3.3. Serialization Pickle¶

3.3.1. What is `pickle`?¶

Python object serialization format
pickle vs. cPickle

3.3.2. Serialize data types¶

3.3.3. Dump to string¶

import pickle


pickle.dumps('Jan Twardowski')
# b'\x80\x03X\x0e\x00\x00\x00Jan Twardowskiq\x00.'

pickle.dumps(1)
# b'\x80\x03K\x01.'

pickle.dumps(1.0)
# b'\x80\x03G?\xf0\x00\x00\x00\x00\x00\x00.'

pickle.dumps(1.2)
# b'\x80\x03G?\xf3333333.'

pickle.dumps(1.5)
# b'\x80\x03G?\xf8\x00\x00\x00\x00\x00\x00.'

3.3.4. Load from string¶

import pickle


pickle.loads(b'\x80\x03X\x0e\x00\x00\x00Jan Twardowskiq\x00.')
# 'Jan Twardowski'

pickle.loads(b'\x80\x03K\x01.')
# 1

pickle.loads(b'\x80\x03G?\xf0\x00\x00\x00\x00\x00\x00.')
# 1.0

pickle.loads(b'\x80\x03G?\xf3333333.')
# 1.2

pickle.loads(b'\x80\x03G?\xf8\x00\x00\x00\x00\x00\x00.')
# 1.5

3.3.5. Serialize sequences¶

3.3.6. Dump to string¶

import pickle


pickle.dumps([1, 2, 3])
# b'\x80\x03]q\x00(K\x01K\x02K\x03e.

pickle.dumps((1, 2, 3))
# b'\x80\x03K\x01K\x02K\x03\x87q\x00.'

pickle.dumps({1, 2, 3})
# b'\x80\x03cbuiltins\nset\nq\x00]q\x01(K\x01K\x02K\x03e\x85q\x02Rq\x03.'

pickle.dumps({'a': 1, 'b': 2, 'c': 3})
# b'\x80\x03}q\x00(X\x01\x00\x00\x00aq\x01K\x01X\x01\x00\x00\x00bq\x02K\x02X\x01\x00\x00\x00cq\x03K\x03u.'

3.3.7. Load from string¶

import pickle


pickle.loads(b'\x80\x03]q\x00(K\x01K\x02K\x03e.)
# [1, 2, 3]

pickle.loads(b'\x80\x03K\x01K\x02K\x03\x87q\x00.')
# (1, 2, 3)

pickle.loads(b'\x80\x03cbuiltins\nset\nq\x00]q\x01(K\x01K\x02K\x03e\x85q\x02Rq\x03.')
# {1, 2, 3}

pickle.loads(b'\x80\x03}q\x00(X\x01\x00\x00\x00aq\x01K\x01X\x01\x00\x00\x00bq\x02K\x02X\x01\x00\x00\x00cq\x03K\x03u.')
# {'a': 1, 'b': 2, 'c': 3}

3.3.8. Serialize Dates and Datetimes¶

import pickle


dt = datetime(1969, 7, 21, 2, 56, 15)
pickle.dumps(dt)
# b'\x80\x03cdatetime\ndatetime\nq\x00C\n\x07\xb1\x07\x15\x028\x0f\x00\x00\x00q\x01\x85q\x02Rq\x03.'

import pickle


pickle.loads(b'\x80\x03cdatetime\ndatetime\nq\x00C\n\x07\xb1\x07\x15\x028\x0f\x00\x00\x00q\x01\x85q\x02Rq\x03.')
# datetime.datetime(1969, 7, 21, 2, 56, 15)

3.3.9. Serialize and deserialize objects¶

import pickle


class Astronaut:
    def __init__(self, firstname, lastname):
        self.firstname = firstname
        self.lastname = lastname

jan = Astronaut('Jan', 'Twardowski')

pickle.dumps(jan)
# b'\x80\x03c__main__\nAstronaut\nq\x00)\x81q\x01}q\x02(X\n\x00\x00\x00firstnameq\x03X\x03\x00\x00\x00Janq\x04X\t\x00\x00\x00lastnameq\x05X\n\x00\x00\x00Twardowskiq\x06ub.'

pickle.loads(b'\x80\x03c__main__\nAstronaut\nq\x00)\x81q\x01}q\x02(X\n\x00\x00\x00firstnameq\x03X\x03\x00\x00\x00Janq\x04X\t\x00\x00\x00lastnameq\x05X\n\x00\x00\x00Twardowskiq\x06ub.')
# <__main__.Astronaut object at 0x10585f8d0>

3.3.10. Serialize and deserialize to file¶

File extension pkl

3.3.11. Dump to file¶

Dump to file:

import pickle

DATA = [1, 2, 3]

with open('filename.pkl', mode='wb') as file:
    pickle.dump(DATA, file)

3.3.12. Load from file¶

Load from file:

import pickle

with open('filename.pkl', mode='rb') as file:
    result = pickle.load(file)

print(result)

3.3.13. Examples¶

Advanced Example:

import pickle
from datetime import datetime, timezone, timedelta


def month_ago(dt):
    return dt - timedelta(days=30)


class Astronaut:
    agency = 'NASA'

    def __init__(self, name):
        self.name = name


jose = Astronaut(name='José Jiménez')
now = datetime.now(tz=timezone.utc)


DATA = [
    jose,
    Astronaut,
    month_ago(now),
    str(now),
    now.__str__(),
    '{}'.format(now),
    f'{now}',
    {'imie': 'Иван', 'nazwisko': 'Иванович'},
    {10, 20, 30},
    (1,),
    10,
    10.5,
]

pickle.dumps(DATA)
# b'\x80\x03]q\x00(c__main__\nAstronaut\nq\x01)\x81q\x02}q\x03X\x04\x00\x00\x00nameq\x04X\x0c\x00\x00\x00Jose Jimenezq\x05sbh\x01cdatetime\ndatetime\nq\x06C\n\x07\xe2\t\x0b\r\n\x05\x04\xa9\xfdq\x07cdatetime\ntimezone\nq\x08cdatetime\ntimedelta\nq\tK\x00K\x00K\x00\x87q\nRq\x0b\x85q\x0cRq\r\x86q\x0eRq\x0fX \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x10X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x11X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x12X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x13}q\x14(X\x04\x00\x00\x00imieq\x15X\x08\x00\x00\x00\xd0\x98\xd0\xb2\xd0\xb0\xd0\xbdq\x16X\x08\x00\x00\x00nazwiskoq\x17X\x10\x00\x00\x00\xd0\x98\xd0\xb2\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2\xd0\xb8\xd1\x87q\x18ucbuiltins\nset\nq\x19]q\x1a(K\nK\x14K\x1ee\x85q\x1bRq\x1cK\x01\x85q\x1dK\nG@%\x00\x00\x00\x00\x00\x00e.'

pickle.loads(b'\x80\x03]q\x00(c__main__\nAstronaut\nq\x01)\x81q\x02}q\x03X\x04\x00\x00\x00nameq\x04X\x0c\x00\x00\x00Jose Jimenezq\x05sbh\x01cdatetime\ndatetime\nq\x06C\n\x07\xe2\t\x0b\r\n\x05\x04\xa9\xfdq\x07cdatetime\ntimezone\nq\x08cdatetime\ntimedelta\nq\tK\x00K\x00K\x00\x87q\nRq\x0b\x85q\x0cRq\r\x86q\x0eRq\x0fX \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x10X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x11X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x12X \x00\x00\x002018-10-11 13:10:05.305661+00:00q\x13}q\x14(X\x04\x00\x00\x00imieq\x15X\x08\x00\x00\x00\xd0\x98\xd0\xb2\xd0\xb0\xd0\xbdq\x16X\x08\x00\x00\x00nazwiskoq\x17X\x10\x00\x00\x00\xd0\x98\xd0\xb2\xd0\xb0\xd0\xbd\xd0\xbe\xd0\xb2\xd0\xb8\xd1\x87q\x18ucbuiltins\nset\nq\x19]q\x1a(K\nK\x14K\x1ee\x85q\x1bRq\x1cK\x01\x85q\x1dK\nG@%\x00\x00\x00\x00\x00\x00e.')
# [
#   <__main__.Astronaut object at 0x10585f850>,
#   <class '__main__.Astronaut'>,
#   datetime.datetime(2018, 9, 11, 13, 10, 5, 305661, tzinfo=datetime.timezone.utc),
#   '2018-10-11 13:10:05.305661+00:00',
#   '2018-10-11 13:10:05.305661+00:00',
#   '2018-10-11 13:10:05.305661+00:00',
#   '2018-10-11 13:10:05.305661+00:00',
#   {'imie': 'Иван', 'nazwisko': 'Иванович'},
#   {10, 20, 30},
#   (1,),
#   10,
#   10.5
# ]

3.3.14. Assignments¶

Code 3.12. Solution¶

"""
* Assignment: Pickle Serialization
* Complexity: easy
* Lines of code: 4 lines
* Time: 5 min

English:
    1. Use data from "Given" section (see below)
    2. Using `pickle` save data structure to file
    3. Recreate data structure from file
    4. Compare result with "Tests" section (see below)

Polish:
    1. Użyj danych z sekcji "Given" (patrz poniżej)
    2. Za pomocą `pickle` zapisz strukturę danych do pliku
    3. Odtwórz strukturę danych na podstawie danych z pliku
    4. Porównaj wyniki z sekcją "Tests" (patrz poniżej)

Tests:
    >>> result  # doctest: +NORMALIZE_WHITESPACE
    [Astronaut(name='Jan Twardowski', missions=[Mission(year=1969, name='Apollo 18'), Mission(year=2024, name='Artemis 3')]),
     Astronaut(name='Mark Watney', missions=[Mission(year=2035, name='Ares 3')]),
     Astronaut(name='Melissa Lewis', missions=[])]
    >>> from os import remove
    >>> remove(FILE)
"""


# Given
import pickle
from dataclasses import dataclass, field

FILE = r'_temporary.pkl'


@dataclass
class Astronaut:
    name: str
    missions: list = field(default_factory=list)


@dataclass
class Mission:
    year: int
    name: str


CREW = [
    Astronaut('Jan Twardowski', missions=[
        Mission(1969, 'Apollo 18'),
        Mission(2024, 'Artemis 3')]),

    Astronaut('Mark Watney', missions=[
        Mission(2035, 'Ares 3')]),

    Astronaut('Melissa Lewis'),
]

3.3. Serialization Pickle¶

3.3.1. What is pickle?¶

3.3.2. Serialize data types¶

3.3.3. Dump to string¶

3.3.4. Load from string¶

3.3.5. Serialize sequences¶

3.3.6. Dump to string¶

3.3.7. Load from string¶

3.3.8. Serialize Dates and Datetimes¶

3.3.9. Serialize and deserialize objects¶

3.3.10. Serialize and deserialize to file¶

3.3.11. Dump to file¶

3.3.12. Load from file¶

3.3.13. Examples¶

3.3.14. Assignments¶

3.3.1. What is `pickle`?¶