Determine whether a key is present in a dictionary [duplicate]

Possible Duplicate:
'has_key()' or 'in'?

I have a Python dictionary like :

mydict = {'name':'abc','city':'xyz','country','def'}

I want to check if a key is in dictionary or not. I am eager to know that which is more preferable from the following two cases and why?

1> if mydict.has_key('name'):
2> if 'name' in mydict:

if 'name' in mydict:

is the preferred, pythonic version. Use of has_key() is discouraged, and this method has been removed in Python 3.

In the same vein as martineau's response, the best solution is often not to check. For example, the code

if x in d:
    foo = d[x]
else:
    foo = bar

is normally written

foo = d.get(x, bar)

which is shorter and more directly speaks to what you mean.

Another common case is something like

if x not in d:
    d[x] = []

d[x].append(foo)

which can be rewritten

d.setdefault(x, []).append(foo)

or rewritten even better by using a collections.defaultdict(list) for d and writing

d[x].append(foo)

In terms of bytecode, in saves a LOAD_ATTR and replaces a CALL_FUNCTION with a COMPARE_OP.

>>> dis.dis(indict)
  2           0 LOAD_GLOBAL              0 (name)
              3 LOAD_GLOBAL              1 (d)
              6 COMPARE_OP               6 (in)
              9 POP_TOP             


>>> dis.dis(haskey)
  2           0 LOAD_GLOBAL              0 (d)
              3 LOAD_ATTR                1 (haskey)
              6 LOAD_GLOBAL              2 (name)
              9 CALL_FUNCTION            1
             12 POP_TOP             

My feelings are that in is much more readable and is to be preferred in every case that I can think of.

In terms of performance, the timing reflects the opcode

$ python -mtimeit -s'd = dict((i, i) for i in range(10000))' "'foo' in d"
 10000000 loops, best of 3: 0.11 usec per loop

$ python -mtimeit -s'd = dict((i, i) for i in range(10000))' "d.has_key('foo')"
  1000000 loops, best of 3: 0.205 usec per loop

in is almost twice as fast.