356 lines
11 KiB
Plaintext
356 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e5905a69",
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"metadata": {},
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"source": [
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"# CSE6242 - HW3 - Q1"
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]
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},
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{
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"cell_type": "markdown",
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"id": "09289981",
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"metadata": {},
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"source": [
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"Pyspark Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "139318cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"### DO NOT MODIFY THIS CELL ###\n",
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"import pyspark\n",
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"from pyspark.sql import SQLContext\n",
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"from pyspark.sql.functions import hour, when, col, date_format, to_timestamp, round, coalesce\n",
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"from pyspark.sql.functions import *"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fd9e0f8",
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"metadata": {},
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"source": [
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"Initialize PySpark Context"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "b0c18c6c",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Setting default log level to \"WARN\".\n",
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"To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\n",
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"23/10/18 14:58:22 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\n",
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"23/10/18 14:58:22 WARN Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.\n",
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"23/10/18 14:58:22 WARN Utils: Service 'SparkUI' could not bind on port 4041. Attempting port 4042.\n",
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"23/10/18 14:58:22 WARN Utils: Service 'SparkUI' could not bind on port 4042. Attempting port 4043.\n",
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"/usr/local/lib/python3.9/dist-packages/pyspark/sql/context.py:113: FutureWarning: Deprecated in 3.0.0. Use SparkSession.builder.getOrCreate() instead.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"### DO NOT MODIFY THIS CELL ###\n",
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"sc = pyspark.SparkContext(appName=\"HW3-Q1\")\n",
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"sqlContext = SQLContext(sc)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d68ae314",
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"metadata": {},
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"source": [
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"Define function for loading data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "7e5bbdda",
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"metadata": {},
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"outputs": [],
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"source": [
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"### DO NOT MODIFY THIS CELL ###\n",
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"def load_data():\n",
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" df = sqlContext.read.option(\"header\",True) \\\n",
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" .csv(\"yellow_tripdata_2019-01_short.csv\")\n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0d52409d",
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"metadata": {},
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"source": [
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"### Q1.a"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e43f6e00",
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"metadata": {},
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"source": [
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"Perform data casting to clean incoming dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "11f801b4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def clean_data(df):\n",
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" '''\n",
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" input: df a dataframe\n",
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" output: df a dataframe with the all the original columns\n",
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" '''\n",
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" \n",
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" # START YOUR CODE HERE ---------\n",
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" from pyspark.sql.types import StructField, StructType, IntegerType, TimestampType, FloatType, StringType\n",
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"\n",
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" df = df.withColumn(\"passenger_count\", df[\"passenger_count\"].cast(IntegerType()))\n",
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" df = df.withColumn(\"total_amount\", df[\"total_amount\"].cast(FloatType()))\n",
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" df = df.withColumn(\"tip_amount\", df[\"tip_amount\"].cast(FloatType()))\n",
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" df = df.withColumn(\"trip_distance\", df[\"trip_distance\"].cast(FloatType()))\n",
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" df = df.withColumn(\"fare_amount\", df[\"fare_amount\"].cast(FloatType()))\n",
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" df = df.withColumn(\"tpep_pickup_datetime\", df[\"tpep_pickup_datetime\"].cast(TimestampType()))\n",
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" df = df.withColumn(\"tpep_dropoff_datetime\", df[\"tpep_dropoff_datetime\"].cast(TimestampType()))\n",
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"\n",
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" # END YOUR CODE HERE -----------\n",
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" \n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d4f565d0",
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"metadata": {},
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"source": [
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"### Q1.b"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72b4f712",
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"metadata": {},
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"source": [
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"Find rate per person for based on how many passengers travel between pickup and dropoff locations. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "4e115152",
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"metadata": {},
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"outputs": [],
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"source": [
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"def common_pair(df):\n",
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" '''\n",
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" input: df a dataframe\n",
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" output: df a dataframe with following columns:\n",
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" - PULocationID\n",
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" - DOLocationID\n",
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" - passenger_count\n",
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" - per_person_rate\n",
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" \n",
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" per_person_rate is the total_amount per person for a given pair.\n",
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" \n",
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" '''\n",
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" \n",
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" # START YOUR CODE HERE ---------\n",
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" from pyspark.sql import Window\n",
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"\n",
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" partition_cols = ['PULocationID','DOLocationID']\n",
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"\n",
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" group_by_result = df.groupBy(partition_cols).count()\n",
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" # group_by_result.show()\n",
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"\n",
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" # Filter out any trips that have the same pick-up and drop-off location. \n",
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" df_temp = df.filter((df.PULocationID != df.DOLocationID))\n",
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" # group_by_result_difference_location.show()\n",
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"\n",
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" # # [4 pts] You will be modifying the function common_pair. \n",
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" # # Return the top 10 pickup-dropoff location pairs that have the highest number of total passengers who have traveled between them. \n",
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" # # Sort the location pairs by total passengers. \n",
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" df_temp = df_temp.withColumn(\"passenger_count\", sum(\"passenger_count\").over(Window.partitionBy(*partition_cols)))\n",
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" \n",
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" # # For each location pair, also compute \n",
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" # # the average amount per passenger over all trips (name this per_person_rate), utilizing total_amount.\n",
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" df_temp = df_temp.withColumn(\"total_amount_partition\", sum(\"total_amount\").over(Window.partitionBy(*partition_cols)))\n",
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" df_temp = df_temp.withColumn(\"per_person_rate\",col(\"total_amount_partition\")/col(\"passenger_count\"))\n",
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" \n",
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" # # For pairs with the same total passengers, \n",
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" # # sort them in descending order of per_person_rate.\n",
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" # # Rename the column for total passengers to passenger_count. \n",
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" df_temp = df_temp.select(['PULocationID','DOLocationID','passenger_count','per_person_rate']).distinct()\n",
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" df_joined = group_by_result.join(df_temp, partition_cols)\n",
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" df_joined = df_joined.orderBy(['passenger_count','per_person_rate'], ascending=False).limit(10)\n",
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" df_output = df_joined.drop('count')\n",
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" # END YOUR CODE HERE -----------\n",
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" \n",
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" return df_output"
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]
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},
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{
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"cell_type": "markdown",
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"id": "127574ab",
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"metadata": {},
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"source": [
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"### Q1.c"
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]
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},
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{
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"cell_type": "markdown",
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"id": "36a8fd27",
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"metadata": {},
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"source": [
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"Find trips which trip distances generate the highest tip percentage."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "376c981c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def distance_with_most_tip(df):\n",
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" '''\n",
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" input: df a dataframe\n",
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" output: df a dataframe with following columns:\n",
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" - trip_distance\n",
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" - tip_percent\n",
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" \n",
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" trip_percent is the percent of tip out of fare_amount\n",
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" \n",
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" '''\n",
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" \n",
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" # START YOUR CODE HERE ---------\n",
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" \n",
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" # END YOUR CODE HERE -----------\n",
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" \n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f0172fe6",
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"metadata": {},
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"source": [
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"### Q1.d"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4613c906",
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"metadata": {},
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"source": [
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"Determine the average speed at different times of day."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "abff9e24",
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"metadata": {},
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"outputs": [],
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"source": [
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"def time_with_most_traffic(df):\n",
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" '''\n",
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" input: df a dataframe\n",
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" output: df a dataframe with following columns:\n",
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" - time_of_day\n",
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" - am_avg_speed\n",
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" - pm_avg_speed\n",
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" \n",
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" trip_percent is the percent of tip out of fare_amount\n",
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" \n",
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" '''\n",
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" \n",
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" # START YOUR CODE HERE ---------\n",
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"\n",
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" # END YOUR CODE HERE -----------\n",
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" \n",
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" return df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "34cbd7b9",
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"metadata": {},
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"source": [
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"### The below cells are for you to investigate your solutions and will not be graded\n",
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"## Ensure they are commented out prior to submitting to Gradescope to avoid errors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "bf9abefb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"+------------+------------+---------------+------------------+\n",
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"|PULocationID|DOLocationID|passenger_count| per_person_rate|\n",
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"+------------+------------+---------------+------------------+\n",
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"| 239| 238| 62| 4.26274198870505|\n",
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"| 237| 236| 60| 4.482500068346659|\n",
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"| 263| 141| 52|3.4190384974846473|\n",
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"| 161| 236| 42| 5.368571440378825|\n",
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"| 148| 79| 42| 4.711904752822149|\n",
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"| 142| 238| 39| 5.05487182812813|\n",
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"| 141| 236| 37| 4.355675723101641|\n",
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"| 239| 143| 37| 4.252162224537617|\n",
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"| 239| 142| 35| 3.817714350564139|\n",
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"| 79| 170| 34| 6.394705884596881|\n",
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"+------------+------------+---------------+------------------+\n",
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"\n"
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]
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}
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],
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"source": [
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"# df = load_data()\n",
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"# df = clean_data(df)\n",
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"# common_pair(df).show()\n",
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"# distance_with_most_tip(df).show()\n",
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"# time_with_most_traffic(df).show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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