Python: is "except KeyError" faster than "if key in dict"?

Question

Edit 2: It was suggested that this is a copy of a similar question. I'd disagree since my question focuses on speed, while the other question asks what is more "readable" or "better" (without defining better). While the questions are similar, there is a big difference in the discussion/answers given.

EDIT: I realise from the questions that I could have been clearer. Sorry for code typos, yes it should be using the proper python operator for addition.

Regarding the input data, I just chose a list of random numbers since that's a common sample. In my case I'm using a dict where I expect a lot of keyerrors, probably 95% of the keys will not exist, and the few that exist will contain clusters of data.

I'm interested in a general discussion though, regardless of the input data set, but of course samples with running times are interesting.

My standard approach would be like so many other posts to write something like

list =  (100 random numbers)
d = {}
for x in list:
    if x in d:
        d[x]+=1
    else:
        d[x]=1

But I just came to think of this being faster, since we dont have to check if the dictionary contains the key. We just assume it does, and if not, we handle that. Is there any difference or is Python smarter than I am?

list =  (100 random numbers)
d = {}
for x in list:
    try:
        d[x]+=1
    except KeyError:
        d[x] = 1

The same approach with indexes in an array, out of bounds, negative indexes etc.

Answer 1

Your claim is absolutely false depends on the input.

If you have a diverse set of keys, and hits the except block often, the performance is not good. If the try block is dominant the try/except idiom can be performant on smaller lists.

Here is a benchmark showing several ways to do the same thing:

from __future__ import print_function
import timeit
import random
import collections

def f1():
    d={}
    for x in tgt:
        if x in d:
            d[x]+=1
        else:
            d[x]=1
    return d

def f2():
    d = {}
    for x in tgt:
        try:
            d[x]+=1
        except KeyError:
            d[x] = 1    
    return d

def f3():
    d={}.fromkeys(tgt, 0)
    for x in tgt:
        d[x]+=1    
    return d    


def f4():
    d=collections.defaultdict(int)
    for x in tgt:
        d[x]+=1    
    return d    

def f5():
    return collections.Counter(tgt)        

def f6():
    d={}
    for x in tgt:
        d[x]=d.setdefault(x, 0)+1
    return d

def f7():
    d={}
    for x in tgt:
        d[x]=d.get(x,0)+1
    return d    

def cmpthese(funcs, c=10000, rate=True, micro=False):
    """Generate a Perl style function benchmark"""                   
    def pprint_table(table):
        """Perl style table output"""
        def format_field(field, fmt='{:,.0f}'):
            if type(field) is str: return field
            if type(field) is tuple: return field[1].format(field[0])
            return fmt.format(field)     

        def get_max_col_w(table, index):
            return max([len(format_field(row[index])) for row in table])         

        col_paddings=[get_max_col_w(table, i) for i in range(len(table[0]))]
        for i,row in enumerate(table):
            # left col
            row_tab=[row[0].ljust(col_paddings[0])]
            # rest of the cols
            row_tab+=[format_field(row[j]).rjust(col_paddings[j]) for j in range(1,len(row))]
            print(' '.join(row_tab))                

    results={k.__name__:timeit.Timer(k).timeit(c) for k in funcs}
    fastest=sorted(results,key=results.get, reverse=True)
    table=[['']]
    if rate: table[0].append('rate/sec')
    if micro: table[0].append('usec/pass')
    table[0].extend(fastest)
    for e in fastest:
        tmp=[e]
        if rate:
            tmp.append('{:,}'.format(int(round(float(c)/results[e]))))

        if micro:
            tmp.append('{:.3f}'.format(1000000*results[e]/float(c)))

        for x in fastest:
            if x==e: tmp.append('--')
            else: tmp.append('{:.1%}'.format((results[x]-results[e])/results[e]))
        table.append(tmp) 

    pprint_table(table)                    

if __name__=='__main__':
    import sys
    print(sys.version)
    for j in [100,1000]:
        for t in [(0,5), (0,50), (0,500)]:
            tgt=[random.randint(*t) for i in range(j)]
            print('{} rand ints between {}:'.format(j,t))
            print('=====')
            cmpthese([f1,f2,f3,f4,f5,f6,f7])
            print()

I have included a small benchmark function based on timeit that prints the functions from Slowest to Fastest with a percent difference between them.

Here is the results for Python 3:

3.4.1 (default, May 19 2014, 13:10:29) 
[GCC 4.2.1 Compatible Apple LLVM 5.1 (clang-503.0.40)]
100 rand ints between (0, 5):
=====
   rate/sec    f6    f7     f1     f2     f3     f4     f5
f6   52,756    -- -1.6% -26.2% -27.9% -30.7% -36.7% -46.8%
f7   53,624  1.6%    -- -25.0% -26.7% -29.6% -35.7% -46.0%
f1   71,491 35.5% 33.3%     --  -2.3%  -6.1% -14.2% -28.0%
f2   73,164 38.7% 36.4%   2.3%     --  -3.9% -12.2% -26.3%
f3   76,148 44.3% 42.0%   6.5%   4.1%     --  -8.7% -23.3%
f4   83,368 58.0% 55.5%  16.6%  13.9%   9.5%     -- -16.0%
f5   99,247 88.1% 85.1%  38.8%  35.6%  30.3%  19.0%     --

100 rand ints between (0, 50):
=====
   rate/sec     f2     f6     f7     f4     f3     f1     f5
f2   39,405     -- -17.9% -18.7% -19.1% -41.8% -47.8% -56.3%
f6   47,980  21.8%     --  -1.1%  -1.6% -29.1% -36.5% -46.8%
f7   48,491  23.1%   1.1%     --  -0.5% -28.4% -35.8% -46.2%
f4   48,737  23.7%   1.6%   0.5%     -- -28.0% -35.5% -46.0%
f3   67,678  71.7%  41.1%  39.6%  38.9%     -- -10.4% -24.9%
f1   75,511  91.6%  57.4%  55.7%  54.9%  11.6%     -- -16.3%
f5   90,175 128.8%  87.9%  86.0%  85.0%  33.2%  19.4%     --

100 rand ints between (0, 500):
=====
   rate/sec     f2     f4     f6     f7     f3     f1     f5
f2   25,748     -- -22.0% -41.4% -42.6% -57.5% -66.2% -67.8%
f4   32,996  28.1%     -- -24.9% -26.4% -45.6% -56.7% -58.8%
f6   43,930  70.6%  33.1%     --  -2.0% -27.5% -42.4% -45.1%
f7   44,823  74.1%  35.8%   2.0%     -- -26.1% -41.2% -44.0%
f3   60,624 135.5%  83.7%  38.0%  35.3%     -- -20.5% -24.2%
f1   76,244 196.1% 131.1%  73.6%  70.1%  25.8%     --  -4.7%
f5   80,026 210.8% 142.5%  82.2%  78.5%  32.0%   5.0%     --

1000 rand ints between (0, 5):
=====
   rate/sec     f7     f6     f1     f3     f2     f4     f5
f7    4,993     --  -6.7% -34.6% -39.4% -44.4% -50.1% -71.1%
f6    5,353   7.2%     -- -29.9% -35.0% -40.4% -46.5% -69.0%
f1    7,640  53.0%  42.7%     --  -7.3% -14.9% -23.6% -55.8%
f3    8,242  65.1%  54.0%   7.9%     --  -8.2% -17.6% -52.3%
f2    8,982  79.9%  67.8%  17.6%   9.0%     -- -10.2% -48.1%
f4   10,004 100.4%  86.9%  30.9%  21.4%  11.4%     -- -42.1%
f5   17,293 246.4% 223.0% 126.3% 109.8%  92.5%  72.9%     --

1000 rand ints between (0, 50):
=====
   rate/sec     f7     f6     f1     f2     f3     f4     f5
f7    5,051     --  -7.1% -26.5% -29.0% -34.1% -45.7% -71.2%
f6    5,435   7.6%     -- -20.9% -23.6% -29.1% -41.5% -69.0%
f1    6,873  36.1%  26.5%     --  -3.4% -10.3% -26.1% -60.8%
f2    7,118  40.9%  31.0%   3.6%     --  -7.1% -23.4% -59.4%
f3    7,661  51.7%  41.0%  11.5%   7.6%     -- -17.6% -56.3%
f4    9,297  84.0%  71.1%  35.3%  30.6%  21.3%     -- -47.0%
f5   17,531 247.1% 222.6% 155.1% 146.3% 128.8%  88.6%     --

1000 rand ints between (0, 500):
=====
   rate/sec     f2     f4     f6     f7     f3     f1     f5
f2    3,985     -- -11.0% -13.6% -14.8% -25.7% -40.4% -66.9%
f4    4,479  12.4%     --  -2.9%  -4.3% -16.5% -33.0% -62.8%
f6    4,613  15.8%   3.0%     --  -1.4% -14.0% -31.0% -61.6%
f7    4,680  17.4%   4.5%   1.4%     -- -12.7% -30.0% -61.1%
f3    5,361  34.5%  19.7%  16.2%  14.6%     -- -19.8% -55.4%
f1    6,683  67.7%  49.2%  44.9%  42.8%  24.6%     -- -44.4%
f5   12,028 201.8% 168.6% 160.7% 157.0% 124.3%  80.0%     --

And Python 2:

2.7.6 (default, Dec  1 2013, 13:26:15) 
[GCC 4.2.1 Compatible Apple LLVM 5.0 (clang-500.2.79)]
100 rand ints between (0, 5):
=====
   rate/sec     f5     f7     f6     f2     f1     f3     f4
f5   24,955     -- -41.8% -42.5% -51.3% -55.7% -61.6% -65.2%
f7   42,867  71.8%     --  -1.2% -16.4% -23.9% -34.0% -40.2%
f6   43,382  73.8%   1.2%     -- -15.4% -23.0% -33.2% -39.5%
f2   51,293 105.5%  19.7%  18.2%     --  -9.0% -21.0% -28.5%
f1   56,357 125.8%  31.5%  29.9%   9.9%     -- -13.2% -21.4%
f3   64,924 160.2%  51.5%  49.7%  26.6%  15.2%     --  -9.5%
f4   71,709 187.3%  67.3%  65.3%  39.8%  27.2%  10.5%     --

100 rand ints between (0, 50):
=====
   rate/sec     f2     f5     f7     f6     f4     f3     f1
f2   22,439     --  -4.7% -45.1% -45.5% -50.7% -63.3% -64.5%
f5   23,553   5.0%     -- -42.4% -42.8% -48.3% -61.5% -62.8%
f7   40,878  82.2%  73.6%     --  -0.7% -10.2% -33.2% -35.4%
f6   41,164  83.4%  74.8%   0.7%     --  -9.6% -32.7% -34.9%
f4   45,525 102.9%  93.3%  11.4%  10.6%     -- -25.6% -28.0%
f3   61,167 172.6% 159.7%  49.6%  48.6%  34.4%     --  -3.3%
f1   63,261 181.9% 168.6%  54.8%  53.7%  39.0%   3.4%     --

100 rand ints between (0, 500):
=====
   rate/sec     f2     f5     f4     f6     f7     f3     f1
f2   13,122     -- -39.9% -56.2% -63.2% -63.8% -75.8% -80.0%
f5   21,837  66.4%     -- -27.1% -38.7% -39.8% -59.6% -66.7%
f4   29,945 128.2%  37.1%     -- -16.0% -17.4% -44.7% -54.3%
f6   35,633 171.6%  63.2%  19.0%     --  -1.7% -34.2% -45.7%
f7   36,257 176.3%  66.0%  21.1%   1.8%     -- -33.0% -44.7%
f3   54,113 312.4% 147.8%  80.7%  51.9%  49.2%     -- -17.5%
f1   65,570 399.7% 200.3% 119.0%  84.0%  80.8%  21.2%     --

1000 rand ints between (0, 5):
=====
   rate/sec     f5     f7     f6     f1     f2     f3     f4
f5    2,787     -- -37.7% -38.4% -53.3% -59.9% -60.4% -67.0%
f7    4,477  60.6%     --  -1.1% -25.0% -35.6% -36.3% -47.0%
f6    4,524  62.3%   1.1%     -- -24.2% -34.9% -35.6% -46.5%
f1    5,972 114.3%  33.4%  32.0%     -- -14.1% -15.0% -29.3%
f2    6,953 149.5%  55.3%  53.7%  16.4%     --  -1.1% -17.7%
f3    7,030 152.2%  57.0%  55.4%  17.7%   1.1%     -- -16.8%
f4    8,452 203.3%  88.8%  86.8%  41.5%  21.6%  20.2%     --

1000 rand ints between (0, 50):
=====
   rate/sec     f5     f7     f6     f2     f1     f3     f4
f5    2,667     -- -37.8% -38.7% -53.0% -55.9% -61.1% -65.3%
f7    4,286  60.7%     --  -1.5% -24.5% -29.1% -37.5% -44.2%
f6    4,351  63.1%   1.5%     -- -23.4% -28.0% -36.6% -43.4%
f2    5,677 112.8%  32.4%  30.5%     --  -6.1% -17.3% -26.1%
f1    6,045 126.6%  41.0%  39.0%   6.5%     -- -11.9% -21.4%
f3    6,862 157.3%  60.1%  57.7%  20.9%  13.5%     -- -10.7%
f4    7,687 188.2%  79.3%  76.7%  35.4%  27.2%  12.0%     --

1000 rand ints between (0, 500):
=====
   rate/sec     f2     f5     f7     f6     f4     f3     f1
f2    2,018     -- -16.1% -44.1% -46.2% -53.4% -61.8% -63.0%
f5    2,405  19.1%     -- -33.4% -35.9% -44.5% -54.4% -55.9%
f7    3,609  78.8%  50.1%     --  -3.8% -16.7% -31.6% -33.8%
f6    3,753  85.9%  56.1%   4.0%     -- -13.4% -28.9% -31.2%
f4    4,334 114.7%  80.2%  20.1%  15.5%     -- -17.9% -20.5%
f3    5,277 161.5% 119.5%  46.2%  40.6%  21.8%     --  -3.2%
f1    5,454 170.2% 126.8%  51.1%  45.3%  25.8%   3.3%     --

So -- it depends.

Conclusions:

1. The Counter method is almost always among the slowest
2. The Counter method is among the slowest on Python 2 but by far the fastest on Python 3.4
3. The try/except version is usually among the slowest
4. The if key in dict version is predictably one of the best/fastest regardless of the size or key count
5. The {}.fromkeys(tgt, 0) is very predictable
6. The defaultdict version is fastest on larger lists. Smaller lists the longer setup time is amortized over too few elements.

Answer 2

There is another point when it comes to coding style. As it's common python coding style to use EAFP (Easier to ask for forgiveness than permission) which assumes the existence of valid keys and catches exceptions if the assumption proves false.

Due this common coding style I've always used the try/except approach and was sure that this is faster than LBYL style (Look before you leap). As I learned by the answers here it definitely depends. As long as you can expect an existing key I would go for the try/except approach.

Answer 3

Update: Not sure if I was testing the right thing anymore, but still found the results interesting.

Python 2:

0% missing keys, Standard access: 0.419198036194
0% missing keys, try/except access: 0.309811115265
50% missing keys, Standard access: 0.417014837265
50% missing keys, try/except access: 0.309100866318
100% missing keys, Standard access: 0.416236877441
100% missing keys, try/except access: 0.310797929764

I tested 3 dictionaries with varying amounts of keys, using the normal and the try/except method. The try/except method was faster each time for me.

My code:

from timeit import timeit

size = 2**10
allkeys = "0% missing keys", dict([(i, 0) for i in range(size)])
somekeys= "50% missing keys", dict([(i*2, 0) for i in range(size//2)])
nokeys = "100% missing keys", dict([])

def test_normal():
    """Standard access"""
    for i in xrange(size):
        if i in d:
            d[i] += 1
        else:
            d[i] = 1

def test_try():
    """try/except access"""
    for i in xrange(size):
        try:
            d[i] += 1
        except KeyError:
            d[i] = 1

for trial in (allkeys, somekeys, nokeys):
    d = trial[1]
    for test in (test_normal, test_try):
        trial_time = timeit("test()",
                            setup="from __main__ import test",
                            number=2**10)
        print "{0}, {1}: {2}".format(trial[0], test.__doc__, trial_time)

The code now uses timeit, which is probably more accurate.

Answer 4

NOTE: purely speculative

I think the first would be slower as it locates the key in the dictionary twice, first in the if statement, then in the C code for dictionary access. The try-except could be slower when many of the keys aren't in the dictionary, as handling the exception involves some overhead.

I set the list to range(100) and left the dictionary empty. The first using if takes 8.003 seconds and the second using try-except takes 30.976 seconds! The overhead is quite significant in a case like this where there is nothing much else being done.

Answer 5

import random
from pip._vendor.distlib.compat import raw_input

x=random.randint(1,99)
guess = int(raw_input("Enter a integer from 1 to 99:"))
    while x !="guess":
        print
        if guess<x:
            print ("guess is low")
            guess= int(raw_input("Enter a integer from 1 to 99:"))
        elif guess >x:
            print ("guess is high")
            guess = int(raw_input("Enter a integer from 1 to 99:"))
        else:
            print (" you guessed it !")
        break
        print