Dictionary Basics: Creating and Accessing

From Mail Log to Useful Summary

Imagine a mail log containing thousands of messages. The raw messages are difficult to compare directly, but a dictionary can transform them into a frequency summary. In a histogram, each unique value becomes a dictionary key and the value stored for that key is the number of times it appears.

Building the Email Histogram

A histogram uses a dictionary as a frequency counter. Each email address is a key. When an address appears for the first time, its count starts at zero and is immediately increased to one. When the address appears again, the existing count is retrieved and increased by one. The pattern counts[key] = counts.get(key, 0) + 1 handles both cases in one line.

emails = [ "ana@example.org", "bo@sample.net", "ana@example.org", "ana@example.org", "bo@sample.net" ] counts = {} for email in emails: counts[email] = counts.get(email, 0) + 1 print(counts)

Finding the Largest Sender Count

After building the histogram, the most prolific sender can be found with a maximum loop. The loop examines each dictionary entry and keeps two pieces of information: the key belonging to the best entry so far and that entry's count. When a larger count is found, both tracked values are replaced.

largest_sender = None
largest_count = 0

for sender, count in counts.items():
    if count > largest_count:
        largest_sender = sender
        largest_count = count

print(largest_sender)
print(largest_count)

ana@example.org
3

The two tracking variables must be updated together. The count identifies whether the current entry is better, while the sender records which key produced that count. At the end of the loop, the tracked sender and tracked count describe the highest-frequency dictionary entry.

From Sender Names to Domains

A full email address answers an individual-sender question. A domain answers an organization-level question. To extract the domain, split the email address at the @ symbol and use the second part: email.split('@')[1]. The find method can also locate the @ symbol, after which string slicing can take the text from the position after that symbol.

domain_counts = {} for email in emails: domain = email.split('@')[1] domain_counts[domain] = domain_counts.get(domain, 0) + 1 print(domain_counts)

Mistakes in Histogram Analysis

• Using a new count of one every time instead of increasing the existing count.

  A repeated key is overwritten with one, so the dictionary does not retain the frequency of earlier appearances.

  Fix: Use counts[email] = counts.get(email, 0) + 1.

• Tracking only the largest count during the maximum loop.

  The final number identifies the frequency but not which dictionary key produced it.

  Fix: Track both the sender key and its count whenever a larger count is found.

• Counting full email addresses when the question is about organizations.

  Different people at the same domain remain separate keys.

  Fix: Extract domain = email.split('@')[1] and use domain as the histogram key.

• Selecting the first part after splitting an email address.

  The first part is the text before the @ symbol, not the domain.

  Fix: Use the second part, email.split('@')[1], for the domain.

Practice the Three Transformations

Key Takeaways

1. A dictionary histogram maps each unique key to the number of times that key appears.
2. The pattern counts[key] = counts.get(key, 0) + 1 handles both new and existing keys.
3. A maximum loop must track both the highest count and the key associated with it.
4. email.split('@')[1] extracts the domain for organization-level counting.
5. The choice of key controls the granularity and meaning of the analysis.