add_one = lambda x:x+1       # 1個參數(shù)，執(zhí)行操作為+1
add_nums = lambda x,y:x+y    # 2個參數(shù)，執(zhí)行操作為相加

print(add_one(2))            # 調(diào)用add_one
print(add_nums(3,7))         # 調(diào)用add_nums

>>>   3 
      10

numbers = [1,2,3,4,5]
add_one = list(map(lambda n:n+1,numbers))  #map(fun,sequence)

print(list(add_one))
print(tuple(add_one))

Out: [2, 3, 4, 5, 6]
     (2, 3, 4, 5, 6)

def add(num):
    return num+1

numbers = [1,2,3,4,5]
add_one = list(map(add,numbers))
print(add_one)
print(tuple(add_one))

from datetime import datetime as dt
logs = ['serverLog','appLog','paymentLog']
format ='_{}.py'.format(dt.now().strftime('%d-%m-%y'))
result =list(map(lambda x:x+format,logs))   # 利用map+lambda 實現(xiàn)字符串拼接
print(result)

Out:['serverLog_11-02-19.py', 'appLog_11-02-19.py', 'paymentLog_11-02-19.py']

person =[{'name':'Lilei',
          'city':'beijing'},
         {'name':'HanMeiMei',
          'city':'shanghai'}]

names=list(map(lambda x:x['name'],person))
print(names)

Out：['Lilei', 'HanMeiMei']

numbers = [0, 1, 2, -3, 5, -8, 13]

# 提取奇數(shù)
result = filter(lambda x: x % 2, numbers)
print("Odd Numbers are :",list(result))

# 提取偶數(shù)
result = filter(lambda x: x % 2 == 0, numbers)
print("Even Numbers are :",list(result))

#提取正數(shù)
result = filter(lambda x: x>0, numbers)
print("Positive Numbers are :",list(result))

Out：Odd Numbers are : [1, -3, 5, 13]
     Even Numbers are : [0, 2, -8]
     Positive Numbers are : [1, 2, 5, 13]

person =[{'name':'Lilei',
          'city':'beijing'},
         {'name':'HanMeiMei',
          'city':'shanghai'}]

names=list(filter(lambda x:x['name']=='Lilei',person)) # 提取李雷的信息
print(names)

Out：[{'name': 'Lilei', 'city': 'beijing'}]

from functools import reduce          # Only Python 3
numbers = [1,2,3,4]
result_multiply = reduce((lambda x, y: x * y), numbers)
result_add = reduce((lambda x,y: x+y), numbers)

print(result_multiply)
print(result_add)

Out：24
     10

from functools import reduce          # Only Python 3
numbers = [1,2,3,4]
result_multiply = reduce((lambda x, y: x * y), numbers)
result_add = reduce((lambda x,y: x+y), numbers)

from functools import reduce
from operator import mul

numbers = [1,2,3,4]
result_add = sum(numbers)
result_multiply =reduce(mul,numbers)

print(result_add)
print(result_multiply)

Out: 10
     24

colors = ['red','purple','green','blue']
result = map(lambda c:c.capitalize(),colors)
print(list(result))

Out：['Red', 'Purple', 'Green', 'Blue']
看著似乎不錯，挺簡潔的，但是我們有更好的方法：


colors = ['red','purple','green','blue']
result = [c.capitalize() for c in colors]
print(result)

Out：['Red', 'Purple', 'Green', 'Blue']
用sorted還能處理首字母不規(guī)范的情況，連排序都省了：

colors = ['Red','purple','Green','blue']
print(sorted(colors,key=str.capitalize))

Out:['blue', 'Green', 'purple', 'Red']

def function(a,b):  
    print(a,b)  
apply(function,('good','better'))  
apply(function,(2,3+6))  
apply(function,('cai','quan'))  
apply(function,('cai',),{'b':'caiquan'})  
apply(function,(),{'a':'caiquan','b':'Tom'})  

DataFrame.apply(func, axis=0, broadcast=False, raw=False,
                 reduce=None, args=(), *kwds)

data[‘cut_review'].apply(lambda x: [i for i in x s if i not in stopwords])

#函數(shù)應(yīng)用和映射
import numpy as np
import pandas as pd
df=pd.DataFrame(np.random.randn(4,3),columns=list('bde'),index=['utah','ohio','texas','oregon'])
print(df)
"""
               b         d         e
utah   -0.667969  1.974801  0.738890
ohio   -0.896774 -0.790914  0.474183
texas   0.043476  0.890176 -0.662676
oregon  0.701109 -2.238288 -0.154442
"""

#將函數(shù)應(yīng)用到由各列或行形成的一維數(shù)組上。DataFrame的apply方法可以實現(xiàn)此功能
f=lambda x:x.max()-x.min()
#默認(rèn)情況下會以列為單位，分別對列應(yīng)用函數(shù)
t1=df.apply(f)
print(t1)
t2=df.apply(f,axis=1)
print(t2)

"""
b    1.597883
d    4.213089
e    1.401566
dtype: float64
utah      2.642770
ohio      1.370957
texas     1.552852
oregon    2.939397
dtype: float64
"""

#除標(biāo)量外，傳遞給apply的函數(shù)還可以返回由多個值組成的Series
def f(x):
    return pd.Series([x.min(),x.max()],index=['min','max'])
t3=df.apply(f)
#從運(yùn)行的結(jié)果可以看出，按列調(diào)用的順序，調(diào)用函數(shù)運(yùn)行的結(jié)果在右邊依次追加
print(t3)

"""
            b         d         e
min -0.896774 -2.238288 -0.662676
max  0.701109  1.974801  0.738890
"""

#元素級的python函數(shù)，將函數(shù)應(yīng)用到每一個元素
#將DataFrame中的各個浮點(diǎn)值保留兩位小數(shù)
f=lambda x: '%.2f'%x
t3=df.applymap(f)
print(t3)
"""
            b      d      e
utah    -0.67   1.97   0.74
ohio    -0.90  -0.79   0.47
texas    0.04   0.89  -0.66
oregon   0.70  -2.24  -0.15
"""

#注意，之所以這里用map,是因為Series有一個元素級函數(shù)的map方法。而dataframe只有applymap。
t4=df['e'].map(f)
print(t4)

"""
utah     0.74
ohio     0.47
texas   -0.66
oregon  -0.15
"""