Python

"a_list.pop(n)"

"Specific function for a class of objects "

"a_list.insert(2, ""Hello"") "

"a_list.append(""Element"")"

"list_1.extend(list_2) geht auch mit plus list_1 = list_1 + list_2"

"Tuple with or without paranthesis (brauchen keine klammern), tuples are immutable "

"Assignment of values to serveral variables simultaneously // x, y, z = a_tuple // a, b = b, a"

"a_dict = {key1 : value1, key2 : value2, ...} "

"a_dict[""key1""] = value1"

"a_dict[""new key""] = a_value"

"a_dict.pop(""a key"")"

"Argument: index, decription: removes element from the list located at the specified index "

"Argument, index, value Description Adds a new element tot he list at the secified index "

"Argument value

"Argument list: merges the list caling the method with the list in argument "

"// reflects how many time the numer on the right can fully divide the number on the left (6 // 4 retunrs 1) "

"Gives the remainder of the floor division 6 % Modulus 4 gives 2 "

"x += 3 -> steht für x = x + 3 gleiches gilt für total += number /// oder modulus %="

"True or False "

"x < y"

"x <= y "

"x != y"

"x == y "

"x in a_list /oder/ x not in a_list"

"5 % 3 ==0 "

"print(expression 1 and expression2)"

"print(P or Q) "

"print(not P)"

"shifted by one tabulation or 4 spaces"

"elif (wie eine unterteilung zwischen if und else: elif grade <15: oder elif grade < 10: "

"redouble = True if average < 10 else False"

"if i==1: break"

"range(start,stop,increment). by default it is start 0 and step 1/ das ist super für for loop functions "

"word[::-1] (from the beginning to the end with two : and-1 indicates the step direction"

"When you have a list of lists, it is possible to browse all its elements with two nesdet loops."

"palindromes = [word for word in l if word==word[::-1]]"

"for index, element in enumerate(sequence): (einfach variablen in index und element eintragen, und sequence ist die liste"

"max(l) / min(l)"

"sum(list) = gibt mir schon die summe zurück"

"for element1, element2 in zip(sequence1, sequence2) #at each iteration we take an element from each sequence "

"Able to store the result as a variable and use it globaly later "

"if element in List: >>> gives back bolean // or if element not in List:>>> bolean "

""""""" define the start and end of the definition in a function "

"help(function) bsp.: help(len) --- no print is needed "

" A ""container"" is any object that can be iterated over such as a list, a tuple, a string, etc."

"Simplify a problem until it is trivial, heist die funktion ruft sich so lange selbst wieder auf bis sie bei bswp 2 Personen angekommen ist. Macht bei fakultät berechnungen sinn N! (immer schauen das am ende soetwas steht wie return function(N-1) damit nicht ewiglich selbst aufrufend "

"A constructor (function that initializes an object of the class), attributes (variables, specific to the created object used to define its properties and store information, Methos (class-specific functions that allow you to interact with an object) "

"This allows us to start a block of code where we can define the constructor of the class and its methods. "

"Definition of the constructor, allows to initialize the attributes of the object."

"The self argument corresponds to the object calling the method. This argument allows us to access the attributes of the object within the method. All the moethods of a class must have self as first argument. "

"Process of building an object and assigning it to a variable. (C1 = Complex(3,2)"

"will print out all the comments and also divides into the methods (subfunctions) within the object class "

"Also known as package or library / python file containing definitions of classes and functions. "

"Remember that a dictionary is a data structure where the data is indexed by keys. To access a key from a dictionary, all you have to do is enter the key in square brackets"

"N-dimensional matrices, can contain diverse data, such as table, data, time series or images"

"X = np.ones(shape = (5,10,10))"

"print(X[4,3])"

"LinkeHandRegel // X[linie from top down, spalte from left to right, Z-direciton from fron to back]"

"X[2,:] oder anderes beispiel X[1:4, 2:5]"

"np.round(x, decimals = n)"

"array.shape"

"X[X < 0] = 0 (to all neg elements we assign value 0) // X[y == 1] = -1 value -1 for all elements which are same in X and y arrays"

"print(X[y == 0])"

"X = np.array([i for i in range(1, 11)]) # 1, 2, ..., 10"

"X_reshaped = X.reshape((2, 5))"

" both shapes contain the same number of elements"

"incapable of processing matrix data, the original 1797 images of 8x8 dimensions were flattened into vectors of size 8x8 = 64."

"np.concatenate([X_1, X_2], axis = 0) // The arrays to concatenate must be passed as a list or a tuple. //The axis argument determines the dimension on which the arrays should be concatenated. The arrays must have the same length on this dimension. //In the case of a 2-dimensional array, an operation along axis 0 corresponds to an operation along the row-axis (the first dimension), while an operation along axis 1 pertains to the column-axis (the second dimension)."

"M = np.array([a,b],[c,d]) -> erste reihe is a und b, zweite reihe in der Matrix is c, d"

"Kreuzprodukt = M.dot(N)"

"performing operation between elements of different dimension // Constant C + each element on matrix (like a c filled ones matrix)"

"The dimensions are equal, one of the dimension is equal to 1. If for each dimension one of these conditions are verified it is compatible. (but dimension 1 with dimension 1 and dimension 2 with 2 and so on) "

"normalized_data = (data - data.min()) / (data.max() - data.min()) "

"Array transposition X_T = X.T"

"X.min() und X.max() und X.mean() // X.mean(axis = 0) berechnet von jeder spalte column den mean bei axis = 1, mean von jeder Zeile"

"sum / std: standard deviation / min, max, argmin (find minimum and gives back indized), argmax (same) "