def plot_power():
'''Reads maximum net power information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
power = read_column(20)
rdata = col_distribution(power)
rdata.pop('')
data = {'0 - 50 kW': 0, '50 - 75 kW': 0, '75 - 100 kW': 0, '100 - 150 kW': 0, '150 - 200 kW': 0, 'Greater': 0}
for k, v in rdata.items():
pwr = float(k)
if pwr <= 50.0:
data['0 - 50 kW'] += v
elif pwr > 50.0 and pwr <=75.0:
data['50 - 75 kW'] += v
elif pwr > 75.0 and pwr <= 100.0:
data['75 - 100 kW'] += v
elif pwr > 100.0 and pwr <= 150.0:
data['100 - 150 kW'] += v
elif pwr > 150.0 and pwr <= 200.0:
data['150 - 200 kW'] += v
else:
data['Greater'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Maximum net power', legend=1, sort=1)
def plot_fuel():
'''Reads fuel type information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
fuel = read_column(18)
rdata = col_distribution(fuel)
rdata.pop('')
data = {'Gasoline': 0, 'Diesel': 0, 'Others': 0}
data['Gasoline'] += rdata.pop('01')
data['Diesel'] += rdata.pop('02')
data['Others'] += sum(rdata.values())
values = data.values()
labels = data.keys()
plot_pie(values, labels, title='Fuels', legend=1)
def plot_seats():
'''Reads seat information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
seats = read_column(11)
data = col_distribution(seats)
data.pop('') # Missing values
data.pop('0') # Only 1 Vechile
keys = [int(val) for val in data.keys()]
data['More than 5'] = 0
for key in keys:
if key > 5:
data['More than 5'] += data.pop(str(key))
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Number of Seats', legend=1, sort=1)
def plot_usage():
'''Reads usage information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
usages = read_column(3)
keys = {'': 'Others', 'nul': 'Others', '01': 'Private', '02': 'Subject to permit', '03': 'School vechile', '04': 'Rental', '05': 'Sales storage'}
data = col_distribution(usages)
for old_key, new_key in keys.items():
if new_key not in data:
data[new_key] = data.pop(old_key)
else:
data[new_key] += data.pop(old_key)
data.pop('Others')
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Vehicle usage', legend=1, sort=1)
def plot_cylinders():
'''Reads cylider information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
cylinders = read_column(21)
data = col_distribution(cylinders)
data.pop('')
keys = [int(val) for val in data.keys()]
data['More than 4'] = 0
data['Less than 4'] = 0
for key in keys:
if key < 4:
data['Less than 4'] += data.pop(str(key))
if key > 4:
data['More than 4'] += data.pop(str(key))
values = data.values()
labels = data.keys()
plot_pie(values, labels, title='Number of Cylinders', legend=1, sort=1)
def plot_co2():
'''Reads co2 emission information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
co2 = read_column(33)
rdata = col_distribution(co2)
rdata.pop('')
data = {'0 - 150 g': 0, '150 - 200 g': 0, 'More than 200 g': 0}
for k, v in rdata.items():
g = int(k)
if g < 150:
data['0 - 150 g'] += v
elif g >= 150 and g < 200:
data['150 - 200 g'] += v
else:
data['More than 200 g'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Carbon dioxide emissions')
def plot_init_reg_date():
'''Reads initial registration date information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
date = read_column(1)
rdata = col_distribution(date)
rdata.pop('')
data = {'1958 - 1979': 0, '1980 - 1989': 0, '1990 - 1999': 0, '2000 - 2009': 0, '2010 - 2016': 0}
for k, v in rdata.items():
year = k[:4]
for interval in data.keys():
lower = interval[:4]
upper = interval[-4:]
if year >= lower and year <= upper:
data[interval] += v
break
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, colors, title = 'Initial registration year', legend=1, sort=1)
def plot_length():
'''Reads length information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
length = read_column(15)
rdata = col_distribution(length)
rdata.pop('')
data = {'0 - 4300 mm': 0, '4300 - 4700 mm': 0, 'Longer than 4700 mm': 0}
for k, v in rdata.items():
lng = int(k)
if lng < 4300:
data['0 - 4300 mm'] += v
elif lng >= 4300 and lng < 4700:
data['4300 - 4700 mm'] += v
else:
data['Longer than 4700 mm'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Lenght', sort=1)
def plot_km():
'''Reads driven distance information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
km = read_column(34)
rdata = col_distribution(km)
rdata.pop('')
data = {'0 - 100000 km': 0, '100000 - 200000 km': 0, 'More than 200000 km': 0}
for k, v in rdata.items():
dist = int(k)
if dist < 100000:
data['0 - 100000 km'] += v
elif dist >= 100000 and dist < 200000:
data['100000 - 200000 km'] += v
else:
data['More than 200000 km'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Driven distance', sort=1, legend=1)
def plot_vechicle_classes():
'''Reads vehicle class information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
vclasses = read_column(0)
rdata = col_distribution(vclasses)
data = {'Light': 0, 'Car': 0, 'Heavy': 0, 'Trailer': 0, 'Others': 0}
keys = {'Light': ['L1', 'L1e', 'L2', 'L2e', 'L3', 'L3e', 'L4', 'L4e', 'L5', 'L5e', 'L6e', 'L7e', 'KNP'],
'Car': ['M1', 'M1G'],
'Trailer': ['O1', 'O2', 'O3', 'O4'],
'Heavy': ['C1', 'C2', 'T', 'T1', 'T2', 'T3', 'T4', 'T5', 'LTR', 'M2', 'M2G', 'M3', 'N1', 'N1G', 'N2', 'N2G', 'N3', 'N3G'],
'Others': ['MUU', 'MTK', 'MA']}
for rkey, rvalue in rdata.items():
for dkey, dvalue in keys.items():
if rkey in dvalue:
data[dkey] += rvalue
values = list(data.values())
labels = list(data.keys())
colors = ['yellowgreen', 'lightskyblue', 'red', 'orangered', 'gold']
plot_pie(values, labels, colors, title = 'Customized Vehicle Classes')
def plot_height():
'''Reads height information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
height = read_column(17)
rdata = col_distribution(height)
rdata.pop('')
data = {'0 - 1450 mm': 0, '1450 - 1500 mm': 0, '1500 - 1550 mm': 0, 'Greater than 1550 mm': 0}
for k, v in rdata.items():
hgt = int(k)
if hgt < 1450:
data['0 - 1450 mm'] += v
elif hgt >= 1450 and hgt < 1500:
data['1450 - 1500 mm'] += v
elif hgt >= 1500 and hgt < 1550:
data['1500 - 1550 mm'] += v
else:
data['Greater than 1550 mm'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Height', sort=1)
def plot_mass():
'''Reads mass information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
mass = read_column(12)
rdata = col_distribution(mass)
rdata.pop('')
data = {'0 - 1000 kg': 0, '1000 - 1500 kg': 0, '1500 - 2000 kg': 0, 'Greater': 0}
for k, v in rdata.items():
mss = int(k)
if mss < 1000:
data['0 - 1000 kg'] += v
elif mss >= 1000 and mss < 1500:
data['1000 - 1500 kg'] += v
elif mss >= 1500 and mss < 2000:
data['1500 - 2000 kg'] += v
else:
data['Greater'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Mass', sort=1)
def plot_doors():
'''Reads door information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
doors = read_column(8)
rdata = col_distribution(doors)
rdata.pop('')
data = {'Less than 4': 0, '4': 0, '5': 0, 'More than 5': 0}
for k, v in rdata.items():
drs = int(k)
if drs < 4:
data['Less than 4'] += v
if drs == 4:
data['4'] += v
if drs == 5:
data['5'] += v
if drs > 5:
data['More than 5'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Number of doors', sort=1, legend=1)
def plot_commencement_date():
'''Reads commencement date information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
date = read_column(6)
rdata = col_distribution(date)
rdata.pop('')
rdata.pop('00000000') # False date
rdata.pop('00050812') # Year probably 2005, but theres only 1 such case
data = {'1900 - 1989': 0, '1990 - 1999': 0, '2000 - 2004': 0, '2005 - 2009': 0, '2010 - 2017': 0}
for k, v in rdata.items():
year = k[:4]
for interval in data.keys():
lower = interval[:4]
upper = interval[-4:]
if year >= lower and year <= upper:
data[interval] += v
break
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, colors, title = 'Year of commencement', legend=1, sort=1)
def plot_width():
'''Reads width information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
width = read_column(16)
rdata = col_distribution(width)
rdata.pop('')
data = {'0 - 1700 mm': 0, '1700 - 1800 mm': 0, '1800 - 1900 mm': 0, '1900 - 2000 mm': 0, 'Wider than 2000 mm': 0}
for k, v in rdata.items():
wdt = int(k)
if wdt < 1700:
data['0 - 1700 mm'] += v
elif wdt >= 1700 and wdt < 1800:
data['1700 - 1800 mm'] += v
elif wdt >= 1800 and wdt < 1900:
data['1800 - 1900 mm'] += v
elif wdt >= 1900 and wdt < 2000:
data['1900 - 2000 mm'] += v
else:
data['Wider than 2000 mm'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Width', sort=1)
def plot_displacement():
'''Reads engine displacement information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
displacement = read_column(19)
rdata = col_distribution(displacement)
rdata.pop('')
data = {'Small': 0, 'Large': 0, '1000 - 1499 cc': 0, '1500 - 1999 cc': 0 , '2000 - 2999 cc': 0}
for k, v in rdata.items():
disp = int(k)
if disp < 1000:
data['Small'] += v
if disp >= 3000:
data['Large'] += v
if disp >= 1000 and disp < 1500:
data['1000 - 1499 cc'] += v
if disp >= 1500 and disp < 2000:
data['1500 - 1999 cc'] += v
if disp >= 2000 and disp < 3000:
data['2000 - 2999 cc'] += v
values = list(data.values())
labels = list(data.keys())
plot_pie(values, labels, title='Engine displacement', sort=1)
def plot_colors():
'''Reads color information from the data, re-organizes the data
and then uses plot_pie to plot a pie chart.
'''
colors = read_column(7)
data = {}
keys = {'': '', '0': 'Black', '1': 'Brown', '2': 'Red', '3': 'Orange', '4': 'Yellow',
'5': 'Green', '6': 'Blue', '7': 'Violet', '8': 'Grey', '9': 'White', 'X': 'Multi-colored',
'Y': 'Silver', 'Z': 'Turquoise'}
for color in colors:
if keys[color] not in data:
data[keys[color]] = 1
else:
data[keys[color]] += 1
data.pop('') # Missing values
data['Blue'] += data.pop('Turquoise')
data['Others'] = 0
data['Others'] = data.pop('Orange') + data.pop('Violet') + data.pop('Multi-colored') + data.pop('Yellow')
values= list(data.values())
labels=list(data.keys())
colors = labels.copy()
colors[colors.index('Others')] = 'Pink'
colors[colors.index('Brown')] = 'Sienna'
plot_pie(values, labels, colors, title='Colors', legend=1)