What is the fastest way to know if a value exists in a list (a list with millions of values in it) and what its index is?
I know that all values in the list are uniqu
As stated by others, in
can be very slow for large lists. Here are some comparisons of the performances for in
, set
and bisect
. Note the time (in second) is in log scale.
Code for testing:
import random
import bisect
import matplotlib.pyplot as plt
import math
import time
def method_in(a, b, c):
start_time = time.time()
for i, x in enumerate(a):
if x in b:
c[i] = 1
return time.time() - start_time
def method_set_in(a, b, c):
start_time = time.time()
s = set(b)
for i, x in enumerate(a):
if x in s:
c[i] = 1
return time.time() - start_time
def method_bisect(a, b, c):
start_time = time.time()
b.sort()
for i, x in enumerate(a):
index = bisect.bisect_left(b, x)
if index < len(a):
if x == b[index]:
c[i] = 1
return time.time() - start_time
def profile():
time_method_in = []
time_method_set_in = []
time_method_bisect = []
# adjust range down if runtime is to great or up if there are to many zero entries in any of the time_method lists
Nls = [x for x in range(10000, 30000, 1000)]
for N in Nls:
a = [x for x in range(0, N)]
random.shuffle(a)
b = [x for x in range(0, N)]
random.shuffle(b)
c = [0 for x in range(0, N)]
time_method_in.append(method_in(a, b, c))
time_method_set_in.append(method_set_in(a, b, c))
time_method_bisect.append(method_bisect(a, b, c))
plt.plot(Nls, time_method_in, marker='o', color='r', linestyle='-', label='in')
plt.plot(Nls, time_method_set_in, marker='o', color='b', linestyle='-', label='set')
plt.plot(Nls, time_method_bisect, marker='o', color='g', linestyle='-', label='bisect')
plt.xlabel('list size', fontsize=18)
plt.ylabel('log(time)', fontsize=18)
plt.legend(loc='upper left')
plt.yscale('log')
plt.show()
profile()