Python I: Extras

This extra material is intended for self study between the Python I and Python II workshops. It should help you review and expand on the topics covered in Python I.

This material may also be a useful reference if you're already familiar with these ideas and want to learn about Python's syntax.

We'll do additional review at the start of Python II.

Contents

Review

  • Numeric types (int,float): 
    my_int=4
  • Strings (str): 
    my_string="cat"
  • Lists (list): 
    my_list=[my_int,my_string]
  • For loops: 
    for k in range(10):
  print(k)
  • Conditionals 
    if my_string=="cat":
  print("This is a cat!")
else:
  print("This is not a cat!")

Review Exercises

  1. Create a list, exlist, with at least two of each data type below:

    • Numeric (Integer)
    • String
    • List

  2. Create empty lists and write a loop to sort exlist into three different lists:

    • numlist
    • strlist

    • listlist

      by each element's type. Remember that the type function can tell you what type each object is.

  3. Adapt your loop to work with the test loop below.

In [1]:
test=["basin",78,["obtainable","public"],89,90,[],[10,20,30,40,50],"orange"]
  1. Which element in the list test has the longest length? (Check length with the len function.)

Data Objects

In Python I, we introduced a number of important data structures in Python: string and numeric types, as well as lists. We used indexing to specify particular parts of the sequential objects - strings and lists. Here we introduce dictionaries, which provide a useful alternative format for some types of information. List and dictionary comprehensions provide a more succinct way to generate lists and dictionaries.

Dictionaries

Dictionaries provide a "mapping object"; instead of an index, they used named "keys" to organized data. Dictionaries also benefit from faster performance than lists in most cases, due to their use of hash tables.

A dictionary is defined as follows:

In [2]:
class_dict = {"course":"Python II","location":"Davis Library","time":"4pm"}
type(class_dict)
Out[2]:
dict

In this case, "course", "location", and "time" serve as the "keys" for this dictionary. Keys play a similar role to the indices we use for lists (or strings). We can print a particular value by placing its key in the same square brackets [] used by list indices.

In [3]:
print(class_dict["location"])

Davis Library

A numeric index will not work with dictionaries.

We can also generate a list of all of the keys for a dictionary using the .keys() method.

In [4]:
print(class_dict.keys())

dict_keys(['course', 'location', 'time'])

List and Dictionary Comprehensions

Python provides some shortcuts to generating lists and dictionaries, especially those that you might (now) generate with a list. For example, let's generate a list of the square of each number from 1 to 15.

In [5]:
squares=[]
for n in range(1,16):
    squares.append(n**2)
print(squares)

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225]

Using a "comprehension", we can shorten this to a single line, effectively bringing the loop inside the [] used to define the list.

In [6]:
squares=[x**2 for x in range(1,16)]
print(squares)

[1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225]

The same general format holds for defining dictionaries.

In [7]:
squaresdict={k:k**2 for k in range(1,16)}
print(squaresdict)

{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100, 11: 121, 12: 144, 13: 169, 14: 196, 15: 225}

We can include conditional statements at the end of the comprehension to build more flexible comprehensions.

In [8]:
sentence="the quick brown fox jumped over the lazy dog"
sentence=sentence.split(" ") #splits the string into a list with each space
print(sentence)
print([w for w in sentence if len(w)>4])

['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog']
['quick', 'brown', 'jumped']

Exercises

  1. Write a list comprehension to create a list of just the values (i.e. the squares) from squaresdict.

  2. Write a list comprehension with os.listdir to list all of the files in a directory that have a particular extension (e.g. end with .txt). Try it against a folder in your computer with at least one such file.

Combining Conditionals

As our needs become more complex, we can combine conditions using Boolean Algebra operators and and or.

In [9]:
num=5
letter="a"
(letter in ["a","b","c"]) and num<3
Out[9]:
False

This is equivalent to using Boolean values directly:

In [10]:
True and False
Out[10]:
False
In [11]:
#similarly...
(letter in ["a","b","c"]) or num<3
Out[11]:
True
In [12]:
True or False
Out[12]:
True

Conditions can be grouped with parentheses, or negated with not)

In [13]:
True or (False and False)
Out[13]:
True
In [14]:
(True or False) and not 5<3
Out[14]:
True

Remember, complex conditions can be simplified:

(True or False) and False is equivalent to True and False since

(True or False) evaluates to True.

Exercise

  1. Define a and b as conditions evaluating to True and False. Write a line corresponding to exclusive or - it should retrun True if only one, but not both of a and b are True. Check your function against the built-in exclusive or operator, ^.
a=True
b=False
# should return True
a=False
b=False
# should return False

Want to learn more?

The topics we've introduced are covered in significantly more depth in Automate the Boring Stuff with Python