/df2['pipe_id'].nunique()*100, 2), '%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CB5ALh-Q4SXt",
        "outputId": "0d94a6dd-4828-4af0-b128-1fe716d1a940"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of unique pipes in the dataset: 24451\n",
            "Number of pipes that have breakeage record: 1161 - 4.75 %\n",
            "Number of pipes that have NO breakeage record: 23290 - 95.25 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis of numerical variables Part 1\n",
        "\n",
        "I will start analysing numerical variables starting with two temporal variables of install_year and break_year\n",
        "\n",
        "I will quicky check if any records have its break_year values smaller(older) than values in install_year,\n",
        "\n",
        "just to make sure that there is no invalid records entered by mistakes or so"
      ],
      "metadata": {
        "id": "mnV3Bn4K6JGH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "temp = df2.copy()\n",
        "temp = temp[temp['break_year'].notnull()]"
      ],
      "metadata": {
        "id": "_htbQw_RAgMt"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp[temp['break_year'] < temp['install_year']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "JEukUo-iAggY",
        "outputId": "a390f0f8-b5ab-4cc7-c8ef-ee0ef3228cd8"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [pipe_id, install_year, material, diameter, break_year, history]\n",
              "Index: []"
            ],
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              "      <th></th>\n",
              "      <th>pipe_id</th>\n",
              "      <th>install_year</th>\n",
              "      <th>material</th>\n",
              "      <th>diameter</th>\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for variable in ['install_year', 'break_year']:\n",
        "  print(variable)\n",
        "  print(df2[variable].min(), '~', df2[variable].max())\n",
        "  print('record of', str(df2[variable].max() - df2[variable].min()), 'years')\n",
        "  print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_IKbBc9CIm3",
        "outputId": "6985b503-8b73-4764-a1d1-9afe8ec3e6d2"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "install_year\n",
            "1861 ~ 2016\n",
            "record of 155 years\n",
            "\n",
            "break_year\n",
            "1980.0 ~ 2016.0\n",
            "record of 36.0 years\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This finding is quite important,\n",
        "\n",
        "the dataset has records of pipe installed since 1861 up to 2017, range of 156 years\n",
        "\n",
        "however when it comes to break_year variable, only breakage after 1980 up to 2016 is in the dataset\n",
        "\n",
        "This means any breakages pipes may have had before year 1980 is not part of the dataset, and this must be a contributor for why the break_year variable is so imbalance when transformed into categorical variable as breakage records are certainly missing in contrast to all the data of install_year data since 1861"
      ],
      "metadata": {
        "id": "HrPHWpTg_HFY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(x=df2['install_year'], data=df)\n",
        "plt.title('install_year distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ugrUNaoL9ylB",
        "outputId": "212aed8e-fc2d-4882-f637-8375c3637861"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ISgAAABYISgAAABYISgAAABYISgAAABaqNSglJCTo5ptvVsOGDRUUFKShQ4cqNTXVqczly5cVGxurJk2aqEGDBho2bJgyMjKcypw4cUKDBw9WvXr1FBQUpGeeeUb5+flOZbZt26bu3bvLx8dH7dq109KlSyu7eQCqmcPhUEFBQYmLw+Go7qoCcFHVGpS2b9+u2NhYff7550pKSlJeXp6io6N14cIFs8yUKVP0r3/9Sx988IG2b9+u06dP6/777zf3FxQUaPDgwcrNzdXOnTv19ttva+nSpXr22WfNMseOHdPgwYPVt29fpaSkaPLkyRo/frw+/vjjKm0vgKrjcDjUPCxcXl5eJS7Nw8IJSwCKZTMMw6juShT66aefFBQUpO3bt6tXr17Kzs5WYGCgli9frgceeECS9M033+jGG29UcnKybr31Vv373//W3XffrdOnTys4OFiStGjRIk2bNk0//fSTvL29NW3aNK1fv16HDh0yz/XQQw8pKytLGzduLLFedrtd/v7+ys7Olp+fX+U0HkCFKigokJeXlx5I3C6bh/XfhIbDoQ9jeys/P1+enp5VWEMAla0ifn+71Bil7OxsSVJAQIAkad++fcrLy9OAAQPMMjfccINatmyp5ORkSVJycrI6depkhiRJiomJkd1u1+HDh80yvz5GYZnCY/xWTk6O7Ha70wLAPdk8POTh4Wm5XC1EAYDLXCEcDocmT56s22+/XR07dpQkpaeny9vbW40aNXIqGxwcrPT0dLPMr0NS4f7CfVcrY7fbdenSpSJ1SUhIkL+/v7mEhYVVSBsBAIB7cZmgFBsbq0OHDmnFihXVXRVNnz5d2dnZ5vLjjz9Wd5UAAEA18KruCkhSXFyc1q1bpx07dqhFixbm9pCQEOXm5iorK8vprlJGRoZCQkLMMrt373Y6XuFTcb8u89sn5TIyMuTn56e6desWqY+Pj498fHwqpG0AAMB9VesdJcMwFBcXp9WrV2vLli1q3bq10/7IyEjVqVNHmzdvNrelpqbqxIkTioqKkiRFRUXp4MGDyszMNMskJSXJz89PERERZplfH6OwTOExAAAAilOtd5RiY2O1fPly/fOf/1TDhg3NMUX+/v6qW7eu/P39NW7cOE2dOlUBAQHy8/PTpEmTFBUVpVtvvVWSFB0drYiICI0aNUovvvii0tPTNWPGDMXGxpp3hf7whz/o1Vdf1R//+Ec9+uij2rJli95//32tX7++2toOAABcX7XeUXr99deVnZ2tPn36qFmzZuaycuVKs8z8+fN19913a9iwYerVq5dCQkK0atUqc7+np6fWrVsnT09PRUVF6eGHH9bo0aM1e/Zss0zr1q21fv16JSUlqUuXLnrppZf0j3/8QzExMVXaXgAA4F5cah4lV8U8SoD7KZxH6XevfyoPD+v5kRyOAn0wsSfzKAE1UI2bRwkAAMCVEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsVGtQ2rFjh4YMGaLQ0FDZbDatWbPGab/NZit2mTNnjlmmVatWRfa/8MILTsc5cOCAevbsKV9fX4WFhenFF1+siuYBAAA3V61B6cKFC+rSpYsSExOL3Z+Wlua0LF68WDabTcOGDXMqN3v2bKdykyZNMvfZ7XZFR0crPDxc+/bt05w5cxQfH6833nijUtsGAADcn1d1nnzQoEEaNGiQ5f6QkBCn9X/+85/q27ev2rRp47S9YcOGRcoWWrZsmXJzc7V48WJ5e3urQ4cOSklJ0bx58zRhwoRrbwQAAKix3GaMUkZGhtavX69x48YV2ffCCy+oSZMm6tatm+bMmaP8/HxzX3Jysnr16iVvb29zW0xMjFJTU3X27Nliz5WTkyO73e60AACA2qda7yiVxdtvv62GDRvq/vvvd9r+5JNPqnv37goICNDOnTs1ffp0paWlad68eZKk9PR0tW7d2ukzwcHB5r7GjRsXOVdCQoJmzZpVSS0BAADuwm2C0uLFizVy5Ej5+vo6bZ86dar5defOneXt7a3HH39cCQkJ8vHxKde5pk+f7nRcu92usLCw8lUcAAC4LbcISp9++qlSU1O1cuXKEsv26NFD+fn5On78uNq3b6+QkBBlZGQ4lSlctxrX5OPjU+6QBQAAag63GKP01ltvKTIyUl26dCmxbEpKijw8PBQUFCRJioqK0o4dO5SXl2eWSUpKUvv27YvtdgMAAChUrUHp/PnzSklJUUpKiiTp2LFjSklJ0YkTJ8wydrtdH3zwgcaPH1/k88nJyVqwYIG+/PJLff/991q2bJmmTJmihx9+2AxBI0aMkLe3t8aNG6fDhw9r5cqVWrhwoVPXGgAAQHGqtett79696tu3r7leGF7GjBmjpUuXSpJWrFghwzA0fPjwIp/38fHRihUrFB8fr5ycHLVu3VpTpkxxCkH+/v765JNPFBsbq8jISDVt2lTPPvssUwMAAIAS2QzDMKq7Eq7ObrfL399f2dnZ8vPzq+7qACiFgoICeXl56XevfyoPD0/Lcg5HgT6Y2FP5+fny9LQuB8D9VMTvb7cYowQAAFAdCEoAAAAWCEoAAAAWCEoAAAAWCEoAAAAWCEoAAAAW3OIVJgDwaw6HQyXNbFJQUFBFtQFQkxGUALgVh8Oh5mHhSj99snQfYKY4ANeAoATArRiGofTTJ/VA4nbZPKxHDzjy8/TRpH7kJADXhKAEwC3ZPDyuOuO24UHXG4Brx2BuAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAAC9UalHbs2KEhQ4YoNDRUNptNa9ascdo/duxY2Ww2p2XgwIFOZc6cOaORI0fKz89PjRo10rhx43T+/HmnMgcOHFDPnj3l6+ursLAwvfjii5XdNAAAUANUa1C6cOGCunTposTERMsyAwcOVFpamrm89957TvtHjhypw4cPKykpSevWrdOOHTs0YcIEc7/dbld0dLTCw8O1b98+zZkzR/Hx8XrjjTcqrV0AAKBm8KrOkw8aNEiDBg26ahkfHx+FhIQUu+/rr7/Wxo0btWfPHt10002SpFdeeUV33XWX5s6dq9DQUC1btky5ublavHixvL291aFDB6WkpGjevHlOgQoAAOC3XH6M0rZt2xQUFKT27dtr4sSJ+uWXX8x9ycnJatSokRmSJGnAgAHy8PDQrl27zDK9evWSt7e3WSYmJkapqak6e/ZssefMycmR3W53WgAAQO3j0kFp4MCBeuedd7R582b9/e9/1/bt2zVo0CAVFBRIktLT0xUUFOT0GS8vLwUEBCg9Pd0sExwc7FSmcL2wzG8lJCTI39/fXMLCwiq6aQAAwA1Ua9dbSR566CHz606dOqlz585q27attm3bpv79+1faeadPn66pU6ea63a7nbAEAEAt5NJ3lH6rTZs2atq0qY4cOSJJCgkJUWZmplOZ/Px8nTlzxhzXFBISooyMDKcyhetWY598fHzk5+fntAAAgNrHrYLSyZMn9csvv6hZs2aSpKioKGVlZWnfvn1mmS1btsjhcKhHjx5mmR07digvL88sk5SUpPbt26tx48ZV2wAAAOBWqjUonT9/XikpKUpJSZEkHTt2TCkpKTpx4oTOnz+vZ555Rp9//rmOHz+uzZs3695771W7du0UExMjSbrxxhs1cOBAPfbYY9q9e7c+++wzxcXF6aGHHlJoaKgkacSIEfL29ta4ceN0+PBhrVy5UgsXLnTqWgMAAChOtQalvXv3qlu3burWrZskaerUqerWrZueffZZeXp66sCBA7rnnnt0/fXXa9y4cYqMjNSnn34qHx8f8xjLli3TDTfcoP79++uuu+7SHXfc4TRHkr+/vz755BMdO3ZMkZGReuqpp/Tss88yNQAAAChRtQ7m7tOnjwzDsNz/8ccfl3iMgIAALV++/KplOnfurE8//bTM9QMAALWbW41RAgAAqEoEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAsEJQAAAAvVGpR27NihIUOGKDQ0VDabTWvWrDH35eXladq0aerUqZPq16+v0NBQjR49WqdPn3Y6RqtWrWSz2ZyWF154wanMgQMH1LNnT/n6+iosLEwvvvhiVTQPAAC4uWoNShcuXFCXLl2UmJhYZN/Fixe1f/9+/fWvf9X+/fu1atUqpaam6p577ilSdvbs2UpLSzOXSZMmmfvsdruio6MVHh6uffv2ac6cOYqPj9cbb7xRqW0DAADuz6s6Tz5o0CANGjSo2H3+/v5KSkpy2vbqq6/qlltu0YkTJ9SyZUtze8OGDRUSElLscZYtW6bc3FwtXrxY3t7e6tChg1JSUjRv3jxNmDCh4hoDAABqHLcao5SdnS2bzaZGjRo5bX/hhRfUpEkTdevWTXPmzFF+fr65Lzk5Wb169ZK3t7e5LSYmRqmpqTp79myx58nJyZHdbndaAABA7VOtd5TK4vLly5o2bZqGDx8uPz8/c/uTTz6p7t27KyAgQDt37tT06dOVlpamefPmSZLS09PVunVrp2MFBweb+xo3blzkXAkJCZo1a1YltgYAALgDtwhKeXl5evDBB2UYhl5//XWnfVOnTjW/7ty5s7y9vfX4448rISFBPj4+5Trf9OnTnY5rt9sVFhZWvsoDAAC35fJBqTAk/fDDD9qyZYvT3aTi9OjRQ/n5+Tp+/Ljat2+vkJAQZWRkOJUpXLca1+Tj41PukAUAAGoOlx6jVBiSvvvuO23atElNmjQp8TMpKSny8PBQUFCQJCkqKko7duxQXl6eWSYpKUnt27cvttsNAACgULXeUTp//ryOHDlirh87dkwpKSkKCAhQs2bN9MADD2j//v1at26dCgoKlJ6eLkkKCAiQt7e3kpOTtWvXLvXt21cNGzZUcnKypkyZoocfftgMQSNGjNCsWbM0btw4TZs2TYcOHdLChQs1f/78amkzAABwH9UalPbu3au+ffua64XjgsaMGaP4+HitXbtWktS1a1enz23dulV9+vSRj4+PVqxYofj4eOXk5Kh169aaMmWK0/gif39/ffLJJ4qNjVVkZKSaNm2qZ599lqkBAAAVwuFwyDCMEsvZbDZ5eLh0Rw6KUa1BqU+fPlf94SrpB6979+76/PPPSzxP586d9emnn5a5fgAAXI3D4VDzsHClnz5ZYtmQ0BY69eMPhCU34/KDuQHUHqX5y7ygoKCKagOUzDAMpZ8+qQcSt8t2lQBkOBz6MLZ3qe48wbUQlAC4hLL8ZS5J4vcNXIjNw0MeHp6W+x1VWBdULIISAJdQ2r/MHfl5+mhSP3ISgCpBUALgUkr6y9zwoOsNQNVhRBkAAIAFghIAAIAFghIAAIAFghIAAICFcgWlNm3a6JdffimyPSsrS23atLnmSgEAALiCcgWl48ePFzvpW05Ojk6dOnXNlQIAAHAFZZoeoPDda5L08ccfy9/f31wvKCjQ5s2b1apVqwqrHAAAQHUqU1AaOnSopCsv9hszZozTvjp16qhVq1Z66aWXKqxyAAAA1alMQcnhuDIJe+vWrbVnzx41bdq0UioFAADgCso1M/exY8cquh4AAAAup9yvMNm8ebM2b96szMxM805TocWLF19zxQAAAKpbuYLSrFmzNHv2bN10001q1qyZbDZbRdcLAACg2pUrKC1atEhLly7VqFGjKro+AAAALqNc8yjl5ubqtttuq+i6AAAAuJRyBaXx48dr+fLlFV0XAAAAl1KurrfLly/rjTfe0KZNm9S5c2fVqVPHaf+8efMqpHIAAADVqVxB6cCBA+ratask6dChQ077GNgNAABqinIFpa1bt1Z0PQAAAFxOucYoAQAA1AbluqPUt2/fq3axbdmypdwVAgAAcBXlCkqF45MK5eXlKSUlRYcOHSryslwAAAB3Va6gNH/+/GK3x8fH6/z589dUIQAAAFdRoWOUHn74Yd7zBgAAaowKDUrJycny9fWtyEMCAABUm3J1vd1///1O64ZhKC0tTXv37tVf//rXCqkYAABAdStXUPL393da9/DwUPv27TV79mxFR0dXSMUAAACqW7mC0pIlSyq6HgAAVDqHwyHDMEpV1mazycOD6QZru3IFpUL79u3T119/LUnq0KGDunXrViGVAgCgojkcDjUPC1f66ZOlKh8S2kKnfvyBsFTLlSsoZWZm6qGHHtK2bdvUqFEjSVJWVpb69u2rFStWKDAwsFTH2bFjh+bMmaN9+/YpLS1Nq1ev1tChQ839hmFo5syZevPNN5WVlaXbb79dr7/+uq677jqzzJkzZzRp0iT961//koeHh4YNG6aFCxeqQYMGZpkDBw4oNjZWe/bsUWBgoCZNmqQ//vGP5Wk6AMBNGYah9NMn9UDidtlKCD+Gw6EPY3uX+u4Taq5yxeRJkybp3LlzOnz4sM6cOaMzZ87o0KFDstvtevLJJ0t9nAsXLqhLly5KTEwsdv+LL76ol19+WYsWLdKuXbtUv359xcTE6PLly2aZkSNH6vDhw0pKStK6deu0Y8cOTZgwwdxvt9sVHR2t8PBw7du3T3PmzFF8fLzeeOON8jQdAODmbB4e8vDwvOpSUpBC7VGuO0obN27Upk2bdOONN5rbIiIilJiYWKbB3IMGDdKgQYOK3WcYhhYsWKAZM2bo3nvvlSS98847Cg4O1po1a/TQQw/p66+/1saNG7Vnzx7ddNNNkqRXXnlFd911l+bOnavQ0FAtW7ZMubm5Wrx4sby9vdWhQwelpKRo3rx5ToEKAADgt8oVmR0Oh+rUqVNke506deRwOK65UpJ07Ngxpaena8CAAeY2f39/9ejRQ8nJyZKuzNvUqFEjMyRJ0oABA+Th4aFdu3aZZXr16iVvb2+zTExMjFJTU3X27Nliz52TkyO73e60AACA2qdcQalfv376r//6L50+fdrcdurUKU2ZMkX9+/evkIqlp6dLkoKDg522BwcHm/vS09MVFBTktN/Ly0sBAQFOZYo7xq/P8VsJCQny9/c3l7CwsGtvEAAAcDvlCkqvvvqq7Ha7WrVqpbZt26pt27Zq3bq17Ha7XnnllYquY5WbPn26srOzzeXHH3+s7ioBgMtzOBwqKCgo1VJRvQ9AZSvXGKWwsDDt379fmzZt0jfffCNJuvHGG526ya5VSEiIJCkjI0PNmjUzt2dkZKhr165mmczMTKfP5efn68yZM+bnQ0JClJGR4VSmcL2wzG/5+PjIx8enQtoBALUBj96jpipTUNqyZYvi4uL0+eefy8/PT3feeafuvPNOSVJ2drY6dOigRYsWqWfPntdcsdatWyskJESbN282g5HdbteuXbs0ceJESVJUVJSysrK0b98+RUZGmnV0OBzq0aOHWeYvf/mL8vLyzHFVSUlJat++vRo3bnzN9YT7Ke2Ec0w2B5Qej96jpirTb4EFCxbosccek5+fX5F9/v7+evzxxzVv3rxSH+/8+fNKSUlRSkqKpCsDuFNSUnTixAnZbDZNnjxZ//3f/621a9fq4MGDGj16tEJDQ825lm688UYNHDhQjz32mHbv3q3PPvtMcXFxeuihhxQaGipJGjFihLy9vTVu3DgdPnxYK1eu1MKFCzV16tSyNB01ROFfvV5eXiUuzcPC6R4AyohH71HTlOmO0pdffqm///3vlvujo6M1d+7cUh9v79696tu3r7leGF7GjBmjpUuX6o9//KMuXLigCRMmKCsrS3fccYc2btwoX19f8zPLli1TXFyc+vfvb044+fLLL5v7/f399cknnyg2NlaRkZFq2rSpnn32WaYGqKVK+1cvf/ECAKQyBqWMjIxipwUwD+blpZ9++qnUx+vTp89VfxHZbDbNnj1bs2fPtiwTEBCg5cuXX/U8nTt31qefflrqeqHmK/yr1wr3kQAAUhm73po3b65Dhw5Z7j9w4IDTwGsAAAB3VqagdNddd+mvf/2r0ytECl26dEkzZ87U3XffXWGVAwAAqE5l6nqbMWOGVq1apeuvv15xcXFq3769JOmbb75RYmKiCgoK9Je//KVSKgoAAFDVyhSUgoODtXPnTk2cOFHTp083xxfZbDbFxMQoMTGxyCzYAAAA7qrME06Gh4drw4YNOnv2rI4cOSLDMHTdddcxJxEAAKhxyjUztyQ1btxYN998c0XWBQAAwKUw6xcAAIAFghIAAIAFghIAAICFco9RAgC4N14QDZSMn3wAqIV4QTRQOtxRAoBaiBdEA6VDUAKAWowXRANXR9cbAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABd71BgBuwOFwlPrFtDabTR5XedEtgNLj/yQAcHEOh0PNw8Ll5eVVqqV5WLgcDl5nC1QE7igBgIszDEPpp0/qgcTtspVwp8hwOPRhbO9S332qTgUFBSWW4e4YqhtBCQDchM3DQx4enlct4w73kQyHQ/LwlI+PT4llQ0Jb6NSPPxCWUG0ISgCAKmZIjgINe3W7PDytA5A73R1DzUVQAgCUqKRustJ0o/1WSXfI3OHuGGo+ghIAwFJZusmufKBy6wNUNYISAOAqStdN5sjP00eT+pGTKgBTQbgWghIAoEQldZMZHmXvekNRhVNBpJ8+WaryDHavfC4flFq1aqUffvihyPYnnnhCiYmJ6tOnj7Zv3+607/HHH9eiRYvM9RMnTmjixInaunWrGjRooDFjxighIUFeXi7ffABALVJTp4JwZy6fFPbs2eM0SPDQoUO688479bvf/c7c9thjj2n27Nnmer169cyvCwoKNHjwYIWEhGjnzp1KS0vT6NGjVadOHT3//PNV0wgAAMqgpkwFURO4fFAKDAx0Wn/hhRfUtm1b9e7d29xWr149hYSEFPv5Tz75RF999ZU2bdqk4OBgde3aVc8995ymTZum+Ph4eXt7V2r9AQCA+3KrTs3c3Fy9++67evTRR2Wz2czty5YtU9OmTdWxY0dNnz5dFy9eNPclJyerU6dOCg4ONrfFxMTIbrfr8OHDxZ4nJydHdrvdaQEAALWPy99R+rU1a9YoKytLY8eONbeNGDFC4eHhCg0N1YEDBzRt2jSlpqZq1apVkqT09HSnkCTJXE9PTy/2PAkJCZo1a1blNAIAALgNtwpKb731lgYNGqTQ0FBz24QJE8yvO3XqpGbNmql///46evSo2rZtW67zTJ8+XVOnTjXX7Xa7wsLCyl9xAADgltwmKP3www/atGmTeafISo8ePSRJR44cUdu2bRUSEqLdu3c7lcnIyJAky3FNPj4+pZ9cDQAA1FhuM0ZpyZIlCgoK0uDBg69aLiUlRZLUrFkzSVJUVJQOHjyozMxMs0xSUpL8/PwUERFRafUFAADuzy3uKDkcDi1ZskRjxoxxmvvo6NGjWr58ue666y41adJEBw4c0JQpU9SrVy917txZkhQdHa2IiAiNGjVKL774otLT0zVjxgzFxsZy1wgAAFyVWwSlTZs26cSJE3r00Uedtnt7e2vTpk1asGCBLly4oLCwMA0bNkwzZswwy3h6emrdunWaOHGioqKiVL9+fY0ZM8Zp3iUAqGilfQ0Fr6AAXJtbBKXo6OhiLzhhYWFFZuUuTnh4uDZs2FAZVQOAIsryGgpeQQG4NrcISgDgTkr7GgpeQQG4PoISAEhOr0q6mrJ0lZX0GgpeQQG4PoISgEpV2rE6pQ0qFc1wOCQPz1I/3EFXGVC7EJQAVJqyjNUxVXkvlCE5CjTs1e3y8ORt7QCcEZQAVJrSjtWRJEd+nj6a1K/qc9L/4m3tAIpDUAJQ6UoTQgyP6ul6A4CroZMdAADAAkEJAADAAkEJAADAAkEJAADAAoO5AQCoIiXNF1Zd84nBGkEJAIBKVtaJTattngwUQVACAKDSlW5i0+qeTwxFEZQAAKgiJc0pxnxiroegBACABcYUgaAEAMBvMKYIhQhKAAAUwZgiXEFQAgC4tNJ0b9lsNnmU8OLl8mBM0bVzOBwyjJKjZGV9D6+V69UIAAA5d395eXlddWkeFi6Hw1HdVcZvOBwONQ8LL/H758rfQ+4oAQBcVOm6vwyHQx/G9i7VXQtULcMwlH76pB5I3C7bVe4WufL3kKAEAHBpJXV/ud49CNdT3d1f7vw9JCgBQA3EY+0oVNj9lX76ZIllQ0Jb6NSPP7jkWKHqQlACgBqEx9rxWzWh+6s6EZQAoEbhsXYUz527v6oTQQkAaiAeawcqBkEJuIrSjuNw1fk/AADXhqAEFKOs4zwYAAkANRNBCShW6cZ5SLV3AGRpHjfmySoA7o6gBFxFSeM8pNo5ALIsjxtL4skqAG6LoASgzEr7uDFPVgFwdwQlAOXGk1UAajpGngIAAFhw6aAUHx8vm83mtNxwww3m/suXLys2NlZNmjRRgwYNNGzYMGVkZDgd48SJExo8eLDq1aunoKAgPfPMM8rPz6/qpgAAADfk8l1vHTp00KZNm8x1L6//q/KUKVO0fv16ffDBB/L391dcXJzuv/9+ffbZZ5KuPHEzePBghYSEaOfOnUpLS9Po0aNVp04dPf/881XeFgAA4F5cPih5eXkpJCSkyPbs7Gy99dZbWr58ufr16ydJWrJkiW688UZ9/vnnuvXWW/XJJ5/oq6++0qZNmxQcHKyuXbvqueee07Rp0xQfHy9vb++qbg4AAHAjLt31JknfffedQkND1aZNG40cOVInTpyQJO3bt095eXkaMGCAWfaGG25Qy5YtlZycLElKTk5Wp06dFBwcbJaJiYmR3W7X4cOHq7YhAADA7bj0HaUePXpo6dKlat++vdLS0jRr1iz17NlThw4dUnp6ury9vdWoUSOnzwQHBys9PV2SlJ6e7hSSCvcX7rOSk5OjnJwcc91ut1dQiwAAgDtx6aA0aNAg8+vOnTurR48eCg8P1/vvv6+6detW2nkTEhI0a9asSjs+AABwDy7f9fZrjRo10vXXX68jR44oJCREubm5ysrKciqTkZFhjmkKCQkp8hRc4Xpx454KTZ8+XdnZ2eby448/VmxDAACAW3CroHT+/HkdPXpUzZo1U2RkpOrUqaPNmzeb+1NTU3XixAlFRUVJkqKionTw4EFlZmaaZZKSkuTn56eIiAjL8/j4+MjPz89pAQAAtY9Ld709/fTTGjJkiMLDw3X69GnNnDlTnp6eGj58uPz9/TVu3DhNnTpVAQEB8vPz06RJkxQVFaVbb71VkhQdHa2IiAiNGjVKL774otLT0zVjxgzFxsaW+q3wAACg9nLpoHTy5EkNHz5cv/zyiwIDA3XHHXfo888/V2BgoCRp/vz58vDw0LBhw5STk6OYmBi99tpr5uc9PT21bt06TZw4UVFRUapfv77GjBmj2bNnV1eTAABwew6HQ4ZR8lscCwrc/zVGLh2UVqxYcdX9vr6+SkxMVGJiomWZ8PBwbdiwoaKrBgBAreRwONQ8LFzpp0+W/kNu/GZslw5KAADg6kq6a1PRd3UMw1D66ZN6IHG7bB5XH+rsyM/TR5P6uXNOIigBAOCODIdD8vAs/ZjbCk4rNg8PeXh4Xv2UHnS9AQCAamFIjgINe3W7PDyt7+zUhLs61YmgBACAGyvpzk5NuKtTndxqHiUAAICqRFACAACwQFACAACwQFACAACwwGBuAECNUNXzCaF2ICgBFaQ0F2GbzSaPEiZoA1A21T2fEGo2ghJwjcpykQ4JbaFTP/5AWAIqFPMJofIQlIBrVrqLtOFw6MPY3qV6kSSAsmM+IVQGghJQQUq6SDv+97900QFwZYz1ckZQAqoIXXQAXBljvYpHUAKqDF10AFwZY72KQ1ACqlhpu+gAoDow1ssZQQmoJRwOR6nuUjE+quoxJgRwXQQloBZwOBxqHhau9NMnSyzL+Kiqw5gQwPURlIBawDAMpZ8+qQcSt8t2lQDE+KiqxpgQwNURlIBahPFRrokxIYDr4t46AACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABV6KixrD4XCU+Nb7ggJeLgoAKD2XvqOUkJCgm2++WQ0bNlRQUJCGDh2q1NRUpzJ9+vSRzWZzWv7whz84lTlx4oQGDx6sevXqKSgoSM8884zy8/OrsimoZA6HQ83DwuXl5XXVxcfH58oHrp6nAACQ5OJ3lLZv367Y2FjdfPPNys/P15///GdFR0frq6++Uv369c1yjz32mGbPnm2u16tXz/y6oKBAgwcPVkhIiHbu3Km0tDSNHj1aderU0fPPP1+l7UHlMQxD6adP6oHE7bJ5WOd/R36ePprUj5wEACgVlw5KGzdudFpfunSpgoKCtG/fPvXq1cvcXq9ePYWEhBR7jE8++URfffWVNm3apODgYHXt2lXPPfecpk2bpvj4eHl7e1dqG1C1bB4e8vDwtNxveND1BgAoPZfuevut7OxsSVJAQIDT9mXLlqlp06bq2LGjpk+frosXL5r7kpOT1alTJwUHB5vbYmJiZLfbdfjw4WLPk5OTI7vd7rQAAIDax6XvKP2aw+HQ5MmTdfvtt6tjx47m9hEjRig8PFyhoaE6cOCApk2bptTUVK1atUqSlJ6e7hSSJJnr6enpxZ4rISFBs2bNqqSWAAAAd+E2QSk2NlaHDh3Sf/7zH6ftEyZMML/u1KmTmjVrpv79++vo0aNq27Ztuc41ffp0TZ061Vy32+0KCwsrX8UBAIDbcouut7i4OK1bt05bt25VixYtrlq2R48ekqQjR45IkkJCQpSRkeFUpnDdalyTj4+P/Pz8nBYAAFD7uHRQMgxDcXFxWr16tbZs2aLWrVuX+JmUlBRJUrNmzSRJUVFROnjwoDIzM80ySUlJ8vPzU0RERKXUGwAA1Awu3fUWGxur5cuX65///KcaNmxojiny9/dX3bp1dfToUS1fvlx33XWXmjRpogMHDmjKlCnq1auXOnfuLEmKjo5WRESERo0apRdffFHp6emaMWOGYmNj/29OHbg0JpIEAFQXlw5Kr7/+uqQrk0r+2pIlSzR27Fh5e3tr06ZNWrBggS5cuKCwsDANGzZMM2bMMMt6enpq3bp1mjhxoqKiolS/fn2NGTPGad4luK7CiSTTT58s3QeYIAkAUIFcOiiVdBchLCxM27dvL/E44eHh2rBhQ0VVC1WIiSQBANXJpYMSUIiJJAEA1cGlB3MDAABUJ4ISAACABYISAACABYISAACABYISAACABYISAACABYISAACABYISAACABSacBFBESe/O4916AGoLghLgokoTRmw2mzyu8mqXsjIcDsnDs/QvjOadMQBqOIIS4GLKElZCQlvo1I8/VGBYMiRHgYa9ul0enrxbDwAISoDLKV1YMRwOfRjbu8SXR5cH79a7OromgdqDoAS4qJLCiqMK64Ir6JoEah+CEgCUGl2TQG1DUAKAMqJrEqg9mEcJAADAAneUADdXmoHDDC4GgPIhKKFaOByOUj2txS94a2UeWCwxuBgAyoighCrncDjUPCxc6adPlv5D/IIvRukGFksMLgaA8iIoocoZhqH00yf1QOJ22UqYKJFf8CUraWCxxOBiACgvghKqDb/gAQCujqCEClWasUeMOwIAuAuCUg1U2oHSUsW+VLXMY4/oTwMAuDiCUg1T1rBSkS9VLe3YI8YdAQDcBUGphinLQOnKeqkqsxYDAGoKglINVZqB0rxUFQCAqyMooVQYpA0AqI0ISigRg7QBALUVQcmNVNZdnZI+U1BQwCBtAECtRFByE5VxV6es7wqz2RikDQCoXQhKbqJyHr0v3bvCuFMEAKitKmamQTeRmJioVq1aydfXVz169NDu3buru0qSrtwtKigoKHGR/u9pNqulpCkBilMZxwQAoCaoNb8BV65cqalTp2rmzJnav3+/unTpopiYGGVmZlZrvQq71Ly8vK66mN1j3NYBAKDK1Jqut3nz5umxxx7TI488IklatGiR1q9fr8WLF+tPf/pTtdWL2awBAHBdteKOUm5urvbt26cBAwaY2zw8PDRgwAAlJydXY83+D91fAAC4nlpxR+nnn39WQUGBgoODnbYHBwfrm2++KVI+JydHOTk55np2drYkyW63V3jdCsce5V44V+IdpcJyJQ28Lk25yjgmdaSO1JE6UkfqWJ6yhuPKuyLsdrs8Pa/+VomyKPy9fU2v6jJqgVOnThmSjJ07dzptf+aZZ4xbbrmlSPmZM2caujIaiIWFhYWFhcXNlx9//LHcGaJW3FFq2rSpPD09lZGR4bQ9IyNDISEhRcpPnz5dU6dONdcdDofOnDmjJk2ayGazXXN97Ha7wsLC9OOPP8rPz++aj+eqaGfNU1vaSjtrFtpZs5SlnYZh6Ny5cwoNDS33+WpFUPL29lZkZKQ2b96soUOHSroSfjZv3qy4uLgi5X18fIpMwtioUaMKr5efn1+N/mEuRDtrntrSVtpZs9DOmqW07fT397+m89SKoCRJU6dO1ZgxY3TTTTfplltu0YIFC3ThwgXzKTgAAIDfqjVB6fe//71++uknPfvss0pPT1fXrl21cePGIgO8AQAACtWaoCRJcXFxxXa1VTUfHx/NnDmz1O9Yc1e0s+apLW2lnTUL7axZqrqdNsO4lmfmAAAAai5mMQQAALBAUAIAALBAUAIAALBAUAIAALBAUCqnHTt2aMiQIQoNDZXNZtOaNWuc9p8/f15xcXFq0aKF6tatq4iICC1atKjIcZKTk9WvXz/Vr19ffn5+6tWrly5dumTuP3PmjEaOHCk/Pz81atRI48aN0/nz5yu7eaaKaGd6erpGjRqlkJAQ1a9fX927d9dHH33kVMbV25mRkaGxY8cqNDRU9erV08CBA/Xdd985lbl8+bJiY2PVpEkTNWjQQMOGDSsyG/yJEyc0ePBg1atXT0FBQXrmmWeUn59f2c1zcq1tPXPmjCZNmqT27durbt26atmypZ588knznYiFqrutFfE9LWQYhgYNGlTscWpKO939WlSadrrDtSghIUE333yzGjZsqKCgIA0dOlSpqalOZSrqWrNt2zZ1795dPj4+ateunZYuXVrZzTNVRDu//PJLDR8+XGFhYapbt65uvPFGLVy4sMi5rrWdBKVyunDhgrp06aLExMRi90+dOlUbN27Uu+++q6+//lqTJ09WXFyc1q5da5ZJTk7WwIEDFR0drd27d2vPnj2Ki4uTx69ejjty5EgdPnxYSUlJWrdunXbs2KEJEyZUevsKVUQ7R48erdTUVK1du1YHDx7U/fffrwcffFBffPGFWcaV22kYhoYOHarvv/9e//znP/XFF18oPDxcAwYM0IULF8xyU6ZM0b/+9S998MEH2r59u06fPq3777/f3F9QUKDBgwcrNzdXO3fu1Ntvv62lS5fq2WefrZI2FrrWtp4+fVqnT5/W3LlzdejQIS1dulQbN27UuHHjzOO4Qlsr4ntaaMGCBcW+vqimtNPdr0Wlbac7XIu2b9+u2NhYff7550pKSlJeXp6io6Mr/Fpz7NgxDR48WH379lVKSoomT56s8ePH6+OPP3abdu7bt09BQUF69913dfjwYf3lL3/R9OnT9eqrr1ZsO8v9ljiYJBmrV6922tahQwdj9uzZTtu6d+9u/OUvfzHXe/ToYcyYMcPyuF999ZUhydizZ4+57d///rdhs9mMU6dOVUzly6C87axfv77xzjvvOJUJCAgw3nzzTcMwXL+dqamphiTj0KFD5raCggIjMDDQbENWVpZRp04d44MPPjDLfP3114YkIzk52TAMw9iwYYPh4eFhpKenm2Vef/11w8/Pz8jJyankVhWvPG0tzvvvv294e3sbeXl5hmG4XluvpZ1ffPGF0bx5cyMtLa3IcWpKO939WlTadrrbtcgwDCMzM9OQZGzfvt0wjIq71vzxj380OnTo4HSu3//+90ZMTExlN6lY5WlncZ544gmjb9++5npFtJM7SpXktttu09q1a3Xq1CkZhqGtW7fq22+/VXR0tCQpMzNTu3btUlBQkG677TYFBwerd+/e+s9//mMeIzk5WY0aNdJNN91kbhswYIA8PDy0a9euKm9TcUpqZ2GZlStX6syZM3I4HFqxYoUuX76sPn36SHL9dubk5EiSfH19zW0eHh7y8fExv1/79u1TXl6eBgwYYJa54YYb1LJlSyUnJ0u60s5OnTo5zQYfExMju92uw4cPV0VTSlSathYnOztbfn5+8vK6Moetq7e1tO28ePGiRowYocTExGJfoF0T2lkTrkWl/X6647WosEs7ICBAUsVda5KTk52OUVim8BhVrTzttDpO4TGkimknQamSvPLKK4qIiFCLFi3k7e2tgQMHKjExUb169ZIkff/995Kk+Ph4PfbYY9q4caO6d++u/v37m/3q6enpCgoKcjqul5eXAgIClJ6eXrUNslBSOyXp/fffV15enpo0aSIfHx89/vjjWr16tdq1ayfJ9dtZ+D/n9OnTdfbsWeXm5urvf/+7Tp48qbS0NElX2uDt7V3k5cnBwcFmG9LT04u8Mqdw3RXaKZWurb/1888/67nnnnPqnnD1tpa2nVOmTNFtt92me++9t9jj1IR21oRrUWm/n+52LXI4HJo8ebJuv/12dezY0axjRVxrrMrY7XansWlVobzt/K2dO3dq5cqVpboWlaWdBKVK8sorr+jzzz/X2rVrtW/fPr300kuKjY3Vpk2bJF35wZCkxx9/XI888oi6deum+fPnq3379lq8eHF1Vr1MSmqnJP31r39VVlaWNm3apL1792rq1Kl68MEHdfDgwWqseenVqVNHq1at0rfffquAgADVq1dPW7du1aBBg5zGcNQEZW2r3W7X4MGDFRERofj4+KqvcDmVpp1r167Vli1btGDBguqt7DUoTTtrwrWotD+37nYtio2N1aFDh7RixYrqrkqlqoh2Hjp0SPfee69mzpzp1KNREWrVu96qyqVLl/TnP/9Zq1ev1uDBgyVJnTt3VkpKiubOnasBAwaoWbNmkqSIiAinz9544406ceKEJCkkJESZmZlO+/Pz83XmzJliuwGqWmnaefToUb366qs6dOiQOnToIEnq0qWLPv30UyUmJmrRokUu305JioyMVEpKirKzs5Wbm6vAwED16NHDvEUfEhKi3NxcZWVlOf0FlJGRYbYhJCREu3fvdjpu4RMcrtJOqeS2Fjp37pwGDhyohg0bavXq1apTp465zx3aWlI7t2zZoqNHjxb5i3bYsGHq2bOntm3bViPaWROuRVLJ7XS3a1FcXJw5mLxFixbm9oq61oSEhBR5Ui4jI0N+fn6qW7duZTSpWNfSzkJfffWV+vfvrwkTJmjGjBlO+yqinTXrz2EXkZeXp7y8vCJ/gXt6epp/vbVq1UqhoaFFHof89ttvFR4eLkmKiopSVlaW9u3bZ+7fsmWLHA6HevToUcmtKFlp2nnx4kVJumoZV2/nr/n7+yswMFDfffed9u7da3bJREZGqk6dOtq8ebNZNjU1VSdOnFBUVJSkK+08ePCg04U4KSlJfn5+RX5JuQKrtkpX7iRFR0fL29tba9eudRobIrlXW63a+ac//UkHDhxQSkqKuUjS/PnztWTJEkk1o5014Vr0a1btdJdrkWEYiouL0+rVq7Vlyxa1bt3aaX9FXWuioqKcjlFYpvAYla0i2ilJhw8fVt++fTVmzBj97W9/K3KeCmlnqYd9w8m5c+eML774wvjiiy8MSca8efOML774wvjhhx8MwzCM3r17Gx06dDC2bt1qfP/998aSJUsMX19f47XXXjOPMX/+fMPPz8/44IMPjO+++86YMWOG4evraxw5csQsM3DgQKNbt27Grl27jP/85z/GddddZwwfPtxt2pmbm2u0a9fO6Nmzp7Fr1y7jyJEjxty5cw2bzWasX7/ebdr5/vvvG1u3bjWOHj1qrFmzxggPDzfuv/9+p2P84Q9/MFq2bGls2bLF2Lt3rxEVFWVERUWZ+/Pz842OHTsa0dHRRkpKirFx40YjMDDQmD59epW1syLamp2dbfTo0cPo1KmTceTIESMtLc1c8vPzXaatFfE9/S395mmrmtLOmnAtKqmd7nItmjhxouHv729s27bN6f+tixcvmmUq4lrz/fffG/Xq1TOeeeYZ4+uvvzYSExMNT09PY+PGjW7TzoMHDxqBgYHGww8/7HSMzMzMCm0nQamctm7dakgqsowZM8YwDMNIS0szxo4da4SGhhq+vr5G+/btjZdeeslwOBxOx0lISDBatGhh1KtXz4iKijI+/fRTp/2//PKLMXz4cKNBgwaGn5+f8cgjjxjnzp2rqmZWSDu//fZb4/777zeCgoKMevXqGZ07dy7yiK6rt3PhwoVGixYtjDp16hgtW7Y0ZsyYUeTx70uXLhlPPPGE0bhxY6NevXrGfffdZ6SlpTmVOX78uDFo0CCjbt26RtOmTY2nnnrKfKS+qlxrW60+L8k4duyYy7S1Ir6nv/XboGQYNaed7n4tKk073eFaZPX/1pIlS8wyFXWt2bp1q9G1a1fD29vbaNOmjdM5KltFtHPmzJnFHiM8PNzpXNfaTtv/VhgAAAC/wRglAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAFWmT58+mjx5crXWoVWrVk4vurXZbFqzZk211QeAa+OluACqzKpVq5xennstbDabVq9eraFDh1bI8QCgOAQlAFUmICCguqvgdnJzc+Xt7V3d1QBqLbreAFSZX3e9tWrVSs8//7weffRRNWzYUC1bttQbb7xhls3NzVVcXJyaNWsmX19fhYeHKyEhwfysJN13332y2Wzm+tGjR3XvvfcqODhYDRo00M0336xNmzZVSN379eunuLg4p20//fSTvL29zbeT5+Tk6Omnn1bz5s1Vv3599ejRQ9u2bTPL//LLLxo+fLiaN2+uevXqqVOnTnrvvfeK/BvFxcVp8uTJatq0qWJiYiqk/gDKh6AEoNq89NJLuummm/TFF1/oiSee0MSJE5WamipJevnll7V27Vq9//77Sk1N1bJly8xAtGfPHknSkiVLlJaWZq6fP39ed911lzZv3qwvvvhCAwcO1JAhQ3TixIlrruv48eO1fPly5eTkmNveffddNW/eXP369ZMkxcXFKTk5WStWrNCBAwf0u9/9TgMHDtR3330nSbp8+bIiIyO1fv16HTp0SBMmTNCoUaO0e/dup3O9/fbb8vb21meffaZFixZdc90BXINyv/oXAMqod+/exn/9138ZhmEY4eHhxsMPP2zuczgcRlBQkPH6668bhmEYkyZNMvr162c4HI5ijyXJWL16dYnn7NChg/HKK6+Y6+Hh4cb8+fPLfJxLly4ZjRs3NlauXGlu69y5sxEfH28YhmH88MMPhqenp3Hq1Cmnz/Xv39+YPn265XEHDx5sPPXUU+Z67969jW7dupVYHwBVgzFKAKpN586dza9tNptCQkKUmZkpSRo7dqzuvPNOtW/fXgMHDtTdd9+t6Ojoqx7v/Pnzio+P1/r165WWlqb8/HxdunSpQu4o+fr6atSoUVq8eLEefPBB7d+/X4cOHdLatWslSQcPHlRBQYGuv/56p8/l5OSoSZMmkqSCggI9//zzev/993Xq1Cnl5uYqJydH9erVc/pMZGTkNdcXQMUgKAGoNr99As5ms8nhcEiSunfvrmPHjunf//63Nm3apAcffFADBgzQhx9+aHm8p59+WklJSZo7d67atWununXr6oEHHlBubm6F1Hf8+PHq2rWrTp48qSVLlqhfv34KDw+XdCWkeXp6at++ffL09HT6XIMGDSRJc+bM0cKFC7VgwQJ16tRJ9evX1+TJk4vUr379+hVSXwDXjqAEwGX5+fnp97//vX7/+9/rgQce0MCBA3XmzBkFBASoTp06KigocCr/2WefaezYsbrvvvskXQkvx48fr7D6dOrUSTfddJPefPNNLV++XK+++qq5r1u3biooKFBmZqZ69uxZ7Oc/++wz3XvvvXr44YclSQ6HQ99++60iIiIqrI4AKhaDuQG4pHnz5um9997TN998o2+//VYffPCBQkJC1KhRI0lXnnzbvHmz0tPTdfbsWUnSddddp1WrViklJUVffvmlRowYYd6hqijjx4/XCy+8IMMwzEAmSddff71Gjhyp0aNHa9WqVTp27Jh2796thIQErV+/3qxfUlKSdu7cqa+//lqPP/64MjIyKrR+ACoWQQmAS2rYsKFefPFF3XTTTbr55pt1/PhxbdiwQR4eVy5bL730kpKSkhQWFqZu3bpJuhKuGjdurNtuu01DhgxRTEyMunfvXqH1Gj58uLy8vDR8+HD5+vo67VuyZIlGjx6tp556Su3bt9fQoUO1Z88etWzZUpI0Y8YMde/eXTExMerTp49CQkKYMBNwcTbDMIzqrgQAuIvjx4+rbdu22rNnT4WHMACuh6AEAKWQl5enX375RU8//bSOHTumzz77rLqrBKAK0PUGAJKef/55NWjQoNhl0KBB+uyzz9SsWTPt2bOHSSCBWoQ7SgAg6cyZMzpz5kyx++rWravmzZtXcY0AuAKCEgAAgAW63gAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACwQlAAAACz8f6JvfFK0caFKAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram shows that install_year count does span from 1861 and continuously,\n",
        "\n",
        "so for further analysis, it must be taken into account that break_year data is not present for years before 1980\n",
        "\n",
        "and breakage is only recorded up to 2016, meaning I should remove records with install_year in 2017 as whether these pipes have already broken or not in 2017 is unknown/not recorded so should be removed for this analysis"
      ],
      "metadata": {
        "id": "jMsT1QafB7wR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2[df2['install_year'] != 2017]\n",
        "len(df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edAH34JL7qrz",
        "outputId": "47e2d7cb-52ee-4a8e-86cb-96a8c6384715"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24766"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now looking at relation between install_year and break_year"
      ],
      "metadata": {
        "id": "L-Ua_IDmFtKM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('install_year')['break_year'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yF57TlXBFrnT",
        "outputId": "1daf56f9-6397-45b2-87db-ce76bf087e34"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "install_year\n",
              "1861    1\n",
              "1862    0\n",
              "1863    0\n",
              "1864    0\n",
              "1865    1\n",
              "       ..\n",
              "2012    0\n",
              "2013    0\n",
              "2014    0\n",
              "2015    0\n",
              "2016    0\n",
              "Name: break_year, Length: 156, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('install_year')['break_year'].count().plot()\n",
        "plt.xlabel('install_year')\n",
        "plt.ylabel('count of breakage')\n",
        "plt.title(\"install_year\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Owi5eDC6Fr04",
        "outputId": "97108c49-37b7-4ead-a9c8-84962f32e4fe"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('break_year')['pipe_id'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtMZYwoOFr-v",
        "outputId": "0b240881-7d2d-4cea-87be-d7a54d4e8fb6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "break_year\n",
              "1980.0    36\n",
              "1981.0    37\n",
              "1982.0    56\n",
              "1983.0    44\n",
              "1984.0    48\n",
              "1985.0    34\n",
              "1986.0    42\n",
              "1987.0    43\n",
              "1988.0    62\n",
              "1989.0    43\n",
              "1990.0    65\n",
              "1991.0    36\n",
              "1992.0    37\n",
              "1993.0    57\n",
              "1994.0    30\n",
              "1995.0    34\n",
              "1996.0    22\n",
              "1997.0    24\n",
              "1998.0    26\n",
              "1999.0    22\n",
              "2000.0    22\n",
              "2001.0    34\n",
              "2002.0    26\n",
              "2003.0    39\n",
              "2004.0    47\n",
              "2005.0    29\n",
              "2006.0    32\n",
              "2007.0    37\n",
              "2008.0    53\n",
              "2009.0    45\n",
              "2010.0    35\n",
              "2011.0    38\n",
              "2012.0    35\n",
              "2013.0    45\n",
              "2014.0    38\n",
              "2015.0    75\n",
              "2016.0    48\n",
              "Name: pipe_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('break_year')['pipe_id'].count().plot()\n",
        "plt.xlabel('break_year')\n",
        "plt.ylabel('count of breakage')\n",
        "plt.title('break_year')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YrN12i1t73v3",
        "outputId": "3000d7a7-701e-41b2-cd44-b0814926537d"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('install_year')['break_year'].count().sort_values(ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bip7rTsrPYLl",
        "outputId": "3df5da66-53b6-4042-b37c-11ae91620cb8"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "install_year\n",
              "1935    102\n",
              "1936     79\n",
              "1934     77\n",
              "1931     63\n",
              "1961     60\n",
              "Name: break_year, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('break_year')['pipe_id'].count().sort_values(ascending=False).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDgIISGyQp-M",
        "outputId": "a13ae743-cd33-4346-b462-04438311c4e7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "break_year\n",
              "2015.0    75\n",
              "1990.0    65\n",
              "1988.0    62\n",
              "1993.0    57\n",
              "1982.0    56\n",
              "Name: pipe_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('break_year')['pipe_id'].count().sort_values(ascending=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5mlctuhRC7g",
        "outputId": "749203c6-8f2c-4570-e72a-c6890e006b8b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "break_year\n",
              "2000.0    22\n",
              "1999.0    22\n",
              "1996.0    22\n",
              "1997.0    24\n",
              "1998.0    26\n",
              "Name: pipe_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For install_year, pipes installed in year 1935 had most number of breakage,\n",
        "\n",
        "and 1934 and 1936 also having high numbers of breakage as well as another hike in 1961\n",
        "\n",
        "For break_year, 2015 had most numbers of breakage and least numbers in 1996\n",
        "\n",
        "In the next step, instead of looking at install_year in the year the pipe was installed, I will interpret how install_year relates to break_year by looking at the number of years since install_year with break_year"
      ],
      "metadata": {
        "id": "wcVnLMaWPWwT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "New variable - pipe_year"
      ],
      "metadata": {
        "id": "2SN8yMk1RqkV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = df2.copy()"
      ],
      "metadata": {
        "id": "kFSone5mJFen"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3['install_year'] = df3['install_year'].astype('float64')"
      ],
      "metadata": {
        "id": "P9pXiijVhu0c"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3['break_year'] = df3['break_year'].fillna(0)"
      ],
      "metadata": {
        "id": "JOwfFms0jHkz"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2['pipe_year'] = [df3['break_year'][x] - df3['install_year'][x] if df3['break_year'][x] > 0 else df2['break_year'][x] for x in df3.index]"
      ],
      "metadata": {
        "id": "QwJXn_P-fc7v"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2BTGiU6mIRMl",
        "outputId": "cde42ff2-cf83-4ede-ba1c-b8b45243e414"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       pipe_id  install_year material  diameter  break_year  history  \\\n",
              "0  P0000000000          1985        D     20.32         NaN      0.0   \n",
              "1  P0000000001          1934        C     15.24         NaN      0.0   \n",
              "2  P0000000002          1904        C     20.32         NaN      0.0   \n",
              "3  P0000000003          1979        D     15.24         NaN      0.0   \n",
              "4  P0000000004          1987        D     20.32         NaN      0.0   \n",
              "\n",
              "   pipe_year  \n",
              "0        NaN  \n",
              "1        NaN  \n",
              "2        NaN  \n",
              "3        NaN  \n",
              "4        NaN  "
            ],
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              "      <th>pipe_id</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>P0000000004</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "</style>\n",
              "\n",
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              "</div>\n",
              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "New variable pipe_year has been added,\n",
        "\n",
        "so for every breakage record, this variable shows the number of years between the year the pipe was installed and broke\n",
        "\n",
        "In earlier section I looked into the actual years the pipes were installed and broke, this time looking into the pipe_year values of pipes when pipes broke"
      ],
      "metadata": {
        "id": "_2aI-cZwliLB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis findings #2"
      ],
      "metadata": {
        "id": "J1AM6d9hJa1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_year_grouped = df2.groupby('pipe_year', as_index=False)['break_year'].count()\n",
        "pipe_year_grouped.sort_values('break_year', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "wpOHpYLToypJ",
        "outputId": "1b105ae3-695e-45ea-9ee6-b5a837121c8e"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     pipe_year  break_year\n",
              "59        59.0          32\n",
              "51        51.0          31\n",
              "53        53.0          28\n",
              "57        57.0          28\n",
              "45        45.0          27\n",
              "..         ...         ...\n",
              "11        11.0           1\n",
              "131      134.0           1\n",
              "132      138.0           1\n",
              "133      139.0           1\n",
              "134      140.0           1\n",
              "\n",
              "[135 rows x 2 columns]"
            ],
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              "      <th>51</th>\n",
              "      <td>51.0</td>\n",
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              "      <td>57.0</td>\n",
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              "      <th>45</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>11.0</td>\n",
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              "    <tr>\n",
              "      <th>131</th>\n",
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              "      <td>1</td>\n",
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              "      <th>132</th>\n",
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              "      <th>134</th>\n",
              "      <td>140.0</td>\n",
              "      <td>1</td>\n",
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              "</table>\n",
              "<p>135 rows × 2 columns</p>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_year_grouped['pipe_year'].agg(['min', 'max', 'mean'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5UBREP7pvwb2",
        "outputId": "470c415b-9ccf-4515-83a9-f74c06878766"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "min       0.000000\n",
              "max     140.000000\n",
              "mean     67.244444\n",
              "Name: pipe_year, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The current dataset has 3128 breakage records and\n",
        "\n",
        "pipe_year variable values range from 0 to 140 years\n",
        "\n",
        "\n",
        "surprisingly, 0 pipe_year had the second highest numbers of breakage meaning 57 pipes had its breakage in its first year of installation already and possibly another breakages later on in its pipe_years, many breakages do happen in its first years of installaion, but whether it is because of particular materials, diameter, factors other than pipe_year need to be looked at\n",
        "\n",
        "I can also see small numbers of breakage at pipe_year with more than 130 years, I think breakage with such old pipe_year is natural however assuming their breakages are limited in this particular dataset as number of unique pipes with such long pipe_year must be limited in the data to start with (mean of pipe_year is 68 years), so I will do a quick check"
      ],
      "metadata": {
        "id": "wMrilHDErOLZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, looking at breakage with 0 pipe_year"
      ],
      "metadata": {
        "id": "TOFs1CyMvfwX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] == 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "tbNFVzI3oy3H",
        "outputId": "97fde112-9885-43ea-f1b8-6c0a4c484836"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           pipe_id  install_year material  diameter  break_year  history  \\\n",
              "402    P0000000400          1986        D     20.32      1986.0      1.0   \n",
              "947    P0000000936          1983        D     20.32      1983.0      1.0   \n",
              "1435   P0000001422          1984        D     15.24      1984.0      1.0   \n",
              "1747   P0000001727          1998        D     30.48      1998.0      1.0   \n",
              "2893   P0000002861          1991        D     20.32      1991.0      1.0   \n",
              "6816   P0000006705          1992        D     15.24      1992.0      1.0   \n",
              "9714   P0000009559          1983        D     20.32      1983.0      1.0   \n",
              "10737  P0000010565          1983        D     20.32      1983.0      1.0   \n",
              "12066  P0000011873          1983        D     20.32      1983.0      1.0   \n",
              "13489  P0000013270          1990        S    121.92      1990.0      1.0   \n",
              "13857  P0000013626          1986        D     20.32      1986.0      1.0   \n",
              "16380  P0000016099          1996        D     15.24      1996.0      1.0   \n",
              "16677  P0000016386          1990        D     10.16      1990.0      1.0   \n",
              "17135  P0000016832          1988        D     30.48      1988.0      1.0   \n",
              "17328  P0000017020          1992        D     20.32      1992.0      1.0   \n",
              "17866  P0000017553          1983        D     15.24      1983.0      1.0   \n",
              "18000  P0000017687          1988        D     15.24      1988.0      1.0   \n",
              "22490  P0000022045          1992        D     20.32      1992.0      1.0   \n",
              "22681  P0000022227          2008        D     50.80      2008.0      1.0   \n",
              "\n",
              "       pipe_year  \n",
              "402          0.0  \n",
              "947          0.0  \n",
              "1435         0.0  \n",
              "1747         0.0  \n",
              "2893         0.0  \n",
              "6816         0.0  \n",
              "9714         0.0  \n",
              "10737        0.0  \n",
              "12066        0.0  \n",
              "13489        0.0  \n",
              "13857        0.0  \n",
              "16380        0.0  \n",
              "16677        0.0  \n",
              "17135        0.0  \n",
              "17328        0.0  \n",
              "17866        0.0  \n",
              "18000        0.0  \n",
              "22490        0.0  \n",
              "22681        0.0  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pipe_id</th>\n",
              "      <th>install_year</th>\n",
              "      <th>material</th>\n",
              "      <th>diameter</th>\n",
              "      <th>break_year</th>\n",
              "      <th>history</th>\n",
              "      <th>pipe_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>402</th>\n",
              "      <td>P0000000400</td>\n",
              "      <td>1986</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>947</th>\n",
              "      <td>P0000000936</td>\n",
              "      <td>1983</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>1435</th>\n",
              "      <td>P0000001422</td>\n",
              "      <td>1984</td>\n",
              "      <td>D</td>\n",
              "      <td>15.24</td>\n",
              "      <td>1984.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1747</th>\n",
              "      <td>P0000001727</td>\n",
              "      <td>1998</td>\n",
              "      <td>D</td>\n",
              "      <td>30.48</td>\n",
              "      <td>1998.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2893</th>\n",
              "      <td>P0000002861</td>\n",
              "      <td>1991</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1991.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6816</th>\n",
              "      <td>P0000006705</td>\n",
              "      <td>1992</td>\n",
              "      <td>D</td>\n",
              "      <td>15.24</td>\n",
              "      <td>1992.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9714</th>\n",
              "      <td>P0000009559</td>\n",
              "      <td>1983</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10737</th>\n",
              "      <td>P0000010565</td>\n",
              "      <td>1983</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12066</th>\n",
              "      <td>P0000011873</td>\n",
              "      <td>1983</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1983.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13489</th>\n",
              "      <td>P0000013270</td>\n",
              "      <td>1990</td>\n",
              "      <td>S</td>\n",
              "      <td>121.92</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13857</th>\n",
              "      <td>P0000013626</td>\n",
              "      <td>1986</td>\n",
              "      <td>D</td>\n",
              "      <td>20.32</td>\n",
              "      <td>1986.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16380</th>\n",
              "      <td>P0000016099</td>\n",
              "      <td>1996</td>\n",
              "      <td>D</td>\n",
              "      <td>15.24</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16677</th>\n",
              "      <td>P0000016386</td>\n",
              "      <td>1990</td>\n",
              "      <td>D</td>\n",
              "      <td>10.16</td>\n",
              "      <td>1990.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17135</th>\n",
              "      <td>P0000016832</td>\n",
              "      <td>1988</td>\n",
              "      <td>D</td>\n",
              "      <td>30.48</td>\n",
              "      <td>1988.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328</th>\n",
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              "      <th>17866</th>\n",
              "      <td>P0000017553</td>\n",
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              "      <td>2008.0</td>\n",
              "      <td>1.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] == 0].groupby('material')['diameter'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wKVR5RP0yJik",
        "outputId": "c83dac48-7e6a-4951-cbc9-45f15ad02134"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "material\n",
              "D     21.166667\n",
              "S    121.920000\n",
              "Name: diameter, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quick look on the 57 records of pipe_year 0 shows most records are with material D or S with exceptionally large diameters,\n",
        "\n",
        "I will take a note on this when I look into the diameter variable"
      ],
      "metadata": {
        "id": "iwwoOu7RxpKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Secondly, looking at breakage with 53 pipe_year"
      ],
      "metadata": {
        "id": "hRC4ph6lyZ8H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] == 53]"
      ],
      "metadata": {
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          "height": 927
        },
        "id": "OSTI_O7lxnAa",
        "outputId": "7392e9f7-1d28-478d-c54d-53e9fe95f8e0"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           pipe_id  install_year material  diameter  break_year  history  \\\n",
              "1184   P0000001173          1959        C     10.16      2012.0      1.0   \n",
              "1685   P0000001665          1930        C     15.24      1983.0      1.0   \n",
              "4167   P0000004111          1949        C     40.64      2002.0      1.0   \n",
              "4681   P0000004616          1947        C     30.48      2000.0      2.0   \n",
              "5748   P0000005654          1939        C     15.24      1992.0      1.0   \n",
              "5998   P0000005899          1936        C     30.48      1989.0      1.0   \n",
              "7846   P0000007727          1937        C     20.32      1990.0      1.0   \n",
              "9184   P0000009033          1940        C     20.32      1993.0      1.0   \n",
              "13163  P0000012958          1939        C     15.24      1992.0      1.0   \n",
              "13177  P0000012972          1931        C     15.24      1984.0      1.0   \n",
              "13322  P0000013103          1955        C     20.32      2008.0      1.0   \n",
              "14323  P0000014085          1935        C     15.24      1988.0      1.0   \n",
              "16407  P0000016124          1932        C     15.24      1985.0      1.0   \n",
              "16743  P0000016451          1929        C     15.24      1982.0      2.0   \n",
              "17015  P0000016716          1929        C     10.16      1982.0      1.0   \n",
              "17339  P0000017031          1927        C     15.24      1980.0      1.0   \n",
              "17873  P0000017560          1930        C     15.24      1983.0      1.0   \n",
              "17945  P0000017632          1933        C     15.24      1986.0      1.0   \n",
              "18855  P0000018525          1933        C     15.24      1986.0      1.0   \n",
              "19067  P0000018725          1931        C     20.32      1984.0      1.0   \n",
              "19458  P0000019102          1934        C     20.32      1987.0      1.0   \n",
              "21394  P0000020982          1961        C     15.24      2014.0      2.0   \n",
              "21734  P0000021308          1936        C     15.24      1989.0      2.0   \n",
              "23947  P0000023460          1935        C     15.24      1988.0      2.0   \n",
              "24060  P0000023564          1936        C     15.24      1989.0      1.0   \n",
              "24256  P0000023752          1933        C     20.32      1986.0      2.0   \n",
              "24373  P0000023865          1934        C     20.32      1987.0      1.0   \n",
              "24388  P0000023880          1941        C     30.48      1994.0      1.0   \n",
              "\n",
              "       pipe_year  \n",
              "1184        53.0  \n",
              "1685        53.0  \n",
              "4167        53.0  \n",
              "4681        53.0  \n",
              "5748        53.0  \n",
              "5998        53.0  \n",
              "7846        53.0  \n",
              "9184        53.0  \n",
              "13163       53.0  \n",
              "13177       53.0  \n",
              "13322       53.0  \n",
              "14323       53.0  \n",
              "16407       53.0  \n",
              "16743       53.0  \n",
              "17015       53.0  \n",
              "17339       53.0  \n",
              "17873       53.0  \n",
              "17945       53.0  \n",
              "18855       53.0  \n",
              "19067       53.0  \n",
              "19458       53.0  \n",
              "21394       53.0  \n",
              "21734       53.0  \n",
              "23947       53.0  \n",
              "24060       53.0  \n",
              "24256       53.0  \n",
              "24373       53.0  \n",
              "24388       53.0  "
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              "      <th>pipe_id</th>\n",
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              "      <th>9184</th>\n",
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              "      <td>20.32</td>\n",
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              "      <th>13163</th>\n",
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              "      <th>16407</th>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] == 53].groupby('material')['diameter'].mean()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V1l2fQ4drZZQ",
        "outputId": "6ef90609-788c-49ee-a599-11ea68032c47"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "material\n",
              "C    18.687143\n",
              "Name: diameter, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] == 53].groupby('history')['pipe_id'].count()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vL2vbUSDrZgB",
        "outputId": "b6934afd-a33a-42f2-fa62-c336810a9aa9"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "history\n",
              "1.0    22\n",
              "2.0     6\n",
              "Name: pipe_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Differences I see between breakage with 0 pipe_year and 53 pipe_year are:\n",
        "\n",
        "*   Most records for 0 pipe_year was material D, but with 53 pipe_year material C\n",
        "*   0 pipe_year records are all history 1/first breakage (which makes sense), with 53 pipe_year majority is also the same, but also with second and third breakages for the same pipe\n",
        "\n",
        "These notes will be saved for later when I look into other variables and look into whether there is different patterns in breakages with different pipe_year"
      ],
      "metadata": {
        "id": "1KJBJXxZzYHf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Thirdly, breakage with more than 130 pipe_year"
      ],
      "metadata": {
        "id": "W1IlhfJ10vV_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['pipe_year'] >= 130]"
      ],
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          "height": 300
        },
        "id": "12z4dQTyzDMP",
        "outputId": "6a8fa753-fb2f-4c1b-d6fe-e8a049f7e897"
      },
      "execution_count": 69,
      "outputs": [
        {
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              "           pipe_id  install_year material  diameter  break_year  history  \\\n",
              "1492   P0000001477          1867        C     15.24      1998.0      1.0   \n",
              "6558   P0000006447          1883        C     15.24      2013.0      1.0   \n",
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              "24652  P0000024135          1875        C     20.32      2015.0      2.0   \n",
              "\n",
              "       pipe_year  \n",
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              "7256       133.0  \n",
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "The latest install_year in the dataset is 2016,\n",
        "so any pipes with breakage must be installed before 1886 in order to be included in this group"
      ],
      "metadata": {
        "id": "Z90ZJ_rp1c0a"
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      "execution_count": 70,
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              "           pipe_id  install_year material  diameter  break_year  history  \\\n",
              "55     P0000000055          1868        C     20.32         NaN      0.0   \n",
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              "      <td>NaN</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>502 rows × 7 columns</p>\n",
              "</div>\n",
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              "        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",
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              "\n",
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              "</svg>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "</div>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['install_year'] <= 1886].groupby('material').count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "iNAL5KJi9BOQ",
        "outputId": "13eb4c9e-133a-4ac6-e2ea-533fc750d932"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          pipe_id  install_year  diameter  break_year  history  pipe_year\n",
              "material                                                                 \n",
              "C             502           502       502          37      502         37"
            ],
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              "      <th>C</th>\n",
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              "      <td>37</td>\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-20672929-2e49-4450-a7ba-b36be0521ce7');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
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              "    fill: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-be01a28e-c682-4a0e-b40e-22ffe7e036f0 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(len(df2[df2['pipe_year'] >= 130]) / 853 * 100, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6W8F-S2Q1Oed",
        "outputId": "51f2b785-8012-45db-bdf4-8722257ed482"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.94"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have to note here that out of all 853 records of pipes installed before 1886, only 1.41% of the records are of breakage, meaning most of 853 records are of pipes installed before 1886 but looks as though still no record of breakage in 2016, and even the records of breakage are mostly of the first breakage of the pipe probably largely due to how breakage is only recorded from 1980 onwards in this dataset as covered earlier in this notebook\n",
        "\n",
        "Still, this is quite surprising considering the ratio of breakage with no breakage ratio of the entire dataset earlier, as well as how significant number of pipes already broke in its first year of installation\n",
        "\n",
        "Only D and S material pipes broke in 0 pipe_year,\n",
        "\n",
        "only C material broke in pipe_year more than 130 years, but only material C pipes were installed at the time so there is strong relation between install_year, material, history and pipe_year variables\n",
        "\n",
        "Still an asuumption at this stage with not looking at other variables yet, certain pipes do break in its first year, at the same time certain pipes with possibly other conditions, may manage to keep itself break-free even 130 years after its installation\n"
      ],
      "metadata": {
        "id": "w_1r3wqG6l1o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis of numerical variables Part 2"
      ],
      "metadata": {
        "id": "wt_NlidvROLX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "history variable"
      ],
      "metadata": {
        "id": "k0xi7hg7RbT6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 3128 breakage in the dataset, by hisotry\n",
        "\n",
        "df2[df2['history'] > 0].groupby('history', as_index=False)['pipe_year'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Pyp5vPf6NrvM",
        "outputId": "70053244-7112-4d1e-f8a4-d87c355cc65e"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   history  pipe_year\n",
              "0      1.0       1161\n",
              "1      2.0        219\n",
              "2      3.0         60\n",
              "3      4.0         20\n",
              "4      5.0          9\n",
              "5      6.0          3\n",
              "6      7.0          2\n",
              "7      8.0          2"
            ],
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-39922100-9a9f-4ddf-b0c5-3675fbcbf7af 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-39922100-9a9f-4ddf-b0c5-3675fbcbf7af');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bd195c1d-c68b-422f-b821-5a03ab83a6a8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bd195c1d-c68b-422f-b821-5a03ab83a6a8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bd195c1d-c68b-422f-b821-5a03ab83a6a8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# average pipe_year by history\n",
        "\n",
        "df2[df2['history'] > 0].groupby('history', as_index=False)['pipe_year'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "5_EMVI0t1Ov5",
        "outputId": "30de0ed7-8163-4aa6-ff1e-70b0b05640ab"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   history  pipe_year\n",
              "0      1.0  58.272179\n",
              "1      2.0  62.461187\n",
              "2      3.0  63.983333\n",
              "3      4.0  66.300000\n",
              "4      5.0  62.777778\n",
              "5      6.0  60.666667\n",
              "6      7.0  56.000000\n",
              "7      8.0  61.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f202b5f1-144c-4923-bd3b-6f5b4cfd8188\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>history</th>\n",
              "      <th>pipe_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>58.272179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>62.461187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>63.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>66.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>62.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.0</td>\n",
              "      <td>60.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.0</td>\n",
              "      <td>56.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8.0</td>\n",
              "      <td>61.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f202b5f1-144c-4923-bd3b-6f5b4cfd8188')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f202b5f1-144c-4923-bd3b-6f5b4cfd8188 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-f202b5f1-144c-4923-bd3b-6f5b4cfd8188');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ac96e955-81c2-4b8c-82ea-314dad7413b4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ac96e955-81c2-4b8c-82ea-314dad7413b4')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ac96e955-81c2-4b8c-82ea-314dad7413b4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "diameter variable\n",
        "\n",
        "This is a continuous variable, so I will check and understand its distribution with histogram and check for outliers"
      ],
      "metadata": {
        "id": "P5fClgzVPpjS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].hist(bins=20, figsize=(10,5))\n",
        "plt.xlabel('diameter')\n",
        "plt.ylabel('count')\n",
        "plt.title('diameter')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "5dQGZil5UK8y",
        "outputId": "4656a7ef-dbbc-496c-882f-062676f24717"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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AAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQ06kFVXV2vixImKjY1VUFCQOnXqpClTpsiyLHuOZVnKzMxU27ZtFRQUpMTERO3Zs8dtnYMHDyolJUWhoaEKCwvT8OHDdfToUbc5n3zyia677joFBgYqJiZGOTk59fIcAQAAADRdDTqQTZs2TS+88IKef/557dq1S9OmTVNOTo7mzJljz8nJydHs2bOVm5urwsJCNWvWTMnJyTp+/Lg9JyUlRTt27FBeXp6WLl2qdevWaeTIkfb+srIyJSUlqUOHDioqKtKzzz6rrKwsvfTSS/X6fAEAAAA0LX6mCziTDz/8UHfddZcGDhwoSerYsaP+8Y9/aOPGjZJ+ODs2a9YsPfHEE7rrrrskSa+99poiIyO1ZMkSDRkyRLt27dLy5cu1adMmxcXFSZLmzJmj2267TX/84x8VHR2t+fPnq7KyUq+88ooCAgJ02WWXaevWrZoxY4ZbcAMAAAAAT2rQgaxv37566aWX9Nlnn+nSSy/Vxx9/rPXr12vGjBmSpL1796q4uFiJiYn2MS1atFB8fLwKCgo0ZMgQFRQUKCwszA5jkpSYmCgfHx8VFhbq7rvvVkFBgfr376+AgAB7TnJysqZNm6ZDhw6pZcuWtWqrqKhQRUWFvV1WViZJqqqqUlVVlf39yV/RMDh9rbNPOpd1fCy3r3XBa6Nh4z3s/eixd6O/3o8ee7fG3N/zqblBB7Lx48errKxMXbt2la+vr6qrqzV16lSlpKRIkoqLiyVJkZGRbsdFRkba+4qLixUREeG238/PT+Hh4W5zYmNja61Rs+9UgSw7O1uTJk2qNb5y5UoFBwe7jeXl5Z3zc8aFl3O1Z9ebEueq87HvvvuuByvBhcJ72PvRY+9Gf70fPfZujbG/5eXl5zy3QQeyhQsXav78+VqwYIF9GWF6erqio6M1bNgwo7VNmDBBGRkZ9nZZWZliYmKUlJSk0NBQST8k47y8PN18883y9/c3VSp+pEfWCo+s4/SxNCXOpYkf+ajC5ajTGtuzkj1SCy4M3sPejx57N/rr/eixd2vM/a25eu5cNOhANmbMGI0fP15DhgyRJPXs2VNffvmlsrOzNWzYMEVFRUmSSkpK1LZtW/u4kpIS9erVS5IUFRWlAwcOuK174sQJHTx40D4+KipKJSUlbnNqtmvm/JjT6ZTT6aw17u/vX+sFc6oxmFNRXbfwdNr1XI46r8nronHgPez96LF3o7/ejx57t8bY3/Opt0HfZbG8vFw+Pu4l+vr6yuX64RKx2NhYRUVFadWqVfb+srIyFRYWKiEhQZKUkJCg0tJSFRUV2XNWr14tl8ul+Ph4e866devcrvXMy8tTly5dTnm5IgAAAAB4QoMOZHfccYemTp2qZcuW6YsvvtDixYs1Y8YM3X333ZIkh8Oh9PR0PfXUU3r77be1bds2DR06VNHR0Ro0aJAkqVu3brrllls0YsQIbdy4UR988IHS0tI0ZMgQRUdHS5J+8YtfKCAgQMOHD9eOHTv0xhtv6LnnnnO7JBEAAAAAPK1BX7I4Z84cTZw4UaNHj9aBAwcUHR2t3/zmN8rMzLTnjB07VseOHdPIkSNVWlqqa6+9VsuXL1dgYKA9Z/78+UpLS9OAAQPk4+OjwYMHa/bs2fb+Fi1aaOXKlUpNTVWfPn3UunVrZWZmcst7AAAAABdUgw5kzZs316xZszRr1qzTznE4HJo8ebImT5582jnh4eFasGDBGR/r8ssv1/vvv1/XUgEAAADgvDXoSxYBAAAAwJsRyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACG+JkuAE1Dx/HLTJcAAAAANDicIQMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQxp8IPv666/1y1/+Uq1atVJQUJB69uypjz76yN5vWZYyMzPVtm1bBQUFKTExUXv27HFb4+DBg0pJSVFoaKjCwsI0fPhwHT161G3OJ598ouuuu06BgYGKiYlRTk5OvTw/AAAAAE1Xgw5khw4dUr9+/eTv76/33ntPO3fu1PTp09WyZUt7Tk5OjmbPnq3c3FwVFhaqWbNmSk5O1vHjx+05KSkp2rFjh/Ly8rR06VKtW7dOI0eOtPeXlZUpKSlJHTp0UFFRkZ599lllZWXppZdeqtfnCwAAAKBp8avLQTfddJPefPNNhYWFuY2XlZVp0KBBWr16tSdq07Rp0xQTE6NXX33VHouNjbW/tyxLs2bN0hNPPKG77rpLkvTaa68pMjJSS5Ys0ZAhQ7Rr1y4tX75cmzZtUlxcnCRpzpw5uu222/THP/5R0dHRmj9/viorK/XKK68oICBAl112mbZu3aoZM2a4BTcAAAAA8KQ6BbK1a9eqsrKy1vjx48f1/vvv/+Siarz99ttKTk7Wvffeq/z8fF100UUaPXq0RowYIUnau3eviouLlZiYaB/TokULxcfHq6CgQEOGDFFBQYHCwsLsMCZJiYmJ8vHxUWFhoe6++24VFBSof//+CggIsOckJydr2rRpOnTokNsZuRoVFRWqqKiwt8vKyiRJVVVVqqqqsr8/+WtT5vS1TJfgcU4fy+1rXfDaaNh4D3s/euzd6K/3o8ferTH393xqPq9A9sknn9jf79y5U8XFxfZ2dXW1li9frosuuuh8ljyjzz//XC+88IIyMjL0+OOPa9OmTXrkkUcUEBCgYcOG2Y8fGRnpdlxkZKS9r7i4WBEREW77/fz8FB4e7jbn5DNvJ69ZXFx8ykCWnZ2tSZMm1RpfuXKlgoOD3cby8vLO52l7pZyrTVdw4UyJc9X52HfffdeDleBC4T3s/eixd6O/3o8ee7fG2N/y8vJznntegaxXr15yOBxyOBy66aabau0PCgrSnDlzzmfJM3K5XIqLi9PTTz8tSerdu7e2b9+u3NxcDRs2zGOPUxcTJkxQRkaGvV1WVqaYmBglJSUpNDRU0g/JOC8vTzfffLP8/f1Nldog9MhaYboEj3P6WJoS59LEj3xU4XLUaY3tWckergqexHvY+9Fj70Z/vR899m6Nub81V8+di/MKZHv37pVlWbr44ou1ceNGtWnTxt4XEBCgiIgI+fr6ns+SZ9S2bVt1797dbaxbt27617/+JUmKioqSJJWUlKht27b2nJKSEvXq1cuec+DAAbc1Tpw4oYMHD9rHR0VFqaSkxG1OzXbNnB9zOp1yOp21xv39/Wu9YE411tRUVNctsDQGFS5HnZ9fU39dNBa8h70fPfZu9Nf70WPv1hj7ez71ntddFjt06KCOHTvaZ646dOhg/2rbtq1Hw5gk9evXT7t373Yb++yzz9ShQwdJP9zgIyoqSqtWrbL3l5WVqbCwUAkJCZKkhIQElZaWqqioyJ6zevVquVwuxcfH23PWrVvndq1nXl6eunTpcsrLFQEAAADAE+p0Uw9J2rNnj9asWaMDBw7I5XL/DE1mZuZPLkySHnvsMfXt21dPP/207rvvPm3cuFEvvfSSfTt6h8Oh9PR0PfXUU+rcubNiY2M1ceJERUdHa9CgQZJ+OKN2yy23aMSIEcrNzVVVVZXS0tI0ZMgQRUdHS5J+8YtfaNKkSRo+fLjGjRun7du367nnntPMmTM98jwAAAAA4FTqFMhefvlljRo1Sq1bt1ZUVJQcjv9/uZbD4fBYILvqqqu0ePFiTZgwQZMnT1ZsbKxmzZqllJQUe87YsWN17NgxjRw5UqWlpbr22mu1fPlyBQYG2nPmz5+vtLQ0DRgwQD4+Pho8eLBmz55t72/RooVWrlyp1NRU9enTR61bt1ZmZia3vAcAAABwQdUpkD311FOaOnWqxo0b5+l6arn99tt1++23n3a/w+HQ5MmTNXny5NPOCQ8P14IFC874OJdffrlHb9kPAAAAAGdzXp8hq3Ho0CHde++9nq4FAAAAAJqUOgWye++9VytXrvR0LQAAAADQpNTpksVLLrlEEydO1IYNG9SzZ89at3V85JFHPFIcAAAAAHizOgWyl156SSEhIcrPz1d+fr7bPofDQSADAAAAgHNQp0C2d+9eT9cBAAAAAE1OnT5DBgAAAAD46ep0huyhhx464/5XXnmlTsUAAAAAQFNSp0B26NAht+2qqipt375dpaWluummmzxSGAAAAAB4uzoFssWLF9cac7lcGjVqlDp16vSTiwIAAACApsBjnyHz8fFRRkaGZs6c6aklAQAAAMCrefSmHv/zP/+jEydOeHJJAAAAAPBadbpkMSMjw23bsix98803WrZsmYYNG+aRwgAAAADA29UpkG3ZssVt28fHR23atNH06dPPegdGAAAAAMAP6hTI1qxZ4+k6AAAAAKDJqVMgq/Htt99q9+7dkqQuXbqoTZs2HikKAAAAAJqCOt3U49ixY3rooYfUtm1b9e/fX/3791d0dLSGDx+u8vJyT9cIAAAAAF6pToEsIyND+fn5euedd1RaWqrS0lK99dZbys/P1+9+9ztP1wgAAAAAXqlOlyz+61//0j//+U/dcMMN9thtt92moKAg3XfffXrhhRc8VR8AAAAAeK06nSErLy9XZGRkrfGIiAguWQQAAACAc1SnQJaQkKAnn3xSx48ft8e+//57TZo0SQkJCR4rDgAAAAC8WZ0uWZw1a5ZuueUWtWvXTldccYUk6eOPP5bT6dTKlSs9WiAAAAAAeKs6BbKePXtqz549mj9/vj799FNJ0v3336+UlBQFBQV5tEAAAAAA8FZ1CmTZ2dmKjIzUiBEj3MZfeeUVffvttxo3bpxHigMAAAAAb1anz5C9+OKL6tq1a63xyy67TLm5uT+5KAAAAABoCuoUyIqLi9W2bdta423atNE333zzk4sCAAAAgKagToEsJiZGH3zwQa3xDz74QNHR0T+5KAAAAABoCur0GbIRI0YoPT1dVVVVuummmyRJq1at0tixY/W73/3OowUCAAAAgLeqUyAbM2aMvvvuO40ePVqVlZWSpMDAQI0bN04TJkzwaIEAAAAA4K3qFMgcDoemTZumiRMnateuXQoKClLnzp3ldDo9XR8AAAAAeK06BbIaISEhuuqqqzxVCwAAAAA0KXW6qQcAAAAA4KcjkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhjSqQPbMM8/I4XAoPT3dHjt+/LhSU1PVqlUrhYSEaPDgwSopKXE7bt++fRo4cKCCg4MVERGhMWPG6MSJE25z1q5dqyuvvFJOp1OXXHKJ5s2bVw/PCAAAAEBT1mgC2aZNm/Tiiy/q8ssvdxt/7LHH9M4772jRokXKz8/X/v37dc8999j7q6urNXDgQFVWVurDDz/UX//6V82bN0+ZmZn2nL1792rgwIG68cYbtXXrVqWnp+vXv/61VqxYUW/PDwAAAEDT0ygC2dGjR5WSkqKXX35ZLVu2tMcPHz6sv/zlL5oxY4Zuuukm9enTR6+++qo+/PBDbdiwQZK0cuVK7dy5U3//+9/Vq1cv3XrrrZoyZYrmzp2ryspKSVJubq5iY2M1ffp0devWTWlpafrZz36mmTNnGnm+AAAAAJoGP9MFnIvU1FQNHDhQiYmJeuqpp+zxoqIiVVVVKTEx0R7r2rWr2rdvr4KCAl1zzTUqKChQz549FRkZac9JTk7WqFGjtGPHDvXu3VsFBQVua9TMOfnSyB+rqKhQRUWFvV1WViZJqqqqUlVVlf39yV+bMqevZboEj3P6WG5f64LXRsPGe9j70WPvRn+9Hz32bo25v+dTc4MPZK+//ro2b96sTZs21dpXXFysgIAAhYWFuY1HRkaquLjYnnNyGKvZX7PvTHPKysr0/fffKygoqNZjZ2dna9KkSbXGV65cqeDgYLexvLy8szxL75dztekKLpwpca46H/vuu+96sBJcKLyHvR899m701/vRY+/WGPtbXl5+znMbdCD76quv9OijjyovL0+BgYGmy3EzYcIEZWRk2NtlZWWKiYlRUlKSQkNDJf2QjPPy8nTzzTfL39/fVKkNQo8s7/s8ntPH0pQ4lyZ+5KMKl6NOa2zPSvZwVfAk3sPejx57N/rr/eixd2vM/a25eu5cNOhAVlRUpAMHDujKK6+0x6qrq7Vu3To9//zzWrFihSorK1VaWup2lqykpERRUVGSpKioKG3cuNFt3Zq7MJ4858d3ZiwpKVFoaOgpz45JktPplNPprDXu7+9f6wVzqrGmpqK6boGlMahwOer8/Jr666Kx4D3s/eixd6O/3o8ee7fG2N/zqbdB39RjwIAB2rZtm7Zu3Wr/iouLU0pKiv29v7+/Vq1aZR+ze/du7du3TwkJCZKkhIQEbdu2TQcOHLDn5OXlKTQ0VN27d7fnnLxGzZyaNQAAAADgQmjQZ8iaN2+uHj16uI01a9ZMrVq1sseHDx+ujIwMhYeHKzQ0VL/97W+VkJCga665RpKUlJSk7t2764EHHlBOTo6Ki4v1xBNPKDU11T7D9fDDD+v555/X2LFj9dBDD2n16tVauHChli1bVr9PGAAAAECT0qAD2bmYOXOmfHx8NHjwYFVUVCg5OVl/+tOf7P2+vr5aunSpRo0apYSEBDVr1kzDhg3T5MmT7TmxsbFatmyZHnvsMT333HNq166d/vznPys5mc/3AAAAALhwGl0gW7t2rdt2YGCg5s6dq7lz5572mA4dOpz1bnY33HCDtmzZ4okSAQAAAOCcNOjPkAEAAACANyOQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAM8TNdAICGoeP4ZaZLcPPFMwNNlwAAAHDBcYYMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAED/TBeDC6Dh+mekSAAAAAJwFZ8gAAAAAwBACGQAAAAAY0qADWXZ2tq666io1b95cERERGjRokHbv3u025/jx40pNTVWrVq0UEhKiwYMHq6SkxG3Ovn37NHDgQAUHBysiIkJjxozRiRMn3OasXbtWV155pZxOpy655BLNmzfvQj89AAAAAE1cgw5k+fn5Sk1N1YYNG5SXl6eqqiolJSXp2LFj9pzHHntM77zzjhYtWqT8/Hzt379f99xzj72/urpaAwcOVGVlpT788EP99a9/1bx585SZmWnP2bt3rwYOHKgbb7xRW7duVXp6un79619rxYoV9fp8AQAAADQtDfqmHsuXL3fbnjdvniIiIlRUVKT+/fvr8OHD+stf/qIFCxbopptukiS9+uqr6tatmzZs2KBrrrlGK1eu1M6dO/Xvf/9bkZGR6tWrl6ZMmaJx48YpKytLAQEBys3NVWxsrKZPny5J6tatm9avX6+ZM2cqOTm53p83AAAAgKahQQeyHzt8+LAkKTw8XJJUVFSkqqoqJSYm2nO6du2q9u3bq6CgQNdcc40KCgrUs2dPRUZG2nOSk5M1atQo7dixQ71791ZBQYHbGjVz0tPTT1tLRUWFKioq7O2ysjJJUlVVlaqqquzvT/5an5y+Vr0/ZlPj9LHcvtaFidfG6TS010xD+NmYfA+jftBj70Z/vR899m6Nub/nU3OjCWQul0vp6enq16+fevToIUkqLi5WQECAwsLC3OZGRkaquLjYnnNyGKvZX7PvTHPKysr0/fffKygoqFY92dnZmjRpUq3xlStXKjg42G0sLy/vPJ6pZ+RcXe8P2WRNiXPV+dh3333Xg5X8NA3tNdOQfjYm3sOoX/TYu9Ff70ePvVtj7G95efk5z200gSw1NVXbt2/X+vXrTZciSZowYYIyMjLs7bKyMsXExCgpKUmhoaGSfkjGeXl5uvnmm+Xv71+v9fXI4vNvF5rTx9KUOJcmfuSjCpejTmtsz2o4l8Q2tNdMQ/jZmHwPo37QY+9Gf70fPfZujbm/NVfPnYtGEcjS0tK0dOlSrVu3Tu3atbPHo6KiVFlZqdLSUrezZCUlJYqKirLnbNy40W29mrswnjznx3dmLCkpUWho6CnPjkmS0+mU0+msNe7v71/rBXOqsQutorpuAQHnr8LlqPPPuyH95tLQXjMN6Wdj4j2M+kWPvRv99X702Ls1xv6eT70N+i6LlmUpLS1Nixcv1urVqxUbG+u2v0+fPvL399eqVavssd27d2vfvn1KSEiQJCUkJGjbtm06cOCAPScvL0+hoaHq3r27PefkNWrm1KwBAAAAABdCgz5DlpqaqgULFuitt95S8+bN7c98tWjRQkFBQWrRooWGDx+ujIwMhYeHKzQ0VL/97W+VkJCga665RpKUlJSk7t2764EHHlBOTo6Ki4v1xBNPKDU11T7D9fDDD+v555/X2LFj9dBDD2n16tVauHChli1bZuy5AwAAAPB+DfoM2QsvvKDDhw/rhhtuUNu2be1fb7zxhj1n5syZuv322zV48GD1799fUVFRevPNN+39vr6+Wrp0qXx9fZWQkKBf/vKXGjp0qCZPnmzPiY2N1bJly5SXl6crrrhC06dP15///GdueQ8AAADggmrQZ8gs6+y34Q4MDNTcuXM1d+7c087p0KHDWe/YdsMNN2jLli3nXSMAAAAA1FWDPkMGAAAAAN6MQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhfqYLAICGrkfWClVUO0yXIUn64pmBpksAAAAexBkyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhEAGAAAAAIYQyAAAAADAEAIZAAAAABhCIAMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAAAAAMAQAhkAAAAAGEIgAwAAAABDCGQAAAAAYAiBDAAAAAAM8TNdAAAAP1XH8cvqdJzT11LO1VKPrBWqqHZ4rJ4vnhnosbUAAN6NM2QAAAAAYAiBDAAAAAAMIZABAAAAgCEEMgAAAAAwhJt6AEAjUtebV1wI3LgCAICfjjNkAAAAAGAIgQwAAAAADOGSRQAAUC/q+5LbM/0/c1xyC6Ch4AwZAAAAABjCGTIAAADDuGEP0HRxhuxH5s6dq44dOyowMFDx8fHauHGj6ZIAAAAAeCnOkJ3kjTfeUEZGhnJzcxUfH69Zs2YpOTlZu3fvVkREhOnyAKBBaUj/og8AQGNFIDvJjBkzNGLECD344IOSpNzcXC1btkyvvPKKxo8fb7g6AAAAoOH9gxiXuf40BLL/U1lZqaKiIk2YMMEe8/HxUWJiogoKCmrNr6ioUEVFhb19+PBhSdLBgwdVVVUlSaqqqlJ5ebm+++47+fv7X+Bn4M7vxLF6fbymyM9lqbzcJb8qH1W7HGc/4BQu+f1CD1dVdw3tN4PvvvvOdAn2e/in9BgNmyfex6fSEF6/DVF9/9l0pv42tB41pD+3G9KfTYUTBpxxf33+XSs+e9UFXf98NLQ/sy/Ua8bpY+mJ3i71+sObqjjH36PP9pqpL0eOHJEkWZZ11rkO61xmNQH79+/XRRddpA8//FAJCQn2+NixY5Wfn6/CwkK3+VlZWZo0aVJ9lwkAAACgkfjqq6/Url27M85paAG70ZgwYYIyMjLsbZfLpYMHD6pVq1ZyOH5I8GVlZYqJidFXX32l0NBQU6XiAqG/3o8eez967N3or/ejx96tMffXsiwdOXJE0dHRZ51LIPs/rVu3lq+vr0pKStzGS0pKFBUVVWu+0+mU0+l0GwsLCzvl2qGhoY3uRYRzR3+9Hz32fvTYu9Ff70ePvVtj7W+LFi3OaR63vf8/AQEB6tOnj1at+v/XB7tcLq1atcrtEkYAAAAA8BTOkJ0kIyNDw4YNU1xcnK6++mrNmjVLx44ds++6CAAAAACeRCA7yc9//nN9++23yszMVHFxsXr16qXly5crMjKyTus5nU49+eSTtS5thHegv96PHns/euzd6K/3o8feran0l7ssAgAAAIAhfIYMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDILpC5c+eqY8eOCgwMVHx8vDZu3Gi6JNRRdna2rrrqKjVv3lwREREaNGiQdu/e7Tbn+PHjSk1NVatWrRQSEqLBgwfX+k/G0Tg888wzcjgcSk9Pt8fob+P39ddf65e//KVatWqloKAg9ezZUx999JG937IsZWZmqm3btgoKClJiYqL27NljsGKcq+rqak2cOFGxsbEKCgpSp06dNGXKFJ18zzL627isW7dOd9xxh6Kjo+VwOLRkyRK3/efSz4MHDyolJUWhoaEKCwvT8OHDdfTo0Xp8FjiTM/W4qqpK48aNU8+ePdWsWTNFR0dr6NCh2r9/v9sa3tRjAtkF8MYbbygjI0NPPvmkNm/erCuuuELJyck6cOCA6dJQB/n5+UpNTdWGDRuUl5enqqoqJSUl6dixY/acxx57TO+8844WLVqk/Px87d+/X/fcc4/BqlEXmzZt0osvvqjLL7/cbZz+Nm6HDh1Sv3795O/vr/fee087d+7U9OnT1bJlS3tOTk6OZs+erdzcXBUWFqpZs2ZKTk7W8ePHDVaOczFt2jS98MILev7557Vr1y5NmzZNOTk5mjNnjj2H/jYux44d0xVXXKG5c+eecv+59DMlJUU7duxQXl6eli5dqnXr1mnkyJH19RRwFmfqcXl5uTZv3qyJEydq8+bNevPNN7V7927deeedbvO8qscWPO7qq6+2UlNT7e3q6morOjrays7ONlgVPOXAgQOWJCs/P9+yLMsqLS21/P39rUWLFtlzdu3aZUmyCgoKTJWJ83TkyBGrc+fOVl5ennX99ddbjz76qGVZ9NcbjBs3zrr22mtPu9/lcllRUVHWs88+a4+VlpZaTqfT+sc//lEfJeInGDhwoPXQQw+5jd1zzz1WSkqKZVn0t7GTZC1evNjePpd+7ty505Jkbdq0yZ7z3nvvWQ6Hw/r666/rrXacmx/3+FQ2btxoSbK+/PJLy7K8r8ecIfOwyspKFRUVKTEx0R7z8fFRYmKiCgoKDFYGTzl8+LAkKTw8XJJUVFSkqqoqt5537dpV7du3p+eNSGpqqgYOHOjWR4n+eoO3335bcXFxuvfeexUREaHevXvr5Zdftvfv3btXxcXFbj1u0aKF4uPj6XEj0LdvX61atUqfffaZJOnjjz/W+vXrdeutt0qiv97mXPpZUFCgsLAwxcXF2XMSExPl4+OjwsLCeq8ZP93hw4flcDgUFhYmyft67Ge6AG/z3//+V9XV1YqMjHQbj4yM1KeffmqoKniKy+VSenq6+vXrpx49ekiSiouLFRAQYP8mUSMyMlLFxcUGqsT5ev3117V582Zt2rSp1j762/h9/vnneuGFF5SRkaHHH39cmzZt0iOPPKKAgAANGzbM7uOpft+mxw3f+PHjVVZWpq5du8rX11fV1dWaOnWqUlJSJIn+eplz6WdxcbEiIiLc9vv5+Sk8PJyeN0LHjx/XuHHjdP/99ys0NFSS9/WYQAach9TUVG3fvl3r1683XQo85KuvvtKjjz6qvLw8BQYGmi4HF4DL5VJcXJyefvppSVLv3r21fft25ebmatiwYYarw0+1cOFCzZ8/XwsWLNBll12mrVu3Kj09XdHR0fQXaOSqqqp03333ybIsvfDCC6bLuWC4ZNHDWrduLV9f31p3YCspKVFUVJShquAJaWlpWrp0qdasWaN27drZ41FRUaqsrFRpaanbfHreOBQVFenAgQO68sor5efnJz8/P+Xn52v27Nny8/NTZGQk/W3k2rZtq+7du7uNdevWTfv27ZMku4/8vt04jRkzRuPHj9eQIUPUs2dPPfDAA3rssceUnZ0tif56m3PpZ1RUVK0bqZ04cUIHDx6k541ITRj78ssvlZeXZ58dk7yvxwQyDwsICFCfPn20atUqe8zlcmnVqlVKSEgwWBnqyrIspaWlafHixVq9erViY2Pd9vfp00f+/v5uPd+9e7f27dtHzxuBAQMGaNu2bdq6dav9Ky4uTikpKfb39Ldx69evX63/quKzzz5Thw4dJEmxsbGKiopy63FZWZkKCwvpcSNQXl4uHx/3v874+vrK5XJJor/e5lz6mZCQoNLSUhUVFdlzVq9eLZfLpfj4+HqvGeevJozt2bNH//73v9WqVSu3/V7XY9N3FfFGr7/+uuV0Oq158+ZZO3futEaOHGmFhYVZxcXFpktDHYwaNcpq0aKFtXbtWuubb76xf5WXl9tzHn74Yat9+/bW6tWrrY8++shKSEiwEhISDFaNn+LkuyxaFv1t7DZu3Gj5+flZU6dOtfbs2WPNnz/fCg4Otv7+97/bc5555hkrLCzMeuutt6xPPvnEuuuuu6zY2Fjr+++/N1g5zsWwYcOsiy66yFq6dKm1d+9e680337Rat25tjR071p5DfxuXI0eOWFu2bLG2bNliSbJmzJhhbdmyxb7D3rn085ZbbrF69+5tFRYWWuvXr7c6d+5s3X///aaeEn7kTD2urKy07rzzTqtdu3bW1q1b3f7uVVFRYa/hTT0mkF0gc+bMsdq3b28FBARYV199tbVhwwbTJaGOJJ3y16uvvmrP+f77763Ro0dbLVu2tIKDg627777b+uabb8wVjZ/kx4GM/jZ+77zzjtWjRw/L6XRaXbt2tV566SW3/S6Xy5o4caIVGRlpOZ1Oa8CAAdbu3bsNVYvzUVZWZj366KNW+/btrcDAQOviiy+2/vCHP7j9xY3+Ni5r1qw55Z+7w4YNsyzr3Pr53XffWffff78VEhJihYaGWg8++KB15MgRA88Gp3KmHu/du/e0f/das2aNvYY39dhhWSf9V/YAAAAAgHrDZ8gAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAwhkAEAAACAIQQyAAAAADCEQAYAAAAAhhDIAABNyg033KD09HRJUseOHTVr1iyj9QAAmjYCGQCgydq0aZNGjhxpugzNmzdPYWFhpssAABjgZ7oAAABMadOmjekSPKq6uloOh0M+Pvx7KwA0FvyODQDwWseOHdPQoUMVEhKitm3bavr06W77f3zJ4owZM9SzZ081a9ZMMTExGj16tI4ePWrvrzmTtXTpUnXp0kXBwcH62c9+pvLycv31r39Vx44d1bJlSz3yyCOqrq62j6uoqNDvf/97XXTRRWrWrJni4+O1du1aSdLatWv14IMP6vDhw3I4HHI4HMrKyjrrcSfX8/bbb6t79+5yOp3at2+fx3+OAIALhzNkAACvNWbMGOXn5+utt95SRESEHn/8cW3evFm9evU65XwfHx/Nnj1bsbGx+vzzzzV69GiNHTtWf/rTn+w55eXlmj17tl5//XUdOXJE99xzj+6++26FhYXp3Xff1eeff67BgwerX79++vnPfy5JSktL086dO/X6668rOjpaixcv1i233KJt27apb9++mjVrljIzM7V7925JUkhIyFmP69y5s13PtGnT9Oc//1mtWrVSRETEBfyJAgA8zgIAwAsdOXLECggIsBYuXGiPfffdd1ZQUJD16KOPWpZlWR06dLBmzpx52jUWLVpktWrVyt5+9dVXLUnWf/7zH3vsN7/5jRUcHGwdOXLEHktOTrZ+85vfWJZlWV9++aXl6+trff31125rDxgwwJowYYK9bosWLdz2n+txkqytW7ee5acBAGioOEMGAPBK//M//6PKykrFx8fbY+Hh4erSpctpj/n3v/+t7OxsffrppyorK9OJEyd0/PhxlZeXKzg4WJIUHBysTp062cdERkaqY8eO9lmtmrEDBw5IkrZt26bq6mpdeumlbo9VUVGhVq1anbaWcz0uICBAl19++Zl+FACABoxABgCApC+++EK33367Ro0apalTpyo8PFzr16/X8OHDVVlZaQcyf39/t+McDscpx1wulyTp6NGj8vX1VVFRkXx9fd3mnRzifuxcjwsKCpLD4Tj/JwwAaBAIZAAAr9SpUyf5+/ursLBQ7du3lyQdOnRIn332ma6//vpa84uKiuRyuTR9+nT7LoULFy78yXX07t1b1dXVOnDggK677rpTzgkICHC7Cci5HgcAaPy4yyIAwCuFhIRo+PDhGjNmjFavXq3t27frV7/61WlvCX/JJZeoqqpKc+bM0eeff66//e1vys3N/cl1XHrppUpJSdHQoUP15ptvau/evdq4caOys7O1bNkyST/c7fHo0aNatWqV/vvf/6q8vPycjgMANH4EMgCA13r22Wd13XXX6Y477lBiYqKuvfZa9enT55Rzr7jiCs2YMUPTpk1Tjx49NH/+fGVnZ3ukjldffVVDhw7V7373O3Xp0kWDBg3Spk2b7DN3ffv21cMPP6yf//znatOmjXJycs7pOABA4+ewLMsyXQQAAAAANEWcIQMAAAAAQwhkAAAAAGAIgQwAAAAADCGQAQAAAIAhBDIAAAAAMIRABgAAAACGEMgAAAAAwBACGQAAAAAYQiADAAAAAEMIZAAAAABgCIEMAAAAAAz5fysXq6lheTHxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diameter_plot = df2.iloc[:, 3].to_frame()"
      ],
      "metadata": {
        "id": "KmByQZlhUnaL"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diameter_plot.boxplot(figsize=(13, 6))\n",
        "plt.title('diameter')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "980743d8-27bc-4658-aa1a-341f341b2b12",
        "id": "dsgTgEEpUnaP"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The histogram shows the values are not continuous, there are certain gaps in the diameter values and the boxplot shows some outliers\n",
        "\n",
        "What I see here and what I have noted earlier from other variables are telling me that diameter lengths (values) may be in numbers of fixed values and the values differ by material variables and I will start on checking this"
      ],
      "metadata": {
        "id": "eGs4nOQaVghW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_JeaXaQSWzp",
        "outputId": "cd570ef0-fc7d-4a9e-d5e4-c1e1c09c4e67"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHhRLV7ySEtY",
        "outputId": "395606b1-ef96-4d6b-989c-68501135b293"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.32     10569\n",
              "15.24      6864\n",
              "30.48      2611\n",
              "10.16      1775\n",
              "40.64      1308\n",
              "60.96       397\n",
              "5.08        390\n",
              "50.80       270\n",
              "76.20       186\n",
              "91.44       129\n",
              "55.88        66\n",
              "7.62         48\n",
              "111.76       36\n",
              "45.72        29\n",
              "121.92       28\n",
              "93.98        17\n",
              "59.69        16\n",
              "106.68       11\n",
              "25.40         8\n",
              "83.82         5\n",
              "35.56         3\n",
              "Name: diameter, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So the pipes do come in 21 different fixed lengths which explains the gap in the histogram earlier"
      ],
      "metadata": {
        "id": "LKj8m5X1W_A0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby(['material', 'diameter'])['diameter'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XMHmo7VOSdIP",
        "outputId": "3d6e7ea2-d3cd-4d83-b357-fc212a37a9bd"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "material  diameter\n",
              "A         15.24          1\n",
              "          20.32          1\n",
              "C         5.08         277\n",
              "          7.62          41\n",
              "          10.16       1319\n",
              "          15.24       5202\n",
              "          20.32       5023\n",
              "          25.40          5\n",
              "          30.48       1580\n",
              "          35.56          3\n",
              "          40.64        884\n",
              "          45.72          3\n",
              "          50.80        183\n",
              "          55.88         51\n",
              "          59.69         16\n",
              "          60.96        210\n",
              "          76.20         69\n",
              "          83.82          5\n",
              "          91.44         39\n",
              "          93.98         17\n",
              "          106.68         4\n",
              "          111.76         6\n",
              "          121.92        10\n",
              "D         5.08          96\n",
              "          7.62           7\n",
              "          10.16        454\n",
              "          15.24       1658\n",
              "          20.32       5542\n",
              "          25.40          3\n",
              "          30.48       1021\n",
              "          40.64        321\n",
              "          50.80          5\n",
              "          60.96         21\n",
              "          106.68         4\n",
              "O         5.08          17\n",
              "S         10.16          2\n",
              "          15.24          3\n",
              "          20.32          3\n",
              "          30.48         10\n",
              "          40.64        103\n",
              "          45.72         26\n",
              "          50.80         82\n",
              "          55.88         15\n",
              "          60.96        166\n",
              "          76.20        117\n",
              "          91.44         90\n",
              "          106.68         3\n",
              "          111.76        30\n",
              "          121.92        18\n",
              "Name: diameter, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now I can see how each material pipe has different certain fixed length of diameter,\n",
        "\n",
        "for an example material A pipe comes in 2 different diameters\n",
        "\n",
        "and material C pipe comes in almost all of the fixed lengths of diameter available\n",
        "\n",
        "This finding explains the outliers seen in boxplot as well as how I saw the obvious different mean of diameters with different pipe material when looking into pipe_year variable"
      ],
      "metadata": {
        "id": "B2Wqh8j0X5Wj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(y = df2['material'], x=df2['diameter'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4YnGRIlPZTRv",
        "outputId": "2c813cfb-089b-4df8-b031-baf996d7354c"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('material', as_index=False)['diameter'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5Ao9geDeZnvB",
        "outputId": "6d709c90-d7a9-444e-aadf-e202b17b3b78"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  material   diameter\n",
              "0        A  17.780000\n",
              "1        C  21.407746\n",
              "2        D  20.722473\n",
              "3        O   5.080000\n",
              "4        S  65.781437"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].agg(['min', 'max'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgMB1-KFPoMY",
        "outputId": "ae284595-c82b-46dd-e8b7-32c1e0dec43b"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "min      5.08\n",
              "max    121.92\n",
              "Name: diameter, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is strong relation between material and diameter variables,\n",
        "\n",
        "material O pipe only comes in one fixed diameter of 5.08,\n",
        "\n",
        "which is also the mean diameter value for material O as well as the minimum value of the diameter variable\n",
        "\n",
        "so having more breakage records of material O pipe will significanty lower the mean value of diameter of broken pipes\n",
        "\n",
        "whereas material S pipe has the biggest mean value of diameter as the pipe diameter range is the biggest and also includes the outlier values shown in the boxplot,\n",
        "\n",
        "and having more material S pipe breakage will raise the mean value of diameter of broken pipes"
      ],
      "metadata": {
        "id": "iPvng3LBbTq_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['diameter'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nas72dGQ8KDZ",
        "outputId": "e0c72204-fd03-405f-a938-fdec17ad66c3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen earlier, diameter values come in 21 fixed lengths"
      ],
      "metadata": {
        "id": "q8mo5axu8OYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['break_year'] > 0]['diameter'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9hqsz5L8V-3",
        "outputId": "500ceac1-3aab-4e52-9da9-2b2071d634ad"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And pipes that have breakage records come in 18 fixed lengths"
      ],
      "metadata": {
        "id": "_fshLXg28cPB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "diameter_all = df2['diameter'].unique()\n",
        "diameter_broke = df2[df2['break_year'] > 0]['diameter'].unique()\n",
        "\n",
        "diff = [x for x in diameter_all if x not in diameter_broke]\n",
        "diff"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "69K7m3c_8pWE",
        "outputId": "c144fb8a-c414-4891-a492-d398275e755f"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[106.68, 35.56, 25.4]"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "meaning pipes with 3 fixed lengths in the list above do not have breakage records in the dataset\n",
        "\n",
        "and certain combinations of material and diameter also do not have any breakages in the dataset as per dataframe below"
      ],
      "metadata": {
        "id": "h8RLtKr-9qZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby(['material', 'diameter'])['diameter', 'break_year'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sJoCL-Ie8poc",
        "outputId": "5063475c-dca0-4623-d3ad-3323953c8213"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   diameter  break_year\n",
              "material diameter                      \n",
              "A        15.24            1           0\n",
              "         20.32            1           0\n",
              "C        5.08           277          20\n",
              "         7.62            41           4\n",
              "         10.16         1319         100\n",
              "         15.24         5202         516\n",
              "         20.32         5023         399\n",
              "         25.40            5           0\n",
              "         30.48         1580         154\n",
              "         35.56            3           0\n",
              "         40.64          884          56\n",
              "         45.72            3           0\n",
              "         50.80          183           5\n",
              "         55.88           51           1\n",
              "         59.69           16           3\n",
              "         60.96          210          14\n",
              "         76.20           69           4\n",
              "         83.82            5           3\n",
              "         91.44           39           3\n",
              "         93.98           17           2\n",
              "         106.68           4           0\n",
              "         111.76           6           1\n",
              "         121.92          10           2\n",
              "D        5.08            96           4\n",
              "         7.62             7           1\n",
              "         10.16          454           9\n",
              "         15.24         1658          34\n",
              "         20.32         5542          74\n",
              "         25.40            3           0\n",
              "         30.48         1021          28\n",
              "         40.64          321           4\n",
              "         50.80            5           1\n",
              "         60.96           21           0\n",
              "         106.68           4           0\n",
              "O        5.08            17           4\n",
              "S        10.16            2           0\n",
              "         15.24            3           0\n",
              "         20.32            3           1\n",
              "         30.48           10           0\n",
              "         40.64          103           3\n",
              "         45.72           26           2\n",
              "         50.80           82           5\n",
              "         55.88           15           0\n",
              "         60.96          166           4\n",
              "         76.20          117           3\n",
              "         91.44           90           6\n",
              "         106.68           3           0\n",
              "         111.76          30           3\n",
              "         121.92          18           3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1bd3448a-2801-4bf2-a639-9f3ce21c3b13\" class=\"colab-df-container\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>diameter</th>\n",
              "      <th>break_year</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>material</th>\n",
              "      <th>diameter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
              "      <th>15.24</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.32</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"21\" valign=\"top\">C</th>\n",
              "      <th>5.08</th>\n",
              "      <td>277</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.62</th>\n",
              "      <td>41</td>\n",
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              "      <td>1580</td>\n",
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              "      <th>35.56</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <th>40.64</th>\n",
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              "      <td>56</td>\n",
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              "      <th>45.72</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <th>55.88</th>\n",
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              "      <td>1</td>\n",
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              "      <td>3</td>\n",
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              "      <td>210</td>\n",
              "      <td>14</td>\n",
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              "    <tr>\n",
              "      <th>76.20</th>\n",
              "      <td>69</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>83.82</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>91.44</th>\n",
              "      <td>39</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93.98</th>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106.68</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111.76</th>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121.92</th>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"11\" valign=\"top\">D</th>\n",
              "      <th>5.08</th>\n",
              "      <td>96</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.62</th>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10.16</th>\n",
              "      <td>454</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15.24</th>\n",
              "      <td>1658</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20.32</th>\n",
              "      <td>5542</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25.40</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>30.48</th>\n",
              "      <td>1021</td>\n",
              "      <td>28</td>\n",
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              "      <th>40.64</th>\n",
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              "      <th>106.68</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "      <th>5.08</th>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"14\" valign=\"top\">S</th>\n",
              "      <th>10.16</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>4</td>\n",
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              "      <th>76.20</th>\n",
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              "      <td>3</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1bd3448a-2801-4bf2-a639-9f3ce21c3b13 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-1bd3448a-2801-4bf2-a639-9f3ce21c3b13');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e75e8276-b747-4b4f-b66d-2d263ff89e19\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e75e8276-b747-4b4f-b66d-2d263ff89e19')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e75e8276-b747-4b4f-b66d-2d263ff89e19 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis of categorial variable\n"
      ],
      "metadata": {
        "id": "YRky4daqM_Hi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "material variable"
      ],
      "metadata": {
        "id": "41XHUE0VW6f6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['material'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YukE7AhWflp",
        "outputId": "91207a33-6577-48a7-965e-92a5fcbfa8cd"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "C    14947\n",
              "D     9132\n",
              "S      668\n",
              "O       17\n",
              "A        2\n",
              "Name: material, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('material')['pipe_id'].count().plot.bar()\n",
        "plt.xlabel('material')\n",
        "plt.ylabel('count')\n",
        "plt.title('material')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "nFtHDz5oJsA-",
        "outputId": "4a1d5a9f-53b1-4c6f-981d-90b42e04c4f0"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "material_df = df2.groupby('material', as_index=False)[['pipe_id', 'break_year']].count()\n",
        "material_df = material_df.sort_values('pipe_id', ascending=False)\n",
        "material_df = material_df.rename(columns={'pipe_id':'count', 'break_year':'breakage count'})\n",
        "material_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4cEDsExGW-Qa",
        "outputId": "9887cd75-f096-4dbc-95cc-13c6543b360d"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  material  count  breakage count\n",
              "1        C  14947            1287\n",
              "2        D   9132             155\n",
              "4        S    668              30\n",
              "3        O     17               4\n",
              "0        A      2               0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75f28e2c-3e20-4708-929c-101845bc4f0b\" class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>material</th>\n",
              "      <th>count</th>\n",
              "      <th>breakage count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C</td>\n",
              "      <td>14947</td>\n",
              "      <td>1287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>D</td>\n",
              "      <td>9132</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S</td>\n",
              "      <td>668</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>O</td>\n",
              "      <td>17</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-75f28e2c-3e20-4708-929c-101845bc4f0b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Majority of the records are material C or D types, which creates bias and overall mean, medians, etc. will be heavily affected by the dominant material values\n",
        "\n",
        "Target variable is heavily imbalance to start with, so not having enough records for rest of the materials mean lack of breakage records for such material values like material A has zero breakage record"
      ],
      "metadata": {
        "id": "RVX_txER_YaN"
      }
    },
    {
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      "source": [
        "df2.groupby('material', as_index=False)['pipe_year'].mean().sort_values('pipe_year', ascending=False)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "sLDMNVldA6re",
        "outputId": "92687015-1e23-4725-f53e-3637230f422f"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  material  pipe_year\n",
              "1        C  65.216006\n",
              "4        S  40.533333\n",
              "3        O  21.000000\n",
              "2        D  14.496774\n",
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Mean pipe_year by material variable shows wide gaps, and this make sense looking back at how pipes installed more than 130 years ago were all material C pipes\n",
        "\n",
        "This means relation between material and install_year variables needs to be looked at"
      ],
      "metadata": {
        "id": "wOtXlr8fCrya"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis findings #3"
      ],
      "metadata": {
        "id": "1Q80nS8ENREw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('material', as_index=False)['install_year', 'break_year'].agg(['min', 'max'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "Syc7RoVZA7Dv",
        "outputId": "a1678969-0987-4cc1-acb6-fca83219f30e"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         install_year       break_year        \n",
              "                  min   max        min     max\n",
              "material                                      \n",
              "A                1941  1953        NaN     NaN\n",
              "C                1861  1969     1980.0  2016.0\n",
              "D                1963  2016     1980.0  2016.0\n",
              "O                1918  2014     1983.0  2016.0\n",
              "S                1906  2009     1981.0  2016.0"
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              "    [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-b0b0706e-820c-4a8a-93be-c979ccdf6138 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-b0b0706e-820c-4a8a-93be-c979ccdf6138');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed4a5ccc-76f5-4248-b78b-8c426052fdc2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed4a5ccc-76f5-4248-b78b-8c426052fdc2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This shows pipe breakage is recorded from 1980 onwards for all material values (except material O, assuming its first breakage since 1980 is 1983 due to its smalll number of breakage records)\n",
        "\n",
        "but for install_year, material values have records from different year and range which explains why:\n",
        "\n",
        "1. any pipes installed more than 130 years ago are all material C - other material pipes are only installed or recorded in the dataset from 1906 onwards whereas only material C has records since 1861\n",
        "\n",
        "2. different record range giving imbalance of records by material values, material C records range from 1861 to 1969, explains why big difference in number or records compared to material A, only having records of pipes installed between 1941 and 1953\n",
        "\n",
        "3. pipe_year behaviour is very different across different material values, material C pipes have longer pipe_year mean as its latest install_year is 1969 but breakage is only recorded from 1980 so shortest/smallest pipe_year value it can have is 11\n",
        "\n",
        "  whereas material D and S have 0 pipe_year breakage records as their install_year values overlap with break_year values, making their pipe_year mean significantly lower than mean of material C\n",
        "\n",
        "  material C pipes may also break in its first installation year, but as its install_year values do not overlap with break_year in this particular dataset, such data is not present in the dataset"
      ],
      "metadata": {
        "id": "cA6723aEH9cQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(y = df2['material'], x=df2['install_year'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "MYRK_FCdCXO6",
        "outputId": "1cb022b0-79c1-4b82-b741-219e78883314"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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d1OWEqvowVNU53sXePISkgMucCkfyeMiRyT17nmATMqS2BijaGiHZmiE5W93BxWUHXHZILgckpw1wtrmv99PicryoqCiMHj0aY8aMwZgxYzBq1CjExcX5tLjY7XaUlZWhpKQEdXV1sNvtSExMxJgxYxAXF9ebNaZewCndREQB5HA48OCDD2LXrl2Q1Xq0jb28e1O9hQxV9RGoS36Aon1WlUKhQGpqKkwmEywWC6qqqrx3d8YMhW3IDEDdT2blCAFFiwXKukIoG8ugsNZAEp0fZK1QKBATE4O4uDjEx8cjNjYWCQkJiI+PR3JyMtLT0xEVFdV75aeACPTnN0MNEVGANTU1YcmSJcjJyYGsNqBtzBwIQ+dbbKSWWmjz/gtlczUAICkpCddddx1mz57t80FdVFSEjz76CJ999hlkWYas1sM28iLIobxgnqMV6qpsqGqOQNHW6HOTXq/HoEGDkJSUhNjYWERGRiIiIgJGoxEmkwkmkwlmsxkxMTGIioqCUqkMUiUoUBhq/GCoIaJQU19fjyVLliAvLw9CqYZt+Cy4ogef+kEuJ9Rlu6Au3wdJyDAajbjllltw1VVXQaU6+WiB7OxsPPPMM8jPz4eQFLAPPRfO+FEBrlHPSK11UJfvg6rmKCQhA3BvADlt2jRMmzYNEyZMcE+N50ryAwpDjR8MNUQUipqamvDb3/4Wu3btAgA44kbCMWjKiTtQyy6oLLlQl/wIhd0KADjvvPPwq1/9qtPjOlpaWvDss89i06ZNAAB76mQ4UicBQZ6xo7BWQV22B6q6Qu91Y8aMwVVXXYWZM2fCYOi7PaUo9DDU+MFQQ0Shym63480338RHH30EIQSEJMEVmeqeuaRQQtHaAGVDsXsQLICEhATcd999OO+887r8XLIs46233sIHH3wAAHDEjYB96LmAou/nhCiaKqAp+RHKxjIA7n2zzj33XFx33XXIzMzs8/JQaGKo8YOhhohC3b59+7B8+XL8+OOPfm+Pi4vDNddcg6uvvrrHK9auXr0aL774ImRZhssQC9uIC/psXyqpxQJN0Q6oGkoAuAf0XnLJJfjZz37GqdF0AoYaPxhqiKi/yM/Px44dO1BSUgK73Y7k5GRkZGRg8uTJAR34unPnTjz55JNoaGiAkBRwJmbAkZgBoeulv5FOGzQlP0BdeQiAgFKpxGWXXYYbbrgBKSkpvfOc1O8x1PjBUENEdKLq6mr86U9/wrZt27zXyTozZEMMhMYAoTZA1prc3+uiuj3+RllbCE3BZigcLQCACy64ALfddhsGDQrs4oMUfhhq/GCoISLyTwiBbdu2YeXKlfjxxx8hy7Lf+8lqPVzRg+GMH+0e79OZgONog7ZwK1SWXABAWloaHnjgAUyePDmQVaAwxlDjB0MNEdHp1dfXIzs7G4WFhbBYLLBYLCgtLUVubi7a2tq893MZ4+FIPgOu6CGAwk+XmCxDVXMEmuIdkJw2KBQKXHfddfjFL37BHaypSxhq/GCoISLqPofDgd27d2P9+vXYuHEjHA4HgPbWm5ihcEUmQ2iMkJx2KKyVUNXkQGFzTz0fPnw4HnroIWRkZASzCtRPMdT4wVBDRBQYdXV1+M9//oPVq1ejtrb2pPeLjo7GjTfeeNqFAYlOhaHGD4YaIqLAcjqd2LZtG7Zs2YKcnBxYLBaYTCakp6fj3HPPxfnnnw+9vp/sM0Uhi6HGD4YaIiKi/ifQn9/cZIOIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisKAKdgGIKDTY7XYcOXIE2dnZyM7OhlarxYwZMzB58mRotdpgF4+I6LQYaogGOCEENm3ahL/+9a+orq72uW3VqlXQ6/WYM2cO7rzzTuh0uiCVkojo9BhqiAawsrIyvPjii9i+fTsAwKSWMcLsxNAIF6wOCT9Ua1Db2opPP/0U+/fvxx/+8AekpKQEudRERP5JQggR7EL0VGNjI8xmMxoaGhAZGRns4hD1C9u2bcMTTzyB5uZmqCSB+UPaMH9IGzTKY/cRAthXq8Lr+41odChgMpnwxBNP4KyzzgpewYkobAT685sDhYkGGCEEVq5ciYcffhjNzc0YaXbi2bMbsWC4b6ABAEkCxsc68ftpjRhhdsJqtWLp0qX44YcfglN4IqJTYKghGkCcTieee+45/PWvf4Usy5iZYsOjU5qQZJBP+bhYncCjU5pwZoIdDocDjzzyCA4dOtRHpSYi6hyGGqIBoqmpCQ8++CDWrFkDCQI3jmrBorEtUHXyr4BaAdx5RjPGxTjQ2tqKXz/0EAoKCnq1zEREXcFQQzQAlJWV4c4778SPP/4IrVLg/olWzE23QZK6dh61Algy3ophkU40NDbioYcegsVi6Z1CExF1EUMNUZj7/vvvcdttt6GwsBDRWhmPTW3CpDhnt8+nVwEPTbQiyeBCZWUlli5ditbW1gCWmIioexhqiMKUy+XCO++8g4cffhhNTU0YFunEk2c2YnCEq8fnjtAIPDTRigi1jMOHD+Opp56Cy9Xz8xIR9QRDDVEYKi0txZIlS/Dee+9BCIELB7Xhd1ObEKML3AoOiQYZv5pghVohsHnzZvzlL39BGKwQQUT9GEMNURiRZRkff/wxFi5ciD179kCrFLh9XDN+MaYV6l54t4+KcuGX45ohQeDTTz/F66+/zmBDREHDFYWJwsS+ffvw8ssvIzs7GwCQEe3AoowWJOhPPV27p6YnOtDiaME7h4348MMPodVqccstt/TqcxIR+cNQQ9TPlZeX46233sKGDRsAAHqlQNbIFsxOtUPRxdlN3TV7kB12WcL7Rwx477334HK5cOutt0KhCN3GYCEEGhsbYbFY0NTUhKamJu+AZ0mSoNFoEBsb6z3UanWQS0xEp8NQQ/1GU1MTysvLIUkSVCoVdDodEhISoFQqT//gMGSxWPD+++9j1apVcDqdkCAwK9WOnw5vhVnT911Ac9NtsMvAyqMG/OMf/0Bubi5+97vfwWg09nlZOqqrq0N+fj4KCgpQXFyMkpISlJaWorq6GjabrVPnUKvVGDlyJDIyMjBx4kRMmzaNO5cThSDu/UQhq7KyEt9++y22bduG/Px81NTUnHAfrVaLoUOHYvjw4Zg0aRImT56MuLi4IJS271gsFvzrX//CqlWr0NbWBgA4I8aBrBGtGBoZ/BlI/y3T4J3DBjhkCenp6fjd736H0aNH9/rztra2oqCgAHl5ecjLy0Nubi5yc3PR0NBwysdFqGUY1QJGlYBWeezPoc0locEuod6mgFP4NnkZDAacf/75mDt3LiZNmgSpqwv+EBGAwH9+M9RQSGltbcWGDRuwZs0av8vwmzUyJAAuAbQ6pRM+bABg8ODBOPPMM3HmmWdi4sSJ0Ov1fVDy3ldWVoaVK1dizWefwe5wAACGRzpx7YhWjIvp/rozvSG3QYk/7zWhzubufpo9ezYWLVqEQYMG9ei8TqcTVVVVKC0tRVlZGYqLi1FUVISioiKUl5f7HaQsQSBBLyPV6EKyUUaSwYVEvYw4nYworXzCflfHEwKoblXgaKMSOfUq/FitgcV2rFtt1KhRuP766zFz5kyoVGz8JuoKhho/GGr6v8LCQnz66adYt24dmpubAbg/jEZFOXFmggMjzE6kGF0wdPjMkAVQ2aJAsVWJvEYVDtSqUNCkhMCxoKNSqZCRkYFJkyZh4sSJyMjI6FchRwiBPXv24KOPPsL//vc/74f2SLMTVw5txfhYZ5dXBe4rDTYJH+bosblCAwEJCoUCEyZMwPTp0zFlyhQkJiYiIiLCO+7G5XLBarWipqYGNTU1qKqqQmVlJSorK1FRUYGKigpUV1dDlk8+8NmscYeXdJMLaREupJlcSDW6oA1gD6UsgJwGJTaXa/G/cg3ssvsFSE1Nxa233orZs2eH9FgiolDCUOMHQ03/5HA4sGXLFnz66ac+uz4n6l24cJANM5LsiNJ27dfT6pBwsFaFfbVq7LWoYGnz/TRTKhQYOWoUzjjjDIwfPx5nnHFGSHZXFRcX46uvvsKXX36JsrIy7/WZsQ5cPrgN46JDN8wcr7BJiRVH9dhrOXGgrVKphMFggM1mg91u79T51AqBeL2MBL0LSXoZyUYXkg0yBplciOzjsURNdgkbSrRYX6yF1eEOMsOHD8dtt92Gs88+m91SRKfBUOMHQ03/4XQ6cfDgQWzYsAHffPMNGhsbAbhbZSbFOXBRmg1nxDgDMmtHCKCqVYGDdSocqlMhu07t023gkZqaigkTJmDChAmYOHEikpOTe/7kXSqnQGVlJbKzs/Hjjz9i+/btKC0t9d6uUwqcnWTH3LQ2pJp6d3p2b6psUWCPRY09NWocbVCi2em/NSNCLSNaKyNaKxCndyFOJx879DLMGtFns7o6q9UJrC/SYW2hDq0ud+HOOOMMLF68GBMnTgxu4YhCWFiGmoqKCjz99NNYu3YtSktLkZCQgIkTJ2LJkiW48MILT/t4hprQ5XQ6kZubi8OHD+OHH37Azp07YbVavbdHaWScl2LDhal2xPXyeioAUNMm4Ui9Cjn1KmTXq1Bs9e2uAoDExERMnDgRmZmZyMjIwJAhQ3o8VsLpdMJisaCqqgpVVVUoLy9HaWkpSktLkZ+ff8JgVqUkkBHtxLnJdkxJsEMXhhO8nDLQaJfQ6pKgVQBapYBOJXplkcC+YnVIWFOgw5fFWm+31OTJk3HDDTdg6tSpbLkhOk7YhZqCggKcc845iIqKwlNPPYXMzEw4HA6sX78eb775Jg4fPnzaczDUBJdnvY+ysjKUlZWhpKTEO3gzPz//hG4Fo0rGpDgHzkm2Y1yAWmW6q8UJ5NSrcKhOjcP1KuQ3KuE6bvCxTqfDkCFDkJ6ejrS0NMTExCAyMhImkwmSJEEIAafTCavViqamJjQ0NMBisXjHhlRXV592J2sFBJINLgyOcGFMtBMjzE7oOebUR4xWhrKfBJ46m4RP83XYVKr1/j6NHDkSCxYswKxZs2AwGIJcQqLQEHah5rLLLsPevXuRnZ19wnoW9fX1iIqKOu05GGp6jxACzc3NqKmp8X5QV1ZW+gzirKysPOUuzQaVjGGRLow0OzEhzoFhka6Q6z7waHO5Q87hehVyG1Q42qBCmytECzvAvHROA+L7oDUvkGraJHxR6A43tvaWG51Oh/PPPx/nnnsuJk2aBLPZHORSEgVPWIWa2tpaxMXF4emnn8bSpUu7fZ7+GGpcLhfsdjtsNpt3kKTNZkNbW9sJ19ntdjidTjidTrhcLsiyDFmWvS0EgHsFVM+MC4VCAUmSvAfgDieeQ5ZluFwuOJ1OOBwOn+duaWmB1WpFc3MzGhsbUV9f732O04nSyIjTuxCvOzZ4M8XoQoJe7jeDWo8nBFDRokB5ixIVzUpUtijQ5JTQ7FCg2SlBAiDB3V1kUAsYVO71Tsxa93ThKI1AjFaGEMAffuwfv5uh6tnpDX3SRdkbrHYJmys0+F+5FuUtx/oSJUnCyJEjMWrUKAwdOhRDhgxBYmIiYmNjYTAY2F1FYS/Qn99BbeA+evQohBAYM2ZMlx7n+dD38Aw27Q2bN2/uUeAaSOrtCtTbFTh66rXOKMTNnz8fWVlZWLFiBdasWRMyG1T+5vv+3aLx9gV1+MkQG3Iblfi+QoP9tWqUNCtx5MgRHDly5JSPnTp1Kp566imYTKY+Ki1R/xTUHuru/rFctmwZzGaz90hLSwtwyY756KOPeu3cRKEoKysL6enpyMrKCplAEy4kCRhhduHG0a14aFITrh3RAp3y9D/jnTt3Iicnpw9KSNS/BbWlZuTIkZAkqVODgTtaunQp7r//fu/3jY2NvRZsHn74Ybz99tsoLCwE4A5iHbt0TsczkPRk3wPu9Vo8rU+ebiCXK/jL3fsnEKkWiNHJ7kN7bLptfPv0W22YD3AVArC5gBan+/dAIbkPg0pAdZJ/E2paFf2mpWHFihXelhp/v6/B0p+7nwBAq3AvMfC/cg2+r9CgrOXEKW0qlQpxcXEwGo0+3ccXX3wxMjMz+7rIRP1O0AcKX3rppdi3bx8HCh/H6XTCbrd7x7u4XC44HA7v+BbPuJqOf/iOH0vT8Tbg2Lgal8t1wpiatrY275iapqYmWK1WNDQ0oL6+HnV1dbBYLLBYLJ0aX2PWyEg2uJBilDHI6MIwsxPpJtdpl6MPNXYXUNDkXq24tFmJ8mYFKlqUaHJIJ8yQ8tAqBExq95ia6PZxNTFaAZUk8OHR/jHjxRNkQinQAP1zoDDgXoF4V7UaXxRpcbj+2AKECoUCo0ePxpQpUzBq1CgMGzYMKSkp3GqBBpSwGigMAHl5eTjnnHMQExODp556CuPHj4fT6cRXX32F1157ze/+P8cLx1ATimRZRkNDg3etFc/y9Z51V8rLy086vkkpCQyJcGF8rCNkZ0A1OyQcrlfhcJ179lNR04nTuztSqVSQJMk78LorjCr3QnJxWhcSDQKDjE4MipARoQ5+iKi3SahpU7TvjRT88nj0pyndAOCSgc0VGqwp1KGs2Z3oJUnClClTMGfOHMyYMQMRERFBLiVRcIVdqAGA8vJyPP3001izZg3Ky8sRHx+PKVOm4Fe/+hVmzZp12scz1ISOpqYmlJaWoqioCIWFhTh69CgOHTqE+vp6n/tFaWScnWTHucl2pJtcQZkdVW9zL8TnCTL+FuKLiYnB2LFjMXz4cJ91asxmM7RarbclTJZlNDc3+6xTY7FYUF1d7V2rprKyEtXV1aec/h6vd2F8jBPjYx0YF+OAjv+09zuyALZXqvFxnh4V7V1MJpMRV155Fa688kokJCQEuYREoSMsQ01PMdSENiEEKioq8MMPP2DHjh3YsWOHz6rCg01OXJTm3uspkBsPdiQLoLxZgSMNKhxpX024qvXEJ0tLS8PEiRMxYcIEjB8/HomJiQGdViuEgNVqRXl5uXehwry8PBw5cgTFxcU+3T1ahcDUBHfwC/YihYFkdwHFViUa7Ao02iW0uSSoFQJapXtckqfbLhS3QzgVIYA9FhU+OqpHodWdRs1mM66//npcccUVJ3SvExFDjV8MNf2Lw+HAtm3b8OWXX2LL5s2wOxwA3Iv0nZ9ix4WpNiQbezZ2QgigvEWBA7VqHKxzt8Q0OXz7LiRJwrBhw5CZmekNMrGxsT163p5oaWnB7t27sX37dnz//fc+G1nG61y4JM2Gmak2n53K+4tmh4QtFRrssahwsFbt3ULgVJSSe0D68Xs/xetkJBhciNGGRugRAjhcr8JHuTocaR8zYzQacd111+Gaa67h6sFEp8BQ4wdDTf/V0NCAzz//HKtWrfL5ED8jxoFzk+2YFOeAsZPjTBrtEg7WqbDfosa+2hN36NZqtcjIyEBmZiYyMzMxbty4kF33QwiBgwcPYv369fj666/R1NQEwL255exBNlyW3hZS411Oxu4CNpRosSpf57OBZXR0NBITExEVFQWj0eid9dfU1ASLxYLa2lrI8qmDrUoSSDB4BqW7kGqUkWZyIdnQN4PSZQH8WK3G2kIdchrcSVOj0WDBggX42c9+xpWCiTqBocYPhpr+T5ZlbNu2DatWrcLWrVu93TBKSSAjxokRkU4MMrmQZJAhAXAJoNkpodSqRLFVibxGJYqsvk0YGo0aZ5yRicmTJ2PSpEkYM2YM1Gq1n2cPbW1tbfjyyy/x0UcfeZcWUCsELki1Yf6QNkSHaLjZZ1Hh7YNG787ogwcPxpw5c3D22Wdj2LBhp+zW67gBaEVFBSorK1FeXo6Kigrv5clm4ikkgSS9jLQIF9JNLgwyujDI5EK8Xg5Iy06JVYGtFRpsrdR4uzA1ajUuvewy3HTTTYiPj+/5kxANEAw1fjDUhJfy8nJ88cUX2LRpEwoKCrr02GHDhmHKlCk466yzMGHCBOh0ut4pZBB4gt/f//53HDhwAIA73FySZsO8wW2I0ITGW1kI4PMiLf6Vo4eAhPj4eNx6662YM2cOlMrANKG4XC5UVVWhuLgYxcXFKCwsRH5+PvLy8rytWsfTKAQSDS4k6WUkGmTEdlhryah2b2+h7bAQnl0G6m0K1NkUKGtWIrdBiZwGFSo7jMUymUy46qqrcPXVVwe165Kov2Ko8YOhJnwVFhZi27ZtyMvLQ0FBAcrKyqBQKKBUKqHT6TB48GAMGzYMI0aMwIQJExAdHR3sIvc6IQR++OEHvPPOO9i/fz8AQK8U+MnQVsxNt0EdxGnPdhfw9iEDtlRoAbg3rP3Vr34FrVbbJ88vhIDFYkFeXh5yc3ORm5uL/Px8FBYWnrBbfHepVCqcddZZuPjiizFjxgzo9fqAnJdoIGKo8YOhhgYiIQS+//57vPXWWzh69CgAIEHvwg0jWzE53tHn0+QdMvDSHhP2WtRQKBS45557cPXVV4fEpowul8s726ykpARlZWWoqqryTrm3Wq1oa2s74XFGoxGxsbFISkpCRkYGxo0bh4yMDK4vQxQgDDV+MNTQQCbLMr788ku88cYbsFgsAIAp8XYsHNPSZ+NtnDLw131G7KzWQKfTYdmyZZgyZUqfPHegeFbWBtwz49RqdVh1XxKFIoYaPxhqiNxTwt9//33861//gtPphFEl4+ejW3FOkr1XW21kAby234itlRqo1Wr88Y9/xNSpU3vvCYkobAT687sfLTpORKdiMBiwePFivPXWWxg1ahSanQq8fsCI1w4Y0NaL+6O+f0SPrZUaqFQq/OEPf2CgIaKgYaghCjPDhw/H66+/jkWLFkGhUGBLhRZPbI9EeXPg3+7ri7T4stjdRfPb3/4WZ599dsCfg4iosxhqiMKQSqXCTTfdhD//+c+IiYlBSbMSv9seiT01gVuO+IcqNd4/4p75c8cdd2D27NkBOzcRUXcw1BCFsYkTJ+Ltt9/G+PHj0eaS8PxuE74q7vn06qMNSry63wgBCT/5yU9w3XXXBaC0REQ9w1BDFObi4uLw0ksv4bLLLoOAhL9lG/B+th5yN6cIlFgVeH63CTZZwrRp07BkyZKQmLZNRMRQQzQAqNVqPPzww1i8eDEAYF2xDi/sNqHF/04DJ1XdqsAfd0XA6lBg3LhxeOqpp6BS9cMdNokoLDHUEA0QkiThxhtvxBNPPAGNRoM9FjWe2BGJypbO/RmoaFHg2R9NqLMpMHToUPzxj3/karpEFFIYaogGmNmzZ+P//u//EB8fj7JmJX63PQIbSzWn7I76sVqN322PRGWrEklJSXj++ee5JhQRhRyGGqIBaPTo0XjjjTeQkZGBFqcCyw8Z8dTOCBypV8Ipu+8jhHv8zPtH9HhxjwmtTgmZmZl49dVXuRM1EYUkrihMNIA5nU588sknWL58OVpbWwG4d/4eHOFCo11CVYcdqRcsWIC77rqLY2iIKGC4TYIfDDVEPVNdXY033ngDmzdvRnNzs/d6jVqNyVOmYP78+TjvvPOCWEIiCkcMNX4w1BAFhizLKC0tRXZ2NrRaLaZMmQKDwRDsYhFRmAr05zfbkYnIS6FQIC0tDWlpacEuChFRl3GgMBEREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorCg6uwdo6OjIUlSp+5bW1vb7QIRERERdUenQ82f//znXiwGERERUc90OtTcfPPNvVkOIiIioh7pdKg5mba2Ntjtdp/rIiMje3paIiIioi7p1kDh5uZm3H333UhISIDRaER0dLTPQURERNTXuhVqfv3rX+Obb77Ba6+9Bq1Wi7fffhtPPvkkUlJS8Pe//z3QZSQiIiI6rW51P3322Wf4+9//jlmzZuEXv/gFzjvvPIwYMQKDBw/GBx98gBtuuCHQ5SQi6rdcLhcsFgucTqf3uujoaOj1+iCWiij8dCvU1NbWYtiwYQDc42c8U7jPPfdc3HHHHYErHRFRP9PW1oaDBw9iz5492L9/P0pLS1FZWQmXy3XCfaOiopCSkoKRI0di4sSJmDhxImJjY4NQaqLw0K1QM2zYMOTn5yM9PR1jxozBypUrcdZZZ+Gzzz5DVFRUgItIRBTaGhsbsXnzZmzatAk7d+6Ew+E44T5CEoDS8w0guSTU19ejvr4eBw8exKpVqwAAI0eOxIUXXogLLrgAycnJfVgLov5PEkKIrj7opZdeglKpxL333osNGzZg/vz5EELA4XDgxRdfxH333dcbZT2pxsZGmM1mNDQ0cOYVEfUJp9OJ77//Hl988QW2bNni0xIjdAIiXgDxgIgUgBGAHkDH9UvtAJoBWAHJIkGqliDV+y5wOn78eFxxxRWYOXMmNBpNH9SKqG8F+vO7W6HmeIWFhfjhhx8wYsQIjB8/vseF6iqGGiLqK4WFhVi7di3Wr1+Puro67/XCLCAGCYhUAUTCN8B0lg2QSiVIxRKkqmMnMJvNmDdvHq6++mrEx8f3vBJEISIkQ02wMdQQUW9qamrCpk2bsG7dOuzbt897vdAKiMECYogAzAF+0lZAypcg5UmQWt0BR6lU4oILLsC1116LMWPGBPgJifpe0ELNyy+/jMWLF0On0+Hll18+5X3vvffeHhesKxhqiCjQmpqasH37dmzatAlbtmw5Nk5GAkSSgDxUBpLR+9sCywDKAUWOAlL1sdab8ePHIysrCzNmzIBSqTz544lCWNBCzdChQ7Fz507ExsZi6NChJz+hJCEvL6/HBesKhhoi6qm2tjYcPnwY+/btw48//ojdu3f7jpOJbG+VGSzc42OCoQ6QjkhQFCuA9r/cKSkpuPLKK3H55ZcjIiIiSAUj6h52P/nBUEPUO+rq6pCfn4/q6mpYLBbU1tbCbrfD6XTC6XRCrVZDp9NBp9PBZDIhMjLSe5jNZpjNZphMJqjV6k49nxACNpsNzc3NaGlpQUtLC1pbW9HS0uLdksXhcMDpdEIIASEEJEmCSqXyHhqNBmq1Gmq1GkqlEiqVCgqFAi6XCy6XC3a7HQ0NDWhoaIDFYkFJSQmKi4tRUlJywrRrESEgUgREugCieuEH3F2tgHRUgpQrQXK4W2+0Wi0uuugizJs3DxkZGZCk7gzqIepbQQ81DocDY8aMwZo1azB27NgeFyAQGGqIes7lciEnJwc//PAD9uzZg5ycHFgsloCcW6PRwGAwQKvVQqVSebtLOgaN1tZW2Gw2yLIckOfsDqETQBwg4gREkgBCveHDCUhFkjvgNBwLMUOGDMGll16KCy64AElJSUEsINGpBT3UAEBqaio2bNjAUEPUz7W1tWH79u347rvvsHXrVjQ1NfncLiAAEwADIPQC0MG91ooC7tk9MgAXACcAOyDZJfdUZTvcM3kc3WstEBDuVbTUcF96DkX780vuQ0BAguQux/GHC+4uGrn90lNmJQANIDQC0AKIAISpfcbS8dOu+wsBoKZ9YHGJBMl1rBLjxo3DzJkzMX36dAwePJgtOBRSQiLUPPPMMzhy5AjefvttqFQ93ui7xxhqiDrPbrdj27Zt2LBhA7Zu3Yq2tjbvbUIlgARAJAiImPYZPT15iwsADrhDjwPuoOEJGYA3nEDZ/jxKuIOMJ7hQ1zngnhJeJPkMLAaApKQknHXWWZg4cSLGjx+PhISEIBWSyC0kQs1VV12Fr7/+GiaTCZmZmTAajT63f/LJJz0uWFcw1BCdmsvlwq5du7BhwwZ89913sFqt3tuEwb22ihgkgBj0/mwe6jttcLfclElANSDJJ4ac0aNHY/To0Rg1ahSGDh2KuLg4tuZQnwn053e3/geLiorCggULevzkRNR7nE4ndu/eje+++w7fffedd482oH3F23QBkSaAaLBVJFzpADFCQIwQ7tayKkCqkiDVuFcvrqioQEVFBb799lvvQ0wmE4YMGYK0tDQMGjQIgwYNQnJyMpKTkxEZGcnAQyEtZGY/VVdX47HHHsPatWtRWVmJ6OhoTJgwAY899hjOOeecUz6WLTVEblVVVdi5cyd27NiB7du3+4yREZr2FW/T3Mv3M8gMcA64p4jXSe7Lesm9ZYM4+S+GwWBAYmIiEhISkJCQgPj4eMTFxSE+Ph6xsbGIiYmB2WzmujnUaSHRUgO4/wvctGkTcnNz8bOf/QwREREoKytDZGQkTCZTl8+3YMEC2O12/O1vf8OwYcNQWVmJr7/+OmCzL4jCiRAC1dXVKC4uRm5uLg4dOoSDBw+ivLzc937a9inJqQJIRGh2LckAWoNdiBCjR++/Vmp4x08B7YOzXQCaAKlJAprgDjlWCWgGpDYJLS0tyM/PR35+/klPq1AoTpjSbzQaYTQaodfrodVqvdPulUolFAp3RT1T9GVZ9s6K8ywd4HK5vNd7pvErFApIkgS1Wg2tVgutVguDwQCTyeRdXiA6OhpRUVGdXlKA+r9utdQUFhZi7ty5KCoqgs1mw5EjRzBs2DDcd999sNlseP3117t0vvr6ekRHR2PTpk2YOXNmV4vDlhoKGLvd7t052bOWSX19PRobG9HU1ISmpiY0Nzd7D5vNBrvdDrvd7v3D6/mjq1QqoVQqoVarodFooNFovH98PWu7eI6Oa6soFAp43pYulwutra1oa2uD1WpFbW0tLBYLqqur0drqJwlIgIgWEIntU5JjEfotMs2A8nP+Z9+R6zKXexPMUOKCewPOVkBqkYCW9q9bJXcobYN7xlsI/sJFRkYiNjbW26rkaWXyXMbHx8NkMrFrLQhCoqXmvvvuw9SpU7Fnzx7ExsZ6r7/qqqtw2223dfl8nmT96aefYvr06dBqtd0pFg1wLpcLDocDbW1tsNlsaG1t9S7c1tLSAqvViubmZlitVm9I8QQXT3hpaWkJdjU6TaB99+cIQMQIiOj28TEd/yl1neTBocQZ7AKEICdC8+dicB8i9tj/wgId/i+WAdhw4rT+jrPfPEf7LDhPd5f3PJ6p91KHrzted+yJj03Zbz+n5JS8ywt4nx8SGhsb0djYeMoWJp1Oh9jYWO8RFRXlbemJiIjwHgaDwXt41l2i0NGtV+O///0vtmzZAo1G43P9kCFDUFpa2vVCqFR47733cNttt+H111/H5MmTMXPmTFx33XV+d/222Wyw2Wze7xsbG7teCQp5n3/+OZ599tk+f14hCUAD9xomWncXDjQ4dqjdh1AJQAKU/w1OK4MEd7cAmgGpgv9hBtP8+fORlZWFFStWYM2aNejpUEXllwOv5ao3W3hcs13uqe6eViVPa5Pna7uEtrY2lJaWduszrKPU1FQ8//zzSE1NDUjZqWu61Wvr6ds8XklJSbf3HlmwYAHKysqwevVqzJ07F5s2bcLkyZPx3nvvnXDfZcuWeftrzWYz0tLSuvWcFNq++uqrPnsuoRMQCQLycBligoCYKCBPlCFPlCEmur8XmQJitIAY1j7YNhlAXJ8VkUJYVlYW0tPTkZWV1eNAQ73ADCAJEEMFRIaAmOD7/pbHyhBJwv0PTA+Vlpbi8OHDPS8zdUu3xtRkZWXBbDbjzTffREREBPbu3Yv4+HhcccUVSE9Px7vvvhuQwi1atAhfffUVCgsLfa7311KTlpbGMTVhxmKxYOXKlaiurvZeJ0lSpz407HY72travN1PHcfBdHcZfqEU3lYa76EAhEL4rHLr5VlkToZ7hVfPSrdOHFuF13Mpn/w/VZ/VdT2tRwZ315OIaF/K34DQHztzMs39v2Ui0C01rktCcExNIHi6jDouwuh5O3p+f/11Px3/3uqoQ/eTt5vL0b66dRuOdUO1+bbMdJZarUZERIR3EozRaITBYPCZ4eUZRwcAmZmZmDdvHgcnd1JILL5XUlKCOXPmQAiBnJwcTJ06FTk5OYiLi8N3330XsFUqX3zxRTzzzDOoqak55f04UJg6SwiBlpYWNDU1efvZPeNpPJfHDxRuaGjw2zIZSoTGvXCeiHUPEkY0QnOmkz9hMFDYE7Y7G7pPJyQHCvvjgHeQsNTWHiI8A4ZtHbbM8Kwq7ex+N5OA8A037QGpu+fT6/U+g4Xj4uK8g4ljYmIQHR2N6Oho6PV6DiDuRSERagD3lO4VK1Zgz549sFqtmDx5Mm644Qbo9foun8tiseCaa67BLbfcgvHjxyMiIgI7d+7EPffcg8svvxzLly8/5eMZaqg3ybKMlpYW7+BiT4tPS0uLd/aTZyNGT9esZ6qqQqHwTjn1zH46fubTqWY/OZ1O7+ynpqYm1NbWora2FpWVlSguLkZxcTGKiorgdPqOKhXq9u0OUgVEcvuYoFAVDlO6W+GeDWSAezp2T/XFlO7OsMM9rbu5w/gtz8ynlvaBuT10fItHTzY01ev1fqd0x8TEeGc+xcXFISEhgbOdQkRIhJrvvvsOM2bMOGHUt9PpxJYtW3D++ed36Xw2mw1PPPEEvvzyS+Tm5sLhcCAtLQ3XXHMNHnnkkdMGJYYaGsjsdjuOHj2KgwcPYs+ePfjhhx98tkGA1L6XU1r7ejWhHHAoOFoB1ANSowQ0tl9aO9dNYzAYvK0csbGx3haOyMhIREZGIiIiAiaTCQaDwbtOjWfWkGeNmo6EEN51ajquVeP5p0GWZe+SCR3XqfH8Q0D9S0iEGqVSifLy8hO6mSwWCxISEvq8qZ6hhugYl8uF7OxsbN26Fd99953vNFYF3AMi09tbcDgbdeBxAaiFe6sES/tqwm0nDy9xcXFISUlBcnIykpKSkJiY6F1VOD4+HgaDoe/KTmEnJNap6TgoqiOLxXLC5pZE1LeUSiUyMjKQkZGBW2+9FcXFxdi0aRM2bNiA/Px8SGXuDQ6FssNGlolgwAlXAu5Vgssl99T/mhM3tlQoFEhLS8OwYcMwZMgQDBkyBOnp6UhJSenWkAKiYOlSS83VV18NAFi1ahXmzp3rs0iey+XC3r17MXr0aKxbty7wJT0FttQQdU5eXh6+/vprbNiwwWdLBaEU7imvSe0Djfm/Sf8mADQAUrHkPpp9Q0xMTAwmTJiAM844A2PGjMGIESMYXigogtpSYzabAbhbaiIiInzeBBqNBtOnT+/WisJE1DeGDRuGYcOGYdGiRTh48CA2btyI7777DhUVFUApIJW2r+5qEBAxAogCRJQ4NgBWDf9TawWOzXRpP7yzXxwdjvZp7d6WAkX7YocKuP8aeQ61+1Kohe/1ChybPo/2y44ry3qO46cMCxybHqyE72KK4TQMow2QCiVI+ZJ7/6Z2arUakyZNwvTp03HWWWchLS2Ng2QpLHVrTM2TTz6JBx98MGS6mthSQ9R9QggcOXIEmzdvxo8//ogDBw6cdFycz5o8Chxbnl7unx+QPltNRLZPi48TgZnB1FcEgGpAcVQBqUyCZ7cBzz+as2fPxtlnn82WGApJITFQONQw1BAFTktLCw4cOICjR4/i6NGjyMvLQ01NDRoaGk77WKPRiIiICJjNZu9OzR0XLPPMevHMnPTMbum4WKJnwUSr1erzfVtbGxwOh3fzUM+uzpIkec+pVCq9m4d6rlOpVJAkyee5GhsbYbVaT7qmjDC4B1KLFAHEwx3kQo2zvVXmqOSerdRu3LhxuOyyyzB79uyQ+ceT6GRCJtR8/PHHWLlyJYqKimC3231u+/HHH3tcsK5gqCHqfTabDfX19bDb7XA6nXC5XFCr1dDpdNBqtTCZTP1qcz+Xy4W6ujqUlJSguLgYubm52LdvH3Jzc33WShGq9gHVg91r/wR95eZWuINMnuSdcq3T6TB37lxceeWVGDZsWJALSNR5ITH76eWXX8ajjz6KhQsXYtWqVfjFL36B3Nxc7NixA3fddVePC0VEoUer1SIxMTHYxQgYpVKJuLg4xMXFYeLEid7rW1pasHv3bmzevBlbtmyBxWKBVCgBhYDQu8ONGCoAUx8XuA6QjkhQlCi8WwukpKRgwYIFuPTSS2Ey9XWBiEJPt1pqxowZg8cffxzXX389IiIisGfPHgwbNgyPPfYYamtr8de//rU3ynpSbKkhot4gyzL279+PL7/8Ehs3bkRTU5P3NhEvIIa0T4nvrQYqGZBKJEi5EqSaY01EmZmZyMrKwjnnnOOzIi9RfxMS3U8GgwGHDh3C4MGDkZCQgK+++goTJkxATk4Opk+fDovF0uOCdQVDDRH1Nrvdji1btmDt2rXYvn27dzyOULWv1pwu3Lu293Q2lWc6dpEEqUByzyKDu2XpggsuwDXXXIOxY8f28EmIQkNIdD8lJSWhtrYWgwcPRnp6Or7//ntMmDAB+fn5AdnMjYgo1Gg0GsyaNQuzZs1CZWUl1q1bhy+++AJlZWWQ8iUg372xqEgVEEntAUfXyZO7AFgAqUpyt8x0mI4dGxuL+fPnY968eQHbLJgoXHUr1MyePRurV6/GpEmT8Itf/AK/+tWv8PHHH2Pnzp3eBfqIiMJVYmIibr75Ztx0003Ys2cPvvzyS/z3v/9FQ0ODN+AAgDAJwAwIY/taP56/uALu/ZaaAcnavlWB7LuuzPTp03HJJZfgnHPO6VcDsImCqVvdT55NxTxvtBUrVmDz5s0YOXIkbr/9dqjV6oAX9FTY/UREweZ0OrFnzx7873//w+7du5GXl9elluvY2FhMnDgR06ZNw7nnnsuBvzQghMSYGgBoa2vD3r17UVVV5TP9UZIkzJ8/v8cF6wqGGiIKNU1NTThw4ABKS0tRXl6OyspKOBwO7+0xMTHejSJHjRqFQYMGcZVfGnBCYkzNunXr8POf/9zvgGDPIldERANZREQEpk+fHuxiEA0o3Rqnf8899+Daa69FeXm5tyvKczDQEBERUTB0K9RUVlbi/vvvD6uFuIiIiKh/61ao+elPf4pNmzYFuChERERE3detgcItLS245pprEB8fj8zMzBNmO917770BK2BncKAwERFR/xMSA4U//PBDfPnll9DpdNi0aZPPiH1Jkvo81BARERF1K9Q8+uijePLJJ/Gb3/wGCkVP1wQnIiIi6rluJRK73Y6srCwGGiIiIgoZ3UolN998M1asWBHoshARERF1W7e6n1wuF/70pz9h/fr1GD9+/AkDhV988cWAFI6IiIios7oVavbt24dJkyYBAPbv3+9zG5f5JiIiomDoVqjZuHFjoMtBRERE1CMc6UtERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBYYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1REREFBZUwS4AERGdmt1uR11dHerr69HQ0ID6+no0NjaiubkZVqsVzc3NaGtr8x42mw1OpxNOpxMulwuyLPucT6FQQKVSeQ+NRgOdTgetVgutVguj0Qij0Qi9Xg+TyYSIiAjvYTabYTabodPpgvTTIDo5hhoioiASQqC+vh4lJSWoqKhAZWWl97KmpgY1NTVoaGgIdjFPoNPpYDabER0djZiYGO9lbGws4uLiEBcXh9jYWMTGxkKl4kcN9Q3+phER9YGWlhYUFRV5j+LiYhQXF6O0tBStra2nfbwCgBGAocOlrv3Qth/q9kPVfijaD6n9EO3nktsPV/vhBODocNg6HG3tR0uHSxfgbRWqrKw8dbkVCsTExCAuLg4JCQlITEz0uUxOTkZUVBQkSTrtz4DodBhqiIgCyG63o6ioCHl5ecjNzUV+fj4KCgpQUVFx0sdIACIBRAOI6nBEAohoP/QAFAj+B7+AgA3ucNPcflg7HE3tR2P7pSzL3hanw4cP+z2nVqtFYmIikpOTkZSUhOTkZJ8jMjKSoYc6haGGiKib6urqkJOTg9zcXO9lUVERXC6X3/sbAcQDiOtwxMAdYNQhEFg6Q4LkbSGKOc19ZQi0AGhoPxrbL+s7XFoB2Gw2bwuWP0ajESkpKUhOTkZqaipSUlKQkpKC1NRUJCQksHuLvPibQER0GrIso7S0FDk5OTh69Kj30mKx+L2/DkBi+5HQ4TD2k+ASKApIMAEwAUg9yX2cEN6AUw+g7rjDCqC5uRk5OTnIyck54fFKpRKJiYlISUnBoEGDvGHHE344oHlgkYQQ4vR3C22NjY0wm81oaGhAZGRksItDRP1Ya2sr8vPzvS0vR48eRW5urt9xLxLcrRVJAJLbL5Pg7jaSBliA6S12CG/Yqe1w6fnaeZrHx8bGegPO8Zdms5ndWkEW6M9vttQQ0YDkcrlQVlaGvLw85OfnIzc3F7m5uSgtLYW///VUOBZaktuPRAAahpdepYHkbek6ngwBK46FnFoAFrjDjgXugc0WiwUWiwV79+494fGebq2OR3JyMlJSUpCYmAi1Wt1r9aLewVBDRGHNZrOhrKwMhYWFJxx2u93vY0w4FmA8ISYWgJIBJqQoICES7paxIX5ub4HwCTwdjyacultLoVAgPj7eO3jZM4A5MTERSUlJiI+PZ+gJQUEPNcXFxXj88cexbt061NTUIDk5GVdeeSUee+wxxMbGBrt4RBTihBBoampCeXm59ygrK0NpaSmKi4tRVVXlt+UFcE9/9rQCJOHYOBgTw0tYMECCAcAgP7d5urX8dWnVA3DIMiorK086ZV2SJO809Y5T1OPj4xEfH4+EhARER0dDqVT2RtXoJII6piYvLw9nn302Ro0ahT/84Q8YOnQoDhw4gIceegh2ux3ff/89YmJON76eY2ooeIQQcDgccDqd3suOh8vlgtPphCzL3kMI4XN4SJLkPRQKBSRJglKpPOHouBKsWq32Xobb2AAhBKxWK+rr61FXV4e6ujpvV4LFYkF1dTWqqqpQVVV12nVetHDPOvLMPIqHO7xEIXjTpF0QaAzKM4eGSIRuy5do79byDFaux7FBzPVwz9w63VgewHeNntjYWO/ihJ6vo6KivAsX6vX6sHsPd0agP7+DGmouvfRS7N+/H0eOHIFer/deX1FRgeHDh+Omm27Ca6+9dtrzMNTQ8ZxO5wnLxre2tsJms/lcf/xhs9m89+n4td1uh81m87l0OBwn7b4IBk+40Wg03kvPcfz3x1/f8bLjoVQqvcFJqVRCoVB4Lz3BS6FwbyHn+VMihPAuze8JdU6nE3a7HU6n0+fn29raitbWVrS0tKC5uRnNzc1oampCY2MjrFbrSadG+2OEe9BudPsRA3eXURzcC9WF2sDdOgi8GOxCBNH9AKJD7DXpLBkCzTg2Vb3j0YgOa/R04ZwajQZmsxlRUVHerSgiIyMRGRnp3aKi45YVBoMBJpMJBoOhX7cGhc1A4draWqxfvx5PP/20T6ABgKSkJNxwww1YsWIFXn311QGZXsNJc3MzKioqTmjFcDgcPq0cnsMTGDxfH390/FD0fN8xfLS1tcHp7Mz/UYEnAVC2H4rjDum4Ax0ugWOrvYoOh+zncLVfHv/fiOfn2pnVafsTLdyBxYhjC9F5DjOOLVLXX9Z5ITcH3F1AoUiNU4dgBSTv76C/ri3gWPDxBJxG+C5QaMWxhQsdcC/aWF1djerq6i6XV6/Xew+DweD9WqfTQa/Xe/f08uzvpdFovJeef2A6ft2xJbhj63BMTAw0Gk2Xy9eXghZqcnJyIITA2LFj/d4+duxY1NXVobq6GgkJvuPePR9iHo2NA7kRN7S1trYiKysraK+RBPcfKA2OLSHf8Wt/t6n8fK3u8LUK7tDS8WslPMvS990Hqwzhs8x9x6+dHa7zLIHf8TrHcbc7jzuPE77L6LtwLGR5vu54eHhqf3yg8/x8PJeen7kK7tCia79OC3erir7DwbDSffPnz0dWVhZWrFiBNWvWnHRsUTC8EuwCnMLv4P597ImOwed0bO2LFHpWaW7pcLTi2BYVnqMV7i0sPP+6eVo8e1tsbCw+/PDDkF77J+gDhbvzJlu2bBmefPLJXigNBVpzc3OfBRoV3B+CHffGcX8oHvsQPV2g6XibEqHXZdGRAhIUcJe1KzxhyAn/Acjl5/DXYnR8oPHwtESdLNSojzs8+xaF6viK/iwrKwvp6enIysrCZ599Fuzi0EloIUELd7ephx0CrfANNa3wDTeeliBPa1Bvt09bLBZYrVaGGn9GjBgBSZJw6NAhXHXVVSfcfujQIURHRyM+Pv6E25YuXYr777/f+31jYyPS0tJ6tbzUPXFxcfjNb36D7Oxs78DZjgNqT9X95OlucjgcsNlskOVT91A7cWzfmUBwt/IInxaajpfHtz4ojztO1v2k6HB+z8e4v66njt1PHVtJjm9B6UxLzfHXhSIVBLTwbaXxBFR/3U+hOE4m1KxYscLbUiNJUki11NwDd9dhKOqNidqu9u6ojl1Qnu89e2h1bKkJZEBRKBTe7qfju56OH1/XcQLC8RMTRo4cibi4uACWLPCCOlB4zpw5OHDgAHJycjhQmE7LM9jUM27G0w3Z8fBc33Hgr7/BwK2trX7vY7PZujQ4tb+TJOmEgcKeP2odv+44+8ozWLjjbK2OH5gdZ3rJsuw3tHZ8Hbo72FoJ91iaKBwbW9NxkHAEQmMDyJPpi4HCntcl1AIN0L8HCh/P3r7Vg2dvq0b4tqA0wh1WuvoKKJVKn4HCnsNoNMJkMnkvDQaDdyyN59Iznkan04X07Miwmv2Uk5ODGTNmYOzYsSdM6bbZbJzSTUHhGWzrGYjsb1Dy8YOZj29xcrlc3pk/nplAHQ/A3fUqy7L3j03HS09o8ASI46d0Hz+Y7/jDE1COHwzo+d5zeJ4nmDw/7+bmZlitVjQ1NXlnQDU0NKCurg4NDQ2ora1FbW0tampq0NDQcNrzquAOOHE4NgvKM61bHwIfpn0xpbsJ7inIUejc2I6+FMpTujsS7d1AHad2dzwa4O4W6gyFQoHo6GjExMR4p3J7jqioKJ+ZT2azGQaDIejvz94WVqEGAAoLC72L79XW1iIpKQlXXnklHn/88U4vvsdQQzSwOBwOWCwWVFVVeRdIq6io8C68V1FRccoWNxOOLbqX2OFS2w8+ZCnw7BAnbKTZ8ehMW6LBYPAuxBcXF+ddhC8uLs67Tk1UVFS/nn7dG8Iu1AQCQw0RdeRyuVBZWYmSkhIUFxejuLgYRUVFKCwsPOWU2WgcW1nYsz1CFEK7G4tOT7TPLjrZlgnWTpwjNjbWu12CZ6uExMRE70rCJpOp9yoQxhhq/GCoIaLOam5uRmFhIQoKCpCXl+e9rKmp8Xt/LXz3gEqCu2WHU81Di9zenXf85paerQ9sp3gsAJhMJu+Glp5NLT1fJyYmQqvV9m4FBqiwWXyPiCgYjEYjMjIykJGR4XN9Q0MD8vLycPToUeTm5uLo0aMoKCiAzW5HIYDCDvdVAIiH8Ak6SQCMDDq9ytHeTXT8fk2e7083xD8+Ph6pqalISUnxXnq+joiICPvxKwMBW2qIiE7C6XSiqKgIR48eRU5OjvfyZGsvRcB3Y8xEuAcmqxh2OsXT2lIP+IQXz3G65RpUKhWSkpKQmprqE15SU1ORnJzM1pYQxO4nPxhqiKivCCFQXV3tE3SOHj2K0tJSv/eX4J5i7hmY7NlUMw4Db2Cyoz20NMB39lDHr0/X2mI0Gr0tLB1DS0pKChISEjgQt59hqPGDoYaIgq2lpQW5ubnIy8vzXubl5cFqPfkw1Agc23jTsxmnZ82dUF9npyO5fSCuZ1E5z9osnktPkGnuxLmUSqV3QG7HcS3JyclITU1FZGQku4nCCEONHww1RBSKhBCoqalBfn4+CgoKUFBQgKKiIhQXF6Ouru6Uj1XAHWwi4buKsmcLEAPce2Z5Ds9eZd1dZVmG8O4TZod7GX5b+9F63NHs5+jsB4lOp/NOffbMIvLMJEpOTkZcXBxbWwYQDhQmIuonJEnyrldy1lln+dzW1NSE4uJilJWVobS0FCUlJaioqEBlZSWqq6vhcrnQAHcLR1eoIbzbeHTcpgM4FjyO3/HdE2Z6ymw2e9dk8VwmJCR4fwbx8fEwm81saaFew1BDRBQEERERfmdhAe51dmpqamCxWFBTU+P9ur6+Hg0NDaivr0dTUxOsViusVqvPDs0O9Dyg6HQ679L7JpMJJpPJu0R/ZGSkz+q3nhVyo6KioFLxI4WCi7+BREQhRqlUehd26wzPvmjH72Hm2abD5XL5tI5IkuSzaaFns0PPNhoMJ9Rf8TeXiKif8+wDZjAYgl0UoqBSnP4uRERERKGPoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCww1BAREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQVVsAtARANXS0sLSkpKUFFRgbq6OjQ3N8PhcAAAdDodIiIiEBcXh5SUFCQnJ0OpVAa5xEQUyhhqiKjP1NbWYtu2bdi1axcOHDiA4uLiTj9Wo9Fg5MiRyMzMxNSpUzFx4kRoNJpeLC0R9TeSEEIEuxA91djYCLPZjIaGBkRGRga7OETUQUtLCzZu3Ij169djz549OP5PjlZlgFFrhk5thEaphULhbo1xuhywO1vR6rDCaquHS3b6PM5gMOD888/HpZdeiokTJ0KSpD6rExEFRqA/vxlqiKhXVFdXY+XKlVizZg2am5u910cbEpFoHoJ40yBEG5OgUxtOey4hBKy2Olis5ahuKkZ5Qx7aHMfOOWTIEGRlZeGSSy6BWq3ulfoQUeAx1PjBUEMUOurr6/H3v/8dq1at8o6PMWmjMDR+PNJjxsKo7fl7VAgBi7UUBZYDKLIcglN2P09iYiJuueUWXHLJJRx/Q9QPMNT4wVBDFHxOpxOffvopli9/B83NVgBAnCkVY5OnI8k8tNe6h+xOG/Kq9+BI5U5v683IkSOxZMkSZGZm9spzElFgMNT4wVBDFFw5OTn44x//iCNHjgAAogwJGD9oJhIjB/fZWBen7MDRyl04VP49HC4bAOCKK67A7bffDqPR2CdlIKKuYajxg6GGKDicTic++OADvPfee3C5XFArtRg/6HwMjR8PhRScZbDaHC3YW/ItCmr2A3B3ST3yyCOYNGlSUMpDRCfHUOMHQw1R36usrMTvf/977N27FwCQGj0SkwdfBL3aFOSSuVU1FmFHwTo02xogSRJuuOEG3HLLLVCpuJIFUagI9Oc3VxQmoi7bunUrbr31VuzduxcqhQZnDb0MM4ZfETKBBgASItNxybiFGBqXCSEE3n//fSxZsgTV1dXBLhoR9RKGGiLqNFmWsXz5cjz88MNobGxEtCEJl4y7GUPixoXkOjFqpQZnDp2Ls4f/BGqlBnv37sWiRYuwe/fuYBeNiHoBQw0RdYrVasUjjzyCv/3tbwCAEQmTMHvs9TDpooJbsE5IixmNizJuglkfj7q6OixZsgT//ve/T1gIkIj6N4YaIjqt4uJi3H777diyZQuUkgpnDb0MkwdfBKWi/4xPidBF48KxNyA9ZixkWcZf/vIX/OlPf4Ldbg920YgoQBhqiOiUdu7ciV/+8pcoKiqCXh2BC8ZejyFx44JdrG5RKdWYNuxyTEibBQkS1q5di1/96leoq6sLdtGIKAAYaojILyEE/v3vf+PBBx+E1WpFrDEZF427ETHGpGAXrUckScLopDNx7qgFUCu12LdvH375y18iNzc32EUjoh5iqCGiEzgcDrzwwgv4y1/+AlmWMTh2HGaNuS6kZjf1VLJ5KC4cewNM2ihUVFTgjjvuwH//+99gF4uIeoChhoh81NfX44EHHsDq1asBAOMHnY+zhl7ar8bPdFakPhYXZtyIhMh0tLW14dFHH8Xf/vY3DiAm6qcYaojI68iRI1i8eDF2794NlUKDc0dejTHJ00JyunagaFV6nD/ypxiR4F5xePny5Xj88cfR0tIS5JIRUVcx1BARAGD9+vW48847UVFRAZM2Chdm3ICUqOHBLlafUCiUmDz4IkwZcgkUkgKbNm3CHXfcgeLi4mAXjYi6gKGGaICz2Wx4/vnn8fTTT8NutyPJPBQXZfwcZn1csIvW54bHT8Cs0VnQqY3Iz8/Hbbfdho0bNwa7WETUSdz7iWgAKygowJNPPumd+ZORcjYyUmYEbTPKUNFqt2Jr7meosZYAcO/2fdddd0Gn0wW5ZEThhXs/EVGPybKMf//731i0aBFyc3OhVRlw/qif4ozUcwd8oAEAvcaEWaOvxZikswAAq1atwm233YbDhw8HuWREdCpsqSEaYIqLi/Hcc8959z9KjByCs4ZeCr0mfKZrB1JFQwG253+ONkczFAoFrr/+eixcuBBarTbYRSPq9wL9+c1QQzRAtLW14YMPPsA///lPOBwOKBVqjB90PkYkTArr2U2BYHO24sfCDSiudbfUJCcn45577sE555zDnx1RDzDU+MFQQ3RyTqcT69atwzvvvIOamhoAQFLkEEwecjFM2qjgFq6fKa3LwY+FG9DqsAIAJk+ejF/+8pcYO3ZskEtG1D8x1PjBUEN0IpvNhnXr1uHDDz9EWVkZAMCoMWN82kwMih7FFoZucrjsOFS2FUcqf4AsXACA6dOn42c/+xkmTJjAnytRF4RtqNm6dSvOPfdczJ07F2vXru3SYxlqiI4pKCjA2rVrsW7dOjQ0NAAAtCoDxiSfhREJk8JyZeBgaLY1YH/pZhRZDkLA/Wd05MiRmD9/Pi688EJEREQEuYREoS9sQ82iRYtgMpmwfPlyZGdnIyUlpdOPZaghAg4dOoTnnnsOR48e9V5n0ERiVOIUDIsfD5VSE7SyyUJGq70poOfUayJCYqZWU1sdsiu2o7DmIFzCCQBQq9U477zz8Jvf/IbTwIlOIdCf3yHxL5vVasWKFSuwc+dOVFRU4L333sMjjzwS7GIR9Str1qzxBpqUqBEYGpeJ5KhhIfHB32pvwtq9bwb0nJePXwyj1hzQc3ZHhC4aU4fMQeag81FQcwAFNfvQ0FqDb775BldccQUmTZoU7CISDRjB/2sHYOXKlRgzZgxGjx6NG2+8Ee+88w43lCPqIpfLPb5jdNKZOHfkVUiNHhGUQCOEgNNlP+5wBPx5nC6Hn+exB+1vh1alx+ikqZhzxi+gUekBHHtNiKhvhERLzfLly3HjjTcCAObOnYuGhgZ8++23mDVrlt/722w22Gw27/eNjY19UUyifsHzgRosLtmBT378S7ceO3/+fGRlZWHFihVYs2bNKQPK+gPv+r3+6sn3BbWrDQD0aiPsztagloFoIAp6S012dja2b9+O66+/HgCgUqmQlZWF5cuXn/Qxy5Ytg9ls9h5paWl9VVwi6kVZWVlIT09HVlYWW2uJqMuC3lKzfPlyOJ1On4HBQghotVr89a9/hdl8Yp/50qVLcf/993u/b2xsZLAhChFKhRpXT77P57pmW+NJW1Y6WrFihbelRpKkUwabOeN+AaP2xIGFSoW664UmorAQ1FDjdDrx97//HS+88AIuueQSn9uuvPJKfPjhh7j99ttPeJxWq+US5UQnsa/kOygkBQbHZkCnNvb580uSdEL3j0rZuaCxZs0afPbZZ6cNNJ5zBrub6Xi1zRUoqNmPhtaaYBeFaEAKaqhZs2YN6urqcOutt57QIrNgwQIsX77cb6ghohOdeeaZWL9+PVwuF/YUb8K+ku8wKHo0RiediWhjYlDLptdE4PLxi097v1a7FS32Rhg0kafdi0qvCY11YGTZheK6bByp+AF1LRXe6+Pj4zFs2LAgloxo4AnqOjXz58+HLMt+F9vbvn07pk2bhj179mD8+PGnPA/XqSFyq6+vx7fffou1a9f67CidZB6KM1LPRYwxKYilCy+ykFFQcwCHyrai2e5e5FCtVuP888/H3LlzMWXKFKhUQe/hJwppYbv4Xk8w1BCd6PDhw1i5ciW++eYbyLIMAEiLHo3xaTNDYn2X/qy8Pg+7izeiqa0WABAVFYUFCxbgiiuuQFRUVHALR9SPMNT4wVBDdHKlpaV477338OWXX0IIAaWkwtiU6RiddBaUCmWwi9evNNsasKvoa5TV5wIAzGYzbrzxRlxxxRVcOZioGxhq/GCoITq9o0eP4pVXXsGuXbsAAGZ9HM4ceim7pDpBCIHc6t3YW/wtnLIDSqUS11xzDW666SaYTKce+0NEJ8dQ4wdDDVHnCCHw1Vdf4ZVXXkFDQwMkSYGMlLMxNnl6SGynEIpa7VZsz/8clY2FAIDx48fjwQcfxJAhQ4JbMKIwwFDjB0MNUdfU19fjpZdewsaNGwEA8RGDMG3YPBhCZEZRqCirz8X2/C9gd7ZCq9Xi9ttvx1VXXQWFggGQKBAYavxgqCHqOiEEvvzyS7z44otobW2FVqXHtGGXI8k8NNhFCzpZyNhf8l8crtgOABg5ciQee+wxDB48OMglIwovgf785r8bRAOUJEmYM2cOli9fjpEjR8LmbMV3Rz7GwbKtA3qLgjZHC77L/sgbaBYsWIDXXnuNgYaoH2BLDRHBZrPh5ZdfxmeffQYASI0eibOGXgZ1iK3Y29vqmiux+eh/0GJvgl6vx29+8xtccMEFwS4WUdhiSw0RBZxWq8VDDz2EX//611CpVCity8E3hz6A1VYf7KL1meLaw/jm8D/RYm/CoEGD8MYbbzDQEPUzDDVE5DVv3jy8/PLLiImJQUNrDb4++D6qm4qDXaxeJYTA/tL/YWvuZ3DJTkybNg1vvPEGZzcR9UMMNUTk44wzzsCbb76J0aNHw+Zsxabslcir3hvsYvUKp8uOLbmrcLBsKwDguuuuw7PPPouICM4CI+qPGGqI6AQJCQl45ZVXcMEFF0AIGTsL1mNX0TeQhRzsogVMs60R3xz+EKV1OVCr1Vi6dCnuvPNOKJVcZZmov2KoISK/dDodnnjiCdxyyy0AgJzKH/DfIx/D7mwLcsl6rrqpBBsO/gP1LVWIiorCn//8Z1x66aXBLhYR9RBDDRGdlCRJWLhwIZ566inodDpUNhZiw8F/oKG1JthF6xYhBHKrduPb7BWwOVswYsQIvPnmm8jMzAx20YgoABhqiOi0Zs2ahVdffRVJSUmw2urx9cH3UWQ5HOxidYlTdmBnwXr8UPgVZCHjggsuwP/93/8hKYl7XxGFC4YaIuoUT6vGlClT4JQd+D7vM/xYuAEu2Rnsop1WU1sdvjn0T+TX7INCocDixYvxxBNPQK/XB7toRBRAXHyPiLrE6XRi+fLl+OCDDwAAUYYETB82D5H62CCX7ERCCBRaDuDHwg1wyg6YzWY8/vjjmDp1arCLRkTg3k9+MdQQ9b2tW7fimWeeQUNDA5QKFTJTz8fIxMmQJCnYRQMAtDma8WPhBpTUHQEATJgwAY899hji4+ODXDIi8mCo8YOhhig4ampqsGzZMuzYsQMAEGtMwZQhlyDKELzg4Gmd2V28CXZnK5RKJRYuXIgbb7yR07WJQgxDjR8MNUTBI4TA6tWr8dprr6GlpQUSJAxPmIiMlBnQqQ19WhaLtQy7izbC0lwGABg+fDiWLl2KUaNG9Wk5iKhzGGr8YKghCr6qqiq88sor+PbbbwEAKoUaIxOnYGTiZOjUxl59bou1HIfKt6KsPheAe42dhQsX4tprr4VKperV5yai7mOo8YOhhih0/PDDD3j99deRnZ0NAFBKKqTFjsGw+PGINaYEbMyN0+VAaX0Ocqt2o8ZaCgBQKBSYO3cuFi1ahLi4uIA8DxH1HoYaPxhqiEKLLMv43//+h/fffx+HDx9bz8aoNSMlagSSzEMRZ0qFWqnp9DmFEGixN6KqqRjl9XmoaMiDU3YAAJRKJS666CL8/Oc/R3p6esDrQ0S9g6HGD4YaotAkhMD+/fuxevVqfPftd2hta/W5PUIXg0h9LEzaKOjURqiVOigVSggh4JIdsDnb0GpvgtVWh4aWarQ5W3wen5ycjEsvvRTz5s1jywxRP8RQ4wdDDVHoa21txfbt27F161bs2rUL5eXlXT6HUqnE6NGjMXXqVMyYMQNjx44NmSnkRNR1DDV+MNQQ9T8WiwVHjx5FUVERKisrUVtbC6vVCqfTvUKxTqdDZGQkYmNjkZycjKFDh2L48OHQ6XRBLjkRBUqgP785LYCIgiI2NhaxsbGYNm1asItCRGGCez8RERFRWGCoISIiorDAUENERERhgaGGiIiIwkJYDBT2TOBqbGwMckmIiIioszyf24GaiB0WoaapqQkAkJaWFuSSEBERUVc1NTXBbDb3+DxhsU6NLMsoKytDREREpxfiamxsRFpaGoqLi8N+bZuBVFdgYNWXdQ1fA6m+A6muwMCq7+nqKoRAU1MTUlJSoFD0fERMWLTUKBQKDBo0qFuPjYyMDPtfKo+BVFdgYNWXdQ1fA6m+A6muwMCq76nqGogWGg8OFCYiIqKwwFBDREREYWHAhhqtVovHH38cWq022EXpdQOprsDAqi/rGr4GUn0HUl2BgVXfvq5rWAwUJiIiIhqwLTVEREQUXhhqiIiIKCww1BAREVFY6Neh5rvvvsP8+fORkpICSZLw6aef+txutVpx9913Y9CgQdDr9cjIyMDrr7/uc59Zs2ZBkiSf4/bbb/e5T1FRES6//HIYDAYkJCTgoYcegtPp7O3q+ehpXQsKCk6op+f46KOPvPfzd/u//vWvvqqm1+nqW1lZiYULFyIlJQUGgwFz585FTk6Oz33a2tpw1113ITY2FiaTCQsWLEBlZaXPffrDa3u6utbW1uKee+7B6NGjodfrkZ6ejnvvvRcNDQ0+5wmF1zYQr2t/ec8CPa9vf3rfLlu2DGeeeSYiIiKQkJCAK6+8EtnZ2T73CdR7ctOmTZg8eTK0Wi1GjBiB9957r7er5yMQdd2zZw+uv/56pKWlQa/XY+zYsfjLX/7ic45Nmzb5fW0rKir6pJ4egXptO/N72uPXVvRjn3/+uXj00UfFJ598IgCI//znPz6333bbbWL48OFi48aNIj8/X7zxxhtCqVSKVatWee8zc+ZMcdttt4ny8nLv0dDQ4L3d6XSKM844Q1x00UVi165d4vPPPxdxcXFi6dKlfVVNIUTP6+p0On3qWF5eLp588klhMplEU1OT9zwAxLvvvutzv9bW1r6sqhDi1PWVZVlMnz5dnHfeeWL79u3i8OHDYvHixSI9PV1YrVbv/W6//XaRlpYmvv76a7Fz504xffp0MWPGDO/t/eG17Uxd9+3bJ66++mqxevVqcfToUfH111+LkSNHigULFvg8Tyi8toF4XfvLe1aInte3P71v58yZI959912xf/9+sXv3bnHZZZf1ynsyLy9PGAwGcf/994uDBw+KV155RSiVSrFu3bp+Vdfly5eLe++9V2zatEnk5uaKf/zjH0Kv14tXXnnFe5+NGzcKACI7O9vntXW5XH1W10DVV4jT/54G4rXt16GmI38f9OPGjRNPPfWUz3WTJ08Wjz76qPf7mTNnivvuu++k5/3888+FQqEQFRUV3utee+01ERkZKWw2W0DK3lXdrevxJk6cKG655ZbTnjvYji9Tdna2ACD279/vvc7lcon4+Hjx1ltvCSGEqK+vF2q1Wnz00Ufe+xw6dEgAEFu3bhVC9I/XtjN19WflypVCo9EIh8Nx0nMHW3fr2h/fs0IE7rXtL+/bqqoqAUB8++23QojAvSd//etfi3Hjxvk8V1ZWlpgzZ05vV+mkulNXf+68805xwQUXeL/3hJq6urpeK3t3dLe+p/s9DcRr26+7n05nxowZWL16NUpLSyGEwMaNG3HkyBFccsklPvf74IMPEBcXhzPOOANLly5FS0uL97atW7ciMzMTiYmJ3uvmzJmDxsZGHDhwoM/qcjqdravHDz/8gN27d+PWW2894ba77roLcXFxOOuss/DOO+8EbPfUQLHZbAAAnU7nvU6hUECr1eJ///sfAHf9HA4HLrroIu99xowZg/T0dGzduhVA/3htO1NXfxoaGhAZGQmVyncnlFB+bbtS13B4z3bnte1P71tP92dMTAyAwL0nt27d6nMOz3085wiG7tT1ZOfxnKOjiRMnIjk5GRdffDE2b94c4NJ3XU/qe6rf00C8tmGx99PJvPLKK1i8eDEGDRoElUoFhUKBt956C+eff773Pj/72c8wePBgpKSkYO/evXj44YeRnZ2NTz75BABQUVHh8wYD4P2+r/s1T6Uzde1o+fLlGDt2LGbMmOFz/VNPPYXZs2fDYDDgyy+/xJ133gmr1Yp77723L6rRKZ43y9KlS/HGG2/AaDTipZdeQklJCcrLywG4XxuNRoOoqCifxyYmJnpft/7w2namrserqanB73//eyxevNjn+lB/bTtb13B5z3bnte0v71tZlrFkyRKcc845OOOMMwAE7j15svs0NjaitbUVer2+N6p0Ut2t6/G2bNmCFStWYO3atd7rkpOT8frrr2Pq1Kmw2Wx4++23MWvWLGzbtg2TJ0/utTqdSk/qe7rf00C8tmEfar7//nusXr0agwcPxnfffYe77roLKSkp3jTY8Q9/ZmYmkpOTceGFFyI3NxfDhw8PVtG7rDN19WhtbcU///lP/O53vzvhPB2vmzRpEpqbm/Hcc8+FzAcfAKjVanzyySe49dZbERMTA6VSiYsuugiXXnpp0P87DbSu1rWxsRGXX345MjIy8MQTT/jcFuqvbWfrGi7v2a6+tv3pfXvXXXdh//79p2xNDBeBqOv+/ftxxRVX4PHHH/dpXR89ejRGjx7t/X7GjBnIzc3FSy+9hH/84x89Knd39aS+ffF7GrbdT62trXjkkUfw4osvYv78+Rg/fjzuvvtuZGVl4fnnnz/p46ZNmwYAOHr0KAAgKSnphBHcnu+TkpJ6qfRd09W6fvzxx2hpacFNN9102nNPmzYNJSUl3qbyUDFlyhTs3r0b9fX1KC8vx7p162CxWDBs2DAA7tfGbrejvr7e53GVlZXe160/vLbA6evq0dTUhLlz5yIiIgL/+c9/oFarT3neUHxtO1vXjvrje9ajK/XtL+/bu+++G2vWrMHGjRsxaNAg7/WBek+e7D6RkZF93krTk7p6HDx4EBdeeCEWL16M3/72t6d9zrPOOsv7u97XAlHfjo7/PQ3Eaxu2ocbhcMDhcECh8K2iUqmELMsnfdzu3bsBuJv9AODss8/Gvn37UFVV5b3PV199hcjISGRkZAS+4N3Q1bouX74cP/nJTxAfH3/ac+/evRvR0dEhu0eJ2WxGfHw8cnJysHPnTlxxxRUA3B8WarUaX3/9tfe+2dnZKCoqwtlnnw2gf7y2HZ2sroC7heaSSy6BRqPB6tWrfcZpnEwov7anquvx+uN79nidqW+ov2+FELj77rvxn//8B9988w2GDh3qc3ug3pNnn322zzk89/Gcoy8Eoq4AcODAAVxwwQW4+eab8fTTT3fquXfv3u39Xe8rgarv8Y7/PQ3Ia9vpIcUhqKmpSezatUvs2rVLABAvvvii2LVrlygsLBRCuGdJjBs3TmzcuFHk5eWJd999V+h0OvHqq68KIYQ4evSoeOqpp8TOnTtFfn6+WLVqlRg2bJg4//zzvc/hmWJ4ySWXiN27d4t169aJ+Pj4Pp8e2tO6euTk5AhJksQXX3xxwnOsXr1avPXWW2Lfvn0iJydHvPrqq8JgMIjHHnusT+rY0enqu3LlSrFx40aRm5srPv30UzF48GBx9dVX+5zj9ttvF+np6eKbb74RO3fuFGeffbY4++yzvbf3l9f2dHVtaGgQ06ZNE5mZmeLo0aM+0yWdTqcQInRe257WtT+9ZwNRX4/+8L694447hNlsFps2bfL5HWxpafHeJxDvSc+034ceekgcOnRI/N///V+fT+kORF337dsn4uPjxY033uhzjqqqKu99XnrpJfHpp5+KnJwcsW/fPnHfffcJhUIhNmzY0Gd1DVR9O/N7GojXtl+HGs90t+OPm2++WQghRHl5uVi4cKFISUkROp1OjB49WrzwwgtClmUhhBBFRUXi/PPPFzExMUKr1YoRI0aIhx56yGfNCyGEKCgoEJdeeqnQ6/UiLi5OPPDAAz5TZftDXT2WLl0q0tLS/K5z8MUXX4iJEycKk8kkjEajmDBhgnj99df7fE0EIU5f37/85S9i0KBBQq1Wi/T0dPHb3/72hOm6ra2t4s477xTR0dHCYDCIq666SpSXl/vcpz+8tqer68keD0Dk5+cLIULnte1pXfvTe1aIwPweC9E/3rcn+x189913vfcJ1Hty48aNYuLEiUKj0Yhhw4b5PEdfCERdH3/8cb/nGDx4sPc+f/zjH8Xw4cOFTqcTMTExYtasWeKbb77pw5q6BaK+nf097elry126iYiIKCyE7ZgaIiIiGlgYaoiIiCgsMNQQERFRWGCoISIiorDAUENERERhgaGGiIiIwgJDDREREYUFhhoiIiIKCww1ROQ1a9YsLFmyJKhlGDJkCP785z97v5ckCZ9++mnQykNE/Ycq2AUgotDxySefnHZ3786SJAn/+c9/cOWVVwbkfEREp8NQQ0ReMTExwS5Cv2O326HRaIJdDCICu5+IqIOO3U9DhgzBM888g1tuuQURERFIT0/Hm2++6b2v3W7H3XffjeTkZOh0OgwePBjLli3zPhYArrrqKkiS5P0+NzcXV1xxBRITE2EymXDmmWdiw4YNASn77Nmzcffdd/tcV11dDY1Gg6+//hoAYLPZ8OCDDyI1NRVGoxHTpk3Dpk2bvPe3WCy4/vrrkZqaCoPBgMzMTHz44Ycn/IzuvvtuLFmyBHFxcZgzZ05Ayk9EPcdQQ0Qn9cILL2Dq1KnYtWsX7rzzTtxxxx3Izs4GALz88stYvXo1Vq5ciezsbHzwwQfe8LJjxw4AwLvvvovy8nLv91arFZdddhm+/vpr7Nq1C3PnzsX8+fNRVFTU47IuWrQI//znP2Gz2bzXvf/++0hNTcXs2bMBAHfffTe2bt2Kf/3rX9i7dy+uueYazJ07Fzk5OQCAtrY2TJkyBWvXrsX+/fuxePFi/PznP8f27dt9nutvf/sbNBoNNm/ejNdff73HZSeiAOn+ZuREFG5mzpwp7rvvPiGEEIMHDxY33nij9zZZlkVCQoJ47bXXhBBC3HPPPWL27NlClmW/5wIg/vOf/5z2OceNGydeeeUV7/eDBw8WL730UpfP09raKqKjo8WKFSu8140fP1488cQTQgghCgsLhVKpFKWlpT6Pu/DCC8XSpUtPet7LL79cPPDAA97vZ86cKSZNmnTa8hBR3+OYGiI6qfHjx3u/liQJSUlJqKqqAgAsXLgQF198MUaPHo25c+di3rx5uOSSS055PqvViieeeAJr165FeXk5nE4nWltbA9JSo9Pp8POf/xzvvPMOrr32Wvz444/Yv38/Vq9eDQDYt28fXC4XRo0a5fM4m82G2NhYAIDL5cIzzzyDlStXorS0FHa7HTabDQaDwecxU6ZM6XF5iSjwGGqI6KSOnwklSRJkWQYATJ48Gfn5+fjiiy+wYcMGXHvttbjooovw8ccfn/R8Dz74IL766is8//zzGDFiBPR6PX7605/CbrcHpLyLFi3CxIkTUVJSgnfffRezZ8/G4MGDAbgDlVKpxA8//AClUunzOJPJBAB47rnn8Je//AV//vOfkZmZCaPRiCVLlpxQPqPRGJDyElFgMdQQUbdFRkYiKysLWVlZ+OlPf4q5c+eitrYWMTExUKvVcLlcPvffvHkzFi5ciKuuugqAO2gUFBQErDyZmZmYOnUq3nrrLfzzn//EX//6V+9tkyZNgsvlQlVVFc477zy/j9+8eTOuuOIK3HjjjQAAWZZx5MgRZGRkBKyMRNR7OFCYiLrlxRdfxIcffojDhw/jyJEj+Oijj5CUlISoqCgA7hlQX3/9NSoqKlBXVwcAGDlyJD755BPs3r0be/bswc9+9jNvy0+gLFq0CM8++yyEEN7wBACjRo3CDTfcgJtuugmffPIJ8vPzsX37dixbtgxr1671lu+rr77Cli1bcOjQIfzyl79EZWVlQMtHRL2HoYaIuiUiIgJ/+tOfMHXqVJx55pkoKCjA559/DoXC/WflhRdewFdffYW0tDRMmjQJgDsIRUdHY8aMGZg/fz7mzJmDyZMnB7Rc119/PVQqFa6//nrodDqf2959913cdNNNeOCBBzB69GhceeWV2LFjB9LT0wEAv/3tbzF58mTMmTMHs2bNQlJSEhcPJOpHJCGECHYhiIgCpaCgAMOHD8eOHTsCHpiIKLQx1BBRWHA4HLBYLHjwwQeRn5+PzZs3B7tIRNTH2P1ERP3CM888A5PJ5Pe49NJLsXnzZiQnJ2PHjh1cEI9ogGJLDRH1C7W1taitrfV7m16vR2pqah+XiIhCDUMNERERhQV2PxEREVFYYKghIiKisMBQQ0RERGGBoYaIiIjCAkMNERERhQWGGiIiIgoLDDVEREQUFhhqiIiIKCz8P/XUE1m5FloXAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.groupby('material', as_index=False)['pipe_year'].mean().sort_values('pipe_year', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N0pH_277CXbs",
        "outputId": "86b8a2be-a135-4d5f-8ae4-95a6d12542a1"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  material  pipe_year\n",
              "1        C  65.216006\n",
              "4        S  40.533333\n",
              "3        O  21.000000\n",
              "2        D  14.496774\n",
              "0        A        NaN"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dd8ae47-bff6-46cf-be81-9904fae22b3a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>material</th>\n",
              "      <th>pipe_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C</td>\n",
              "      <td>65.216006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>S</td>\n",
              "      <td>40.533333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>O</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>D</td>\n",
              "      <td>14.496774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dd8ae47-bff6-46cf-be81-9904fae22b3a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7dd8ae47-bff6-46cf-be81-9904fae22b3a 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-7dd8ae47-bff6-46cf-be81-9904fae22b3a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4ac23e42-f43b-490d-b6ca-8fec5c9399c2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ac23e42-f43b-490d-b6ca-8fec5c9399c2')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4ac23e42-f43b-490d-b6ca-8fec5c9399c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.violinplot(y = df2['material'], x=df2['pipe_year'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "zCRr-MQLOHOt",
        "outputId": "6c72d078-6419-405e-f797-a4a99b9ed3e1"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Different range of temporal variables also affects history variable"
      ],
      "metadata": {
        "id": "fXf15JuRROH0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[df2['history'] > 2].groupby('material')['pipe_id'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLabBjYUYvgG",
        "outputId": "0304aaab-0f92-4cf3-eb3a-312b4ab0a2ca"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "material\n",
              "C    94\n",
              "D     1\n",
              "S     1\n",
              "Name: pipe_id, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# average pipe_year by history\n",
        "\n",
        "df2[df2['history'] > 0].groupby('history', as_index=False)['pipe_year'].mean()"
      ],
      "metadata": {
        "id": "mkUMMQegF_FP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7534b7e0-bb92-4fa8-bb53-1037725fe407"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   history  pipe_year\n",
              "0      1.0  58.272179\n",
              "1      2.0  62.461187\n",
              "2      3.0  63.983333\n",
              "3      4.0  66.300000\n",
              "4      5.0  62.777778\n",
              "5      6.0  60.666667\n",
              "6      7.0  56.000000\n",
              "7      8.0  61.500000"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>history</th>\n",
              "      <th>pipe_year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>62.461187</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>63.983333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>66.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>62.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6.0</td>\n",
              "      <td>60.666667</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7.0</td>\n",
              "      <td>56.000000</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8.0</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-5c6a1423-8dd9-47e9-beef-be126983d345 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-5c6a1423-8dd9-47e9-beef-be126983d345');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "looking at breakage records of pipes with multiple breakage history (history value of more than 2 in this case), again it makes sense to see more material C records here as mean pipe_year positively correlates to history values and only material C can have certain high values of pipe_year\n",
        "\n",
        "Again, other material pipes may also break as much or even more than material C pipes in its lifetime, but as their install_year records only start from certain later point in this dataset, such data is not present"
      ],
      "metadata": {
        "id": "huOLnm8NUPqz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation\n",
        "\n",
        "Lastly I will take a look at correlation of the variables"
      ],
      "metadata": {
        "id": "kZbWJx-2nMSd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.corr()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "tjVjG1KZl5G8",
        "outputId": "15df635d-9f2a-4d8b-cb27-c69f5031bdfe"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              install_year  diameter  break_year   history  pipe_year\n",
              "install_year      1.000000  0.016486    0.001697 -0.088671  -0.915784\n",
              "diameter          0.016486  1.000000   -0.046312 -0.013583  -0.078681\n",
              "break_year        0.001697 -0.046312    1.000000  0.169920   0.400117\n",
              "history          -0.088671 -0.013583    0.169920  1.000000   0.057156\n",
              "pipe_year        -0.915784 -0.078681    0.400117  0.057156   1.000000"
            ],
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              "      <th></th>\n",
              "      <th>install_year</th>\n",
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              "      <td>0.169920</td>\n",
              "      <td>0.400117</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>history</th>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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              "\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df2.corr())\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "-g7Zg1mEoIpp",
        "outputId": "6a1402a4-d92b-41c6-e659-8941d47ae07b"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Negative correlation for pipe_year and history against install_year as more recent the install_year value gets, history and pipe_year values to lower\n",
        "\n",
        "Positive correlation is found between pipe_year and break_year, as more recent the break_year value gets, the year difference with install_year values to get bigger\n",
        "\n"
      ],
      "metadata": {
        "id": "pt6I_pZ0pNIm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusion\n",
        "\n",
        "**Here are insights on potential factors affecting pipe breakage summarizing my findings throughout the analysis:**\n",
        "\n",
        "- The most potential factors affecting pipe breakage is pipe material and number of years since pipe installation (install_year and break_year)\n",
        "\n",
        "- diameter is less potential factor affecting pipe breakage on its own, however the combination of diameter and material is a higher potential factor"
      ],
      "metadata": {
        "id": "s7cMkncUvyi4"
      }
    }
  ]
}