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Commit 7103427

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

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