Saving an Object (Data Persistence)

Saving an Object (Data persistence)

You could use the pickle module in the standard library.
Here's an elementary application of it to your example:

import pickle

class Company(object):
    def __init__(self, name, value):
        self.name = name
        self.value = value

with open('company_data.pkl', 'wb') as outp:
    company1 = Company('banana', 40)
    pickle.dump(company1, outp, pickle.HIGHEST_PROTOCOL)

    company2 = Company('spam', 42)
    pickle.dump(company2, outp, pickle.HIGHEST_PROTOCOL)

del company1
del company2

with open('company_data.pkl', 'rb') as inp:
    company1 = pickle.load(inp)
    print(company1.name)  # -> banana
    print(company1.value)  # -> 40

    company2 = pickle.load(inp)
    print(company2.name) # -> spam
    print(company2.value)  # -> 42

You could also define your own simple utility like the following which opens a file and writes a single object to it:

def save_object(obj, filename):
    with open(filename, 'wb') as outp:  # Overwrites any existing file.
        pickle.dump(obj, outp, pickle.HIGHEST_PROTOCOL)

# sample usage
save_object(company1, 'company1.pkl')

Update

Since this is such a popular answer, I'd like touch on a few slightly advanced usage topics.

cPickle (or _pickle) vs pickle

It's almost always preferable to actually use the cPickle module rather than pickle because the former is written in C and is much faster. There are some subtle differences between them, but in most situations they're equivalent and the C version will provide greatly superior performance. Switching to it couldn't be easier, just change the import statement to this:

import cPickle as pickle

In Python 3, cPickle was renamed _pickle, but doing this is no longer necessary since the pickle module now does it automatically—see What difference between pickle and _pickle in python 3?.

The rundown is you could use something like the following to ensure that your code will always use the C version when it's available in both Python 2 and 3:

try:
    import cPickle as pickle
except ModuleNotFoundError:
    import pickle

Data stream formats (protocols)

pickle can read and write files in several different, Python-specific, formats, called protocols as described in the documentation, "Protocol version 0" is ASCII and therefore "human-readable". Versions > 0 are binary and the highest one available depends on what version of Python is being used. The default also depends on Python version. In Python 2 the default was Protocol version 0, but in Python 3.8.1, it's Protocol version 4. In Python 3.x the module had a pickle.DEFAULT_PROTOCOL added to it, but that doesn't exist in Python 2.

Fortunately there's shorthand for writing pickle.HIGHEST_PROTOCOL in every call (assuming that's what you want, and you usually do), just use the literal number -1 — similar to referencing the last element of a sequence via a negative index.
So, instead of writing:

pickle.dump(obj, outp, pickle.HIGHEST_PROTOCOL)

You can just write:

pickle.dump(obj, outp, -1)

Either way, you'd only have specify the protocol once if you created a Pickler object for use in multiple pickle operations:

pickler = pickle.Pickler(outp, -1)
pickler.dump(obj1)
pickler.dump(obj2)
   etc...

Note: If you're in an environment running different versions of Python, then you'll probably want to explicitly use (i.e. hardcode) a specific protocol number that all of them can read (later versions can generally read files produced by earlier ones).

Multiple Objects

While a pickle file can contain any number of pickled objects, as shown in the above samples, when there's an unknown number of them, it's often easier to store them all in some sort of variably-sized container, like a list, tuple, or dict and write them all to the file in a single call:

tech_companies = [
    Company('Apple', 114.18), Company('Google', 908.60), Company('Microsoft', 69.18)
]
save_object(tech_companies, 'tech_companies.pkl')

and restore the list and everything in it later with:

with open('tech_companies.pkl', 'rb') as inp:
    tech_companies = pickle.load(inp)

The major advantage is you don't need to know how many object instances are saved in order to load them back later (although doing so without that information is possible, it requires some slightly specialized code). See the answers to the related question Saving and loading multiple objects in pickle file? for details on different ways to do this. Personally I liked @Lutz Prechelt's answer the best, so that's the approach used in the sample code below:

class Company:
    def __init__(self, name, value):
        self.name = name
        self.value = value

def pickle_loader(filename):
    """ Deserialize a file of pickled objects. """
    with open(filename, "rb") as f:
        while True:
            try:
                yield pickle.load(f)
            except EOFError:
                break

print('Companies in pickle file:')
for company in pickle_loader('company_data.pkl'):
    print('  name: {}, value: {}'.format(company.name, company.value))

How to save all python objects from working directory to a file

If I understood your need, you want to backup your session.
If that is the case, here is a solution using pickle. Kr.

First solution is:

import pickle

def is_picklable(obj):
    try:
        pickle.dumps(obj)
    except Exception:
        return False
    return True

bk = {}
for k in dir():
    obj =  globals()[k]
    if is_picklable(obj):
        try:
            bk.update({k: obj})
        except TypeError:
            pass

# to save session
with open('./your_bk.pkl', 'wb') as f:
    pickle.dump(bk, f)

    
# to load your session

with open('./your_bk.pkl', 'rb') as f:
    bk_restore = pickle.load(f)

***Second solution is with dill. You might have error if in your workspace, there are some unpicklable objects ***:

import dill

dill.dump_session('./your_bk_dill.pkl')
#to restore session:
dill.load_session('./your_bk_dill.pkl')

Third option go with shelve package:

import shelve

bk = shelve.open('./your_bk_shelve.pkl','n')
for k in dir():
    try:
        bk[k] = globals()[k]
    except Exception:
        pass
bk.close()

# to restore
bk_restore = shelve.open('./your_bk_shelve.pkl')
for k in bk_restore:
    globals()[k]=bk_restore[k]
    tmp[k] = bk_restore[k]
bk_restore.close()

Check and let's know about your trial.

Credits: The second and third solution are nearly a shameless copy/paste from those two links belows. I adapted the handling of errors as the original answer will lead to error for pickling of module.

dill solution

shelve solution

Saving list of data objects persistent internal in android

Look closely at your serialization code.

//Saving
if(appUser == null){
    Log.e("MainActivity", "Create new User");
    appUser = new User();
    try{
        FileOutputStream fos = this.getApplicationContext()
                .openFileOutput("UserData.data", Context.MODE_PRIVATE);
        ObjectOutputStream os = new ObjectOutputStream(fos);
        os.writeObject(this);
        ...

You're creating a new User object but you're serializing this which I'm guessing is either an Activity or a Fragment. Hence, you're receiving a NotSerializableException.

Strings and Longs can be serialized with no issues. However, if your final User implementation would have a list of MyDataStuff, you would have to mark its class Serializable as well.

How can I persist data-objects in a datagrid without using a database

To sum up the options we provided in comments.

Probably the easiest way to persist objects is by serializing them to JSON (a string) then save to a text file. By using NewtonSoft library, it is as easy as

string data = JsonConvert.SerializeObject(cars);
File.WriteAllText(file, data);

Later you can use the DeserializeObject method to re-construct the objects from the saved string.

Another format for serialization is XML. It is as easy as JSON but the output string is more verbose thus less readable.

.NET has built-in support for XML serialization so you don’t need third party libraries. The same might be true for JSON, but NewtonSoft is used by everyone for JSON serialization.

If you are comfortable with SQL, you can try SQLite which is a file-based database (by convention it has a .sqlite file extension) and doesn’t require a stand-alone server. You need to reference SQLite.NET library in you program, and its connection string is like

“Data Source=mydb.sqlite;Version=3;”

then you can use it like other full-fledged databases.

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