Turning a txt. file into a list in sagemath/python

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First, you can put all your lines into a Python list:

sage: with open('/path/to/your/file.txt', 'r') as f:
....:     L = f.readlines()

So, L is a list ot lines of the file:

sage: L
['2.14\n', '3.15\n', '7.8 \n']

As you can see, the entries are strings, with maybe spaces and newlines at the end. You can clean such a string with the strip() method:

sage: [l.strip() for l in L]
['2.14', '3.15', '7.8']

But you want Sage floating point numbers, not strings representing them, so you can transform the string into elements of RDF, the real double field:

sage: [RDF(l.strip()) for l in L]
[2.14, 3.15, 7.8]

If you want to summarize this, you can directly do:

sage: with open('/path/to/your/file.txt', 'r') as f:
....:     L = [RDF(l.strip()) for l in f.readlines()]

sage: L
[2.14, 3.15, 7.8]

UPDATE: we can get the best of this answer and the one of @nbruin as follows:

sage: with open('/path/to/your/file.txt', 'r') as f:
....:     L = [RDF(l.strip()) for l in f]

In general the open.readlines() pattern suggested in another answer works quite well and is easier to work when you're developing the file processing that you have to do, but it does lead to the entire textual file content being allocated in main memory. If you want to process the lines as they are read from the file, you can use that the file itself already knows how to "iterate" over its lines:

sage: L = [ RDF(l.strip()) for l in open('/path/to/your/file.txt', 'r')]

While this works, it's better to use a with clause to ensure the file is closed:

sage: with  open('/path/to/your/file.txt', 'r') as f:
....:     L = [RDF(l.strip()) for l in f]

I typically use the following:

import csv
data=list( csv.reader(open('myfile.csv','rU')) )

The data is read into a list as strings. Then, you can convert to another data type. For example, you can convert to integers as follows:

data=map(lambda x: int(x),data)