{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16047145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/python\n",
      "/usr/lib/jvm/java-21-openjdk\n"
     ]
    }
   ],
   "source": [
    "import sys, os\n",
    "\n",
    "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-21-openjdk\"\n",
    "os.environ[\"PATH\"] = os.environ[\"JAVA_HOME\"] + \"/bin:\" + os.environ[\"PATH\"]\n",
    "print(sys.executable)\n",
    "print(os.environ.get(\"JAVA_HOME\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "00bc6543",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql import types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cd4a0f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Using incubator modules: jdk.incubator.vector\n",
      "Using Spark's default log4j profile: org/apache/spark/log4j2-defaults.properties\n",
      "Setting default log level to \"WARN\".\n",
      "To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
      "26/03/10 00:15:45 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n"
     ]
    }
   ],
   "source": [
    "spark = SparkSession.builder \\\n",
    "    .master(\"local[*]\") \\\n",
    "    .appName('test') \\\n",
    "    .getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eb3e4c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.1.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5236cebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-r--r--. 1 miko miko 68M 12-19 16:51 yellow_tripdata_2025-11.parquet\n"
     ]
    }
   ],
   "source": [
    "!ls -lh yellow_tripdata_2025-11.parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a3399a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "schema = types.StructType([\n",
    "    types.StructField('VendorID', types.IntegerType(), True),\n",
    "    types.StructField('tpep_pickup_datetime', types.TimestampType(), True),\n",
    "    types.StructField('tpep_dropoff_datetime', types.TimestampType(), True),\n",
    "    types.StructField('passenger_count', types.IntegerType(), True),\n",
    "    types.StructField('trip_distance', types.DoubleType(), True),\n",
    "    types.StructField('RatecodeID', types.IntegerType(), True),\n",
    "    types.StructField('store_and_fwd_flag', types.StringType(), True),\n",
    "    types.StructField('PULocationID', types.IntegerType(), True),\n",
    "    types.StructField('DOLocationID', types.IntegerType(), True),\n",
    "    types.StructField('payment_type', types.IntegerType(), True),\n",
    "    types.StructField('fare_amount', types.DoubleType(), True),\n",
    "    types.StructField('extra', types.DoubleType(), True),\n",
    "    types.StructField('mta_tax', types.DoubleType(), True),\n",
    "    types.StructField('tip_amount', types.DoubleType(), True),\n",
    "    types.StructField('tolls_amount', types.DoubleType(), True),\n",
    "    types.StructField('improvement_surcharge', types.DoubleType(), True),\n",
    "    types.StructField('total_amount', types.DoubleType(), True),\n",
    "    types.StructField('congestion_surcharge', types.DoubleType(), True)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68bc8b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                \r"
     ]
    }
   ],
   "source": [
    "df = spark.read.parquet('yellow_tripdata_2025-11.parquet')\n",
    "\n",
    "df.repartition(4).write.parquet('homework_pq', mode='overwrite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58989b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = spark.read.parquet('homework_pq')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b01d2f",
   "metadata": {},
   "source": [
    "**Q3**: How many taxi trips were there on February 15?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f7489aea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import functions as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c2500fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "162604"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df \\\n",
    "    .withColumn('tpep_pickup_datetime', F.to_date(df.tpep_pickup_datetime)) \\\n",
    "    .filter(\"tpep_pickup_datetime = '2025-11-15'\") \\\n",
    "    .count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd7ae60d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.createOrReplaceTempView('yellow_2025_11')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6d47c147",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+\n",
      "|count(1)|\n",
      "+--------+\n",
      "|  162604|\n",
      "+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"\n",
    "SELECT\n",
    "    COUNT(1)\n",
    "FROM \n",
    "    yellow_2025_11\n",
    "WHERE\n",
    "    to_date(tpep_pickup_datetime) = '2025-11-15';\n",
    "\"\"\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae3f533b",
   "metadata": {},
   "source": [
    "**Q4**: What is the length of the longest trip in the dataset in hours?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "279d9161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+\n",
      "|max(duration_hrs)|\n",
      "+-----------------+\n",
      "|90.64666666666666|\n",
      "+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df.withColumn('duration_hrs', \n",
    "    (F.unix_timestamp('tpep_dropoff_datetime') - F.unix_timestamp('tpep_pickup_datetime')) / 3600) \\\n",
    "    .select(F.max('duration_hrs')) \\\n",
    "    .show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d10173a",
   "metadata": {},
   "source": [
    "**Q6**: Least frequent pickup location zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "74b7f664",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zones = spark.read.option(\"header\", \"true\").csv('taxi_zone_lookup.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81642d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LocationID', 'Borough', 'Zone', 'service_zone']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zones.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f460dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VendorID',\n",
       " 'tpep_pickup_datetime',\n",
       " 'tpep_dropoff_datetime',\n",
       " 'passenger_count',\n",
       " 'trip_distance',\n",
       " 'RatecodeID',\n",
       " 'store_and_fwd_flag',\n",
       " 'PULocationID',\n",
       " 'DOLocationID',\n",
       " 'payment_type',\n",
       " 'fare_amount',\n",
       " 'extra',\n",
       " 'mta_tax',\n",
       " 'tip_amount',\n",
       " 'tolls_amount',\n",
       " 'improvement_surcharge',\n",
       " 'total_amount',\n",
       " 'congestion_surcharge',\n",
       " 'Airport_fee',\n",
       " 'cbd_congestion_fee']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad8f0101",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_zones.createOrReplaceTempView('zones')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6f738414",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------+\n",
      "|                Zone|count(1)|\n",
      "+--------------------+--------+\n",
      "|Governor's Island...|       1|\n",
      "|Eltingville/Annad...|       1|\n",
      "|       Arden Heights|       1|\n",
      "|       Port Richmond|       3|\n",
      "|       Rikers Island|       4|\n",
      "+--------------------+--------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"\n",
    "SELECT\n",
    "    pul.Zone,\n",
    "    COUNT(1)\n",
    "FROM \n",
    "    yellow_2025_11 y LEFT JOIN zones pul ON y.PULocationID = pul.LocationID\n",
    "GROUP BY \n",
    "    1\n",
    "ORDER BY\n",
    "    2 ASC\n",
    "LIMIT 5;\n",
    "\"\"\").show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4b754d1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}