MultiDimensional Arrays or Matrices
There are situations that demand multidimensional arrays or
matrices. In many languages (Java, COBOL, BASIC) this notion of
multidimensionality is handled by predeclaring the dimensions (and
limiting the sizes of each dimension). In Python, these are handled
somewhat more simply.
If you have a need for more sophisticated processing than we show
in this section, you'll need to get the Python
Numeric
module, also known as
NumPy
. This is a Source Forge project, and can be
found at https://numpy.sourceforge.net/
.
Let's look at a simple twodimensional tabular summary. When
rolling two dice, there are 36 possible outcomes. We can tabulate these
in a twodimensional table with one die in the rows and one die in the
columns:

1 
2 
3 
4 
5 
6 
1 
2 
3 
4 
5 
6 
7 
2 
3 
4 
5 
6 
7 
8 
3 
4 
5 
6 
7 
8 
9 
4 
5 
6 
7 
8 
9 
10 
5 
6 
7 
8 
9 
10 
11 
6 
7 
8 
9 
10 
11 
12 
In Python, a multidimensional table like this can be implemented
as a sequence of sequences. A table is a sequence of rows. Each row is a
sequence of individual cells. This allows us to use mathematicallike
notation. Where the mathematician might say
A_{i,j}
, in Python we can say
A[i][j]
. In Python, we want the row i
from table A, and column j
from that row.
This looks remarkably like the list
of
tuple
s we discussed in the section called “Lists of Tuples”.
List of Lists Example. We can build a table using a nested list comprehension. The
following example creates a table as a sequence of sequences and then
fills in each cell of the table.
table= [ [ 0 for i in range(6) ] for j in range(6) ]
print table
for d1 in range(6):
for d2 in range(6):
table[d1][d2]= d1+d2+2
print table
This program produced the following output.
[[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, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
[[2, 3, 4, 5, 6, 7], [3, 4, 5, 6, 7, 8], [4, 5, 6, 7, 8, 9],
[5, 6, 7, 8, 9, 10], [6, 7, 8, 9, 10, 11], [7, 8, 9, 10, 11, 12]]
This program did two things. It created a six by six table of
zeroes. It then filled this with each possible combination of two dice.
This is not the most efficient way to do this, but we want to illustrate
several techniques with a simple example. We'll look at each half in
detail.
The first part of this program creates and prints a 6item
list
, named table
; each item
in the table is a 6item list
of zeroes. It uses
a list comprehension to create an object for each value of
j
in the range of 0 to 6. Each of the objects is a
list
of zeroes, one for each value of
i
in the range of 0 to 6. After this initialization,
the twodimensional table of zeroes is printed.
The comprehension can be read from inner to outer, like an
ordinary expression. The inner list
, [ 0
for i in range(6) ]
, creates a simple list
of six zeroes. The outer list
, [ [...] for
j in range(6) ]
creates six copies of these inner
list
s.
The second part of this program then iterates over all
combinations of two dice, filling in each cell of the table. This is
done as two nested loops, one loop for each of the two dice. The outer
enumerates all values of one die, d1
. The loop
enumerates all values of a second die, d2
.
Updating each cell involves selecting the row with
table[d1]
; this is a list
of 6
values. The specific cell in this list
is
selected by ...[d2]
. We set this cell to the number rolled
on the dice, d1+d2+2
.
Additional Examples. The printed list
of
list
s is a little hard to read. The following
loop would display the table in a more readable form.
>>>
for row in table:
...
print row
...
[2, 3, 4, 5, 6, 7]
[3, 4, 5, 6, 7, 8]
[4, 5, 6, 7, 8, 9]
[5, 6, 7, 8, 9, 10]
[6, 7, 8, 9, 10, 11]
[7, 8, 9, 10, 11, 12]
As an exercise, we'll leave it to the reader to add some features
to this to print column and row headings along with the contents. As a
hint, the "%2d" % value
string
operation might be useful to get fixedsize numeric conversions.
Explicit Index Values. We'll summarize our table of die rolls, and accumulate a
frequency table. We'll use a simple list with 13 buckets (numbered
from 0 to 12) for the frequency of each die roll. We can see that the
die roll of 2 occurs just once in our matrix, so we'll expact that
fq[2]
will have the value 1. Let's visit each cell in the
matrix and accumulate a frequency table.
There is an alternative to this approach. Rather than strip out
each row sequence, we could use explicit indexes and look up each
individual value with an integer index into the sequence.
fq= 13*[0]
for i in range(6):
for j in range(6):
c= table[i][j]
fq[ c ] += 1
We initialize the frequency table, fq
, to be a
list
of 13 zeroes.
The outer loop sets the variable i
to the
values from 0 to 5. The inner loop sets the variable
j
to the values from 0 to 5.
We use the index value of i
to select a row
from the table, and the index value of j
to select a
column from that row. This is the value, c
. We then
accumulate the frequency occurances in the frequency table,
fq
.
This looks very mathematical and formal. However, Python gives us
an alternative, which can be somewhat simpler.
Using List Iterators Instead of Index Values. Since our table is a list of lists, we can make use of the power
of the
for
statement to step through the elements
without using an index.
fq= 13*[0]
print fq
for row in table:
for c in row:
fq[c] += 1
print fq[2:]
We initialize the frequency table, fq
, to be a
list
of 13 zeroes.
The outer loop sets the variable row
to each
element of the original table
variable. This
decomposes the table into individual rows, each of which is a 6element
list
.
The inner loop sets the variable c
to each
column's value within the row. This decomposes the row into the
individual values.
We count the actual occurances of each value, c
by using the value as an index into the frequency table,
fq
. The increment the frequency value by 1.
Mathematical Matrices. We use the explicit index technique for managing the
mathematicallydefined matrix operations. Matrix operations are done
more clearly with this style of explicit index operations. We'll show
matrix addition as an example, here, and leave matrix multplication as
an exercise in a later section.
m1 = [ [1, 2, 3, 0], [4, 5, 6, 0], [7, 8, 9, 0] ]
m2 = [ [2, 4, 6, 0], [1, 3, 5, 0], [0, 1, 2, 0] ]
m3= [ 4*[0] for i in range(3) ]
for i in range(3):
for j in range(4):
m3[i][j]= m1[i][j]+m2[i][j]
In this example we created two input matrices,
m1
and m2
, each three by four. We
initialized a third matrix, m3
, to three rows of four
zeroes, using a comprehension. Then we iterated through all rows (using
the i
variable), and all columns (using the
j
variable) and computed the sum of
m1
and m2
.
Python provides a number of modules for handling this kind of
processing. In Part IV, “Components, Modules and Packages” we'll look at modules for more
sophisticated matrix handling.