11. File IO

11.1. Getting data into Python

  • You now know a lot about how to manipulate data.

  • But you probably want to manipulate specific data. Your data. Not toy examples.

  • We need to learn about File I/O.

  • I wont lie to you: file I/O is boring, painful, detail-oriented work.

  • Fortunately, Python makes it less painful than just about any other language I’ve ever used.

11.2. Read/Write to a text file

  • There are a bunch of ways to do it, but here’s one.

>>> my_file = open('aFileName.txt', 'r')

  • Done (many programming languages do NOT make it this easy)

  • This assumes the file is in the working directory

  • Take a wild guess what ‘r’ means

Activity

  1. Create a text in the working directory. If using Colab, you’ll have to get one there.

  2. Open it like the above

  3. Try using the methods .readline() and .read()

  • There are a lot of methods, these are just two.

  • How about writing to a file?

>>> my_other_file = open('anotherFileName.txt', 'w')

Activity

Try to figure out how to write to this file.

  • When done with files…

  • Listen up, this is a very very important thing

  • Pay attention

  • We must close them!

  • Failing to do this can cause serious issues!!

    • Seriously, I’ve spent longer than I would like to admit in my life looking for bugs that were just a result of me not closing my files.

  • Fortunately it’s easy to close them

>>> my_other_file.close()

11.3. Loading a CSV file

  • Regular text files are cool, but CSVs are a great way to format data.

  • CSV stands for “Comma Separated Values”.

  • The file is stored in plain text (you can read it with a text editor)

  • Delivers what it promises.

  • Each line of the file is one item.

  • Within the line, each value associated with that item is in a comma-delimited field.

    • Some people will use tabs or other things, but commas are best.

  • For example, suppose I have recorded height, weight and IQ for 3 subjects:

    name, height, weight, IQ
Subject 1, 170, 68, 100
Subject 2, 182, 80, 110
Subject 3, 155, 54, 105

  • The first line is a header, explaining the values in each field.

  • Headers are not mandatory. Some CSVs have them, some don’t.

  • Good news: Python has a built-in library to read CSV files for you!

  • In fact, we’ve seen this before:

    def load_asn1_data():
    """
    This function loads the file `wordle.csv` and returns a LIST of
    latitudes and longitudes for North American Starbucks'.
    We'll talk about lists formally in class in a few lectures, but maybe
    you can start guessing how they work based on what you see here...
    """

    import csv

    reader = csv.reader(open('wordle.csv', 'r'))
    words = []

    for r in reader:
        words.append( (r[0],r[1]))

    return words

  • How does the csv.reader work?

Activity+

Figure out how it works. Download this csv file to your computer. NOTE: If using Colab, you’ll have to upload it.

Now write a function called load_airports() that loads this CSV file into a list.

Play with this list a bit and get a feel for how the data is organized.

Activity+++

Now write a function get_name_from_code(airport_code, airport_list) that will return a string containing the full name of the airport with the code airport_code.

The parameter airport_list should be the list you loaded using load_airports().

  • Suppose you have some tabular data in Python that you want to save back in to a CSV

    >>> csv_out = csv.writer(open('yourFileName', 'w'))
>>> csv_out.writerow(['First cell','Second cell', 'Third cell'])
write as many rows as you need to... maybe in a loop?

  • CSV files are popular because they’re simple.

  • You can, e.g., export any Excel spreadsheet as a CSV.

  • If you have tabular data, this is a decent choice of format.

  • If you don’t have tabular data… this is an awful choice.

11.4. For next class