我正在尝试运行以下代码：

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 you would like to explore : "chicago" , "new york city" , or "washington"  :' )
        if city not in ('chicago', 'new york city', 'washington'):
            print(" You entered wrong choice , please try again")
            continue
        else:
            break

    # get user input for month (all, january, february, ... , june)
    while True:
        month = input('Enter "all" for all data or chose  a month : "january" , "february" , "march", "april" , "may" or "june "  :')
        if month not in ("all", "january", "february", "march", "april", "may", "june"):
            print(" You entered wrong choice , please try again")
            continue
        else:
            break

    # get user input for day of week (all, monday, tuesday, ... sunday)
    while True:
        day = input('Enter "all" for all days or chose a day : "saturday", "sunday", "monday", "tuesday", "wednesday", "thursday", "friday":  ')
        if day not in ("all","saturday", "sunday", "monday", "tuesday", "wednesday", "thursday", "friday"):
            print(" You entered wrong choice , please try again")
            continue
        else:
            break

    print('-'*60)
    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
    """
    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 , and hour from Start Time to new columns

    df['month'] = df['Start Time'].dt.month
    df['day_of_week'] = df['Start Time'].dt.day_name
    df['hour'] = df['Start Time'].dt.hour

    # filter by month if applicable
    if month != 'all':
        # use the index of the month_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('\n The most popular month is  : \n', popular_month)

    # display the most common day of week

    popular_day = df['day_of_week'].mode()[0]
    print('\n The most popular day of the week is  :  \n', str(popular_day))

    # display the most common start hour
    popular_hour = df['hour'].mode()[0]
    print('\n The most popular hour of the day is :\n ', popular_hour)

    print("\nThis took %s seconds.\n" % (time.time() - start_time))
    print('-'*60)

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
    start_station = df['Start Station'].value_counts().idxmax()
    print('\n The most commonly used start station is :  \n', start_station)

    # display most commonly used end station
    end_station = df['End Station'].value_counts().idxmax()
    print('\nThe most commonly used end station is:  \n', end_station)

    # display most frequent combination of start station and end station trip
    combination = df.groupby(['Start Station','End Station']).value_counts().idxmax()
    print('\nThe most frequent combination of start station and end station are:  \n', combination)

    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."""
    start_time = time.time()

    travel_time = sum(df['Trip Duration'])
    print('Total travel time:', travel_time / 86400, " Days")

    # display total travel time
    total_time = sum(df['Trip Duration'])

    print('\nThe total travel time is {} seconds: \n', total_time)


    # display mean travel time
    mean_time = df['Trip Duration'].mean()
    print('\n The average travel time is \n', mean_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()

    # TO DO: Display counts of user types

    user_types = df['User Type'].value_counts()
    #print(user_types)
    print('User Types:\n', user_types)

    # TO DO: Display counts of gender



    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()

我收到以下错误，有人能帮助我解决这些错误吗？

> Traceback (most recent call last):
  File "C:\Users\DELL\PycharmProjects\Professional\venv\Lib\site-packages\pandas\core\indexes\range.py", line 391, in get_loc
    return self._range.index(new_key)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: 0 is not in range

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "C:\Users\DELL\PycharmProjects\Professional\Bikeshare.py", line 203, in <module>
    main()
  File "C:\Users\DELL\PycharmProjects\Professional\Bikeshare.py", line 192, in main
    time_stats(df)
  File "C:\Users\DELL\PycharmProjects\Professional\Bikeshare.py", line 100, in time_stats
    popular_month = df['month'].mode()[0]
                    ~~~~~~~~~~~~~~~~~~^^^
  File "C:\Users\DELL\PycharmProjects\Professional\venv\Lib\site-packages\pandas\core\series.py", line 981, in __getitem__

Calculating The Most Frequent Times of Travel...

    return self._get_value(key)
           ^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\DELL\PycharmProjects\Professional\venv\Lib\site-packages\pandas\core\series.py", line 1089, in _get_value
    loc = self.index.get_loc(label)
          ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\DELL\PycharmProjects\Professional\venv\Lib\site-packages\pandas\core\indexes\range.py", line 393, in get_loc
    raise KeyError(key) from err
KeyError: 0

我希望过滤PandasDataFrame返回月，星期几，小时执行一些统计。