Python Dictionary - easily explained!

The Python Dictionary is used to store key-value pairs in a variable. It is one of a total of four data structures that are pre-installed in Python. In addition to the dictionary, these also include the tuple, the set, and the list.

We define a Python dictionary by writing the key-value pair in curly braces and separated by a colon. We can store elements with different data types in a dictionary.

We can query the elements of the dictionary by specifying the key in square brackets. Then we get the corresponding value stored for this key.

What are the basic features of a Python dictionary?

Since Python version 3.7 the dictionary is ordered. This means that the order in which we store the key-value pairs also plays a role. In the versions before that, in contrast, the order has no meaning. In addition, the Python dictionary is also modifiable, i.e. after it has been created, elements can be changed, added, or deleted from the dictionary.

The most important feature of the Python dictionary is that duplicate key-value pairs are not allowed. In other data formats in Python, however, duplicate elements are allowed. If we want to add a key-value pair to the dictionary whose key already exists, the old pair is overwritten without notification.

How to query elements?

There are various information and elements that we can query from a dictionary.

As we have already seen, we can query the value by defining the associated key in square brackets. Similarly, the “get()” method returns the same result:

With the commands “.keys()” and “.values()” Python returns us a list of all keys and values. The order of the lists also corresponds to how they are stored in the dictionary. This also means that the list of values may contain duplicates.

On the other hand, if we want to retrieve the full key-value pairs, we use the “.items()” method, which returns the pairs as a list of tuples:

These so-called dictionary views are a powerful and efficient feature in Python that offer significant advantages in terms of efficiency and dynamic updates. These views provide access to keys, values, and key-value pairs in a dictionary without copying the data. Here’s how dictionary views can optimize your work:

1. Efficiency: One of the most notable features of dictionary views is their efficiency. Instead of creating a copy of the entire dictionary, views allow you to iterate through the elements directly within the dictionary. This saves significant memory and computation time, especially with large dictionaries.

2. Real-Time Updates: Dictionary views are dynamic and update in real-time when the underlying dictionary is modified. This means that you will see changes in the views immediately as the dictionary is updated, without the need for additional update or copy operations.

3. Key, Value, and Item Views: There are three types of dictionary views: dict_keys, dict_values, and dict_items. Each of these views provides access to different aspects of the dictionary, whether it’s just the keys, just the values, or key-value pairs. This allows you to select the view that best suits your specific use case.

4. Fast Iteration: With dictionary views, you can efficiently iterate through the elements of a dictionary. This is particularly useful when you want to iterate over specific keys, values, or pairs without having to traverse the entire data structure.

5. Saving Memory: Since dictionary views do not create additional copies of the data, they save memory. This is particularly important when working with large dictionaries, as creating copies can significantly increase memory consumption.

In summary, dictionary views offer an elegant and efficient way to access and work with data in a dictionary. They help save memory, reduce computation time, and enable real-time data updates. This makes them a valuable tool in Python programming, especially in applications dealing with large and dynamically changing dictionaries.

How can you access elements that might not exist in the Dictionary?

In Python dictionaries, accessing elements is a common operation. While you can access dictionary values using square brackets and the key, there are two other methods, get() and setdefault(), that provide additional functionality and safety when working with dictionaries.

Using get() to Access Dictionary Elements:

The get() method allows you to access a dictionary value based on its key. What sets it apart from the square bracket notation is that it provides a default value in case the key does not exist in the dictionary. This can be extremely useful to avoid KeyError exceptions.

Here’s an example:

In this example, student_grades.get("Alice", "N/A") returns 92 because the key "Alice" exists in the dictionary. student_grades.get("David", "N/A") returns "N/A" because the key "David" does not exist, and we provided "N/A" as the default value.

Using setdefault() to Access and Set Dictionary Elements:

The setdefault() method combines accessing and setting dictionary elements. It tries to access a value using a key, just like get(). However, if the key does not exist, it sets the key to a default value and returns that value. This can be handy when you want to ensure a key exists in the dictionary.

Here’s an example:

In this example, fruit_count.setdefault("oranges", 0) checks if the key "oranges" exists in the dictionary. Since it doesn’t, it sets "oranges" to 0 and returns 0.

Both get() and setdefault() are valuable tools when working with dictionaries, providing flexibility and safety in handling dictionary elements, especially when dealing with dynamic data or user inputs.

How can elements of a dictionary be changed?

If we want to change individual values within the Python dictionary, we can do this directly via the key. Since there must not be duplicate keys, the old value is simply overwritten. If we want to change multiple pairs at once, we use the “.update()” method and define the new key-value pairs in it.

How to delete elements?

If we want to delete a single element from the Python dictionary, we can either specify the key and use the “pop()” method to specifically delete the element, or use “popitem()” to delete the last added key-value pair:

Finally, you can clear the entire Python dictionary with the “clear()” method:

What is the difference between a Pandas Series and a Python Dictionary?

Although both Pandas Series and Python Dictionaries are key-value pairs, there are some important differences between them:

Indexing: in a Pandas Series, we can use a user-defined index that does not have to be numeric or sequential. In contrast, a Python dictionary can only use hashable objects as keys.
Order: a Pandas series is an ordered collection of data, while a Python Dictionary is unordered.
Data type: A Pandas series has a specific data type that applies to all elements in the series, while a Python Dictionary can have values of different types for each key.
Functions: A Pandas series has built-in functions for data manipulation and analysis, such as “describe()”, “mean()”, and “count()”. Python dictionaries, on the other hand, have no built-in functions for data manipulation or analysis.
Memory usage: A Pandas series uses more memory than a Python Dictionary because it stores data in a table format with index and column labels. In contrast, the Python dictionary stores only key-value pairs.

How does the Python Dictionary perform in comparison to other data structures?

Python dictionaries are a fundamental data structure known for their efficiency in providing fast access to key-value pairs. Understanding the performance characteristics and complexity of Python dictionaries is essential for optimizing code in various applications. Here, we explore the performance and complexity aspects of a Python dictionary:

Constant Time (O(1)) Average Complexity: Python dictionaries are implemented as hash tables, which means that on average, accessing, inserting, or deleting a key-value pair in a dictionary takes constant time complexity, denoted as O(1). This is true for most common dictionary operations.
Efficient Key Lookup: The Python Dictionary keys are hashed to determine their storage location within the dictionary. This hash allows for nearly instantaneous key lookup, making dictionaries suitable for scenarios where fast access to data is crucial.
Collision Handling: Hash collisions occur when two different keys produce the same hash value. Python’s dictionary implementation handles collisions efficiently using open addressing and probing techniques, ensuring that key-value pairs are stored and retrieved correctly.
Space Efficiency: Python dictionaries are memory-efficient. They do not allocate memory for empty slots but rather dynamically resize themselves as needed. This minimizes memory wastage and contributes to the overall efficiency of dictionary operations.
Worst-Case Scenario: While the average time complexity for dictionary operations is O(1), it’s important to note that in the worst-case scenario, when there are many hash collisions, performance can degrade to O(n), where n is the number of key-value pairs. However, this situation is rare in practice and is mitigated by Python’s collision resolution mechanisms.
Iteration Performance: Iterating over a dictionary is also efficient, with a time complexity of O(n), where n is the number of key-value pairs. This is because you typically iterate through the dictionary once, visiting each key-value pair.
Amortized Complexity for Insertions: While individual dictionary insertions can be considered O(1) on average, there is an amortized cost associated with resizing the dictionary when it reaches a certain capacity. This resizing operation can take O(n) time. However, because resizing occurs infrequently and only when the dictionary size exceeds a threshold, the amortized insertion complexity remains close to O(1).

In conclusion, Python dictionaries provide efficient key-value storage with average constant-time complexity for essential operations. While there can be worst-case scenarios, Python’s dictionary implementation effectively handles them. Understanding these performance characteristics and complexities is crucial for designing efficient algorithms and data structures in Python.

Which Python collections are available?

In Python, there are a total of four data types that are stored by default:

The list is an ordered collection of elements, which is changeable and can also contain duplicate elements.
The tuple is in effect a list, with the difference that it is no longer changeable. So no elements can be added or removed afterward.
The set does not allow duplicate entries. At the same time, the arrangement of the elements within the set is variable. The set itself can be changed, but the individual elements cannot be changed afterward.
Since Python version 3.7, a dictionary is an ordered collection of elements that can be changed. In the earlier versions, the dictionary is unordered.

This is what you should take with you

The Python Dictionary is one of four pre-installed data structures in Python.
It is used to store key-value pairs in a single variable.
The values of a dictionary can have different values. Besides a single scalar, lists, tuples, or new dictionaries can also be stored as values.

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