PyCharm快捷键

Pycharm

常用快捷键

快捷键 功能
Ctrl + Q 快速查看文档
Ctrl + F1 显示错误描述或警告信息
Ctrl + / 行注释(可选中多行)
Ctrl + Alt + L 代码格式化
Ctrl + Alt + O 自动导入
Ctrl + Alt + I 自动缩进
Tab / Shift + Tab 缩进、不缩进当前行(可选中多行)
Ctrl+C/Ctrl+Insert 复制当前行或选定的代码块到剪贴板
Ctrl + D 复制选定的区域
Ctrl + Y 删除当前行
Shift + Enter 换行(不用鼠标操作了)
Ctrl +J 插入模版
Ctrl + Shift +/- 展开/折叠全部代码块
Ctrl + Numpad+ 全部展开
Ctrl + Numpad- 全部折叠
Ctrl + Delete 删除到字符结束
Ctrl + Backspace 删除到字符开始
Ctrl + Shift + F7 将当前单词在整个文件中高亮,F3移动到下一个,ESC取消高亮。
Alt + up/down 方法上移或下移动
Alt + Shift + up/down 当前行上移或下移动
Ctrl + B/鼠标左键 转到方法定义处
Ctrl + W 选中增加的代码块
Shift + F6 方法或变量重命名
Ctrl + E 最近访问的文件
Esc 从其他窗口回到编辑窗口
Shift + Esc 隐藏当前窗口,焦点到编辑窗口
F12 回到先前的工具窗口
Ctrl + Shift + up 快速上移某一行
Ctrl + Shift + down 快速下移某一行
ctrl+alt+左箭头 返回上一个光标的位置(CTRL进入函数后返回)
ctrl+alt+右箭头 前进到后一个光标的位置

全部快捷键

1、编辑(Editing)

快捷键 功能
Ctrl + Space 基本的代码完成(类、方法、属性)
Ctrl + Alt + Space 快速导入任意类
Ctrl + Shift + Enter 语句完成
Ctrl + P 参数信息(在方法中调用参数)
Ctrl + Q 快速查看文档
Shift + F1 外部文档
Ctrl + 鼠标 简介
Ctrl + F1 显示错误描述或警告信息
Alt + Insert 自动生成代码
Ctrl + O 重新方法
Ctrl + Alt + T 选中
Ctrl + / 行注释
Ctrl + Shift + / 块注释
Ctrl + W 选中增加的代码块
Ctrl + Shift + W 回到之前状态
Ctrl + Shift + ]/[ 选定代码块结束、开始
Alt + Enter 快速修正
Ctrl + Alt + L 代码格式化
Ctrl + Alt + O 自动导入
Ctrl + Alt + I 自动缩进
Tab / Shift + Tab 缩进、不缩进当前行
Ctrl+X/Shift+Delete 剪切当前行或选定的代码块到剪贴板
Ctrl+C/Ctrl+Insert 复制当前行或选定的代码块到剪贴板
Ctrl+V/Shift+Insert 从剪贴板粘贴
Ctrl + Shift + V 从最近的缓冲区粘贴
Ctrl + D 复制选定的区域或行到后面或下一行
Ctrl + Y 删除当前行
Ctrl + Shift + J 添加智能线
Ctrl + Enter 智能线切割
Shift + Enter 下一行另起一行
Ctrl + Shift + U 在选定的区域或代码块间切换
Ctrl + Delete 删除到字符结束
Ctrl + Backspace 删除到字符开始
Ctrl + Numpad+/- 展开折叠代码块
Ctrl + Numpad+ 全部展开
Ctrl + Numpad- 全部折叠
Ctrl + F4 关闭运行的选项卡

2、查找/替换(Search/Replace)

快捷键 功能
F3 下一个
Shift + F3 前一个
Ctrl + R 替换
Ctrl + Shift + F 全局查找
Ctrl + Shift + R 全局替换

3、运行(Running)

快捷键 功能
Alt + Shift + F10 运行模式配置
Alt + Shift + F9 调试模式配置
Shift + F10 运行
Shift + F9 调试
Ctrl + Shift + F10 运行编辑器配置
Ctrl + Alt + R 运行manage.py任务

4、调试(Debugging)

快捷键 功能
F8 跳过
F7 进入
Shift + F8 退出
Alt + F9 运行游标
Alt + F8 验证表达式
Ctrl + Alt + F8 快速验证表达式
F9 恢复程序
Ctrl + F8 断点开关
Ctrl + Shift + F8 查看断点

5、导航(Navigation)

快捷键 功能
Ctrl + N 跳转到类
Ctrl + Shift + N 跳转到符号
Alt + Right/Left 跳转到下一个、前一个编辑的选项卡
F12 回到先前的工具窗口
Esc 从其他窗口回到编辑窗口
Shift + Esc 隐藏当前窗口,焦点到编辑窗口
Ctrl + Shift + F4 关闭主动运行的选项卡
Ctrl + G 查看当前行号、字符号
Ctrl + E 最近访问的文件
Ctrl+Alt+Left/Right 后退、前进
Ctrl+Shift+Backspace 导航到最近编辑区域
Alt + F1 查找当前文件或标识
Ctrl+B / Ctrl+Click 跳转到声明
Ctrl + Alt + B 跳转到实现
Ctrl + Shift + I 查看快速定义
Ctrl + Shift + B 跳转到类型声明
Ctrl + U 跳转到父方法、父类
Alt + Up/Down 跳转到上一个、下一个方法
Ctrl + ]/[ 跳转到代码块结束、开始
Ctrl + F12 弹出文件结构
Ctrl + H 类型层次结构
Ctrl + Shift + H 方法层次结构
Ctrl + Alt + H 调用层次结构
F2 / Shift + F2 下一条、前一条高亮的错误
F4 / Ctrl + Enter 编辑资源、查看资源
Alt + Home 显示导航条F11书签开关
Ctrl + Shift +F11 书签助记开关
Ctrl #[0-9] + 跳转到标识的书签
Shift + F11显示书签
快捷键 功能
Alt + F7/Ctrl + F7 文件中查询用法
Ctrl + Shift + F7 文件中用法高亮显示
Ctrl + Alt + F7 显示用法

7、重构(Refactoring)

快捷键 功能
F5 复制
F6 剪切
Alt + Delete 安全删除
Shift + F6 方法或变量重命名
Ctrl + F6 更改签名
Ctrl + Alt + N 内联
Ctrl + Alt + M 提取方法
Ctrl + Alt + V 提取属性
Ctrl + Alt + F 提取字段
Ctrl + Alt + C 提取常量
Ctrl + Alt + P 提取参数

8、控制VCS/Local History

快捷键 功能
Ctrl + K 提交项目
Ctrl + T 更新项目
Alt + Shift + C 查看最近的变化
Alt + BackQuote(’)VCS 快速弹出
Ctrl + Alt + J 当前行使用模版

9、模版(Live Templates)

快捷键 功能
Ctrl + Alt + J 当前行使用模版
Ctrl +J 插入模版

10、基本(General)

快捷键 功能
Alt + #[0-9] 打开相应编号的工具窗口
Ctrl + Alt + Y 同步
Ctrl + Shift + F12 最大化编辑开关
Alt + Shift + F 添加到最喜欢
Alt + Shift + I 根据配置检查当前文件
Ctrl + BackQuote(’) 快速切换当前计划
Ctrl + Alt + S 打开设置页
Ctrl + Shift + A 查找编辑器里所有的动作
Ctrl + Tab 在窗口间进行切换
# Single line comments start with a number symbol.

""" Multiline strings can be written
using three "s, and are often used
as documentation.
"""

####################################################
## 1. Primitive Datatypes and Operators
####################################################

# You have numbers
3 # => 3

# Math is what you would expect
1 + 1 # => 2
8 - 1 # => 7
10 * 2 # => 20
35 / 5 # => 7.0

# 正数和负数的整数除法都会向下舍入。
5 // 3 # => 1
-5 // 3 # => -2
5.0 // 3.0 # => 1.0 # works on floats too
-5.0 // 3.0 # => -2.0

# 除法的结果总是浮点数
10.0 / 3 # => 3.3333333333333335

# 模运算
7 % 3 # => 1
# i % j have the same sign as j, unlike C
-7 % 3 # => 2

# 求幂 (x**y,x 的 y 次幂)
2**3 # => 8

# 用括号强制优先
1 + 3 * 2 # => 7
(1 + 3) * 2 # => 8

# 布尔值是基元(注意:大写)
True # => True
False # => False

# 否定与不
not True # => False
not False # => True

# 布尔运算符
# 注意“and”和“or”区分大小写
True and False # => False
False or True # => True

# True 和 False 实际上是 1 和 0 但关键字不同
True + True # => 2
True * 8 # => 8
False - 5 # => -5

# 较运算符查看 True 和 False 的数值
0 == False # => True
1 == True # => True
2 == True # => False
-5 != False # => True

# 在整数上使用布尔逻辑运算符将它们转换为布尔值进行评估,但返回它们的非转换值
# 不要与 bool(ints) 和按位和/或 (&,|)
bool(0) # => False
bool(4) # => True
bool(-6) # => True
0 and 2 # => 0
-5 or 0 # => -5

# 平等是 ==
1 == 1 # => True
2 == 1 # => False

# 不平等是!=
1 != 1 # => False
2 != 1 # => True

# 更多比较
1 < 10 # => True
1 > 10 # => False
2 <= 2 # => True
2 >= 2 # => True

# 查看一个值是否在一个范围内
1 < 2 and 2 < 3 # => True
2 < 3 and 3 < 2 # => False
# 这样的链接看起来更好
1 < 2 < 3 # => True
2 < 3 < 2 # => False

# (is vs. ==) is 检查两个变量是否指向同一个对象,但 == 检查
# 如果指向的对象具有相同的值。
a = [1, 2, 3, 4] # Point a at a new list, [1, 2, 3, 4]
b = a # Point b at what a is pointing to
b is a # => True, a and b refer to the same object
b == a # => True, a's and b's objects are equal
b = [1, 2, 3, 4] # Point b at a new list, [1, 2, 3, 4]
b is a # => False, a and b do not refer to the same object
b == a # => True, a's and b's objects are equal

# 字符串是用 " or ' 创建的
"This is a string."
'This is also a string.'

# 也可以添加字符串
"Hello " + "world!" # => "Hello world!"
# 字符串文字(但不是变量)可以在不使用“+”的情况下连接
"Hello " "world!" # => "Hello world!"

# 可以将字符串视为字符列表
"Hello world!"[0] # => 'H'

# 你可以找到一个字符串的长度
len("This is a string") # => 16

# 您还可以使用 f 字符串或格式化字符串文字进行格式化(在 Python 3.6+ 中)
name = "Reiko"
f"She said her name is {name}." # => "She said her name is Reiko"
# 您基本上可以将任何 Python 表达式放在大括号内,它将在字符串中输出。
f"{name} is {len(name)} characters long." # => "Reiko is 5 characters long."

# 没有一个对象
None # => None

# 不要使用等号“==”符号将对象与 None 进行比较
# 使用“是”代替。 这会检查对象身份的相等性。
"etc" is None # => False
None is None # => True

# None、0 和空字符串/列表/字典/元组都评估为 False。
# 所有其他值都是 True
bool(0) # => False
bool("") # => False
bool([]) # => False
bool({}) # => False
bool(()) # => False

####################################################
## 2. 变量和集合
####################################################

# Python有打印功能
print("I'm Python. Nice to meet you!") # => I'm Python. Nice to meet you!

# 默认情况下,打印功能还会在末尾打印出换行符。
# 使用可选参数 end 来更改结束字符串。
print("Hello, World", end="!") # => Hello, World!

# 从控制台获取输入数据的简单方法
input_string_var = input("Enter some data: ") # 以字符串形式返回数据

# 没有声明,只有赋值。
# 约定是使用lower_case_with_underscores
some_var = 5
some_var # => 5

# 访问以前未分配的变量是一个例外。
# 请参阅控制流以了解有关异常处理的更多信息。
some_unknown_var # Raises a NameError

# if 可以用作表达式
# 等效于 C 的 '?:' 三元运算符
"yay!" if 0 > 1 else "nay!" # => "nay!"

# Lists store sequences
li = []
# You can start with a prefilled list
other_li = [4, 5, 6]

# Add stuff to the end of a list with append
li.append(1) # li is now [1]
li.append(2) # li is now [1, 2]
li.append(4) # li is now [1, 2, 4]
li.append(3) # li is now [1, 2, 4, 3]
# Remove from the end with pop
li.pop() # => 3 and li is now [1, 2, 4]
# Let's put it back
li.append(3) # li is now [1, 2, 4, 3] again.

# Access a list like you would any array
li[0] # => 1
# Look at the last element
li[-1] # => 3

# Looking out of bounds is an IndexError
li[4] # Raises an IndexError

# You can look at ranges with slice syntax.
# The start index is included, the end index is not
# (It's a closed/open range for you mathy types.)
li[1:3] # Return list from index 1 to 3 => [2, 4]
li[2:] # Return list starting from index 2 => [4, 3]
li[:3] # Return list from beginning until index 3 => [1, 2, 4]
li[::2] # Return list selecting every second entry => [1, 4]
li[::-1] # Return list in reverse order => [3, 4, 2, 1]
# Use any combination of these to make advanced slices
# li[start:end:step]

# Make a one layer deep copy using slices
li2 = li[:] # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false.

# Remove arbitrary elements from a list with "del"
del li[2] # li is now [1, 2, 3]

# Remove first occurrence of a value
li.remove(2) # li is now [1, 3]
li.remove(2) # Raises a ValueError as 2 is not in the list

# Insert an element at a specific index
li.insert(1, 2) # li is now [1, 2, 3] again

# Get the index of the first item found matching the argument
li.index(2) # => 1
li.index(4) # Raises a ValueError as 4 is not in the list

# You can add lists
# Note: values for li and for other_li are not modified.
li + other_li # => [1, 2, 3, 4, 5, 6]

# Concatenate lists with "extend()"
li.extend(other_li) # Now li is [1, 2, 3, 4, 5, 6]

# Check for existence in a list with "in"
1 in li # => True

# Examine the length with "len()"
len(li) # => 6


# Tuples are like lists but are immutable.
tup = (1, 2, 3)
tup[0] # => 1
tup[0] = 3 # Raises a TypeError

# Note that a tuple of length one has to have a comma after the last element but
# tuples of other lengths, even zero, do not.
type((1)) # => <class 'int'>
type((1,)) # => <class 'tuple'>
type(()) # => <class 'tuple'>

# You can do most of the list operations on tuples too
len(tup) # => 3
tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6)
tup[:2] # => (1, 2)
2 in tup # => True

# You can unpack tuples (or lists) into variables
a, b, c = (1, 2, 3) # a is now 1, b is now 2 and c is now 3
# You can also do extended unpacking
a, *b, c = (1, 2, 3, 4) # a is now 1, b is now [2, 3] and c is now 4
# Tuples are created by default if you leave out the parentheses
d, e, f = 4, 5, 6 # tuple 4, 5, 6 is unpacked into variables d, e and f
# respectively such that d = 4, e = 5 and f = 6
# Now look how easy it is to swap two values
e, d = d, e # d is now 5 and e is now 4


# Dictionaries store mappings from keys to values
empty_dict = {}
# Here is a prefilled dictionary
filled_dict = {"one": 1, "two": 2, "three": 3}

# Note keys for dictionaries have to be immutable types. This is to ensure that
# the key can be converted to a constant hash value for quick look-ups.
# Immutable types include ints, floats, strings, tuples.
invalid_dict = {[1,2,3]: "123"} # => Raises a TypeError: unhashable type: 'list'
valid_dict = {(1,2,3):[1,2,3]} # Values can be of any type, however.

# Look up values with []
filled_dict["one"] # => 1

# Get all keys as an iterable with "keys()". We need to wrap the call in list()
# to turn it into a list. We'll talk about those later. Note - for Python
# versions <3.7, dictionary key ordering is not guaranteed. Your results might
# not match the example below exactly. However, as of Python 3.7, dictionary
# items maintain the order at which they are inserted into the dictionary.
list(filled_dict.keys()) # => ["three", "two", "one"] in Python <3.7
list(filled_dict.keys()) # => ["one", "two", "three"] in Python 3.7+


# Get all values as an iterable with "values()". Once again we need to wrap it
# in list() to get it out of the iterable. Note - Same as above regarding key
# ordering.
list(filled_dict.values()) # => [3, 2, 1] in Python <3.7
list(filled_dict.values()) # => [1, 2, 3] in Python 3.7+

# Check for existence of keys in a dictionary with "in"
"one" in filled_dict # => True
1 in filled_dict # => False

# Looking up a non-existing key is a KeyError
filled_dict["four"] # KeyError

# Use "get()" method to avoid the KeyError
filled_dict.get("one") # => 1
filled_dict.get("four") # => None
# The get method supports a default argument when the value is missing
filled_dict.get("one", 4) # => 1
filled_dict.get("four", 4) # => 4

# "setdefault()" inserts into a dictionary only if the given key isn't present
filled_dict.setdefault("five", 5) # filled_dict["five"] is set to 5
filled_dict.setdefault("five", 6) # filled_dict["five"] is still 5

# Adding to a dictionary
filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4}
filled_dict["four"] = 4 # another way to add to dict

# Remove keys from a dictionary with del
del filled_dict["one"] # Removes the key "one" from filled dict

# From Python 3.5 you can also use the additional unpacking options
{'a': 1, **{'b': 2}} # => {'a': 1, 'b': 2}
{'a': 1, **{'a': 2}} # => {'a': 2}



# Sets store ... well sets
empty_set = set()
# Initialize a set with a bunch of values. Yeah, it looks a bit like a dict. Sorry.
some_set = {1, 1, 2, 2, 3, 4} # some_set is now {1, 2, 3, 4}

# Similar to keys of a dictionary, elements of a set have to be immutable.
invalid_set = {[1], 1} # => Raises a TypeError: unhashable type: 'list'
valid_set = {(1,), 1}

# Add one more item to the set
filled_set = some_set
filled_set.add(5) # filled_set is now {1, 2, 3, 4, 5}
# Sets do not have duplicate elements
filled_set.add(5) # it remains as before {1, 2, 3, 4, 5}

# Do set intersection with &
other_set = {3, 4, 5, 6}
filled_set & other_set # => {3, 4, 5}

# Do set union with |
filled_set | other_set # => {1, 2, 3, 4, 5, 6}

# Do set difference with -
{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}

# Do set symmetric difference with ^
{1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5}

# Check if set on the left is a superset of set on the right
{1, 2} >= {1, 2, 3} # => False

# Check if set on the left is a subset of set on the right
{1, 2} <= {1, 2, 3} # => True

# Check for existence in a set with in
2 in filled_set # => True
10 in filled_set # => False

# Make a one layer deep copy
filled_set = some_set.copy() # filled_set is {1, 2, 3, 4, 5}
filled_set is some_set # => False


####################################################
## 3. Control Flow and Iterables
####################################################

# Let's just make a variable
some_var = 5

# Here is an if statement. Indentation is significant in Python!
# Convention is to use four spaces, not tabs.
# This prints "some_var is smaller than 10"
if some_var > 10:
print("some_var is totally bigger than 10.")
elif some_var < 10: # This elif clause is optional.
print("some_var is smaller than 10.")
else: # This is optional too.
print("some_var is indeed 10.")


"""
For loops iterate over lists
prints:
dog is a mammal
cat is a mammal
mouse is a mammal
"""
for animal in ["dog", "cat", "mouse"]:
# You can use format() to interpolate formatted strings
print("{} is a mammal".format(animal))

"""
"range(number)" returns an iterable of numbers
from zero to the given number
prints:
0
1
2
3
"""
for i in range(4):
print(i)

"""
"range(lower, upper)" returns an iterable of numbers
from the lower number to the upper number
prints:
4
5
6
7
"""
for i in range(4, 8):
print(i)

"""
"range(lower, upper, step)" returns an iterable of numbers
from the lower number to the upper number, while incrementing
by step. If step is not indicated, the default value is 1.
prints:
4
6
"""
for i in range(4, 8, 2):
print(i)

"""
To loop over a list, and retrieve both the index and the value of each item in the list
prints:
0 dog
1 cat
2 mouse
"""
animals = ["dog", "cat", "mouse"]
for i, value in enumerate(animals):
print(i, value)

"""
While loops go until a condition is no longer met.
prints:
0
1
2
3
"""
x = 0
while x < 4:
print(x)
x += 1 # Shorthand for x = x + 1

# Handle exceptions with a try/except block
try:
# Use "raise" to raise an error
raise IndexError("This is an index error")
except IndexError as e:
pass # Pass is just a no-op. Usually you would do recovery here.
except (TypeError, NameError):
pass # Multiple exceptions can be handled together, if required.
else: # Optional clause to the try/except block. Must follow all except blocks
print("All good!") # Runs only if the code in try raises no exceptions
finally: # Execute under all circumstances
print("We can clean up resources here")

# Instead of try/finally to cleanup resources you can use a with statement
with open("myfile.txt") as f:
for line in f:
print(line)

# Writing to a file
contents = {"aa": 12, "bb": 21}
with open("myfile1.txt", "w+") as file:
file.write(str(contents)) # writes a string to a file

with open("myfile2.txt", "w+") as file:
file.write(json.dumps(contents)) # writes an object to a file

# Reading from a file
with open('myfile1.txt', "r+") as file:
contents = file.read() # reads a string from a file
print(contents)
# print: {"aa": 12, "bb": 21}

with open('myfile2.txt', "r+") as file:
contents = json.load(file) # reads a json object from a file
print(contents)
# print: {"aa": 12, "bb": 21}


# Python offers a fundamental abstraction called the Iterable.
# An iterable is an object that can be treated as a sequence.
# The object returned by the range function, is an iterable.

filled_dict = {"one": 1, "two": 2, "three": 3}
our_iterable = filled_dict.keys()
print(our_iterable) # => dict_keys(['one', 'two', 'three']). This is an object that implements our Iterable interface.

# We can loop over it.
for i in our_iterable:
print(i) # Prints one, two, three

# However we cannot address elements by index.
our_iterable[1] # Raises a TypeError

# An iterable is an object that knows how to create an iterator.
our_iterator = iter(our_iterable)

# Our iterator is an object that can remember the state as we traverse through it.
# We get the next object with "next()".
next(our_iterator) # => "one"

# It maintains state as we iterate.
next(our_iterator) # => "two"
next(our_iterator) # => "three"

# After the iterator has returned all of its data, it raises a StopIteration exception
next(our_iterator) # Raises StopIteration

# We can also loop over it, in fact, "for" does this implicitly!
our_iterator = iter(our_iterable)
for i in our_iterator:
print(i) # Prints one, two, three

# You can grab all the elements of an iterable or iterator by calling list() on it.
list(our_iterable) # => Returns ["one", "two", "three"]
list(our_iterator) # => Returns [] because state is saved


####################################################
## 4. Functions
####################################################

# Use "def" to create new functions
def add(x, y):
print("x is {} and y is {}".format(x, y))
return x + y # Return values with a return statement

# Calling functions with parameters
add(5, 6) # => prints out "x is 5 and y is 6" and returns 11

# Another way to call functions is with keyword arguments
add(y=6, x=5) # Keyword arguments can arrive in any order.

# You can define functions that take a variable number of
# positional arguments
def varargs(*args):
return args

varargs(1, 2, 3) # => (1, 2, 3)

# You can define functions that take a variable number of
# keyword arguments, as well
def keyword_args(**kwargs):
return kwargs

# Let's call it to see what happens
keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"}


# You can do both at once, if you like
def all_the_args(*args, **kwargs):
print(args)
print(kwargs)
"""
all_the_args(1, 2, a=3, b=4) prints:
(1, 2)
{"a": 3, "b": 4}
"""

# When calling functions, you can do the opposite of args/kwargs!
# Use * to expand tuples and use ** to expand kwargs.
args = (1, 2, 3, 4)
kwargs = {"a": 3, "b": 4}
all_the_args(*args) # equivalent to all_the_args(1, 2, 3, 4)
all_the_args(**kwargs) # equivalent to all_the_args(a=3, b=4)
all_the_args(*args, **kwargs) # equivalent to all_the_args(1, 2, 3, 4, a=3, b=4)

# Returning multiple values (with tuple assignments)
def swap(x, y):
return y, x # Return multiple values as a tuple without the parenthesis.
# (Note: parenthesis have been excluded but can be included)

x = 1
y = 2
x, y = swap(x, y) # => x = 2, y = 1
# (x, y) = swap(x,y) # Again parenthesis have been excluded but can be included.

# Function Scope
x = 5

def set_x(num):
# Local var x not the same as global variable x
x = num # => 43
print(x) # => 43

def set_global_x(num):
global x
print(x) # => 5
x = num # global var x is now set to 6
print(x) # => 6

set_x(43)
set_global_x(6)


# Python has first class functions
def create_adder(x):
def adder(y):
return x + y
return adder

add_10 = create_adder(10)
add_10(3) # => 13

# There are also anonymous functions
(lambda x: x > 2)(3) # => True
(lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5

# There are built-in higher order functions
list(map(add_10, [1, 2, 3])) # => [11, 12, 13]
list(map(max, [1, 2, 3], [4, 2, 1])) # => [4, 2, 3]

list(filter(lambda x: x > 5, [3, 4, 5, 6, 7])) # => [6, 7]

# We can use list comprehensions for nice maps and filters
# List comprehension stores the output as a list which can itself be a nested list
[add_10(i) for i in [1, 2, 3]] # => [11, 12, 13]
[x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7]

# You can construct set and dict comprehensions as well.
{x for x in 'abcddeef' if x not in 'abc'} # => {'d', 'e', 'f'}
{x: x**2 for x in range(5)} # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}


####################################################
## 5. Modules
####################################################

# You can import modules
import math
print(math.sqrt(16)) # => 4.0

# You can get specific functions from a module
from math import ceil, floor
print(ceil(3.7)) # => 4.0
print(floor(3.7)) # => 3.0

# You can import all functions from a module.
# Warning: this is not recommended
from math import *

# You can shorten module names
import math as m
math.sqrt(16) == m.sqrt(16) # => True

# Python modules are just ordinary Python files. You
# can write your own, and import them. The name of the
# module is the same as the name of the file.

# You can find out which functions and attributes
# are defined in a module.
import math
dir(math)

# If you have a Python script named math.py in the same
# folder as your current script, the file math.py will
# be loaded instead of the built-in Python module.
# This happens because the local folder has priority
# over Python's built-in libraries.


####################################################
## 6. Classes
####################################################

# We use the "class" statement to create a class
class Human:

# A class attribute. It is shared by all instances of this class
species = "H. sapiens"

# Basic initializer, this is called when this class is instantiated.
# Note that the double leading and trailing underscores denote objects
# or attributes that are used by Python but that live in user-controlled
# namespaces. Methods(or objects or attributes) like: __init__, __str__,
# __repr__ etc. are called special methods (or sometimes called dunder methods)
# You should not invent such names on your own.
def __init__(self, name):
# Assign the argument to the instance's name attribute
self.name = name

# Initialize property
self._age = 0

# An instance method. All methods take "self" as the first argument
def say(self, msg):
print("{name}: {message}".format(name=self.name, message=msg))

# Another instance method
def sing(self):
return 'yo... yo... microphone check... one two... one two...'

# A class method is shared among all instances
# They are called with the calling class as the first argument
@classmethod
def get_species(cls):
return cls.species

# A static method is called without a class or instance reference
@staticmethod
def grunt():
return "*grunt*"

# A property is just like a getter.
# It turns the method age() into a read-only attribute of the same name.
# There's no need to write trivial getters and setters in Python, though.
@property
def age(self):
return self._age

# This allows the property to be set
@age.setter
def age(self, age):
self._age = age

# This allows the property to be deleted
@age.deleter
def age(self):
del self._age


# When a Python interpreter reads a source file it executes all its code.
# This __name__ check makes sure this code block is only executed when this
# module is the main program.
if __name__ == '__main__':
# Instantiate a class
i = Human(name="Ian")
i.say("hi") # "Ian: hi"
j = Human("Joel")
j.say("hello") # "Joel: hello"
# i and j are instances of type Human, or in other words: they are Human objects

# Call our class method
i.say(i.get_species()) # "Ian: H. sapiens"
# Change the shared attribute
Human.species = "H. neanderthalensis"
i.say(i.get_species()) # => "Ian: H. neanderthalensis"
j.say(j.get_species()) # => "Joel: H. neanderthalensis"

# Call the static method
print(Human.grunt()) # => "*grunt*"

# Static methods can be called by instances too
print(i.grunt()) # => "*grunt*"

# Update the property for this instance
i.age = 42
# Get the property
i.say(i.age) # => "Ian: 42"
j.say(j.age) # => "Joel: 0"
# Delete the property
del i.age
# i.age # => this would raise an AttributeError


####################################################
## 6.1 Inheritance
####################################################

# Inheritance allows new child classes to be defined that inherit methods and
# variables from their parent class.

# Using the Human class defined above as the base or parent class, we can
# define a child class, Superhero, which inherits the class variables like
# "species", "name", and "age", as well as methods, like "sing" and "grunt"
# from the Human class, but can also have its own unique properties.

# To take advantage of modularization by file you could place the classes above in their own files,
# say, human.py

# To import functions from other files use the following format
# from "filename-without-extension" import "function-or-class"

from human import Human


# Specify the parent class(es) as parameters to the class definition
class Superhero(Human):

# If the child class should inherit all of the parent's definitions without
# any modifications, you can just use the "pass" keyword (and nothing else)
# but in this case it is commented out to allow for a unique child class:
# pass

# Child classes can override their parents' attributes
species = 'Superhuman'

# Children automatically inherit their parent class's constructor including
# its arguments, but can also define additional arguments or definitions
# and override its methods such as the class constructor.
# This constructor inherits the "name" argument from the "Human" class and
# adds the "superpower" and "movie" arguments:
def __init__(self, name, movie=False,
superpowers=["super strength", "bulletproofing"]):

# add additional class attributes:
self.fictional = True
self.movie = movie
# be aware of mutable default values, since defaults are shared
self.superpowers = superpowers

# The "super" function lets you access the parent class's methods
# that are overridden by the child, in this case, the __init__ method.
# This calls the parent class constructor:
super().__init__(name)

# override the sing method
def sing(self):
return 'Dun, dun, DUN!'

# add an additional instance method
def boast(self):
for power in self.superpowers:
print("I wield the power of {pow}!".format(pow=power))


if __name__ == '__main__':
sup = Superhero(name="Tick")

# Instance type checks
if isinstance(sup, Human):
print('I am human')
if type(sup) is Superhero:
print('I am a superhero')

# Get the Method Resolution search Order used by both getattr() and super()
# This attribute is dynamic and can be updated
print(Superhero.__mro__) # => (<class '__main__.Superhero'>,
# => <class 'human.Human'>, <class 'object'>)

# Calls parent method but uses its own class attribute
print(sup.get_species()) # => Superhuman

# Calls overridden method
print(sup.sing()) # => Dun, dun, DUN!

# Calls method from Human
sup.say('Spoon') # => Tick: Spoon

# Call method that exists only in Superhero
sup.boast() # => I wield the power of super strength!
# => I wield the power of bulletproofing!

# Inherited class attribute
sup.age = 31
print(sup.age) # => 31

# Attribute that only exists within Superhero
print('Am I Oscar eligible? ' + str(sup.movie))

####################################################
## 6.2 Multiple Inheritance
####################################################

# Another class definition
# bat.py
class Bat:

species = 'Baty'

def __init__(self, can_fly=True):
self.fly = can_fly

# This class also has a say method
def say(self, msg):
msg = '... ... ...'
return msg

# And its own method as well
def sonar(self):
return '))) ... ((('

if __name__ == '__main__':
b = Bat()
print(b.say('hello'))
print(b.fly)


# And yet another class definition that inherits from Superhero and Bat
# superhero.py
from superhero import Superhero
from bat import Bat

# Define Batman as a child that inherits from both Superhero and Bat
class Batman(Superhero, Bat):

def __init__(self, *args, **kwargs):
# Typically to inherit attributes you have to call super:
# super(Batman, self).__init__(*args, **kwargs)
# However we are dealing with multiple inheritance here, and super()
# only works with the next base class in the MRO list.
# So instead we explicitly call __init__ for all ancestors.
# The use of *args and **kwargs allows for a clean way to pass arguments,
# with each parent "peeling a layer of the onion".
Superhero.__init__(self, 'anonymous', movie=True,
superpowers=['Wealthy'], *args, **kwargs)
Bat.__init__(self, *args, can_fly=False, **kwargs)
# override the value for the name attribute
self.name = 'Sad Affleck'

def sing(self):
return 'nan nan nan nan nan batman!'


if __name__ == '__main__':
sup = Batman()

# Get the Method Resolution search Order used by both getattr() and super().
# This attribute is dynamic and can be updated
print(Batman.__mro__) # => (<class '__main__.Batman'>,
# => <class 'superhero.Superhero'>,
# => <class 'human.Human'>,
# => <class 'bat.Bat'>, <class 'object'>)

# Calls parent method but uses its own class attribute
print(sup.get_species()) # => Superhuman

# Calls overridden method
print(sup.sing()) # => nan nan nan nan nan batman!

# Calls method from Human, because inheritance order matters
sup.say('I agree') # => Sad Affleck: I agree

# Call method that exists only in 2nd ancestor
print(sup.sonar()) # => ))) ... (((

# Inherited class attribute
sup.age = 100
print(sup.age) # => 100

# Inherited attribute from 2nd ancestor whose default value was overridden.
print('Can I fly? ' + str(sup.fly)) # => Can I fly? False



####################################################
## 7. Advanced
####################################################

# Generators help you make lazy code.
def double_numbers(iterable):
for i in iterable:
yield i + i

# Generators are memory-efficient because they only load the data needed to
# process the next value in the iterable. This allows them to perform
# operations on otherwise prohibitively large value ranges.
# NOTE: `range` replaces `xrange` in Python 3.
for i in double_numbers(range(1, 900000000)): # `range` is a generator.
print(i)
if i >= 30:
break

# Just as you can create a list comprehension, you can create generator
# comprehensions as well.
values = (-x for x in [1,2,3,4,5])
for x in values:
print(x) # prints -1 -2 -3 -4 -5 to console/terminal

# You can also cast a generator comprehension directly to a list.
values = (-x for x in [1,2,3,4,5])
gen_to_list = list(values)
print(gen_to_list) # => [-1, -2, -3, -4, -5]


# Decorators
# In this example `beg` wraps `say`. If say_please is True then it
# will change the returned message.
from functools import wraps


def beg(target_function):
@wraps(target_function)
def wrapper(*args, **kwargs):
msg, say_please = target_function(*args, **kwargs)
if say_please:
return "{} {}".format(msg, "Please! I am poor :(")
return msg

return wrapper


@beg
def say(say_please=False):
msg = "Can you buy me a beer?"
return msg, say_please


print(say()) # Can you buy me a beer?
print(say(say_please=True)) # Can you buy me a beer? Please! I am poor :(