Python dictionary multiple keys with same name

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Python dictionary multiple keys with same name

Developer of AppleDiscoverDesignDevelopDistributeSupportAccount All types of compound data we have studied in detail so far -- strings, lists and tuples -- are sequence types, which use whole as indices to access the values that contain within them. The dictionaries are another type of compound. They are the integrated mapping type of Python. They map the keys, which can be any unchangeable type, to the values, which can be any type (heterogeneous), just like the elements of a list or tuple. In other languages, they are called associative arrays as they associate a key with a value. As an example, we will create a dictionary to translate English words into Spanish. For this dictionary, the keys are strings. One way to create a dictionary is to start with the empty dictionary and add the key pairs:value. The vacuum dictionary is denoted {} > > eng2sp = {} > > eng2sp["one] = "one" > eng2sp["two"] = "dos" The first assignment creates a dictionary called eng2sp; the other assignments add a new key:value pairs to the dictionary. We can print the current value of the dictionary as usual: > > print(eng2sp) {"two": "dos," "one": "one" The key: dictionary value pairs are separated by commas. Each pair contains a key and a value separated from a colon. Hashing The order of couples may not be what was expected. Python uses complex algorithms, designed for very fast access, to determine where key pairs:value are stored in a dictionary. For our purposes we can think of thisas unpredictable. You might also wonder why we use dictionaries to all when the same concept of mapping a key for a value could be implemented using a list of tuples: > > > (Apples: 430, bananas: 312, oranges: 525, pears: 217} {pearl: 217, apples: 430, oranges: 525, bananas: 312} > [(appli, 430,) (banana, 312,) (orange, 525,) (peri, 217) The reason is that dictionaries are very fast, implemented using a technique called hashing, which allows us to access a value very quickly. On the contrary, the tuples implementation list is slow. If we wanted to find a value associated with a key, we should iterate on each tuple, controlling the 0th element. What if the key wasn't even on the list? We should come to the end to find out. Another way to create a dictionary is to provide a list of key pairs:value using the same syntax as the previous output: > eng2sp = one, two: dos, three: tres No matter what order we write couples. The values in a dictionary are accessible with the keys, not with the indices, so there is no need to worry about ordering. This is how we use a key to search for the corresponding value: > print(eng2sp["two)] dos The key two produces the value. lists, tuples and strings were called sequences, because their elements occur in order. The dictionary is the first compound type we saw that it is not a sequence, so weindex or slice a dictionary. The declaration of removing a key: Value pair from a dictionary. For example, the following dictionary contains the names of various fruits and the number of each fruit in stock: > inventory = 'apples': 430, "bananas": 312, "orange": 525, "pears": 217 > print(inventory) 217, 'apples': 430, 'oranges': 525, 'bananas': 31 If someone buys all pears, we can remove the entrance from the dictionary: > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > 0, 'appli': 430, 'oranges': 525, 'bananas': > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > returns the number of key pairs:value: Dictators have a number of integrated useful methods. The key method returns what Python 3 calls a view of its underlying keys. An object of view has some similarities with the object of range we have seen before -- it is a lazy promise, to provide its elements when they are necessary from the rest of the program. We can iterate over the view, or turn the view into a list like this: for k in eng2sp.keys(): # The order of the k's is not defined printing("Got key", k, "che map to the value", eng2sp[k]) ks =press(ks) This produces this output: I have key three that maps for the tres value. I've got a key that map to value one ['tre', 'two', 'one'] It is so common to iterate over the keys in a dictionary that we can omit the method of keys to call in the loop -- iterating over a dictionary implicitly itera over its keys: for k in eng2sp: press ("Cerca di Got", k) The method of values is similar; returns a view object that can be turned into a list: > list (eng2sp.valori())) ('tres', 'dos', 'one') The method of articles also returns a view, which promises a list of tuples -- a tuple for each key: > > > eng2sp.items()) ('tre', 'tres'), ('due', 'dos'), ('one', 'one')] Tuples are often useful to get both the key and the value at the same time while we are looping: for (k,v) in eng2sp.items(): print("Got",k,"che map a",v) This produces: I have three cards. The inside and not in operators can check if a key is in the dictionary: > > "one" in English "Sei" in eng2sp False > > > > "tres" in eng2sp # Note that 'in' keys test, not values. False This method can be very useful, since looking for an existing key in a dictionary causes a runtime error: > > eng2esp["dog"] Traceback (last most recent call): ... KeyError: 'dog' As in the case of lists, because dictionaries are mutable, we must be aware of the advertising. Whenever two variables refer to the same object, it changes to an effectothers. If we want to change a dictionary and store a copy of the original, use the copy method. For example, opposites are a dictionary containing pairs of opposites: >> opposites = {"up": "down," "right": "wrong," "yes." "no"} > > > > alias = opposites > copy = opposites.copy() # Surface copy alias and opposites refer to the same object; copy refers to a fresh copy of the same dictionary. If we change alias, the opposites have also changed: > > > > > alias["right"] = "left" > > opposites["right"] "left" If we change the copy, the opposites are unvaried: >>>>> copy["right"] = "privilege" >> opposites["right"] "left" We have previously used a list to represent a matrix. This is a good choice for a matrix with mostly non-zero values, but consider a straight matrix like this: The representation of the list contains many zeros: matrix = [0, 0, 0, 1, 0], [0, 0, 0, 0], [0, 2, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]], [0, 0, 0]] An alternative is to use a dictionary. For keys, we can use tuples that contain row numbers and column. Here is the representation of the dictionary of the same matrix: > matrix = {0, 3): 1, (2, 1): 2, (4, 3): 3. We just need three key pairs:value, one for each non-zero element of the matrix. Each key is a tuple, and each value is a whole. To access a matrix element, we could use the operator []: Note that the syntax for the representation of the dictionary is not the same as the syntax for the representation of the nested list. Instead of two whole indices, we use an index,is a tuple of whole. There's a problem. If we specify an element that is zero, we get an error, because there is no voice in the dictionary with that key: > matrix[(1, 3)] (1, 3) The get method solves this problem: > matrix.get(0, 3), 0) 1 The first argument is the key; the second argument is the obtain value should return if the key is not in the dictionary: > matrix.get(1, 3), 0) 0 get surely improves the semantics of access to a radiant matrix. Too bad for syntax. If you played around with the fibula function from the recurrence chapter, you may have noticed that the bigger the argument you provide, the more the function takes to work. In addition, the running time increases very quickly. On one of our machines, fib(20) ends instantly, fib(30) takes about a second, and fib(40) takes approximately forever. To understand why, consider this call chart for fib with n = 4: A call chart shows some frame function (instances when the function was invoked), with lines connecting each frame to the frames of the functions it calls. At the top of the chart, fib with n = 4 fib calls with n = 3 and n = 2. In turn, fib with n = 3 fib calls with n = 2 and n = 1. And so on. Count how many times are called fib(0) and fib(1). This is an inefficient solution to the problem, and it becomes much worse as the subject becomes bigger. A good solution is to keep track of the values that have already been calculated by memorizing them in a dictionary. A previously calculated valueis stored for the next or is called a memo. here is an implementation of fib using memo: already known = {0: 0, 1: 1} def fib(n:) if n not in already known: new_value = fib(n-1) + fib(n-2) already known[n] = new_value return already known[n] the dictionary already known keeps track of the fibonacci numbers we already know. we start with only two couples: 0 maps to 1; and 1 maps to 1. each time the fib is called, check the dictionary to determine if it contains the result. if it is there, the function can return immediately without making multiple recurring calls. If not, it must calculate the new value. the new value is added to the dictionary before the function returns. using this version of fib, our machines can calculate fib(100) in an eyeblink. > fib(100) 354224848179261915075 in chapter 8 (strings) exercises we wrote a function that counted the number of occurrences of a letter in a string. a more general version of this problem is to form a frequency table of letters in the string, i.e., how many times each letter appears. such frequency table could be useful to compress a text file. Since different letters appear with different frequencies, we can compress a file using shorter codes for common letters and longer codes for letters appearing less frequently. dictionaries provide an elegant way to generate a frequency table: > > > > > > > > > > > > > < > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > ... letter_counts[letter] = letter_counts.get(letter, 0) + 1 ...letter_counts {'M'): 1, 's': 4, 'p': 2, 'i': 4. Let's start with an empty dictionary. For each letter in the string, we find the current count (perhaps zero) and increase it. In the end, the dictionary contains pairs of letters and their frequencies. It could be more attractive to see the frequency table in alphabetical order. We can do this with the articles and methods of sorting: > letter_items = list (letter_counts.items()) > letter_items.sort() > print(letter_items) [('M', 1), ('i', 4), ('p', 2), ('s', 4) We had to call the list conversion function type. This transforms the promise we get from elements into a list, a step that you need before you can use the selection method of the list. Graph of call A graph consisting of nodes representing function frames (or invocations), and direct edges (line with arrows) showing which frames originated from other frames. dictionary A collection of key pairs:value that map from keys to values. The keys can be any unchangeable value, and the associated value can be of any type. Unchangeable data value A data value that cannot be changed. The assignments to elements or slices (under-parts) of unchangeable values cause a runtime error. Key A data entry that is mapped to a value in a dictionary. Keys are used to search for values in a dictionary. Each key must be unique through the dictionary. Key: Couple Value One of the pairs of elements in a dictionary. The values are sought in a dictionary by key. type of mapping A type of mapping is a type of dataof a collection of associated keys and values. The only type of built-in mapping of Python is the dictionary. The dictionaries implement the abstract data type of associative arrays. Memo Memo Memo Temporary storage of pre-computed values to avoid duplicating the same calculation. variable data value A data value that can be changed. The types of all mutable values are compound types. lists and dictionaries are mutable; strings and tuple are not. Write a program that reads a string and returns an alphabet letter table in alphabetical order that occur in the string along with the number of times that each letter occurs. The case should be ignored. > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > Then apply what you learned to fill in the body of the function below: def add_fruit (inventory, fruit, quantity=0): I'm going back to do these tests. new_inventory = { add_fruit(new_inventory, "strawberries", 10) test("strawberries" in new_inventory)== 10) add_fruit(new_inventory, strawberries, 25) test(new_inventory["strawberries"] == 35) write a program called alice_words.py that creates a text file called alice_words. txt containing an alphabetical list of all words, and the number of times that occur, in the text version of alice adventures in wonderland. (you can get a free normal text version of the book, along with many others, from .) the first 10 lines of the output file should look like this: ========================================================================================================================================================================================================================================================== How many characters do you have? - Really?

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