簡(jiǎn)介：隨著共享單車的星期，這次探索三大美國(guó)城市的自行車共享系統(tǒng)相關(guān)的數(shù)據(jù)：芝加哥、紐約和華盛頓特區(qū)，幫助共享單車公司得到一些關(guān)鍵性的數(shù)據(jù)信息，例如哪個(gè)起始車站最熱門，哪一趟行程最熱門等等，來(lái)對(duì)共享單車的投放給予一定幫助。

一、分析步驟

• 編寫代碼導(dǎo)入數(shù)據(jù)，并通過(guò)計(jì)算描述性統(tǒng)計(jì)數(shù)據(jù)回答有趣的問(wèn)題。
• 編寫一個(gè)腳本，該腳本會(huì)接受原始輸入并在終端中創(chuàng)建交互式體驗(yàn)，以展現(xiàn)這些統(tǒng)計(jì)信息。
• 提出問(wèn)題
• 終端應(yīng)用腳本

二、提出問(wèn)題

• 起始時(shí)間（Start Time 列）中哪個(gè)月份最常見(jiàn)？
• 起始時(shí)間中，一周的哪一天（比如 Monday, Tuesday）最常見(jiàn)？
• 起始時(shí)間中，一天當(dāng)中哪個(gè)小時(shí)最常見(jiàn)？
• 總騎行時(shí)長(zhǎng)（Trip Duration）是多久，平均騎行時(shí)長(zhǎng)是多久？
• 哪個(gè)起始車站（Start Station）最熱門，哪個(gè)結(jié)束車站（End Station）最熱門？
• 哪一趟行程最熱門（即，哪一個(gè)起始站點(diǎn)與結(jié)束站點(diǎn)的組合最熱門）？
• 每種用戶類型有多少人？
• 每種性別有多少人？
• 出生年份最早的是哪一年、最晚的是哪一年，最常見(jiàn)的是哪一年？

三、代碼實(shí)現(xiàn)

工具：Python
文本編輯器：Pycharm

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
    city = input("Which city do you want to analyze? input ：chicago, new york city, washington\n").lower()
    while True:
        if city not in CITY_DATA.keys():
            city = input('Invalid input======\nwould you like to see data for chicago, '
                         'new youk city, or washington?')
        else:
            break

    # get user input for month (all, january, february, ... , june)
    months = ['all', 'january', 'february', 'march', 'april', 'may', 'june']
    month = input("Which month data do you want to analyze？input ：all，january, february, "
                  "march, april, may, june\n").lower()
    while True:
        if month not in months:
            month = input('Invalid input======\nWhich month data do you want to analyze？input ：all，january, february,'
                  'march, april, may, june\n').lower()
        else:
            break

    # get user input for day of week (all, monday, tuesday, ... sunday)
    days = ['all', 'monday','tuesday','wednesday','thursday','friday','saturday','sunday']
    day = input("Which day of week do you want to analyze? input："
                "all，monday, tuesday, wednesday, thursday, friday, saturday, sunday").lower()
    while True:
        if day not in days:
            day = input("Invalid input======\nWhich day of week do you want to analyze? input："
                "all，monday, tuesday, wednesday, thursday, friday, saturday, sunday").lower()
        else:
            break

    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
    common_month = df['month'].mode()[0]
    print('The most common month: ', common_month)

    # display the most common day of week
    common_day_of_week = df['day_of_week'].mode()[0]
    print('The most common day of week: ', common_day_of_week)

    # display the most common start hour
    df['start_hour'] = df['Start Time'].dt.hour
    common_start_hour = df['start_hour'].mode()[0]
    print('The most common start hour: ', 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
    common_start_station = df['Start Station'].mode()[0]
    print('The most commonly used start station: ', common_start_station)

    # display most commonly used end station
    common_end_station = df['End Station'].mode()[0]
    print('The most commonly used end station: ', common_end_station)

    # display most frequent combination of start station and end station trip
    df['Station'] = df['Start Station'] + df['End Station']
    frequent_station = df['Station'].mode()[0]
    print('The most frequent station: ', frequent_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('The total trabel time: ', total_travel_time)

    # display mean travel time
    mean_trabel_time = df['Trip Duration'].mean()
    print('The mean travel time: ', mean_trabel_time)

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


def user_stats(df):
    """Displays statistics on bikeshare users."""

    print('\nCalculating User Stats...\n')
    start_time = time.time()

    # Display counts of user types
    count_user_types = df['User Type'].value_counts()
    print('Counts of user types: ', count_user_types)

    # Display counts of gender
    try:
        count_gender = df['Gender'].value_counts()
        print('Counts of gender: ', count_gender)
    except KeyError:
        print('Counts of gender:oh sorry, this city have no this data.')

    # Display earliest, most recent, and most common year of birth
    try:
        earliest_birth = df['Birth Year'].min()
        most_recent_birth = df['Birth Year'].max()
        most_common_birth = df['Birth Year'].mode()[0]
        print('Earliest year of birth:',earliest_birth)
        print('Most recent year of birth',most_recent_birth)
        print('Most common year of birth',most_common_birth)
    except KeyError:
        print('oh sorry, this city have no Birth Year data.')

    print("\nThis took %s seconds." % (time.time() - start_time))
    print('-'*40)


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)

        restart = input('\nWould you like to restart? Enter yes or no.\n')
        if restart.lower() != 'yes':
            break


if __name__ == "__main__":
    main()

四、互動(dòng)式體驗(yàn)

該文件是一個(gè)腳本，它接受原始輸入在終端中創(chuàng)建交互式體驗(yàn)，來(lái)回答有關(guān)數(shù)據(jù)集的問(wèn)題。
輸入想要查看的問(wèn)題：

輸入.png

答案.png