Python Sequences | Ordered Data Collections

Introduction

Python Sequences are ordered collections of items. They are indexed by integers.

Sequence Datatypes

Python has three sequence datatypes.

String: 'Hello'. A string is a sequence of characters.
List: [1, 4, 5].
Tuple: ('GOOG', 100, 490.1).

All sequences are ordered, indexed by integers, and have a length.

a = 'Hello'               ## String
b = [1, 4, 5]             ## List
c = ('GOOG', 100, 490.1)  ## Tuple

## Indexed order
a[0]                      ## 'H'
b[-1]                     ## 5
c[1]                      ## 100

## Length of sequence
len(a)                    ## 5
len(b)                    ## 3
len(c)                    ## 3

Sequences can be replicated: s * n.

>>> a = 'Hello'
>>> a * 3
'HelloHelloHello'
>>> b = [1, 2, 3]
>>> b * 2
[1, 2, 3, 1, 2, 3]
>>>

Sequences of the same type can be concatenated: s + t.

>>> a = (1, 2, 3)
>>> b = (4, 5)
>>> a + b
(1, 2, 3, 4, 5)
>>>
>>> c = [1, 5]
>>> a + c
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can only concatenate tuple (not "list") to tuple

Slicing

Slicing means to take a subsequence from a sequence. The syntax is s[start:end]. Where start and end are the indexes of the subsequence you want.

a = [0,1,2,3,4,5,6,7,8]

a[2:5]    ## [2,3,4]
a[-5:]    ## [4,5,6,7,8]
a[:3]     ## [0,1,2]

Indices start and end must be integers.
Slices do not include the end value. It is like a half-open interval from math.
If indices are omitted, they default to the beginning or end of the list.

Slice re-assignment

On lists, slices can be reassigned and deleted.

## Reassignment
a = [0,1,2,3,4,5,6,7,8]
a[2:4] = [10,11,12]       ## [0,1,10,11,12,4,5,6,7,8]

Note: The reassigned slice doesn't need to have the same length.

## Deletion
a = [0,1,2,3,4,5,6,7,8]
del a[2:4]                ## [0,1,4,5,6,7,8]

Sequence Reductions

There are some common functions to reduce a sequence to a single value.

>>> s = [1, 2, 3, 4]
>>> sum(s)
10
>>> min(s)
1
>>> max(s)
4
>>> t = ['Hello', 'World']
>>> max(t)
'World'
>>>

Iteration over a sequence

The for-loop iterates over the elements in a sequence.

>>> s = [1, 4, 9, 16]
>>> for i in s:
...     print(i)
...
1
4
9
16
>>>

On each iteration of the loop, you get a new item to work with. This new value is placed into the iteration variable. In this example, the iteration variable is x:

for x in s:         ## `x` is an iteration variable
    ...statements

On each iteration, the previous value of the iteration variable is overwritten (if any). After the loop finishes, the variable retains the last value.

break statement

You can use the break statement to break out of a loop early.

for name in namelist:
    if name == 'Jake':
        break
    ...
    ...
statements

When the break statement executes, it exits the loop and moves on the next statements. The break statement only applies to the inner-most loop. If this loop is within another loop, it will not break the outer loop.

continue statement

To skip one element and move to the next one, use the continue statement.

for line in lines:
    if line == '\n':    ## Skip blank lines
        continue
    ## More statements
    ...

This is useful when the current item is not of interest or needs to be ignored in the processing.

Looping over integers

If you need to count, use range().

for i in range(100):
    ## i = 0,1,...,99

The syntax is range([start,] end [,step])

for i in range(100):
    ## i = 0,1,...,99
for j in range(10,20):
    ## j = 10,11,..., 19
for k in range(10,50,2):
    ## k = 10,12,...,48
    ## Notice how it counts in steps of 2, not 1.

The ending value is never included. It mirrors the behavior of slices.
start is optional. Default 0.
step is optional. Default 1.
range() computes values as needed. It does not actually store a large range of numbers.

enumerate() function

The enumerate function adds an extra counter value to iteration.

names = ['Elwood', 'Jake', 'Curtis']
for i, name in enumerate(names):
    ## Loops with i = 0, name = 'Elwood'
    ## i = 1, name = 'Jake'
    ## i = 2, name = 'Curtis'

The general form is enumerate(sequence [, start = 0]). start is optional. A good example of using enumerate() is tracking line numbers while reading a file:

with open(filename) as f:
    for lineno, line in enumerate(f, start=1):
        ...

In the end, enumerate is just a nice shortcut for:

i = 0
for x in s:
    statements
    i += 1

Using enumerate is less typing and runs slightly faster.

For and tuples

You can iterate with multiple iteration variables.

points = [
  (1, 4),(10, 40),(23, 14),(5, 6),(7, 8)
]
for x, y in points:
    ## Loops with x = 1, y = 4
    ##            x = 10, y = 40
    ##            x = 23, y = 14
    ##            ...

When using multiple variables, each tuple is unpacked into a set of iteration variables. The number of variables must match the number of items in each tuple.

zip() function

The zip function takes multiple sequences and makes an iterator that combines them.

columns = ['name', 'shares', 'price']
values = ['GOOG', 100, 490.1 ]
pairs = zip(columns, values)
## ('name','GOOG'), ('shares',100), ('price',490.1)

To get the result you must iterate. You can use multiple variables to unpack the tuples as shown earlier.

for column, value in pairs:
    ...

A common use of zip is to create key/value pairs for constructing dictionaries.

d = dict(zip(columns, values))

Exercise 2.13: Counting

Try some basic counting examples:

>>> for n in range(10):            ## Count 0 ... 9
        print(n, end=' ')

0 1 2 3 4 5 6 7 8 9
>>> for n in range(10,0,-1):       ## Count 10 ... 1
        print(n, end=' ')

10 9 8 7 6 5 4 3 2 1
>>> for n in range(0,10,2):        ## Count 0, 2, ... 8
        print(n, end=' ')

0 2 4 6 8
>>>

Exercise 2.14: More sequence operations

Interactively experiment with some of the sequence reduction operations.

>>> data = [4, 9, 1, 25, 16, 100, 49]
>>> min(data)
1
>>> max(data)
100
>>> sum(data)
204
>>>

Try looping over the data.

>>> for x in data:
        print(x)

4
9
...
>>> for n, x in enumerate(data):
        print(n, x)

0 4
1 9
2 1
...
>>>

Sometimes the for statement, len(), and range() get used by novices in some kind of horrible code fragment that looks like it emerged from the depths of a rusty C program.

>>> for n in range(len(data)):
        print(data[n])

4
9
1
...
>>>

Don't do that! Not only does reading it make everyone's eyes bleed, it's inefficient with memory and it runs a lot slower. Just use a normal for loop if you want to iterate over data. Use enumerate() if you happen to need the index for some reason.

Exercise 2.15: A practical enumerate() example

Recall that the file missing.csv contains data for a stock portfolio, but has some rows with missing data. Using enumerate(), modify your pcost.py program so that it prints a line number with the warning message when it encounters bad input.

>>> cost = portfolio_cost('/home/labex/project/missing.csv')
Row 4: Couldn't convert: ['MSFT', '', '51.23']
Row 7: Couldn't convert: ['IBM', '', '70.44']
>>>

To do this, you'll need to change a few parts of your code.

...
for rowno, row in enumerate(rows, start=1):
    try:
        ...
    except ValueError:
        print(f'Row {rowno}: Bad row: {row}')

✨ Check Solution and Practice

Exercise 2.16: Using the zip() function

In the file portfolio.csv, the first line contains column headers. In all previous code, we've been discarding them.

>>> f = open('/home/labex/project/portfolio.csv')
>>> rows = csv.reader(f)
>>> headers = next(rows)
>>> headers
['name', 'shares', 'price']
>>>

However, what if you could use the headers for something useful? This is where the zip() function enters the picture. First try this to pair the file headers with a row of data:

>>> row = next(rows)
>>> row
['AA', '100', '32.20']
>>> list(zip(headers, row))
[ ('name', 'AA'), ('shares', '100'), ('price', '32.20') ]
>>>

Notice how zip() paired the column headers with the column values. We've used list() here to turn the result into a list so that you can see it. Normally, zip() creates an iterator that must be consumed by a for-loop.

This pairing is an intermediate step to building a dictionary. Now try this:

>>> record = dict(zip(headers, row))
>>> record
{'price': '32.20', 'name': 'AA', 'shares': '100'}
>>>

This transformation is one of the most useful tricks to know about when processing a lot of data files. For example, suppose you wanted to make the pcost.py program work with various input files, but without regard for the actual column number where the name, shares, and price appear.

Modify the portfolio_cost() function in pcost.py so that it looks like this:

## pcost.py

def portfolio_cost(filename):
    ...
        for rowno, row in enumerate(rows, start=1):
            record = dict(zip(headers, row))
            try:
                nshares = int(record['shares'])
                price = float(record['price'])
                total_cost += nshares * price
            ## This catches errors in int() and float() conversions above
            except ValueError:
                print(f'Row {rowno}: Bad row: {row}')
        ...

Now, try your function on a completely different data file portfoliodate.csv which looks like this:

name,date,time,shares,price
"AA","6/11/2007","9:50am",100,32.20
"IBM","5/13/2007","4:20pm",50,91.10
"CAT","9/23/2006","1:30pm",150,83.44
"MSFT","5/17/2007","10:30am",200,51.23
"GE","2/1/2006","10:45am",95,40.37
"MSFT","10/31/2006","12:05pm",50,65.10
"IBM","7/9/2006","3:15pm",100,70.44

>>> portfolio_cost('/home/labex/project/portfoliodate.csv')
44671.15
>>>

If you did it right, you'll find that your program still works even though the data file has a completely different column format than before. That's cool!

The change made here is subtle, but significant. Instead of portfolio_cost() being hardcoded to read a single fixed file format, the new version reads any CSV file and picks the values of interest out of it. As long as the file has the required columns, the code will work.

Modify the report.py program you wrote in Section 2.3 so that it uses the same technique to pick out column headers.

Try running the report.py program on the portfoliodate.csv file and see that it produces the same answer as before.

✨ Check Solution and Practice

Exercise 2.17: Inverting a dictionary

A dictionary maps keys to values. For example, a dictionary of stock prices.

>>> prices = {
        'GOOG' : 490.1,
        'AA' : 23.45,
        'IBM' : 91.1,
        'MSFT' : 34.23
    }
>>>

If you use the items() method, you can get (key,value) pairs:

>>> prices.items()
dict_items([('GOOG', 490.1), ('AA', 23.45), ('IBM', 91.1), ('MSFT', 34.23)])
>>>

However, what if you wanted to get a list of (value, key) pairs instead? Hint: use zip().

>>> pricelist = list(zip(prices.values(),prices.keys()))
>>> pricelist
[(490.1, 'GOOG'), (23.45, 'AA'), (91.1, 'IBM'), (34.23, 'MSFT')]
>>>

Why would you do this? For one, it allows you to perform certain kinds of data processing on the dictionary data.

>>> min(pricelist)
(23.45, 'AA')
>>> max(pricelist)
(490.1, 'GOOG')
>>> sorted(pricelist)
[(23.45, 'AA'), (34.23, 'MSFT'), (91.1, 'IBM'), (490.1, 'GOOG')]
>>>

This also illustrates an important feature of tuples. When used in comparisons, tuples are compared element-by-element starting with the first item. Similar to how strings are compared character-by-character.

zip() is often used in situations like this where you need to pair up data from different places. For example, pairing up the column names with column values in order to make a dictionary of named values.

Note that zip() is not limited to pairs. For example, you can use it with any number of input lists:

>>> a = [1, 2, 3, 4]
>>> b = ['w', 'x', 'y', 'z']
>>> c = [0.2, 0.4, 0.6, 0.8]
>>> list(zip(a, b, c))
[(1, 'w', 0.2), (2, 'x', 0.4), (3, 'y', 0.6), (4, 'z', 0.8))]
>>>

Also, be aware that zip() stops once the shortest input sequence is exhausted.

>>> a = [1, 2, 3, 4, 5, 6]
>>> b = ['x', 'y', 'z']
>>> list(zip(a,b))
[(1, 'x'), (2, 'y'), (3, 'z')]
>>>

Summary

Congratulations! You have completed the Sequences lab. You can practice more labs in LabEx to improve your skills.

Python Sequence Fundamentals