PY4E - Python for Everybody

Understanding Dictionaries

• A dictionary is a data structure like a list but more flexible.

• Keys and Values: A dictionary maps keys to values, where keys can be of any data type and values can also vary. The connection between a key and a value is a key-value pair.

• Creating a Dictionary: Use the dict() function or curly brackets {}.

  • Example:

    • eng2sp = dict() creates an empty dictionary.

    • eng2sp = {'one': 'uno', 'two': 'dos', 'three': 'tres'} initializes with items.

Properties of Dictionaries

• Ordered Structure: As of Python 3.7, dictionaries maintain the order of key-value pairs as entered.

• Value Lookup:

  • Access values using keys: eng2sp['two'] returns 'dos'.

  • Attempting to access a non-existing key raises a KeyError.

• Key Functions:

  • len(eng2sp) returns the number of entries.

  • 'key' in dictionary checks key presence.

  • Values can be accessed and checked using dictionary.values().

Performance of Dictionaries

• Efficient Search:

  • Lists: Linear search time increases with size.

  • Dictionaries: Utilizes hashing, allowing for constant time search regardless of size, which greatly enhances efficiency.

Example: Counting Letters in a String

• Implementing Frequency Counter: Use a dictionary to count occurrences of each letter in a string.

  • Initializing:

    • d = dict()

  • For each character, check existence and update count:

    if c not in d:
        d[c] = 1
    else:
        d[c] += 1

• Result example: {'b': 1, 'r': 2, 'o': 2, 'n': 1, 't': 1, 's': 2, 'a': 1, 'u': 2}

Using the get() Method

• Simplifies counting loops by avoiding key existence checks:

  • d[c] = d.get(c, 0) + 1

• This idiom enhances readability and is commonly used.

Dictionaries with Files

• Reading files to count occurrences is a common task. Utilize nested loops:

  • Outer loop iterates through file lines, while the inner counts words.

• Code example:

  for line in fhand:
      words = line.split()
      for word in words:
          counts[word] = counts.get(word, 0) + 1

Advanced Word Counting with Punctuation

• Cleaning Input: Remove punctuation and normalize case using str.translate() and str.lower() to avoid miscounts.

• Complete code now includes steps to clean lines before counting words:

  line = line.translate(line.maketrans('', '', string.punctuation)).lower()

Debugging Tips for Dictionaries

• Scale Down: Test with smaller data sets to isolate errors.

• Summaries: Print summaries instead of entire datasets to identify anomalies.

• Self-checks: Implement sanity checks to validate outputs.

Vocabulary

• Dictionary: A mapping from keys to corresponding values.

• Hashtable: The algorithm behind dictionary implementation.

• Histogram: A set of counters reflecting frequencies of items.

Exercises

• Exercise 1: Read words from a file and store them in a dictionary as keys.

• Exercise 2: Count messages by the day of the week from email logs.

• Exercise 3: Build a histogram of email counts from different addresses.

• Exercise 4: Determine who sent the most emails in the dataset.

• Exercise 5: Modify email counting to aggregate by domain instead of addresses.