Tuple Assignment Index Out Of Range

There are a number of misconceptions here, and I don't think that addressing just the one you've specifically asked about will be properly helpful, so I'm going to do more than that.

First of all, lists. You're creating empty lists gegner and gegnerhp; it looks like you then want to add some items to those lists, and you're trying to do it by specifying an index. In python, the list index starts from 0, not 1.

i.e. in the following list:

The index '1' is the second item in the list.

In your code, your range(1,anzhalgegner) would start at 1 and increment up to whatever you have set anzhalgegner to be. In your first iteration, your code attempts to assign a value to the list gegner at position 1 - however, the list does not have anything at position 0, meaning it can't assign anything to position 1.

The simplest change to get you started would be to start your range from 0.

Once you've tried that out, I would also suggest that instead of manually specifying what position in the list to place an item (list[index] = value), since all you are doing is adding things to the end of the list, consider using the append method. Then it would look something like this:

Python Object Types - Lists 2018

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List object is the more general sequence provided by Python. Lists are ordered collections of arbitrarily typed objects. They have no fixed size. In other words, they can hold arbitrary objects and can expand dynamically as new items are added. They are mutable - unlike strings, lists can be modified in-place by assignment to offsets as well as several list method calls.

Lists - Sequence Operations

Because lists are sequences, they support all the sequence operations for strings. The only difference is that the results are usually lists instead of strings. For example, given a three-item list:

>>> # A list of three different-type objects >>> L = [123, 'poe', 3.1415] >>> # Number of items in the list >>> len(L) 3

We can index, slice ...

>>> # Indexing by position >>> L[0] 123 >>> # Slicing a list returns a new list >>> L[:-1] [123, 'poe'] >>> # Concatenation makes a new list too >>> L + [94550, 98101, 230] [123, 'poe', 3.1415, 94550, 98101, 230] >>> # We're not changing the original list >>> L [123, 'poe', 3.1415] >>>

Lists - Type-Specific Operations

The lists have no fixed type constraint. The list we just look at, for instance, contains three objects of completely different types. Further, lists have no fixed size. In other words, they can grow and shrink on demand in response to list-specific operations:

>>> # Growing: add object at the end of list >>> L.append('Dijkstra') >>> L [123, 'poe', 3.1415, 'Dijkstra'] >>> >>> # Shrinking: delete an item in the middle >>> L.pop(2) 3.1415 >>> >>> L [123, 'poe', 'Dijkstra'] >>>

The append method expands the list's size and inserts an item at the end. The pop method then removes an item at a given offset. Other list methods insert an item at an arbitrary position (insert), remove a given item by value (remove), etc. Because lists are mutable, most list methods also change the list object in-place instead of creating a new one:

>>> >>> M = ['Ludwig', 'van', 'Beethoven'] >>> M.sort() >>> M ['Beethoven', 'Ludwig', 'van'] >>> M.reverse() >>> M ['van', 'Ludwig', 'Beethoven'] >>>

The list sort method orders the list in ascending fashion by default. The reverse reverses it. In both cases, the methods modify the list directly.

Lists - Bound Checking

Even though lists have no fixed size, Python still doesn't allow us to reference items that are not exist. Indexing off the end of a list is always a mistake, but so is assigning off the end. Rather than silently growing the list, Python reports an error. To grow a list, we call list methods such as append.

>>> L [123, 'poe', 'Dijkstra'] >>> L[10] Traceback (most recent call last): File "

Lists - Nesting

Python's core data types support arbitrary nesting. We can nest them in any combination. We can have a list that contains a dictionary, which contains another list, and so on. One immediate application of this feature is to represent matrixes or multidimensional arrays.

>>> >>> # A 3 x 3 matrix, as nested lists >>> M = [ [1, 2, 3], [4, 5, 6], [7, 8, 9] ] >>> M [[1, 2, 3], [4, 5, 6], [7, 8, 9]] >>>

We can access the matrix in several ways:

>>> >>> # Get row 2 >>> M[1] [4, 5, 6] >>> # Get row 2, then get item 3 of that row >>> M[1][2] 6 >>>

The first operation fetches the entire second row, and the second grabs the third item of that row.

Lists - Comprehensions

Python features a more advanced operation known as a list comprehension expression. This turns out to be a powerful way to process structures like the matrix. Suppose, for example, that we need to extract the second column of the example matrix. It's easy to grab rows by simple indexing because the matrix is stored by rows, but it's almost as easy to get a column with a list comprehension:

>>> >>> # Collect the items in column 2 >>> col2 = [A[1] for A in M] >>> col2 [2, 5, 8] >>> >>> M [[1, 2, 3], [4, 5, 6], [7, 8, 9]] >>>

List comprehensions are a way to build a new list by running an expression on each item in a sequence, one at a time, from left to right. List comprehensions are coded in square brackets and are composed of an expression and a looping construct that share a variable name (A, here) for each row in matrix M, in a new list. The result is a new list containing column 2 of the matrix.

List comprehension can be more complicated in practice:

>>> >>> M [[1, 2, 3], [4, 5, 6], [7, 8, 9]] >>> >>> # Add 10 to each item in column 2 >>> [A[1] + 10 for A in M] [12, 15, 18] >>> # Filter out odd items >>> [A[1] for A in M if A[1] % 2 == 0] [2, 8] >>>

The first operation adds 10 to each item as it is collected, and the second used an if clause to filter odd numbers out of the result using the % modulus expression. List comprehensions make new lists of results, but they can be used to iterate over any iterable object. For instance, we use list comprehensions to step over a hardcoded list of coordinates and a string:

>>> # Collect a diagonal from matrix >>> diag = [M[i][i] for i in [0, 1, 2]] >>> diag [1, 5, 9] >>> >>> # Repeat characters in a string >>> doubles = [ c * 2 for c in 'blah'] >>> doubles ['bb', 'll', 'aa', 'hh'] >>>

List comprehensions tend to be handy in practice and often provide a substantial processing speed advantage. They also work on any type that is a sequence in Python as well as some types that are not. The comprehension syntax in parentheses can also be used to create generators that produce results on demand:

>>> M [[1, 2, 3], [4, 5, 6], [7, 8, 9]] >>> >>> # Create a generator of row sums >>> G = (sum(A) for A in M) >>> # iter(G) not required here >>> next(G) 6 >>> # Run the iteration protocol >>> next(G) 15 >>> next(G) 24

The map built-in can do similar work by generating the results of running items through a function. Wrapping it in list forces it to return all its values.

>>> # Map sum over items in M >>> list(map(sum,M)) [6, 15, 24]

Comprehension syntax can also be used to create sets and dictionaries:

>>> >>> # Create a set of row sums >>> {sum(A) for A in M} {24, 6, 15} >>> >>> # Creates key/value table of row sums >>> {i : sum(M[i]) for i in range(3)} {0: 6, 1: 15, 2: 24} >>>

In fact, lists, sets, and dictionaries can all be built with comprehensions:

>>> >>> # List of character ordinals >>> [ord(x) for x in 'google'] [103, 111, 111, 103, 108, 101] >>> # Sets remove duplicates >>> {ord(x) for x in 'google'} {111, 108, 101, 103} >>> # Dictionary keys are unique >>> {x: ord(x) for x in 'google'} {'e': 101, 'o': 111, 'g': 103, 'l': 108} >>>


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