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(b_c.CITY_DATA[city])
# convert the Start Time column to datetime
df['Start Time'] = pd.to_datetime(df['Start Time'])
b_c.COLUMN_HEADINGS = sorted(list(df.columns), key=str.casefold)
# extract month and day of week from Start Time to create new columns
df['Month'] = df['Start Time'].dt.month
#
try:
df['Day of Week'] = df['Start Time'].dt.weekday_name
except AttributeError:
df['Day of Week'] = df['Start Time'].dt.day_name()
#
df['Hour'] = df['Start Time'].dt.hour
df['Year'] = df['Start Time'].dt.year
#
df['Trips'] = df['Start Station'] + '->' + df['End Station']
#
try:
df['Year of Birth'] = df['Birth Year'].dropna().astype(int)
except:
print()
# filter by month if applicable
if month != 'all':
# use the index of the months list to get the corresponding int
months = list(b_c.MONTH_NAMES.values())
month = months.index(month.title()) + 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()]
b_c.clear_screen() # print a set of characters to clear the screen
print(
'Bikeshare data has been loaded for the city: {}!\n\nLets proceed with the data analysis!\n'
.format(city))
return df
def get_inputs( df, city, month, day, proceed ):
"""
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
"""
b_c.clear_screen() # print a set of characters to clear the screen
if proceed:
print('Hello! Let\'s explore some US bikeshare data!')
# Get the user's input for the city
#
city = get_city()
# Get the user's time filter and process accordingly
#
if month is None and day is None:
#
answer = get_time_filter()
#
if answer == "Filter by both day and month":
month = get_month()
day = get_day()
elif answer == "Filter by month":
month = get_month()
day = "all"
elif answer == "Filter by day":
month = "all"
day = get_day()
else:
month = "all"
day = "all"
print( '\nInput complete.\n\nNow data is being loaded ...\n' )
proceed = True
return city, month, day, proceed
def user_stats(df, city, month, day):
"""Displays statistics on bikeshare users."""
# Set up variables in order to store the inputs
#
stats = [{}]
inputs = pd.DataFrame(stats, index=['Inputs: '])
# Set up a variable to store the inputs which will later be printed
#
inputs['City'] = city.title()
inputs['Month'] = month.title()
inputs['Day of Week'] = day.title()
inputs['Number of Records'] = f'{len(df.index):,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(inputs, '\n')
results = pd.DataFrame(stats, index=['Count: ', 'Percentage: '])
# Count the number of trips by user type
#
prt_str = ''
for user_type, count in df['User Type'].agg(
lambda x: x.value_counts().to_dict()).items():
# Combine the user type and the count into a string
# in order to combine the user types as a single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + f'{count:,}'.rjust(7) + ' '
results['Trips by User Type'] = prt_str
# Convert the count of number of trips into percentage by user type
#
# https://blog.softhints.com/pandas-count-percentage-value-column/
#
prt_str = ''
user_type = df['User Type']
items_u_t = user_type.agg(lambda x: (x.value_counts(normalize=True).mul(
100).round(1).astype(str) + '%').to_dict()).items()
for user_type, percent in items_u_t:
#
# Combine the user type and the percentage into a string in order to make a single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + percent.rjust(7) + ' '
results['Trips by User Type']['Percentage: '] = prt_str
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', results, '\n')
if month == 'all':
# Calculate the count and percentage of user type by month
#
results_month = pd.DataFrame(columns=list(b_c.MONTH_NAMES.values()),
index=['Count: ', 'Percentage: '])
title_month = 'Trips by User Type (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
# Get the counts of user types by month
#
prt_str = ''
month_u_t = df.groupby('Month')['User Type']
dict_items = dict(
month_u_t.agg(lambda x: x.value_counts().to_dict())).items()
for m, counts in dict_items:
#
# loop through each month's counts
#
for user_type, count in list(counts.items()):
# Combine the user type and the count into a string in order to make a single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + f'{count:,}'.rjust(7) + ' '
results_month[b_c.MONTH_NAMES[str(m)]] = prt_str + '|'
prt_str = ''
# Calculate the percentages of user types by month
#
month_u_t = df.groupby('Month')['User Type']
dict_items = dict(
month_u_t.agg(lambda x: (x.value_counts(normalize=True).mul(
100).round(1).astype(str) + '%').to_dict())).items()
for m, percentages in dict_items:
# loop through each month's percentages
#
for user_type, percent in list(percentages.items()):
# Combine the user type and the percentages into a string in order to make single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + percent.rjust(7) + ' '
results_month[b_c.MONTH_NAMES[str(
m)]]['Percentage: '] = prt_str + '|'
prt_str = ''
with pd.option_context(*b_c.PD_OPTIONS):
print(results_month, '\n')
if day == 'all':
# Calculate the count and percentage of user type by day
#
results_day = pd.DataFrame(columns=list(b_c.DAY_NAMES.values()),
index=['Count: ', 'Percentage: '])
title_day = 'Trips by User Type (by Day)'
print(' ' * 12, title_day.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_day))).center(50, ' '))
# Get the counts of user types by day
#
prt_str = ''
day_u_t = df.groupby('Day of Week')['User Type']
dict_items = dict(
day_u_t.agg(lambda x: x.value_counts().to_dict())).items()
for d, counts in dict_items:
# loop through each day's counts
#
for user_type, count in list(counts.items()):
# Combine the user type and the count into a string in order to make a single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + f'{count:,}'.rjust(7) + ' '
results_day[d] = prt_str + '|'
prt_str = ''
# Calculate the percentages of user types by day
#
day_u_t = df.groupby('Day of Week')['User Type']
dict_items = dict(
day_u_t.agg(lambda x: (x.value_counts(normalize=True).mul(
100).round(1).astype(str) + '%').to_dict())).items()
for d, percentages in dict_items:
# loop through each day's percentages
#
for user_type, percent in list(percentages.items()):
# Combine the user type and the percentages into a string in order to make a single value
#
prt_str += ('[' + user_type[0] +
']:').ljust(7) + percent.rjust(7) + ' '
results_day[d]['Percentage: '] = prt_str + '|'
prt_str = ''
with pd.option_context(*b_c.PD_OPTIONS):
print(results_day, '\n')
print('\nLegend: [S] -> Subscriber.',
'\n [C] -> Customer.\n [D] -> Dependent.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', inputs, '\n')
# Count the number of trips by gender of the rider and also include that count as a percentage
#
# Define an error if the data field Gender was not included in the dataset
#
try:
stats = [{}]
results = pd.DataFrame(stats, index=['Count: ', 'Percentage: '])
# Count the number of trips by gender
#
prt_str = ''
for gender, count in df['Gender'].agg(
lambda x: x.value_counts().to_dict()).items():
# Combine the gender and count into a string in order to make a single value
#
prt_str += (gender + ':').ljust(7) + f'{count:,}'.rjust(7) + ' '
results['Trips by Gender'] = prt_str
# Calculate the percentage of bike trips by gender
#
prt_str = ''
gender_items = df['Gender'].agg(
lambda x: (x.value_counts(normalize=True).mul(100).round(1).astype(
str) + '%').to_dict()).items()
for gender, percent in gender_items:
# Combine the gender and the percentage into a string in order to make a single value
#
prt_str += (gender + ':').ljust(7) + percent.rjust(7) + ' '
results['Trips by Gender']['Percentage: '] = prt_str
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', results, '\n')
if month == 'all':
# Count the number of bike trips by gender and by month
# and also calculate the percentage
#
results_month = pd.DataFrame(columns=list(
b_c.MONTH_NAMES.values()),
index=['Count: ', 'Percentage: '])
title_month = 'Trips by Gender (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
# Loop through each month getting the counts
#
prt_str = ''
dict_items = dict(
df.groupby('Month')['Gender'].agg(
lambda x: x.value_counts().to_dict())).items()
for m, counts in dict_items:
#
# Loop through each gender getting its percent
#
for gender, count in list(counts.items()):
#
# Combine the gender and the counts into a string
# in order to make a single value
#
prt_str += (gender +
':').ljust(7) + f'{count:,}'.rjust(7) + ' '
results_month[b_c.MONTH_NAMES[str(m)]] = prt_str + '|'
prt_str = ''
# Loop through each month getting the percentages
#
month_g = df.groupby('Month')['Gender']
dict_items = dict(
month_g.agg(lambda x: (x.value_counts(normalize=True).mul(
100).round(1).astype(str) + '%').to_dict())).items()
for m, percentages in dict_items:
#
# Loop through each gender getting its percent
#
for gender, percent in list(percentages.items()):
#
# Combine the gender and the percentages into a string
# in order to make a single value
#
prt_str += (gender + ':').ljust(7) + percent.rjust(7) + ' '
results_month[b_c.MONTH_NAMES[str(
m)]]['Percentage: '] = prt_str + '|'
prt_str = ''
with pd.option_context(*b_c.PD_OPTIONS):
print(results_month, '\n')
if day == 'all':
# Count the number of bike trips by gender and by day
# and also calculate the percentage
#
results_day = pd.DataFrame(columns=list(b_c.DAY_NAMES.values()),
index=['Count: ', 'Percentage: '])
title_day = 'Trips by Gender (by Day)'
print(' ' * 12, title_day.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_day))).center(50, ' '))
# Loop through each day getting the counts
#
prt_str = ''
day_g = df.groupby('Day of Week')['Gender']
dict_items = dict(
day_g.agg(lambda x: x.value_counts().to_dict())).items()
for d, counts in dict_items:
#
# Loop through each gender getting its count
#
for gender, count in list(counts.items()):
#
# Combine the gender and the counts into a string
# in order to make a single value
#
prt_str += (gender +
':').ljust(7) + f'{count:,}'.rjust(7) + ' '
results_day[d] = prt_str + '|'
prt_str = ''
# Loop through each day getting the percentages
#
day_g = df.groupby('Day of Week')['Gender']
dict_items = dict(
day_g.agg(lambda x: (x.value_counts(normalize=True).mul(
100).round(1).astype(str) + '%').to_dict())).items()
for d, percentages in dict_items:
#
# Loop through each gender getting its percent
#
for gender, percent in list(percentages.items()):
#
# Combine the gender and the percentages into a string
# in order to make a single value
#
prt_str += (gender + ':').ljust(7) + percent.rjust(7) + ' '
results_day[d]['Percentage: '] = prt_str + '|'
prt_str = ''
with pd.option_context(*b_c.PD_OPTIONS):
print(results_day, '\n')
except:
print('No data collected for the gender of the bike rider.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', inputs, '\n')
# Display earliest, most recent, and most common year of birth
#
stats = [{}]
results = pd.DataFrame(stats, index=['Max: ', 'Min: ', 'Mode: '])
try:
# Find the max value for the birth year (youngest bike rider)
#
results['Rider Birth Year'] = str(int(df['Year of Birth'].max()))
# Find the min value for the birth year (youngest bike rider)
#
results['Rider Birth Year']['Min: '] = str(
int(df['Year of Birth'].min()))
# Find the mode value for the birth year (most common birth year of the rider)
#
results['Rider Birth Year']['Mode: '] = df['Year of Birth'].mode()[0]
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', results, '\n')
if month == 'all':
# Find the max, min, mode of the bike rider's birth year by month
#
results_month = pd.DataFrame(columns=list(
b_c.MONTH_NAMES.values()),
index=['Max: ', 'Min: ', 'Mode: '])
title_month = 'Rider Birth Year (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
# Loop through each month
#
for m, birth_year in df.groupby('Month')['Year of Birth']:
#
# Find the max, min, mode of the birth year
#
month_name = b_c.MONTH_NAMES[str(m)]
results_month[month_name] = str(int(birth_year.max()))
results_month[month_name]['Min: '] = str(int(birth_year.min()))
results_month[month_name]['Mode: '] = str(
int(birth_year.mode()[0]))
with pd.option_context(*b_c.PD_OPTIONS):
print(results_month, '\n')
if day == 'all':
# Find the max, min, mode of the bike rider's birth year by day
#
results_day = pd.DataFrame(columns=list(b_c.DAY_NAMES.values()),
index=['Max: ', 'Min: ', 'Mode: '])
title_day = 'Rider Birth Year (by Day)'
print(' ' * 12, title_day.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_day))).center(50, ' '))
for day_name, birth_year in df.groupby(
'Day of Week')['Year of Birth']:
#
# Find the max, min, mode of the birth year
#
results_day[day_name] = str(int(birth_year.max()))
results_day[day_name]['Min: '] = str(int(birth_year.min()))
results_day[day_name]['Mode: '] = str(int(
birth_year.mode()[0]))
with pd.option_context(*b_c.PD_OPTIONS):
print(results_day)
print('\nLegend: Max means youngest.', '\n Min means oldest.\n')
except:
print('No data collected on the birth year of the rider.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
def trip_duration_stats(df, city, month, day):
"""Displays statistics on the total and average trip duration."""
# Set up variables in order to store the inputs
#
stats = [{}]
inputs = pd.DataFrame(stats, index=['Inputs: '])
# Set up a variable to store the inputs which will later be printed
#
inputs['City'] = city.title()
inputs['Month'] = month.title()
inputs['Day of Week'] = day.title()
inputs['Number of Records'] = f'{len(df.index):,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(inputs, '\n')
results = pd.DataFrame(stats, index=['Sum: ', 'Mean: '])
# Calculate in minutes the total travel time
#
minutes = f'{int(df["Trip Duration"].sum() // 60):,}' + ' minutes '
seconds = str(int(df["Trip Duration"].sum() % 60)) + ' Seconds'
#
results['Trip Duration'] = minutes + seconds
# Calculate the mean travel time
#
mean_trip_duration = "{0:.2f}".format(df["Trip Duration"].mean() / 60)
results['Trip Duration']['Mean: '] = mean_trip_duration + ' Minutes'
with pd.option_context(*b_c.PD_OPTIONS):
print('\n', results, '\n')
if month == 'all':
# Calculate in minutes the total travel time and the average but by month
#
results_month = pd.DataFrame(columns=list(b_c.MONTH_NAMES.values()),
index=['Sum: ', 'Mean: '])
title_month = 'Trip Duration (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
for m, durations in df.groupby('Month')['Trip Duration']:
# Calculate the sum and then convert to minutes and secs,
# and format the minutes into thousands
#
minutes = f'{int(durations.sum() // 60):,}' + ' minutes '
seconds = str(int(durations.sum() % 60)) + ' Seconds'
results_month[b_c.MONTH_NAMES[str(m)]]['Sum: '] = minutes + seconds
#
# Calculate the mean but format it with two decimal places
#
mean_trip_duration = "{0:.2f}".format(
durations.mean() / 60) + ' Minutes'
results_month[b_c.MONTH_NAMES[str(
m)]]['Mean: '] = mean_trip_duration
with pd.option_context(*b_c.PD_OPTIONS):
print(results_month, '\n')
if day == 'all':
# Calculate in minutes the total travel time and the average but by day
#
results_day = pd.DataFrame(columns=list(b_c.DAY_NAMES.values()),
index=['Sum: ', 'Mean: '])
title_day = 'Trip Duration (by Day)'
print(' ' * 12, title_day.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_day))).center(50, ' '))
for d, durations in df.groupby('Day of Week')['Trip Duration']:
# Calculate the sum and then convert to minutes and secs,
# and format the minutes into thousands
#
minutes = f'{int(durations.sum() // 60):,}' + ' minutes '
seconds = str(int(durations.sum() % 60)) + ' Seconds'
results_day[d]['Sum: '] = minutes + seconds
#
# Calculate the mean but format it with two decimal places
#
mean_trip_duration = "{0:.2f}".format(
durations.mean() / 60) + ' Minutes'
results_day[d]['Mean: '] = mean_trip_duration
with pd.option_context(*b_c.PD_OPTIONS):
print(results_day, '\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
def time_stats(df, city, month, day):
"""Displays statistics on the most frequent times of travel."""
# Set up variables in order to store the statistics
#
stats = [{}]
inputs = pd.DataFrame(stats, index=['Inputs: '])
# Set up a variable to store the inputs which will later be printed
#
inputs['City'] = city.title()
inputs['Month'] = month.title()
inputs['Day'] = day.title()
inputs['Number of Records'] = f'{len(df.index):,}'
# Show the user inputs
#
with pd.option_context(*b_c.PD_OPTIONS):
print(inputs, '\n')
# Define a variable to store the results
#
results = pd.DataFrame(stats, index=['Mode: ', 'Count: '])
# Get the mode and convert to 12 hour AM/PM
#
mode_start_hour = str(df['Hour'].mode()[0])
mode_start_hour = datetime.strptime(mode_start_hour,
"%H").strftime("%I %p")
results['Trip Start hour'] = datetime.strptime(str(df['Hour'].mode()[0]),
"%H").strftime("%I %p")
# Count the number of trips for that hour
#
results['Trip Start hour'][
'Count: '] = f'{list(df["Hour"].value_counts())[0]:,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(results, '\n')
if month == 'all':
# Print a title
#
title_month = 'Trip Start hour (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
results_month = pd.DataFrame(columns=list(b_c.MONTH_NAMES.values()),
index=['Mode: ', 'Count: '])
# Get the mode for the trip start hour by month
#
for m, hours in df.groupby('Month')['Hour']:
# Convert the 24 hour value to 12 hour with the right AM/PM
#
mode_start_hour = str(list(hours.mode())[0])
mode_start_hour = datetime.strptime(mode_start_hour,
"%H").strftime("%I %p")
results_month[b_c.MONTH_NAMES[str(m)]]['Mode: '] = mode_start_hour
# Report the count for that trip start hour
#
results_month[b_c.MONTH_NAMES[str(
m)]]['Count: '] = f'{list(hours.value_counts())[0]:,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(results_month, '\n')
if day == 'all':
# Print a title
#
title_day = 'Trip Start hour (by Day)'
print(' ' * 12, title_day.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_day))).center(50, ' '))
results_day = pd.DataFrame(columns=list(b_c.DAY_NAMES.values()),
index=['Mode: ', 'Count: '])
# Get the mode for the trip start hour by day
#
for d, hours in df.groupby('Day of Week')['Hour']:
# Convert the trip start hour into 12 hour AM/PM format
#
mode_start_hour = str(list(hours.mode())[0])
mode_start_hour = datetime.strptime(mode_start_hour,
"%H").strftime("%I %p")
results_day[d]['Mode: '] = mode_start_hour
# Report the count for that trip start hour
#
results_day[d]['Count: '] = f'{list(hours.value_counts())[0]:,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(results_day, '\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
def station_stats(df, city, month, day):
"""Displays statistics on the most popular stations and trip."""
# Set up variables in order to store the inputs
#
stats = [{}]
inputs = pd.DataFrame(stats, index=['Inputs: '])
# Set up a variable to store the inputs which will later be printed
#
inputs['City'] = city.title()
inputs['Month'] = month.title()
inputs['Day of Week'] = day.title()
inputs['Number of Records'] = f'{len(df.index):,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(inputs, '\n')
title_month = 'Station Stats'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
results = pd.DataFrame(stats, index=['Mode: ', 'Count: ', 'Range: '])
# Collect the mode of the start station,
# plus why it is the mode (count).
# Finally, report how many unique start stations there were (range)
#
results['Start Station'] = df['Start Station'].mode()[0]
results['Start Station'][
'Count: '] = f'{list(df["Start Station"].value_counts())[0]:,}'
results['Start Station'][
'Range: '] = f'{len(df["Start Station"].value_counts()):,}'
# Collect the mode of the end station,
# plus why it is the mode (count).
# Finally, report how many unique end stations there were (range)
#
results['End Station'] = df['End Station'].mode()[0]
results['End Station'][
'Count: '] = f'{list(df["End Station"].value_counts())[0]:,}'
results['End Station'][
'Range: '] = f'{len(df["End Station"].value_counts()):,}'
# Collect the mode of the trip,
# plus why it is the mode (count).
# Finally, report how many unique trips there were (range)
#
results['Trips'] = df['Trips'].mode()[0]
results['Trips']['Count: '] = f'{list(df["Trips"].value_counts())[0]:,}'
results['Trips']['Range: '] = f'{len(df["Trips"].value_counts()):,}'
with pd.option_context(*b_c.PD_OPTIONS):
print(results, '\n')
print(
'\nLegend: Trips show Start Station and End Station separated by \'->\'.',
'\n Count shows how many trips there were for that mode.',
'\n Range shows how many total unique trips there were.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
if month == 'all':
# Print a title
#
title_month = 'Station Stats (by Month)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
results_month = pd.DataFrame(columns=list(b_c.MONTH_NAMES.values()),
index=['Station Stats'])
# Loop through the months
#
for m in list(b_c.MONTH_NAMES.keys()):
#
results = pd.DataFrame(stats,
index=['Mode: ', 'Count: ', 'Range: '])
#
for field_name in b_c.STATION_FIELDS:
#
# get mode, count, range of the three fields
#
mode_f_n = df[df['Month'] == int(m)][field_name].mode()[0]
count_f_n = f'{list(df[df["Month"] == int(m)][field_name].value_counts())[0]:,}'
range_f_n = f'{len(df[df["Month"] == int(m)][field_name].value_counts()):,}'
#
# store the mode, count, range of each field
#
results[field_name] = mode_f_n
results[field_name]['Count: '] = count_f_n
results[field_name]['Range: '] = range_f_n
print(' ' * 12, b_c.MONTH_NAMES[str(m)].center(50, ' '), '\n',
' ' * 11,
('-' * (len(b_c.MONTH_NAMES[str(m)]))).center(50, ' '))
with pd.option_context(*b_c.PD_OPTIONS):
print(results, '\n')
print(
'\nLegend: Trips show Start Station and End Station separated by \'->\'.',
'\n Count shows how many trips there were for that mode.',
'\n Range shows how many total unique trips there were.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()
if day == 'all':
# Print a title
#
title_month = 'Station Stats (by Day)'
print(' ' * 12, title_month.center(50, ' '), '\n', ' ' * 11,
('-' * (len(title_month))).center(50, ' '))
results_month = pd.DataFrame(columns=list(b_c.MONTH_NAMES.values()),
index=['Station Stats'])
# Loop through the months
#
for d in list(b_c.DAY_NAMES.values()):
#
results = pd.DataFrame(stats,
index=['Mode: ', 'Count: ', 'Range: '])
#
for field_name in b_c.STATION_FIELDS:
#
# get mode, count, range of the three fields
#
mode_f_n = df[df['Day of Week'] == d][field_name].mode()[0]
count_f_n = f'{list(df[df["Day of Week"] == d][field_name].value_counts())[0]:,}'
range_f_n = f'{len(df[df["Day of Week"] == d][field_name].value_counts()):,}'
#
# store the mode, count, range of each field
#
results[field_name] = mode_f_n
results[field_name]['Count: '] = count_f_n
results[field_name]['Range: '] = range_f_n
print(' ' * 12, d.center(50, ' '), '\n', ' ' * 11,
('-' * (len(d))).center(50, ' '))
with pd.option_context(*b_c.PD_OPTIONS):
print(results, '\n')
print(
'\nLegend: Trips show Start Station and End Station separated by \'->\'.',
'\n Count shows how many trips there were for that mode.',
'\n Range shows how many total unique trips there were.\n')
b_i.get_yes_or_no("Pause. ")
b_c.clear_screen()