diff --git a/missions/W5/w5m2/NYC_Weather_2016_2022.csv b/missions/W5/w5m2/NYC_Weather_2016_2022.csv
new file mode 100644
index 00000000..19947ed5
--- /dev/null
+++ b/missions/W5/w5m2/NYC_Weather_2016_2022.csv
@@ -0,0 +1,59761 @@
+time,temperature_2m (°C),precipitation (mm),rain (mm),cloudcover (%),cloudcover_low (%),cloudcover_mid (%),cloudcover_high (%),windspeed_10m (km/h),winddirection_10m (°)
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+2022-10-23T17:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T18:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T19:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T20:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T21:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T22:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-23T23:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T00:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T01:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T02:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T03:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T04:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T05:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T06:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T07:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T08:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T09:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T10:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T11:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T12:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T13:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T14:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T15:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T16:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T17:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T18:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T19:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T20:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T21:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T22:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-24T23:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T00:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T01:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T02:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T03:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T04:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T05:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T06:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T07:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T08:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T09:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T10:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T11:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T12:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T13:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T14:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T15:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T16:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T17:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T18:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T19:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T20:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T21:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T22:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
+2022-10-25T23:00,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN
diff --git a/missions/W5/w5m2/README.md b/missions/W5/w5m2/README.md
new file mode 100644
index 00000000..3b659e0e
--- /dev/null
+++ b/missions/W5/w5m2/README.md
@@ -0,0 +1,38 @@
+# TLC 데이터셋 스파크 분석
+
+PySpark를 사용하여 뉴욕시 택시(TLC) 데이터셋 분석하기
+
+## 데이터 
+
+- **픽업 시간** (`tpep_pickup_datetime`): 여행 시작 시간
+- **하차 시간** (`tpep_dropoff_datetime`): 여행 종료 시간
+- **승객 수** (`passenger_count`): 여행 중 승객 수
+- **여행 거리** (`trip_distance`): 여행 거리
+- **총 요금** (`total_amount`): 총 여행 요금
+- **혼잡 추가 요금** (`congestion_surcharge`): 혼잡 때 추가되는 요금
+- **공항 요금** (`airport_fee`): 공항으로 가는 여행에 부과되는 요금
+
+## 데이터 전처리 및 분석
+
+- 데이터 클린징: 비현실적인 데이터(예: 승객 수가 0)의 제거
+- 기본적인 통계 계산: 평균, 최대, 최소 등
+- 날씨 데이터와의 결합하여 최종 데이터 추출
+
+
+## 분석 결과
+
+- 21년도 1월부터 3월까지 총 여행 횟수 및 승객이 2명 이상인 여행의 횟수
+- 21년도 1월부터 3월까지 총 수익
+- 21년도 1월부터 3월까지 평균 여행 시간
+- 21년도 1월부터 3월까지 강수 여부 및 온도에 따른 여행 기간 및 요금  
+
+## 스파크 UI 스크린샷
+
+![Spark Jobs](https://github.com/user-attachments/assets/3baf6a7f-dc6c-42ba-b9b1-2ebaa486775c)
+![DAG Visualization](https://github.com/user-attachments/assets/7704b75d-eb41-4418-baa8-f0f647992ffa)
+![Stages for All Jobs](https://github.com/user-attachments/assets/a54a6421-b8ef-48f0-a3f7-08abb751732b)
+
+## 참고 문헌
+
+- [Apache Spark Documentation](https://spark.apache.org/docs/latest/)
+- [PySpark API Reference](https://spark.apache.org/docs/latest/api/python/)
\ No newline at end of file
diff --git a/missions/W5/w5m2/tlc_analysis.ipynb b/missions/W5/w5m2/tlc_analysis.ipynb
new file mode 100644
index 00000000..370e9bbc
--- /dev/null
+++ b/missions/W5/w5m2/tlc_analysis.ipynb
@@ -0,0 +1,530 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql.functions import col, sum, mean, round, unix_timestamp, date_format, to_timestamp, hour, when, avg\n",
+    "from pyspark.sql import SparkSession\n",
+    "from datetime import datetime\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark = (\n",
+    "    SparkSession.builder.appName(\"TLC_Analysis\")\n",
+    "    .master(\"local[*]\")\n",
+    "    .config(\"spark.driver.host\", \"localhost\")\n",
+    "    .config(\"spark.driver.port\", \"7077\")\n",
+    "    .config(\"spark.ui.port\", \"4050\")\n",
+    "    .config(\"spark.driver.memory\", \"4g\")\n",
+    "    .config(\"spark.executor.memory\", \"4g\")\n",
+    "    .getOrCreate()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_data(file_name, date):\n",
+    "    file_path = f\"your file path\"  \n",
+    "    print(f\"Attempting to read from: {file_path}\")\n",
+    "\n",
+    "    try:\n",
+    "        read_df = spark.read.parquet(file_path)\n",
+    "        return read_df\n",
+    "    except Exception:\n",
+    "        print(f\"File not found: {file_path}\")\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ">>>>> yellow 2021-01\n",
+      "Attempting to read from: /Users/munsoyun/Desktop/Docker/W5M2/parquet_files/yellow_tripdata_2021-01.parquet\n",
+      ">>>>> yellow 2021-02\n",
+      "Attempting to read from: /Users/munsoyun/Desktop/Docker/W5M2/parquet_files/yellow_tripdata_2021-02.parquet\n",
+      ">>>>> yellow 2021-03\n",
+      "Attempting to read from: /Users/munsoyun/Desktop/Docker/W5M2/parquet_files/yellow_tripdata_2021-03.parquet\n"
+     ]
+    }
+   ],
+   "source": [
+    "categories = [\"yellow\"]\n",
+    "\n",
+    "months = (\n",
+    "    pd.date_range(start=\"2021-01-01\", end=\"2021-03-01\", freq=\"MS\")\n",
+    "    .strftime(\"%Y-%m\")\n",
+    "    .tolist()\n",
+    ")\n",
+    "\n",
+    "df = None\n",
+    "for category in categories:\n",
+    "    for month in months:\n",
+    "        print(\">>>>>\", category, month)\n",
+    "        read_df = read_data(category, month)\n",
+    "        if read_df is not None:\n",
+    "            if df is None:\n",
+    "                df = read_df\n",
+    "            else:\n",
+    "                df = df.union(read_df)\n",
+    "df = df.select(\n",
+    "    \"tpep_pickup_datetime\",\n",
+    "    \"tpep_dropoff_datetime\",\n",
+    "    \"passenger_count\",\n",
+    "    \"trip_distance\",\n",
+    "    \"total_amount\",\n",
+    "    \"congestion_surcharge\",\n",
+    "    \"airport_fee\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- tpep_pickup_datetime: timestamp_ntz (nullable = true)\n",
+      " |-- tpep_dropoff_datetime: timestamp_ntz (nullable = true)\n",
+      " |-- passenger_count: double (nullable = true)\n",
+      " |-- trip_distance: double (nullable = true)\n",
+      " |-- total_amount: double (nullable = true)\n",
+      " |-- congestion_surcharge: double (nullable = true)\n",
+      " |-- airport_fee: double (nullable = true)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "weather = spark.read.csv(\"your file path\", header=True)\n",
+    "weather = weather.select(\"time\", \"temperature_2m (°C)\", \"rain (mm)\")\n",
+    "weather = weather.withColumn(\"weather_hour\", date_format(to_timestamp(\"time\"), \"yyyy-MM-dd HH:00:00\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.dropna()\n",
+    "\n",
+    "start_date = \"2021-01-01\"\n",
+    "end_date = \"2021-03-31\"\n",
+    "\n",
+    "filterd_df = df.filter(\n",
+    "    (col(\"passenger_count\") >= 0)\n",
+    "    & (col(\"total_amount\") >= 0)\n",
+    "    & (col(\"congestion_surcharge\") >= 0)\n",
+    "    & (col(\"airport_fee\") >= 0)\n",
+    "    & (col(\"tpep_pickup_datetime\") >= start_date)\n",
+    "    & (col(\"tpep_pickup_datetime\") <= end_date)\n",
+    "    & (col(\"tpep_dropoff_datetime\") >= start_date)\n",
+    "    & (col(\"tpep_dropoff_datetime\") <= end_date)\n",
+    ")\n",
+    "\n",
+    "filterd_df = filterd_df.withColumn(\n",
+    "    \"total_fee\",\n",
+    "    (col(\"total_amount\") + col(\"congestion_surcharge\") + col(\"airport_fee\")),\n",
+    ")\n",
+    "\n",
+    "filterd_df = filterd_df.withColumn(\n",
+    "        \"trip_duration\",\n",
+    "        (unix_timestamp(\"tpep_dropoff_datetime\") - unix_timestamp(\"tpep_pickup_datetime\")) / 60,\n",
+    "    )\n",
+    "filterd_df = filterd_df.withColumn(\"total_fee\", col(\"total_fee\").cast(\"double\"))\n",
+    "filterd_df = filterd_df.filter(col(\"total_fee\").isNotNull())\n",
+    "filterd_df = filterd_df.select(\n",
+    "    \"tpep_pickup_datetime\", \"trip_duration\", \"passenger_count\", \"total_fee\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- tpep_pickup_datetime: timestamp_ntz (nullable = true)\n",
+      " |-- trip_duration: double (nullable = true)\n",
+      " |-- passenger_count: double (nullable = true)\n",
+      " |-- total_fee: double (nullable = true)\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "DataFrame[tpep_pickup_datetime: timestamp_ntz, trip_duration: double, passenger_count: double, total_fee: double]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filterd_df.printSchema()\n",
+    "filterd_df.cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+---------------+---------+\n",
+      "|tpep_pickup_datetime|      trip_duration|passenger_count|total_fee|\n",
+      "+--------------------+-------------------+---------------+---------+\n",
+      "| 2021-01-05 18:55:22|  9.583333333333334|            2.0|    25.65|\n",
+      "| 2021-01-05 19:58:59|  5.983333333333333|            1.0|     13.8|\n",
+      "| 2021-01-05 20:12:38|              10.25|            1.0|    17.85|\n",
+      "| 2021-01-05 20:26:52| 24.583333333333332|            1.0|    42.35|\n",
+      "| 2021-01-12 18:34:41| 2.6166666666666667|            1.0|      6.3|\n",
+      "| 2021-02-21 05:36:21|  57.93333333333333|            3.0|     60.8|\n",
+      "| 2021-03-09 16:30:06|                0.1|            1.0|      3.3|\n",
+      "| 2021-03-13 15:16:15| 10.533333333333333|            1.0|      9.3|\n",
+      "| 2021-03-20 13:47:19| 12.466666666666667|            1.0|     14.8|\n",
+      "| 2021-03-20 14:14:30| 16.766666666666666|            2.0|     22.3|\n",
+      "| 2021-03-20 15:01:29|              13.45|            2.0|     21.6|\n",
+      "| 2021-03-20 15:32:42|              17.95|            1.0|     18.3|\n",
+      "| 2021-03-20 16:16:08|  4.666666666666667|            1.0|     11.8|\n",
+      "| 2021-03-20 16:51:23|               13.4|            1.0|    19.75|\n",
+      "| 2021-03-20 17:07:44|              12.35|            1.0|    19.75|\n",
+      "| 2021-03-22 22:26:25|              22.25|            0.0|     18.3|\n",
+      "| 2021-03-22 23:58:45|               4.25|            1.0|      5.8|\n",
+      "| 2021-03-23 00:35:42| 21.466666666666665|            1.0|    45.57|\n",
+      "| 2021-03-23 03:47:15| 21.616666666666667|            1.0|     21.3|\n",
+      "| 2021-03-26 14:56:13|0.23333333333333334|            1.0|      3.3|\n",
+      "+--------------------+-------------------+---------------+---------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "filterd_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All passenger count: 115571\n",
+      "Over two passenger count: 25135\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_passenger_count = filterd_df.count()\n",
+    "over_two_passenger_count = filterd_df.filter(col(\"passenger_count\") >= 2).count()\n",
+    "\n",
+    "print(f\"All passenger count: {all_passenger_count}\")\n",
+    "print(f\"Over two passenger count: {over_two_passenger_count}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Revenue: 2247081.3100010254\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_revenue = filterd_df.agg(sum(\"total_fee\")).collect()[0][0]\n",
+    "print(f\"Total Revenue: {total_revenue}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean Trip Duration: 12.663630726277693\n"
+     ]
+    }
+   ],
+   "source": [
+    "mean_trip_duration = filterd_df.agg(mean(\"trip_duration\")).collect()[0][0]\n",
+    "print(f\"Mean Trip Duration: {mean_trip_duration}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DataFrame[pickup_hour: string, mean_hourly_fee: double, mean_trip_duration: double, mean_passenger_count: double, temperature_2m (°C): string, rain (mm): string]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "taxi_df = filterd_df.withColumn(\"pickup_hour\", date_format(to_timestamp(\"tpep_pickup_datetime\"), \"yyyy-MM-dd HH:00:00\"))\n",
+    "\n",
+    "hourly_taxi_df = taxi_df.groupBy(\"pickup_hour\").agg(\n",
+    "    round(mean(\"total_fee\"), 3).alias(\"mean_hourly_fee\"),\n",
+    "    round(mean(\"trip_duration\"), 2).alias(\"mean_trip_duration\"),\n",
+    "    round(mean(\"passenger_count\"), 2).alias(\"mean_passenger_count\")\n",
+    ")\n",
+    "final_df = hourly_taxi_df.join(weather, hourly_taxi_df.pickup_hour == weather.weather_hour).drop(\"time\", \"weather_hour\")\n",
+    "final_df = final_df.orderBy(\"pickup_hour\")\n",
+    "final_df.cache()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------------------+---------------+------------------+--------------------+-------------------+---------+\n",
+      "|        pickup_hour|mean_hourly_fee|mean_trip_duration|mean_passenger_count|temperature_2m (°C)|rain (mm)|\n",
+      "+-------------------+---------------+------------------+--------------------+-------------------+---------+\n",
+      "|2021-01-05 18:00:00|          25.65|              9.58|                 2.0|                4.3|     0.00|\n",
+      "|2021-01-05 19:00:00|           13.8|              5.98|                 1.0|                4.2|     0.00|\n",
+      "|2021-01-05 20:00:00|           30.1|             17.42|                 1.0|                4.4|     0.00|\n",
+      "|2021-01-12 18:00:00|            6.3|              2.62|                 1.0|                4.6|     0.00|\n",
+      "|2021-02-21 05:00:00|           60.8|             57.93|                 3.0|               -3.7|     0.00|\n",
+      "|2021-03-09 16:00:00|            3.3|               0.1|                 1.0|               14.3|     0.00|\n",
+      "|2021-03-13 15:00:00|            9.3|             10.53|                 1.0|                4.0|     0.00|\n",
+      "|2021-03-20 13:00:00|           14.8|             12.47|                 1.0|                1.3|     0.00|\n",
+      "|2021-03-20 14:00:00|           22.3|             16.77|                 2.0|                3.0|     0.00|\n",
+      "|2021-03-20 15:00:00|          19.95|              15.7|                 1.5|                5.7|     0.00|\n",
+      "|2021-03-20 16:00:00|         15.775|              9.03|                 1.0|                6.9|     0.00|\n",
+      "|2021-03-20 17:00:00|          19.75|             12.35|                 1.0|                8.1|     0.00|\n",
+      "|2021-03-22 22:00:00|           18.3|             22.25|                 0.0|               10.7|     0.00|\n",
+      "|2021-03-22 23:00:00|            5.8|              4.25|                 1.0|               10.2|     0.00|\n",
+      "|2021-03-23 00:00:00|          45.57|             21.47|                 1.0|                9.3|     0.00|\n",
+      "|2021-03-23 03:00:00|           21.3|             21.62|                 1.0|                7.3|     0.00|\n",
+      "|2021-03-26 14:00:00|           13.0|              6.08|                 1.0|               16.8|     0.00|\n",
+      "|2021-03-26 15:00:00|          22.62|             14.97|                0.83|               17.9|     0.00|\n",
+      "|2021-03-26 16:00:00|          14.86|              7.07|                 1.2|               18.5|     0.00|\n",
+      "|2021-03-26 17:00:00|          20.48|             15.59|                 1.4|               20.6|     0.00|\n",
+      "+-------------------+---------------+------------------+--------------------+-------------------+---------+\n",
+      "only showing top 20 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 40:===================================================>  (109 + 6) / 115]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------------+----------------------+-------------------+\n",
+      "|rain_category|avg_mean_trip_duration|avg_mean_hourly_fee|\n",
+      "+-------------+----------------------+-------------------+\n",
+      "|      No Rain|    13.624059405940598|  21.89006930693069|\n",
+      "|  Little Rain|    11.825714285714286| 21.384642857142858|\n",
+      "+-------------+----------------------+-------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "final_df = final_df.withColumn(\n",
+    "    \"rain_category\",\n",
+    "    when(col(\"rain (mm)\") == 0.00, \"No Rain\")\n",
+    "    .when((col(\"rain (mm)\") > 0.00) & (col(\"rain (mm)\") <= 5.00), \"Little Rain\")\n",
+    "    .otherwise(\"Heavy Rain\"),\n",
+    ")\n",
+    "\n",
+    "result1_df = final_df.groupBy(\"rain_category\").agg(\n",
+    "    avg(\"mean_trip_duration\").alias(\"avg_mean_trip_duration\"),\n",
+    "    avg(\"mean_hourly_fee\").alias(\"avg_mean_hourly_fee\"),\n",
+    ")\n",
+    "\n",
+    "result1_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 47:===============================================>     (102 + 10) / 115]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+----------------------+-------------------+\n",
+      "|temperature_category|avg_mean_trip_duration|avg_mean_hourly_fee|\n",
+      "+--------------------+----------------------+-------------------+\n",
+      "|             Not Hot|    13.394385964912283|  21.84852631578948|\n",
+      "|            Moderate|                 14.63|              19.55|\n",
+      "+--------------------+----------------------+-------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "final_df = final_df.withColumn(\n",
+    "    \"temperature_category\",\n",
+    "    when(col(\"temperature_2m (°C)\") <= 24.1, \"Not Hot\")\n",
+    "    .when(\n",
+    "        (col(\"temperature_2m (°C)\") > 24.1) & (col(\"temperature_2m (°C)\") <= 28.4),\n",
+    "        \"Moderate\",\n",
+    "    )\n",
+    "    .otherwise(\"Hot\"),\n",
+    ")\n",
+    "\n",
+    "result2_df = final_df.groupBy(\"temperature_category\").agg(\n",
+    "    avg(\"mean_trip_duration\").alias(\"avg_mean_trip_duration\"),\n",
+    "    avg(\"mean_hourly_fee\").alias(\"avg_mean_hourly_fee\"),\n",
+    ")\n",
+    "\n",
+    "result2_df.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark.stop()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.11.2 64-bit",
+   "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": "3.11.2"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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