import time
import pandas as pd
import numpy as np
import json

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

MONTHS = ['january', 'february', 'march', 'april', 'may', 'june']

DAYS = ['sunday', 'monday', 'tuesday', 'wednesday', \
        'thursday', 'friday', 'saturday']


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('Which of these cities do you want to explore : Chicago, New York or Washington? \n> ').lower()
if city in cities:
break

    while True:
        month = input("Select month: ").lower()
        if month in MONTHS:
            break

    while True:
        day = input("Select day: ").lower()
        if day in DAYS:
            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
    """
    # 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 and hour from Start Time to create new columns
    df['month'] = df['Start Time'].dt.month
    df['day_of_week'] = df['Start Time'].dt.day_name()
    df['hour'] = df['Start Time'].dt.hour

    # filter by month if applicable
    if month != 'all':
        month = MONTHS.index(month) + 1
        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()

    # display the most common month
    most_common_month = df['month'].value_counts().idxmax()
    print("The most common month is :", most_common_month)

    # display the most common day of week
    most_common_day_of_week = df['day_of_week'].value_counts().idxmax()
    print("The most common day of week is :", most_common_day_of_week)

    # display the most common start hour
    most_common_start_hour = df['hour'].value_counts().idxmax()
    print("The most common start hour is :", most_common_start_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
    most_common_start_station = df['Start Station'].value_counts().idxmax()
    print("The most commonly used start station :", most_common_start_station)

    # display most commonly used end station
    most_common_end_station = df['End Station'].value_counts().idxmax()
    print("The most commonly used end station :", most_common_end_station)

    # display most frequent combination of start station and end station trip
    most_common_start_end_station = df.groupby(['Start Station', 'End Station']).size().idxmax()
    print("The most commonly used start station and end station : {}, {}" \
          .format(most_common_start_end_station[0], most_common_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()

    # display total travel time
    total_travel = df['Trip Duration'].sum()
    print("Total travel time :", total_travel)

    # display mean travel time
    mean_travel = df['Trip Duration'].mean()
    print("Mean travel time :", mean_travel)

    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_counts = df['User Type'].value_counts()
    for index, user_count in enumerate(user_counts):
        print("  {}: {}".format(user_counts.index[index], user_count))

    # Display counts of gender
    try:
        print("Counts of gender:\n")
        gender_counts = df['Gender'].value_counts()
        for index, gender_count in enumerate(gender_counts):
            print("  {}: {}".format(gender_counts.index[index], gender_count))

    except:
        print("Dataset doesn't have Gender column")

    # Display earliest, most recent, and most common year of birth
    try:
        birth_year = df['Birth Year']

        most_common_year = birth_year.value_counts().idxmax()
        print("The most common birth year:", most_common_year)

        most_recent = birth_year.max()
        print("The most recent birth year:", most_recent)

        earliest_year = birth_year.min()
        print("The most earliest birth year:", earliest_year)

    except:
        print("Dataset doesn't have Birth Year column")

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


def display_data(df):
    """Displays raw bikeshare data."""
    row_length = df.shape[0]

    for i in range(0, row_length, 5):

        yes = input('\nWould you like to view 5 rows of individual trip data ? Enter yes or no\n')
        if yes.lower() != 'yes':
            break

        row_data = df.iloc[i: i + 5]
        print(row_data)


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)

        display_data(df)

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


if __name__ == "__main__":
    main()