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< 2.5 Data Structure - Sets | Contents | 2.7 Introducing Numpy Arrays >
Data Structure - Dictionaries¶
We introduced several sequential data type in the previous sections. Now we will introduce you a new and useful type - Dictionaries. It is a mapping type, which makes it a totally different type than the ones we talked before. Instead of using a sequence of numbers to index the elements (such as lists or tuples), dictionaries are indexed by keys, which could be a string, number or even tuple (but not list). A dictionary is a key-value pairs, and each key maps to a corresponding value. It is defined by using a pair of braces { }, while the elements are a list of comma separated key:value pairs (note the key:value pair is separated by the colon, with key at front and value at the end).
dict_1 = {'apple':3, 'oragne':4, 'pear':2}
dict_1
{'apple': 3, 'oragne': 4, 'pear': 2}
Within a dictionary, elements are stored without order, therefore, you can not access a dictionary based on a sequence of index numbers. To get access to a dictionary, we need to use the key of the element - dictionary[key].
TRY IT! Get the element ‘apple’ from dict_1.
dict_1['apple']
3
We could get all the keys in a dictionary by using the keys method, or all the values by using the method values.
TRY IT Get all the keys and values from dict_1.
dict_1.keys()
dict_keys(['apple', 'oragne', 'pear'])
dict_1.values()
dict_values([3, 4, 2])
We could also get the size of a dictionary by using the len function.
len(dict_1)
3
We could define an empty dictionary and then fill in the element later. Or we could turn a list of tuples with (key, value) pairs to a dictionary.
TRY IT! Define an empty dictionary named school_dict and add value “UC Berkeley”:”USA”.
school_dict = {}
school_dict['UC Berkeley'] = 'USA'
school_dict
{'UC Berkeley': 'USA'}
TRY IT! Add another element “Oxford”:”UK” to school_dict.
school_dict['Oxford'] = 'UK'
school_dict
{'UC Berkeley': 'USA', 'Oxford': 'UK'}
TRY IT! Turn the list of tuples [(“UC Berkeley”, “USA”), (‘Oxford’, ‘UK’)] into a dictionary.
dict([("UC Berkeley", "USA"), ('Oxford', 'UK')])
{'UC Berkeley': 'USA', 'Oxford': 'UK'}
We could also check if an element belong to a dictionary using the operator in.
TRY IT! Determine if “UC Berkeley” is in school_dict.
"UC Berkeley" in school_dict
True
TRY IT! Determine whether “Harvard” is not in school_dict.
"Harvard" not in school_dict
True
We could also use the list function to turn a dictionary with a list of keys. For example:
list(school_dict)
['UC Berkeley', 'Oxford']
< 2.5 Data Structure - Sets | Contents | 2.7 Introducing Numpy Arrays >