import time
import pandas as pd
import numpy as np

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


cities = ['washington', 'chicago', 'new york city']
months = ['january', 'february', 'march', 'april', 'may', 'june', 'all']
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!')
    # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs
    while True:
        city = input('Enter the city you wish to analyze:> ').lower()
        if city not in cities:
            print('\n"{}" is not found in our records.'.format(city))
            continue
        else:
            break

    # get user input for month (all, january, february, ... , june)
    while True:
        month = input('What month would you like to filter your data by?:').lower()
        if month not in months:
            print('\n"{}" is not found in our records.'.format(month))
            continue
        else:
            break

    # get user input for day of week (all, monday, tuesday, ... sunday)
    while True:
        day = input('What day would you like to filter your data by?:').lower()
        if day not in days:
            print('\n"{}" is not found in our records.'.format(day))
            continue
        else:
            break


    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])

    df["Start Time"] = pd.to_datetime(df["Start Time"])

    if month != 'all':
        months = ['january', 'february', 'march', 'april', 'may', 'june']
        month = months.index(month) + 1
        # filter to create the new dataframe



    if day != 'all':
        days = ['sunday', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday']
        day = days.index(day) + 1
        # filter to create the new dataframe


    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()

        # convert the Start Time column to datetime
    df['Start Time'] = pd.to_datetime(df['Start Time'])

    # extract month from the Start Time column to create a month column
    df['month'] = df['Start Time'].dt.month

    # find the most popular month
    popular_month = df['month'].mode()[0]
    print('Most Popular Month: {}'.format(popular_month))

    # TO DO: display the most common day of week
    # convert the Start Time column to datetime
    df['Start Time'] = pd.to_datetime(df['Start Time'])

    # create day column
    df['day'] = df['Start Time'].dt.day

    # find most popular day
    popular_day = df['day'].mode()[0]
    print('Most Popular Day: {}'.format(popular_day))

    # TO DO: display the most common start hour
    df['Start Time'] = pd.to_datetime(df['Start Time'])

    #create an hour column
    df['hour'] = df['Start Time'].dt.hour

    #most popular hour
    popular_hour = df['hour'].mode()[0]
    print('Most Popular 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()

    # TO DO: display most commonly used start station
    popular_start_station = df['Start Station'].mode()[0]
    print('Most Popular Start Station: {}'.format(popular_start_station))


    # TO DO: display most commonly used end station
    popular_end_station = df['End Station'].mode()[0]
    print('Most Popular End Station: {}'.format(popular_end_station))


    # TO DO: display most frequent combination of start station and end station trip
    most_popular_start_end_station = df[['Start Station', 'End Station']].mode().loc[0]
    print("The most commonly used start station and end station : {}, {}".format(most_popular_start_end_station[0], most_popular_start_end_station[1]))

    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()

    # TO DO: display total travel time
    travel_time = df['Trip Duration'].sum()
    print('Total Travel Time: {}'.format(travel_time))

    # TO DO: display mean travel time
    mean_time = df['Trip Duration'].mean()
    print('Mean Travel Time: {}'.format(mean_time))

    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('User Types: \n{}\n'.format(user_types))

    try:
         # TO DO: Display counts of gender
         gender_types = df['Gender'].value_counts()

         # TO DO: Display earliest, most recent, and most common year of birth
         earliest_birth_year = df['Birth Year'].min()

         most_recent_birth_year = df['Birth Year'].max()

         popular_birth_year = df['Birth Year'].mode()[0]

    except KeyError:
         # print error statement for missing data
         print('Gender data not available. \nCannot display statistics.\n')

         print('Birth Year data not available.  \nCannot display statistics.')

    else:
         # display counts of gender
         print(gender_types)
         # display earliest birth year
         print('Earliest Birth Year:', earliest_birth_year.min())
         # display most recent birth
         print('Most Recent Birth Year:', most_recent_birth_year.min())
         # display most popular birth
         print('Most Popular Birth Year:', popular_birth_year)


    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
        line_number = 0
def raw_data(df):

    all_data = input('Would you like to see the raw data? Please enter yes or no.')

    while all_data not in ['yes', 'no']:
         print('Please enter yes or no')
    all_data = input('Would you like to see the raw data? Please enter yes or no.')

    if all_data == 'no':
        return
    elif all_data == 'yes':
        print(df.iloc[line_number])

    keep_going = input('Do you want to see more data? Enter yes or no.').lower()

    if keep_going == 'no':
        return
    elif keep_going == 'yes':
        print(df.iloc[line_number : line_number + 5])
        line_number += 5


if __name__ == "__main__":
    main()