Introduction
A standard process in programming is opening a file and parsing its contents. What do you do when the file you are attempting to course of is kind of massive, like a number of GB of information or bigger? The reply to this downside is to learn in chunks of a file at a time, course of it, then free it from reminiscence so you may course of one other chunk till the entire huge file has been processed. Whereas it is as much as you to find out an appropriate dimension for the chunks of information you are processing, for a lot of functions, it is appropriate to course of a file one line at a time.
All through this text, we’ll be overlaying numerous code examples that reveal how one can learn information line by line. In case you wish to check out a few of these examples by your self, the code used on this article may be discovered on the following GitHub repo.
Fundamental File IO in Python
Python is a superb general-purpose programming language, and it has numerous very helpful file IO performance in its customary library of built-in capabilities and modules.
The built-in open() operate is what you employ to open a file object for both studying or writing functions. This is how you should use it to open a file:
fp = open('path/to/file.txt', 'r')
As demonstrated above, the open() operate takes in a number of arguments. We will probably be specializing in two arguments, with the primary being a positional string parameter representing the trail to the file you wish to open. The second (elective) parameter can also be a string, and it specifies the mode of interplay you plan for use on the file object being returned by the operate name. The most typical modes are listed within the desk beneath, with the default being ‘r’ for studying:
| Mode | Description |
|---|---|
r |
Open for studying plain textual content |
w |
Open for writing plain textual content |
a |
Open an present file for appending plain textual content |
rb |
Open for studying binary knowledge |
wb |
Open for writing binary knowledge |
After you have written or learn the entire desired knowledge in a file object, it’s essential shut the file in order that sources may be reallocated on the working system that the code is working on.
fp.shut()
Notice: It is all the time good follow to shut a file object useful resource, however it’s a process that is simple to overlook.
When you can all the time keep in mind to name shut() on a file object, there’s an alternate and extra elegant option to open a file object and make sure that the Python interpreter cleans up after its use:
with open('path/to/file.txt') as fp:
By merely utilizing the with key phrase (launched in Python 2.5) to the code we use to open a file object, Python will do one thing much like the next code. This ensures that it doesn’t matter what the file object is closed after use:
attempt:
fp = open('path/to/file.txt')
lastly:
fp.shut()
Both of those two strategies is appropriate, with the primary instance being extra Pythonic.
The file object returned from the open() operate has three frequent express strategies (learn(), readline(), and readlines()) to learn in knowledge. The learn() technique reads all the information right into a single string. That is helpful for smaller information the place you want to do textual content manipulation on the complete file. Then there may be readline(), which is a helpful option to solely learn in particular person strains, in incremental quantities at a time, and return them as strings. The final express technique, readlines(), will learn all of the strains of a file and return them as a listing of strings.
Notice: For the rest of this text we will probably be working with the textual content of the ebook The “Iliad of Homer”, which may be discovered at gutenberg.org, in addition to within the GitHub repo the place the code is for this text.
Studying a File Line-by-Line in Python with readline()
Let’s begin off with the readline() technique, which reads a single line, which would require us to make use of a counter and increment it:
filepath = 'Iliad.txt'
with open(filepath) as fp:
line = fp.readline()
cnt = 1
whereas line:
print("Line {}: {}".format(cnt, line.strip()))
line = fp.readline()
cnt += 1
This code snippet opens a file object whose reference is saved in fp, then reads in a line one by one by calling readline() on that file object iteratively in a whereas loop. It then merely prints the road to the console.
Operating this code, it’s best to see one thing like the next:
...
Line 567: exceedingly trifling. Now we have no remaining inscription sooner than the
Line 568: fortieth Olympiad, and the early inscriptions are impolite and unskilfully
Line 569: executed; nor can we even guarantee ourselves whether or not Archilochus, Simonides
Line 570: of Amorgus, Kallinus, Tyrtaeus, Xanthus, and the opposite early elegiac and
Line 571: lyric poets, dedicated their compositions to writing, or at what time the
Line 572: follow of doing so grew to become acquainted. The primary optimistic floor which
Line 573: authorizes us to presume the existence of a manuscript of Homer, is within the
Line 574: well-known ordinance of Solon, with regard to the rhapsodies on the
Line 575: Panathenaea: however for what size of time beforehand manuscripts had
Line 576: existed, we're unable to say.
...
Although, this method is crude and express. Most actually not very Pythonic. We are able to make the most of the readlines() technique to make this code rather more succinct.
Learn a File Line-by-Line with readlines()
The readlines() technique reads all of the strains and shops them right into a Checklist. We are able to then iterate over that listing and utilizing enumerate(), make an index for every line for our comfort:
file = open('Iliad.txt', 'r')
strains = file.readlines()
for index, line in enumerate(strains):
print("Line {}: {}".format(index, line.strip()))
file.shut()
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This ends in:
...
Line 160: INTRODUCTION.
Line 161:
Line 162:
Line 163: Scepticism is as a lot the results of information, as information is of
Line 164: scepticism. To be content material with what we at current know, is, for essentially the most
Line 165: half, to close our ears towards conviction; since, from the very gradual
Line 166: character of our training, we should regularly overlook, and emancipate
Line 167: ourselves from, information beforehand acquired; we should put aside previous
Line 168: notions and embrace contemporary ones; and, as we study, we have to be day by day
Line 169: unlearning one thing which it has price us no small labour and nervousness to
Line 170: purchase.
...
Now, though a lot better, we do not even must name the readlines() technique to attain this identical performance. That is the normal manner of studying a file line-by-line, however there is a extra fashionable, shorter one.
Learn a File Line-by-Line with a for Loop – Most Pythonic Strategy
The returned File itself is an iterable. We need not extract the strains through readlines() in any respect – we will iterate the returned object itself. This additionally makes it simple to enumerate() it so we will write the road quantity in every print() assertion.
That is the shortest, most Pythonic method to fixing the issue, and the method favored by most:
with open('Iliad.txt') as f:
for index, line in enumerate(f):
print("Line {}: {}".format(index, line.strip()))
This ends in:
...
Line 277: Mentes, from Leucadia, the trendy Santa Maura, who evinced a information and
Line 278: intelligence hardly ever present in these occasions, persuaded Melesigenes to shut
Line 279: his college, and accompany him on his travels. He promised not solely to pay
Line 280: his bills, however to furnish him with an additional stipend, urging, that,
Line 281: "Whereas he was but younger, it was becoming that he ought to see along with his personal
Line 282: eyes the international locations and cities which could hereafter be the topics of his
Line 283: discourses." Melesigenes consented, and set out along with his patron,
Line 284: "analyzing all of the curiosities of the international locations they visited, and
...
Right here, we’re making the most of the built-in functionalities of Python that permit us to effortlessly iterate over an iterable object, merely utilizing a for loop. If you would like to learn extra about Python’s built-in functionalities on iterating objects, we have you lined:
Functions of Studying Information Line-by-Line
How will you use this virtually? Most NLP functions cope with massive corpora of information. More often than not, it will not be sensible to learn the complete corpora into reminiscence. Whereas rudimentary, you may write a from-scratch answer to depend the frequency of sure phrases, with out utilizing any exterior libraries. Let’s write a easy script that masses in a file, reads it line-by-line, and counts the frequency of phrases, printing the ten most frequent phrases and the variety of their occurrences:
import sys
import os
def most important():
filepath = sys.argv[1]
if not os.path.isfile(filepath):
print("File path {} doesn't exist. Exiting...".format(filepath))
sys.exit()
bag_of_words = {}
with open(filepath) as fp:
for line in fp:
record_word_cnt(line.strip().break up(' '), bag_of_words)
sorted_words = order_bag_of_words(bag_of_words, desc=True)
print("Most frequent 10 phrases {}".format(sorted_words[:10]))
def order_bag_of_words(bag_of_words, desc=False):
phrases = [(word, cnt) for word, cnt in bag_of_words.items()]
return sorted(phrases, key=lambda x: x[1], reverse=desc)
def record_word_cnt(phrases, bag_of_words):
for phrase in phrases:
if phrase != '':
if phrase.decrease() in bag_of_words:
bag_of_words[word.lower()] += 1
else:
bag_of_words[word.lower()] = 1
if __name__ == '__main__':
most important()
The script makes use of the os module to guarantee that the file we’re trying to learn really exists. In that case, its learn line-by-line and every line is handed on into the record_word_cnt() operate. It delimits the areas between phrases and provides the phrase to the dictionary – bag_of_words. As soon as all of the strains are recorded into the dictionary, we order it through order_bag_of_words() which returns a listing of tuples within the (phrase, word_count) format, sorted by the phrase depend.
Lastly, we print the highest ten most typical phrases.
Usually, for this, you’d create a Bag of Phrases Mannequin, utilizing libraries like NLTK, although, this implementation will suffice. Let’s run the script and supply our Iliad.txt to it:
$ python app.py Iliad.txt
This ends in:
Most frequent 10 phrases [('the', 15633), ('and', 6959), ('of', 5237), ('to', 4449), ('his', 3440), ('in', 3158), ('with', 2445), ('a', 2297), ('he', 1635), ('from', 1418)]
Conclusion
On this article, we have explored a number of methods to learn a file line-by-line in Python, in addition to created a rudimentary Bag of Phrases mannequin to calculate the frequency of phrases in a given file.
