====== Introduction to Python ====== ==== Python basics ==== Python is an interpreted, dynamically typed language which is easy to use for scripting and prototyping applications. C and Java programmers quickly adjust to the Python syntax. Unlike C, in Python, lists are predefined and usually more used than arrays: l = [] l.append("1") l.append(0) l.append([]) print(l) **Remarks** * in Python we often use side-effects to modify data * Python is strongly-typed. However, lists may have different 'types' of elements for i in range(0, len(l)): print(l[i]) print(list(range(0, len(l)))) for elem in l: print(elem) **Remarks** * List traversal may be achieved using indexing. Indexes are integers taken from a range. ''range(0, len(l))'' is a list in itself. * Lists (and other data structures) can be iterated using ''in'' **Syntax** * unlike C or Java where we often use ''{...}'' for scoping, in Python we use tabs * control instructions such as ''for'', ''while'', ''if'' do not require ''(...)'' for conditions but they must end with '':'' def func(l1, l2): l1.append(3) return l1 + l2 x = [1] y = [2] print(func(x, y)) print(x) **Remarks** * although it is possible to add type annotations, in Python a function's signature only consists of the number of parameters and their names * objects are generally passed as reference (hence, when printing ''x'', we see the list ''[1, 3]'') (for details see: [[https://docs.python.org/3/reference/datamodel.html | Python data model]]) * ''+'' denotes list concatenation **Exercise 1** Write a function which determines the maximum number from a list. **Exercise 2** Write a function which prints EACH repeating character from a string. (Hint: strings are lists of characters). ==== Useful data structures: dictionaries and tuples ==== Dictionaries are another useful data structure. A dictionary is a '' : '' mapping. Unlike lists, keys may be of any type (integers, strings, or any other datatype). d = {} d["X"] = ["X"] if "X" in d: print("d[X] is defined in the dictionary") if not "Y" in d: print("d[Y] is not defined in the dictionary") **Exercise 3** Write a function which returns the number of repetitions of each character from a text. (Hint: use dictionaries to store the number of repetitions.) **Remarks** * Classes are the usual means for storing heterogeneous data (e.g. for a student, his name, email, list of attended lectures, etc.). However it is sometimes easier to use tuples instead. The example below illustrates the creation of a tuple and accessing parts of it. stud_info = ("Mihai", "mihai@upb.ro", ["AA", "PA", "SD", "Programare"]) str = stud_info[0] + "'s email is " + stud_info[1] print(str) **Exercise 4** Write a function which takes a list of tuples containing student info and returns only those corresponding to students which have attended at least 3 lectures. **Exercise 5** Write a function which returns the number of unique characters from a list. **Exercise 6** Write a function which takes a pattern and a text and prints all indexes where an occurrence of pattern in text are found. **Remark**: * lists can be sliced in Python using the following syntax: ''l[start_index:end_index]'' Test it to see how slicing is performed. **Exercise 7** Modify the previous implementation to use slicing. **Remark**: * in Python we can use arbitrarily nested functions **Exercise 8** Write a function which searches for a list of patterns in a text. def find_patterns(pattern_list, text): # checks if pattern is found at position index in text def inner_search(pattern, index): For example, ''find_patterns(["ab", "cd"], "abcdabcd")'' should print out "0,2,4,6". **Remark**: * Matrices can be represented as lists of lists. matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] **Exercise 9** Write a function which returns the elements from the principal diagonal of a matrix, as a list. Example: ''diag(matrix) = [1, 5, 9]''. **Exercise 10** Write a function which adds two matrices. **Exercise 11** Write a function which implements matrix transposition. **Exercise 12** Write a function which multiplies two matrices.