EDA + Data Cleaning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Install PySpark"
      ],
      "metadata": {
        "id": "64LRFB5HA-ej"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "b7D8anXz-fjx",
        "outputId": "bbbf2634-bd91-43e9-bfe5-942f5b3f2a73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Get:1 http://security.ubuntu.com/ubuntu jammy-security InRelease [110 kB]\n",
            "Hit:2 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease\n",
            "Hit:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64  InRelease\n",
            "Hit:4 http://archive.ubuntu.com/ubuntu jammy InRelease\n",
            "Get:5 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [119 kB]\n",
            "Hit:6 https://ppa.launchpadcontent.net/c2d4u.team/c2d4u4.0+/ubuntu jammy InRelease\n",
            "Hit:7 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease\n",
            "Hit:8 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n",
            "Hit:9 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n",
            "Hit:10 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n",
            "Fetched 229 kB in 2s (110 kB/s)\n",
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "45 packages can be upgraded. Run 'apt list --upgradable' to see them.\n",
            "tar: spark-3.2.1-bin-hadoop3.2.tgz: Cannot open: No such file or directory\n",
            "tar: Error is not recoverable: exiting now\n",
            "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.1)\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
            "Requirement already satisfied: py4j in /usr/local/lib/python3.10/dist-packages (0.10.9.7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7e3d0c333220>"
            ],
            "text/html": [
              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://896e852f327c:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.5.1</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local[*]</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Our First Spark Example</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "!sudo apt update\n",
        "!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
        "#Check this site for the latest download link https://www.apache.org/dyn/closer.lua/spark/spark-3.2.1/spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!wget -q https://dlcdn.apache.org/spark/spark-3.2.1/spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!tar xf spark-3.2.1-bin-hadoop3.2.tgz\n",
        "!pip install -q findspark\n",
        "!pip install pyspark\n",
        "!pip install py4j\n",
        "\n",
        "import os\n",
        "import sys\n",
        "# os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
        "# os.environ[\"SPARK_HOME\"] = \"/content/spark-3.2.1-bin-hadoop3.2\"\n",
        "\n",
        "\n",
        "import findspark\n",
        "findspark.init()\n",
        "findspark.find()\n",
        "\n",
        "import pyspark\n",
        "\n",
        "from pyspark.sql import DataFrame, SparkSession\n",
        "from typing import List\n",
        "import pyspark.sql.types as T\n",
        "import pyspark.sql.functions as F\n",
        "\n",
        "spark= SparkSession \\\n",
        "       .builder \\\n",
        "       .appName(\"Our First Spark Example\") \\\n",
        "       .getOrCreate()\n",
        "\n",
        "spark"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "spark"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "vNg5BZ4yAoQk",
        "outputId": "5aea65bf-db7a-49d6-89ff-f1744c5ca4cd"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pyspark.sql.session.SparkSession at 0x7e3d0c333220>"
            ],
            "text/html": [
              "\n",
              "            <div>\n",
              "                <p><b>SparkSession - in-memory</b></p>\n",
              "                \n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"http://896e852f327c:4040\">Spark UI</a></p>\n",
              "\n",
              "            <dl>\n",
              "              <dt>Version</dt>\n",
              "                <dd><code>v3.5.1</code></dd>\n",
              "              <dt>Master</dt>\n",
              "                <dd><code>local[*]</code></dd>\n",
              "              <dt>AppName</dt>\n",
              "                <dd><code>Our First Spark Example</code></dd>\n",
              "            </dl>\n",
              "        </div>\n",
              "        \n",
              "            </div>\n",
              "        "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Data"
      ],
      "metadata": {
        "id": "jpZQneT-A5c1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = spark.read.csv('train.csv', header = True)"
      ],
      "metadata": {
        "id": "pTWc6HN9A2sH"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Viewing the dataframe schema\n",
        "df.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9_P9A1MCasJ",
        "outputId": "efccf5de-2aab-4cab-dacd-d1d72ad96482"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- PassengerId: string (nullable = true)\n",
            " |-- Survived: string (nullable = true)\n",
            " |-- Pclass: string (nullable = true)\n",
            " |-- Name: string (nullable = true)\n",
            " |-- Sex: string (nullable = true)\n",
            " |-- Age: string (nullable = true)\n",
            " |-- SibSp: string (nullable = true)\n",
            " |-- Parch: string (nullable = true)\n",
            " |-- Ticket: string (nullable = true)\n",
            " |-- Fare: string (nullable = true)\n",
            " |-- Cabin: string (nullable = true)\n",
            " |-- Embarked: string (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summary stats\n",
        "df.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qvewYcwrCfyG",
        "outputId": "1f1e1efd-5443-44f3-ca29-d024cd32580b"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "|PassengerId|Survived|Pclass|                Name|   Sex| Age|SibSp|Parch|          Ticket|   Fare|Cabin|Embarked|\n",
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "|          1|       0|     3|Braund, Mr. Owen ...|  male|  22|    1|    0|       A/5 21171|   7.25| NULL|       S|\n",
            "|          2|       1|     1|Cumings, Mrs. Joh...|female|  38|    1|    0|        PC 17599|71.2833|  C85|       C|\n",
            "|          3|       1|     3|Heikkinen, Miss. ...|female|  26|    0|    0|STON/O2. 3101282|  7.925| NULL|       S|\n",
            "|          4|       1|     1|Futrelle, Mrs. Ja...|female|  35|    1|    0|          113803|   53.1| C123|       S|\n",
            "|          5|       0|     3|Allen, Mr. Willia...|  male|  35|    0|    0|          373450|   8.05| NULL|       S|\n",
            "|          6|       0|     3|    Moran, Mr. James|  male|NULL|    0|    0|          330877| 8.4583| NULL|       Q|\n",
            "|          7|       0|     1|McCarthy, Mr. Tim...|  male|  54|    0|    0|           17463|51.8625|  E46|       S|\n",
            "|          8|       0|     3|Palsson, Master. ...|  male|   2|    3|    1|          349909| 21.075| NULL|       S|\n",
            "|          9|       1|     3|Johnson, Mrs. Osc...|female|  27|    0|    2|          347742|11.1333| NULL|       S|\n",
            "|         10|       1|     2|Nasser, Mrs. Nich...|female|  14|    1|    0|          237736|30.0708| NULL|       C|\n",
            "|         11|       1|     3|Sandstrom, Miss. ...|female|   4|    1|    1|         PP 9549|   16.7|   G6|       S|\n",
            "|         12|       1|     1|Bonnell, Miss. El...|female|  58|    0|    0|          113783|  26.55| C103|       S|\n",
            "|         13|       0|     3|Saundercock, Mr. ...|  male|  20|    0|    0|       A/5. 2151|   8.05| NULL|       S|\n",
            "|         14|       0|     3|Andersson, Mr. An...|  male|  39|    1|    5|          347082| 31.275| NULL|       S|\n",
            "|         15|       0|     3|Vestrom, Miss. Hu...|female|  14|    0|    0|          350406| 7.8542| NULL|       S|\n",
            "|         16|       1|     2|Hewlett, Mrs. (Ma...|female|  55|    0|    0|          248706|     16| NULL|       S|\n",
            "|         17|       0|     3|Rice, Master. Eugene|  male|   2|    4|    1|          382652| 29.125| NULL|       Q|\n",
            "|         18|       1|     2|Williams, Mr. Cha...|  male|NULL|    0|    0|          244373|     13| NULL|       S|\n",
            "|         19|       0|     3|Vander Planke, Mr...|female|  31|    1|    0|          345763|     18| NULL|       S|\n",
            "|         20|       1|     3|Masselmani, Mrs. ...|female|NULL|    0|    0|            2649|  7.225| NULL|       C|\n",
            "+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#EDA"
      ],
      "metadata": {
        "id": "U4t4dubYPe4h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "# Filter the DataFrame for male passengers\n",
        "male_passengers = df.filter(col(\"Sex\") == \"male\")\n",
        "\n",
        "# Filter the DataFrame for female passengers\n",
        "female_passengers = df.filter(col(\"Sex\") == \"female\")\n",
        "\n",
        "# Calculate the total number of male passengers\n",
        "total_male_passengers = male_passengers.count()\n",
        "\n",
        "# Calculate the total number of female passengers\n",
        "total_female_passengers = female_passengers.count()\n",
        "\n",
        "# Calculate the number of male survivors\n",
        "male_survivors = male_passengers.filter(col(\"Survived\") == \"1\").count()\n",
        "\n",
        "# Calculate the number of female survivors\n",
        "female_survivors = female_passengers.filter(col(\"Survived\") == \"1\").count()\n",
        "\n",
        "# Calculate the percentage of survivors among men\n",
        "male_survivor_percentage = (male_survivors / total_male_passengers) * 100\n",
        "\n",
        "# Calculate the percentage of survivors among women\n",
        "female_survivor_percentage = (female_survivors / total_female_passengers) * 100\n",
        "\n",
        "print(\"Percentage of survivors among men: {:.2f}%\".format(male_survivor_percentage))\n",
        "print(\"Percentage of survivors among women: {:.2f}%\".format(female_survivor_percentage))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArA9_s1wPgrF",
        "outputId": "a4193f0a-78e7-4e33-8f6e-bad93bed486a"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of survivors among men: 18.89%\n",
            "Percentage of survivors among women: 74.20%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Data\n",
        "categories = ['Men', 'Women']\n",
        "percentages = [male_survivor_percentage, female_survivor_percentage]\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(categories, percentages, color=['blue', 'pink'])\n",
        "plt.xlabel('Sex')\n",
        "plt.ylabel('Survivor Percentage')\n",
        "plt.title('Percentage of Survivors by Gender')\n",
        "plt.ylim(0, 100)  # Set y-axis limit to 0-100%\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "0odQEJHkPkxD",
        "outputId": "42c6010e-9215-41c1-ea8c-9a5d2f3f66da"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "# Filter data for survivors and casualties\n",
        "survivors_age = df.filter(col(\"Survived\") == \"1\").select(\"Age\").collect()\n",
        "casualties_age = df.filter(col(\"Survived\") == \"0\").select(\"Age\").collect()\n",
        "\n",
        "# Extract ages from Row objects and convert to float\n",
        "survivors_age = [float(row.Age) for row in survivors_age if row.Age is not None]\n",
        "casualties_age = [float(row.Age) for row in casualties_age if row.Age is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_age, casualties_age]\n",
        "labels = ['Survivors', 'Casualties']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Age')\n",
        "plt.title('Box Plot of Age for Survivors and Casualties')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yryYV6f_Pu8K",
        "outputId": "7fe268bb-5ee3-4df5-cd57-e063eea05a50"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the casualties there are more outliers and the boxplot is longer (the age of the casualties tends to be between 66 and 1, while survivors are between 0 and 60)"
      ],
      "metadata": {
        "id": "Y2rwCuotTK9J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "# Convert fare column to FloatType\n",
        "df = df.withColumn(\"Fare\", df[\"Fare\"].cast(\"float\"))\n",
        "\n",
        "# Filter data for survivors and casualties\n",
        "survivors_fare = df.filter(col(\"Survived\") == \"1\").select(\"Fare\").collect()\n",
        "casualties_fare = df.filter(col(\"Survived\") == \"0\").select(\"Fare\").collect()\n",
        "\n",
        "# Extract fares from Row objects\n",
        "survivors_fare = [row.Fare for row in survivors_fare if row.Fare is not None]\n",
        "casualties_fare = [row.Fare for row in casualties_fare if row.Fare is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_fare, casualties_fare]\n",
        "labels = ['Survivors', 'Casualties']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Fare')\n",
        "plt.title('Box Plot of Fare for Survivors and Casualties')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "JT9vRBbyTARa",
        "outputId": "50974955-36be-48d7-e92e-089a9f2a937f"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The casualties payed a lower fare then casualties, with some outliers in both distributions. In the survivors especially there is an extremely high 500+ fare."
      ],
      "metadata": {
        "id": "x3ODpE8-UDe6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Extract fares from the DataFrame\n",
        "fares = df.select(\"Fare\").rdd.flatMap(lambda x: x).collect()\n",
        "\n",
        "# Plot histogram\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(fares, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Fare')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "LmpuPfvrs8pa",
        "outputId": "39e4eb9f-883c-4f08-ac13-1cd315958071"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from pyspark.sql.functions import col\n",
        "\n",
        "# Group the DataFrame by \"Pclass\" and count the number of passengers in each class\n",
        "class_counts = df.groupBy(\"Pclass\").count().orderBy(\"Pclass\")\n",
        "\n",
        "# Convert DataFrame to Pandas for plotting\n",
        "class_counts_pd = class_counts.toPandas()\n",
        "\n",
        "# Plot bar plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(class_counts_pd[\"Pclass\"], class_counts_pd[\"count\"], color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Passenger Class')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Passenger Class')\n",
        "plt.xticks(class_counts_pd[\"Pclass\"])\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "zHVy7sQ3tKXC",
        "outputId": "5fbe26ac-8bf2-4641-8e92-a6df5f8f30c8"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "BpU181xUI9I7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import col, isnan, when, count\n",
        "\n",
        "# Count the number of missing values in each column\n",
        "missing_values = df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in df.columns])\n",
        "\n",
        "# Show the count of missing values\n",
        "missing_values.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FCDIDtakJCZ6",
        "outputId": "52a99839-1bb6-4bda-fed5-d03b1cef46e7"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|PassengerId|Survived|Pclass|Name|Sex|Age|SibSp|Parch|Ticket|Fare|Cabin|Embarked|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|          0|       0|     0|   0|  0|177|    0|    0|     0|   0|  687|       2|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out null values in the Age column\n",
        "age_data = df.select(\"Age\").filter(col(\"Age\").isNotNull()).rdd.flatMap(lambda x: x).collect()\n",
        "\n",
        "# Plot histogram\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(age_data, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Age')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "d56oAnqRK414",
        "outputId": "df693bc1-8d74-4788-cf8f-bcfdcc92e165"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import when, median\n",
        "from pyspark.sql.types import IntegerType  # Import IntegerType\n",
        "\n",
        "# Replace missing values of Age with the median value because as we see from the\n",
        "# distribution above the average might be a less representative number (weird\n",
        "# distribution and outliers)\n",
        "median_age = df.select(median(\"Age\")).collect()[0][0]\n",
        "df = df.withColumn(\"Age\", when(col(\"Age\").isNull(),(median_age)).otherwise(col(\"Age\")))\n",
        "df = df.withColumn(\"Age\", df[\"Age\"].cast(IntegerType()))\n",
        "\n",
        "# Introduce a new category \"Not available\" for missing values of Cabin, as there are many\n",
        "df = df.withColumn(\"Cabin\", when(col(\"Cabin\").isNull(), \"Not available\").otherwise(col(\"Cabin\")))\n",
        "\n",
        "# Drop the 2 missing observations in Embarked\n",
        "df = df.dropna(subset=[\"Embarked\"])\n",
        "\n",
        "# Check the first few rows to verify the changes\n",
        "df.show()\n",
        "\n",
        "# Check the count of missing values to confirm\n",
        "missing_values = df.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in df.columns])\n",
        "missing_values.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gyAzpRd9LCSl",
        "outputId": "dedea123-e5c7-4fbc-a2dd-b44aa1294528"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+\n",
            "|PassengerId|Survived|Pclass|                Name|   Sex|Age|SibSp|Parch|          Ticket|   Fare|        Cabin|Embarked|\n",
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+\n",
            "|          1|       0|     3|Braund, Mr. Owen ...|  male| 22|    1|    0|       A/5 21171|   7.25|Not available|       S|\n",
            "|          2|       1|     1|Cumings, Mrs. Joh...|female| 38|    1|    0|        PC 17599|71.2833|          C85|       C|\n",
            "|          3|       1|     3|Heikkinen, Miss. ...|female| 26|    0|    0|STON/O2. 3101282|  7.925|Not available|       S|\n",
            "|          4|       1|     1|Futrelle, Mrs. Ja...|female| 35|    1|    0|          113803|   53.1|         C123|       S|\n",
            "|          5|       0|     3|Allen, Mr. Willia...|  male| 35|    0|    0|          373450|   8.05|Not available|       S|\n",
            "|          6|       0|     3|    Moran, Mr. James|  male| 28|    0|    0|          330877| 8.4583|Not available|       Q|\n",
            "|          7|       0|     1|McCarthy, Mr. Tim...|  male| 54|    0|    0|           17463|51.8625|          E46|       S|\n",
            "|          8|       0|     3|Palsson, Master. ...|  male|  2|    3|    1|          349909| 21.075|Not available|       S|\n",
            "|          9|       1|     3|Johnson, Mrs. Osc...|female| 27|    0|    2|          347742|11.1333|Not available|       S|\n",
            "|         10|       1|     2|Nasser, Mrs. Nich...|female| 14|    1|    0|          237736|30.0708|Not available|       C|\n",
            "|         11|       1|     3|Sandstrom, Miss. ...|female|  4|    1|    1|         PP 9549|   16.7|           G6|       S|\n",
            "|         12|       1|     1|Bonnell, Miss. El...|female| 58|    0|    0|          113783|  26.55|         C103|       S|\n",
            "|         13|       0|     3|Saundercock, Mr. ...|  male| 20|    0|    0|       A/5. 2151|   8.05|Not available|       S|\n",
            "|         14|       0|     3|Andersson, Mr. An...|  male| 39|    1|    5|          347082| 31.275|Not available|       S|\n",
            "|         15|       0|     3|Vestrom, Miss. Hu...|female| 14|    0|    0|          350406| 7.8542|Not available|       S|\n",
            "|         16|       1|     2|Hewlett, Mrs. (Ma...|female| 55|    0|    0|          248706|   16.0|Not available|       S|\n",
            "|         17|       0|     3|Rice, Master. Eugene|  male|  2|    4|    1|          382652| 29.125|Not available|       Q|\n",
            "|         18|       1|     2|Williams, Mr. Cha...|  male| 28|    0|    0|          244373|   13.0|Not available|       S|\n",
            "|         19|       0|     3|Vander Planke, Mr...|female| 31|    1|    0|          345763|   18.0|Not available|       S|\n",
            "|         20|       1|     3|Masselmani, Mrs. ...|female| 28|    0|    0|            2649|  7.225|Not available|       C|\n",
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+\n",
            "only showing top 20 rows\n",
            "\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|PassengerId|Survived|Pclass|Name|Sex|Age|SibSp|Parch|Ticket|Fare|Cabin|Embarked|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "|          0|       0|     0|   0|  0|  0|    0|    0|     0|   0|    0|       0|\n",
            "+-----------+--------+------+----+---+---+-----+-----+------+----+-----+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we will do some feature engineering to the 'Name' column. We will extract the titles given to names (Mr, Miss etc.) and see if they have any relevance."
      ],
      "metadata": {
        "id": "9Oe8lb07hC_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import col, split\n",
        "\n",
        "# Extract titles from the \"Name\" column using PySpark functions\n",
        "df = df.withColumn(\"Title\", split(split(col(\"Name\"), \",\")[1], \"\\.\")[0].cast(\"string\"))\n",
        "\n",
        "# Show the count of each title\n",
        "title_counts = df.groupBy(\"Title\").count().orderBy(\"count\", ascending=False)\n",
        "title_counts.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39k7NAtEgn5n",
        "outputId": "5f027a41-a3e6-44d6-8be9-c3ac58280dfe"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+-----+\n",
            "|        Title|count|\n",
            "+-------------+-----+\n",
            "|           Mr|  517|\n",
            "|         Miss|  181|\n",
            "|          Mrs|  124|\n",
            "|       Master|   40|\n",
            "|           Dr|    7|\n",
            "|          Rev|    6|\n",
            "|          Col|    2|\n",
            "|         Mlle|    2|\n",
            "|        Major|    2|\n",
            "|         Capt|    1|\n",
            "|         Lady|    1|\n",
            "|     Jonkheer|    1|\n",
            "|          Sir|    1|\n",
            "| the Countess|    1|\n",
            "|          Don|    1|\n",
            "|           Ms|    1|\n",
            "|          Mme|    1|\n",
            "+-------------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import col, split\n",
        "\n",
        "# Extract titles from the \"Name\" column using PySpark functions and create a new column\n",
        "df = df.withColumn(\"Title\", split(split(col(\"Name\"), \",\")[1], \"\\.\")[0].cast(\"string\"))\n",
        "\n",
        "# Show the DataFrame with the new \"Title\" column\n",
        "df.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJsYmeT0QLPT",
        "outputId": "6ba4fdb8-d19b-4ac2-e670-d2c7aaf91078"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+-------+\n",
            "|PassengerId|Survived|Pclass|                Name|   Sex|Age|SibSp|Parch|          Ticket|   Fare|        Cabin|Embarked|  Title|\n",
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+-------+\n",
            "|          1|       0|     3|Braund, Mr. Owen ...|  male| 22|    1|    0|       A/5 21171|   7.25|Not available|       S|     Mr|\n",
            "|          2|       1|     1|Cumings, Mrs. Joh...|female| 38|    1|    0|        PC 17599|71.2833|          C85|       C|    Mrs|\n",
            "|          3|       1|     3|Heikkinen, Miss. ...|female| 26|    0|    0|STON/O2. 3101282|  7.925|Not available|       S|   Miss|\n",
            "|          4|       1|     1|Futrelle, Mrs. Ja...|female| 35|    1|    0|          113803|   53.1|         C123|       S|    Mrs|\n",
            "|          5|       0|     3|Allen, Mr. Willia...|  male| 35|    0|    0|          373450|   8.05|Not available|       S|     Mr|\n",
            "|          6|       0|     3|    Moran, Mr. James|  male| 28|    0|    0|          330877| 8.4583|Not available|       Q|     Mr|\n",
            "|          7|       0|     1|McCarthy, Mr. Tim...|  male| 54|    0|    0|           17463|51.8625|          E46|       S|     Mr|\n",
            "|          8|       0|     3|Palsson, Master. ...|  male|  2|    3|    1|          349909| 21.075|Not available|       S| Master|\n",
            "|          9|       1|     3|Johnson, Mrs. Osc...|female| 27|    0|    2|          347742|11.1333|Not available|       S|    Mrs|\n",
            "|         10|       1|     2|Nasser, Mrs. Nich...|female| 14|    1|    0|          237736|30.0708|Not available|       C|    Mrs|\n",
            "|         11|       1|     3|Sandstrom, Miss. ...|female|  4|    1|    1|         PP 9549|   16.7|           G6|       S|   Miss|\n",
            "|         12|       1|     1|Bonnell, Miss. El...|female| 58|    0|    0|          113783|  26.55|         C103|       S|   Miss|\n",
            "|         13|       0|     3|Saundercock, Mr. ...|  male| 20|    0|    0|       A/5. 2151|   8.05|Not available|       S|     Mr|\n",
            "|         14|       0|     3|Andersson, Mr. An...|  male| 39|    1|    5|          347082| 31.275|Not available|       S|     Mr|\n",
            "|         15|       0|     3|Vestrom, Miss. Hu...|female| 14|    0|    0|          350406| 7.8542|Not available|       S|   Miss|\n",
            "|         16|       1|     2|Hewlett, Mrs. (Ma...|female| 55|    0|    0|          248706|   16.0|Not available|       S|    Mrs|\n",
            "|         17|       0|     3|Rice, Master. Eugene|  male|  2|    4|    1|          382652| 29.125|Not available|       Q| Master|\n",
            "|         18|       1|     2|Williams, Mr. Cha...|  male| 28|    0|    0|          244373|   13.0|Not available|       S|     Mr|\n",
            "|         19|       0|     3|Vander Planke, Mr...|female| 31|    1|    0|          345763|   18.0|Not available|       S|    Mrs|\n",
            "|         20|       1|     3|Masselmani, Mrs. ...|female| 28|    0|    0|            2649|  7.225|Not available|       C|    Mrs|\n",
            "+-----------+--------+------+--------------------+------+---+-----+-----+----------------+-------+-------------+--------+-------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a lot of categories so we might group some of them together"
      ],
      "metadata": {
        "id": "svit6Y8POV2v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a regular expression pattern to match the titles to be replaced\n",
        "titles_to_replace = [\"Capt\", \"Col\", \"Major\", \"Dr\", \"Rev\"]\n",
        "pattern = \"|\".join(titles_to_replace)\n",
        "\n",
        "# Replace titles in the \"Title\" column with \"Officer\"\n",
        "df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), pattern, \"Officer\"))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z41DfKaOgtS",
        "outputId": "24a68c4e-a20b-473c-ba9d-ac51223139c1"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+\n",
            "|Title        |\n",
            "+-------------+\n",
            "| Master      |\n",
            "| Mrs         |\n",
            "| Miss        |\n",
            "| Lady        |\n",
            "| Jonkheer    |\n",
            "| Mr          |\n",
            "| Sir         |\n",
            "| the Countess|\n",
            "| Mlle        |\n",
            "| Officer     |\n",
            "| Don         |\n",
            "| Ms          |\n",
            "| Mme         |\n",
            "+-------------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a regular expression pattern to match the additional titles to be replaced\n",
        "additional_titles_to_replace = [\"Jonkheer\", \"Don\", \"Sir\", \"the Countess\", \"Dona\", \"Lady\"]\n",
        "additional_pattern = \"|\".join(additional_titles_to_replace)\n",
        "\n",
        "# Replace additional titles in the \"Title\" column with \"Noble\"\n",
        "df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), additional_pattern, \"Noble\"))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZqC1wvWjPRF8",
        "outputId": "98554f19-a96a-4d91-df4c-ca7089b92e71"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+\n",
            "|Title   |\n",
            "+--------+\n",
            "| Master |\n",
            "| Mrs    |\n",
            "| Noble  |\n",
            "| Miss   |\n",
            "| Mr     |\n",
            "| Mlle   |\n",
            "| Officer|\n",
            "| Ms     |\n",
            "| Mme    |\n",
            "+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import regexp_replace\n",
        "\n",
        "# Define a dictionary to map titles to their replacements\n",
        "title_replacements = {\n",
        "    \"Mme\": \"Mrs\",\n",
        "    \"Ms\": \"Mrs\",\n",
        "    \"Mlle\": \"Miss\"\n",
        "}\n",
        "\n",
        "# Iterate over the dictionary and replace titles in the \"Title\" column\n",
        "for old_title, new_title in title_replacements.items():\n",
        "    df = df.withColumn(\"Title\", regexp_replace(col(\"Title\"), old_title, new_title))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Title\").distinct().show(truncate=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6J2vvzUTP8Yz",
        "outputId": "66b0a703-2f71-4139-8bbb-9eff05bb8247"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+\n",
            "|Title   |\n",
            "+--------+\n",
            "| Master |\n",
            "| Mrs    |\n",
            "| Noble  |\n",
            "| Miss   |\n",
            "| Mr     |\n",
            "| Officer|\n",
            "+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "now we will clean drop Name, Ticket and PassengerId"
      ],
      "metadata": {
        "id": "R8KJ_LNLqSkw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop the \"Name\" and \"Ticket\" columns\n",
        "df = df.drop(\"Name\", \"Ticket\", \"PassengerId\")"
      ],
      "metadata": {
        "id": "vHYUWGQVqZgR"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0s9ySClKtj0N",
        "outputId": "182137ba-f620-4cde-c946-a8b73338e903"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+------+------+---+-----+-----+-----+-------------+--------+-------+\n",
            "|Survived|Pclass|   Sex|Age|SibSp|Parch| Fare|        Cabin|Embarked|  Title|\n",
            "+--------+------+------+---+-----+-----+-----+-------------+--------+-------+\n",
            "|       0|     3|  male| 22|    1|    0| 7.25|Not available|       S|     Mr|\n",
            "|       1|     1|female| 38|    1|    0|71.28|          C85|       C|    Mrs|\n",
            "|       1|     3|female| 26|    0|    0| 7.93|Not available|       S|   Miss|\n",
            "|       1|     1|female| 35|    1|    0|53.10|         C123|       S|    Mrs|\n",
            "|       0|     3|  male| 35|    0|    0| 8.05|Not available|       S|     Mr|\n",
            "|       0|     3|  male| 28|    0|    0| 8.46|Not available|       Q|     Mr|\n",
            "|       0|     1|  male| 54|    0|    0|51.86|          E46|       S|     Mr|\n",
            "|       0|     3|  male|  2|    3|    1|21.08|Not available|       S| Master|\n",
            "|       1|     3|female| 27|    0|    2|11.13|Not available|       S|    Mrs|\n",
            "|       1|     2|female| 14|    1|    0|30.07|Not available|       C|    Mrs|\n",
            "|       1|     3|female|  4|    1|    1|16.70|           G6|       S|   Miss|\n",
            "|       1|     1|female| 58|    0|    0|26.55|         C103|       S|   Miss|\n",
            "|       0|     3|  male| 20|    0|    0| 8.05|Not available|       S|     Mr|\n",
            "|       0|     3|  male| 39|    1|    5|31.27|Not available|       S|     Mr|\n",
            "|       0|     3|female| 14|    0|    0| 7.85|Not available|       S|   Miss|\n",
            "|       1|     2|female| 55|    0|    0|16.00|Not available|       S|    Mrs|\n",
            "|       0|     3|  male|  2|    4|    1|29.12|Not available|       Q| Master|\n",
            "|       1|     2|  male| 28|    0|    0|13.00|Not available|       S|     Mr|\n",
            "|       0|     3|female| 31|    1|    0|18.00|Not available|       S|    Mrs|\n",
            "|       1|     3|female| 28|    0|    0| 7.22|Not available|       C|    Mrs|\n",
            "+--------+------+------+---+-----+-----+-----+-------------+--------+-------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we proceed treating the outliers in Fare.\n",
        "\n",
        "Note: i will not treat outliers in Age as they are few and also seem like true values (e.g. 70 year olds are outliers but it's a normal age)\n",
        "\n",
        "To treat the outliers in Fare i decided to replace values above 500 (definetly too high) with the next highest values, in order to have really high fare, which is still telling us that those passegers were wealthy and were probably in first class"
      ],
      "metadata": {
        "id": "BiV2S689Ra7z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import when\n",
        "\n",
        "# Replace values above 200 in the \"Fare\" column with 150\n",
        "df = df.withColumn(\"Fare\", when(col(\"Fare\") > 200, 150).otherwise(col(\"Fare\")))\n",
        "\n",
        "# Show the updated DataFrame\n",
        "df.select(\"Fare\").show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1y09KqwtniJ",
        "outputId": "5109dd56-4b77-4dae-b8c0-6466d9809945"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+\n",
            "| Fare|\n",
            "+-----+\n",
            "| 7.25|\n",
            "|71.28|\n",
            "| 7.93|\n",
            "| 53.1|\n",
            "| 8.05|\n",
            "| 8.46|\n",
            "|51.86|\n",
            "|21.08|\n",
            "|11.13|\n",
            "|30.07|\n",
            "| 16.7|\n",
            "|26.55|\n",
            "| 8.05|\n",
            "|31.27|\n",
            "| 7.85|\n",
            "| 16.0|\n",
            "|29.12|\n",
            "| 13.0|\n",
            "| 18.0|\n",
            "| 7.22|\n",
            "+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert fare column to FloatType\n",
        "df = df.withColumn(\"Fare\", df[\"Fare\"].cast(\"float\"))\n",
        "\n",
        "# Filter data for survivors and casualties\n",
        "survivors_fare = df.filter(col(\"Survived\") == \"1\").select(\"Fare\").collect()\n",
        "casualties_fare = df.filter(col(\"Survived\") == \"0\").select(\"Fare\").collect()\n",
        "\n",
        "# Extract fares from Row objects\n",
        "survivors_fare = [row.Fare for row in survivors_fare if row.Fare is not None]\n",
        "casualties_fare = [row.Fare for row in casualties_fare if row.Fare is not None]\n",
        "\n",
        "# Prepare data for box plots\n",
        "data = [survivors_fare, casualties_fare]\n",
        "labels = ['Survivors', 'Casualties']\n",
        "\n",
        "# Create box plots\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.boxplot(data, labels=labels, patch_artist=True, showmeans=True)\n",
        "plt.xlabel('Passenger Status')\n",
        "plt.ylabel('Fare')\n",
        "plt.title('Box Plot of Fare for Survivors and Casualties')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "W4Ze2WvVW31Y",
        "outputId": "bf90fafb-a45b-4962-f1ae-ce323294f6d9"
      },
      "execution_count": 171,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Extract fares from the DataFrame\n",
        "fares = df.select(\"Fare\").rdd.flatMap(lambda x: x).collect()\n",
        "\n",
        "# Plot histogram\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(fares, bins=20, color='skyblue', edgecolor='black')\n",
        "plt.xlabel('Fare')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Fare')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GdIKi3BJZRzM",
        "outputId": "5306759e-4fef-4b45-8b1e-f2cb52d6395e"
      },
      "execution_count": 172,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "now we will clean Fare, to have all observations in the same format (2 decimals)"
      ],
      "metadata": {
        "id": "39dayyA4Z8cH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import format_number\n",
        "\n",
        "# Clean the \"Fare\" column to have two decimal places\n",
        "df = df.withColumn(\"Fare\", format_number(col(\"Fare\"), 2))\n",
        "\n",
        "# Show the cleaned \"Fare\" column\n",
        "df.select(\"Fare\").show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJfBdWZ-ZlXu",
        "outputId": "2166119d-fdc1-42c2-fc87-fc1afec45835"
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----+\n",
            "| Fare|\n",
            "+-----+\n",
            "| 7.25|\n",
            "|71.28|\n",
            "| 7.93|\n",
            "|53.10|\n",
            "| 8.05|\n",
            "| 8.46|\n",
            "|51.86|\n",
            "|21.08|\n",
            "|11.13|\n",
            "|30.07|\n",
            "|16.70|\n",
            "|26.55|\n",
            "| 8.05|\n",
            "|31.27|\n",
            "| 7.85|\n",
            "|16.00|\n",
            "|29.12|\n",
            "|13.00|\n",
            "|18.00|\n",
            "| 7.22|\n",
            "+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d-YMnHADZ7Pt"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}