BikeShare.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62aef565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello! Let's Explore Some US Bikeshare Data!\n",
      "Enter The City You're Curious About: chicago\n",
      "Now Enter The Month You Want To Know More About(all/january:june): all\n",
      "Now Enter The Week Day(all/Monday:Sunday): msd\n",
      "Please Enter A Valid Day Name(all, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday): all\n",
      "----------------------------------------\n",
      "Do You Want To Explore Your Table?(y/n) y\n",
      "\n",
      "Your Filtered Table Result Is:\n",
      "    Unnamed: 0          Start Time             End Time  Trip Duration  \\\n",
      "0     1423854 2017-06-23 15:09:32  2017-06-23 15:14:53            321   \n",
      "1      955915 2017-05-25 18:19:03  2017-05-25 18:45:53           1610   \n",
      "2        9031 2017-01-04 08:27:49  2017-01-04 08:34:45            416   \n",
      "3      304487 2017-03-06 13:49:38  2017-03-06 13:55:28            350   \n",
      "4       45207 2017-01-17 14:53:07  2017-01-17 15:02:01            534   \n",
      "\n",
      "                   Start Station                   End Station   User Type  \\\n",
      "0           Wood St & Hubbard St       Damen Ave & Chicago Ave  Subscriber   \n",
      "1            Theater on the Lake  Sheffield Ave & Waveland Ave  Subscriber   \n",
      "2             May St & Taylor St           Wood St & Taylor St  Subscriber   \n",
      "3  Christiana Ave & Lawrence Ave  St. Louis Ave & Balmoral Ave  Subscriber   \n",
      "4         Clark St & Randolph St  Desplaines St & Jackson Blvd  Subscriber   \n",
      "\n",
      "   Gender  Birth Year  month day_of_week  \n",
      "0    Male      1992.0      6      Friday  \n",
      "1  Female      1992.0      5    Thursday  \n",
      "2    Male      1981.0      1   Wednesday  \n",
      "3    Male      1986.0      3      Monday  \n",
      "4    Male      1975.0      1     Tuesday  \n",
      "----------------------------------------\n",
      "Do You Want To See More?(y/n) y\n",
      "\n",
      "Viewing Next 5 Rows:\n",
      "    Unnamed: 0          Start Time             End Time  Trip Duration  \\\n",
      "5     1473887 2017-06-26 09:01:20  2017-06-26 09:11:06            586   \n",
      "6      961916 2017-05-26 09:41:44  2017-05-26 09:46:25            281   \n",
      "7       65924 2017-01-21 14:28:38  2017-01-21 14:40:41            723   \n",
      "8      606841 2017-04-20 16:08:51  2017-04-20 16:20:20            689   \n",
      "9      135470 2017-02-06 18:00:47  2017-02-06 18:09:00            493   \n",
      "\n",
      "                  Start Station                    End Station   User Type  \\\n",
      "5  Clinton St & Washington Blvd           Canal St & Taylor St  Subscriber   \n",
      "6         Ashland Ave & Lake St           Wood St & Hubbard St  Subscriber   \n",
      "7    Larrabee St & Kingsbury St     Larrabee St & Armitage Ave    Customer   \n",
      "8        Sedgwick St & Huron St  Halsted St & Blackhawk St (*)  Subscriber   \n",
      "9  Stetson Ave & South Water St   Clinton St & Washington Blvd  Subscriber   \n",
      "\n",
      "   Gender  Birth Year  month day_of_week  \n",
      "5    Male      1990.0      6      Monday  \n",
      "6  Female      1983.0      5      Friday  \n",
      "7     NaN         NaN      1    Saturday  \n",
      "8    Male      1984.0      4    Thursday  \n",
      "9    Male      1979.0      2      Monday  \n",
      "----------------------------------------\n",
      "Do You Want To See More?(y/n) n\n",
      "----------------------------------------\n",
      "\n",
      "Calculating The Most Frequent Times Of Travel...\n",
      "\n",
      "The Most Common Month Is: June\n",
      "The Most Common Week Day Is: Tuesday\n",
      "The Most Common Start Hour Is: 5 PM\n",
      "\n",
      "This Took 0.09574508666992188 Seconds.\n",
      "----------------------------------------\n",
      "\n",
      "Calculating The Most Popular Stations And Trip...\n",
      "\n",
      "The Most Common Start Station Is: Streeter Dr & Grand Ave\n",
      "The Most Common End Station Is: Streeter Dr & Grand Ave\n",
      "The Most Frequent Combination Of Start Station And End Station Trip Is: Lake Shore Dr & Monroe St ---> Streeter Dr & Grand Ave\n",
      "\n",
      "This Took 0.21573829650878906 Seconds.\n",
      "----------------------------------------\n",
      "\n",
      "Calculating Trip Duration...\n",
      "\n",
      "The Total Travel Time Is: 78019.941 Hours.\n",
      "The Average Travel Time Is: 15.6 Minutes.\n",
      "\n",
      "This Took 0.0032401084899902344 Seconds.\n",
      "----------------------------------------\n",
      "\n",
      "Calculating User Stats...\n",
      "\n",
      "Counts Of User Types:\n",
      " Subscriber    238889\n",
      "Customer       61110\n",
      "Dependent          1\n",
      "Name: User Type, dtype: int64\n",
      "\n",
      "Counts Of Gender:\n",
      " Male      181190\n",
      "Female     57758\n",
      "Name: Gender, dtype: int64\n",
      "\n",
      "Earliest Birth Year Is: 1899\n",
      "Most Recent Birth Year Is: 2016\n",
      "Most Common Birth Year Is: 1989\n",
      "\n",
      "This Took 0.05633115768432617 Seconds.\n",
      "----------------------------------------\n",
      "\n",
      "Would you like to restart? Enter yes or no.\n",
      "no\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def get_filters():\n",
    "    \"\"\"\n",
    "    Asks user to specify a city, month, and day to analyze.\n",
    "    Returns:\n",
    "        (str) city - name of the city to analyze\n",
    "        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n",
    "        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n",
    "    \"\"\"\n",
    "\n",
    "    print('Hello! Let\\'s Explore Some US Bikeshare Data!')\n",
    "\n",
    "    # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n",
    "    city = input(\"Enter The City You're Curious About: \").lower()\n",
    "    city_name = ['chicago', 'new york city', 'washington']\n",
    "    while True:\n",
    "        if city not in city_name:\n",
    "            city = input(\"Please Enter A Valid City Name(chicago, new york city, washington): \").lower()\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    # get user input for month (all, january, february, ... , june)\n",
    "    month = input(\"Now Enter The Month You Want To Know More About(all/january:june): \").lower()\n",
    "    month_name = ['all', 'january', 'february', 'march', 'april','may', 'june']\n",
    "    while True:\n",
    "        if month not in month_name:\n",
    "            month = input(\"Please Enter A Valid Month Name(all, january, february, march, april, may, june): \").lower()\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    # get user input for day of week (all, monday, tuesday, ... sunday)\n",
    "    day = input(\"Now Enter The Week Day(all/Monday:Sunday): \").title()\n",
    "    day_name = ['All', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
    "    while True:\n",
    "        if day not in day_name:\n",
    "            day = input(\"Please Enter A Valid Day Name(all, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday): \").title()\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    print('-'*40)\n",
    "    return city, month, day\n",
    "\n",
    "def load_data(city, month, day):\n",
    "    \"\"\"\n",
    "    Loads data for the specified city and filters by month and day if applicable.\n",
    "    Args:\n",
    "        (str) city - name of the city to analyze\n",
    "        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n",
    "        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n",
    "    Returns:\n",
    "        df - Pandas DataFrame containing city data filtered by month and day\n",
    "    \"\"\"\n",
    "    \n",
    "    CITY_DATA = { 'chicago': 'chicago.csv', 'new york city': 'new_york_city.csv', 'washington': 'washington.csv' }\n",
    "    df= pd.read_csv(CITY_DATA[city])\n",
    "\n",
    "    df['Start Time'] = pd.to_datetime(df['Start Time'])\n",
    "    df['month'] = df['Start Time'].dt.month\n",
    "    df['day_of_week'] = df['Start Time'].dt.day_name()\n",
    "\n",
    "    months = {'january':1 , 'february':2, 'march':3, 'april':4, 'may':5, 'june':6}\n",
    "    if month != 'all':\n",
    "        df = df[df['month']==months[month]]\n",
    "    \n",
    "    if day != 'All':\n",
    "        df = df[df['day_of_week']==day]\n",
    "\n",
    "    return df\n",
    "\n",
    "\n",
    "def time_stats(df):\n",
    "    \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n",
    "\n",
    "    print('\\nCalculating The Most Frequent Times Of Travel...\\n')\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # display the most common month\n",
    "    if month =='all':\n",
    "        months = {1:'January' , 2:'February', 3:'March', 4:'April', 5:'May', 6:'June'}\n",
    "        print('The Most Common Month Is:', months[df['month'].mode()[0]])\n",
    "        \n",
    "    # display the most common day of week\n",
    "    if day=='All':\n",
    "        print('The Most Common Week Day Is:', df['day_of_week'].mode()[0])\n",
    "    \n",
    "    # display the most common start hour\n",
    "    df['Start Time'] = pd.to_datetime(df['Start Time'])\n",
    "    df['hour'] = df['Start Time'].dt.hour\n",
    "    h=df['hour'].mode()[0]\n",
    "    if h == 12:\n",
    "        print('The Most Common Start Hour Is: 12 PM')\n",
    "    elif h > 12:\n",
    "        h=h-12\n",
    "        print('The Most Common Start Hour Is:', h, 'PM')\n",
    "    else:\n",
    "        print('The Most Common Start Hour Is:', h, 'AM')\n",
    "     \n",
    "    print(\"\\nThis Took %s Seconds.\" % (time.time() - start_time))\n",
    "    print('-'*40)\n",
    "\n",
    "\n",
    "def station_stats(df):\n",
    "    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n",
    "\n",
    "    print('\\nCalculating The Most Popular Stations And Trip...\\n')\n",
    "    start_time = time.time()\n",
    "\n",
    "    # display most commonly used start station\n",
    "    print('The Most Common Start Station Is:', df['Start Station'].mode()[0])\n",
    "\n",
    "    # display most commonly used end station\n",
    "    print('The Most Common End Station Is:', df['End Station'].mode()[0])\n",
    "\n",
    "    # display most frequent combination of start station and end station trip\n",
    "    df['Trip Road']=df['Start Station']+ ' ---> ' +df['End Station']\n",
    "    print('The Most Frequent Combination Of Start Station And End Station Trip Is:', df['Trip Road'].mode()[0])\n",
    "\n",
    "    print(\"\\nThis Took %s Seconds.\" % (time.time() - start_time))\n",
    "    print('-'*40)\n",
    "\n",
    "\n",
    "def trip_duration_stats(df):\n",
    "    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n",
    "\n",
    "    print('\\nCalculating Trip Duration...\\n')\n",
    "    start_time = time.time()\n",
    "\n",
    "    # display total travel time\n",
    "    print('The Total Travel Time Is:', round(df['Trip Duration'].sum()/3600,3), 'Hours.')\n",
    " \n",
    "\n",
    "    # display mean travel time\n",
    "    print('The Average Travel Time Is:', round(df['Trip Duration'].mean()/60,2),'Minutes.')\n",
    "\n",
    "    print(\"\\nThis Took %s Seconds.\" % (time.time() - start_time))\n",
    "    print('-'*40)\n",
    "\n",
    "\n",
    "def user_stats(df):\n",
    "    \"\"\"Displays statistics on bikeshare users.\"\"\"\n",
    "\n",
    "    print('\\nCalculating User Stats...\\n')\n",
    "    start_time = time.time()\n",
    "   \n",
    "    # Display counts of user types\n",
    "    print('Counts Of User Types:\\n', df['User Type'].value_counts())\n",
    "    \n",
    "    if city!= 'washington':\n",
    "      # Display counts of gender\n",
    "      print('\\nCounts Of Gender:\\n', df['Gender'].value_counts())\n",
    "\n",
    "      # Display earliest, most recent, and most common year of birth\n",
    "      print('\\nEarliest Birth Year Is:',int(df['Birth Year'].min()))\n",
    "      print('Most Recent Birth Year Is:',int(df['Birth Year'].max()))\n",
    "      print('Most Common Birth Year Is:',int(df['Birth Year'].mode()[0]))\n",
    "\n",
    "\n",
    "\n",
    "    print(\"\\nThis Took %s Seconds.\" % (time.time() - start_time))\n",
    "    print('-'*40)\n",
    "\n",
    "\n",
    "def raw_data(df):\n",
    "    '''\n",
    "    displaying some rows of the table upon user's request\n",
    "    '''\n",
    "\n",
    "    user_choice= input('Do You Want To Explore Your Table?(y/n) ').lower()\n",
    "\n",
    "    while True:\n",
    "        if user_choice == 'y' or user_choice == 'n' :\n",
    "            break\n",
    "        else:\n",
    "           user_choice=input(\"Please Enter A Valid Answer:(y/n) \").lower()\n",
    "\n",
    "    if user_choice=='n':\n",
    "        print('-'*40)\n",
    "        return\n",
    "\n",
    "    else:\n",
    "        start=0\n",
    "        end=5\n",
    "\n",
    "        print('\\nYour Filtered Table Result Is:\\n',df.iloc[start:end])\n",
    "        print('-'*40)\n",
    "\n",
    "        while True:\n",
    "            start=start+5\n",
    "            end=end+5\n",
    "\n",
    "            #check if the table has more data\n",
    "            if start > len(df.index)-1:\n",
    "                print(\"No More Data To View.\")\n",
    "                print('-'*40)\n",
    "                break\n",
    "\n",
    "            user_choice=input('Do You Want To See More?(y/n) ').lower()\n",
    "            while True:\n",
    "                if user_choice == 'y' or user_choice == 'n' :\n",
    "                    break\n",
    "                else:\n",
    "                    user_choice=input(\"Please Enter A Valid Answer:(y/n) \").lower()\n",
    "                    \n",
    "            if user_choice=='y':\n",
    "                print('\\nViewing Next 5 Rows:\\n',df.iloc[start:end])\n",
    "                print('-'*40)\n",
    "            else:\n",
    "                break\n",
    "    print('-'*40)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    while True:\n",
    "        city, month, day = get_filters()\n",
    "        df = load_data(city, month, day)\n",
    "        \n",
    "        raw_data(df)\n",
    "        time_stats(df)\n",
    "        station_stats(df)\n",
    "        trip_duration_stats(df)\n",
    "        user_stats(df)\n",
    "       \n",
    "\n",
    "        restart = input('\\nWould you like to restart? Enter yes or no.\\n').lower()\n",
    "        while True:\n",
    "            if restart == 'yes' or restart == 'no' :\n",
    "                break\n",
    "            else:\n",
    "                restart=input(\"Please Enter A Valid Answer:(yes/no) \").lower()\n",
    "        if restart != 'yes':\n",
    "            break"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}