bikeshare.py

import time
import pandas as pd
import numpy as np
from tabulate import tabulate

"""
Uses Python to explore data related to bike share systems for three major cities in the United States—Chicago, New York City, and Washington.
It shows statistics such as travel times, popular stations and trips, and bikeshare user data based on user filter selection
"""

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

MONTHS = ['january', 'february', 'march', 'april', 'may', 'june', 'all']
cities = ['chicago','new york', 'washington']
city = 0
days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday', 'All']


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!\n')


    #gets user input for city to analyze (chicago, new york city, washington).
    city_message = 'Which city would you like to see data from? Chicago, New York, or Washington?: \n'
    global city
    city = input(city_message).lower()
    while city not in cities:
        print('Seems you may have misspelled the name the city.\n')
        city = input(city_message)

    #gets user input for month (all, january, february, ... , june)
    month_message = '\nWhich month would you like to see data from? January, February, March, April, May or June? \nYou can type "all" for no filter: \n'
    month = input(month_message).lower()
    while month not in MONTHS:
        print('Seems you may have misspelled the month.\n')
        month = input(month_message).lower()

    #gets user input for day of week (all, monday, tuesday, ... sunday)
    day_message = '\nWhich day of the week would you like to analyze? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday or Sunday?\nYou can type "all" for no filter : \n'
    day = input(day_message).title()
    while day not in days:
        print('Seems you may have misspelled the day.\n')
        day = input(day_message).title()

    print('\nThank you! Your filter selection has been City: {} - Month: {} - Day: {}:\n'.format(city, month, day))
    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
    """
    df = pd.read_csv(CITY_DATA[city])

    # converts the Start Time column to datetime
    df['Start Time'] = pd.to_datetime(df['Start Time'])
    # extracts day of week from the Start Time column to create an 'Day of Week' column
    df['day_of_week'] = df['Start Time'].dt.day_name()
    # extracts month from the Start Time column to create an month column
    df['month'] = df['Start Time'].dt.month
    # extracts hour from the Start Time column to create an hour column
    df['hour'] = df['Start Time'].dt.hour

    #if filter by month  applicable, it will use the index of the months list to get the corresponding int
    if month != 'all':
        month = MONTHS.index(month)
        # filter by month to create the new dataframe
        df = df[df['month'] == month + 1]

    # 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()

    #Displays the most common month
    popular_month = df['month'].mode()[0]
    # use the index of the months list to get the corresponding int
    popular_month = MONTHS[popular_month - 1]
    print('Most popular month for travel is {}\n'.format(popular_month))

    #Displays the most common day of week
    popular_dow = df['day_of_week'].mode()[0]
    print('Most popular day of the week for travel is {}\n'.format(popular_dow))

    # Displays the most common start hour
    popular_hour = df['hour'].mode()
    print('Most popular time of the day for travel is at {}hs\n'.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()

    # Displays most commonly used start station
    popular_station = df['Start Station'].mode()[0]
    print('Most popular start station is {}\n'.format(popular_station))

    # Displays most commonly used end station
    popular_end_station = df['End Station'].mode()[0]
    print('Most popular end station is {}\n'.format(popular_end_station))


    # Displays most frequent combination of start station and end station trip
    popular_combination = df.groupby(['Start Station','End Station']).size().idxmax()
    print('Most popular combination is from {} to {}'.format(popular_combination[0],popular_combination[1]))

    if popular_combination[0] == popular_combination[1]:
        print('This means the most popular travel is a round trip!')


    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()

    # Displays total travel time, converted from seconds to weeks and days
    total_trip =  df['Trip Duration'].sum()
    days = total_trip // (24 * 3600)
    weeks = days // 7
    remainder = days % 7
    print('The total travel time based on your filter selection is {} weeks and {} day(s)'.format(weeks, remainder))

    # Displays mean travel time in minutes
    trip_duration =  df['Trip Duration'].mean()
    print('The average travel time is {} minutes'.format(round(trip_duration / 60, 2)))

    # Displays maximun travel time in minutes
    max_trip = df['Trip Duration'].max()
    print('The max travel time was {} minutes'.format(round(max_trip / 60, 2)))

    # Displays minimun travel time in minutes
    min_trip = df['Trip Duration'].min()
    print('The max travel time was {} minutes'.format(round(min_trip / 60, 2)))  

    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()

        # Displays counts of user types in tabulate. Separated keys and values and converts count values to percentage
    user_types = df['User Type'].value_counts().keys().tolist()
    ut_count = df['User Type'].value_counts().tolist()
    ut_percent = df['User Type'].value_counts(normalize=True)
    ut_data = [[user_types[0], ut_count[0], '{}%'.format(round(ut_percent[0]*100), 2)],
        [user_types[1], ut_count[1], '{}%'.format(round(ut_percent[1]*100), 2)]]

    print(tabulate(ut_data, headers=['User Type', 'Count', 'Percentage']))
    print('')

    # Verifies selected city is not Washington before continue to gender and birth year analysis.
    if city != 'washington':
        # Displays counts of gender in tabulate. Separated keys and values and converts count values to percentage
        gender = df['Gender'].value_counts().keys().tolist()
        gender_count = df['Gender'].value_counts().tolist()
        gender_percent = df['Gender'].value_counts(normalize=True)

        print('')
        gender_data = [[gender[0], gender_count[0], '{}%'.format(round(gender_percent[0]*100), 2)],
            [gender[1], gender_count[1], '{}%'.format(round(gender_percent[1]*100), 2)]]

        print(tabulate(gender_data, headers=['Gender', 'Count', 'Percentage']))
        print('\nThis took %s seconds.' % (time.time() - start_time))
        print('-'*40)

    # Display earliest, most recent, and most common year of birth
        earliest_byear = df['Birth Year'].min()
        recent_byear = df['Birth Year'].max()
        common_byear = df['Birth Year'].mode()[0]

        print('')
        by_data = [['Earliest year', earliest_byear],
            ['Most recent', recent_byear],
            ['Most common', common_byear]]

        print(tabulate(by_data, headers=['Data', 'Birth Year']))
        print('\nThis took %s seconds.' % (time.time() - start_time))
        print('-'*40)

    else:
        print('\nSorry! We don´t have any gender or birth year data for the city of Washington.')

def raw_data(df):
    """
    Asks user to if he/she wants to see raw data for the selected city.
    It will display 10 rows of data at a time until user decides types 'no'

    """
    view_rdata = input('\nWould you like to see raw data of individual trips? Enter yes or no\n').lower()
    rdata_index = 0
    while view_rdata.lower() == 'yes':
        print(df.iloc[rdata_index:rdata_index+10])
        rdata_index += 10
        rdata_cont = input('Would you like to see 10 more rows?:Enter yes or no.\n' ).lower()
        if rdata_cont.lower() != 'yes':
            break

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)
        raw_data(df)

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


if __name__ == "__main__":
	main()