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Difficulty level: ♦♦♦♦♢
❝ Every Saturday since we’ve lived in this apartment, I have awakened at 6:15, poured myself a bowl of cereal, added
a quarter-cup of 2% milk, sat on this end of this couch, turned on BBC America, and watched Doctor Who. ❞
— Sheldon, The Big Bang Theory
On the surface, the concept of serialization is simple. You have a data structure in memory that you want to save, reuse, or send to someone else. How would you do that? Well, that depends on how you want to save it, how you want to reuse it, and to whom you want to send it. Many games allow you to save your progress when you quit the game and pick up where you left off when you relaunch the game. (Actually, many non-gaming applications do this as well.) In this case, a data structure that captures “your progress so far” needs to be stored on disk when you quit, then loaded from disk when you relaunch. The data is only meant to be used by the same program that created it, never sent over a network, and never read by anything other than the program that created it. Therefore, the interoperability issues are limited to ensuring that later versions of the program can read data written by earlier versions.
For cases like this, the pickle module is ideal. It’s part of the Python standard library, so it’s always available. It’s fast; the bulk of it is written in C, like the Python interpreter itself. It can store arbitrarily complex Python data structures.
What can the pickle module store?
bytes objects, byte arrays, and None.
If this isn’t enough for you, the pickle module is also extensible. If you’re interested in extensibility, check out the links in the Further Reading section at the end of the chapter.
This chapter tells a tale with two Python Shells. All of the examples in this chapter are part of a single story arc. You will be asked to switch back and forth between the two Python Shells as I demonstrate the pickle and json modules.
To help keep things straight, open the Python Shell and define the following variable:
>>> shell = 1
Keep that window open. Now open another Python Shell and define the following variable:
>>> shell = 2
Throughout this chapter, I will use the shell variable to indicate which Python Shell is being used in each example.
⁂
The pickle module works with data structures. Let’s build one.
>>> shell ① 1 >>> entry = {} ② >>> entry['title'] = 'Dive into history, 2009 edition' >>> entry['article_link'] = 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition' >>> entry['comments_link'] = None >>> entry['internal_id'] = b'\xDE\xD5\xB4\xF8' >>> entry['tags'] = ('diveintopython', 'docbook', 'html') >>> entry['published'] = True >>> import time >>> entry['published_date'] = time.strptime('Fri Mar 27 22:20:42 2009') ③ >>> entry['published_date'] time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1)
pickle module. Don’t read too much into these values.
time module contains a data structure (time_struct) to represent a point in time (accurate to one millisecond) and functions to manipulate time structs. The strptime() function takes a formatted string an converts it to a time_struct. This string is in the default format, but you can control that with format codes. See the time module for more details.
That’s a handsome-looking Python dictionary. Let’s save it to a file.
>>> shell ① 1 >>> import pickle >>> with open('entry.pickle', 'wb') as f: ② ... pickle.dump(entry, f) ③ ...
open() function to open a file. Set the file mode to 'wb' to open the file for writing in binary mode. Wrap it in a with statement to ensure the file is closed automatically when you’re done with it.
dump() function in the pickle module takes a serializable Python data structure, serializes it into a binary, Python-specific format using the latest version of the pickle protocol, and saves it to an open file.
That last sentence was pretty important.
pickle module takes a Python data structure and saves it to a file.
entry.pickle file you just created and do anything useful with it in Perl, PHP, Java, or any other language.
pickle module. The pickle protocol has changed several times as new data types have been added to the Python language, but there are still limitations.
pickle module will use the latest version of the pickle protocol. This ensures that you have maximum flexibility in the types of data you can serialize, but it also means that the resulting file will not be readable by older versions of Python that do not support the latest version of the pickle protocol.
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Now switch to your second Python Shell — i.e. not the one where you created the entry dictionary.
>>> shell ① 2 >>> entry ② Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'entry' is not defined >>> import pickle >>> with open('entry.pickle', 'rb') as f: ③ ... entry = pickle.load(f) ④ ... >>> entry ⑤ {'comments_link': None, 'internal_id': b'\xDE\xD5\xB4\xF8', 'title': 'Dive into history, 2009 edition', 'tags': ('diveintopython', 'docbook', 'html'), 'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition', 'published_date': time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1), 'published': True}
entry.pickle file you created in Python Shell #1. The pickle module uses a binary data format, so you should always open pickle files in binary mode.
pickle.load() function takes a stream object, reads the serialized data from the stream, creates a new Python object, recreates the serialized data in the new Python object, and returns the new Python object.
The pickle.dump() / pickle.load() cycle results in a new data structure that is equal to the original data structure.
>>> shell ① 1 >>> with open('entry.pickle', 'rb') as f: ② ... entry2 = pickle.load(f) ③ ... >>> entry2 == entry ④ True >>> entry2 is entry ⑤ False >>> entry2['tags'] ⑥ ('diveintopython', 'docbook', 'html') >>> entry2['internal_id'] b'\xDE\xD5\xB4\xF8'
entry.pickle file.
entry.pickle file. Now you’ve read the serialized data from that file and created a perfect replica of the original data structure.
'tags' key is a tuple, and the value of the 'internal_id' key is a bytes object.
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The examples in the previous section showed how to serialize a Python object directly to a file on disk. But what if you don’t want or need a file? You can also serialize to a bytes object in memory.
>>> shell 1 >>> b = pickle.dumps(entry) ① >>> type(b) ② <class 'bytes'> >>> entry3 = pickle.loads(b) ③ >>> entry3 == entry ④ True
pickle.dumps() function (note the 's' at the end of the function name) performs the same serialization as the pickle.dump() function. Instead of taking a stream object and writing the serialized data to a file on disk, it simply returns the serialized data.
pickle.dumps() function returns a bytes object.
pickle.loads() function (again, note the 's' at the end of the function name) performs the same deserialization as the pickle.load() function. Instead of taking a stream object and reading the serialized data from a file, it takes a bytes object containing serialized data, such as the one returned by the pickle.dumps() function.
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The pickle protocol has been around for many years, and it has matured as Python itself has matured. There are now four different versions of the pickle protocol.
bytes objects and byte arrays. It is a binary format.
Oh look, the difference between bytes and strings rears its ugly head again. (If you’re surprised, you haven’t been paying attention.) What this means in practice is that, while Python 3 can read data pickled with protocol version 2, Python 2 can not read data pickled with protocol version 3.
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What does the pickle protocol look like? Let’s jump out of the Python Shell for a moment and take a look at that entry.pickle file we created.
you@localhost:~/diveintopython3/examples$ ls -l entry.pickle -rw-r--r-- 1 you you 358 Aug 3 13:34 entry.pickle you@localhost:~/diveintopython3/examples$ cat entry.pickle comments_linkqNXtagsqXdiveintopythonqXdocbookqXhtmlq?qX publishedq? XlinkXJhttp://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition q Xpublished_dateq ctime struct_time ?qRqXtitleqXDive into history, 2009 editionqu.
That wasn’t terribly helpful. You can see the strings, but other datatypes end up as unprintable (or at least unreadable) characters. Fields are not obviously delimited by tabs or spaces. This is not a format you would want to debug by yourself.
>>> shell
1
>>> import pickletools
>>> with open('entry.pickle', 'rb') as f:
... pickletools.dis(f)
0: \x80 PROTO 3
2: } EMPTY_DICT
3: q BINPUT 0
5: ( MARK
6: X BINUNICODE 'published_date'
25: q BINPUT 1
27: c GLOBAL 'time struct_time'
45: q BINPUT 2
47: ( MARK
48: M BININT2 2009
51: K BININT1 3
53: K BININT1 27
55: K BININT1 22
57: K BININT1 20
59: K BININT1 42
61: K BININT1 4
63: K BININT1 86
65: J BININT -1
70: t TUPLE (MARK at 47)
71: q BINPUT 3
73: } EMPTY_DICT
74: q BINPUT 4
76: \x86 TUPLE2
77: q BINPUT 5
79: R REDUCE
80: q BINPUT 6
82: X BINUNICODE 'comments_link'
100: q BINPUT 7
102: N NONE
103: X BINUNICODE 'internal_id'
119: q BINPUT 8
121: C SHORT_BINBYTES 'ÞÕ´ø'
127: q BINPUT 9
129: X BINUNICODE 'tags'
138: q BINPUT 10
140: X BINUNICODE 'diveintopython'
159: q BINPUT 11
161: X BINUNICODE 'docbook'
173: q BINPUT 12
175: X BINUNICODE 'html'
184: q BINPUT 13
186: \x87 TUPLE3
187: q BINPUT 14
189: X BINUNICODE 'title'
199: q BINPUT 15
201: X BINUNICODE 'Dive into history, 2009 edition'
237: q BINPUT 16
239: X BINUNICODE 'article_link'
256: q BINPUT 17
258: X BINUNICODE 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition'
337: q BINPUT 18
339: X BINUNICODE 'published'
353: q BINPUT 19
355: \x88 NEWTRUE
356: u SETITEMS (MARK at 5)
357: . STOP
highest protocol among opcodes = 3
The most interesting piece of information in that disassembly is on the last line, because it includes the version of the pickle protocol with which this file was saved. There is no explicit version marker in the pickle protocol. To determine which protocol version was used to store a pickle file, you need to look at the markers (“opcodes”) within the pickled data and use hard-coded knowledge of which opcodes were introduced with each version of the pickle protocol. The pickle.dis() function does exactly that, and it prints the result in the last line of the disassembly output. Here is a function that returns just the version number, without printing anything:
import pickletools
def protocol_version(file_object):
maxproto = -1
for opcode, arg, pos in pickletools.genops(file_object):
maxproto = max(maxproto, opcode.proto)
return maxprotoAnd here it is in action:
>>> import pickleversion
>>> with open('entry.pickle', 'rb') as f:
... v = pickleversion.protocol_version(f)
>>> v
3
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The data format used by the pickle module is Python-specific. It makes no attempt to be compatible with other programming languages. If cross-language compatibility is one of your requirements, you need to look at other serialization formats. One such format is JSON. “JSON” stands for “JavaScript Object Notation,” but don’t let the name fool you — JSON is explicitly designed to be usable across multiple programming languages.
Python 3 includes a json module in the standard library. Like the pickle module, the json module has functions for serializing data structures, storing the serialized data on disk, loading serialized data from disk, and unserializing the data back into a new Python object. But there are some important differences, too. First of all, the JSON data format is text-based, not binary. RFC 4627 defines the JSON format and how different types of data must be encoded as text. For example, a boolean value is stored as either the five-character string 'false' or the four-character string 'true'. All JSON values are case-sensitive.
Second, as with any text-based format, there is the issue of whitespace. JSON allows arbitrary amounts of whitespace (spaces, tabs, carriage returns, and line feeds) between values. This whitespace is “insignificant,” which means that JSON encoders can add as much or as little whitespace as they like, and JSON decoders are required to ignore the whitespace between values. This allows you to “pretty-print” your JSON data, nicely nesting values within values at different indentation levels so you can read it in a standard browser or text editor. Python’s json module has options for pretty-printing during encoding.
Third, there’s the perennial problem of character encoding. JSON encodes values as plain text, but as you know, there ain’t no such thing as “plain text.” JSON must be stored in a Unicode encoding (UTF-32, UTF-16, or the default, UTF-8), and section 3 of RFC 4627 defines how to tell which encoding is being used.
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JSON looks remarkably like a data structure you might define manually in JavaScript. This is no accident; you can actually use the JavaScript eval() function to “decode” JSON-serialized data. (The usual caveats about untrusted input apply, but the point is that JSON is valid JavaScript.) As such, JSON may already look familiar to you.
>>> shell
1
>>> basic_entry = {} ①
>>> basic_entry['id'] = 256
>>> basic_entry['title'] = 'Dive into history, 2009 edition'
>>> basic_entry['tags'] = ('diveintopython', 'docbook', 'html')
>>> basic_entry['published'] = True
>>> basic_entry['comments_link'] = None
>>> import json
>>> with open('basic.json', mode='w', encoding='utf-8') as f: ②
... json.dump(basic_entry, f) ③
pickle module, the json module defines a dump() function which takes a Python data structure and a writeable stream object. The dump() function serializes the Python data structure and writes it to the stream object. Doing this inside a with statement will ensure that the file is closed properly when we’re done.
So what does the resulting JSON serialization look like?
you@localhost:~/diveintopython3/examples$ cat basic.json
{"published": true, "tags": ["diveintopython", "docbook", "html"], "comments_link": null,
"id": 256, "title": "Dive into history, 2009 edition"}
That’s certainly more readable than a pickle file. But JSON can contain arbitrary whitespace between values, and the json module provides an easy way to take advantage of this to create even more readable JSON files.
>>> shell
1
>>> with open('basic-pretty.json', mode='w', encoding='utf-8') as f:
... json.dump(basic_entry, f, indent=2) ①
json.dump() function, it will make the resulting JSON file more readable, at the expense of larger file size. The indent parameter is an integer. 0 means “put each value on its own line.” A number greater than 0 means “put each value on its own line, and use this number of spaces to indent nested data structures.”
And this is the result:
you@localhost:~/diveintopython3/examples$ cat basic-pretty.json
{
"published": true,
"tags": [
"diveintopython",
"docbook",
"html"
],
"comments_link": null,
"id": 256,
"title": "Dive into history, 2009 edition"
}
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Since JSON is not Python-specific, there are some mismatches in its coverage of Python datatypes. Some of them are simply naming differences, but there is two important Python datatypes that are completely missing. See if you can spot them:
| Notes | JSON | Python 3 |
|---|---|---|
| object | dictionary | |
| array | list | |
| string | string | |
| integer | integer | |
| real number | float | |
| * | true
| True
|
| * | false
| False
|
| * | null
| None
|
| * All JSON values are case-sensitive. | ||
Did you notice what was missing? Tuples & bytes! JSON has an array type, which the json module maps to a Python list, but it does not have a separate type for “frozen arrays” (tuples). And while JSON supports strings quite nicely, it has no support for bytes objects or byte arrays.
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Even if JSON has no built-in support for bytes, that doesn’t mean you can’t serialize bytes objects. The json module provides extensibility hooks for encoding and decoding unknown datatypes. (By “unknown,” I mean “not defined in JSON.” Obviously the json module knows about byte arrays, but it’s constrained by the limitations of the JSON specification.) If you want to encode bytes or other datatypes that JSON doesn’t support natively, you need to provide custom encoders and decoders for those types.
>>> shell 1 >>> entry ① {'comments_link': None, 'internal_id': b'\xDE\xD5\xB4\xF8', 'title': 'Dive into history, 2009 edition', 'tags': ('diveintopython', 'docbook', 'html'), 'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition', 'published_date': time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1), 'published': True} >>> import json >>> with open('entry.json', 'w', encoding='utf-8') as f: ② ... json.dump(entry, f) ③ ... Traceback (most recent call last): File "<stdin>", line 5, in <module> File "C:\Python31\lib\json\__init__.py", line 178, in dump for chunk in iterable: File "C:\Python31\lib\json\encoder.py", line 408, in _iterencode for chunk in _iterencode_dict(o, _current_indent_level): File "C:\Python31\lib\json\encoder.py", line 382, in _iterencode_dict for chunk in chunks: File "C:\Python31\lib\json\encoder.py", line 416, in _iterencode o = _default(o) File "C:\Python31\lib\json\encoder.py", line 170, in default raise TypeError(repr(o) + " is not JSON serializable") TypeError: b'\xDE\xD5\xB4\xF8' is not JSON serializable
None value, a string, a tuple of strings, a bytes object, and a time structure.
Here’s what happened: the json.dump() function tried to serialize the bytes object b'\xDE\xD5\xB4\xF8', but it failed, because JSON has no support for bytes objects. However, if storing bytes is important to you, you can define your own “mini-serialization format.”
[download customserializer.py]
def to_json(python_object): ①
if isinstance(python_object, bytes): ②
return {'__class__': 'bytes',
'__value__': list(python_object)} ③
raise TypeError(repr(python_object) + ' is not JSON serializable') ④json.dump() function is unable to serialize by itself — in this case, the bytes object b'\xDE\xD5\xB4\xF8'.
json.dump() function passed to it. This is not strictly necessary if your function only serializes one datatype, but it makes it crystal clear what case your function is covering, and it makes it easier to extend if you need to add serializations for more datatypes later.
bytes object into a dictionary. The __class__ key will hold the original datatype (as a string, 'bytes'), and the __value__ key will hold the actual value. Of course this can’t be a bytes object; the entire point is to convert it into something that can be serialized in JSON! A bytes object is just a sequence of integers; each integer is somewhere in the range 0–255. We can use the list() function to convert the bytes object into a list of integers. So b'\xDE\xD5\xB4\xF8' becomes [222, 213, 180, 248]. (Do the math! It works! The byte \xDE in hexadecimal is 222 in decimal, \xD5 is 213, and so on.)
TypeError so that the json.dump() function knows that your custom serializer did not recognize the type.
That’s it; you don’t need to do anything else. In particular, this custom serialization function returns a Python dictionary, not a string. You’re not doing the entire serializing-to-JSON yourself; you’re only doing the converting-to-a-supported-datatype part. The json.dump() function will do the rest.
>>> shell 1 >>> import customserializer ① >>> with open('entry.json', 'w', encoding='utf-8') as f: ② ... json.dump(entry, f, default=customserializer.to_json) ③ ... Traceback (most recent call last): File "<stdin>", line 9, in <module> json.dump(entry, f, default=customserializer.to_json) File "C:\Python31\lib\json\__init__.py", line 178, in dump for chunk in iterable: File "C:\Python31\lib\json\encoder.py", line 408, in _iterencode for chunk in _iterencode_dict(o, _current_indent_level): File "C:\Python31\lib\json\encoder.py", line 382, in _iterencode_dict for chunk in chunks: File "C:\Python31\lib\json\encoder.py", line 416, in _iterencode o = _default(o) File "/Users/pilgrim/diveintopython3/examples/customserializer.py", line 12, in to_json raise TypeError(repr(python_object) + ' is not JSON serializable') ④ TypeError: time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1) is not JSON serializable
customserializer module is where you just defined the to_json() function in the previous example.
json.dump() function, pass your function into the json.dump() function in the default parameter. (Hooray, everything in Python is an object!)
json.dump() function is no longer complaining about being unable to serialize the bytes object. Now it’s complaining about a completely different object: the time.struct_time object.
While getting a different exception might not seem like progress, it really is! It’ll just take one more tweak to get past this.
import time
def to_json(python_object):
if isinstance(python_object, time.struct_time): ①
return {'__class__': 'time.asctime',
'__value__': time.asctime(python_object)} ②
if isinstance(python_object, bytes):
return {'__class__': 'bytes',
'__value__': list(python_object)}
raise TypeError(repr(python_object) + ' is not JSON serializable')customserializer.to_json() function, we need to check whether the Python object (that the json.dump() function is having trouble with) is a time.struct_time.
bytes object: convert the time.struct_time object to a dictionary that only contains JSON-serializable values. In this case, the easiest way to convert a datetime into a JSON-serializable value is to convert it to a string with the time.asctime() function. The time.asctime() function will convert that nasty-looking time.struct_time into the string 'Fri Mar 27 22:20:42 2009'.
With these two custom conversions, the entire entry data structure should serialize to JSON without any further problems.
>>> shell
1
>>> with open('entry.json', 'w', encoding='utf-8') as f:
... json.dump(entry, f, default=customserializer.to_json)
...
you@localhost:~/diveintopython3/examples$ ls -l example.json
-rw-r--r-- 1 you you 391 Aug 3 13:34 entry.json
you@localhost:~/diveintopython3/examples$ cat example.json
{"published_date": {"__class__": "time.asctime", "__value__": "Fri Mar 27 22:20:42 2009"},
"comments_link": null, "internal_id": {"__class__": "bytes", "__value__": [222, 213, 180, 248]},
"tags": ["diveintopython", "docbook", "html"], "title": "Dive into history, 2009 edition",
"article_link": "http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition",
"published": true}
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Like the pickle module, the json module has a load() function which takes a stream object, reads JSON-encoded data from it, and creates a new Python object that mirrors the JSON data structure.
>>> shell 2 >>> del entry ① >>> entry Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'entry' is not defined >>> import json >>> with open('entry.json', 'r', encoding='utf-8') as f: ... entry = json.load(f) ② ... >>> entry ③ {'comments_link': None, 'internal_id': {'__class__': 'bytes', '__value__': [222, 213, 180, 248]}, 'title': 'Dive into history, 2009 edition', 'tags': ['diveintopython', 'docbook', 'html'], 'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition', 'published_date': {'__class__': 'time.asctime', '__value__': 'Fri Mar 27 22:20:42 2009'}, 'published': True}
pickle module.
json.load() function works the same as the pickle.load() function. You pass in a stream object and it returns a new Python object.
json.load() function successfully read the entry.json file you created in Python Shell #1 and created a new Python object that contained the data. Now the bad news: it didn’t recreate the original entry data structure. The two values 'internal_id' and 'published_date' were recreated as dictionaries — specifically, the dictionaries with JSON-compatible values that you created in the to_json() conversion function.
json.load() doesn’t know anything about any conversion function you may have passed to json.dump(). What you need is the opposite of the to_json() function — a function that will take a custom-converted JSON object and convert it back to the original Python datatype.
# add this to customserializer.py
def from_json(json_object): ①
if '__class__' in json_object: ②
if json_object['__class__'] == 'time.asctime':
return time.strptime(json_object['__value__']) ③
if json_object['__class__'] == 'bytes':
return bytes(json_object['__value__']) ④
return json_object'__class__' key that the to_json() function created. If so, the value of the '__class__' key will tell you how to decode the value back into the original Python datatype.
time.asctime() function, you use the time.strptime() function. This function takes a formatted datetime string (in a customizable format, but it defaults to the same format that time.asctime() defaults to) and returns a time.struct_time.
bytes object, you can use the bytes() function.
That was it; there were only two datatypes handled in the to_json() function, and now those two datatypes are handled in the from_json() function. This is the result:
>>> shell
2
>>> import customserializer
>>> with open('entry.json', 'r', encoding='utf-8') as f:
... entry = json.load(f, object_hook=customserializer.from_json) ①
...
>>> entry ②
{'comments_link': None,
'internal_id': b'\xDE\xD5\xB4\xF8',
'title': 'Dive into history, 2009 edition',
'tags': ['diveintopython', 'docbook', 'html'],
'article_link': 'http://diveintomark.org/archives/2009/03/27/dive-into-history-2009-edition',
'published_date': time.struct_time(tm_year=2009, tm_mon=3, tm_mday=27, tm_hour=22, tm_min=20, tm_sec=42, tm_wday=4, tm_yday=86, tm_isdst=-1),
'published': True}
from_json() function into the deserialization process, pass it as the object_hook parameter to the json.load() function. Functions that take functions; it’s so handy!
'internal_id' key whose value is a bytes object. It also contains a 'published_date' key whose value is a time.struct_time object.
There is one final glitch, though.
>>> shell
1
>>> import customserializer
>>> with open('entry.json', 'r', encoding='utf-8') as f:
... entry2 = json.load(f, object_hook=customserializer.from_json)
...
>>> entry2 == entry ①
False
>>> entry['tags'] ②
('diveintopython', 'docbook', 'html')
>>> entry2['tags'] ③
['diveintopython', 'docbook', 'html']
to_json() function into the serialization, and hooking the from_json() function into the deserialization, we still haven’t recreated a perfect replica of the original data structure. Why not?
'tags' key was a tuple of three strings.
'tags' key is a list of three strings. JSON doesn’t distinguish between tuples and lists; it only has a single list-like datatype, the array, and the json module silently converts both tuples and lists into JSON arrays during serialization. For most uses, you can ignore the difference between tuples and lists, but it’s something to keep in mind as you work with the json module.
☞Many articles about the
picklemodule make references tocPickle. In Python 2, there were two implementations of thepicklemodule, one written in pure Python and another written in C (but still callable from Python). In Python 3, these two modules have been consolidated, so you should always justimport pickle. You may find these articles useful, but you should ignore the now-obsolete information aboutcPickle.
On pickling with the pickle module:
pickle module
pickle and cPickle — Python object serialization
pickle
On JSON and the json module:
json — JavaScript Object Notation Serializer
On pickle extensibility:
© 2001–10 Mark Pilgrim