When we pass in a function, sometimes it is not necessary to define the function explicitly, and it is more convenient to pass in the anonymous function directly. In Python, there is limited support for anonymous functions
lambda x, y: x+y
1 The purpose of
anonymity is to have no name, it is meaningless to assign a name to an anonymous function
2 The parameter rules and scope relationships of anonymous functions are the same as those of famous functions
3 The function body of an anonymous function should usually be an expression, which must have a return value
f=lambda x,n:x ** n
print(f(2,3))
Application of lambda anonymous functions: **max, min, sorted, map, reduce, filter**
The person with the highest salary: max
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
def get(k):
return salaries[k]
print(max(salaries,key=get)) #'alex'
print(max(salaries,key=lambda x:salaries[x]))
info = [
{'name':'egon','age': '18','salary': '3000'},
{'name':'wxx','age': '28','salary': '1000'},
{'name':'lxx','age': '38','salary': '2000'}
]
max(info, key=lambda dic: int(dic['salary']))
max([11, 22, 33, 44, 55])
The person with the lowest wage: min
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
print(min(salaries,key=lambda x:salaries[x])) #'yuanhao'
info=[
{'name':'egon','age':'18','salary':'3000'},
{'name':'wxx','age':'28','salary':'1000'},
{'name':'lxx','age':'38','salary':'2000'}
]
min(info,key=lambda dic:int(dic['salary']))
Sort the salary dictionary, sort by salary level
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
alaries=sorted(salaries) # sort by dictionary keys by default
print(salaries)
# salaries=sorted(salaries,key=lambda x:salaries[x]) #default is ascending sort
alaries=sorted(salaries,key=lambda x:salaries[x],reverse=True) #descending order
print(salaries)
info=[
{'name':'egon','age':'18','salary':'3000'},
{'name':'wxx','age':'28','salary':'1000'},
{'name':'lxx','age':'38','salary':'2000'}
]
l=sorted(info,key=lambda dic:int(dic['salary']))
map mapping, loop to let each element execute the function, save the results of each function execution to a new list
v1 = [11,22,33,44]
result = map(lambda x:x+100,v1) # The first parameter is the executed function, and the second parameter is the iterable element.
print(list(result)) # [111,122,133,144]
names=['alex','wupeiqi','yuanhao','egon']
res=map(lambda x:x+'_NB' if x =='egon' else x +'_SB',names)
print(list(res))
reduce, accumulate the elements in the parameter sequence.
import functools
v1 = ['wo','hao','e']
def func(x,y):
return x+y
result = functools.reduce(func,v1)
print(result) # wohaoe
result = functools.reduce(lambda x,y:x+y,v1)
print(result) # wohaoe
from functools import reduce
l=['my','name','is','alex','alex','is','sb']
res=reduce(lambda x,y:x+''+y+'',l)
print(res)
#my name is alex alex is sb
filter, filter by conditions.
result=filter(lambda x:x> 2,[1,2,3,4])
print(list(result))
v1 = [11,22,33,'asd',44,'xf']
# General practice
def func(x):
if type(x) == int:
return True
return False
result = filter(func,v1)
print(list(result)) # [11,22,33,44]
# Simplified approach
result = filter(lambda x: True if type(x) == int else False, v1)
print(list(result))
# Minimalist approach
result = filter(lambda x: type(x) == int, v1)
print(list(result))
names=['alex_sb','wxx_sb','yxx_sb','egon']
res=filter(lambda x:True if x.endswith('sb') else False,names)
res=filter(lambda x:x.endswith('sb'),names)
print(list(res)) #['alex_sb','wxx_sb','yxx_sb']
ages=[18,19,10,23,99,30]
res=filter(lambda n:n >= 30,ages)
print(list(res)) #[99, 30]
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
res=filter(lambda k:salaries[k] >= 10000,salaries)
print(list(res)) #['alex','wupeiqi']
