使用pickle进行数据序列化

pickle可以进行数据序列化。即数据对象之前是什么类型，将它原封不动的保存起来，load之后还是之前类型

import pickle
data1=[1,2,3,4]
#数据序列化
with open('data1.dat','wb') as f:
    pickle.dump(data1,f)
#反序列化
with open('data1.dat','rb') as f:
    data1=pickle.load(f)
print(data1)
print(type(data1))

[1, 2, 3, 4]
<class 'list'>

import glob
glob.glob('*.dat')

['data1.dat']

#使用压缩
import gzip
import pickle
data1=[1,2,3,4]
f=gzip.open('data2.dat','wb')
pickle.dump(data1,f,pickle.HIGHEST_PROTOCOL)
with gzip.open('data2.dat','rb') as f:
    data=pickle.load(f)
print(data)

[1, 2, 3, 4]

#字典
def tochr(val):
    result={}
    result[val]=chr(val)
    with open('data3.pickle','ab') as f:
        pickle.dump(result,file=f)
        
for i in [65,97]:
    tochr(i)  

with open('data3.pickle','rb') as f:
    data=pickle.load(f)
print(data)

{97: 'a'}

#注意，因为上面例子dump了两次，所以，load时候只load了最后1次dump的数据
#字典
def tochr(val):
    result={}
    result[val]=chr(val)
    with open('data4.pickle','ab') as f:
        pickle.dump(result,file=f)
        
for i in [65,97]:
    tochr(i)  

with open('data4.pickle','rb') as f:
    print(pickle.load(f))
    print(pickle.load(f))

{65: 'A'}
{97: 'a'}

#一次性读取
def tochr(val):
    result={}
    result[val]=chr(val)
    with open('data4.pickle','ab') as f:
        pickle.dump(result,file=f)
        
for i in [65,97,86]:
    tochr(i)  

f=open('data4.pickle','rb')
data2={}
while True:
    try:
        
        data=pickle.load(f)
        data2.update(data)
    except EOFError:
        break
print(data2)

{65: 'A', 97: 'a', 86: 'V'}

#dump一次，load一次
#一次性读取
result={}
def tochr(val):
    
    result[val]=chr(val)
    return result
    
for i in [65,97,86]:
    res=tochr(i)  
with open('data5.pickle','ab') as f:
    pickle.dump(res,file=f)
    
f=open('data5.pickle','rb')
data=pickle.load(f)
print(data)

{65: 'A', 97: 'a', 86: 'V'}