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--- aa:lab:introduction-to-python [2020/09/29 13:08]
pdmatei
+++ aa:lab:introduction-to-python [2020/10/05 13:58] (current)
claudiu.dorobantu [Python basics]
@@ Line 13: / Line 13: @@
 l.append([])
-print l
+print(l)
 </code>
 **Remarks**
   * in Python we often use side-effects to modify data
-  * Python is not strongly-typed. As a consequence, lists may have different 'types' of elements
+  * Python is strongly-typed. However, lists may have different 'types' of elements
 <code python>
-for i in range(0,len(l)):
+for i in range(0, len(l)):
-	print l[i]
+	print(l[i])
-print range(0,len(l))
+print(list(range(0, len(l))))
 for elem in l:
-	print elem
+	print(elem)
 </code>
 **Remarks**
-  * List traversal may be achieved using indexing. Indexes are integers taken from a range. ''range(0,len(l))'' is a list in itself.
+  * 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''
@@ Line 37: / Line 37: @@
 <code python>
-def func (l1, l2):
+def func(l1, l2):
 	l1.append(3)
 	return l1 + l2
 x = [1]
 y = [2]
-print func(x,y)
+print(func(x, y))
-print x
+print(x)
 </code>
 **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 are 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]])
+  * 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 ====
+==== Useful data structures: dictionaries and tuples ====
 Dictionaries are another useful data structure. A dictionary is a ''<key> : <value>'' mapping. Unlike lists, keys may be of any type (integers, strings, or any other datatype).
@@ Line 63: / Line 64: @@
 if "X" in d:
-	print "d[X] is defined in the dictionary"
+	print("d[X] is defined in the dictionary")
 if not "Y" in d:
-        print "d[Y] is not defined in the dictionary"
+        print("d[Y] is not defined in the dictionary")
 </code>
-**Exercise 3** Write a function returns the number of repetitions of each character from a text. (Hint: use dictionaries to store the number of repetitions.)
+**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.)
-**Exercise 4** Write a function returns the number of unique characters from a list.
+**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.
+<code python>
+stud_info = ("Mihai", "mihai@upb.ro", ["AA", "PA", "SD", "Programare"])
+str = stud_info[0] + "'s email is " + stud_info[1]
+print(str)
+</code>
+**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 5** Write a function which takes a pattern and a text and prints all indexes where an occurrence of pattern in text are found.
+**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:
+**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 6** Modify the previous implementation to use slicing.
+**Exercise 7** Modify the previous implementation to use slicing.
-Remark:
+**Remark**:
   * in Python we can use arbitrarily nested functions
-**Exercise 7** Write a function which searches for a list of patterns in a text.
+**Exercise 8** Write a function which searches for a list of patterns in a text.
 <code python>
-def find_patterns (pattern_list, text):
+def find_patterns(pattern_list, text):
 	# checks if pattern is found at position index in text
-	def inner_search (pattern,index):
+	def inner_search(pattern, index):
 </code>
-For example, ''find_patterns(["ab","cd"], "abcdabcd")'' should print out "0,2,4,6".
+For example, ''find_patterns(["ab", "cd"], "abcdabcd")'' should print out "0,2,4,6".
-Remark:
+**Remark**:
-  * Python supports functional-style programming to some extent.
+  * Matrices can be represented as lists of lists.
 <code python>
-def plus1(x):
+matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
-	return x + 1
-print map(plus1,[1,2,3])
-print map(lambda x:x+1, [1,2,3])
 </code>
+**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.