My gitHub Bikeshare project

.gitignore

@@ -1 +1,3 @@
-.github/**
+chicago.csv
+new_york_city.csv
+washington.csv

README.md

@@ -2,16 +2,21 @@
 
 ### Date created
 Include the date you created this project and README file.
-
+23rd May, 2021
 ### Project Title
 Replace the Project Title
-
+Ngbede_Alike_Bikeshare_project
 ### Description
 Describe what your project is about and what it does
-
+This project explores the statistical bikeshare data in chicago, new york city and washington
 ### Files used
 Include the files used
-
+chicago.csv
+washington.csv
+new_york_city.csv
 ### Credits
 It's important to give proper credit. Add links to any repo that inspired you or blogposts you consulted.
-
+gitHub
+hackerrank
+Stackoverflow
+wikipedia

bikeshare.py

@@ -0,0 +1,233 @@
+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 correct_input(str_inp, runtype):
+    """to check the correctness of the input
+    """
+    while True:
+        line_input = input(str_inp)
+        try:
+            if line_input in ['chicago','new york city','washington'] and runtype == 1:
+                break
+            elif line_input  in ['january', 'february', 'march', 'april', 'may', 'june','all'] and runtype == 2:
+                break
+            elif line_input in ['sunday','monday','tuesday','wednesday','thursday','friday','saturday','all'] and runtype == 3:
+                break
+            else:
+                if runtype == 1:
+                    print("please enter: chicago, new york city or washington")
+                if runtype == 2:
+                    print("please enter: january-june or all")
+                if runtype == 3:
+                    print("please enter: sunday-saturday or all")
+        except ValueError:
+            print("wrong input!")
+    return line_input
+
+
+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!')
+    # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs
+    city = correct_input("Enter a city: ", 1)
+
+
+    # TO DO: get user input for month (all, january, february, ... , june)
+    month = correct_input("Enter a month between January and June: ", 2)
+
+    # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)
+    day = correct_input("Enter a day betwwen monday and saturday: ", 3)
+
+    print('-'*40)
+    return city, month, day
+
+# load bikeshare data with respect to stated city, month and 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, day of week, hour from Start Time to create new columns
+    df['month'] = df['Start Time'].dt.month
+    df['day_of_week'] = df['Start Time'].dt.weekday_name
+    df['hour'] = df['Start Time'].dt.hour
+
+    # 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
+    popular_month = df['month'].mode()[0]
+
+    print('Most Popular Month:', popular_month)
+
+    # display the most common day of week
+    most_common_day = df['day_of_week'].mode()[0]
+
+    print('Most Day Of Week:', most_common_day)
+
+    # display the most common start hour
+    most_common_start_hour = df['hour'].mode()[0]
+
+    print('Most Common Start Hour:', most_common_start_hour)
+
+    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
+    popular_start_station = df['Start Station'].mode()[0]
+
+    print('Most Start Station:', popular_start_station)
+
+    # display most commonly used end station
+    popular_end_station = df['End Station'].mode()[0]
+
+    print('Most End Station:', popular_end_station)
+
+    # display most frequent combination of start station and end station trip
+    frequent_combination=df.groupby(['Start Station','End Station'])
+    popular_frequent_combo_station = frequent_combination.size().sort_values(ascending=False).head(1)
+    print('Most frequent combination of Start Station and End Station trip:\n', popular_frequent_combo_station)
+
+    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
+    total_travel_time = df['Trip Duration'].sum()
+
+    print('Total Travel Time:', total_travel_time)
+
+    # display mean travel time
+    mean_travel_time = df['Trip Duration'].mean()
+
+    print('Mean Travel Time:', mean_travel_time)
+
+    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('User Type Stats:')
+    print(df['User Type'].value_counts())
+    if city != 'washington':
+        # Display counts of gender
+        print('Gender Stats:')
+        print(df['Gender'].value_counts())
+        # Display earliest, most recent, and most common year of birth
+        print('Birth Year Stats:')
+        most_common_year = df['Birth Year'].mode()[0]
+        print('Most Common Year:',most_common_year)
+        most_recent_year = df['Birth Year'].max()
+        print('Most Recent Year:',most_recent_year)
+        earliest_year = df['Birth Year'].min()
+        print('Earliest Year:',earliest_year)
+    print("\nThis took %s seconds." % (time.time() - start_time))
+    print('-'*40)
+
+def data_info(df):
+    """
+    displays five rows of bikeshare data
+    """
+    response_locket = ['yes', 'no']
+    read_data = ''
+    counter = 0
+    while read_data not in response_locket:
+        print('\nDo you wish to view the raw bikeshare data?')
+        print('\nright responses: \nyes or no')
+        read_data = input().lower()
+        if read_data == 'yes':
+           print(df.head())
+        elif read_data not in response_locket:
+            print('wrong input!')
+            print('nreloading...\n')
+    while read_data == 'yes':
+        print('wish to view more data?')
+        counter +=5
+        read_data = input().lower()
+        if read_data == 'yes':
+              print(df[counter:counter+5])
+        elif read_data != 'yes':
+              break
+
+print('_'*40)
+
+
+def main():
+    while True:
+        city, month, day = get_filters()
+        df = load_data(city, month, day)
+        data_info(df)
+        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()