文件修改，map函数的一些练习

1.写函数，用户传入修改的文件名，与要修改的内容，执行函数，完成整个文件的批量修改操作

# 输入文件名，被修改的内容，修改成的内容
def update_file(file_name, updated, updating):
    import os
    if not os.path.exists(file_name):
        print('输入的文件不存在！')
        exit()
    new_file = file_name + '_bak'
    change_num = 0
    with open(file_name, 'r', encoding='utf-8') as f:
        with open(new_file, 'w', encoding='utf-8') as f_new:
            for line in f:
                if updated in line:
                    new_line = line.replace(updated, updating)
                    change_num += 1
                else:
                    new_line = line
                f_new.write(new_line)
    os.replace(new_file, file_name)  # 覆盖原来的文件
    return '修改了%s处' % change_num


print(update_file('update.txt', 'M', 'm'))

2.用map来处理字符串列表,把列表中所有人都变成sb,比方alex_sb

l_name = ['alex', 'eric', 'penny']
res = map(lambda x: x + '_sb', l_name)
print(list(res))

3.用filter函数处理数字列表，将列表中所有的偶数筛选出来

num = [1, 3, 5, 6, 7, 8]
res = filter(lambda x: x % 2 == 0, num)
print(list(res))

4.如下，每个小字典的name对应股票名字，shares对应多少股，price对应股票的价格

portfolio = [
    {'name': 'IBM', 'shares': 100, 'price': 91.1},
    {'name': 'AAPL', 'shares': 50, 'price': 543.22},
    {'name': 'FB', 'shares': 200, 'price': 21.09},
    {'name': 'HPQ', 'shares': 35, 'price': 31.75},
    {'name': 'YHOO', 'shares': 45, 'price': 16.35},
    {'name': 'ACME', 'shares': 75, 'price': 115.65}
]

计算购买每支股票的总价

用filter过滤出，单价大于100的股票有哪些

portfolio = [
    {'name': 'IBM', 'shares': 100, 'price': 91.1},
    {'name': 'AAPL', 'shares': 50, 'price': 543.22},
    {'name': 'FB', 'shares': 200, 'price': 21.09},
    {'name': 'HPQ', 'shares': 35, 'price': 31.75},
    {'name': 'YHOO', 'shares': 45, 'price': 16.35},
    {'name': 'ACME', 'shares': 75, 'price': 115.65}
]
for item in portfolio:
    item['total_price'] = item['price'] * item['shares']
print(portfolio)
print(list(filter(lambda x: x['price'] > 100, portfolio)))

 

序列化，反序列化

pickle的方式：

import pickle

info = {'name': 'alex', 'age': '22'}
with open('test.test', 'wb') as f:
    f.write(pickle.dumps(info))  # 序列化
    # pickle.dump(info, f)  # 跟上一句完全一个意思

with open('test.test', 'rb') as f2:
    data = pickle.loads(f2.read())  # 反序列化
    # data = pickle.load(f2)    # 跟上一句完全一个意思
print(data['age'])

json的方式：

import json

info = {'name': 'alex', 'age': '22'}
with open('test.test', 'w') as f:
    # f.write(json.dumps(info))  # 序列化
    json.dump(info, f)  # 跟上一句完全一个意思

with open('test.test', 'r') as f2:
    # data = json.loads(f2.read())  # 反序列化
    data = json.load(f2)    # 跟上一句完全一个意思
print(data['age'])