Python string formatting: is '%' more efficient than 'format' function?

I wanted to compare different to build a string in Python from different variables:

• using + to concatenate (referred to as 'plus')
• using %
• using "".join(list)
• using format function
• using "{0.<attribute>}".format(object)

I compared for 3 types of scenari

• string with 2 variables
• string with 4 variables
• string with 4 variables, each used twice

I measured 1 million operations of each time and performed an average over 6 measures. I came up with the following timings:

           test_plus:   0.29480
        test_percent:   0.47540
           test_join:   0.56240
         test_format:   0.72760
        test_formatC:   0.90000
      test_plus_long:   0.50520
   test_percent_long:   0.58660
      test_join_long:   0.64540
    test_format_long:   1.03400
   test_formatC_long:   1.28020
     test_plus_long2:   0.95220
  test_percent_long2:   0.81580
     test_join_long2:   0.88400
   test_format_long2:   1.51500
  test_formatC_long2:   1.97160

In each scenario, I came up with the following conclusion

• Concatenation seems to be one of the fastest method
• Formatting using % is much faster than formatting with format function

I believe format is much better than % (e.g. in this question) and % was almost deprecated.

I have therefore several questions:

1. Is % really faster than format?
2. If so, why is that?
3. Why is "{} {}".format(var1, var2) more efficient than "{0.attribute1} {0.attribute2}".format(object)?

---

For reference, I used the following code to measure the different timings.

import time
def timing(f, n, show, *args):
	if show: print f.__name__ + ":\t",
	r = range(n/10)
	t1 = time.clock()
	for i in r:
		f(*args); f(*args); f(*args); f(*args); f(*args); f(*args); f(*args); f(*args); f(*args); f(*args)
	t2 = time.clock()
	timing = round(t2-t1, 3)
	if show: print timing
	return timing
	

class values(object):
	def __init__(self, a, b, c="", d=""):
		self.a = a
		self.b = b
		self.c = c
		self.d = d

	
def test_plus(a, b):
	return a + "-" + b

def test_percent(a, b):
	return "%s-%s" % (a, b)

def test_join(a, b):
	return ''.join([a, '-', b])
		
def test_format(a, b):
	return "{}-{}".format(a, b)

def test_formatC(val):
	return "{0.a}-{0.b}".format(val)

	
def test_plus_long(a, b, c, d):
	return a + "-" + b + "-" + c + "-" + d

def test_percent_long(a, b, c, d):
	return "%s-%s-%s-%s" % (a, b, c, d)
		
def test_join_long(a, b, c, d):
	return ''.join([a, '-', b, '-', c, '-', d])
	
def test_format_long(a, b, c, d):
	return "{0}-{1}-{2}-{3}".format(a, b, c, d)

def test_formatC_long(val):
	return "{0.a}-{0.b}-{0.c}-{0.d}".format(val)

	
def test_plus_long2(a, b, c, d):
	return a + "-" + b + "-" + c + "-" + d + "-" + a + "-" + b + "-" + c + "-" + d

def test_percent_long2(a, b, c, d):
	return "%s-%s-%s-%s-%s-%s-%s-%s" % (a, b, c, d, a, b, c, d)
	
def test_join_long2(a, b, c, d):
	return ''.join([a, '-', b, '-', c, '-', d, '-', a, '-', b, '-', c, '-', d])
			
def test_format_long2(a, b, c, d):
	return "{0}-{1}-{2}-{3}-{0}-{1}-{2}-{3}".format(a, b, c, d)

def test_formatC_long2(val):
	return "{0.a}-{0.b}-{0.c}-{0.d}-{0.a}-{0.b}-{0.c}-{0.d}".format(val)


def test_plus_superlong(lst):
	string = ""
	for i in lst:
		string += str(i)
	return string
	

def test_join_superlong(lst):
	return "".join([str(i) for i in lst])
	

def mean(numbers):
    return float(sum(numbers)) / max(len(numbers), 1)
		

nb_times = int(1e6)
n = xrange(5)
lst_numbers = xrange(1000)
from collections import defaultdict
metrics = defaultdict(list)
list_functions = [
	test_plus, test_percent, test_join, test_format, test_formatC,
	test_plus_long, test_percent_long, test_join_long, test_format_long, test_formatC_long,
	test_plus_long2, test_percent_long2, test_join_long2, test_format_long2, test_formatC_long2,
	# test_plus_superlong, test_join_superlong,
]
val = values("123", "456", "789", "0ab")
for i in n:
	for f in list_functions:
		print ".",
		name = f.__name__
		if "formatC" in name:
			t = timing(f, nb_times, False, val)
		elif '_long' in name:
			t = timing(f, nb_times, False, "123", "456", "789", "0ab")
		elif '_superlong' in name:
			t = timing(f, nb_times, False, lst_numbers)
		else:
			t = timing(f, nb_times, False, "123", "456")
		metrics[name].append(t) 

# Get Average
print "\n===AVERAGE OF TIMINGS==="
for f in list_functions:
	name = f.__name__
	timings = metrics[name]
	print "{:>20}:\t{:0.5f}".format(name, mean(timings))

1. Yes, % string formatting is faster than the .format method
2. most likely (this may have a much better explanation) due to % being a syntactical notation (hence fast execution), whereas .format involves at least one extra method call
3. because attribute value access also involves an extra method call, viz. __getattr__

I ran a slightly better analysis (on Python 3.8.2) using timeit of various formatting methods, results of which are as follows (pretty-printed with BeautifulTable) -

Type \ num_vars	1	2	5	10	50	250
f_str_str	0.056	0.063	0.115	0.173	0.754	3.717
f_str_int	0.055	0.148	0.354	0.656	3.186	15.747
concat_str	0.012	0.044	0.169	0.333	1.888	10.231
pct_s_str	0.091	0.114	0.182	0.313	1.213	6.019
pct_s_int	0.090	0.141	0.248	0.479	2.179	10.768
dot_format_str	0.143	0.157	0.251	0.461	1.745	8.259
dot_format_int	0.141	0.192	0.333	0.620	2.735	13.298
dot_format2_str	0.159	0.195	0.330	0.634	3.494	18.975
dot_format2_int	0.158	0.227	0.422	0.762	4.337	25.498

The trailing _str & _int represent the operation was carried out on respective value types.

Kindly note that the concat_str result for a single variable is essentially just the string itself, so it shouldn't really be considered.

My setup for arriving at the results -

from timeit import timeit
from beautifultable import BeautifulTable  # pip install beautifultable

times = {}

for num_vars in (250, 50, 10, 5, 2, 1):
    f_str = "f'{" + '}{'.join([f'x{i}' for i in range(num_vars)]) + "}'"
    # "f'{x0}{x1}'"
    concat = '+'.join([f'x{i}' for i in range(num_vars)])
    # 'x0+x1'
    pct_s = '"' + '%s'*num_vars + '" % (' + ','.join([f'x{i}' for i in range(num_vars)]) + ')'
    # '"%s%s" % (x0,x1)'
    dot_format = '"' + '{}'*num_vars + '".format(' + ','.join([f'x{i}' for i in range(num_vars)]) + ')'
    # '"{}{}".format(x0,x1)'
    dot_format2 = '"{' + '}{'.join([f'{i}' for i in range(num_vars)]) + '}".format(' + ','.join([f'x{i}' for i in range(num_vars)]) + ')'
    # '"{0}{1}".format(x0,x1)'

    vars = ','.join([f'x{i}' for i in range(num_vars)])
    vals_str = tuple(map(str, range(num_vars))) if num_vars > 1 else '0'
    setup_str = f'{vars} = {vals_str}'
    # "x0,x1 = ('0', '1')"
    vals_int = tuple(range(num_vars)) if num_vars > 1 else 0
    setup_int = f'{vars} = {vals_int}'
    # 'x0,x1 = (0, 1)'

    times[num_vars] = {
        'f_str_str': timeit(f_str, setup_str),
        'f_str_int': timeit(f_str, setup_int),
        'concat_str': timeit(concat, setup_str),
        # 'concat_int': timeit(concat, setup_int), # this will be summation, not concat
        'pct_s_str': timeit(pct_s, setup_str),
        'pct_s_int': timeit(pct_s, setup_int),
        'dot_format_str': timeit(dot_format, setup_str),
        'dot_format_int': timeit(dot_format, setup_int),
        'dot_format2_str': timeit(dot_format2, setup_str),
        'dot_format2_int': timeit(dot_format2, setup_int),
    }

table = BeautifulTable()
table.column_headers = ['Type \ num_vars'] + list(map(str, times.keys()))
# Order is preserved, so I didn't worry much
for key in ('f_str_str', 'f_str_int', 'concat_str', 'pct_s_str', 'pct_s_int', 'dot_format_str', 'dot_format_int', 'dot_format2_str', 'dot_format2_int'):
    table.append_row([key] + [times[num_vars][key] for num_vars in (1, 2, 5, 10, 50, 250)])
print(table)

I couldn't go beyond num_vars=250 because of the max arguments (255) limit with timeit.

tl;dr - Python string formatting performance : f-strings are fastest and more elegant, but at times (due to some implementation restrictions & being Py3.6+ only), you might have to use other formatting options as necessary.