# Pickle data serialisation
# Using Pickle to serialize and deserialize an object
The pickle
module implements an algorithm for turning an arbitrary Python object into a series of bytes. This process is also called serializing the object. The byte stream representing the object can then be transmitted or stored, and later reconstructed to create a new object with the same characteristics.
For the simplest code, we use the dump()
and load()
functions.
# To serialize the object
import pickle
# An arbitrary collection of objects supported by pickle.
data = {
'a': [1, 2.0, 3, 4+6j],
'b': ("character string", b"byte string"),
'c': {None, True, False}
}
with open('data.pickle', 'wb') as f:
# Pickle the 'data' dictionary using the highest protocol available.
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
# To deserialize the object
import pickle
with open('data.pickle', 'rb') as f:
# The protocol version used is detected automatically, so we do not
# have to specify it.
data = pickle.load(f)
# Using pickle and byte objects
It is also possible to serialize into and deserialize out of byte objects, using
the dumps
and loads
function, which are equivalent to dump
and load
.
serialized_data = pickle.dumps(data, pickle.HIGHEST_PROTOCOL)
# type(serialized_data) is bytes
deserialized_data = pickle.loads(serialized_data)
# deserialized_data == data
# Customize Pickled Data
Some data cannot be pickled. Other data should not be pickled for other reasons.
What will be pickled can be defined in __getstate__
method. This method must return something that is picklable.
On the oposite side is __setstate__
: it will receive what __getstate__
created and has to initialize the object.
class A(object):
def __init__(self, important_data):
self.important_data = important_data
# Add data which cannot be pickled:
self.func = lambda: 7
# Add data which should never be pickled, because it expires quickly:
self.is_up_to_date = False
def __getstate__(self):
return [self.important_data] # only this is needed
def __setstate__(self, state):
self.important_data = state[0]
self.func = lambda: 7 # just some hard-coded unpicklable function
self.is_up_to_date = False # even if it was before pickling
Now, this can be done:
>>> a1 = A('very important')
>>>
>>> s = pickle.dumps(a1) # calls a1.__getstate__()
>>>
>>> a2 = pickle.loads(s) # calls a1.__setstate__(['very important'])
>>> a2
<__main__.A object at 0x0000000002742470>
>>> a2.important_data
'very important'
>>> a2.func()
7
The implementation here pikles a list with one value: [self.important_data]
. That was just an example, __getstate__
could have returned anything that is picklable, as long as __setstate__
knows how to do the oppoisite. A good alternative is a dictionary of all values: {'important_data': self.important_data}
.
Constructor is not called!
Note that in the previous example instance a2
was created in pickle.loads
without ever calling A.__init__
, so A.__setstate__
had to initialize everything that __init__
would have initialized if it were called.
# Syntax
# Parameters
Parameter | Details |
---|---|
object | The object which is to be stored |
file | The open file which will contain the object |
protocol | The protocol used for pickling the object (optional parameter) |
buffer | A bytes object that contains a serialized object |
# Remarks
# Pickleable types
The following objects are picklable.
None
,True
, andFalse
- numbers (of all types)
- strings (of all types)
tuple
s,list
s,set
s, anddict
s containing only picklable objects- functions defined at the top level of a module
- built-in functions
- classes that are defined at the top level of a module
- instances of such classes whose
__dict__
or the result of calling__getstate__()
is picklable (see the official docs (opens new window) for details).
Based on the official Python documentation (opens new window).
# pickle
and security
The pickle module is not secure. It should not be used when receiving the serialized data from an untrusted party, such as over the Internet.