Tutorial4.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PySpark Tutorial 4\n",
    "\n",
    "## PySpark GroupBy and Aggregate Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession\n",
    "\n",
    "spark = SparkSession.builder.appName(\"Aggregate\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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://192.168.134.114:4042\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v3.1.2</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>Aggregate</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x7ff5f88092b0>"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+------------+------+\n",
      "|     Name| Departments|salary|\n",
      "+---------+------------+------+\n",
      "|    Krish|Data Science| 10000|\n",
      "|    Krish|         IOT|  5000|\n",
      "|   Mahesh|    Big Data|  4000|\n",
      "|    Krish|    Big Data|  4000|\n",
      "|   Mahesh|Data Science|  3000|\n",
      "|Sudhanshu|Data Science| 20000|\n",
      "|Sudhanshu|         IOT| 10000|\n",
      "|Sudhanshu|    Big Data|  5000|\n",
      "|    Sunny|Data Science| 10000|\n",
      "|    Sunny|    Big Data|  2000|\n",
      "+---------+------------+------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Read the data\n",
    "df = spark.read.csv(\"test3.csv\", header=True, inferSchema=True)\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- Name: string (nullable = true)\n",
      " |-- Departments: string (nullable = true)\n",
      " |-- salary: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.printSchema()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GroupBy Operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-----------+\n",
      "|     Name|sum(salary)|\n",
      "+---------+-----------+\n",
      "|Sudhanshu|      35000|\n",
      "|    Sunny|      12000|\n",
      "|    Krish|      19000|\n",
      "|   Mahesh|       7000|\n",
      "+---------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouped to find the maximum salary\n",
    "df.groupBy(\"Name\").sum().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----------+\n",
      "| Departments|sum(salary)|\n",
      "+------------+-----------+\n",
      "|         IOT|      15000|\n",
      "|    Big Data|      15000|\n",
      "|Data Science|      43000|\n",
      "+------------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Groupby Departments which gives maximum salary\n",
    "df.groupBy(\"Departments\").sum().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----------+\n",
      "| Departments|avg(salary)|\n",
      "+------------+-----------+\n",
      "|         IOT|     7500.0|\n",
      "|    Big Data|     3750.0|\n",
      "|Data Science|    10750.0|\n",
      "+------------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df.groupBy(\"Departments\").mean().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------+-----+\n",
      "| Departments|count|\n",
      "+------------+-----+\n",
      "|         IOT|    2|\n",
      "|    Big Data|    4|\n",
      "|Data Science|    4|\n",
      "+------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Grouped Departments to count the each Departmentt\n",
    "df.groupBy(\"Departments\").count().show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+-----------+\n",
      "|     Name|max(salary)|\n",
      "+---------+-----------+\n",
      "|Sudhanshu|      20000|\n",
      "|    Sunny|      10000|\n",
      "|    Krish|      10000|\n",
      "|   Mahesh|       4000|\n",
      "+---------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# which Person has maximum salary\n",
    "df.groupBy(\"Name\").max().show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is lot of method you can use that"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+\n",
      "|sum(Salary)|\n",
      "+-----------+\n",
      "|      73000|\n",
      "+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# total sum of Salary\n",
    "df.agg({\"Salary\":\"sum\"}).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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