# Advanced structures

# Lists

A list is like an apartment house. Each room has it’s own number (index) and contains some data - maybe amount of people in it, maybe something else.

Another way is to think about it like a box with boxes. Outside box is a list itself, inner boxes are values (almost like variables), each of them stores some information. We can access this information using name of the list and index of a inner box.

When can it be helpful? One of the common situations is when we need to store some similar pieces of data - maybe list with names, or phone numbers, or maybe coordinates or else.

grocery = ['Juice', 'Tomatoes', 'Potatoes', 'Bananas']
item = grocery[0]  # >>> 'Juice'

In other programming languages lists are often named as array.

We can change the value stored in a list (thing that we can’t do with strings)

grocery[0] = "Green Juice"
# >>> ['Green Juice', 'Tomatoes', 'Potatoes', 'Bananas']

As with strings, we can get a subset of the list with [start_index:end_index] but not including value on end_index.

grocery[1:3]  # >>> ['Tomatoes', 'Potatoes']

We can put any data type in a a list including another lists. As a result we will have multi-dimensional lists

events = ['Wash Car', 'Pick up Kids', 'Cash Check']
to_do = [events, grocery]
# >>>

to_do[1][0]
# >>> (first item in the second sublist)

The good practice is not to store different types of data in one list. Use dict instead (you can read about it in corresponding chapter).

# Basic operations

new_list = events + grocery  # merge  lists
# >>>

grocery.append('onions')
# >>>

grocery.insert(1, "Pickle")
# >>>

grocery.sort()
# >>>

grocery.reverse()
# >>>

grocery.remove("Pickle")
# >>>

del grocery[4]
# >>>

Another very useful block of operations

values = [1, 3, -2, 0]

len(values)  # >>> 4 (amount of values in list)
min(values)  # >>> -2 (minimum value in list)
max(values)  # >>> 3 (maximum value in list)
sum(values)  # >>> 2 (sum of all value in list)

# Range

There is handsome range operation to generate lists with numbers in Python

a = range(4)  # >>> range(4)
list(range(5, 9))  # >>> [5, 6, 7, 8]

It can even more, read the docs.

# Tuples

Values in a tuple can’t be changed like values in a list

pi_tuple = (3, 1, 4, 1, 5, 9)

Operations as len, max, min and sum also available with tuples.

# Sets

arr = [1, 2, 3, 2, 4, 1, 1]
set(arr)  # >>> (1, 2, 3, 4)

# Converting

We can use list, tuple and set commands to convert from one array type to another

new_tuple = list(pi_tuple)
new_list = tuple(grocery)
new_set = set(pi_tuple)

If we try to convert string into list

list("Hello")  # >>> ['H', 'e', 'l', ....]

# Matrices

Note. It is recommended to study Cycles chapter first.

One specific application of lists and tuples is mathematical matrices. We can obtain multi-dimensional structures.

cols = 8
rows = 5

M = [[0 for x in range(cols)] for x in range(rows)]  # create zero matrix

# M[6][0] = 3  # >>> ERROR - out of range
M[0][6] = 3
print(M[0][6])  # >>> 3

Or with short syntax

M = [[0] * cols for i in range(rows)]

You can use numpy, pandas or any similar package to work with matrices and other math stuff in Python.

import numpy

numpy.zeros((5, 5))
'''array([[ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.]])'''
numpy.matrix([[1, 2], [3, 4]])
'''matrix([[1, 2],
        [3, 4]])'''

numpy.matrix('1 2; 3 4')
numpy.arange(25).reshape((5, 5))
numpy.array(range(25)).reshape((5, 5))
numpy.ndarray((5, 5))