US_Bikeshare.ipynb

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      "Let's explore some US bikeshare data!\n",
      "\n",
      "Would you like to see data for Chicago, New York City, or Washington?\n",
      "chicgo\n",
      "\n",
      "You have entered an INVALID CITY. Please select from Chicago, New York City or Washington.\n",
      "\n",
      "Would you like to see data for Chicago, New York City, or Washington?\n",
      "chicago\n",
      "\n",
      "Which month would you like to filter the data by: January, February, March, April, May, or June? \n",
      "Enter \"all\" if you would like to see data for months from January to June.\n",
      "MACH\n",
      "\n",
      "You have entered an invalid MONTH. Please select from January, February, March, April, May, June or all.\n",
      "\n",
      "Which month would you like to filter the data by: January, February, March, April, May, or June? \n",
      "Enter \"all\" if you would like to see data for months from January to June.\n",
      "MARCH\n",
      "\n",
      "Which day would you like to filter the data by: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? \n",
      "Enter \"all\" if you would like to see data for all days.\n",
      "husday\n",
      "\n",
      "You have entered an invalid DAY. Please select from Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all.\n",
      "\n",
      "Which day would you like to filter the data by: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? \n",
      "Enter \"all\" if you would like to see data for all days.\n",
      "thursday\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "Calculating The Most Frequent Times of Travel...\n",
      "\n",
      "Most popular month of traveling: March\n",
      "Most popular day of traveling: Thursday\n",
      "Most popular hour of the day to start traveling: 17\n",
      "\n",
      "This took 0.0029969215393066406 seconds.\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "Calculating The Most Popular Stations and Trip...\n",
      "\n",
      "Most commonly used start station: Clinton St & Washington Blvd\n",
      "Most commonly used end station: Clinton St & Washington Blvd\n",
      "Most common trip from start station to end station: Canal St & Madison St to Michigan Ave & Washington St\n",
      "\n",
      "This took 0.004998683929443359 seconds.\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "Calculating Trip Duration...\n",
      "\n",
      "Total travel time: 3075092 seconds\n",
      "Average travel time: 618.9798711755234 seconds\n",
      "\n",
      "This took 0.0010001659393310547 seconds.\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "Calculating User Stats...\n",
      "\n",
      "User Type Count:\n",
      " Subscriber    4788\n",
      "Customer       180\n",
      "Name: User Type, dtype: int64\n",
      "\n",
      "Gender Count:\n",
      " Male      3857\n",
      "Female     930\n",
      "Name: Gender, dtype: int64\n",
      "\n",
      "Earliest year of birth: 1918\n",
      "Most recent year of birth: 2016\n",
      "Most common year of birth: 1989\n",
      "\n",
      "This took 0.0043735504150390625 seconds.\n",
      "------------------------------------------------------------------------------------------------------------------------\n",
      "\n",
      "Would you like to view 5 rows of raw data? yes or no:\n",
      "yes\n",
      "     Unnamed: 0          Start Time             End Time  Trip Duration  \\\n",
      "26       385517 2017-03-23 09:38:27  2017-03-23 09:42:41            254   \n",
      "34       389463 2017-03-23 20:50:57  2017-03-23 20:57:19            382   \n",
      "137      325584 2017-03-09 13:08:17  2017-03-09 13:14:22            365   \n",
      "265      286790 2017-03-02 17:44:10  2017-03-02 17:46:50            160   \n",
      "313      326052 2017-03-09 15:01:06  2017-03-09 15:11:02            596   \n",
      "\n",
      "                    Start Station                      End Station  \\\n",
      "26       Loomis St & Jackson Blvd         Peoria St & Jackson Blvd   \n",
      "34            Ellis Ave & 55th St            Kimbark Ave & 53rd St   \n",
      "137  Financial Pl & Congress Pkwy      Michigan Ave & Jackson Blvd   \n",
      "265  Sheridan Rd & Irving Park Rd  Pine Grove Ave & Irving Park Rd   \n",
      "313           Wabash Ave & 8th St             Wabash Ave & 16th St   \n",
      "\n",
      "      User Type  Gender  Birth Year  Month Day_of_week  Hour  \\\n",
      "26   Subscriber  Female      1985.0      3    Thursday     9   \n",
      "34   Subscriber  Female      1993.0      3    Thursday    20   \n",
      "137  Subscriber    Male      1955.0      3    Thursday    13   \n",
      "265  Subscriber    Male      1980.0      3    Thursday    17   \n",
      "313  Subscriber    Male      1966.0      3    Thursday    15   \n",
      "\n",
      "                                         Frequent Trip  \n",
      "26   Loomis St & Jackson Blvd to Peoria St & Jackso...  \n",
      "34        Ellis Ave & 55th St to Kimbark Ave & 53rd St  \n",
      "137  Financial Pl & Congress Pkwy to Michigan Ave &...  \n",
      "265  Sheridan Rd & Irving Park Rd to Pine Grove Ave...  \n",
      "313        Wabash Ave & 8th St to Wabash Ave & 16th St  \n",
      "\n",
      "Would you like to view 5 more rows of data? yes or no:\n",
      "yes\n",
      "     Unnamed: 0          Start Time             End Time  Trip Duration  \\\n",
      "406      326132 2017-03-09 15:18:09  2017-03-09 15:27:43            574   \n",
      "504      387672 2017-03-23 17:05:57  2017-03-23 17:10:51            294   \n",
      "607      386642 2017-03-23 15:09:08  2017-03-23 15:21:36            748   \n",
      "650      351520 2017-03-16 20:22:23  2017-03-16 20:29:03            400   \n",
      "815      328839 2017-03-09 21:27:39  2017-03-09 21:36:37            538   \n",
      "\n",
      "                      Start Station                End Station   User Type  \\\n",
      "406     Sheridan Rd & Noyes St (NU)     Benson Ave & Church St  Subscriber   \n",
      "504       LaSalle St & Jackson Blvd   Jefferson St & Monroe St  Subscriber   \n",
      "607      Larrabee St & Kingsbury St     Clinton St & Tilden St  Subscriber   \n",
      "650  Sheffield Ave & Wellington Ave  Lincoln Ave & Belmont Ave  Subscriber   \n",
      "815          Kingsbury St & Erie St      Canal St & Madison St  Subscriber   \n",
      "\n",
      "     Gender  Birth Year  Month Day_of_week  Hour  \\\n",
      "406    Male      1992.0      3    Thursday    15   \n",
      "504    Male      1979.0      3    Thursday    17   \n",
      "607    Male      1975.0      3    Thursday    15   \n",
      "650  Female      1979.0      3    Thursday    20   \n",
      "815    Male      1957.0      3    Thursday    21   \n",
      "\n",
      "                                         Frequent Trip  \n",
      "406  Sheridan Rd & Noyes St (NU) to Benson Ave & Ch...  \n",
      "504  LaSalle St & Jackson Blvd to Jefferson St & Mo...  \n",
      "607  Larrabee St & Kingsbury St to Clinton St & Til...  \n",
      "650  Sheffield Ave & Wellington Ave to Lincoln Ave ...  \n",
      "815    Kingsbury St & Erie St to Canal St & Madison St  \n",
      "\n",
      "Would you like to view 5 more rows of data? yes or no:\n",
      "no\n",
      "\n",
      "Would you like to restart? Enter yes or no.\n",
      "no\n"
     ]
    }
   ],
   "source": [
    "# import libraries and packages \n",
    "\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import calendar\n",
    "\n",
    "CITY_DATA = { 'chicago': '../data/chicago.csv',\n",
    "              'new york city': '../data/city_new_york.csv',\n",
    "              'washington': '../data/washington.csv' }\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",
    "    print('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",
    "    cities = ('chicago', 'new york city', 'washington')\n",
    "    city = ''\n",
    "    while city not in cities:\n",
    "        city = input('\\nWould you like to see data for Chicago, New York City, or Washington?\\n').lower()\n",
    "        if city not in cities:\n",
    "            print('\\nYou have entered an INVALID CITY. Please select from Chicago, New York City or Washington.')\n",
    "            continue\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    # get user input for month (all, january, february, ... , june)\n",
    "    months = ('all', 'january', 'february', 'march', 'april', 'may', 'june')\n",
    "    month = ''\n",
    "    while month not in months:\n",
    "        month = input('\\nWhich month would you like to filter the data by: January, February, March, April, May, or June? \\nEnter \"all\" if you would like to see data for months from January to June.\\n').lower()\n",
    "        if month not in months:\n",
    "            print('\\nYou have entered an INVALID MONTH. Please select from January, February, March, April, May, June or all.')\n",
    "            continue\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    # get user input for day of week (all, monday, tuesday, ... sunday)\n",
    "    days = ('all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday')\n",
    "    day = ''\n",
    "    while day not in days:\n",
    "        day = input('\\nWhich day would you like to filter the data by: Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, or Sunday? \\nEnter \"all\" if you would like to see data for all days.\\n').lower()\n",
    "        if day not in days:\n",
    "            print('\\nYou have entered an INVALID DAY. Please select from Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday or all.')\n",
    "            continue\n",
    "        else:\n",
    "            break\n",
    "\n",
    "    print('-'*120)\n",
    "    return city, month, day\n",
    "\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",
    "\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",
    "    # load data file into a dataframe\n",
    "    df = pd.read_csv(CITY_DATA[city])\n",
    "\n",
    "    # convert the Start Time column to datetime \n",
    "    df['Start Time'] = pd.to_datetime(df['Start Time'])\n",
    "    \n",
    "    # extract month from the Start Time column to create a month column\n",
    "    df['Month'] = df['Start Time'].dt.month\n",
    "   \n",
    "    # extract day from the Start Time column to create a day_of_week column\n",
    "    df['Day_of_week'] = df['Start Time'].dt.day_name()\n",
    "\n",
    "    # filter by month if applicable and create new dataframe\n",
    "    if month != 'all':\n",
    "        months = ['january', 'february', 'march', 'april', 'may', 'june']\n",
    "        month = months.index(month) + 1\n",
    "        df = df[df['Month'] == month]\n",
    "\n",
    "    # filter by day of week if applicable and create new dataframe\n",
    "    if day != 'all':\n",
    "        df = df[df['Day_of_week'] == day.title()]\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",
    "    common_month = df['Month'].mode()[0]\n",
    "    print('Most popular month of traveling:', calendar.month_name[common_month])\n",
    "\n",
    "    # display the most common day of week\n",
    "    common_day = df['Day_of_week'].mode()[0]\n",
    "    print('Most popular day of traveling:', common_day)\n",
    "\n",
    "    # display the most common start hour\n",
    "    df['Hour'] = df['Start Time'].dt.hour\n",
    "    common_hour = df['Hour'].mode()[0]\n",
    "    print('Most popular hour of the day to start traveling:', common_hour)\n",
    "\n",
    "    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n",
    "    print('-'*120)\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",
    "    common_start = df['Start Station'].mode()[0]\n",
    "    print('Most commonly used start station:', common_start)\n",
    "\n",
    "    # display most commonly used end station\n",
    "    common_end = df['End Station'].mode()[0]\n",
    "    print('Most commonly used end station:', common_end)\n",
    "\n",
    "    # display most frequent combination of start station and end station trip\n",
    "    df['Frequent Trip'] = df['Start Station'] + ' to ' + df['End Station']\n",
    "    common_trip = df['Frequent Trip'].mode()[0]\n",
    "    print('Most common trip from start station to end station:', common_trip)\n",
    "\n",
    "    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n",
    "    print('-'*120)\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",
    "    total_travel_time = df['Trip Duration'].sum()\n",
    "    print('Total travel time:', total_travel_time, 'seconds')\n",
    "\n",
    "    # display mean travel time\n",
    "    mean_travel_time = df['Trip Duration'].mean()\n",
    "    print('Average travel time:', mean_travel_time, 'seconds')\n",
    "\n",
    "    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n",
    "    print('-'*120)\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",
    "    user_type_count = df['User Type'].value_counts()\n",
    "    print('User Type Count:\\n',user_type_count)\n",
    "\n",
    "    # Display counts of gender\n",
    "    try:\n",
    "        gender_count = df['Gender'].value_counts()\n",
    "        print('\\nGender Count:\\n', gender_count)\n",
    "    except KeyError:\n",
    "        print('\\nGender Count: data not available.')\n",
    "\n",
    "    # Display earliest, most recent, and most common year of birth\n",
    "    try:\n",
    "        birth_min = int(df['Birth Year'].min())\n",
    "        print('\\nEarliest year of birth:', birth_min)\n",
    "    except KeyError:\n",
    "        print('\\nEarliest birth year: data not available.')\n",
    "\n",
    "    try:\n",
    "        birth_max = int(df['Birth Year'].max())\n",
    "        print('Most recent year of birth:', birth_max)\n",
    "    except KeyError:\n",
    "        print('\\nMost recent birth year: data not available.')\n",
    "\n",
    "    try:\n",
    "        birth_mode = int(df['Birth Year'].mode()[0])\n",
    "        print('Most common year of birth:', birth_mode)\n",
    "    except KeyError:\n",
    "        print('\\nMost common birth year: data not available.')\n",
    "\n",
    "    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n",
    "    print('-'*120)\n",
    "\n",
    "\n",
    "def display_data(df):\n",
    "    \"\"\"Displays raw data 5 rows at a time, if requested.\"\"\"\n",
    "\n",
    "    view_data = input('\\nWould you like to view 5 rows of raw data? yes or no:\\n').lower()\n",
    "    if view_data != 'no':\n",
    "        start_loc = 0\n",
    "        while (start_loc < df['Start Time'].count() and view_data != 'no'):\n",
    "            print(df.iloc[start_loc:start_loc+5])\n",
    "            start_loc += 5\n",
    "            more_data = input('\\nWould you like to view 5 more rows of data? yes or no:\\n').lower()\n",
    "            if more_data != 'yes':\n",
    "                break\n",
    "                \n",
    "    \n",
    "def main():\n",
    "    while True:\n",
    "        city, month, day = get_filters()\n",
    "        df = load_data(city, month, day)\n",
    "\n",
    "        time_stats(df)\n",
    "        station_stats(df)\n",
    "        trip_duration_stats(df)\n",
    "        user_stats(df)\n",
    "        display_data(df)\n",
    "\n",
    "        restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n",
    "        if restart.lower() != 'yes':\n",
    "            break\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()\n"
   ]
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