bikeshare_2.py

"""
This program analyzes rideshare data for several cities and
interactively displays important summary statistics for eachself.

Justin Lynn Reid
"""

import time
import pandas as pd
import numpy as np


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
    city = input("Please input city name: ").lower()

    while city not in ['chicago', 'new york city', 'washington']:
        city = input(
        "City is name is invalid! Please input another name: ").lower()

    # get user input for month (all, january, february, ... , june)
    month = input("Please input month name: ").lower()

    # get user input for day of week (all, monday, tuesday, ... sunday)
    day = input("Please input day of week: ").lower()

    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("{}.csv".format(city.replace(" ","_")))

    # Convert the Start and End Time columns to datetime
    df['Start Time'] = pd.to_datetime(df['Start Time'])
    df['End Time'] = pd.to_datetime(df['End Time'])

    # extract month and day of week from Start Time to create new columns
    df['month'] = df['Start Time'].apply(lambda x: x.month)
    df['day_of_week'] = df['Start Time'].apply(lambda x: x.strftime('%A').lower())


    # 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.loc[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.loc[df['day_of_week'] == day,:]

    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
    print("The most common month is: {}".format(
        str(df['month'].mode().values[0]))
    )

    # display the most common day of week
    print("The most common day of the week: {}".format(
        str(df['day_of_week'].mode().values[0]))
    )

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


    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
    print("The most common start station is: {} ".format(
        df['Start Station'].mode().values[0])
    )

    # display most commonly used end station
    print("The most common end station is: {}".format(
        df['End Station'].mode().values[0])
    )

    # display most frequent combination of start station and end station trip
    df['routes'] = df['Start Station']+ " " + df['End Station']
    print("The most common start and end station combo is: {}".format(
        df['routes'].mode().values[0])
    )

    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()

    df['duration'] = df['End Time'] - df['Start Time']

    # display total travel time
    print("The total travel time is: {}".format(
        str(df['duration'].sum()))
    )

    # display mean travel time
    print("The mean travel time is: {}".format(
        str(df['duration'].mean()))
    )

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


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

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

    # Display counts of user types
    print("Here are the counts of various user types:")
    print(df['User Type'].value_counts())

    if city != 'washington':
        # Display counts of gender
        print("Here are the counts of gender:")
        print(df['Gender'].value_counts())


        # Display earliest, most recent, and most common year of birth
        print("The earliest birth year is: {}".format(
            str(int(df['Birth Year'].min())))
        )
        print("The latest birth year is: {}".format(
            str(int(df['Birth Year'].max())))
        )
        print("The most common birth year is: {}".format(
            str(int(df['Birth Year'].mode().values[0])))
        )

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


def display_data(df):
    """
    Display contents of the CSV file to the display as requested by
    the user.
    """

    start_loc = 0
    end_loc = 5

    display_active = input("Do you want to see the raw data?: ").lower()

    if display_active == 'yes':
        while end_loc <= df.shape[0] - 1:

            print(df.iloc[start_loc:end_loc,:])
            start_loc += 5
            end_loc += 5

            end_display = input("Do you wish to continue?: ").lower()
            if end_display == 'no':
                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, city)
        display_data(df)

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


if __name__ == "__main__":
	main()