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A Complete Guide to Python Dictionaries by@smpnjn
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A Complete Guide to Python Dictionaries

by Johnny SimpsonOctober 2nd, 2022
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Python Dictionary is an important way to store data in Python. They let us store information as key-value pairs which are indexed by key name rather than an index. In this guide, we will cover everything you need to know about Python dictionaries. We use curly brackets to define a dictionary, just like in Python sets. We define both keys and values, which is slightly different than sets. You can use the `dict()` function to create a dictionary from variables and update it using the update() function.

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Dictionaries are an important way to store data in Python. They let us store information as key-value pairs which are indexed by key name rather than an index like in Python Lists. In this guide, we will cover everything you need to know about Python dictionaries. If you are interested in other data collections in Python, such as lists, sets, and tuples, you can find out more about them here.


Let's begin by defining a dictionary. We use curly brackets, just like in Python sets - but we define both keys and values, which is slightly different than sets:


dictionary = { "name" : "Johnny", "age" : 152 }


While these methods of creating dictionaries are great, we are not limited by them. If you are familiar with Python lists, you can turn them straight into dictionaries using the dict() function:


dictionary = dict([['name', 'Johnny'], ['age', 152]])
print(dictionary) # { "name" : "Johnny", "age" : 152 }


Or, we can use the dict function to create a dictionary from variables:


dictionary = dict(name="Johnny", age=153)
print(dictionary) # { "name" : "Johnny", "age" : 152 }


Accessing Python dictionary values can be accessed using the square bracket notation:


dictionary = { "name" : "Johnny", "age" : 152 }
print(dictionary["name"]) # Johnny


Or, if you want, you can get this information using the get method:


dictionary = { "name" : "Johnny", "age" : 152 }
print(dictionary.get("name")) # Johnny


Python dictionaries are also mutable, so using the square bracket notation above, we can update values:


dictionary = { "name" : "Johnny", "age" : 152 }
dictionary["age"] = 153

print(dictionary["age"]) # 153


Or, you can use the update() method, based on your preference:


dictionary = { "name" : "Johnny", "age" : 152 }
dictionary.update({"age" : 153})

print(dictionary["age"]) # 153


Dictionaries can also be multi-dimensional. So this is also a valid dictionary:


dictionary = {
    "name" : {
        "firstName" : "Johnny",
        "lastName" : "Simpson"
    },
    "age": 152
}


Finally, you can get the length of a dictionary (i.e. the number of key-value pairs) using the len() function:


dictionary = { "name" : "Johnny", "age" : 152 }
dictionary.update("age", 153)

print(len(dictionary)) # 2


Python Dictionary Methods

We've covered both get and update so far, there are a bunch of other methods which are useful too. Here's a full list of them:

  • dict.clear() - deletes all items from a python dictionary.
  • dict.copy() - makes a copy of a dictionary, which has the same value, but a different reference.
  • dict.popitem() - removes the last key-value pair from the dictionary.
  • dict.pop("keyItem") - removes the key-value pair with a key of "keyItem".
  • dict.update(newDictionary) - updates the dictionary with keys and values from, overwriting any existing ones.
  • dict.setdefault("key", "default") - will return the value for the item key, and if it doesn't exist, will create a new key value pair of {'key' : 'default'}
  • dict.fromkeys(keys, values) - takes two sets of data for both keys and values, and creates a new dictionary based on them.
  • dict.items() - returns an iterable set of tuples for each key-value pair. The returned data is known as a view object.
  • dict.keys() - returns an iterable set of keys for the dictionary. The returned data is known as a view object.
  • dict.values() - returns an iterable set of values for the dictionary. The returned data is known as a view object.


View Objects in Python Dictionaries

You might notice that the last three methods, items(), keys(), and values(), all return a view object. A view object is a dynamic way type of object which will update automatically should the dictionary be updated. They are also iterable. Let's look at a quick example, using dict.items():


dictionary = dict(name="Johnny", age=153)
getDictionaryItems = dictionary.items()

for x in setDictionary:
    print(x) 

# Returns 
# ('name', 'Johnny')
# ('age', 153)


dict.items() returns a list of tuples, so we can easily iterate over them using a for loop.

View Objects also support membership checks. For example, we can check if a certain key exists in a dictionary using the keys() method, along with the in and not in operators:


dictionary = dict(name="Johnny", age=153)
getDictionaryItems = dictionary.keys()

print("name" in getDictionaryItems) # True, as name is a key in dictionary
print("name" not in getDictionaryItems) # False, as name is a key in dictionary


Deleting Items from Dictionaries in Python

There are a number of ways to delete items from a dictionary, as shown in the methods listed above. The main ones are popitem(), pop(), and clear(). Let's look at a few examples, so it's clear how they work. To remove a specific item from a dictionary, we use pop. In the example below, I remove the dictionary item with the key favouriteDrink:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
dictionary.pop("favouriteDrink")
print(dictionary) # {'name': 'Johnny', 'age': 153}


If methods aren't your thing, you can just use the del keyword to remove favouriteDrink in the same way:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
del dictionary["favouriteDrink"]
print(dictionary) # {'name': 'Johnny', 'age': 153}


If we only wanted to remove the last item, without specifying it, we can use popitem:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
dictionary.popitem()
print(dictionary) # {'name': 'Johnny', 'age': 153}


Or if we wanted to clear the dictionary completely, we can use clear():


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
dictionary.clear()
print(dictionary) # {}


Using setdefault with Python Dictionaries

The setdefault method on Python dictionaries either returns a value for a key if it exists or creates that key-value pair if it doesn't. The first argument is the key, and the second is the value you wish to set the key to should it not exist - for example, dict.setdefault("age", 152). If you do not set the second argument, it defaults to the string None.


Here is an example, which leads to a new key-value pair being added to the object dictionary below:


dictionary = { "name" : "Johnny", "age": 152 }

dictionary.setdefault("age", 152) # Does nothing but returns age, as age exists on dictionary
dictionary.setdefault("favouriteDrink", "tea") # Adds `favouriteDrink` to dictionary
print(dictionary) # {'name': 'Johnny', 'age': 152, 'favouriteDrink': 'tea'}


Creating a New Dictionary from two lists of data in Python

If you wish to create a new dictionary in python from a set of data, we can use the dict.fromkeys method. Here, we can define a list, set, or tuple of data for keys, and a single value for the value that each key should be. For example, below I've defined a tuple and a single value of 1. When combined with fromkeys, we get a brand new dictionary:


myKeys = ("name", "age", "favouriteDrink")
myValues = 1

newDict = dict.fromkeys(myKeys, myValues)

print(newDict) # {'name': 1, 'age': 1, 'favouriteDrink': 1}


Iterating Over a Dictionaries Keys

An easy way to iterate over dictionaries keys, along with the dict.keys() method, is to simply call iter(), which returns an iterable for the keys in your dictionary. This essentially performs the same task as dict.keys() most of the time:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")

dictKeys = iter(dictionary)

for x in dictKeys:
    print(x)

# Returns name age favouriteDrink


The difference between iter and dict.keys(), is you cannot alter the keys after calling iter. To show you what I mean, below I add a new key after calling iter and try to iterate over with a for loop. This produces an error, as the keys changed during iteration:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
dictKeys = iter(dictionary)

dictionary['birthday'] = "Monday"
for x in dictKeys:
    print(x)

# RuntimeError: dictionary changed size during iteration


Meanwhile, no error will be produced for dict.keys(), since dict.keys() produces a view object which is a dynamic live view of the dictionaries contents:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")
dictKeys = dictionary.keys()

dictionary['birthday'] = "Monday"
for x in dictKeys:
    print(x)

# Returns name age favouriteDrink birthday


Iterating Over a Dictionaries Keys in Reverse Order

Another useful function you might find yourself using is reversed. This function takes dictionary keys and returns an iterable list of them in reverse order. As with iter, you will produce errors if you try to update the dictionary while iterating over it in reversed form.

Once reversed, the keys can be iterated upon to do some function of your choosing. Here is an example, which returns the string value of each key in reverse order for dictionary:


dictionary = dict(name="Johnny", age=153, favouriteDrink="tea")

reversedDict = reversed(dictionary)

for x in reversedDict:
    print(x)

# Returns favouriteDrink age name


Checking Membership in a Python Dictionary

Just like in sets and other data collections in Python, we can also check if something exists as a key in a dictionary. For this, we can use the in or not in operators. Remember, dictionary membership is checked by key, not value.


dictionary = { "name" : "Johnny", "age" : 152 }

print('name' in dictionary) # True
print('name' not in dictionary) # False


Conclusion

That's everything for dictionaries - I hope you've enjoyed this Python tutorial. To learn more about engineering in general, you can check out my other work here. Otherwise, click the links below to learn more about the different types of data collections in Python:



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