CODEWITHSUNDEEP
pythonarrayslisttext
user2037744
user2037744

Reputation: 2325

How to read a text file into a list or an array with Python

I am trying to read the lines of a text file into a list or array in python. I just need to be able to individually access any item in the list or array after it is created.

The text file is formatted as follows:

0,0,200,0,53,1,0,255,...,0.

Where the ... is above, there actual text file has hundreds or thousands more items.

I'm using the following code to try to read the file into a list:

text_file = open("filename.dat", "r")
lines = text_file.readlines()
print lines
print len(lines)
text_file.close()

The output I get is:

['0,0,200,0,53,1,0,255,...,0.']
1

Apparently it is reading the entire file into a list of just one item, rather than a list of individual items. What am I doing wrong?

Upvotes: 230

Views: 1258641

Answers (7)

Chris Papadimitriou
Chris Papadimitriou

Reputation: 46

You can use the build in Python function .eval()

with open('test.txt', 'r') as f:
    text = f.read()
    text_list = eval(text)

The output is:

text:     '[0,0,200,0,53,1,0,255]'
text_list: [0, 0, 200, 0, 53, 1, 0, 255]

Python's eval() allows you to evaluate arbitrary Python expressions from a string-based or compiled-code-based input. This function can be handy when you're trying to dynamically evaluate Python expressions from any input that comes as a string or a compiled code object. source, documentation

Upvotes: 1

Pranab Sarkar
Pranab Sarkar

Reputation: 31

Better this way,

 def txt_to_lst(file_path):

    try:
        stopword=open(file_path,"r")
        lines = stopword.read().split('\n')
        print(lines)

    except Exception as e:
        print(e)

Upvotes: 1

Shreyas H.V
Shreyas H.V

Reputation: 129

Im a bit late but you can also read the text file into a dataframe and then convert corresponding column to a list.

lista=pd.read_csv('path_to_textfile.txt', sep=",", header=None)[0].tolist()

example.

lista=pd.read_csv('data/holdout.txt',sep=',',header=None)[0].tolist()

Note: the column name of the corresponding dataframe will be in the form of integers and i choose 0 because i was extracting only the first column

Upvotes: 3

Achrome
Achrome

Reputation: 7821

You will have to split your string into a list of values using split()

So,

lines = text_file.read().split(',')

EDIT: I didn't realise there would be so much traction to this. Here's a more idiomatic approach.

import csv
with open('filename.csv', 'r') as fd:
    reader = csv.reader(fd)
    for row in reader:
        # do something

Upvotes: 192

Blairg23
Blairg23

Reputation: 12065

This question is asking how to read the comma-separated value contents from a file into an iterable list:

0,0,200,0,53,1,0,255,...,0.

The easiest way to do this is with the csv module as follows:

import csv
with open('filename.dat', newline='') as csvfile:
    spamreader = csv.reader(csvfile, delimiter=',')

Now, you can easily iterate over spamreader like this:

for row in spamreader:
    print(', '.join(row))

See documentation for more examples.

Upvotes: 8

gboffi
gboffi

Reputation: 25023

So you want to create a list of lists... We need to start with an empty list

list_of_lists = []

next, we read the file content, line by line

with open('data') as f:
    for line in f:
        inner_list = [elt.strip() for elt in line.split(',')]
        # in alternative, if you need to use the file content as numbers
        # inner_list = [int(elt.strip()) for elt in line.split(',')]
        list_of_lists.append(inner_list)

A common use case is that of columnar data, but our units of storage are the rows of the file, that we have read one by one, so you may want to transpose your list of lists. This can be done with the following idiom

by_cols = zip(*list_of_lists)

Another common use is to give a name to each column

col_names = ('apples sold', 'pears sold', 'apples revenue', 'pears revenue')
by_names = {}
for i, col_name in enumerate(col_names):
    by_names[col_name] = by_cols[i]

so that you can operate on homogeneous data items

 mean_apple_prices = [money/fruits for money, fruits in
                     zip(by_names['apples revenue'], by_names['apples_sold'])]

Most of what I've written can be speeded up using the csv module, from the standard library. Another third party module is pandas, that lets you automate most aspects of a typical data analysis (but has a number of dependencies).

Update While in Python 2 zip(*list_of_lists) returns a different (transposed) list of lists, in Python 3 the situation has changed and zip(*list_of_lists) returns a zip object that is not subscriptable.

If you need indexed access you can use

by_cols = list(zip(*list_of_lists))

that gives you a list of lists in both versions of Python.

On the other hand, if you don't need indexed access and what you want is just to build a dictionary indexed by column names, a zip object is just fine...

file = open('some_data.csv')
names = get_names(next(file))
columns = zip(*((x.strip() for x in line.split(',')) for line in file)))
d = {}
for name, column in zip(names, columns): d[name] = column

Upvotes: 29

Thiru
Thiru

Reputation: 3373

You can also use numpy loadtxt like

from numpy import loadtxt
lines = loadtxt("filename.dat", comments="#", delimiter=",", unpack=False)

Upvotes: 68

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