Submit the BikeShare Dataset Project

Author: sondosaabed

Project Explore BikeShare dataset/Explore BikeShare.py

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+#####
+##### Sondos Aabed Explores the Bokeshare Dataset
+#####
+
+### Importing the necessary libraries
+import time
+import pandas as pd
+import numpy as np
+
+#### this is the csv files dictionary
+CITY_DATA = { 'chicago': 'chicago.csv',
+ 'new york city': 'new_york_city.csv',
+ 'washington': 'washington.csv' }
+
+#### in this method get the filters inputted by the user
+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('\nHello! Let\'s explore some US bikeshare data!')
+ #####
+ # In those cases an invalid input is handled by asking the user to try again until it's true input
+ ####
+ # get user input for city (chicago, new york city, washington). 
+ while True:
+ city= input("\n Which City would like to explore? All, Chicago, New york city, Or Washington?\n")
+ city=city.lower()
+ if city not in ('all', 'new york city', 'chicago','washington'):
+ print("Try to enter another city that is either: Chicago, New york city, Or Washington ")
+ continue
+ else:
+ break
+
+ # get user input for month (all, january, february, ... , june)
+ while True:
+ month = input("\n In which of the months you want to explore? is it (all, january, february, ... , june)\n")
+ month = month.lower()
+ if month not in ('all','january','february','march','april','may','june','july','august','september','october','november','december'):
+ print("Try to enter the month again, it wasn't a valid month!")
+ continue
+ else:
+ break
+
+ # get user input for day of week (all, monday, tuesday, ... sunday)
+ while True: 
+ day = input("\n What about the day you are looking for? is it (all, monday, tuesday, ... sunday)?\n")
+ day = day.lower()
+ if day not in ('sunday','monday','all','tuesday','wednesday','thursday','friday','saturday'):
+ print("You entered a not valid day, try again")
+ continue
+ else:
+ break
+
+ print('-'*40)
+ return city, month, day
+
+# in this method load the dataset based on which city the user inputs 
+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
+ """
+ # read the csv file using read_csv pandas based on the user input of cit
+ # I have decided to add the option all because why not exploring all of them together giving a broader view 
+ if city not in ('all'):
+ df = pd.read_csv(CITY_DATA[city])
+ else:
+ # for all dataframes if the user choses all combine them
+ dfs = []
+ for city, path in CITY_DATA.items(all):
+ dfC = pd.read_csv(path)
+ dfs.append(dfC)
+ 
+ df = pd.concat(dfs, ignore_index=True)
+ ## print(df)
+ return df
+
+## this metohd I created to clean the data 
+## cleaning the data included handling missing data 
+# also handle the high cardinality of dates
+def clean_data(df):
+ df = handle_dates(df)
+ df = handle_missing(df)
+ return df
+
+# this method I created to handle the missing data
+def handle_missing(df):
+ # I chose to fill them with Unknown 
+ print('We have {} missing enteries'.format(df.isnull().sum().sum()) )
+ # fill Nan values using fillna method
+ df.fillna('Unknown', inplace=True)
+ print('These were filled by (Unknown) ')
+ return df
+
+## this method I created to handle teh dates
+def handle_dates(df):
+ """
+ Handle the dates as their datatypes using to_datetime pandas
+ """
+ df['Start Time'] = pd.to_datetime(df['Start Time'])
+ df['End Time'] = pd.to_datetime(df['End Time'])
+ df['Birth Year'] = pd.to_datetime(df['Birth Year'])
+
+ ## this coulmn has high cardinality so I better create new coulmns that I can filter by
+ # Like the day of the week and the month and the year and the time
+ df['start_month'] = df['Start Time'].dt.strftime('%B').str.lower()
+ df['start_day'] = df['Start Time'].dt.strftime('%A').str.lower()
+ df['start_year'] = df['Start Time'].dt.strftime('%Y')
+ df['start_time'] = df['Start Time'].dt.strftime('%X')
+ 
+ df['end_month'] = df['End Time'].dt.strftime('%B').str.lower()
+ df['end_day'] = df['End Time'].dt.strftime('%A').str.lower()
+ df['end_year'] = df['End Time'].dt.strftime('%Y')
+ df['end_time'] = df['End Time'].dt.strftime('%X')
+ 
+ # we have also the coulmn of Birth year 
+ # df['Birth Year'] = pd.to_datetime(df['Birth Year'], format='%Y')
+ # this is not working for users stats 
+ # I have decided to handle this one as integer to get the min and max values
+ df['Birth Year'] = pd.to_numeric(df['Birth Year'],errors='coerce' , downcast='integer')
+
+ # dropped them after I handeld them
+ df.drop('Start Time', axis=1, inplace=True) 
+ df.drop('End Time', axis=1, inplace=True) 
+
+ return df
+
+# this method get the time travel frequent times
+# to get that I used the mode built-in method
+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()
+
+ # the most common month
+ print('The most frequent month is: ', df['start_month'].mode()[0])
+ 
+ # the most common day of week
+ print('The most frequent day is: ', df['start_day'].mode()[0])
+
+ # the most common start hour
+ print('The most commoon start hour is: ', df['start_time'].mode()[0])
+
+ print("\nThis took %s seconds." % (time.time() - start_time))
+ print('-'*40)
+
+# in this method I get some statics about the stations of the trip
+# used mode and groupby 
+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()
+
+ # most commonly used start station
+ print('The most commonly used start station is: ', df['Start Station'].mode()[0] )
+
+ # most commonly used end station
+ print('The most commonly used end station is: ', df['End Station'].mode()[0] )
+
+ # most frequent combination of start station and end station trip
+ print('The most frequent combination of start station and end station trip is: ', 
+ df.groupby(['Start Station','End Station']).size().idxmax())
+
+ print("\nThis took %s seconds." % (time.time() - start_time))
+ print('-'*40)
+
+# In this method I get some statics about the trip duration 
+# used the sum, mean aggregation functions
+def trip_duration_stats(df):
+ """Displays statistics on the total and average trip duration."""
+
+ print('\nCalculating Trip Duration...\n')
+ start_time = time.time()
+
+ # total travel time
+ # the trip duration coulmn is in seconds 
+ # to make it more readable I convert it to days by dividing it on 86400
+ print('The total travel time in hours is: ', df['Trip Duration'].sum()/86400)
+
+ # mean travel time
+ print('The average travel time in minutes is: ', df['Trip Duration'].mean()/60)
+
+ print("\nThis took %s seconds." % (time.time() - start_time))
+ print('-'*40)
+
+# In this method I get some statics about the users
+# Using 
+def user_stats(df):
+ """Displays statistics on bikeshare users."""
+
+ print('\nCalculating User Stats...\n')
+ start_time = time.time()
+
+ # counts of user types
+ print('In this city, we have diffrent types of users as follows: ')
+ print(df['User Type'].value_counts())
+
+ # counts users based on gender
+ print('The total count of each gender is as follow: ')
+ print('Females:', df['Gender'].value_counts().get("Female", 0))
+ print('Males:', df['Gender'].value_counts().get("Male", 0))
+ print('Unknown:', df['Gender'].value_counts().get("Unknown", 0))
+
+ # So because I don't want to include the unknown value of these I will use a filter on the dataset 
+ # earliest year of birth 
+ print('The earliest year of birth is: ', df['Birth Year'].min())
+
+ # Something doesn't add up here because it first displays to me the (unknown) so because I used it to fill the missing data
+ # I am thinking to impute the missing birth year with the mode of it 
+ # but this will effect the time since I already imputed why impute twice
+ # so what can I do ?
+
+ # most recent of birth 
+ print('The most recent year of birth is: ', df['Birth Year'].max())
+
+ # most common year of birth
+ print('The most common year of birth is: ', df['Birth Year'].mode()[0])
+
+ print("\nThis took %s seconds." % (time.time() - start_time))
+ print('-'*40)
+
+def main():
+ # start the program until the user hits no
+ while True:
+ # gets the filters 
+ city, month, day = get_filters()
+
+ # load the dataset
+ df = load_data(city, month, day)
+
+ # clean the dataset
+ df= clean_data(df)
+
+ # Display diffrent statics of the dataset
+ 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
+
+############################
+
+# In this project the dataset of diffrent city is explored
+# by the user interactivly of diffrent cities
+
+############################
+if __name__ == "__main__":
+main()