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bikeshare_2.py
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bikeshare_2.py
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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' }
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 city would you like to explore ?")
city = city.lower()
if city in ['chicago', 'new york city', 'washington']:
break
else:
print("invalid input. Please enter a valid input")
# get user input for month (all, january, february, ... , june)
while True:
month = input("Do you want details specific to a particular month? If yes, type month name from within first six months else type 'all'")
month = month.lower()
if month in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:
break
else:
print("invalid input. Please enter a valid input")
# get user input for day of week (all, monday, tuesday, ... sunday)
while True:
day = input("Do you want details specific to a particular day? If yes, type day name else type 'all'")
day = day.lower()
if day in ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday', 'all']:
break
else:
print("invalid input. Please enter a valid input")
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 from Start Time to create new columns
df['month'] = df['Start Time'].dt.month
df['day_of_week'] = df['Start Time'].dt.weekday_name
# 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[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
print("The most common month is ", df['month'].mode()[0], "\n")
# display the most common day of week
print("The most common day of week is ", df['day_of_week'].mode()[0], "\n")
# display the most common start hour
df['hour'] = df['Start Time'].dt.hour
print("The most common start hour is ", df['hour'].mode()[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 commonly used start station is ", df['Start Station'].mode()[0], "\n")
# display most commonly used end station
print("The most commonly used end station is ", df['End Station'].mode()[0], "\n")
# display most frequent combination of start station and end station trip
df['combination'] = df['Start Station'] + " " + df['End Station']
print("The most frequent combination of start station and end station trip is: ", df['combination'].mode()[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()
# display total travel time
print("The total travel time is", df['Trip Duration'].sum(), "\n")
# display mean travel time
print("The total mean time is", df['Trip 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
user_types = df.groupby(['User Type'])['User Type'].count()
print(user_types, "\n")
if city != 'washington':
# Display counts of gender
gen = df.groupby(['Gender'])['Gender'].count()
print(gen)
# Display earliest, most recent, and most common year of birth
mryob = sorted(df.groupby(['Birth Year'])['Birth Year'], reverse=True)[0][0]
eyob = sorted(df.groupby(['Birth Year'])['Birth Year'])[0][0]
mcyob = df['Birth Year'].mode()[0]
print("The earliest year of birth is ", eyob, "\n")
print("The most recent year of birth is ", mryob, "\n")
print("The most common year of birth is ", mcyob, "\n")
print("\nThis took %s seconds." % (time.time() - start_time))
print('-'*40)
x = 1
while True:
raw = input('\nWould you like to see some raw data? Enter yes or no.\n')
if raw.lower() == 'yes':
print(df[x:x+5])
x = x+5
else:
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)
restart = input('\nWould you like to restart? Enter yes or no.\n')
if restart.lower() != 'yes':
break
if __name__ == "__main__":
main()