DEV Community: Esther Wavinya

Why Geospatial Technology

Esther Wavinya — Sun, 07 Jun 2020 10:37:47 +0000

Geospatial technology is an emerging field of study that includes Geographic Information System (GIS), Remote Sensing (RS) and Global Positioning System (GPS).

Here is the link to the article in my medium account Why Geospatial Technology

Python Frameworks and libraries

Esther Wavinya — Sun, 07 Jun 2020 09:48:54 +0000

I wrote the article in my medium account. The link is What I should have known about Python

Functional Programming

Esther Wavinya — Mon, 11 May 2020 08:16:23 +0000

6. Functional Programming
6.1. Recursion
Defining solution of a problem in terms of the same problem, typically of smaller size, is called recursion. Recursion makes it possible to express solution of a problem very concisely and elegantly.

A function is called recursive if it makes call to itself. Typically, a recursive function will have a terminating condition and one or more recursive calls to itself.

6.1.1. Example: Computing Exponent
Mathematically we can define exponent of a number in terms of its smaller power.

def exp(x, n):
    """
    Computes the result of x raised to the power of n.

        >>> exp(2, 3)
        8
        >>> exp(3, 2)
        9
    """
    if n == 0:
        return 1
    else:
        return x * exp(x, n-1)

Lets look at the execution pattern.

exp(2, 4)
+-- 2 * exp(2, 3)
|       +-- 2 * exp(2, 2)
|       |       +-- 2 * exp(2, 1)
|       |       |       +-- 2 * exp(2, 0)
|       |       |       |       +-- 1
|       |       |       +-- 2 * 1
|       |       |       +-- 2
|       |       +-- 2 * 2
|       |       +-- 4
|       +-- 2 * 4
|       +-- 8
+-- 2 * 8
+-- 16

Number of calls to the above exp function is proportional to size of the problem, which is n here.

We can compute exponent in fewer steps if we use successive squaring.

def fast_exp(x, n):
    if n == 0:
        return 1
    elif n % 2 == 0:
        return fast_exp(x*x, n/2))
    else:
        return x * fast_exp(x, n-1)

Lets look at the execution pattern now.

fast_exp(2, 10)
+-- fast_exp(4, 5) # 2 * 2
|   +-- 4 * fast_exp(4, 4)
|   |       +-- fast_exp(16, 2) # 4 * 4
|   |       |   +-- fast_exp(256, 1) # 16 * 16
|   |       |   |   +-- 256 * fast_exp(256, 0)
|   |       |   |             +-- 1
|   |       |   |   +-- 256 * 1
|   |       |   |   +-- 256
|   |       |   +-- 256
|   |       +-- 256
|   +-- 4 * 256
|   +-- 1024
+-- 1024
1024

Problem 1: Implement a function product to multiply 2 numbers recursively using + and - operators only.

6.1.2. Example: Flatten a list
Supposed you have a nested list and want to flatten it.

def flatten_list(a, result=None):
    """Flattens a nested list.

        >>> flatten_list([ [1, 2, [3, 4] ], [5, 6], 7])
        [1, 2, 3, 4, 5, 6, 7]
    """
    if result is None:
        result = []

    for x in a:
        if isinstance(x, list):
            flatten_list(x, result)
        else:
            result.append(x)

    return result

Problem 2: Write a function flatten_dict to flatten a nested dictionary by joining the keys with . character.

>>> flatten_dict({'a': 1, 'b': {'x': 2, 'y': 3}, 'c': 4})
{'a': 1, 'b.x': 2, 'b.y': 3, 'c': 4}

Problem 3: Write a function unflatten_dict to do reverse of flatten_dict.

>>> unflatten_dict({'a': 1, 'b.x': 2, 'b.y': 3, 'c': 4})
{'a': 1, 'b': {'x': 2, 'y': 3}, 'c': 4}

Problem 4: Write a function treemap to map a function over nested list.

>>> treemap(lambda x: x*x, [1, 2, [3, 4, [5]]])
[1, 4, [9, 16, [25]]]

Problem 5: Write a function tree_reverse to reverse elements of a nested-list recursively.

>>> tree_reverse([[1, 2], [3, [4, 5]], 6])
[6, [[5, 4], 3], [2, 1]]

6.1.3. Example: JSON Encode
Lets look at more commonly used example of serializing a python datastructure into JSON (JavaScript Object Notation).

Here is an example of JSON record.

{
    "name": "Advanced Python Training",
    "date": "October 13, 2012",
    "completed": false,
    "instructor": {
        "name": "Anand Chitipothu",
        "website": "http://anandology.com/"
    },
    "participants": [
        {
            "name": "Participant 1",
            "email": "email1@example.com"
        },
        {
            "name": "Participant 2",
            "email": "email2@example.com"
        }
    ]
}

It looks very much like Python dictionaries and lists. There are some differences though. Strings are always enclosed in double quotes, booleans are represented as true and false.

The standard library module json provides functionality to work in JSON. Lets try to implement it now as it is very good example of use of recursion.

For simplicity, lets assume that strings will not have any special characters and can have space, tab and newline characters.

def json_encode(data):
    if isinstance(data, bool):
        if data:
            return "true"
        else:
            return "false"
    elif isinstance(data, (int, float)):
        return str(data)
    elif isinstance(data, str):
        return '"' + escape_string(data) + '"'
    elif isinstance(data, list):
        return "[" + ", ".join(json_encode(d) for d in data) + "]"
    else:
        raise TypeError("%s is not JSON serializable" % repr(data))

def escape_string(s):
    """Escapes double-quote, tab and new line characters in a string."""
    s = s.replace('"', '\\"')
    s = s.replace("\t", "\\t")
    s = s.replace("\n", "\\n")
    return s

This handles booleans, integers, strings, floats and lists, but doesn’t handle dictionaries yet. That is left an exercise to the readers.

If you notice the block of code that is handling lists, we are calling json_encode recursively for each element of the list, that is required because each element can be of any type, even a list or a dictionary.

Problem 6: Complete the above implementation of json_encode by handling the case of dictionaries.

Problem 7: Implement a program dirtree.py that takes a directory as argument and prints all the files in that directory recursively as a tree.

Hint: Use os.listdir and os.path.isdir funtions.

$ python dirtree.py foo/
foo/
|-- a.txt
|-- b.txt
|-- bar/
|   |-- p.txt
|   `-- q.txt
`-- c.txt

Problem 8: Write a function count_change to count the number of ways to change any given amount. Available coins are also passed as argument to the function.

>>> count_change(10, [1, 5])
3
>>> count_change(10, [1, 2])
6
>>> count_change(100, [1, 5, 10, 25, 50])
292

Problem 9: Write a function permute to compute all possible permutations of elements of a given list.

>>> permute([1, 2, 3])
[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]]

6.2. Higher Order Functions & Decorators
In Python, functions are first-class objects. They can be passed as arguments to other functions and a new functions can be returned from a function call.

6.2.1. Example: Tracing Function Calls
For example, consider the following fib function.

def fib(n):
    if n is 0 or n is 1:
        return 1
    else:
        return fib(n-1) + fib(n-2)

Suppose we want to trace all the calls to the fib function. We can write a higher order function to return a new function, which prints whenever fib function is called.

def trace(f):
    def g(x):
        print(f.__name__, x)
        value = f(x)
        print('return', repr(value))
        return value
    return g

fib = trace(fib)
print(fib(3))
This produces the following output.

fib 3
fib 2
fib 1
return 1
fib 0
return 1
return 2
fib 1
return 1
return 3
3

Noticed that the trick here is at fib = trace(fib). We have replaced the function fib with a new function, so whenever that function is called recursively, it is the our new function, which prints the trace before calling the orginal function.

To make the output more readable, let us indent the function calls.

def trace(f):
    f.indent = 0
    def g(x):
        print('|  ' * f.indent + '|--', f.__name__, x)
        f.indent += 1
        value = f(x)
        print('|  ' * f.indent + '|--', 'return', repr(value))
        f.indent -= 1
        return value
    return g

fib = trace(fib)
print(fib(4))

This produces the following output.

$ python fib.py
|-- fib 4
|  |-- fib 3
|  |  |-- fib 2
|  |  |  |-- fib 1
|  |  |  |  |-- return 1
|  |  |  |-- fib 0
|  |  |  |  |-- return 1
|  |  |  |-- return 2
|  |  |-- fib 1
|  |  |  |-- return 1
|  |  |-- return 3
|  |-- fib 2
|  |  |-- fib 1
|  |  |  |-- return 1
|  |  |-- fib 0
|  |  |  |-- return 1
|  |  |-- return 2
|  |-- return 5
5

This pattern is so useful that python has special syntax for specifying this concisely.

@trace
def fib(n):
    ...

It is equivalant of adding fib = trace(fib) after the function definition.

6.2.2. Example: Memoize
In the above example, it is clear that number of function calls are growing exponentially with the size of input and there is lot of redundant computation that is done.

Suppose we want to get rid of the redundant computation by caching the result of fib when it is called for the first time and reuse it when it is needed next time. Doing this is very popular in functional programming world and it is called memoize.

def memoize(f):
    cache = {}
    def g(x):
        if x not in cache:
            cache[x] = f(x)
        return cache[x]
    return g

fib = trace(fib)
fib = memoize(fib)
print(fib(4))

If you notice, after memoize, growth of fib has become linear.

|-- fib 4
|  |-- fib 3
|  |  |-- fib 2
|  |  |  |-- fib 1
|  |  |  |  |-- return 1
|  |  |  |-- fib 0
|  |  |  |  |-- return 1
|  |  |  |-- return 2
|  |  |-- return 3
|  |-- return 5
5

Problem 10: Write a function profile, which takes a function as argument and returns a new function, which behaves exactly similar to the given function, except that it prints the time consumed in executing it.

>>> fib = profile(fib)
>>> fib(20)
time taken: 0.1 sec
10946

Problem 11: Write a function vectorize which takes a function f and return a new function, which takes a list as argument and calls f for every element and returns the result as a list.

>>> def square(x): return x * x
...
>>> f = vectorize(square)
>>> f([1, 2, 3])
[1, 4, 9]
>>> g = vectorize(len)
>>> g(["hello", "world"])
[5, 5]
>>> g([[1, 2], [2, 3, 4]])
[2, 3]

6.2.3. Example: unixcommand decorator
Many unix commands have a typical pattern. They accept multiple filenames as arguments, does some processing and prints the lines back. Some examples of such commands are cat and grep.

def unixcommand(f):
    def g(filenames):
        printlines(out for line in readlines(filenames)
                           for out in f(line))
    return g
Lets see how to use it.

@unixcommand
def cat(line):
    yield line

@unixcommand
def lowercase(line):
    yield line.lower()

6.3. exec & eval
Python privides the whole interpreter as a built-in function. You can pass a string and ask it is execute that piece of code at run time.

For example:

>>> exec("x = 1")
>>> x
1

By default exec works in the current environment, so it updated the globals in the above example. It is also possible to specify an environment to exec.

>>> env = {'a' : 42}
>>> exec('x = a+1', env)
>>> print(env['x'])
43

It is also possible to create functions or classes dynamically using exec, though it is usually not a good idea.

>>> code = 'def add_%d(x): return x + %d'
>>> for i in range(1, 5):
...     exec(code % (i, i))
...
>>> add_1(3)
4
>>> add_3(3)
6

eval is like exec but it takes an expression and returns its value.

>>> eval("2+3")
5
>>> a = 2
>>> eval("a * a")
4
>>> env = {'x' : 42}
>>> eval('x+1', env)
43

Iterators & Generators

Esther Wavinya — Wed, 06 May 2020 15:15:39 +0000

5. Iterators & Generators
5.1. Iterators
We use for statement for looping over a list.

>>> for i in [1, 2, 3, 4]:
...     print(i)
...
1
2
3
4

If we use it with a string, it loops over its characters.

>>> for c in "python":
...     print(c)
...
p
y
t
h
o
n

If we use it with a dictionary, it loops over its keys.

>>> for k in {"x": 1, "y": 2}:
...     print(k)
...
y
x

If we use it with a file, it loops over lines of the file.

>>> for line in open("a.txt"):
...     print(line, end="")
...
first line
second line

So there are many types of objects which can be used with a for loop. These are called iterable objects.

There are many functions which consume these iterables.

>>> ",".join(["a", "b", "c"])
'a,b,c'
>>> ",".join({"x": 1, "y": 2})
'y,x'
>>> list("python")
['p', 'y', 't', 'h', 'o', 'n']
>>> list({"x": 1, "y": 2})
['y', 'x']

5.1.1. The Iteration Protocol
The built-in function iter takes an iterable object and returns an iterator.

>>> x = iter([1, 2, 3])
>>> x
<listiterator object at 0x1004ca850>
>>> next(x)
1
>>> next(x)
2
>>> next(x)
3
>>> next(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

Each time we call the next method on the iterator gives us the next element. If there are no more elements, it raises a StopIteration.

Iterators are implemented as classes. Here is an iterator that works like built-in range function.

class yrange:
    def __init__(self, n):
        self.i = 0
        self.n = n

    def __iter__(self):
        return self

    def __next__(self):
        if self.i < self.n:
            i = self.i
            self.i += 1
            return i
        else:
            raise StopIteration()

The __iter__ method is what makes an object iterable. Behind the scenes, the iter function calls __iter__ method on the given object.

The return value of __iter__ is an iterator. It should have a __next__ method and raise StopIteration when there are no more elements.

Lets try it out:

>>> y = yrange(3)
>>> next(y)
0
>>> next(y)
1
>>> next(y)
2
>>> next(y)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 14, in __next__
StopIteration

Many built-in functions accept iterators as arguments.

>>> list(yrange(5))
[0, 1, 2, 3, 4]
>>> sum(yrange(5))
10

In the above case, both the iterable and iterator are the same object. Notice that the __iter__ method returned self. It need not be the case always.

class zrange:
    def __init__(self, n):
        self.n = n

    def __iter__(self):
        return zrange_iter(self.n)

class zrange_iter:
    def __init__(self, n):
        self.i = 0
        self.n = n

    def __iter__(self):
        # Iterators are iterables too.
        # Adding this functions to make them so.
        return self

    def __next__(self):
        if self.i < self.n:
            i = self.i
            self.i += 1
            return i
        else:
            raise StopIteration()

If both iteratable and iterator are the same object, it is consumed in a single iteration.

>>> y = yrange(5)
>>> list(y)
[0, 1, 2, 3, 4]
>>> list(y)
[]
>>> z = zrange(5)
>>> list(z)
[0, 1, 2, 3, 4]
>>> list(z)
[0, 1, 2, 3, 4]

Problem 1: Write an iterator class reverse_iter, that takes a list and iterates it from the reverse direction. ::

>>> it = reverse_iter([1, 2, 3, 4])
>>> next(it)
4
>>> next(it)
3
>>> next(it)
2
>>> next(it)
1
>>> next(it)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

5.2. Generators
Generators simplifies creation of iterators. A generator is a function that produces a sequence of results instead of a single value.

def yrange(n):
    i = 0
    while i < n:
        yield i
        i += 1

Each time the yield statement is executed the function generates a new value.

>>> y = yrange(3)
>>> y
<generator object yrange at 0x401f30>
>>> next(y)
0
>>> next(y)
1
>>> next(y)
2
>>> next(y)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration

So a generator is also an iterator. You don’t have to worry about the iterator protocol.

The word “generator” is confusingly used to mean both the function that generates and what it generates. In this chapter, I’ll use the word “generator” to mean the genearted object and “generator function” to mean the function that generates it.

Can you think about how it is working internally?

When a generator function is called, it returns a generator object without even beginning execution of the function. When next method is called for the first time, the function starts executing until it reaches yield statement. The yielded value is returned by the next call.

The following example demonstrates the interplay between yield and call to __next__ method on generator object.

>>> def foo():
...     print("begin")
...     for i in range(3):
...         print("before yield", i)
...         yield i
...         print("after yield", i)
...     print("end")
...
>>> f = foo()
>>> next(f)
begin
before yield 0
0
>>> next(f)
after yield 0
before yield 1
1
>>> next(f)
after yield 1
before yield 2
2
>>> next(f)
after yield 2
end
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
StopIteration
>>>

Lets see an example:

def integers():
    """Infinite sequence of integers."""
    i = 1
    while True:
        yield i
        i = i + 1

def squares():
    for i in integers():
        yield i * i

def take(n, seq):
    """Returns first n values from the given sequence."""
    seq = iter(seq)
    result = []
    try:
        for i in range(n):
            result.append(next(seq))
    except StopIteration:
        pass
    return result

print(take(5, squares())) # prints [1, 4, 9, 16, 25]

5.3. Generator Expressions
Generator Expressions are generator version of list comprehensions. They look like list comprehensions, but returns a generator back instead of a list.

>>> a = (x*x for x in range(10))
>>> a
<generator object <genexpr> at 0x401f08>
>>> sum(a)
285

We can use the generator expressions as arguments to various functions that consume iterators.

>>> sum((x*x for x in range(10)))
285

When there is only one argument to the calling function, the parenthesis around generator expression can be omitted.

>>> sum(x*x for x in range(10))
285

Another fun example:

Lets say we want to find first 10 (or any n) pythogorian triplets. A triplet (x, y, z) is called pythogorian triplet if x*x + y*y == z*z.

It is easy to solve this problem if we know till what value of z to test for. But we want to find first n pythogorian triplets.

>>> pyt = ((x, y, z) for z in integers() for y in range(1, z) for x in range(1, y) if x*x + y*y == z*z)
>>> take(10, pyt)
[(3, 4, 5), (6, 8, 10), (5, 12, 13), (9, 12, 15), (8, 15, 17), (12, 16, 20), (15, 20, 25), (7, 24, 25), (10, 24, 26), (20, 21, 29)]

5.3.1. Example: Reading multiple files
Lets say we want to write a program that takes a list of filenames as arguments and prints contents of all those files, like cat command in unix.

The traditional way to implement it is:

def cat(filenames):
    for f in filenames:
        for line in open(f):
            print(line, end="")

Now, lets say we want to print only the line which has a particular substring, like grep command in unix.

def grep(pattern, filenames):
    for f in filenames:
        for line in open(f):
            if pattern in line:
                print(line, end="")

Both these programs have lots of code in common. It is hard to move the common part to a function. But with generators makes it possible to do it.

def readfiles(filenames):
    for f in filenames:
        for line in open(f):
            yield line

def grep(pattern, lines):
    return (line for line in lines if pattern in line)

def printlines(lines):
    for line in lines:
        print(line, end="")

def main(pattern, filenames):
    lines = readfiles(filenames)
    lines = grep(pattern, lines)
    printlines(lines)

The code is much simpler now with each function doing one small thing. We can move all these functions into a separate module and reuse it in other programs.

Problem 2: Write a program that takes one or more filenames as arguments and prints all the lines which are longer than 40 characters.

Problem 3: Write a function findfiles that recursively descends the directory tree for the specified directory and generates paths of all the files in the tree.

Problem 4: Write a function to compute the number of python files (.py extension) in a specified directory recursively.

Problem 5: Write a function to compute the total number of lines of code in all python files in the specified directory recursively.

Problem 6: Write a function to compute the total number of lines of code, ignoring empty and comment lines, in all python files in the specified directory recursively.

Problem 7: Write a program split.py, that takes an integer n and a filename as command line arguments and splits the file into multiple small files with each having n lines.

5.4. Itertools
The itertools module in the standard library provides lot of intersting tools to work with iterators.

Lets look at some of the interesting functions.

chain – chains multiple iterators together.

>>> it1 = iter([1, 2, 3])
>>> it2 = iter([4, 5, 6])
>>> itertools.chain(it1, it2)
[1, 2, 3, 4, 5, 6]

izip – iterable version of zip

>>> for x, y in itertools.izip(["a", "b", "c"], [1, 2, 3]):
...     print(x, y)
...
a 1
b 2
c 3

Problem 8: Write a function peep, that takes an iterator as argument and returns the first element and an equivalant iterator.

>>> it = iter(range(5))
>>> x, it1 = peep(it)
>>> print(x, list(it1))
0 [0, 1, 2, 3, 4]

Problem 9: The built-in function enumerate takes an iteratable and returns an iterator over pairs (index, value) for each value in the source.

>>> list(enumerate(["a", "b", "c"])
[(0, "a"), (1, "b"), (2, "c")]
>>> for i, c in enumerate(["a", "b", "c"]):
...     print(i, c)
...
0 a
1 b
2 c

Object Oriented Programming

Esther Wavinya — Wed, 06 May 2020 14:55:36 +0000

4. Object Oriented Programming
4.1. State
Suppose we want to model a bank account with support for deposit and withdraw operations. One way to do that is by using global state as shown in the following example.

balance = 0

def deposit(amount):
    global balance
    balance += amount
    return balance

def withdraw(amount):
    global balance
    balance -= amount
    return balance

The above example is good enough only if we want to have just a single account. Things start getting complicated if want to model multiple accounts.

We can solve the problem by making the state local, probably by using a dictionary to store the state.

def make_account():
    return {'balance': 0}

def deposit(account, amount):
    account['balance'] += amount
    return account['balance']

def withdraw(account, amount):
    account['balance'] -= amount
    return account['balance']

With this it is possible to work with multiple accounts at the same time.

>>> a = make_account()
>>> b = make_account()
>>> deposit(a, 100)
100
>>> deposit(b, 50)
50
>>> withdraw(b, 10)
40
>>> withdraw(a, 10)
90

4.2. Classes and Objects

class BankAccount:
    def __init__(self):
        self.balance = 0

    def withdraw(self, amount):
        self.balance -= amount
        return self.balance

    def deposit(self, amount):
        self.balance += amount
        return self.balance

>>> a = BankAccount()
>>> b = BankAccount()
>>> a.deposit(100)
100
>>> b.deposit(50)
50
>>> b.withdraw(10)
40
>>> a.withdraw(10)
90

4.3. Inheritance
Let us try to create a little more sophisticated account type where the account holder has to maintain a pre-determined minimum balance.

class MinimumBalanceAccount(BankAccount):
    def __init__(self, minimum_balance):
        BankAccount.__init__(self)
        self.minimum_balance = minimum_balance

    def withdraw(self, amount):
        if self.balance - amount < self.minimum_balance:
            print('Sorry, minimum balance must be maintained.')
        else:
            BankAccount.withdraw(self, amount)

Problem 1: What will the output of the following program.

class A:
    def f(self):
        return self.g()

    def g(self):
        return 'A'

class B(A):
    def g(self):
        return 'B'

a = A()
b = B()
print(a.f(), b.f())
print(a.g(), b.g())

Example: Drawing Shapes

class Canvas:
    def __init__(self, width, height):
        self.width = width
        self.height = height
        self.data = [[' '] * width for i in range(height)]

    def setpixel(self, row, col):
        self.data[row][col] = '*'

    def getpixel(self, row, col):
        return self.data[row][col]

    def display(self):
        print("\n".join(["".join(row) for row in self.data]))

class Shape:
    def paint(self, canvas): pass

class Rectangle(Shape):
    def __init__(self, x, y, w, h):
        self.x = x
        self.y = y
        self.w = w
        self.h = h

    def hline(self, x, y, w):
        pass

    def vline(self, x, y, h):
        pass

    def paint(self, canvas):
        hline(self.x, self.y, self.w)
        hline(self.x, self.y + self.h, self.w)
        vline(self.x, self.y, self.h)
        vline(self.x + self.w, self.y, self.h)

class Square(Rectangle):
    def __init__(self, x, y, size):
        Rectangle.__init__(self, x, y, size, size)

class CompoundShape(Shape):
    def __init__(self, shapes):
        self.shapes = shapes

    def paint(self, canvas):
        for s in self.shapes:
            s.paint(canvas)

4.4. Special Class Methods
In Python, a class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators.

For example, the + operator invokes __add__ method.

>>> a, b = 1, 2
>>> a + b
3
>>> a.__add__(b)
3

Just like __add__ is called for + operator, __sub__, __mul__ and __div__ methods are called for -, *, and / operators.

Example: Rational Numbers

Suppose we want to do arithmetic with rational numbers. We want to be able to add, subtract, multiply, and divide them and to test whether two rational numbers are equal.

We can add, subtract, multiply, divide, and test equality by using the following relations:

n1/d1 + n2/d2 = (n1*d2 + n2*d1)/(d1*d2)
n1/d1 - n2/d2 = (n1*d2 - n2*d1)/(d1*d2)
n1/d1 * n2/d2 = (n1*n2)/(d1*d2)
(n1/d1) / (n2/d2) = (n1*d2)/(d1*n2)

n1/d1 == n2/d2 if and only if n1*d2 == n2*d1

Lets write the rational number class.

class RationalNumber:
    """
    Rational Numbers with support for arthmetic operations.

        >>> a = RationalNumber(1, 2)
        >>> b = RationalNumber(1, 3)
        >>> a + b
        5/6
        >>> a - b
        1/6
        >>> a * b
        1/6
        >>> a/b
        3/2
    """
    def __init__(self, numerator, denominator=1):
        self.n = numerator
        self.d = denominator

    def __add__(self, other):
        if not isinstance(other, RationalNumber):
            other = RationalNumber(other)

        n = self.n * other.d + self.d * other.n
        d = self.d * other.d
        return RationalNumber(n, d)

    def __sub__(self, other):
        if not isinstance(other, RationalNumber):
            other = RationalNumber(other)

        n1, d1 = self.n, self.d
        n2, d2 = other.n, other.d
        return RationalNumber(n1*d2 - n2*d1, d1*d2)

    def __mul__(self, other):
        if not isinstance(other, RationalNumber):
            other = RationalNumber(other)

        n1, d1 = self.n, self.d
        n2, d2 = other.n, other.d
        return RationalNumber(n1*n2, d1*d2)

    def __div__(self, other):
        if not isinstance(other, RationalNumber):
            other = RationalNumber(other)

        n1, d1 = self.n, self.d
        n2, d2 = other.n, other.d
        return RationalNumber(n1*d2, d1*n2)

    def __str__(self):
        return "%s/%s" % (self.n, self.d)

    __repr__ = __str__

4.5. Errors and Exceptions
We’ve already seen exceptions in various places. Python gives NameError when we try to use a variable that is not defined.

>>> foo
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'foo' is not defined

try adding a string to an integer:

>>> "foo" + 2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: cannot concatenate 'str' and 'int' objects

try dividing a number by 0:

>>> 2/0
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ZeroDivisionError: integer division or modulo by zero

or, try opening a file that is not there:

>>> open("not-there.txt")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IOError: [Errno 2] No such file or directory: 'not-there.txt'

Python raises exception in case errors. We can write programs to handle such errors. We too can raise exceptions when an error case in encountered.

Exceptions are handled by using the try-except statements.

def main():
    filename = sys.argv[1]
    try:
        for row in parse_csv(filename):
            print row
    except IOError:
        print("The given file doesn't exist: ", filename, file=sys.stderr)
        sys.exit(1)

The above example prints an error message and exits with an error status when an IOError is encountered.

The except statement can be written in multiple ways:

# catch all exceptions
try:
    ...
except:

# catch just one exception
try:
    ...
except IOError:
    ...

# catch one exception, but provide the exception object
try:
    ...
except IOError as e:
    ...

# catch more than one exception
try:
    ...
except (IOError, ValueError) as e:
    ...

It is possible to have more than one except statements with one try.

try:
    ...
except IOError as e:
    print("Unable to open the file (%s): %s" % (str(e), filename), file=sys.stderr)
    sys.exit(1)
except FormatError as e:
    print("File is badly formatted (%s): %s" % (str(e), filename), file=sys.stderr)

The try statement can have an optional else clause, which is executed only if no exception is raised in the try-block.

try:
    ...
except IOError as e:
    print("Unable to open the file (%s): %s" % (str(e), filename), file=sys.stderr)
    sys.exit(1)
else:
    print("successfully opened the file", filename)

There can be an optional else clause with a try statement, which is executed irrespective of whether or not exception has occured.

try:
    ...
except IOError as e:
    print("Unable to open the file (%s): %s" % (str(e), filename), file=sys.stderr)
    sys.exit(1)
finally:
    delete_temp_files()

Exception is raised using the raised keyword.

raise Exception("error message")

All the exceptions are extended from the built-in Exception class.

class ParseError(Exception):
pass

Problem 2: What will be the output of the following program?

try:
    print "a"
except:
    print "b"
else:
    print "c"
finally:
    print "d"

Problem 3: What will be the output of the following program?

try:
    print("a")
    raise Exception("doom")
except:
    print("b")
else:
    print("c")
finally:
    print("d")

Problem 4: What will be the output of the following program?

def f():
    try:
        print("a")
        return
    except:
        print("b")
    else:
        print("c")
    finally:
        print("d")

f()

Modules

Esther Wavinya — Wed, 06 May 2020 13:55:34 +0000

3. Modules
Modules are reusable libraries of code in Python. Python comes with many standard library modules.
A module is imported using the import statement.

>>> import time
>>> print(time.asctime())
'Fri Mar 30 12:59:21 2012'

In this example, we’ve imported the time module and called the asctime function from that module, which returns current time as a string.

There is also another way to use the import statement.

>>> from time import asctime
>>> asctime()
'WED May 06 16:50:37 2020'

Here we just imported the asctime function from the time module.

The pydoc command provides help on any module or a function.

$ pydoc time
Help on module time:

NAME
    time - This module provides various functions to manipulate time values.
...

$ pydoc time.asctime
Help on built-in function asctime in time:

time.asctime = asctime(...)
    asctime([tuple]) -> string
...

On Windows, the pydoc command is not available. The work-around is to use, the built-in help function.

>>> help('time')
Help on module time:

NAME
    time - This module provides various functions to manipulate time values.
...

Writing our own modules is very simple.

For example, create a file called num.py with the following content.

def square(x):
    return x * x

def cube(x):
    return x * x * x

Now open Python interpreter:

>>> import num
>>> num.square(3)
9
>>> num.cube(3)
27

That's all we’ve written a python library.

Try pydoc num (pydoc.bat numbers on Windows) to see documentation for this numbers modules. It won’t have any documentation as we haven’t provided anything yet.

In Python, it is possible to associate documentation for each module, function using docstrings. Docstrings are strings written at the top of the module or at the beginning of a function.

Lets try to document our num module by changing the contents of num.py

"""The num module provides utilties to work on numbers.

Current it provides square and cube.
"""

def square(x):
    """Computes square of a number."""
    return x * x

def cube(x):
    """Computes cube of a number."""
    return x * x

The pydoc command will now show us the doumentation nicely formatted.

Help on module num:

NAME
    num - The num module provides utilties to work on numbers.

FILE
    /Users/anand/num.py

DESCRIPTION
    Current it provides square and cube.

FUNCTIONS
    cube(x)
        Computes cube of a number.

    square(x)
        Computes square of a number.

Under the hood, python stores the documentation as a special field called doc.

>>> import os
>>> print(os.getcwd.__doc__)
getcwd() -> path

Return a string representing the current working directory.

3.1. Standard Library
Python comes with many standard library modules. Lets look at some of the most commonly used ones.

3.1.1. os module
The os and os.path modules provides functionality to work with files, directories etc.

Problem 1: Write a program to list all files in the given directory.

Problem 2: Write a program extcount.py to count number of files for each extension in the given directory. The program should take a directory name as argument and print count and extension for each available file extension.

$ python extcount.py src/
14 py
4 txt
1 csv

Problem 3: Write a program to list all the files in the given directory along with their length and last modification time. The output should contain one line for each file containing filename, length and modification date separated by tabs.

Hint: see help for os.stat.

Problem 4: Write a program to print directory tree. The program should take path of a directory as argument and print all the files in it recursively as a tree.

$ python dirtree.py foo
foo
|-- a.txt
|-- b.txt
|-- code
|   |-- a.py
|   |-- b.py
|   |-- docs
|   |   |-- a.txt
|   |   \-- b.txt
|   \-- x.py
\-- z.txt

3.1.2. urllib module
The urllib module provides functionality to download webpages.

>>> from urllib.request import urlopen
>>> response = urlopen("http://python.org/")
>>> print(response.headers)
Date: Fri, 30 Mar 2012 09:24:55 GMT
Server: Apache/2.2.16 (Debian)
Last-Modified: Fri, 30 Mar 2012 08:42:25 GMT
ETag: "105800d-4b7b-4bc71d1db9e40"
Accept-Ranges: bytes
Content-Length: 19323
Connection: close
Content-Type: text/html
X-Pad: avoid browser bug

>>> response.header['Content-Type']
'text/html'

>>> content = request.read()

Problem 5: Write a program wget.py to download a given URL. The program should accept a URL as argument, download it and save it with the basename of the URL. If the URL ends with a /, consider the basename as index.html.

$ python wget.py http://docs.python.org/tutorial/interpreter.html
saving http://docs.python.org/tutorial/interpreter.html as interpreter.html.

$ python wget.py http://docs.python.org/tutorial/
saving http://docs.python.org/tutorial/ as index.html.

3.1.3. re module
Problem 6: Write a program antihtml.py that takes a URL as argument, downloads the html from web and print it after stripping html tags.

$ python antihtml.py index.html
...
The Python interpreter is usually installed as /usr/local/bin/python on
those machines where it is available; putting /usr/local/bin in your
...

Problem 7: Write a function make_slug that takes a name converts it into a slug. A slug is a string where spaces and special characters are replaced by a hyphen, typically used to create blog post URL from post title. It should also make sure there are no more than one hyphen in any place and there are no hyphens at the beginning and end of the slug.

>>> make_slug("hello world")
'hello-world'
>>> make_slug("hello  world!")
'hello-world'
>>> make_slug(" --hello-  world--")
'hello-world'

Problem 8: Write a program links.py that takes URL of a webpage as argument and prints all the URLs linked from that webpage.

Problem 9: Write a regular expression to validate a phone number.

3.1.4. json module
Problem 10: Write a program myip.py to print the external IP address of the machine. Use the response from http://httpbin.org/ get and read the IP address from there. The program should print only the IP address and nothing else.

3.1.5. zipfile module
The zipfile module provides interface to read and write zip files.

Here are some examples to demonstate the power of zipfile module.

The following example prints names of all the files in a zip archive.

import zipfile
z = zipfile.ZipFile("a.zip")
for name in z.namelist():
    print(name)

The following example prints each file in the zip archive.

import zipfile
z = zipfile.ZipFile("a.zip")
for name in z.namelist():
    print()
    print("FILE:", name)
    print()
    print(z.read(name))

Problem 11: Write a python program zip.py to create a zip file. The program should take name of zip file as first argument and files to add as rest of the arguments.

$ python zip.py foo.zip file1.txt file2.txt

Problem 12: Write a program mydoc.py to implement the functionality of pydoc. The program should take the module name as argument and print documentation for the module and each of the functions defined in that module.

$ python mydoc.py os
Help on module os:

DESCRIPTION

os - OS routines for Mac, NT, or Posix depending on what system we're on.
...

FUNCTIONS

getcwd()
    ...

Hints:
-The dir function to get all entries of a module

-The inspect.isfunction function can be used to test if given object is a function

-x.doc gives the docstring for x.

-The import function can be used to import a module by name

3.2. Installing third-party modules
PyPI, The Python Package Index maintains the list of Python packages available. The third-party module developers usually register at PyPI and uploads their packages there.

The standard way to installing a python module is using pip or easy_install. Pip is more modern and preferred.

Lets start with installing easy_install.

Download the easy_install install script ez_setup.py.

Run it using Python.

That will install easy_install, the script used to install third-party python packages.

Before installing new packages, lets understand how to manage virtual environments for installing python packages.

Earlier the only way of installing python packages was system wide. When used this way, packages installed for one project can conflict with other and create trouble. So people invented a way to create isolated Python environment to install packages. This tool is called virtualenv.

To install virtualenv:

$ easy_install virtualenv

Installing virtualenv also installs the pip command, a better replace for easy_install.

Once it is installed, create a new virtual env by running the virtualenv command.

$ virtualenv testenv

Now to switch to that env.

On UNIX/Mac OS X:

$ source testenv/bin/activate

On Windows:

> testenv\Scripts\activate

Now the virtualenv testenv is activated.

Now all the packages installed will be limited to this virtualenv. Lets try to install a third-party package.

$ pip install tablib

This installs a third-party library called tablib.

The tablib library is a small little library to work with tabular data and write csv and Excel files.

Here is a simple example.

# create a dataset
data = tablib.Dataset()

# Add rows
data.append(["A", 1])
data.append(["B", 2])
data.append(["C", 3])

# save as csv
with open('test.csv', 'wb') as f:
    f.write(data.csv)

# save as Excel
with open('test.xls', 'wb') as f:
    f.write(data.xls)

# save as Excel 07+
with open('test.xlsx', 'wb') as f:
    f.write(data.xlsx)

It is even possible to create multi-sheet excel files.

sheet1 = tablib.Dataset()
sheet1.append(["A1", 1])
sheet1.append(["A2", 2])

sheet2 = tablib.Dataset()
sheet2.append(["B1", 1])
sheet2.append(["B2", 2])


book = tablib.Databook([data1, data2])
with open('book.xlsx', 'wb') as f:
    f.write(book.xlsx)

Problem 13: Write a program csv2xls.py that reads a csv file and exports it as Excel file. The program should take two arguments. The name of the csv file to read as first argument and the name of the Excel file to write as the second argument.

Problem 14: Create a new virtualenv and install BeautifulSoup. BeautifulSoup is very good library for parsing HTML. Try using it to extract all HTML links from a webpage.

Read the BeautifulSoup documentation to get started.

Working with Data

Esther Wavinya — Wed, 06 May 2020 13:08:53 +0000

2. Working with Data
2.1. Lists
We’ve already seen quick introduction to lists in the previous article.

>>> [1, 2, 3, 4]
[1, 2, 3, 4]
>>> ["hello", "world"]
["hello", "world"]
>>> [0, 1.5, "hello"]
[0, 1.5, "hello"]
>>> [0, 1.5, "hello"]
[0, 1.5, "hello"]

A List can contain another list as member.

>>> a = [1, 2]
>>> b = [1.5, 2, a]
>>> b
[1.5, 2, [1, 2]]

The built-in function range can be used to create a sequence of consecutive integers.

The range function returns a special range object that behaves like a list. To get a real list from it, you can use the list function.

>>> x = range(1, 4)
>>> x
range(1, 4)
>>> x[0]
1
>>> len(x)
3

The built-in function len can be used to find the length of a list.

>>> a = [1, 2, 3, 4]
>>> len(a)
4

The + and * operators work even on lists.

>>> a = [1, 2, 3]
>>> b = [4, 5]
>>> a + b
[1, 2, 3, 4, 5]
>>> b * 3
[4, 5, 4, 5, 4, 5]

List can be indexed to get individual entries. Value of index can go from 0 to (length of list - 1).

>>> x = [1, 2]
>>> x[0]
1
>>> x[1]
2

When a wrong index is used, python gives an error.

>>> x = [1, 2, 3, 4]
>>> x[6]
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
IndexError: list index out of range
Negative indices can be used to index the list from right.

>>> x = [1, 2, 3, 4]
>>> x[-1]
4
>>> x [-2]
3

We can use list slicing to get part of a list.

>>> x = [1, 2, 3, 4]
>>> x[0:2]
[1, 2]
>>> x[1:4]
[2, 3, 4]

Even negative indices can be used in slicing. For example, the following examples strips the last element from the list.

>>> x[0:-1]
[1, 2, 3]

Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the list being sliced.

>>> x = [1, 2, 3, 4]
>>> a[:2]
[1, 2]
>>> a[2:]
[3, 4]
>>> a[:]
[1, 2, 3, 4]

An optional third index can be used to specify the increment, which defaults to 1.

>>> x = range(10)
>>> x
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> x[0:6:2]
[0, 2, 4]

We can reverse a list, just by providing -1 for increment.

>>> x[::-1]
[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

List members can be modified by assignment.

>>> x = [1, 2, 3, 4]
>>> x[1] = 5
>>> x
[1, 5, 3, 4]

Presence of a key in a list can be tested using in operator.

>>> x = [1, 2, 3, 4]
>>> 2 in x
True
>>> 10 in x
False

Values can be appended to a list by calling append method on list. A method is just like a function, but it is associated with an object and can access that object when it is called. We will learn more about methods when we study classes.

>>> a = [1, 2]
>>> a.append(3)
>>> a
[1, 2, 3]

Problem 1: What will be the output of the following program?

x = [0, 1, [2]]
x[2][0] = 3
print(x)
x[2].append(4)
print(x)
x[2] = 2
print(x)

2.1.1. The for Statement
Python provides for statement to iterate over a list. A for statement executes the specified block of code for every element in a list.

for x in [1, 2, 3, 4]:
    print(x)

for i  in range(10):
   print(i, i*i, i*i*i)

The built-in function zip takes two lists and returns list of pairs.

>>> zip(["a", "b", "c"], [1, 2, 3])
[('a', 1), ('b', 2), ('c', 3)]

It is handy when we want to iterate over two lists together.

names = ["a", "b", "c"]
values = [1, 2, 3]
for name, value in zip(names, values):
    print(name, value)

Problem 2: Python has a built-in function sum to find sum of all elements of a list. Provide an implementation for sum.

>>> sum([1, 2, 3])
>>> 6

Problem 3: What happens when the above sum function is called with a list of strings? Can you make your sum function work for a list of strings as well.

>>> sum(["hello", "world"])
"helloworld"
>>> sum(["aa", "bb", "cc"])
"aabbcc"

Problem 4: Implement a function product, to compute product of a list of numbers.

>>> product([1, 2, 3])
6

Problem 5: Write a function factorial to compute factorial of a number. Can you use the product function defined in the previous example to compute factorial?

>>> factorial(4)
24

Problem 6: Write a function reverse to reverse a list. Can you do this without using list slicing?

>>> reverse([1, 2, 3, 4])
[4, 3, 2, 1]
>>> reverse(reverse([1, 2, 3, 4]))
[1, 2, 3, 4]

Problem 7: Python has built-in functions min and max to compute minimum and maximum of a given list. Provide an implementation for these functions. What happens when you call your min and max functions with a list of strings?

Problem 8: Cumulative sum of a list [a, b, c, ...] is defined as [a, a+b, a+b+c, ...]. Write a function cumulative_sum to compute cumulative sum of a list. Does your implementation work for a list of strings?

>>> cumulative_sum([1, 2, 3, 4])
[1, 3, 6, 10]
>>> cumulative_sum([4, 3, 2, 1])
[4, 7, 9, 10]

Problem 9: Write a function cumulative_product to compute cumulative product of a list of numbers.

>>> cumulative_product([1, 2, 3, 4])
[1, 2, 6, 24]
>>> cumulative_product([4, 3, 2, 1])
[4, 12, 24, 24]

Problem 10: Write a function unique to find all the unique elements of a list.

>>> unique([1, 2, 1, 3, 2, 5])
[1, 2, 3, 5]

Problem 11: Write a function dups to find all duplicates in the list.

>>> dups([1, 2, 1, 3, 2, 5])
[1, 2]

Problem 12: Write a function group(list, size) that take a list and splits into smaller lists of given size.

>>> group([1, 2, 3, 4, 5, 6, 7, 8, 9], 3)
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> group([1, 2, 3, 4, 5, 6, 7, 8, 9], 4)
[[1, 2, 3, 4], [5, 6, 7, 8], [9]]

2.1.2. Sorting Lists
The sort method sorts a list in place.

>>> a = [2, 10, 4, 3, 7]
>>> a.sort()
>>> a
[2, 3, 4, 7 10]

The built-in function sorted returns a new sorted list without modifying the source list.

>>> a = [4, 3, 5, 9, 2]
>>> sorted(a)
[2, 3, 4, 5, 9]
>>> a
[4, 3, 5, 9, 2]

The behavior of sort method and sorted function is exactly same except that sorted returns a new list instead of modifying the given list.

The sort method works even when the list has different types of objects and even lists.

>>> a = ["hello", 1, "world", 45, 2]
>>> a.sort()
>>> a
[1, 2, 45, 'hello', 'world']
>>> a = [[2, 3], [1, 6]]
>>> a.sort()
>>> a
[[1, 6], [2, 3]]

We can optionally specify a function as sort key.

>>> a = [[2, 3], [4, 6], [6, 1]]
>>> a.sort(key=lambda x: x[1])
>>> a
[[6, 1], [2, 3],  [4 6]]

This sorts all the elements of the list based on the value of second element of each entry.

Problem 13: Write a function lensort to sort a list of strings based on length.

>>> lensort(['python', 'perl', 'java', 'c', 'haskell', 'ruby'])
['c', 'perl', 'java', 'ruby', 'python', 'haskell']

Problem 14: Improve the unique function written in previous problems to take an optional key function as argument and use the return value of the key function to check for uniqueness.

>>> unique(["python", "java", "Python", "Java"], key=lambda s: s.lower())
["python", "java"]

2.2. Tuples
Tuple is a sequence type just like list, but it is immutable. A tuple consists of a number of values separated by commas.

>>> a = (1, 2, 3)
>>> a[0]
1

The enclosing braces are optional.

>>> a = 1, 2, 3
>>> a[0]
1

The built-in function len and slicing works on tuples too.

>>> len(a)
3
>>> a[1:]
2, 3

Since parenthesis are also used for grouping, tuples with a single value are represented with an additional comma.

>>> a = (1)
>> a
1
>>> b = (1,)
>>> b
(1,)
>>> b[0]
1

2.3. Sets
Sets are unordered collection of unique elements.

>>> x = set([3, 1, 2, 1])
set([1, 2, 3])

Python 2.7 introduced a new way of writing sets.

>>> x = {3, 1, 2, 1}
set([1, 2, 3])

New elements can be added to a set using the add method.

>>> x = set([1, 2, 3])
>>> x.add(4)
>>> x
set([1, 2, 3, 4])
```


Just like lists, the existence of an element can be checked using the `in` operator. However, this operation is faster in sets compared to lists.


```
>>> x = set([1, 2, 3])
>>> 1 in x
True
>>> 5 in x
False
```


**Problem 15:** Reimplement the `unique` function implemented in the earlier examples using sets.

**2.4. Strings**
Strings also behave like lists in many ways. Length of a string can be found using built-in function `len`.


```
>>> len("abrakadabra")
11
```


Indexing and slicing on strings behave similar to that of lists.


```
>>> a = "helloworld"
>>> a[1]
'e'
>>> a[-2]
'l'
>>> a[1:5]
"ello"
>>> a[:5]
"hello"
>>> a[5:]
"world"
>>> a[-2:]
'ld'
>>> a[:-2]
'hellowor'
>>> a[::-1]
'dlrowolleh'
```


The `in` operator can be used to check if a string is present in another string.


```
>>> 'hell' in 'hello'
True
>>> 'full' in 'hello'
False
>>> 'el' in 'hello'
True
```


There are many useful methods on strings.

The `split` method splits a string using a delimiter. If no delimiter is specified, it uses any whitespace char as delimiter.


```
>>> "hello world".split()
['hello', 'world']
>>> "a,b,c".split(',')
['a', 'b', 'c']
```


The `join` method joins a list of strings.


```
>>> " ".join(['hello', 'world'])
'hello world'
>>> ','.join(['a', 'b', 'c'])
```


The `strip` method returns a copy of the given string with leading and trailing whitespace removed. Optionally a string can be passed as argument to remove characters from that string instead of whitespace.


```
>>> ' hello world\n'.strip()
'hello world'
>>> 'abcdefgh'.strip('abdh')
'cdefg'
```


Python supports formatting values into strings. Although this can include very complicated expressions, the most basic usage is to insert values into a string with the %s placeholder.


```
>>> a = 'hello'
>>> b = 'python'
>>> "%s %s" % (a, b)
'hello python'
>>> 'Chapter %d: %s' % (2, 'Data Structures')
```


**2.5. Working With Files**
Python provides a built-in function `open` to open a file, which returns a file object.


```
f = open('foo.txt', 'r') # open a file in read mode
f = open('foo.txt', 'w') # open a file in write mode
f = open('foo.txt', 'a') # open a file in append mode
```


The second argument to `open` is optional, which defaults to `'r'` when not specified.

Unix does not distinguish binary files from text files but windows does. On windows `'rb'`, `'wb'`, `'ab'` should be used to open a binary file in read, write and append mode respectively.

Easiest way to read contents of a file is by using the `read` method.


```
>>> open('foo.txt').read()
'first line\nsecond line\nlast line\n'
```


Contents of a file can be read line-wise using `readline` and `readlines` methods. The `readline` method returns empty string when there is nothing more to read in a file.


```
>>> open('foo.txt').readlines()
['first line\n', 'second line\n', 'last line\n']
>>> f = open('foo.txt')
>>> f.readline()
'first line\n'
>>> f.readline()
'second line\n'
>>> f.readline()
'last line\n'
>>> f.readline()
''
```


The `write` method is used to write data to a file opened in write or append mode.


```
>>> f = open('foo.txt', 'w')
>>> f.write('a\nb\nc')
>>> f.close()

>>> f.open('foo.txt', 'a')
>>> f.write('d\n')
>>> f.close()
```


The `writelines` method is convenient to use when the data is available as a list of lines.


```
>>> f = open('foo.txt')
>>> f.writelines(['a\n', 'b\n', 'c\n'])
>>> f.close()
```


**2.5.1. Example: Word Count**
Lets try to compute the number of characters, words and lines in a file.

Number of characters in a file is same as the length of its contents.


```
def charcount(filename):
    return len(open(filename).read())
```


Number of words in a file can be found by splitting the contents of the file.


```
def wordcount(filename):
    return len(open(filename).read().split())
```


Number of lines in a file can be found from readlines method.


```
def linecount(filename):
    return len(open(filename).readlines())
```


**Problem 17:** Write a program `reverse.py` to print lines of a file in reverse order.


```
$ cat she.txt
She sells seashells on the seashore;
The shells that she sells are seashells I'm sure.
So if she sells seashells on the seashore,
I'm sure that the shells are seashore shells.

$ python reverse.py she.txt
I'm sure that the shells are seashore shells.
So if she sells seashells on the seashore,
The shells that she sells are seashells I'm sure.
She sells seashells on the seashore;
```


**Problem 18:** Write a program to print each line of a file in reverse order.

**Problem 19:** Implement unix commands `head` and `tail`. The `head` and `tail` commands take a file as argument and prints its first and last 10 lines of the file respectively.

**Problem 20:** Implement unix command `grep`. The `grep` command takes a string and a file as arguments and prints all lines in the file which contain the specified string.


```
$ python grep.py she.txt sure
The shells that she sells are seashells I'm sure.
I'm sure that the shells are seashore shells.
```


**Problem 21:** Write a program `wrap.py` that takes filename and width as aruguments and wraps the lines longer than `width`.


```
$ python wrap.py she.txt 30
I'm sure that the shells are s
eashore shells.
So if she sells seashells on t
he seashore,
The shells that she sells are
seashells I'm sure.
She sells seashells on the sea
shore;
```


**Problem 22:** The above wrap program is not so nice because it is breaking the line at middle of any word. Can you write a new program `wordwrap.py` that works like `wrap.py`, but breaks the line only at the word boundaries?


```
$ python wordwrap.py she.txt 30
I'm sure that the shells are
seashore shells.
So if she sells seashells on
the seashore,
The shells that she sells are
seashells I'm sure.
She sells seashells on the
seashore;
```


**Problem 23:** Write a program `center_align.py` to center align all lines in the given file.


```
$ python center_align.py she.txt
  I'm sure that the shells are seashore shells.
    So if she sells seashells on the seashore,
The shells that she sells are seashells I'm sure.
       She sells seashells on the seashore;
```


**2.6. List Comprehensions**
List Comprehensions provide a concise way of creating lists. Many times a complex task can be modelled in a single line.

Here are some simple examples for transforming a list.


```
>>> a = range(10)
>>> a
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> [x for x in a]
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> [x*x for x in a]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> [x+1 for x in a]
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
```


It is also possible to filter a list using `if` inside a list comprehension.


```
>>> a = range(10)
>>> [x for x in a if x % 2 == 0]
[0, 2, 4, 6, 8]
>>> [x*x for x in a if x%2 == 0]
[0, 4, 8, 36, 64]
```


It is possible to iterate over multiple lists using the built-in function `zip`.


```
>>> a = [1, 2, 3, 4]
>>> b = [2, 3, 5, 7]
>>> zip(a, b)
[(1, 2), (2, 3), (3, 5), (4, 7)]
>>> [x+y for x, y in zip(a, b)]
[3, 5, 8, 11]
```


we can use multiple `for` clauses in single list comprehension.


```
>>> [(x, y) for x in range(5) for y in range(5) if (x+y)%2 == 0]
[(0, 0), (0, 2), (0, 4), (1, 1), (1, 3), (2, 0), (2, 2), (2, 4), (3, 1), (3, 3), (4, 0), (4, 2), (4, 4)]

>>> [(x, y) for x in range(5) for y in range(5) if (x+y)%2 == 0 and x != y]
[(0, 2), (0, 4), (1, 3), (2, 0), (2, 4), (3, 1), (4, 0), (4, 2)]

>>> [(x, y) for x in range(5) for y in range(x) if (x+y)%2 == 0]
[(2, 0), (3, 1), (4, 0), (4, 2)]
```


The following example finds all Pythagorean triplets using numbers below 25. `(x, y, z)` is a called pythagorean triplet if `x*x + y*y == z*z`.


```
>>> n = 25
>>> [(x, y, z) for x in range(1, n) for y in range(x, n) for z in range(y, n) if x*x + y*y == z*z]
[(3, 4, 5), (5, 12, 13), (6, 8, 10), (8, 15, 17), (9, 12, 15), (12, 16, 20)]
```


**Problem 24:** Provide an implementation for zip function using list comprehensions.


```
>>> zip([1, 2, 3], ["a", "b", "c"])
[(1, "a"), (2, "b"), (3, "c")]
```


**Problem 25:** Python provides a built-in function `map` that applies a function to each element of a list. Provide an implementation for map using list comprehensions.


```
>>> def square(x): return x * x
...
>>> map(square, range(5))
[0, 1, 4, 9, 16]
```


**Problem 26:** Python provides a built-in function `filter(f, a)` that returns items of the list a for which `f(item)` returns true. Provide an implementation for `filter` using list comprehensions.


```
>>> def even(x): return x %2 == 0
...
>>> filter(even, range(10))
[0, 2, 4, 6, 8]
```


**Problem 27:** Write a function `triplets` that takes a number `n` as argument and returns a list of triplets such that sum of first two elements of the triplet equals the third element using numbers below n. Please note that `(a, b, c)` and `(b, a, c)` represent same triplet.


```
>>> triplets(5)
[(1, 1, 2), (1, 2, 3), (1, 3, 4), (2, 2, 4)]
```


**Problem 28:** Write a function `enumerate` that takes a list and returns a list of tuples containing `(index,item)` for each item in the list.


```
>>> enumerate(["a", "b", "c"])
[(0, "a"), (1, "b"), (2, "c")]
>>> for index, value in enumerate(["a", "b", "c"]):
...     print(index, value)
0 a
1 b
2 c
```


**Problem 29:** Write a function `array` to create an 2-dimensional array. The function should take both dimensions as arguments. Value of each element can be initialized to None:


```
>>> a = array(2, 3)
>>> a
[[None, None, None], [None, None, None]]
>>> a[0][0] = 5
[[5, None, None], [None, None, None]]
```


**Problem 30:** Write a python function `parse_csv` to parse csv (comma separated values) files.


```
>>> print(open('a.csv').read())
a,b,c
1,2,3
2,3,4
3,4,5
>>> parse_csv('a.csv')
[['a', 'b', 'c'], ['1', '2', '3'], ['2', '3', '4'], ['3', '4', '5']]
```


**Problem 31:** Generalize the above implementation of csv parser to support any delimiter and comments.


```
>>> print(open('a.txt').read())
# elements are separated by ! and comment indicator is #
a!b!c
1!2!3
2!3!4
3!4!5
>>> parse('a.txt', '!', '#')
[['a', 'b', 'c'], ['1', '2', '3'], ['2', '3', '4'], ['3', '4', '5']]
```


**Problem 32:** Write a function `mutate` to compute all words generated by a single mutation on a given word. A mutation is defined as inserting a character, deleting a character, replacing a character, or swapping 2 consecutive characters in a string. For simplicity consider only letters from `a` to `z`.


```
>>> words = mutate('hello')
>>> 'helo' in words
True
>>> 'cello' in words
True
>>> 'helol' in words
True
```


**Problem 33:** Write a function `nearly_equal` to test whether two strings are nearly equal. Two strings `a` and `b` are nearly equal when `a` can be generated by a single mutation on `b`.


```
>>> nearly_equal('python', 'perl')
False
>>> nearly_equal('perl', 'pearl')
True
>>> nearly_equal('python', 'jython')
True
>>> nearly_equal('man', 'woman')
False
```


**2.7. Dictionaries**
Dictionaries are like lists, but they can be indexed with non integer keys also. Unlike lists, dictionaries are not ordered.


```
>>> a = {'x': 1, 'y': 2, 'z': 3}
>>> a['x']
1
>>> a['z']
3
>>> b = {}
>>> b['x'] = 2
>>> b[2] = 'foo'
>>> b[(1, 2)] = 3
>>> b
{(1, 2): 3, 'x': 2, 2: 'foo'}
```


The `del` keyword can be used to delete an item from a dictionary.


```
>>> a = {'x': 1, 'y': 2, 'z': 3}
>>> del a['x']
>>> a
{'y': 2, 'z': 3}
```


The `keys` method returns all keys in a dictionary, the `values` method returns all values in a dictionary and `items` method returns all key-value pairs in a dictionary.


```
>>> a.keys()
['x', 'y', 'z']
>>> a.values()
[1, 2, 3]
>>> a.items()
[('x', 1), ('y', 2), ('z', 3)]
```


The `for` statement can be used to iterate over a dictionary.


```
>>> for key in a: print(key)
...
x
y
z
>>> for key, value in a.items(): print(key, value)
...
x 1
y 2
z 3
```


Presence of a key in a dictionary can be tested using `in` operator or `has_key` method.


```
>>> 'x' in a
True
>>> 'p' in a
False
>>> a.has_key('x')
True
>>> a.has_key('p')
False
```


Other useful methods on dictionaries are `get` and `setdefault`.


```
>>> d = {'x': 1, 'y': 2, 'z': 3}
>>> d.get('x', 5)
1
>>> d.get('p', 5)
5
>>> d.setdefault('x', 0)
1
>>> d
{'x': 1, 'y': 2, 'z': 3}
>>> d.setdefault('p', 0)
0
>>> d
{'y': 2, 'x': 1, 'z': 3, 'p': 0}
```


Dictionaries can be used in string formatting to specify named parameters.


```
>>> 'hello %(name)s' % {'name': 'python'}
'hello python'
>>> 'Chapter %(index)d: %(name)s' % {'index': 2, 'name': 'Data Structures'}
'Chapter 2: Data Structures'
```


**2.7.1. Example: Word Frequency**
Suppose we want to find number of occurrences of each word in a file. Dictionary can be used to store the number of occurrences for each word.

Lets first write a function to count frequency of words, given a list of words.


```
def word_frequency(words):
    """Returns frequency of each word given a list of words.

        >>> word_frequency(['a', 'b', 'a'])
        {'a': 2, 'b': 1}
    """
    frequency = {}
    for w in words:
        frequency[w] = frequency.get(w, 0) + 1
    return frequency
```


Getting words from a file is very trivial.


```
def read_words(filename):
    return open(filename).read().split()
```


We can combine these two functions to find frequency of all words in a file.


```
def main(filename):
    frequency = word_frequency(read_words(filename))
    for word, count in frequency.items():
        print(word, count)

if __name__ == "__main__":
    import sys
    main(sys.argv[1])
```


**Problem 34:** Improve the above program to print the words in the descending order of the number of occurrences.

**Problem 35:** Write a program to count frequency of characters in a given file. Can you use character frequency to tell whether the given file is a Python program file, C program file or a text file?

**Problem 36:** Write a program to find anagrams in a given list of words. Two words are called anagrams if one word can be formed by rearranging letters of another. For example 'eat', 'ate' and 'tea' are anagrams.


```
>>> anagrams(['eat', 'ate', 'done', 'tea', 'soup', 'node'])
[['eat', 'ate', 'tea], ['done', 'node'], ['soup']]
```


**Problem 37:** Write a function `valuesort` to sort values of a dictionary based on the key.


```
>>> valuesort({'x': 1, 'y': 2, 'a': 3})
[3, 1, 2]
```


**Problem 38:** Write a function `invertdict` to interchange keys and values in a dictionary. For simplicity, assume that all values are unique.


```
>>> invertdict({'x': 1, 'y': 2, 'z': 3})
{1: 'x', 2: 'y', 3: 'z'}
```


**2.7.2. Understanding Python Execution Environment**
Python stores the variables we use as a dictionary. The `globals()` function returns all the globals variables in the current environment.


```
>>> globals()
{'__builtins__': <module '__builtin__' (built-in)>, '__name__': '__main__', '__doc__': None}
>>> x = 1
>>> globals()
{'__builtins__': <module '__builtin__' (built-in)>, '__name__': '__main__', '__doc__': None, 'x': 1}
>>> x = 2
>>> globals()
{'__builtins__': <module '__builtin__' (built-in)>, '__name__': '__main__', '__doc__': None, 'x': 2}
>>> globals()['x'] = 3
>>> x
3
```


Just like `globals` python also provides a function `locals` which gives all the local variables in a function.


```
>>> def f(a, b): print(locals())
...
>>> f(1, 2)
{'a': 1, 'b': 2}
```


One more example:


```
>>> def f(name):
...     return "Hello %(name)s!" % locals()
...
>>> f("Guido")
Hello Guido!
```

Getting started with Python

Esther Wavinya — Tue, 28 Apr 2020 08:10:19 +0000

As a beginner in code I am going to help my fellow beginners in studying python. I am writing what I have been learning personally according to Python Practice Book by Anand Chitipothu. It is going to be kind of a review. According to my previous article in my medium account I mentioned several Python libraries, frameworks and micro-frameworks. That depends on exactly what you are doing with this programming language because it is too wide.

For instance: Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. If you haven't read the article here is the link What I should have known about python

Now let's get back to business.

1.0 Getting Started
1.1. Running Python Interpreter
Python comes with an interactive interpreter. When you type python in your shell or command prompt, the python interpreter becomes active with a >>> prompt and waits for your commands.


$ python
Python 3.7.4 (v3.7.4:e09359112e, Jul  8 2019, 14:54:52)
[Clang 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>

Now you can type any valid python expression at the prompt. python reads the typed expression, evaluates it and prints the result.

>>> 42
42
>>> 4 + 2
6

Problem 1: Open a new Python interpreter and use it to find the value of 2 + 3.

1.2. Running Python Scripts
Open your text editor, type the following text and save it as hello.py.

print("hello, world!")

And run this program by calling python hello.py. Make sure you change to the directory where you saved the file before doing it.

$ python hello.py
hello, world!

1.3. Datatypes
Python has support for all basic datatypes and also have very powerful compound datatypes.

Python has integers.

>>> 1 + 2
3

Python is pretty good at handling very large numbers as well. For example, let us try to compute 2 raises to the power of 1000.

>>> 2 ** 1000
10715086071862673209484250490600018105614048117055336074437503883703510511249361224931983788156958581275946729175531468251871452856923140435984577574698574803934567774824230985421074605062371141877954182153046474983581941267398767559165543946077062914571196477686542167660429831652624386837205668069376

That is a pretty big number, isn’t it? Can you count how many digits it has?

Python has floating point numbers.

>>> 1.2 + 2.3
3.5

Python has strings.

>>> "hello world"
'hello world'
>>> print("hello world")
hello world

String can be enclosed either in single quotes or double quotes. Both are exactly the same. In Python, strings are very versatile and it very easy to work with them.

>>> 'hello' + 'world'
'helloworld'

>>> "hello" * 3
'hellohellohello'

>>> print("=" * 40)
========================================

The built-in function len is used to find the length of a string.

>>> len('helloworld')
10

Python supports multi-line strings too. They are enclosed in three double quotes or three single quotes.

text = """This is a multi-line string.
Line 2
Line 3
and the text may have "quotes" too.
"""

>>> print(text)
This is a multi-line string.
Line 2
Line 3
and the text may have "quotes" too.

Python supports the usual escape codes. \n indicates new line, \t indicates a tab etc.

>>> print "a\nb\nc"
a
b
c

Python has lists. Lists are one of the most useful data types of Python.

>>> x = ["a", "b", "c"]
>>> x
['a', 'b', 'c']
>>> len(x)
3
>>> x[1]
'b'

Python has another datatype called tuple for representing fixed width records. Tuples behave just like lists, but they are immutable.

>>> point = (2, 3)
>>> point
(2, 3)

When writing tuples, the parenthesis can be omitted most of the times.

>>> point = 2, 3
>>> point
(2, 3)

It is also possible to assign a tuple multiple values at once:

>>> yellow = (255, 255, 0)
>>> r, g, b = yellow
>>> print(r, g, b)
255 255 0

Python has a dictionary datatype for representing name-value pairs.

>>> person = {"name": "Alice", "email": "alice@example.com"}
>>> person['name']
'Alice'
>>> person['email']
'alice@example.com'

Python has a set datatype too. A set is an unordered collection of elements.

>>> x = {1, 2, 3, 2, 1}
>>> x
{1, 2, 3}

Python has a boolean type. It has two special values True and False to represent truth and false.

Finally, Python has a special type called None to represent nothing.

>>> x = None
>>> print(x)
None

Now you know most of the common data structures of Python. While they look very simple, mastering them takes a bit of practice. Make sure you go through all the examples and the practice problems in the subsequent sections.

1.4. Variables
You’ve already seen variables in the previous section. Let us look at them closely now.

In Python, variables don’t have a type. They are just placeholders which can hold any type of values.

>>> x = 5
>>> x
5
>>> x = 'hello'
>>> x
'hello'

It is important to notice the difference between variables and strings. Often new programmers get tricked by this. Can you spot any error in the following example?

name = “Alice” print(“name”)

1.5. Functions
Python has many built-in functions. The print is the most commonly used built-in function.

>>> print('hello')
hello
>>> print('hello', 1, 2, 3)
hello 1 2 3

We’ve also see the len function in the previous sections. The len function computes the length of a string, list or other collections.

>>> len("hello")
5
>>> len(['a', 'b', 'c'])
3

One important thing about Python is that it doesn’t allow operations on incompatible data types. For instance:

>>> 5 + "2"
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'

That is because it is not possible to add a number to a string. We need to either convert 5 into a string or "2" into a number. The built-in function int converts a string into a number and the str function converts any value into a string.


>>> int("5")
5
>>> str(5)
'5'
>>> 5 + int("2")
7
>>> str(5) + "2"
'52'

1.5.1. Example: Counting the number of digits in a number
Let us write a program to compute number of digits in a number. Let us look at some numbers first.


>>> 12345
12345
>>> 2 ** 100
1267650600228229401496703205376
>>> 2 ** 1000
10715086071862673209484250490600018105614048117055336074437503883703510511249361224931983788156958581275946729175531468251871452856923140435984577574698574803934567774824230985421074605062371141877954182153046474983581941267398767559165543946077062914571196477686542167660429831652624386837205668069376

We can combile the previously mentioned built-in functions to solve this.


>>> len(str(12345))
5
>>> len(str(2 ** 100))
31
>>> len(str(2 * 1000))
302

1.6. Writing Custom Functions
Just like a value can be associated with a name, a piece of logic can also be associated with a name by defining a function.


>>> def square(x):
...    return x * x
...
>>> square(5)
25

The body of the function is indented. Indentation is the Python’s way of grouping statements.

The ... is the secondary prompt, which the Python interpreter uses to denote that it is expecting some more input.

The functions can be used in any expressions.


>>> square(2) + square(3)
13
>>> square(square(3))
81

Existing functions can be used in creating new functions.


>>> def sum_of_squares(x, y):
...    return square(x) + square(y)
...
>>> sum_of_squares(2, 3)
13

Functions are just like other values, they can assigned, passed as arguments to other functions etc.


>>> f = square
>>> f(4)
16
>>> def fxy(f, x, y):
...     return f(x) + f(y)
...
>>> fxy(square, 2, 3)
13

It is important to understand, the scope of the variables used in functions.

Lets look at an example.


x = 0
y = 0
def incr(x):
    y = x + 1
    return y
incr(5)
print x, y

Variables assigned in a function, including the arguments are called the local variables to the function. The variables defined in the top-level are called global variables.

Changing the values of x and y inside the function incr won’t effect the values of global x and y.

But, we can use the values of the global variables.


pi = 3.14
def area(r):
    return pi * r * r

When Python sees use of a variable not defined locally, it tries to find a global variable with that name.

However, you have to explicitly declare a variable as global to modify it.


numcalls = 0
def square(x):
    global numcalls
    numcalls = numcalls + 1
    return x * x

Problem 2: How many multiplications are performed when each of the following lines of code is executed?


print square(5)
print square(2*5)

Problem 3: What will be the output of the following program?


x = 1
def f():
    return x
print x
print f()

Problem 4: What will be the output of the following program?


x = 1
def f():
    x = 2
    return x
print x
print f()
print x

Problem 5: What will be the output of the following program?


x = 1
def f():
        y = x
        x = 2
        return x + y
print x
print f()
print x

Problem 6: What will be the output of the following program?


x = 2
def f(a):
    x = a * a
    return x
y = f(3)
print x, y

Functions can be called with keyword arguments.


>>> def difference(x, y):
...    return x - y
...
>>> difference(5, 2)
3
>>> difference(x=5, y=2)
3
>>> difference(5, y=2)
3
>>> difference(y=2, x=5)
3

And some arguments can have default values.


>>> def increment(x, amount=1):
...    return x + amount
...
>>> increment(10)
11
>>> increment(10, 5)
15
>>> increment(10, amount=2)
12

There is another way of creating functions, using the lambda operator.


>>> cube = lambda x: x ** 3
>>> fxy(cube, 2, 3)
35
>>> fxy(lambda x: x ** 3, 2, 3)
35

Notice that unlike function definition, lambda doesn’t need a return. The body of the lambda is a single expression.

The lambda operator becomes handy when writing small functions to be passed as arguments etc. We’ll see more of it as we get into solving more serious problems.

Python provides some useful built-in functions.


>>> min(2, 3)
2
>>> max(3, 4)
4

The built-in function len computes length of a string.


>>> len("helloworld")
10

The built-in function int converts string to integer and built-in function strconverts integers and other type of objects to strings.


>>> int("50")
50
>>> str(123)
"123"

Problem 7: Write a function count_digits to find number of digits in the given number.

>>> count_digits(5)
1
>>> count_digits(12345)
5

Methods are special kind of functions that work on an object.For example, upper is a method available on string objects.

>>> x = "hello"
>>> print x.upper()
HELLO

As already mentioned, methods are also functions. They can be assigned to other variables and can be called separately.

>>> f = x.upper
>>> f()
'HELLO'

Problem 8: Write a function istrcmp to compare two strings, ignoring the
case.

>>> istrcmp('python', 'Python')
True
>>> istrcmp('LaTeX', 'Latex')
True
>>> istrcmp('a', 'b')
False

1.7. Conditional Expressions
Python provides various operators for comparing values. The result of a comparison is a boolean value, either True or False.

>>> 2 < 3
True
>>> 2 > 3
False

Here is the list of available conditional operators.

-== equal to

-!= not equal to

-< less than

-> greater than

-<= less than or equal to

->= greater than or equal to

It is even possible to combine these operators.

>>> x = 5
>>> 2 < x < 10
True
>>> 2 < 3 < 4 < 5 < 6
True

The conditional operators work even on strings - the ordering being the lexical order.

>>> "python" > "perl"
True
>>> "python" > "java"
True

There are few logical operators to combine boolean values.

-a and b is True only if both a and b are True.

-a or b is True if either a or b is True.

-not a is True only if a is False.

>>> True and True
True
>>> True and False
False
>>> 2 < 3 and 5 < 4
False
>>> 2 < 3 or 5 < 4
True

Problem 9: What will be output of the following program?

print 2 < 3 and 3 > 1
print 2 < 3 or 3 > 1
print 2 < 3 or not 3 > 1
print 2 < 3 and not 3 > 1

Problem 10: What will be output of the following program?

x = 4
y = 5
p = x < y or x < z
print(p)

The if statement is used to execute a piece of code only when a boolean expression is true.

>>> x = 42
>>> if x % 2 == 0: print('even')
even
>>>

In this example, print('even') is executed only when x % 2 == 0 is True.
The code associated with if can be written as a separate indented block of code, which is often the case when there is more than one statement to be executed.

>>> if x % 2 == 0:
...     print('even')
...
even
>>>

The if statement can have optional else clause, which is executed when the boolean expression is False.

>>> x = 3
>>> if x % 2 == 0:
...     print('even')
... else:
...     print('odd')
...
odd
>>>

The if statement can have optional elif clauses when there are more conditions to be checked. The elif keyword is short for else if, and is useful to avoid excessive indentation.

>>> x = 42
>>> if x < 10:
...        print('one digit number')
... elif x < 100:
...     print('two digit number')
... else:
...     print('big number')
...
two digit number
>>>

Problem 11: What happens when the following code is executed? Will it give any error? Explain the reasons.

x = 2
if x == 2:
    print(x)
else:
    print(y)

Problem 12: What happens the following code is executed? Will it give any error? Explain the reasons.

x = 2
if x == 2:
    print(x)
else:
    x +

1.8. Lists
Lists are one of the great data structures in Python. We are going to learn a little bit about lists now. Basic knowledge of lists is requrired to be able to solve some problems that we want to solve in this chapter.

Here is a list of numbers.

>>> x = [1, 2, 3]

And here is a list of strings.

>>> x = ["hello", "world"]

List can be heterogeneous. Here is a list containing integers, strings and another list.


>>> x = [1, 2, "hello", "world", ["another", "list"]]

The built-in function len works for lists as well.


>>> x = [1, 2, 3]
>>> len(x)
3

The [] operator is used to access individual elements of a list.


>>> x = [1, 2, 3]
>>> x[1]
2
>>> x[1] = 4
>>> x[1]
4

The first element is indexed with 0, second with 1 and so on.
We’ll learn more about lists in my next article.

1.9. Modules
Modules are libraries in Python. Python ships with many standard library modules.
A module can be imported using the import statement.
Lets look at time module for example:


>>> import time
>>> time.asctime()
'Tue Sep 11 21:42:06 2012'

The asctime function from the time module returns the current time of the system as a string.
The sys module provides access to the list of arguments passed to the program, among the other things.
The sys.argv variable contains the list of arguments passed to the program. As a convention, the first element of that list is the name of the program.

Lets look at the following program echo.py that prints the first argument passed to it.


import sys
print(sys.argv[1])

Lets try running it.


$ python echo.py hello
hello
$ python echo.py hello world
hello

There are many more interesting modules in the standard library. We’ll learn more about them in the coming articles.

Problem 13: Write a program add.py that takes 2 numbers as command line arguments and prints its sum.


$ python add.py 3 5
8
$ python add.py 2 9
11