1、list can hold arbitrary objects and can expand dynamically as new items are added. A list is an ordered set of items.
2、A tuple is an
immutable list. A tuple can not be changed in any way once it is created.
immutable list. A tuple can not be changed in any way once it is created.
3、A set is an
unordered “bag”
of unique values. A single set can contain values of any immutable datatype.
unordered “bag”
of unique values. A single set can contain values of any immutable datatype.
4、A dictionary is an
unordered set of key-value pairs. keys are
unique and
immutable
unordered set of key-value pairs. keys are
unique and
immutable
5、import os, glob, humansize
metadata_list = [(f, os.stat(f)) for f in glob.glob('*test*.py')]
metadata_dict = {f:os.stat(f) for f in glob.glob('*')}
humansize_dict = {os.path.splitext(f)[0]:humansize.approximate_size(meta.st_size) \
for f, meta in metadata_dict.items() if meta.st_size > 6000}
a_set = {2**x for x in range(10)}
6、Bytes are not characters; bytes are bytes. Characters are an abstraction. A string is a sequence of those
abstractions. In Python 3, all strings are
immutable sequences of Unicode characters.The built-in len()
immutable sequences of Unicode characters.The built-in len()
function returns the length of the string, i.e. the
number of characters. A string is like a tuple of characters.
number of characters. A string is like a tuple of characters.
An
immutable sequence of numbers-between-0-and-255 is called a bytes object.
immutable sequence of numbers-between-0-and-255 is called a bytes object.
Each item in a string is a string, each item in a byte array is an integer.
aBuf = b'\xEF\xBB\xBF'
aBuf[-1] #191
aBuf[-1:] #b'\xbf' byte array
7、To define a bytes object, use the
b' ' “byte literal” syntax. Each byte within the byte literal can be an
b' ' “byte literal” syntax. Each byte within the byte literal can be an
ASCII character or an encoded hexadecimal number from \x00 to \xff (0–255).To convert a bytes object into
a mutable bytearray object, use the built-in bytearray() function.
8、bytes objects have a
decode() method that takes acharacter encoding and returns a string, and strings
decode() method that takes acharacter encoding and returns a string, and strings
have an
encode() method that takes a characterencoding and returns a bytes object.
encode() method that takes a characterencoding and returns a bytes object.
9、'1MB = 1000{0.modules[humansize].SUFFIXES[1000][0]}'.format(sys) #compound field names
10、“The rules for parsing an item key are very simple. If it starts with a digit, then it is treated as a number, otherwise it is used as a string.”
11、Within a replacement field, a colon (:) marks the start of the format specifier.
12、compact regular expressions
import re
s = '100 BROAD ROAD APT. 3'
re.sub(r'\bROAD\b', 'RD.', s)
re.sub(r'\bROAD\b', 'RD.', s)
# re.sub(b'\bROAD\b', 'RD.', s)
# search bytes
pattern = '^M{0,3}(CM|CD|D?C{0,3})(XC|XL|L?X{0,3})(IX|IV|V?I{0,3})$'
re.search(pattern, 'MDLV')
re.search(pattern, 'MDLV')
phonePattern = re.compile(r'(\d{3})\D*(\d{3})\D*(\d{4})\D*(\d*)$')
#Putting it all in parentheses means “match exactly three numeric digits, and then remember them as a #group that I can ask for later”.
phonePattern.search('(800)5551212 ext. 1234').groups()
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212').groups()
('800', '555', '1212', '')
>>> phonePattern.search('work 1-(800) 555.1212 #1234')
phonePattern.search('(800)5551212 ext. 1234').groups()
('800', '555', '1212', '1234')
>>> phonePattern.search('800-555-1212').groups()
('800', '555', '1212', '')
>>> phonePattern.search('work 1-(800) 555.1212 #1234')
('800', '555', '1212', '1234')
if phonePattern.match('800-555-1212'):
do_something
if phonePattern.search('800-555-1212'):
do_something
re.sub('([^aeiou])y$', r'\1ies', 'vacancy')
'vacancies'
# \1, which means “hey, that first group you remembered? put it right here.”
re.findall('[0-9]+', '16 2-by-4s in rows of 8')
['16', '2', '4', '8']
re.findall('[A-Z]+', 'SEND + MORE == MONEY')
['SEND', 'MORE', 'MONEY']
['16', '2', '4', '8']
re.findall('[A-Z]+', 'SEND + MORE == MONEY')
['SEND', 'MORE', 'MONEY']
re.findall(' s.*? s', "The sixth sick sheikh's sixth sheep's sick.")
# (.*?) means the shortest possible series of any character
[' sixth s', " sheikh's s", " sheep's s"]
#doesn’t return overlapping matches.
#doesn’t return overlapping matches.
13、verbose regular expressions
Python allows you to do this with something called verbose regular expressions. A verbose regular expression
is different from a compact regular expression in two ways:
is different from a compact regular expression in two ways:
• Whitespace is ignored. Spaces, tabs, and carriage returns are not matched as spaces, tabs, and carriage
returns. They’re not matched at all. (If you want to match a space in a verbose regular expression, you’ll
need to escape it by putting a backslash in front of it.)
returns. They’re not matched at all. (If you want to match a space in a verbose regular expression, you’ll
need to escape it by putting a backslash in front of it.)
• Comments are ignored. A comment in a verbose regular expression is just like a comment in Python code:
it starts with a # character and goes until the end of the line. In this case it’s a comment within a multi-line
string instead of within your source code, but it works the same way.
it starts with a # character and goes until the end of the line. In this case it’s a comment within a multi-line
string instead of within your source code, but it works the same way.
pattern = '''
^ # beginning of string
M{0,3} # thousands - 0 to 3 Ms
(CM|CD|D?C{0,3}) # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 Cs),
# or 500-800 (D, followed by 0 to 3 Cs)
(XC|XL|L?X{0,3}) # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 Xs),
# or 50-80 (L, followed by 0 to 3 Xs)
(IX|IV|V?I{0,3}) # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 Is),
# or 5-8 (V, followed by 0 to 3 Is)
$ # end of string
'''
re.search(pattern, 'M', re.VERBOSE)
^ # beginning of string
M{0,3} # thousands - 0 to 3 Ms
(CM|CD|D?C{0,3}) # hundreds - 900 (CM), 400 (CD), 0-300 (0 to 3 Cs),
# or 500-800 (D, followed by 0 to 3 Cs)
(XC|XL|L?X{0,3}) # tens - 90 (XC), 40 (XL), 0-30 (0 to 3 Xs),
# or 50-80 (L, followed by 0 to 3 Xs)
(IX|IV|V?I{0,3}) # ones - 9 (IX), 4 (IV), 0-3 (0 to 3 Is),
# or 5-8 (V, followed by 0 to 3 Is)
$ # end of string
'''
re.search(pattern, 'M', re.VERBOSE)
phonePattern = re.compile(r'''
# don't match beginning of string, number can start anywhere
(\d{3}) # area code is 3 digits (e.g. '800')
\D* # optional separator is any number of non-digits
(\d{3}) # trunk is 3 digits (e.g. '555')
\D* # optional separator
(\d{4}) # rest of number is 4 digits (e.g. '1212')
\D* # optional separator
(\d*) # extension is optional and can be any number of digits
$ # end of string
''', re.VERBOSE)
# don't match beginning of string, number can start anywhere
(\d{3}) # area code is 3 digits (e.g. '800')
\D* # optional separator is any number of non-digits
(\d{3}) # trunk is 3 digits (e.g. '555')
\D* # optional separator
(\d{4}) # rest of number is 4 digits (e.g. '1212')
\D* # optional separator
(\d*) # extension is optional and can be any number of digits
$ # end of string
''', re.VERBOSE)
phonePattern.search('work 1-(800) 555.1212 #1234').groups()
14、regular expressions
• ^ matches the beginning of a string.
• $ matches the end of a string.
• \b matches a word boundary.
• \d matches any numeric digit.
• \D matches any non-numeric character.
• x? matches an optional x character (in other words, it matches an x zero or one times).
• x* matches x zero or more times.
• x+ matches x one or more times.
• x{n,m} matches an x character at least n times, but not more than m times.
• (a|b|c) matches exactly one of a, b or c.
• (x) in general is a
remembered group. You can get the value of what matched by using the groups() methodof the object returned by re.search.
• $ matches the end of a string.
• \b matches a word boundary.
• \d matches any numeric digit.
• \D matches any non-numeric character.
• x? matches an optional x character (in other words, it matches an x zero or one times).
• x* matches x zero or more times.
• x+ matches x one or more times.
• x{n,m} matches an x character at least n times, but not more than m times.
• (a|b|c) matches exactly one of a, b or c.
• (x) in general is a
remembered group. You can get the value of what matched by using the groups() methodof the object returned by re.search.
15、This technique of using the values of outside parameters within a dynamic function is called
closures.
closures.
16、The
with statement creates what’s called a context: when the with block ends, Python will automatically close the file, even if an exception is raised inside the with block.
with statement creates what’s called a context: when the with block ends, Python will automatically close the file, even if an exception is raised inside the with block.
There’s nothing file-specific about the with statement; it’s just a generic framework for creating runtime
contexts and telling objects that they’re entering and exiting a runtime context. If the object in question is a
stream object, then it does useful file-like things (like closing the file automatically). But that behavior is
defined in the stream object, not in the with statement.
contexts and telling objects that they’re entering and exiting a runtime context. If the object in question is a
stream object, then it does useful file-like things (like closing the file automatically). But that behavior is
defined in the stream object, not in the with statement.
17、The first argument to the
split() method is
None, which means “split on any whitespace (tabs or spaces, it makes no difference).” The second argument is 3, which means “split on whitespace 3 times, then leave the rest of the line alone.”
split() method is
None, which means “split on any whitespace (tabs or spaces, it makes no difference).” The second argument is 3, which means “split on whitespace 3 times, then leave the rest of the line alone.”
18、The presence of the
yield x keyword in function body means that this is not a normal function. It is a special kind of function which generates values one at a time. You can think of it as a
resumable function. Calling it will return a
generator that can be used to generate successive values of x. The
next() function takes a generator object and returns its next value.
yield x keyword in function body means that this is not a normal function. It is a special kind of function which generates values one at a time. You can think of it as a
resumable function. Calling it will return a
generator that can be used to generate successive values of x. The
next() function takes a generator object and returns its next value.
"yield" pause a function, and "next()" resumes where it left off.
Generators are just a simple form of iterators. A function that yields values is a nice, compact way of building an
iterator without building an iterator. File objects are iterators too! It’s iterators all the way down.
iterator without building an iterator. File objects are iterators too! It’s iterators all the way down.
This is a useful idiom: pass a generator to the list() function, and it will iterate through the entire
generator (just like the
for loop) and return a list of all the values.
generator (just like the
for loop) and return a list of all the values.
The for loop will automatically call the next() function to get values from the
generator and assign them to the for loop index variable.
generator and assign them to the for loop index variable.
19、‘
pass' is a Python reserved word that just means “move along, nothing to see here”.
pass' is a Python reserved word that just means “move along, nothing to see here”.
20、The first argument of every class method, including the
__init__() method, is always a reference to the
__init__() method, is always a reference to the
current instance of the class. By convention, this argument is named
self
.
21、An
iterator is just a class that defines an
__iter__() method.
iterator is just a class that defines an
__iter__() method.
Python Code
|
1
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |
class Fib:
'''iterator that yields numbers in the Fibonacci sequence''' def __init__(self, maxn): self.maxn = maxn def self.b = return self def __next__(self): for n |
After performing beginning-of-iteration initialization, the __iter__() method can return any object that implements a
__next__() method. The __next__() method is called whenever someone calls
next() on an iterator of an instance of a class.
__next__() method. The __next__() method is called whenever someone calls
next() on an iterator of an instance of a class.
iter(object) calls object.__iter__(), return an iterator object;
next(iterator_object) calls iterator_object.__next__(), return a value;
C++ Code
|
1
2 3 |
for n in Fib(
): print(n, end= ' ') |
a for loop calls __init__() (if the object exists, ignore) and __iter__() once, but calls __next__() several times until encounter raise
StopIteration exception.
StopIteration exception.
When the __next__() method raises a StopIteration exception, this signals to the caller that the iteration
is exhausted. Unlike most exceptions, this is not an error; it’s a normal condition that just means that the
iterator has no more values to generate. If the caller is a for loop, it will notice this StopIteration
exception and gracefully
exit the loop.
is exhausted. Unlike most exceptions, this is not an error; it’s a normal condition that just means that the
iterator has no more values to generate. If the caller is a for loop, it will notice this StopIteration
exception and gracefully
exit the loop.
22、when the variable was not defined within any method. It’s defined at the
class level. It’s a
class variable, and although you can access it just like an instance variable (self.rules_filename), it is shared across all instances of the same class.
class level. It’s a
class variable, and although you can access it just like an instance variable (self.rules_filename), it is shared across all instances of the same class.
23、A
generator expression is like an anonymous function that yields values. The expression itself looks like a list comprehension, but it’s wrapped in
parentheses instead of square brackets.
generator expression is like an anonymous function that yields values. The expression itself looks like a list comprehension, but it’s wrapped in
parentheses instead of square brackets.
unique_characters = {'E', 'D', 'M', 'O', 'N', 'S', 'R', 'Y'}
gen = (ord(c) for c in unique_characters)
gen = (ord(c) for c in unique_characters)
If you like, you can iterate through all the possible values and return a tuple, list, or set, by passing the
generator expression to tuple(), list(), or set(). In these cases, you don’t need an extra set of
parentheses — just pass the “bare” expression ord(c) for c in unique_characters to the tuple()
function, and Python figures out that it’s a generator expression.
generator expression to tuple(), list(), or set(). In these cases, you don’t need an extra set of
parentheses — just pass the “bare” expression ord(c) for c in unique_characters to the tuple()
function, and Python figures out that it’s a generator expression.
tuple(ord(c) for c in unique_characters)
(69, 68, 77, 79, 78, 83, 82, 89)
(69, 68, 77, 79, 78, 83, 82, 89)
24、The
itertools.permutations() function doesn’t have to take a list. It can take any sequence — even a string.The permutations() function takes a sequence and a number, which is the number of items you want in each smaller group. The function returns an iterator.
itertools.permutations() function doesn’t have to take a list. It can take any sequence — even a string.The permutations() function takes a sequence and a number, which is the number of items you want in each smaller group. The function returns an iterator.
The
itertools.combinations() function returns an iterator containing all the possible combinations of the
given sequence of the given length.
itertools.combinations() function returns an iterator containing all the possible combinations of the
given sequence of the given length.
The itertools.
groupby() function takes a sequence and a key function, and returns an iterator that
generates pairs. Each pair contains the result of key_function(each item) and another iterator containing
all the items that shared that key result.
groupby() function takes a sequence and a key function, and returns an iterator that
generates pairs. Each pair contains the result of key_function(each item) and another iterator containing
all the items that shared that key result.
The itertools.groupby() function only works if the input sequence is already sorted by the grouping function.
The
itertools.chain() function takes two iterators and returns an iterator that contains all the items
from the first iterator, followed by all the items from the second iterator. (Actually, it can take any number
of iterators, and it chains them all in the order they were passed to the function.)
itertools.chain() function takes two iterators and returns an iterator that contains all the items
from the first iterator, followed by all the items from the second iterator. (Actually, it can take any number
of iterators, and it chains them all in the order they were passed to the function.)
25、
rstrip() string method to strip trailing whitespace from each line. (Strings also have an
lstrip() method to strip leading whitespace, and a
strip() method which strips both.)
rstrip() string method to strip trailing whitespace from each line. (Strings also have an
lstrip() method to strip leading whitespace, and a
strip() method which strips both.)
26、zip()、tuple()、dict()、translate()、eval()
characters = ('S', 'M', 'E', 'D', 'O', 'N', 'R', 'Y') guess = ('1', '2', '0', '3', '4', '5', '6', '7')
tuple(zip(characters, guess))
(('S', '1'), ('M', '2'), ('E', '0'), ('D', '3'), ('O', '4'), ('N', '5'), ('R', '6'), ('Y', '7'))
dict(zip(characters, guess))
{'E': '0', 'D': '3', 'M': '2', 'O': '4', 'N': '5', 'S': '1', 'R': '6', 'Y': '7'}
'SEND + MORE == MONEY'.translate(translation_table)
'1053 + 2460 == 24507'
'1053 + 2460 == 24507'
The second and third parameters passed to the
eval() function act as the global and local namespaces for
evaluating the expression.
eval() function act as the global and local namespaces for
evaluating the expression.
The
subprocess module allows you to run arbitrary shell commands and get the result as a Python string.
subprocess module allows you to run arbitrary shell commands and get the result as a Python string.
eval("__import__('subprocess').getoutput('rm -rf /')", {"__builtins__":None}, {})
#error. the __import__() function is also a builtin function
27、Running the script runs
unittest.main(), which runs each test case. Each test case is a method within a
unittest.main(), which runs each test case. Each test case is a method within a
class in xxxtest.py. There is no required organization of these test classes; they can each contain a single
test method, or you can have one class that contains multiple test methods. The only requirement is that
each test class must inherit from
unittest.TestCase.
The unittest.TestCase class provides the
assertRaises method, which takes the following arguments: the
exception you’re expecting, the function you’re testing, and the arguments you’re passing to that function. (If the function you’re testing takes more than one argument, pass them all to assertRaises, in order, and it
will pass them right along to the function you’re testing.)
assertRaises method, which takes the following arguments: the
exception you’re expecting, the function you’re testing, and the arguments you’re passing to that function. (If the function you’re testing takes more than one argument, pass them all to assertRaises, in order, and it
will pass them right along to the function you’re testing.)
28、It is important to understand that modules are only imported once, then cached. If you import an already-imported module, it does nothing.
29、a file on disk is a sequence of bytes.The default encoding is platform dependent.
30、Python has a built-in function called
open(). The open() function returns a
stream object, which has methods and attributes for getting information about and manipulating a stream of characters.
open(). The open() function returns a
stream object, which has methods and attributes for getting information about and manipulating a stream of characters.
31、Once you open a file (with the correct encoding), reading from it is just a matter of calling the stream
object’s
read() method. The result is a string.The read() method can take an optional parameter, the number of characters to read.
object’s
read() method. The result is a string.The read() method can take an optional parameter, the number of characters to read.
32、The
seek() and
tell() methods always count
bytes, but since you opened this file as text, the read() method counts
characters. Chinese characters require multiple bytes to encode in UTF -8 .
seek() and
tell() methods always count
bytes, but since you opened this file as text, the read() method counts
characters. Chinese characters require multiple bytes to encode in UTF -8 .
33、The stream object file still exists; calling its
close() method doesn’t destroy the object itself. But it’s
close() method doesn’t destroy the object itself. But it’s
not terribly useful.Closed stream objects do have one useful attribute: the
closed
attribute will confirm that the file is closed.
34、Read a file one line at a time
Python Code
|
1
2 3 4 5 |
line_number =
with print( |
the stream object is also an
iterator which spits out a single line every time you ask for a value.
iterator which spits out a single line every time you ask for a value.
The format specifier {:>4} means “print this argument right-justified within 4 spaces.”
The
rstrip() string method removes the trailing whitespace, including the carriage return characters.
rstrip() string method removes the trailing whitespace, including the carriage return characters.
35、Reading a “string” from a
text file only works because you told Python what encoding to use to read a stream of bytes and convert it to a string.
text file only works because you told Python what encoding to use to read a stream of bytes and convert it to a string.
36、Opening a file in
binary mode is simple but subtle. The only difference from opening it in text mode is that the mode parameter contains a
'b' character. a binary stream object has no
encoding attribute.
binary mode is simple but subtle. The only difference from opening it in text mode is that the mode parameter contains a
'b' character. a binary stream object has no
encoding attribute.
Reading a file in “binary” mode? You’ll get a stream of bytes. Fetching a web page? Calling a web API ? They return a stream of bytes, too.
37、Since you opened the file in binary mode, the read() method takes the number of
bytes to read, not the number of characters.
bytes to read, not the number of characters.
38、As long as your functions take a stream object and simply call the object’s read() method, you can handle any
input source that acts like a file, without specific code to handle each kind of input.
input source that acts like a file, without specific code to handle each kind of input.
39、
io.StringIO lets you treat a string as a text file. There’s also a
io.BytesIO class, which lets you treat a byte array as a binary file.
io.StringIO lets you treat a string as a text file. There’s also a
io.BytesIO class, which lets you treat a byte array as a binary file.
40、The
gzip module lets you create a stream object for reading or writing a gzip-compressed file.
gzip module lets you create a stream object for reading or writing a gzip-compressed file.
You should always open gzipped files in binary mode.
import gzip
with gzip.open('out.log.gz', mode='wb') as z_file:
z_file.write('A nine mile walk is no joke, especially in the rain.'.encode('utf-8'))
with gzip.open('out.log.gz', mode='wb') as z_file:
z_file.write('A nine mile walk is no joke, especially in the rain.'.encode('utf-8'))
41、The print function adds a carriage return to the end of the string you’re printing, and calls
sys.stdout.write.
sys.stdout.write.
42、Any class can be a
context manager by defining two special methods:
__enter__() and
__exit__().
context manager by defining two special methods:
__enter__() and
__exit__().
43、The ElementTree library is part of the Python standard library, in
xml.etree.ElementTree
xml.etree.ElementTree
ElementTree represents XML elements as
{namespace}localname.
{namespace}localname.
In the ElementTree API, an element acts like a
list. The items of the list are the element’s children.
list. The items of the list are the element’s children.
XML isn’t just a collection of elements; each element can also have its own set of
attributes（.attrib）. Once you have a reference to a specific element, you can easily get its attributes as a Python dictionary.
attributes（.attrib）. Once you have a reference to a specific element, you can easily get its attributes as a Python dictionary.
In a boolean context, ElementTree element objects will evaluate to False if they contain no children (i.e. if
len(element) is 0). This means that if element.find('...') is not testing whether the find() method found a matching element; it’s testing whether that matching element has any child elements! To test whether the find() method returned an element, use
if element.find('...') is not None
len(element) is 0). This means that if element.find('...') is not testing whether the find() method found a matching element; it’s testing whether that matching element has any child elements! To test whether the find() method returned an element, use
if element.find('...') is not None
44、The time module contains a data structure
(time_struct) to represent a point in time (accurate to one
(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.
45、The
dump() function in the
pickle module takes a serializable Python data structure, serializes it into a
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.
The
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.
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.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 (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.
The
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.
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.
46、Like the pickle module, the
json module defines a dump() function which takes a Python data structure
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.
JSON is a
text-based format. Always open JSON files in text mode with a UTF -8 character encoding.
text-based format. Always open JSON files in text mode with a UTF -8 character encoding.
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.
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.
47、The
time.asctime() function will convert that nasty-looking time.struct_time into the string 'Fri Mar 27 22:20:42 2009'.
time.asctime() function will convert that nasty-looking time.struct_time into the string 'Fri Mar 27 22:20:42 2009'.
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].
import customserializer
with open('entry.json', 'w', encoding='utf-8') as f:
json.dump(entry, f, default=customserializer.to_json)
#shell 1
with open('entry.json', 'w', encoding='utf-8') as f:
json.dump(entry, f, default=customserializer.to_json)
#shell 1
with open('entry.json', 'r', encoding='utf-8') as f:
entry = json.load(f, object_hook=customserializer.from_json)
#shell 2
entry = json.load(f, object_hook=customserializer.from_json)
#shell 2
48、The
urllib.request.urlopen().read() method always returns a bytes object, not a string. Remember, bytes are
bytes;characters are an abstraction. HTTP servers don’t deal in abstractions. If you request a resource, you get bytes. If you want it as a string, you’ll need to determine the character encoding and explicitly convert it to a string.
urllib.request.urlopen().read() method always returns a bytes object, not a string. Remember, bytes are
bytes;characters are an abstraction. HTTP servers don’t deal in abstractions. If you request a resource, you get bytes. If you want it as a string, you’ll need to determine the character encoding and explicitly convert it to a string.
The
response returned from the
urllib.request.urlopen() function contains all the
HTTP headers the
server sent back. download the actual data by calling
response.read()
response returned from the
urllib.request.urlopen() function contains all the
HTTP headers the
server sent back. download the actual data by calling
response.read()
49、The primary interface to
httplib2 is the
Http object.Once you have an Http object, retrieving data is as simple as calling the
request() method with the address of the data you want. This will issue an
HTTP GET request for that URL .
httplib2 is the
Http object.Once you have an Http object, retrieving data is as simple as calling the
request() method with the address of the data you want. This will issue an
HTTP GET request for that URL .
The request() method returns two values. The first is an
httplib2.Response object, which contains all
the
HTTP headers the server returned. For example, a status code of 200 indicates that the request was
successful.
httplib2.Response object, which contains all
the
HTTP headers the server returned. For example, a status code of 200 indicates that the request was
successful.
The content variable contains the
actual data that was returned by the HTTP server. The data is returned
as a bytes object, not a string. If you want it as a string, you’ll need to determine the character encoding
and convert it yourself.
actual data that was returned by the HTTP server. The data is returned
as a bytes object, not a string. If you want it as a string, you’ll need to determine the character encoding
and convert it yourself.
you should always create an
httplib2.Http object with a directory name. Caching is the reason.
httplib2.Http object with a directory name. Caching is the reason.
httplib2 allows you to add arbitrary HTTP headers to any outgoing request. In order to bypass all caches
(not just your local disk cache, but also any caching proxies between you and the remote server), add a
(not just your local disk cache, but also any caching proxies between you and the remote server), add a
no-cache header in the headers dictionary.
HTTP defines
Last-Modified and
Etag headers for this purpose. These headers are called
validators. If the local cache is no longer fresh(expired), a client can send the validators with the next request to see if the data has actually changed. If the data hasn’t changed, the server sends back a 304 status code and no data.
Last-Modified and
Etag headers for this purpose. These headers are called
validators. If the local cache is no longer fresh(expired), a client can send the validators with the next request to see if the data has actually changed. If the data hasn’t changed, the server sends back a 304 status code and no data.
which caused httplib2 to look in its cache.
httplib2 sends the ETag validator back to the server in the
If-None-Match header.
httplib2 also sends the Last-Modified validator back to the server in the
If-Modified-Since header.
If-None-Match header.
httplib2 also sends the Last-Modified validator back to the server in the
If-Modified-Since header.
50、Python comes with a utility function to URL -encode a dictionary: urllib.parse.
urlencode()
urlencode()
Store your username and password with the
add_credentials() method.
add_credentials() method.
51、If Python sees an
__init__.py file in a directory, it assumes that all of the files in that directory are part of the same
module. The module’s name is the name of the directory. Files within the directory can reference other files within the same directory, or even within subdirectories. But the entire collection of files is presented to other Python code as a single module — as if all the functions and classes were in a single .py file.
__init__.py file in a directory, it assumes that all of the files in that directory are part of the same
module. The module’s name is the name of the directory. Files within the directory can reference other files within the same directory, or even within subdirectories. But the entire collection of files is presented to other Python code as a single module — as if all the functions and classes were in a single .py file.
The __init__.py file doesn’t need to define anything; it can literally be an empty file. Or you can use it to define your main entry point functions. Or you put all your functions in it. Or all but one.
A directory with an __init__.py file is always treated as a
multi-file module.
Without an __init__.py file, a directory is just a directory of unrelated .py files.
multi-file module.
Without an __init__.py file, a directory is just a directory of unrelated .py files.
52、Within lists, tuples, sets, and dictionaries, whitespace can appear before and after commas with no ill effects.
53、In Python 2, the global
file() function was an alias for the open() function, which was the standard way of opening text files for reading. In Python 3, the global file() function no longer exists, but the open() function still exists.
file() function was an alias for the open() function, which was the standard way of opening text files for reading. In Python 3, the global file() function no longer exists, but the open() function still exists.
54、In Python 2, a string was an array of bytes whose character encoding was tracked separately. If you wanted Python 2 to keep track of the character encoding, you had to use a Unicode string
(u'') instead. But in Python 3, a string is always what Python 2 called a Unicode string — that is, an array of Unicode characters
(of possibly varying byte lengths).
(u'') instead. But in Python 3, a string is always what Python 2 called a Unicode string — that is, an array of Unicode characters
(of possibly varying byte lengths).
摘自《dive into python3》