bikeshare_2.py

import time
import pandas as pd
from pick import pick

CITY_DATA = { 'chicago': 'chicago.csv',
              'new york city': 'new_york_city.csv',
              'washington': 'washington.csv' }

def get_filters():
    """
    Asks user to specify a city, month, and day to analyze.

    Returns:
        (str) city - name of the city to analyze
        (str) month - name of the month to filter by, or "all" to apply no month filter
        (str) day - name of the day of week to filter by, or "all" to apply no day filter
    """
    print('Hello! Let\'s explore some US bikeshare data!')
    # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs
    title = 'Please choose a city: '
    options = list(CITY_DATA)
    city = pick(options, title)[0]

    # get user input for month (all, january, february, ... , june)
    title = 'Please choose a month: '
    options = ['all', 'january', 'february', 'march', 'april', 'may', 'june']
    month = pick(options, title)[0]

    # get user input for day of week (all, monday, tuesday, ... sunday)
    title = 'Please choose a day: '
    options = ['all', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']
    day = pick(options, title)[0]

    print('-'*40)
    return city, month, day


def load_data(city, month, day):
    """
    Loads data for the specified city and filters by month and day if applicable.

    Args:
        (str) city - name of the city to analyze
        (str) month - name of the month to filter by, or "all" to apply no month filter
        (str) day - name of the day of week to filter by, or "all" to apply no day filter
    Returns:
        df - Pandas DataFrame containing city data filtered by month and day
    """
    # load data file into a dataframe
    df = pd.read_csv(CITY_DATA[city])
    
    # convert the Start Time column to datetime
    df['Start Time'] = pd.to_datetime(df['Start Time'])

    # extract month and day of week from Start Time to create new columns
    df['month'] = df['Start Time'].dt.month
    df['day_of_week'] = df['Start Time'].dt.weekday_name

     # filter by month if applicable
    if month != 'all':
        # use the index of the months list to get the corresponding int
        months = ['january', 'february', 'march', 'april', 'may', 'june']
        month = months.index(month)+1
    
        # filter by month to create the new dataframe
        df = df[df['month'] == month]

    
    # filter by day of week if applicable
    if day != 'all':
        # filter by day of week to create the new dataframe
        df = df[df['day_of_week'] == day.title()]

    return df


def time_stats(df):
    """Displays statistics on the most frequent times of travel."""

    print('\nCalculating The Most Frequent Times of Travel...\n')
    start_time = time.time()

    df['Start Time'] = pd.to_datetime(df['Start Time'])
    # display the most common month
    df['month'] = df['Start Time'].dt.month
    popular_month = df['month'].mode()[0]
    print("Most common month: {}".format(popular_month))

    # display the most common day of week
    df['day'] = df['Start Time'].dt.weekday_name
    popular_day = df['day'].mode()[0]
    print("Most common day: {}".format(popular_day))

    # display the most common start hour
    df['hour'] = df['Start Time'].dt.hour
    popular_hour = df['hour'].mode()[0]
    print("Most common hour: {}".format(popular_hour))

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


def station_stats(df):
    """Displays statistics on the most popular stations and trip."""

    print('\nCalculating The Most Popular Stations and Trip...\n')
    start_time = time.time()

    # display most commonly used start station
    start_station = df['Start Station'].value_counts().idxmax()
    print("Most popular start station: {}".format(start_station))

    # display most commonly used end station
    end_station = df['End Station'].value_counts().idxmax()
    print("Most popular end station: {}".format(end_station))

    # display most frequent combination of start station and end station trip
    df['trip']="{} <-> {}".format(df['Start Station'], df['End Station'])
    combination = df['trip'].value_counts().idxmax()
    print("Most popular combination: {}".format(combination))

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


def trip_duration_stats(df):
    """Displays statistics on the total and average trip duration."""

    print('\nCalculating Trip Duration...\n')
    start_time = time.time()

    # display total travel time
    print('Total travel time: {}'.format(df['Trip Duration'].sum()))

    # display mean travel time
    print('Mean travel time: {}'.format(df['Trip Duration'].mean()))

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


def user_stats(df):
    """Displays statistics on bikeshare users."""

    print('\nCalculating User Stats...\n')
    start_time = time.time()

    # Display counts of user types
    user_types = df['User Type'].value_counts()
    print('Count of user types:\n{}\n'.format(user_types))

    # Display counts of gender
    genders = 'N\A'
    if 'Gender' in df:
        genders = df['Gender'].value_counts()
    print('Count of genders:\n{}\n'.format(genders))

    # Display earliest, most recent, and most common year of birth
    if 'Birth Year' in df:
        min_year = df['Birth Year'].min()
        max_year = df['Birth Year'].max()
        common_year = df['Birth Year'].mode()
        print('Earliest birth year: {}'.format(int(min_year))) 
        print('Most recent birth year: {}'.format(int(max_year)))
        print('Most common year: {}'.format(int(common_year)))
    else:
        print('Birth year statistics not available for your selection!')

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


def main():
    while True:
        city, month, day = get_filters()
        df = load_data(city, month, day)

        time_stats(df)
        station_stats(df)
        trip_duration_stats(df)
        user_stats(df)

        restart = input('\nWould you like to restart? Enter yes or no.\n')
        if restart.lower() != 'yes':
            break


if __name__ == "__main__":
	main()