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• There is nothing to say

• If the function does not return a value, you can return to None

• Used for occupying

Three. Built in data structure

• list

• set

• dict

• tuple

list(List)

# 1, Create an empty list L1 =

[]

# 2. Create a list of values L2 = [100

]

# 3. Create a list with multiple values L3 = [2, 3, 1, 4, 6, 4, 6

]

# 4. Using list () L4 = list ()

• # Using list a to create a list B B code, the following code means that for all elements in a, add the new list B one by one, B = [i * 10 for I in

a]

# You can also filter the content disease in the original list into a new list, such as the original list a, to generate a new list of B a = [x for X in range (1, 35)) to generate a list from 1 to 34.Generate all the even numbers in a to a new list B B = [m for m in a if M% 2 = =

0]

# The list generation can be nested by two lists of a, B a = [i for I in range (1, 4)] list generates a list = B, a (100, 400)F I% 100 = = = =

0]

# List generation is nested, which is equal to two for loops nested C = [M + n for m in a for N in

b]

# The above code is equivalent to the following code for m in

a:

for n in

b:

print (m + n, end = ” “

)

# Nested list cities can also use conditional expression C = [M + n for m in a for N in B N + + 250;]

• On the common functions of lists

• copy: Copy, this function is a shallow copy

b = a.copy()

• count:Find the number of specified values or elements in the list

a_len = a.count( 8 )

• extend:Expand the list, two lists, and splice one directly into the next one.

a = [ 1 , 2 , 3 , 4 , 5

]

b

= [ 6 , 7 , 8 , 9 , 10

]

a.extend(b)

• remove:Deletes the element of the specified value in the list (if the deleted value is not in list, it is wrong).

• clear:empty

• reverse:Flip the content of the list and turn it in

a = [ 1 , 2 , 3 , 4 , 5

]

a.reverse()

• del delete

• pop，Take an element from the counterpoint, that is, take the last element out.

last_ele = a.pop()

• insert: Enact position insertion

# insert(index, data), The insertion position is a.insert (3, 666) in front of index.

• append Insert a content, add to the end

a = [ i for i in range( 1 , 5

)]

a.append(

100 )

• min

• list：Convert data from other formats into list

# Convert the content generated by range to list print (list (range (12, 19))).

• max:Find the maximum in the list

b = [ ‘ man ‘ , ‘ film ‘ , ‘ python ‘

]

print (max(b))

• len:List length

a = [x for x in range( 1 , 100

)]

print (len(a))

tuple(Tuple)

# Create the empty tuple t =

()

# Create a tuple with only one value t = (1

,)

t

= 1

,

# Create a tuple of tuples t = (1, 2, 3, 4, 5).

)

t

= 1 , 2 , 3 , 4 , 5 # Use other structures to create L = [1, 2, 3, 4, 5

]

t

= tuple(l)

The characteristics of the tuple

• Sequence table, order

• The value of the tuple data can be accessed and can not be modified

• The tuple data can be any type

• listAll features except for modifiable tuples are available.

Function of a tuple

• len: Get the length of a tuple

• max, min:Maximum minimum

• count: Calculate the number of times the data appears

• index:Find the index position of elements in the tuple

set(Set)

# The definition of the set s =

set()

# At this time, brackets must have values, otherwise it is defined as a dict s = {1, 2, 3, 4, 5, 6, 7.

}

# If it is only defined with braces, it is defined as a dict type D = {}.

The features of a set

• Data in the collection is out of order, that is, index and fragmentation can not be used.

• The collection of internal data elements is unique and can be used to exclude duplicate data.

• Data in the collection, STR, int, float, tuple, frozen set, etc., that is, only hash data can be placed inside.

Set sequence operation

• Member detection (in, not in)

• ergodic

# for Cyclic s = {{4, 5, ‘I’

}

for i in

s:

print (i, end = ” “

)

# Sets with tuples are traversed by S = {(1, 2, 3), (“just”, “for”, “fun”), (4, 5, 6).

)}

for k,m,n in

s:

print (k, ” — ” , m, ” — “

, n)

for k in

s:

print (k)

Connotations of set

# The following sets are automatically filtered out after initialization. S = {23, 223, 545, 3, 1, 2, 3, 4, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,

}

# Common set connotation SS = {i for I in

s}

# Conditional set connotations SSS = {i for I in s if I% 2 = =

0}

# The connotations of the set of multiple cycles S1 = {{{1, 2, 3, 4

}

s2

= { ” just ” , ” for ” , ” fun “

}

s

= {m * n for m in s2 for n in

s1}

s

= {m * n for m in s2 for n in s1 if n == 2 }

Set function

• len, max, min

• set:Generating a set

• clear

• copy

• remove:Remove the set value and change the original value directly. If the value to be deleted does not exist, it is wrong.

• discard:Removing the specified value in the collection is the same as remove, but if you want to delete it, you will not report it wrong.

• pop Random removal of an element

• function

• intersection: intersection

• difference:Difference set

• union: Union

• issubset: Check whether a set is another subset

• issuperset: Check whether one set is another superset

• # Create s = frozenset ()

dict(Dictionary)

• There is no sequential combination of data, and data appears in key value pairs.

# Create an empty dictionary 1 d =

{}

# Create an empty dictionary 2 D =

dict()

# Create a value dictionary. Each set of data is separated by colon. Each pair of key values is separated by commas. D = {“one”: 1, “two”: 2, “three”: 3).

}

# Using dict to create a content Dictionary 1 d = dict ({“one”: 1, “two”: 2, “three”: 3)

})

# Using dict to create a content dictionary 2, using keyword parameter d = dict (one = 1, two = 2, three = 3

)

# d = dict( [( ” one ” , 1 ), ( ” two ” , 2 ), ( ” three ” , 3 )])

The characteristics of the dictionary

• A dictionary is a sequence type, but it is an unordered sequence, so there is no fragmentation and index.

• Every data in the dictionary has a pair of key pairs, that is, kV pairs.

• key: Must be the value of a hash, such as int, string, float, tuple, but list, set, dict are not.

• value: Any value

Dictionary common operation

# Access data d = {“one”: 1, “two”: 2, “three”: 3

}

# Note that the parentheses in the access format are key values print (d[“one”).

])

d[

” one ” ] = ” eins ” # Delete an operation using del to operate del d[“one”.

]

# Member detection, in, not in in detection detected key content d = {“one”: 1, “two”: 2, “three”: 3).

}

if 2 in

d:

print ( ” value “

)

if ” two ” in

d:

print ( ” key “

)

if ( ” two ” , 2 ) in

d:

print ( ” kv “

)

# Use key to use for loop d = {“one”: 1, “two”: 2, “three”: 3).

}

# Using the for loop, for K in is accessed directly by the key value

d:

print

(k, d[k])

# The above code can be rewritten as for K in

d.keys():

print

(k, d[k])

# Only access to the value of the dictionary for V in

d.values():

print

(v)

# Pay attention to the following special usage: for K, V in

d.items():

print (k, ‘ — ‘ ,v)

Dictionary generation

d = { ” one ” : 1 , ” two ” : 2 , ” three ” : 3

}

# Conventional dictionary generation DD = {k:v for K, V in

d.items()}

# Lexicon generation with restricted conditions DD = {k:v for K, V in d.items () if v% 2 = = 0}

Dictionary related functions

• len, max, min, dict

• str(Dictionary: return the string format of the dictionary

• clear: Empty dictionary

• items: Return the key value of the dictionary to the composition of the tuple format

• keys:A structure that returns the keys of a dictionary

• values: In the same way, an Iterable structure

• get: Return the corresponding value based on the set key

d = { ” one ” : 1 , ” two ” : 2 , ” three ” : 3

}

print (d.get( ” on333 “

))

# getThe default value is None, and you can set print (d.get (“one”, 100)).

• fromkeys: Using a specified sequence as a key, using a value as the value of all keys in the dictionary.

l = [ ” eins ” , ” zwei ” , ” drei “

]

# Note the type of fromkeys two parameters. Note that the calling body of fromkeys is d = dict.fromkeys (L, “oops”).

Four. Expression

operator

• Arithmetic operator

• Comparison or relation operator

• Assignment Operators

• Logical operator

• Bit operation

• Member operation

• Identity operator

Arithmetic operator

• Plus +

• Minus –

• By *

• Except /

• Remainder%

• The dealer (floor) / / /

• Power operation * *

• :warning:Python No self incrementing operator

Comparison operator

• ==

• !=

• >=

• <=

• The result of comparison is the Boolean value (True/False)

Assignment Operators

• += （-=， ×=， /=, //=, %=, **=）

Logical operator

• and Logic and

• or Logic or

• not Logic non

• As a result, if 0 is False, or True

• short circuit

• Python There is no other or operation in it

Member operation symbol

• Used to detect whether a variable is a member of another variable.

• in

• not in

Identity operation

• is: It is used to detect whether two variables are the same variable

• is not: Two variables are not the same variable

Operator precedence

• Parentheses have the highest priority

** Index (highest priority)

~ + – Bit by bit flipping

,

The one plus plus and minus sign (the last two methods are called + / and].

* / % // Multiplying, removing, drawing and removing

\>> << Right shift, left shift operator

& Bit’AND’

^ | Bitwise Operators

<

= < > > =

Comparison operator

<>

== ! =

equal operator

= % = / = // = – = + = * = ** =

Assignment Operators

is is not Identity operator

in not in member operator

not or and Logical operator

Five. Program structure

order

branch

• Basic grammar

# The colon behind the expression can not be small. Indent is used to represent the same block if.

Conditional expression:

Statement 1

Statement 2

Statement 3

..

• Bi-directional branch

# if And else a hierarchy, the rest of the statement is a level if.

Conditional expression:

Statement 1

Statement 2

else

:

Statement 1

Statement 2

• switch

if

Conditional expression:

Statement 1

.

elif

Conditional expression:

Statement 1

elif

Conditional expression:

Statement 1

..

else

:

Statement 1

.

• Python No switch-case statement

loop

for loop

for Variable in

Sequence:

Statement 1

Statement 2

range

• Generating a digital sequence

• Scope: [m, n)

# Print 1~10 for I in range (1, 11)

):

print (i)

for-else

• When the for loop ends, the else statement is executed

• elseStatement selectable

while loop

while

Conditional expression:

Statement block

# Another expression while

Conditional expression:

Statement block 1

else

Statement block 2

Cyclic break, continue, pass

• break： Unconditionally end the whole cycle, short of sudden death

• continue：Unconditionally terminate this cycle, from the new to the next cycle.

• pass：Indicates skipping, usually used for standing, without skipping function.

Six. Function

• defKeyword

• Code indentation

# Define def

func():

print ( ” This is a function “

)

# Call func ()

Parameter and return value of a function

• Parameter: responsible for transferring necessary data or information to function.

• Formal parameter (formal parameter): the parameter used in function definition is not a specific value, it is just a symbol of occupation, and becomes a parameter.

• Argument (real parameter): the value entered when calling the function.

• Return value: the result of the function

• Using the return keyword

• If there is no return, the default returns a None

• Once the function executes the return statement, it returns unconditionally, that is, the execution of the ending function.

# returnThe basic use of the sentence is to return a sentence def after the greeting function.

hello(person):

print ( ” {0}, Are you swollen? “

.format(person))

print ( ” Sir, If you ignore the amount, you go. “

)

return ” I’ve greeted {0}, and {1} doesn’t look at me. “

.format(person, person)

p

= ” “Ming moon” RST =

hello(p)

print (rst)

# Define a function to print a 99 multiplication table def

printLine(row):

for col in range( 1 , row + 1

):

# printThe default function of the function is printed after completing the line print (row * col, end = “”).

)

print ( “”

)

# 99 multiplication tables, version 2 for row in range (1, 10)

):

printLine(row)

Parameter detailed solution

• Parameter classification

• Common parameters

• Default parameters

• Keyword parameters

• Collection of parameters

• Common parameters

• See the above example

• Defining a variable name directly at the time of definition

• When you call, put the variable or value directly in the specified location.

def Function name (parameter 1, parameter 2,

.):

Function body

# Call the function name (value1, Value2,

.)

• Default parameters

• The parameter has the default value

• If you do not assign values to the corresponding parameters when invoking, use default values.

def func_name(p1 = v1, p2 = v2

.):

func_block

# Call 1

func_name()

# Call 2 value1 = 100 Value2 = 200 func_name (value1, Value2)

• Keyword parameters

def func(p1 = v1, p2 = v2

..):

func_body

# Call function: func (P1 = value1, P2 = Value2)

.)

• Collection of parameters

• Place a parameter that has no location and cannot correspond to the parameter position of the definition, and place it in a specific data structure.

def func( *

args):

func_body

# Access to args according to the way list is used to get incoming parameters: func (P1, P2, P3).

..)

The sequential problem of collecting parameter mixed calls

• Collection parameters, keyword parameters, general parameters can be mixed.

• The rule of use is that common parameters and keyword parameters are preferred.

• When it is defined, it usually looks for common parameters, keyword parameters, collection parameters tuple, and collection of parameters Dict

Return value

• The difference between function and process

• Whether there is a return value

• You need to display the content with return,

Recursive function

• pythonThere is a limit to the depth of recursion, exceeding the limit of the error

# F (1) = 1, f (2) = 1, f (n) = f (n-1), and f (n) = f (n-1).+ F (n-2), for example: 1,1, 2, 3,5,8,13……. N indicates the value of the Fibonacci sequence of the N number def.

fib(n):

if n == 1

:

return 1 if n == 2

:

return 1 return fib(n – 1 ) + fib(n – 2

)

print (fib( 3

))

print (fib( 10 ))

• Hanoa problem

• Rules:

• Method：

1. n=1： Directly move a plate on A to C, A-> C

2. n=2:

3. n=3:

4. n = n：

1. Put small plates from A to B, A-> B

2. Put the big plate from A to C, A-> C

3. Put small plates from B to C, B-> C

1. Move the two plates on the A to the B through C, and invoke the recursive implementation.

2. Move the remaining largest plate on A to C, A-> C

3. The two plates on the B are moved to C by means of A, calling recursion.

1. N-1 plates on A are moved to B through C, calling recursion.

2. The largest plate on A is also the only one moving to C, A-> C

3. N-1 plates on B are moved to C through A, calling recursion.

1. Move one plate at a time

2. At any time, the big plate is underneath, and the small plate is on it.

def

hano(n, a, b, c):

”’

Recursive implementation of Hanoi Tower

n：Represent a few plates

a：The first tower, the beginning of the tower

b：A tower representing second towers.

c：Representing third towers, target towers

”’ if n == 1

:

print (a, ” –> “

, c)

return

None

”’

if n == 2:

print(a, “–>”, b)

print(a, “–>”, c)

print(b, “–>”, c)

return None

”’ # N-1 plates, from the a tower to the B tower with the help of C tower, are added to Hano (n – 1).

, a, c, b)

print (a, ” –> “

, c)

# N-1 plates from the B tower, with the help of a tower, moved to the C tower to Hano (n – 1, B, a, c).

a = ” A ” b = ” B ” c = ” C ” n = 1

hano(n, a, b, c)

n

= 2

hano(n, a, b, c)

n

= 3 hano(n, a, b, c)

Lookup function help document

• Using help function

help( print )

• Using __doc__

def

stu(name, age):

”’

This is the text content of the document

:param name: Express the name of the student

:param age: Indicate the age of the student

:return: This function has no return value

”’ pass print

(help(stu))

print ( ” * ” * 20

)

print (stu. __doc__ )

Seven, variable scope

• Classification: according to the scope of action

• Global (global): external definition of function

• Local (local): definition within a function

• LEGBPrinciple

• L（Local）Local scope

• E（Enclosing function locale）Outer nested function scope

• G（Global module）Function definition in the module scope

• B（Buildin）： pythonBuilt-in magic area

Lifting local variables as global variables (using global)

def

fun():

global

b1

b1

= 100 print

(b1)

print ( ” I am in fun “

)

# b2The scope of action is fun B2 = 99 print

(b2)

fun()

print (b1)

globals, localsfunction

• Local variables and global variables can be displayed through globals and locals.

# globals Locals and globals and locals are called built-in functions a = 1 b = 2 def.

fun(c,d):

e

= 111 print ( ” Locals={0} “

.format(locals()))

print ( ” Globals={0} “

.format(globals()))

fun(

100 , 200 )

eval()function

• Executes a string as an expression and returns the result after the expression is executed.

eval(string_code, globals = None, locals = None)

exec()function

Similar to the eval function, but do not return the result

exec (string_code, globals = None, locals = None)