Objects

• A value is some data in memory together with its type (int, float, etc.)
• An object is similar; however,
  1. Tends to be more complex (e.g, have many parts)
  2. Tends to have operations assigned to it.
• Examples: List, String, Date
• The difference between values and objects is not precisely defined (and not highly important)
• Sometimes functions are tightly bound to an object (i.e., don’t make much sense in a more global context)
  • String#startswith
  • This function doesn’t really make sense as a “global” function, since it really only applies to strings.
  • So, we bundle it with the class, call it a method, and call it like this:
  • my_string.startswith('foo')
• Other common string methods:
  • startswith
  • endswith
  • index
  • strip
  • upper
  • lower
  • split
  • join
  • More: https://www.w3schools.com/python/python_ref_string.asp

Files

• Calling the global open function returns a File object that can be used to read or write files.
  • f = open('my_file.txt', 'r')
• Filename can be (a) absolute or (b) relative to the current working directory.
  • More on this later
• Four common methods to call on a file object
  • read — entire file as one string
  • readlines — entire file as a list
  • readline — one line at a time.
  • close — close the file when finished.
• Take care to only use read and readlines with small files.
• Short-cut that will automatically close the file for you.

with open('the_file.txt', 'r') as f:
    for line in f:
        # do stuff

• finish IC-8

CSV

• Comma-separated value
• In principle, we can simply take a line, call split and look at the parts.
• When might this fail?
  • What if data contains a comma?
• A more robust solution is to use a library that handles all of the “what ifs”

import csv

def sample_open_csv(filename):
  with open(filename) as csv_file:
    f = csv.reader(csv_file)
    next(f, None) # skip the header row
    for row in f:
        print(row)

• remember the include csv
• Short-cut: List comprehension: row = [int(val) for val in raw_row]
• Show the “long” version of above.

DictReader

import csv

def sample_open_csv(filename):
  with open(filename) as csv_file:
    f = csv.DictReader(csv_file)
    # _don't_ call next
    for row in f:
        print(row)

Topics for later:

• Use pandas or numpy to read csv
• Dictionary comprehensions