Data_Engineering_Pyspark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Engineering with Pyspark"
   ]
  },
  {
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   "execution_count": 271,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyspark in /opt/conda/lib/python3.7/site-packages (3.2.0)\n",
      "Requirement already satisfied: py4j==0.10.9.2 in /opt/conda/lib/python3.7/site-packages (from pyspark) (0.10.9.2)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install pyspark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pyspark_dist_explore in /opt/conda/lib/python3.7/site-packages (0.1.8)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from pyspark_dist_explore) (1.3.3)\n",
      "Requirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from pyspark_dist_explore) (3.4.3)\n",
      "Requirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from pyspark_dist_explore) (1.7.1)\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from pyspark_dist_explore) (1.19.5)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pyspark_dist_explore) (2.4.7)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pyspark_dist_explore) (8.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pyspark_dist_explore) (1.3.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pyspark_dist_explore) (2.8.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->pyspark_dist_explore) (0.10.0)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from cycler>=0.10->matplotlib->pyspark_dist_explore) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->pyspark_dist_explore) (2021.1)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "! pip install pyspark_dist_explore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "execution": {
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   "source": [
    "# Spark\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import when\n",
    "from pyspark.sql.types import StructType\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql.functions import isnan, when, count, col\n",
    "\n",
    "\n",
    "# EDA\n",
    "from pyspark_dist_explore import hist\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ML\n",
    "from pyspark.ml import Pipeline\n",
    "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler, StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre Processing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "spark_new = SparkSession \\\n",
    "    .builder \\\n",
    "    .appName(\"Data Pre Processing with Spark\") \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "execution": {
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   "source": [
    "# local do arquivo\n",
    "path = 'data_transient.csv'\n",
    "\n",
    "# Lendo o arquivo em um objeto Spark\n",
    "#df = spark.read.option(\"delimiter\", \";\").option(\"header\", \"true\").option(\"nullValue\", \"unknown\").option(\"nullValue\", \"null\").csv(path)\n",
    "\n",
    "# Define csv input schema\n",
    "schema = StructType([\n",
    "    StructField(\"age\", IntegerType()),\n",
    "    StructField(\"job\", StringType()),\n",
    "    StructField(\"marital\", StringType()),\n",
    "    StructField(\"education\", StringType()),\n",
    "    StructField(\"default\", StringType()),\n",
    "    StructField(\"housing\", StringType()),\n",
    "    StructField(\"loan\", StringType()),\n",
    "    StructField(\"contact\", StringType()),\n",
    "    StructField(\"month\", StringType()),\n",
    "    StructField(\"day_of_week\", StringType()),\n",
    "    StructField(\"campaign\", IntegerType()),\n",
    "    StructField(\"pdays\", IntegerType()),\n",
    "    StructField(\"previous\", IntegerType()),\n",
    "    StructField(\"poutcome\", IntegerType()),\n",
    "    StructField(\"emp.var.rate\", FloatType()),\n",
    "    StructField(\"cons.price.idx\", FloatType()),\n",
    "    StructField(\"cons.conf.idx\", FloatType()),\n",
    "    StructField(\"euribor3m\", FloatType()),\n",
    "    StructField(\"nr.employed\", IntegerType()),\n",
    "    StructField(\"y\", IntegerType()),\n",
    "])\n",
    "\n",
    "\n",
    "df = spark_new.read.option(\"delimiter\", \";\").option(\"header\", \"true\").csv(path, schema=schema)"
   ]
  },
  {
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    "execution": {
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   },
   "outputs": [],
   "source": [
    "df.createOrReplaceTempView(\"df_transient\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- age: integer (nullable = true)\n",
      " |-- job: string (nullable = true)\n",
      " |-- marital: string (nullable = true)\n",
      " |-- education: string (nullable = true)\n",
      " |-- default: string (nullable = true)\n",
      " |-- housing: string (nullable = true)\n",
      " |-- loan: string (nullable = true)\n",
      " |-- contact: string (nullable = true)\n",
      " |-- month: string (nullable = true)\n",
      " |-- day_of_week: string (nullable = true)\n",
      " |-- campaign: integer (nullable = true)\n",
      " |-- pdays: integer (nullable = true)\n",
      " |-- previous: integer (nullable = true)\n",
      " |-- poutcome: integer (nullable = true)\n",
      " |-- emp.var.rate: float (nullable = true)\n",
      " |-- cons.price.idx: float (nullable = true)\n",
      " |-- cons.conf.idx: float (nullable = true)\n",
      " |-- euribor3m: float (nullable = true)\n",
      " |-- nr.employed: integer (nullable = true)\n",
      " |-- y: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
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     "name": "stdout",
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     "text": [
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "|age|      job|marital|  education|default|housing|loan|  contact|month|day_of_week|campaign|pdays|previous|poutcome|emp.var.rate|cons.price.idx|cons.conf.idx|euribor3m|nr.employed|  y|\n",
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "| 56|housemaid|married|   basic.4y|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 57| services|married|high.school|   null|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 37| services|married|high.school|     no|    yes|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 40|   admin.|married|   basic.6y|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 56| services|married|high.school|     no|     no| yes|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark_new.sql('''Select * FROM df_transient''').show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some Exploratory Analisys - Numerical Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "execution": {
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    {
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     "text": [
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "|age|      job|marital|  education|default|housing|loan|  contact|month|day_of_week|campaign|pdays|previous|poutcome|emp_var_rate|cons_price_idx|cons_conf_idx|euribor3m|nr_employed|  y|\n",
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "| 56|housemaid|married|   basic.4y|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 57| services|married|high.school|   null|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 37| services|married|high.school|     no|    yes|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 40|   admin.|married|   basic.6y|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 56| services|married|high.school|     no|     no| yes|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "+---+---------+-------+-----------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df_eda = df.withColumnRenamed(\"emp.var.rate\",\"emp_var_rate\") \\\n",
    "                        .withColumnRenamed(\"cons.price.idx\",\"cons_price_idx\")\\\n",
    "                        .withColumnRenamed(\"cons.conf.idx\",\"cons_conf_idx\")\\\n",
    "                        .withColumnRenamed(\"nr.employed\",\"nr_employed\")\n",
    "df_eda.show(5)"
   ]
  },
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    {
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\n",
      "text/plain": [
       "<Figure size 1008x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histogram\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3)\n",
    "fig.set_size_inches(14, 8)\n",
    "\n",
    "# age histogram\n",
    "hist(axes[0], df_eda.select('age'), bins = 50, color=['red']);\n",
    "axes[0].set_title('Histogram of age');\n",
    "\n",
    "# nr_employed histogram\n",
    "hist(axes[1], df_eda.select('campaign'), bins = 50, color=['red']);\n",
    "axes[1].set_title('Histogram of campaign');\n",
    "\n",
    "# euribor3m histogram\n",
    "hist(axes[2], df_eda.select('y'), bins = 50, color=['red']);\n",
    "axes[2].set_title('Histogram of target');\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:38.977873Z",
     "iopub.status.busy": "2021-12-05T02:56:38.977109Z",
     "iopub.status.idle": "2021-12-05T02:56:44.347249Z",
     "shell.execute_reply": "2021-12-05T02:56:44.346258Z",
     "shell.execute_reply.started": "2021-12-05T02:56:38.977830Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting histogram - Economic data\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=3)\n",
    "fig.set_size_inches(16, 10)\n",
    "\n",
    "# pdays histogram\n",
    "hist(axes[0,0], df_eda.select('pdays'), bins = 50, color=['red']);\n",
    "axes[0,0].set_title('Histogram of pdays');\n",
    "\n",
    "# nr_employed histogram\n",
    "hist(axes[0,1], df_eda.select('nr_employed'), bins = 50, color=['red']);\n",
    "axes[0,1].set_title('Histogram of nr_employed');\n",
    "\n",
    "# euribor3m histogram\n",
    "hist(axes[0,2], df_eda.select('euribor3m'), bins = 50, color=['red']);\n",
    "axes[0,2].set_title('Histogram of euribor3m');\n",
    "\n",
    "# emp_var_rate histogram\n",
    "hist(axes[1,0], df_eda.select('emp_var_rate'), bins = 50, color=['red']);\n",
    "axes[1,0].set_title('Histogram of emp_var_rate');\n",
    "\n",
    "# cons_conf_idx histogram\n",
    "hist(axes[1,1], df_eda.select('cons_conf_idx'), bins = 50, color=['red']);\n",
    "axes[1,1].set_title('Histogram of cons_conf_idx');\n",
    "\n",
    "# cons_price_idx histogram\n",
    "hist(axes[1,2], df_eda.select('cons_price_idx'), bins = 50, color=['red']);\n",
    "axes[1,2].set_title('Histogram of cons_price_idx');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some Exploratory Analisys - Categorical Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:44.349699Z",
     "iopub.status.busy": "2021-12-05T02:56:44.349135Z",
     "iopub.status.idle": "2021-12-05T02:56:44.846999Z",
     "shell.execute_reply": "2021-12-05T02:56:44.845889Z",
     "shell.execute_reply.started": "2021-12-05T02:56:44.349651Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------+-------------+------------------+\n",
      "|CAMPAIGN|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+--------+-------+-------------+------------------+\n",
      "|       1|  17642|         2300|13.037070626913048|\n",
      "|       2|  10570|         1211|11.456953642384105|\n",
      "|       3|   5341|          574|10.747051114023591|\n",
      "|       4|   2651|          249|  9.39268200678989|\n",
      "|       6|    979|           75|7.6608784473953015|\n",
      "|       5|   1599|          120|   7.5046904315197|\n",
      "|      17|     58|            4| 6.896551724137931|\n",
      "|      11|    177|           12| 6.779661016949152|\n",
      "|      23|     16|            1|              6.25|\n",
      "|       7|    629|           38| 6.041335453100159|\n",
      "|       9|    283|           17| 6.007067137809187|\n",
      "|      10|    225|           12| 5.333333333333334|\n",
      "|      13|     92|            4|4.3478260869565215|\n",
      "|       8|    400|           17|              4.25|\n",
      "|      15|     51|            2|3.9215686274509802|\n",
      "|      12|    125|            3|               2.4|\n",
      "|      14|     69|            1|1.4492753623188406|\n",
      "|      31|      7|            0|               0.0|\n",
      "|      34|      3|            0|               0.0|\n",
      "|      16|     51|            0|               0.0|\n",
      "|      39|      1|            0|               0.0|\n",
      "|      28|      8|            0|               0.0|\n",
      "|      20|     30|            0|               0.0|\n",
      "|      41|      1|            0|               0.0|\n",
      "|      25|      8|            0|               0.0|\n",
      "|      21|     24|            0|               0.0|\n",
      "|      26|      8|            0|               0.0|\n",
      "|      27|     11|            0|               0.0|\n",
      "|      40|      2|            0|               0.0|\n",
      "|      43|      2|            0|               0.0|\n",
      "|      37|      1|            0|               0.0|\n",
      "|      24|     15|            0|               0.0|\n",
      "|      32|      4|            0|               0.0|\n",
      "|      56|      1|            0|               0.0|\n",
      "|      42|      2|            0|               0.0|\n",
      "|      18|     33|            0|               0.0|\n",
      "|      22|     17|            0|               0.0|\n",
      "|      19|     26|            0|               0.0|\n",
      "|      35|      5|            0|               0.0|\n",
      "|      29|     10|            0|               0.0|\n",
      "|      33|      4|            0|               0.0|\n",
      "|      30|      7|            0|               0.0|\n",
      "+--------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping Campaign sucess\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT CAMPAIGN,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      campaign,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY campaign, y\n",
    "              )\n",
    "            GROUP BY CAMPAIGN\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:44.848224Z",
     "iopub.status.busy": "2021-12-05T02:56:44.848010Z",
     "iopub.status.idle": "2021-12-05T02:56:45.220280Z",
     "shell.execute_reply": "2021-12-05T02:56:45.219181Z",
     "shell.execute_reply.started": "2021-12-05T02:56:44.848196Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------+-------------+------------------+\n",
      "|          job|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+-------------+-------+-------------+------------------+\n",
      "|      student|    875|          275|31.428571428571427|\n",
      "|      retired|   1720|          434|25.232558139534884|\n",
      "|   unemployed|   1014|          144|14.201183431952662|\n",
      "|       admin.|  10422|         1352|12.972558050278257|\n",
      "|   management|   2924|          328| 11.21751025991792|\n",
      "|         null|    330|           37|11.212121212121213|\n",
      "|   technician|   6743|          730|10.826041821147857|\n",
      "|self-employed|   1421|          149| 10.48557353976073|\n",
      "|    housemaid|   1060|          106|              10.0|\n",
      "| entrepreneur|   1456|          124| 8.516483516483516|\n",
      "|     services|   3969|          323| 8.138070042831949|\n",
      "|  blue-collar|   9254|          638| 6.894315971471795|\n",
      "+-------------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by job the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT job,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      job,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY job, y\n",
    "              )\n",
    "            GROUP BY job\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:45.222586Z",
     "iopub.status.busy": "2021-12-05T02:56:45.221874Z",
     "iopub.status.idle": "2021-12-05T02:56:45.540750Z",
     "shell.execute_reply": "2021-12-05T02:56:45.539680Z",
     "shell.execute_reply.started": "2021-12-05T02:56:45.222532Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------+-------------+------------------+\n",
      "|          education|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+-------------------+-------+-------------+------------------+\n",
      "|         illiterate|     18|            4| 22.22222222222222|\n",
      "|               null|   1731|          251|14.500288850375506|\n",
      "|  university.degree|  12168|         1670|13.724523339907954|\n",
      "|professional.course|   5243|          595|11.348464619492656|\n",
      "|        high.school|   9515|         1031|10.835522858644246|\n",
      "|           basic.4y|   4176|          428| 10.24904214559387|\n",
      "|           basic.6y|   2292|          188| 8.202443280977311|\n",
      "|           basic.9y|   6045|          473|  7.82464846980976|\n",
      "+-------------------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by education the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT education,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      education,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY education, y\n",
    "              )\n",
    "            GROUP BY education\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:45.546289Z",
     "iopub.status.busy": "2021-12-05T02:56:45.546048Z",
     "iopub.status.idle": "2021-12-05T02:56:45.827292Z",
     "shell.execute_reply": "2021-12-05T02:56:45.826402Z",
     "shell.execute_reply.started": "2021-12-05T02:56:45.546258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------+-------------+------------------+\n",
      "| marital|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+--------+-------+-------------+------------------+\n",
      "|    null|     80|           12|              15.0|\n",
      "|  single|  11568|         1620|14.004149377593361|\n",
      "|divorced|   4612|          476|10.320901994796184|\n",
      "| married|  24928|         2532|10.157252888318355|\n",
      "+--------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by marital status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT marital,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      marital,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY marital, y\n",
    "              )\n",
    "            GROUP BY marital\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:45.828827Z",
     "iopub.status.busy": "2021-12-05T02:56:45.828541Z",
     "iopub.status.idle": "2021-12-05T02:56:46.161508Z",
     "shell.execute_reply": "2021-12-05T02:56:46.160590Z",
     "shell.execute_reply.started": "2021-12-05T02:56:45.828788Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-------+-------------+------------------+\n",
      "|  contact|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+---------+-------+-------------+------------------+\n",
      "| cellular|  26144|         3853|14.737607099143208|\n",
      "|telephone|  15044|          787| 5.231321457059293|\n",
      "+---------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by contact status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT contact,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      contact,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY contact, y\n",
    "              )\n",
    "            GROUP BY contact\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:46.162992Z",
     "iopub.status.busy": "2021-12-05T02:56:46.162684Z",
     "iopub.status.idle": "2021-12-05T02:56:46.477772Z",
     "shell.execute_reply": "2021-12-05T02:56:46.476797Z",
     "shell.execute_reply.started": "2021-12-05T02:56:46.162951Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------+-------------+------------------+\n",
      "|default|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+-------+-------+-------------+------------------+\n",
      "|     no|  32588|         4197|12.878973855406898|\n",
      "|   null|   8597|          443| 5.152960335000581|\n",
      "|    yes|      3|            0|               0.0|\n",
      "+-------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by default status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT default,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      default,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY default, y\n",
    "              )\n",
    "            GROUP BY default\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:46.479092Z",
     "iopub.status.busy": "2021-12-05T02:56:46.478880Z",
     "iopub.status.idle": "2021-12-05T02:56:46.779082Z",
     "shell.execute_reply": "2021-12-05T02:56:46.778157Z",
     "shell.execute_reply.started": "2021-12-05T02:56:46.479065Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+-------+-------------+------------------+\n",
      "|housing|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+-------+-------+-------------+------------------+\n",
      "|    yes|  21576|         2507|11.619391916944753|\n",
      "|     no|  18622|         2026|10.879604768553323|\n",
      "|   null|    990|          107|10.808080808080808|\n",
      "+-------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by housing status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT housing,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      housing,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY housing, y\n",
    "              )\n",
    "            GROUP BY housing\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:46.780798Z",
     "iopub.status.busy": "2021-12-05T02:56:46.780478Z",
     "iopub.status.idle": "2021-12-05T02:56:47.019737Z",
     "shell.execute_reply": "2021-12-05T02:56:47.018811Z",
     "shell.execute_reply.started": "2021-12-05T02:56:46.780756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+-------+-------------+------------------+\n",
      "|loan|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+----+-------+-------------+------------------+\n",
      "|  no|  33950|         3850| 11.34020618556701|\n",
      "| yes|   6248|          683|10.931498079385403|\n",
      "|null|    990|          107|10.808080808080808|\n",
      "+----+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by loan status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT loan,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      loan,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY loan, y\n",
    "              )\n",
    "            GROUP BY loan\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:47.021221Z",
     "iopub.status.busy": "2021-12-05T02:56:47.020940Z",
     "iopub.status.idle": "2021-12-05T02:56:47.337703Z",
     "shell.execute_reply": "2021-12-05T02:56:47.336745Z",
     "shell.execute_reply.started": "2021-12-05T02:56:47.021181Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-------+-------------+------------------+\n",
      "|day_of_week|QTD_CLI|QTD_CONTRATOS|    PERC_CONVERSAO|\n",
      "+-----------+-------+-------------+------------------+\n",
      "|        thu|   8623|         1045|12.118752174417256|\n",
      "|        tue|   8090|          953|11.779975278121137|\n",
      "|        wed|   8134|          949|11.667076469141874|\n",
      "|        fri|   7827|          846| 10.80873898045228|\n",
      "|        mon|   8514|          847| 9.948320413436692|\n",
      "+-----------+-------+-------------+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouping by day_of_week status the campaign conversion\n",
    "\n",
    "spark_new.sql('''\n",
    "            SELECT day_of_week,\n",
    "                SUM(QTD) AS QTD_CLI,\n",
    "                SUM(QTD*FLAG_CONTRATOU) AS QTD_CONTRATOS,\n",
    "                (SUM(QTD*FLAG_CONTRATOU)/SUM(QTD))*100 AS PERC_CONVERSAO\n",
    "            FROM (\n",
    "                  SELECT DISTINCT \n",
    "                      day_of_week,\n",
    "                      y as flag_contratou,\n",
    "                      COUNT(*) as qtd \n",
    "                  FROM df_transient \n",
    "                  GROUP BY day_of_week, y\n",
    "              )\n",
    "            GROUP BY day_of_week\n",
    "            ORDER BY PERC_CONVERSAO DESC\n",
    "              ''').show(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Engeneering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-09aa658342f2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprintSchema\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:47.348140Z",
     "iopub.status.busy": "2021-12-05T02:56:47.347536Z",
     "iopub.status.idle": "2021-12-05T02:56:47.472566Z",
     "shell.execute_reply": "2021-12-05T02:56:47.471684Z",
     "shell.execute_reply.started": "2021-12-05T02:56:47.348094Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+-------+----------------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "|age|      job|marital|       education|default|housing|loan|  contact|month|day_of_week|campaign|pdays|previous|poutcome|emp.var.rate|cons.price.idx|cons.conf.idx|euribor3m|nr.employed|  y|\n",
      "+---+---------+-------+----------------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "| 56|housemaid|married|Basic incomplete|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 57| employed|married|     High School|   null|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 37| employed|married|     High School|     no|    yes|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 40| employed|married|Basic incomplete|     no|     no|  no|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 56| employed|married|     High School|     no|     no| yes|telephone|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "+---+---------+-------+----------------+-------+-------+----+---------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Task 1 - Replace statements  in education and job columns\n",
    "\n",
    "# Rules (education):\n",
    "# 'Basic incomplete' : 'illiterate' 'basic.4y', 'basic.6y'\n",
    "# 'Basic Degree': 'basic.9y'\n",
    "# 'High School ': 'high.school'\n",
    "# 'Higher Education Degree': 'university.degree', 'professional.course'\n",
    "\n",
    "# Rules (job):\n",
    "# 'retired' : 'retired' \n",
    "# 'student': 'student'\n",
    "# 'unemployed' : 'unemployed'\n",
    "# 'housemaid' : housemaid'\n",
    "# 'employed': 'admin.' , 'technician', 'services', 'management'\n",
    "# 'enterpreneur': 'self-employed', 'blue-collar' , 'entrepreneur'\n",
    "\n",
    "df_1_new = df.withColumn('education',when(df.education == 'illiterate','Basic incomplete')  \\\n",
    "           .when(df.education == 'basic.4y','Basic incomplete')\\\n",
    "           .when(df.education == 'basic.6y','Basic incomplete') \\\n",
    "           .when(df.education == 'basic.9y','Basic Degree') \\\n",
    "           .when(df.education == 'high.school', 'High School') \\\n",
    "           .when(df.education == 'university.degree', 'Higher Education Degree') \\\n",
    "           .when(df.education == 'professional.course','Higher Education Degree') \\\n",
    "           .otherwise(df.education))\\\n",
    "            .withColumn('job', when(df.job == 'blue-collar','enterpreneur')\\\n",
    "                        .when(df.job == 'self-employed', 'enterpreneur') \\\n",
    "                        .when(df.job == 'management','employed') \\\n",
    "                        .when(df.job == 'admin.','employed') \\\n",
    "                        .when(df.job == 'technician','employed')\\\n",
    "                        .when(df.job == 'services','employed').otherwise(df.job))\n",
    "\n",
    "df_1_new.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:47.474068Z",
     "iopub.status.busy": "2021-12-05T02:56:47.473772Z",
     "iopub.status.idle": "2021-12-05T02:56:47.608729Z",
     "shell.execute_reply": "2021-12-05T02:56:47.607990Z",
     "shell.execute_reply.started": "2021-12-05T02:56:47.474026Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+-------+----------------+-------+-------+----+-------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "|age|      job|marital|       education|default|housing|loan|contact|month|day_of_week|campaign|pdays|previous|poutcome|emp.var.rate|cons.price.idx|cons.conf.idx|euribor3m|nr.employed|  y|\n",
      "+---+---------+-------+----------------+-------+-------+----+-------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "| 56|housemaid|      0|Basic incomplete|     no|      0|   0|      0|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 57| employed|      0|     High School|   null|      0|   0|      0|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 37| employed|      0|     High School|     no|      1|   0|      0|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 40| employed|      0|Basic incomplete|     no|      0|   0|      0|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "| 56| employed|      0|     High School|     no|      0|   1|      0|  may|        mon|       1| null|       0|    null|         1.1|        93.994|        -36.4|    4.857|       5191|  0|\n",
      "+---+---------+-------+----------------+-------+-------+----+-------+-----+-----------+--------+-----+--------+--------+------------+--------------+-------------+---------+-----------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Task 2 - Replace statements by Boolean type\n",
    "# contact,  housing, loan, contact, marital\n",
    "\n",
    "\n",
    "df_2_new = df_1_new.withColumn('contact', when(df_1_new.contact == 'telephone', 0).otherwise(1))\\\n",
    "    .withColumn('marital', when(df_1_new.marital == 'single', 1).otherwise(0))\\\n",
    "    .withColumn('loan', when(df_1_new.loan == 'yes', 1).otherwise(0))\\\n",
    "    .withColumn('housing', when(df_1_new.housing == 'no', 0).otherwise(1))\n",
    "\n",
    "df_2_new.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:56:47.609852Z",
     "iopub.status.busy": "2021-12-05T02:56:47.609647Z",
     "iopub.status.idle": "2021-12-05T02:56:47.700440Z",
     "shell.execute_reply": "2021-12-05T02:56:47.699472Z",
     "shell.execute_reply.started": "2021-12-05T02:56:47.609825Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "|age|      job|marital|       education|housing|loan|contact|day_of_week|pdays|previous|emp.var.rate|cons.price.idx|cons.conf.idx|euribor3m|  y|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "| 56|housemaid|      0|Basic incomplete|      0|   0|      0|        mon| null|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 57| employed|      0|     High School|      0|   0|      0|        mon| null|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 37| employed|      0|     High School|      1|   0|      0|        mon| null|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 40| employed|      0|Basic incomplete|      0|   0|      0|        mon| null|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 56| employed|      0|     High School|      0|   1|      0|        mon| null|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Task 3 - Drop default, month and campaign\n",
    "# contact,  housing, loan, contact, marital\n",
    "\n",
    "columns_to_drop = ['default', 'month', 'campaign', 'poutcome', 'nr.employed']\n",
    "df_trusted = df_2_new.drop(*columns_to_drop)\n",
    "\n",
    "df_trusted.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:57:17.341208Z",
     "iopub.status.busy": "2021-12-05T02:57:17.340890Z",
     "iopub.status.idle": "2021-12-05T02:57:17.483421Z",
     "shell.execute_reply": "2021-12-05T02:57:17.482667Z",
     "shell.execute_reply.started": "2021-12-05T02:57:17.341176Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "|age|      job|marital|       education|housing|loan|contact|day_of_week|pdays|previous|emp_var_rate|cons_price_idx|cons_conf_idx|euribor3m|  y|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "| 56|housemaid|      0|Basic incomplete|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 57| employed|      0|     High School|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 37| employed|      0|     High School|      1|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 40| employed|      0|Basic incomplete|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 56| employed|      0|     High School|      0|   1|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Task 4 -> Fill na\n",
    "# columns-> all columns\n",
    "\n",
    "df_trusted = df_trusted.fillna( { 'job':'employed',\n",
    "                              'education':'Higher Education Degree',\n",
    "                              'pdays':0} )\n",
    "df_trusted.show(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:57:18.428690Z",
     "iopub.status.busy": "2021-12-05T02:57:18.428316Z",
     "iopub.status.idle": "2021-12-05T02:57:18.505720Z",
     "shell.execute_reply": "2021-12-05T02:57:18.504888Z",
     "shell.execute_reply.started": "2021-12-05T02:57:18.428657Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "|age|      job|marital|       education|housing|loan|contact|day_of_week|pdays|previous|emp_var_rate|cons_price_idx|cons_conf_idx|euribor3m|  y|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "| 56|housemaid|      0|Basic incomplete|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 57| employed|      0|     High School|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 37| employed|      0|     High School|      1|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 40| employed|      0|Basic incomplete|      0|   0|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "| 56| employed|      0|     High School|      0|   1|      0|        mon|    0|       0|         1.1|        93.994|        -36.4|    4.857|  0|\n",
      "+---+---------+-------+----------------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_trusted.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting null values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:57:31.525431Z",
     "iopub.status.busy": "2021-12-05T02:57:31.525021Z",
     "iopub.status.idle": "2021-12-05T02:57:32.516347Z",
     "shell.execute_reply": "2021-12-05T02:57:32.515648Z",
     "shell.execute_reply.started": "2021-12-05T02:57:31.525365Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---+---+-------+---------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "|age|job|marital|education|housing|loan|contact|day_of_week|pdays|previous|emp_var_rate|cons_price_idx|cons_conf_idx|euribor3m|  y|\n",
      "+---+---+-------+---------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "|  0|  0|      0|        0|      0|   0|      0|          0|    0|       0|           0|             0|            0|        0|  0|\n",
      "+---+---+-------+---------+-------+----+-------+-----------+-----+--------+------------+--------------+-------------+---------+---+\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df_trusted.select([count(when(isnan(c) | col(c).isNull(), c)).alias(c) for c in df_trusted.columns]).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-05T02:57:32.517896Z",
     "iopub.status.busy": "2021-12-05T02:57:32.517668Z",
     "iopub.status.idle": "2021-12-05T02:57:32.990133Z",
     "shell.execute_reply": "2021-12-05T02:57:32.989341Z",
     "shell.execute_reply.started": "2021-12-05T02:57:32.517866Z"
    }
   },
   "outputs": [],
   "source": [
    "df_trusted.write.option(\"header\",True).mode(\"overwrite\").option(\"delimiter\",\";\").csv('data_trusted.csv')"
   ]
  }
 ],
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