How to save a list to a file and read it as a list type?

Answer #1:

You can use pickle module for that.
This module have two methods,

1. Pickling(dump): Convert Python objects into string representation.
2. Unpickling(load): Retrieving original objects from stored string representstion.

https://docs.python.org/3.3/library/pickle.html

Code:

>>> import pickle
>>> l = [1,2,3,4]
>>> with open("test.txt", "wb") as fp:   #Pickling
...   pickle.dump(l, fp)
... 
>>> with open("test.txt", "rb") as fp:   # Unpickling
...   b = pickle.load(fp)
... 
>>> b
[1, 2, 3, 4]

Also Json

1. dump/dumps: Serialize
2. load/loads: Deserialize

https://docs.python.org/3/library/json.html

Code:

>>> import json
>>> with open("test.txt", "w") as fp:
...     json.dump(l, fp)
...
>>> with open("test.txt", "r") as fp:
...     b = json.load(fp)
...
>>> b
[1, 2, 3, 4]

Answer #2:

I decided I didn’t want to use a pickle because I wanted to be able to open the text file and change its contents easily during testing. Therefore, I did this:

score = [1,2,3,4,5]

with open("file.txt", "w") as f:
    for s in score:
        f.write(str(s) +"n")

score = []
with open("file.txt", "r") as f:
  for line in f:
    score.append(int(line.strip()))

So the items in the file are read as integers, despite being stored to the file as strings.

Answer #3:

Although the accepted answer works, you should really be using python’s json module:

import json

score=[1,2,3,4,5]

with open("file.json", 'w') as f:
    # indent=2 is not needed but makes the file human-readable
    json.dump(score, f, indent=2) 

with open("file.json", 'r') as f:
    score = json.load(f)

print(score)

Advantages:

1. json is a widely adopted and standardized data format, so non-python programs can easily read and understand the json files
2. json files are human-readable
3. Any nested or non-nested list/dictionary structure can be saved to a json file (as long as all the contents are serializable).

Disadvantages:

1. The data is stored in plain-text (ie it’s uncompressed), which makes it a slow and bloated option for large amounts of data (ie probably a bad option for storing large numpy arrays, that’s what hdf5 is for).
2. The contents of a list/dictionary need to be serializable before you can save it as a json, so while you can save things like strings, ints, and floats, you’ll need to write custom serialization and deserialization code to save objects, classes, and functions

When to use json vs pickle:

• If you want to store something you know you’re only ever going to use in the context of a python program, use pickle
• If you need to save data that isn’t serializable by default (ie objects), save yourself the trouble and use pickle.
• If you need a platform agnostic solution, use json
• If you need to be able to inspect and edit the data directly, use json

Common use cases:

• Configuration files (for example, node.js uses a package.json file to track project details, dependencies, scripts, etc …)
• Most REST APIs use json to transmit and receive data
• Data that requires a nested list/dictionary structure, or requires variable length lists/dicts
• Can be an alternative to csv, xml or yaml files

Answer #4:

If you don’t want to use pickle, you can store the list as text and then evaluate it:

data = [0,1,2,3,4,5]
with open("test.txt", "w") as file:
    file.write(str(data))

with open("test.txt", "r") as file:
    data2 = eval(file.readline())

# Let's see if data and types are same.
print(data, type(data), type(data[0]))
print(data2, type(data2), type(data2[0]))

[0, 1, 2, 3, 4, 5] class ‘list’ class ‘int’

[0, 1, 2, 3, 4, 5] class ‘list’ class ‘int’

Answer #5:

If you want you can use numpy’s save function to save the list as file.
Say you have two lists

sampleList1=['z','x','a','b']
sampleList2=[[1,2],[4,5]]

here’s the function to save the list as file, remember you need to keep the extension .npy

def saveList(myList,filename):
    # the filename should mention the extension 'npy'
    np.save(filename,myList)
    print("Saved successfully!")

and here’s the function to load the file into a list

def loadList(filename):
    # the filename should mention the extension 'npy'
    tempNumpyArray=np.load(filename)
    return tempNumpyArray.tolist()

a working example

>>> saveList(sampleList1,'sampleList1.npy')
>>> Saved successfully!
>>> saveList(sampleList2,'sampleList2.npy')
>>> Saved successfully!

# loading the list now 
>>> loadedList1=loadList('sampleList1.npy')
>>> loadedList2=loadList('sampleList2.npy')

>>> loadedList1==sampleList1
>>> True

>>> print(loadedList1,sampleList1)

>>> ['z', 'x', 'a', 'b'] ['z', 'x', 'a', 'b']

Answer #6:

pickle and other serialization packages work. So does writing it to a .py file that you can then import.

>>> score = [1,2,3,4,5]
>>> 
>>> with open('file.py', 'w') as f:
...   f.write('score = %s' % score)
... 
>>> from file import score as my_list
>>> print(my_list)
[1, 2, 3, 4, 5]

Answer #7:

What I did not like with many answers is that it makes way too many system calls by writing to the file line per line. Imho it is best to join list with ‘n’ (line return) and then write it only once to the file:

mylist = ["abc", "def", "ghi"]
myfile = "file.txt"
with open(myfile, 'w') as f:
    f.write("n".join(mylist))

and then to open it and get your list again:

with open(myfile, 'r') as f:
    mystring = f.read()
my_list = mystring.split("n")

Answer #8:

I am using pandas.

import pandas as pd
x = pd.Series([1,2,3,4,5])
x.to_excel('temp.xlsx')
y = list(pd.read_excel('temp.xlsx')[0])
print(y)

Use this if you are anyway importing pandas for other computations.