2/12/2019.

Github Project

def city_input(): ''' This function starts the user interface by introduction and asking the user with the city he/she wants to analyze ''' print('Hello! Let's explore some US bikeshare data!') print(' ') # get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs city = input('Please choose the city for which you would like to see the Statistics: Chicago, New York, or Washington ') city = city.lower() while True: # for handling the unexpected input by user if city == 'chicago': print("\nChicago City! Okay Let's go further\n") return 'chicago' if city == 'new york': print("\nNew York City! Okay let's go further\n") return 'new york city' elif city == 'washington': print("\nWashington! Okay let's go further\n") return 'washington' # error handled by implementing 'else' and provided another option to input data else: print('\nPlease enter the names of cities\n') city = input('Please choose from Chicago, New York, or Washington: ') city = city.lower() return city

def get_time(): ''' the code below asks the user to choose between month and day of month, day of the week or no filters ''' period = input('\nWould you like to filter the data by month and day of the month, day of the week, or you do not want to filter at all? Type "no" for no period filter.\n') period = period.lower()

I recieved helped from Lasantha R.

def month_info(m): # get user input for month (all, january, february, ... , june) if m == 'month': month = input('\nChoose month! January, February, March, April, May, or June? Please type the full month name.\n') while month.strip().lower() not in ['january', 'february', 'march', 'april', 'may', 'june']: month = input('\nPlease choose between January, February, March, April, May, or June? Please type the full month name.\n') return month.strip().lower() else: return 'none'

def month_day_info(df, day_m): # Asks the user for a month and a day of month, month_day = [] if day_m == "day_of_month": month = month_info("month") month_day.append(month) maximum_day_month = max_day_month(df, month)

    while (True):
        ask = """ \n Which day of the month? \n
        Please type your response as an integer between 1 and 7 """                 
        ask  = ask + str(maximum_day_month) + "\n"
        day_m = input(ask)

        try:
            day_m = int(day_m)
            if 1 <= day_m <= maximum_day_month:
                month_day.append(day_m)
                return month_day
        except ValueError:
            print("That's not a numeric value")
else:
    return 'none'

def day_info(d): # Asks the user for a day and returns the specified day if d == 'day_of_week': day = input('\nWhich day? Please type a day Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday. \n') while day.lower().strip() not in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']: day = input('\nPlease type a day as a choice from Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday. \n') return day.lower().strip() else: return 'none'

def load_data(city): # Loads data for the specified city print('\nLoading the data... .. .. ..\n') df = pd.read_csv(CITY_DATA[city])

#extracting from Start Time
df['Start Time'] = pd.to_datetime(df['Start Time'])
df['day_of_week'] = df['Start Time'].dt.weekday_name
df['month'] = df['Start Time'].dt.month
df["day_of_month"] = df["Start Time"].dt.day
return df

def time_filters(df, time, month, week_day, md): ''' Filters the data according to the criteria specified by the user. Local Variables: df - city dataframe time - indicates the specified time (either "month", "day_of_month", or "day_of_week") month - indicates the month used to filter the data week_day - indicates the week day used to filter the data md - list that indicates the month (at index [0]) used to filter the data and the day number (at index [1]) Result: df - dataframe to be used for final calculation ''' print('Data loaded. Now computing statistics... \n') #Filter by Month if time == 'month': months = ['january', 'february', 'march', 'april', 'may', 'june'] month = months.index(month) + 1 df = df[df['month'] == month]

#Filter by day of week
if time == 'day_of_week':
    days = ['Monday', 'Tuesday',
    'Wednesday', 'Thursday',
    'Friday', 'Saturday', 'Sunday']
    for d in days:
        if week_day.capitalize() in d:
            day_of_week = d
    df = df[df['day_of_week'] == day_of_week]

if time == "day_of_month":
    months = ['january', 'february', 'march', 'april', 'may', 'june']
    month = md[0]
    month = months.index(month) + 1
    df = df[df['month']==month]
    day = md[1]
    df = df[df['day_of_month'] == day]

return df

def max_day_month(df, month): '''Gets the max day of the month '''

months = {"january": 1, "february": 2, "march": 3, "april":4, "may": 5, "june":6}
df = df[df["month"] == months[month]]
max_day = max(df["day_of_month"])
return max_day

def month_freq(df): '''What is the most popular month for start time? ''' # df - dataframe returned from time_filters print('\n * Q1. What is the most popular month for bike traveling?') m = df.month.mode()[0] months = ['january', 'february', 'march', 'april', 'may', 'june'] popular_month = months[m - 1].capitalize() return popular_month

def day_freq(df): '''What is the most popular day of week for start time? ''' # df - dataframe returned from time_filters print('\n * Q2. What is the most popular day of the week for bike rides?') return df['day_of_week'].value_counts().reset_index()['index'][0]

def hour_freq(df): '''What is the most popular hour of day for start time? ''' # df - dataframe returned from time_filters print('\n * Q3. What is the most popular hour of the day for bike rides?') df['hour'] = df['Start Time'].dt.hour return df.hour.mode()[0]

def ride_duration(df): ''' What is the total ride duration and average ride duration? Result: tuple = total ride duration, average ride durations ''' # df - dataframe returned from time_filters print('\n * Q4. What was the total traveling done for 2017 through June, and what was the average time spent on each trip?') df['End Time'] = pd.to_datetime(df['End Time']) df['Travel Time'] = df['End Time'] - df['Start Time'] #sum for total trip time, mean for avg trip time total_ride_time = np.sum(df['Travel Time']) total_days = str(total_ride_time).split()[0]

print ("\nThe total travel time on 2017 through June was " + total_days + " days \n")
avg_ride_time = np.mean(df['Travel Time'])
avg_days = str(avg_ride_time).split()[0]
print("The average travel time on 2017 through June was " + avg_days + " days \n")

return total_ride_time, avg_ride_time

def stations_freq(df): '''What is the most popular start station and most popular end station? ''' # df - dataframe returned from time_filters print("\n* Q5. What is the most popular start station?\n") start_station = df['Start Station'].value_counts().reset_index()['index'][0] print (start_station) print("\n* Q6. What is the most popular end station?\n") end_station = df['End Station'].value_counts().reset_index()['index'][0] print(end_station) return start_station, end_station

def common_trip(df): '''What is the most popular trip? ''' # df - dataframe returned from time_filters result = df[['Start Station', 'End Station']].groupby(['Start Station', 'End Station']).size().nlargest(1) print('\n* Q7. What was the most popular trip from start to end?') return result

def bike_users(df): '''What are the counts of each user type? ''' # df - dataframe returned from time_filters print('\n* Q8. Types of users: subscribers, customers, others\n') return df['User Type'].value_counts()

def gender_data(df): '''What are the counts of gender?''' # df - dataframe returned from time_filters try: print('\n* Q9. What is the breakdown of gender among users?\n') return df['Gender'].value_counts() except: print('There is no gender data in the source.')

def birth_years(df): '''What is the earliest, latest, and most frequent birth year?''' # df - dataframe returned from time_filters try: print('\n* Q10. What is the earliest, latest, and most frequent year of birth, respectively?') earliest = np.min(df['Birth Year']) print ("\nThe earliest year of birth is " + str(earliest) + "\n") latest = np.max(df['Birth Year']) print ("The latest year of birth is " + str(latest) + "\n") most_frequent= df['Birth Year'].mode()[0] print ("The most frequent year of birth is " + str(most_frequent) + "\n") return earliest, latest, most_frequent except: print('No available birth date data for this period.')

def process(f, df): '''Calculates the time it takes to commpute a statistic ''' start_time = time.time() statToCompute = f(df) print(statToCompute) print("Computing this stat took %s seconds." % (time.time() - start_time))

def disp_raw_data(df): ''' Displays the data used to compute the stats Input: the df with all the bikeshare data Returns: none ''' #omit irrelevant columns from visualization df = df.drop(['month', 'day_of_month'], axis = 1) row_index = 0

see_data = input("\nWould you like to see five rows of raw data used to compute the stats? 'yes' or 'no'? \n").lower()
while True:
    if see_data =='no':
        return
    if see_data =='yes':
        print(df[row_index: row_index + 5])
        row_index = row_index +5
    see_data = input("\nWould you like to see five more rows of the data used to compute the stats? Please write 'yes' or 'no' \n").lower()

def main(): '''The main function calculates and prints out the descriptive statistics about a requested city ''' # calling all the functions step by step city = city_input() df = load_data(city) period = get_time() month = month_info(period) day = day_info(period) month_day = month_day_info(df, period)

df = time_filters(df, period, month, day, month_day)
disp_raw_data(df)

# all the conclusions
stats_funcs_list = [month_freq,
 day_freq, hour_freq,
 ride_duration, common_trip,
 stations_freq, bike_users, birth_years, gender_data]

for x in stats_funcs_list:	# displays processing time for each function block
    process(x, df)

# Restarting option
restart = input("\n * Would you like to do it again and perform another analysis? Type \'yes\' or \'no\'.\n")
if restart.upper() == 'YES' or restart.upper() == "Y":
    main()

if name == 'main': main()