The Beginning
So we are going to look at our first code. As Python is an interpreted language , you can directly write the code into the Python interpreter or write in a file and then run the file. First we are going to do that using the interpreter, to start type python in the command prompt (shell or terminal). I am using the latest Python built from the source code, so the version number can be different.
Python 3.5.0a0 (default:d6ac4b6020b9+, Jun 9 2014, 12:15:05)
[GCC 4.8.2 20131212 (Red Hat 4.8.2-7)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
In our first code we are going to print “Hello World!” , so do it as below,
>>> print("Hello World!")
Hello World!
helloworld.py
Now as a serious programmer you may want to write the above code into a source file. We will create a helloworld.py. Use any text editor you like to create the file. I used vi, you can even use GUI based tools like Kate, gedit too.
#!/usr/bin/env python3
print("Hello World!")
To run the code first you have to make the file executable, in GNU/Linux you can do that by giving the command in a shell or terminal
Then
$ ./helloworld.py
Hello World!
On the first line you can #! , we call it sha-bang. Using this we are telling that use Python interpreter to run this code. In the next line we are printing a text message. In Python we call all the line of texts as strings.
Whitespaces and indentation
In Python whitespace is an important thing. We divide different identifiers using spaces.Whitespace in the beginning of the line is known as indentation, but if you give wrong indentation it will throw an error. Examples are given below:
>>> a = 12
>>> a = 12
File "<stdin>", line 1
a = 12
^
IndentationError: unexpected indent
Warning
There is an extra space in the beginning of the second line which is causing the error, so always look for the proper indentation.
You can even get into this indentation errors if you mix up tabs and spaces. Like if you use spaces and only use spaces for indentation, don’t use tabs in that case. For you it may look same, but the code will give you error if you try to run it.
So we can have few basic rules ready for spaces and indentation.
- Use 4 spaces for indentation.
- Never mix tab and spaces.
- One blank line between functions.
- Two blank lines between classes.
There are more places where you should be following same type of rules of whitespace, they are like
- Add a space after ”,” in dicts, lists, tuples, and argument lists and after ”:” in dicts.
- Spaces around assignments and comparisons (except in argument list)
- No spaces just inside parentheses.
Modules
Modules are Python files which contain different function definitions , variables which we can reuse, it should always end with a .py extension. Python itself is having a vast module library with the default installation. We are going to use some of them. To use a module you have to import it first.
>>> import math
>>> print(math.e)
2.71828182846
We are going to learn more about modules in the Modules chapter.
Variables and Datatypes
Every programming language is having own grammar rules just like the other languages we speak.
Keywords and Identifiers
The following identifiers are used as reserved words, or keywords of the language, and cannot be used as ordinary identifiers. They must be spelled exactly as written here:
False class finally is return
None continue for lambda try
True def from nonlocal while
and del global not with
as elif if or yield
assert else import pass
break except in raise
In Python we don’t specify what kind of data we are going to put in a variable. So you can directly write abc = 1 and abc will become an integer datatype. If you write abc = 1.0 abc will become of floating type. Here is a small program to add two given numbers
>>> a = 13
>>> b = 23
>>> a + b
36
From the above example you can understand that to declare a variable in Python , what you need is just to type the name and the value. Python can also manipulate strings They can be enclosed in single quotes or double quotes like
>>> 'India'
'India'
>>> 'India\'s best'
"India's best"
>>> "Hello World!"
'Hello World!'
Some Examples
Some examples of variables and datatypes:
Average of N numbers
In the next program we will do an average of N numbers.
#!/usr/bin/env python
N = 10
sum = 0
count = 0
while count < N:
number = float(input(""))
sum = sum + number
count = count + 1
average = float(sum)/N
print("N = %d , Sum = %f" % (N, sum))
print("Average = %f") % average
The output
$ ./averagen.py
1
2.3
4.67
1.42
7
3.67
4.08
2.2
4.25
8.21
N = 10 , Sum = 38.800000
Average = 3.880000
Temperature conversion
In this program we will convert the given temperature to Celsius from Fahrenheit by using the formula C=(F-32)/1.8
#!/usr/bin/env python3
fahrenheit = 0.0
print("Fahrenheit Celsius")
while fahrenheit <= 250:
celsius = ( fahrenheit - 32.0 ) / 1.8 # Here we calculate the Celsius value
print("%5.1f %7.2f" % (fahrenheit , celsius))
fahrenheit = fahrenheit + 25
The output
[kd@kdlappy book]$ ./temperature.py
Fahrenheit Celsius
0.0 -17.78
25.0 -3.89
50.0 10.00
75.0 23.89
100.0 37.78
125.0 51.67
150.0 65.56
175.0 79.44
200.0 93.33
225.0 107.22
250.0 121.11
Multiple assignments in a single line
You can even assign values to multiple variables in a single line, like
>>> a , b = 45, 54
>>> a
45
>>> b
54
Using this swapping two numbers becomes very easy
>>> a, b = b , a
>>> a
54
>>> b
45
To understand how this works, you will have to learn about a data type called tuple. We use comma to create tuple. In the right hand side we create the tuple (we call this as tuple packing) and in the left hand side we do tuple unpacking into a new tuple.
Below we have another example of tuple unpacking.
>>> data = ("Kushal Das", "India", "Python")
>>> name, country, language = data
>>> name
'Kushal Das'
>>> country
'India'
>>> language
'Python'
Operators and expressions
In Python most of the lines you will write will be expressions. Expressions are made of operators and operands. An expression is like 2 + 3 .
Operators
Operators are the symbols which tells the Python interpreter to do some mathematical or logical operation. Few basic examples of mathematical operators are given below:
>>> 2 + 3
5
>>> 23 - 3
20
>>> 22.0 / 12
1.8333333333333333
To get floating result you need to the division using any of operand as floating number. To do modulo operation use % operator
Example of integer arithmetic
The code
#!/usr/bin/env python3
days = int(input("Enter days: "))
months = days / 30
days = days % 30
print("Months = %d Days = %d" % (months, days))
The output
$ ./integer.py
Enter days: 265
Months = 8 Days = 25
In the first line I am taking the input of days, then getting the months and days and at last printing them. You can do it in a easy way
#!/usr/bin/env python3
days = int(input("Enter days: "))
print("Months = %d Days = %d" % (divmod(days, 30)))
The divmod(num1, num2) function returns two values , first is the division of num1 and num2 and in second the modulo of num1 and num2.
Relational Operators
You can use the following operators as relational operators
Relational Operators
| Operator |
Meaning |
| < |
Is less than |
| <= |
Is less than or equal to |
| > |
Is greater than |
| >= |
Is greater than or equal to |
| == |
Is equal to |
| != |
Is not equal to |
Some examples
>>> 1 < 2
True
>>> 3 > 34
False
>>> 23 == 45
False
>>> 34 != 323
True
// operator gives the floor division result
>>> 4.0 // 3
1.0
>>> 4.0 / 3
1.3333333333333333
Logical Operators
To do logical AND , OR we use and ,*or* keywords. x and y returns False if x is False else it returns evaluation of y. If x is True, it returns True.
>>> 1 and 4
4
>>> 1 or 4
1
>>> -1 or 4
-1
>>> 0 or 4
4
Shorthand Operator
x op = expression is the syntax for shorthand operators. It will be evaluated like x = x op expression , Few examples are
>>> a = 12
>>> a += 13
>>> a
25
>>> a /= 3
>>> a
8
>>> a += (26 * 32)
>>> a
840
shorthand.py example
#!/usr/bin/env python3
N = 100
a = 2
while a < N:
print("%d" % a)
a *= a
The output
Expressions
Generally while writing expressions we put spaces before and after every operator so that the code becomes clearer to read, like
a = 234 * (45 - 56.0 / 34)
One example code used to show expressions
#!/usr/bin/env python3
a = 9
b = 12
c = 3
x = a - b / 3 + c * 2 - 1
y = a - b / (3 + c) * (2 - 1)
z = a - (b / (3 + c) * 2) - 1
print ("X = ", x)
print ("Y = ", y)
print ("Z = ", z)
The output
$ ./evaluationexp.py
X = 10
Y = 7
Z = 4
At first x is being calculated. The steps are like this
9 - 12 / 3 + 3 * 2 -1
9 - 4 + 3 * 2 - 1
9 - 4 + 6 - 1
5 + 6 - 1
11 - 1
10
Now for y and z we have parentheses, so the expressions evaluated in different way. Do the calculation yourself to check them.
Type Conversions
We have to do the type conversions manually. Like
float(string) -> float value
int(string) -> integer value
str(integer) or str(float) -> string representation
>>> a = 8.126768
>>> str(a)
'8.126768'
evaluateequ.py
This is a program to evaluate 1/x+1/(x+1)+1/(x+2)+ ... +1/n series upto n, in our case x = 1 and n =10
#!/usr/bin/env python3
sum = 0.0
for i in range(1, 11):
sum += 1.0 / i
print("%2d %6.4f" % (i , sum))
The output
$ ./evaluateequ.py
1 1.0000
2 1.5000
3 1.8333
4 2.0833
5 2.2833
6 2.4500
7 2.5929
8 2.7179
9 2.8290
10 2.9290
In the line sum += 1.0 / i what is actually happening is sum = sum + 1.0 / i.
quadraticequation.py
This is a program to evaluate the quadratic equation
#!/usr/bin/env python
import math
a = int(input("Enter value of a: "))
b = int(input("Enter value of b: "))
c = int(input("Enter value of c: "))
d = b * b - 4 * a * c
if d < 0:
print("ROOTS are imaginary")
else:
root1 = (-b + math.sqrt(d)) / (2.0 * a)
root2 = (-b - math.sqrt(d)) / (2.0 * a)
print("Root 1 = ", root1)
print("Root 2 = ", root2)
salesmansalary.py
In this example we are going to calculate the salary of a camera salesman. His basic salary is 1500, for every camera he will sell he will get 200 and the commission on the month’s sale is 2 %. The input will be number of cameras sold and total price of the cameras.
#!/usr/bin/env python
basic_salary = 1500
bonus_rate = 200
commision_rate = 0.02
numberofcamera = int(input("Enter the number of inputs sold: "))
price = float(input("Enter the total prices: "))
bonus = (bonus_rate * numberofcamera)
commision = (commision_rate * numberofcamera * price)
print("Bonus = %6.2f" % bonus)
print("Commision = %6.2f" % commision)
print("Gross salary = %6.2f" % (basic_salary + bonus + commision))
The output
$ ./salesmansalary.py
Enter the number of inputs sold: 5
Enter the total prices: 20450
Bonus = 1000.00
Commision = 2045.00
Gross salary = 4545.00
If-else , the control flow
While working on real life of problems we have to make decisions. Decisions like which camera to buy or which cricket bat is better. At the time of writing a computer program we do the same. We make the decisions using if-else statements, we change the flow of control in the program by using them.
If statement
The syntax looks like
If the value of expression is true (anything other than zero), do the what is written below under indentation. Please remember to give proper indentation, all the lines indented will be evaluated on the True value of the expression. One simple example is to take some number as input and check if the number is less than 100 or not.
#!/usr/bin/env python3
number = int(input("Enter a number: "))
if number < 100:
print("The number is less than 100")
Then we run it
$ ./number100.py
Enter a number: 12
The number is less than 100
Else statement
Now in the above example we want to print “Greater than” if the number is greater than 100. For that we have to use the else statement. This works when the *if*statement is not fulfilled.
#!/usr/bin/env python3
number = int(input("Enter a number: "))
if number < 100:
print("The number is less than 100")
else:
print("The number is greater than 100")
The output
$ ./number100.py
Enter a number: 345
The number is greater than 100
Another very basic example
>>> x = int(input("Please enter an integer: "))
>>> if x < 0:
... x = 0
... print('Negative changed to zero')
... elif x == 0:
... print('Zero')
... elif x == 1:
... print('Single')
... else:
... print('More')
Truth value testing
The elegant way to test Truth values is like
Looping
In the examples we used before , sometimes it was required to do the same work couple of times. We use a counter to check how many times the code needs to be executed. This technique is known as looping. First we are going to look into while statement for looping.
While loop
The syntax for while statement is like
while condition:
statement1
statement2
The code we want to reuse must be indented properly under the while statement. They will be executed if the condition is true. Again like in if-else statement any non zero value is true. Let us write a simple code to print numbers 0 to 10
>>> n = 0
>>> while n < 11:
... print(n)
... n += 1
...
0
1
2
3
4
5
6
7
8
9
10
In the first line we are setting n = 0, then in the while statement the condition is n < 11, that means what ever line indented below that will execute until n becomes same or greater than 11. Inside the loop we are just printing the value of n and then increasing it by one.
Fibonacci Series
Let us try to solve Fibonacci series. In this series we get the next number by adding the previous two numbers. So the series looks like 1,1,2,3,5,8,13 .......
#!/usr/bin/env python3
a, b = 0, 1
while b < 100:
print(b)
a, b = b, a + b
The output
$ ./fibonacci1.py
1
1
2
3
5
8
13
21
34
55
89
In the first line of the code we are initializing a and b, then looping while b’s value is less than 100. Inside the loop first we are printing the value of b and then in the next line putting the value of b to a and a + b to b in the same line.
In your print function call if you pass another argument called end and pass a space string, it will print in the same line with space delimiter. The default value for end is ‘n’.
#!/usr/bin/env python3
a, b = 0, 1
while b < 100:
print(b,)
a, b = b, a + b
The output
$ ./fibonacci2.py
1 1 2 3 5 8 13 21 34 55 89
Power Series
Let us write a program to evaluate the power series. The series looks like e**x =1+x+x**2/2! +x**3/3! +....+ x**n/n! where 0 < x < 1
#!/usr/bin/env python3
x = float(raw_input("Enter the value of x: "))
n = term = num = 1
sum = 1.0
while n <= 100:
term *= x / n
sum += term
n += 1
if term < 0.0001:
break
print("No of Times= %d and Sum= %f" % (n, sum))
The output
$ ./powerseries.py
Enter the value of x: 0
No of Times= 2 and Sum= 1.000000
$ ./powerseries.py
Enter the value of x: 0.1
No of Times= 5 and Sum= 1.105171
$ ./powerseries.py
Enter the value of x: 0.5
No of Times= 7 and Sum= 1.648720
In this program we introduced a new keyword called break. What break does is stop the innermost loop. In this example we are using break under the if statement
This means if the value of term is less than 0.0001 then get out of the loop.
Multiplication Table
In this example we are going to print the multiplication table up to 10.
#!/usr/bin/env python3
i = 1
print("-" * 50)
while i < 11:
n = 1
while n <= 10:
print("%4d" % (i * n), end=' ')
n += 1
print()
i += 1
print("-" * 50)
The output
$ ./multiplication.py
--------------------------------------------------
1 2 3 4 5 6 7 8 9 10
2 4 6 8 10 12 14 16 18 20
3 6 9 12 15 18 21 24 27 30
4 8 12 16 20 24 28 32 36 40
5 10 15 20 25 30 35 40 45 50
6 12 18 24 30 36 42 48 54 60
7 14 21 28 35 42 49 56 63 70
8 16 24 32 40 48 56 64 72 80
9 18 27 36 45 54 63 72 81 90
10 20 30 40 50 60 70 80 90 100
--------------------------------------------------
Here we used one while loop inside another loop, this is known as nested looping. You can also see one interesting statement here
In a print statement if we multiply the string with an integer n , the string will be printed n many times. Some examples
>>> print("*" * 10)
**********
>>> print("#" * 20)
####################
>>> print("--" * 20)
----------------------------------------
>>> print("-" * 40)
----------------------------------------
Some printing * examples
Here are some examples which you can find very often in college lab reports
Design 1
#!/usr/bin/env python3
row = int(input("Enter the number of rows: "))
n = row
while n >= 0:
x = "*" * n
print(x)
n -= 1
The output
$ ./design1.py
Enter the number of rows: 5
*****
****
***
**
*
Design 2
#!/usr/bin/env python3
n = int(input("Enter the number of rows: "))
i = 1
while i <= n:
print("*" * i)
i += 1
The output
$ ./design2.py
Enter the number of rows: 5
*
**
***
****
*****
Design 3
#!/usr/bin/env python
row = int(input("Enter the number of rows: "))
n = row
while n >= 0:
x = "*" * n
y = " " * (row - n)
print(y + x)
n -= 1
The output
$ ./design3.py
Enter the number of rows: 5
*****
****
***
**
*
Lists
We are going to learn a data structure called list before we go ahead to learn more on looping. Lists can be written as a list of comma-separated values (items) between square brackets.
>>> a = [ 1, 342, 2233423, 'India', 'Fedora']
>>> a
[1, 342, 2233423, 'India', 'Fedora']
Lists can keep any other data inside it. It works as a sequence too, that means
>>> a[0]
1
>>> a[4]
'Fedora'
You can even slice it into different pieces, examples are given below
>>> a[4]
'Fedora'
>>> a[-1]
'Fedora'
>>> a[-2]
'India'
>>> a[0:-1]
[1, 342, 2233423, 'India']
>>> a[2:-2]
[2233423]
>>> a[:-2]
[1, 342, 2233423]
>>> a[0::2]
[1, 2233423, 'Fedora']
In the last example we used two :(s) , the last value inside the third brackets indicates step. s[i:j:k] means slice of s from i to j with step k.
To check if any value exists within the list or not you can do
>>> a = ['Fedora', 'is', 'cool']
>>> 'cool' in a
True
>>> 'Linux' in a
False
That means we can use the above statement as if clause expression. The built-in function len() can tell the length of a list.
Note
If you want to test if the list is empty or not, do it like this
if list_name: #This means the list is not empty
pass
else: #This means the list is empty
pass
For loop
There is another to loop by using for statement. In Python the for statement is different from the way it works in C. Here for statement iterates over the items of any sequence (a list or a string). Example given below
>>> a = ['Fedora', 'is', 'powerfull']
>>> for x in a:
... print x,
...
Fedora is powerfull
We can also do things like
>>> a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
>>> for x in a[::2]:
... print(x)
1
3
5
7
9
range() function
range() is a buitin class. From the help document
- class range(object)
range(stop) -> range object
range(start, stop[, step]) -> range object
Return a virtual sequence of numbers from start to stop by step.
Methods defined here:
Now if you want to see this help message on your system type help(range) in the Python interpreter. help(s) will return help message on the object s. Examples of range function
>>> range(1, 5)
[1, 2, 3, 4]
>>> range(1, 15, 3)
[1, 4, 7, 10, 13]
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Continue statement
Just like break we have another statement, continue, which skips the execution of the code after itself and goes back to the start of the loop. That means it will help you to skip a part of the loop. In the below example we will ask the user to input an integer, if the input is negative then we will ask again, if positive then we will square the number. To get out of the infinite loop user must input 0.
#!/usr/bin/env python3
while True:
n = int(input("Please enter an Integer: "))
if n < 0:
continue #this will take the execution back to the starting of the loop
elif n == 0:
break
print("Square is ", n ** 2)
print("Goodbye")
The output
$ ./continue.py
Please enter an Integer: 34
Square is 1156
Please enter an Integer: 4
Square is 16
Please enter an Integer: -9
Please enter an Integer: 0
Goodbye
Else loop
We can have an optional else statement after any loop. It will be executed after the loop unless a break statement stopped the loop.
>>> for i in range(0, 5):
... print(i)
... else:
... print("Bye bye")
...
0
1
2
3
4
Bye bye
We will see more example of break and continue later in the book.
Game of sticks
This is a very simple game of sticks. There are 21 sticks, first the user picks number of sticks between 1-4, then the computer picks sticks(1-4). Who ever will pick the last stick will loose. Can you find out the case when the user will win ?
#!/usr/bin/env python3
sticks = 21
print("There are 21 sticks, you can take 1-4 number of sticks at a time.")
print("Whoever will take the last stick will loose")
while True:
print("Sticks left: " , sticks)
sticks_taken = int(input("Take sticks(1-4):"))
if sticks == 1:
print("You took the last stick, you loose")
break
if sticks_taken >= 5 or sticks_taken <= 0:
print("Wrong choice")
continue
print("Computer took: " , (5 - sticks_taken) , "\n")
sticks -= 5
Data Structures
Python is having a few built-in data structure. If you are still wondering what is a data structure, then it is nothing a but a way to store data and the having particular methods to retrieve or manipulate it. We already saw lists before, now we will go in depth.
Lists
>>> a = [23, 45, 1, -3434, 43624356, 234]
>>> a.append(45)
>>> a
[23, 45, 1, -3434, 43624356, 234, 45]
At first we created a list a. Then to add 45 at the end of the list we call a.append(45) method. You can see that 45 added at the end of the list. Sometimes it may require to insert data at any place within the list, for that we have insert() method.
>>> a.insert(0, 1) # 1 added at the 0th position of the list
>>> a
[1, 23, 45, 1, -3434, 43624356, 234, 45]
>>> a.insert(0, 111)
>>> a
[111, 1, 23, 45, 1, -3434, 43624356, 234, 45]
count(s) will return you number of times s is in the list. Here we are going to check how many times 45 is there in the list.
If you want to remove any particular value from the list you have to use remove() method.
>>> a.remove(234)
>>> a
[111, 1, 23, 45, 1, -3434, 43624356, 45]
Now to reverse the whole list
>>> a.reverse()
>>> a
[45, 43624356, -3434, 1, 45, 23, 1, 111]
We can store anything in the list, so first we are going to add another list b in a, then we will learn how to add the values of b into a.
>>> b = [45, 56, 90]
>>> a.append(b)
>>> a
[45, 43624356, -3434, 1, 45, 23, 1, 111, [45, 56, 90]]
>>> a[-1]
[45, 56, 90]
>>> a.extend(b) #To add the values of b not the b itself
>>> a
[45, 43624356, -3434, 1, 45, 23, 1, 111, [45, 56, 90], 45, 56, 90]
>>> a[-1]
90
Above you can see how we used a.extend() method to extend the list. To sort any list we have sort() method.
>>> a.sort()
>>> a
[-3434, 1, 1, 23, 45, 45, 45, 56, 90, 111, 43624356, [45, 56, 90]]
You can also delete element at any particular position of the list using the del keyword.
>>> del a[-1]
>>> a
[-3434, 1, 1, 23, 45, 45, 45, 56, 90, 111, 43624356]
Using lists as stack and queue
Stacks are often known as LIFO (Last In First Out) structure. It means the data will enter into it at the end , and the last data will come out first. The easiest example can be of couple of marbles in an one side closed pipe. So if you want to take the marbles out of it you have to do that from the end where you entered the last marble. To achieve the same in code
>>> a
[1, 2, 3, 4, 5, 6]
>>> a.pop()
6
>>> a.pop()
5
>>> a.pop()
4
>>> a.pop()
3
>>> a
[1, 2]
>>> a.append(34)
>>> a
[1, 2, 34]
We learned a new method above pop(). pop(i) will take out the ith data from the list.
In our daily life we have to encounter queues many times, like in ticket counters or in library or in the billing section of any supermarket. Queue is the data structure where you can append more data at the end and take out data from the beginning. That is why it is known as FIFO (First In First Out).
>>> a = [1, 2, 3, 4, 5]
>>> a.append(1)
>>> a
[1, 2, 3, 4, 5, 1]
>>> a.pop(0)
1
>>> a.pop(0)
2
>>> a
[3, 4, 5, 1]
To take out the first element of the list we are using a.pop(0).
List Comprehensions
List comprehensions provide a concise way to create lists. Each list comprehension consists of an expression followed by a for clause, then zero or more for or if clauses. The result will be a list resulting from evaluating the expression in the context of the for and if clauses which follow it.
For example if we want to make a list out of the square values of another list, then
>>> a = [1, 2, 3]
>>> [x ** 2 for x in a]
[1, 4, 9]
>>> z = [x + 1 for x in [x ** 2 for x in a]]
>>> z
[2, 5, 10]
Above in the second case we used two list comprehensions in a same line.
Tuples
Tuples are data separated by comma.
>>> a = 'Fedora', 'Debian', 'Kubuntu', 'Pardus'
>>> a
('Fedora', 'Debian', 'Kubuntu', 'Pardus')
>>> a[1]
'Debian'
>>> for x in a:
... print(x, end=' ')
...
Fedora Debian Kubuntu Pardus
You can also unpack values of any tuple in to variables, like
>>> divmod(15,2)
(7, 1)
>>> x, y = divmod(15,2)
>>> x
7
>>> y
1
Tuples are immutable, that means you can not del/add/edit any value inside the tuple. Here is another example
>>> a = (1, 2, 3, 4)
>>> del a[0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'tuple' object doesn't support item deletion
Above you can see Python is giving error when we are trying to delete a value in the tuple.
To create a tuple which contains only one value you have to type a trailing comma.
>>> a = (123)
>>> a
123
>>> type(a)
<type 'int'>
>>> a = (123, ) #Look at the trailing comma
>>> a
(123,)
>>> type(a)
<type 'tuple'>
Using the built in function type() you can know the data type of any variable. Remember the len() function we used to find the length of any sequence ?
>>> type(len)
<type 'builtin_function_or_method'>
Sets
Sets are another type of data structure with no duplicate items. We can also mathematical set operations on sets.
>>> a = set('abcthabcjwethddda')
>>> a
set(['a', 'c', 'b', 'e', 'd', 'h', 'j', 't', 'w'])
And some examples of the set operations
>>> a = set('abracadabra')
>>> b = set('alacazam')
>>> a # unique letters in a
set(['a', 'r', 'b', 'c', 'd'])
>>> a - b # letters in a but not in b
set(['r', 'd', 'b'])
>>> a | b # letters in either a or b
set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
>>> a & b # letters in both a and b
set(['a', 'c'])
>>> a ^ b # letters in a or b but not both
set(['r', 'd', 'b', 'm', 'z', 'l'])
To add or pop values from a set
>>> a
set(['a', 'c', 'b', 'e', 'd', 'h', 'j', 'q', 't', 'w'])
>>> a.add('p')
>>> a
set(['a', 'c', 'b', 'e', 'd', 'h', 'j', 'q', 'p', 't', 'w'])
Dictionaries
Dictionaries are unordered set of key: value pairs where keys are unique. We declare dictionaries using {} braces. We use dictionaries to store data for any particular key and then retrieve them.
>>> data = {'kushal':'Fedora', 'kart_':'Debian', 'Jace':'Mac'}
>>> data
{'kushal': 'Fedora', 'Jace': 'Mac', 'kart_': 'Debian'}
>>> data['kart_']
'Debian'
We can add more data to it by simply
>>> data['parthan'] = 'Ubuntu'
>>> data
{'kushal': 'Fedora', 'Jace': 'Mac', 'kart_': 'Debian', 'parthan': 'Ubuntu'}
To delete any particular key:value pair
>>> del data['kushal']
>>> data
{'Jace': 'Mac', 'kart_': 'Debian', 'parthan': 'Ubuntu'
To check if any key is there in the dictionary or not you can use in keyword.
>>> 'Soumya' in data
False
You must remember that no mutable object can be a key, that means you can not use a list as a key.
dict() can create dictionaries from tuples of key,value pair.
>>> dict((('Indian','Delhi'),('Bangladesh','Dhaka')))
{'Indian': 'Delhi', 'Bangladesh': 'Dhaka'}
If you want to loop through a dict use iteritems() method.
>>> data
{'Kushal': 'Fedora', 'Jace': 'Mac', 'kart_': 'Debian', 'parthan': 'Ubuntu'}
>>> for x, y in data.iteritems():
... print("%s uses %s" % (x, y))
...
Kushal uses Fedora
Jace uses Mac
kart_ uses Debian
parthan uses Ubuntu
Many times it happens that we want to add more data to a value in a dictionary and if the key does not exists then we add some default value. You can do this efficiently using dict.setdefault(key, default).
>>> data = {}
>>> data.setdefault('names', []).append('Ruby')
>>> data
{'names': ['Ruby']}
>>> data.setdefault('names', []).append('Python')
>>> data
{'names': ['Ruby', 'Python']}
>>> data.setdefault('names', []).append('C')
>>> data
{'names': ['Ruby', 'Python', 'C']}
When we try to get value for a key which does not exists we get KeyError. We can use dict.get(key, default) to get a default value when they key does not exists before.
>>> data['foo']
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 'foo'
>>> data.get('foo', 0)
0
If you want to loop through a list (or any sequence) and get iteration number at the same time you have to use enumerate().
>>> for i, j in enumerate(['a', 'b', 'c']):
... print(i, j)
...
0 a
1 b
2 c
You may also need to iterate through two sequences same time, for that use zip() function.
>>> a = ['Pradeepto', 'Kushal']
>>> b = ['OpenSUSE', 'Fedora']
>>> for x, y in zip(a, b):
... print("%s uses %s" % (x, y))
...
Pradeepto uses OpenSUSE
Kushal uses Fedora
students.py
In this example , you have to take number of students as input , then ask marks for three subjects as ‘Physics’, ‘Maths’, ‘History’, if the total marks for any student is less 120 then print he failed, or else say passed.
#!/usr/bin/env python3
n = int(input("Enter the number of students:"))
data = {} # here we will store the data
languages = ('Physics', 'Maths', 'History') #all languages
for i in range(0, n): #for the n number of students
name = input('Enter the name of the student %d: ' % (i + 1)) #Get the name of the student
marks = []
for x in languages:
marks.append(int(input('Enter marks of %s: ' % x))) #Get the marks for languages
data[name] = marks
for x, y in data.iteritems():
total = sum(y)
print("%s 's total marks %d" % (x, total))
if total < 120:
print("%s failed :(" % x)
else:
print("%s passed :)" % x)
The output
[kd@kdlappy book]$ ./students.py
Enter the number of students:2
Enter the name of the student 1: Babai
Enter marks of Physics: 12
Enter marks of Maths: 45
Enter marks of History: 40
Enter the name of the student 2: Tesla
Enter marks of Physics: 99
Enter marks of Maths: 98
Enter marks of History: 99
Babai 's total marks 97
Babai failed :(
Tesla 's total marks 296
Tesla passed :)
matrixmul.py
In this example we will multiply two matrices. First we will take input the number of rows/columns in the matrix (here we assume we are using n x n matrix). Then values of the matrices.
#!/usr/bin/env python
n = int(raw_input("Enter the value of n: "))
print("Enter values for the Matrix A")
a = []
for i in range(0, n):
a.append([int(x) for x in raw_input("").split(" ")])
print("Enter values for the Matrix B")
b = []
for i in range(0, n):
b.append([int(x) for x in raw_input("").split(" ")])
c = []
for i in range(0, n):
c.append([a[i][j] * b[j][i] for j in range(0,n)])
print("After matrix multiplication")
print("-" * 10 * n)
for x in c:
for y in x:
print("%5d" % y, end=' ')
print("")
print("-" * 10 * n)
The output
[kd@kdlappy book]$ ./matrixmul.py
Enter the value of n: 3
Enter values for the Matrix A
1 2 3
4 5 6
7 8 9
Enter values for the Matrix B
9 8 7
6 5 4
3 2 1
After matrix multiplication
------------------------------
9 12 9
32 25 12
49 32 9
------------------------------
Here we have used list comprehensions couple of times. [int(x) for x in raw_input(“”).split(” ”)] here first it takes the input as string by raw_input(), then split the result by ” ”, then for each value create one int. We are also using [a[i][j] * b[j][i] for j in range(0,n)] to get the resultant row in a single line.
Strings
Strings are nothing but simple text. In Python we declare strings in between “” or ‘’ or ‘’’ ‘’’ or “”” “””. The examples below will help you to understand string in a better way.
>>> s = "I am Indian"
>>> s
'I am Indian'
>>> s = 'I am Indian'
>>> s = "Here is a line \
... splitted in two lines"
>>> s
'Here is a line split in two lines'
>>> s = "Here is a line \n split in two lines"
>>> s
'Here is a line \n split in two lines'
>>> print s
Here is a line
split in two lines
Now if you want to multiline strings you have to use triple single/double quotes.
>>> s = """ This is a
... multiline string, so you can
... write many lines"""
>>> print s
This is a
multiline string, so you can
write many lines
Different methods available for Strings
Every string object is having couple of buildin methods available, we already saw some of them like s.split(” ”).
>>> s = "kushal das"
>>> s.title()
'Kushal Das'
title() method returns a titlecased version of the string, words start with uppercase characters, all remaining cased characters are lowercase.
>>> z = s.upper()
>>> z
'KUSHAL DAS'
>>> z.lower()
'kushal das'
upper() returns a total uppercase version whereas lower() returns a lower case version of the string.
>>> s = "I am A pRoGraMMer"
>> s.swapcase()
'i AM a PrOgRAmmER'
swapcase() returns the string with case swapped :)
>>> s = "jdwb 2323bjb"
>>> s.isalnum()
False
>>> s = "jdwb2323bjb"
>>> s.isalnum()
True
Because of the space in the first line isalnum() returned False , it checks for all charecters are alpha numeric or not.
>>> s = "SankarshanSir"
>>> s.isalpha()
True
>>> s = "Sankarshan Sir"
>>> s.isalpha()
False
isalpha() checkes for only alphabets.
>>> s = "1234"
>>> s.isdigit() #To check if all the characters are digits or not
True
>>> s = "Fedora9 is coming"
>>> s.islower() # To check if all chracters are lower case or not
False
>>> s = "Fedora9 Is Coming"
>>> s.istitle() # To check if it is a title or not
True
>>> s = "INDIA"
>>> s.isupper() # To check if characters are in upper case or not
True
To split any string we have split(). It takes a string as an argument , depending on that it will split the main string and returns a list containing splitted strings.
>>> s = "We all love Python"
>>> s.split(" ")
['We', 'all', 'love', 'Python']
>>> x = "Nishant:is:waiting"
>>> x.split(':')
['Nishant', 'is', 'waiting']
The opposite method for split() is join(). It takes a list contains strings as input and join them.
>>> "-".join("GNU/Linux is great".split(" "))
'GNU/Linux-is-great'
In the above example first we are splitting the string “GNU/Linux is great” based on the white space, then joining them with “-”.
Strip the strings
Strings do have few methods to do striping. The simplest one is strip(chars). If you provide the chars argument then it will strip any combination of them. By default it strips only whitespace or newline characters.
>>> s = " abc\n "
>>> s.strip()
'abc'
You can particularly strip from the left hand or right hand side also using lstrip(chars) or rstrip(chars).
>>> s = "www.foss.in"
>>> s.lstrip("cwsd.")
'foss.in'
>>> s.rstrip("cnwdi.")
'www.foss'
Finding text
Stings have some methods which will help you in finding text/substring in a string. Examples are given below:
>>> s = "faulty for a reason"
>>> s.find("for")
7
>>> s.find("fora")
-1
>>> s.startswith("fa") #To check if the string startswith fa or not
True
>>> s.endswith("reason") #
True
find() helps to find the first occurrence of the substring given, if not found it returns -1.
Palindrome checking
Palindrome are the kind of strings which are same from left or right whichever way you read them. Example “madam”. In this example we will take the word as input from the user and say if it is palindrome or not.
#!/usr/bin/env python
s = raw_input("Please enter a string: ")
z = s[::-1]
if s == z:
print("The string is a palindrome")
else:
print("The string is not a palindrome")
The output
$ ./palindrome.py
Please enter a string: madam1
The string is not a palindrome
$ ./palindrome.py
Please enter a string: madam
The string is a palindrome
Number of words
In this example we will count the number of words in a given line
#!/usr/bin/env python
s = input("Enter a line: ")
print("The number of words in the line are %d" % (len(s.split(" "))))
The output
$ ./countwords.py
Enter a line: Sayamindu is a great programmer
The number of words in the line are 5
Functions
Reusing the same code is required many times within a same program. Functions help us to do so. We write the things we have to do repeatedly in a function then call it where ever required. We already saw build in functions like len(), divmod().
Defining a function
We use def keyword to define a function. General syntax is like
def functionname(params):
statement1
statement2
Let us write a function which will take two integers as input and then return the sum.
>>> def sum(a, b):
... return a + b
In the second line with the return keyword, we are sending back the value of a + b to the caller. You must call it like
>>> res = sum(234234, 34453546464)
>>> res
34453780698L
Remember the palindrome program we wrote in the last chapter. Let us write a function which will check if a given string is palindrome or not, then return True or False.
#!/usr/bin/env python3
def palindrome(s):
return s == s[::-1]
if __name__ == '__main__':
s = input("Enter a string: ")
if palindrome(s):
print("Yay a palindrome")
else:
print("Oh no, not a palindrome")
Now run the code :)
Local and global variables
To understand local and global variables we will go through two examples.
#!/usr/bin/env python3
def change(b):
a = 90
print(a)
a = 9
print("Before the function call ", a)
print("inside change function", end=' ')
change(a)
print("After the function call ", a)
The output
$ ./local.py
Before the function call 9
inside change function 90
After the function call 9
First we are assigning 9 to a, then calling change function, inside of that we are assigning 90 to a and printing a. After the function call we are again printing the value of a. When we are writing a = 90 inside the function, it is actually creating a new variable called a, which is only available inside the function and will be destroyed after the function finished. So though the name is same for the variable a but they are different in and out side of the function.
#!/usr/bin/env python3
def change(b):
global a
a = 90
print(a)
a = 9
print("Before the function call ", a)
print("inside change function", end=' ')
change(a)
print("After the function call ", a)
Here by using global keyword we are telling that a is globally defined, so when we are changing a’s value inside the function it is actually changing for the a outside of the function also.
Default argument value
In a function variables may have default argument values, that means if we don’t give any value for that particular variable it will assigned automatically.
>>> def test(a , b=-99):
... if a > b:
... return True
... else:
... return False
In the above example we have written b = -99 in the function parameter list. That means of no value for b is given then b’s value is -99. This is a very simple example of default arguments. You can test the code by
>>> test(12, 23)
False
>>> test(12)
True
Important
Important
Remember that you can not have an argument without default argument if you already have one argument with default values before it. Like f(a, b=90, c) is illegal as b is having a default value but after that c is not having any default value.
Also remember that default value is evaluated only once, so if you have any mutable object like list it will make a difference. See the next example
>>> def f(a, data=[]):
... data.append(a)
... return data
...
>>> print f(1)
[1]
>>> print f(2)
[1, 2]
>>> print f(3)
[1, 2, 3]
To avoid this you can write more idiomatic Python, like the following
>>> def f(a, data=None):
... if data is None:
... data = []
... data.append(a)
... return data
...
>>> print f(1)
[1]
>>> print f(2)
[2]
Keyword arguments
>>> def func(a, b=5, c=10):
... print('a is', a, 'and b is', b, 'and c is', c)
...
>>> func(12, 24)
a is 12 and b is 24 and c is 10
>>> func(12, c = 24)
a is 12 and b is 5 and c is 24
>>> func(b=12, c = 24, a = -1)
a is -1 and b is 12 and c is 24
In the above example you can see we are calling the function with variable names, like func(12, c = 24), by that we are assigning 24 to c and b is getting its default value. Also remember that you can not have without keyword based argument after a keyword based argument. like
>>> def func(a, b=13, v):
... print(a, b, v)
...
File "<stdin>", line 1
SyntaxError: non-default argument follows default argument
Keyword only argument
We can also mark the arguments of function as keyword only. That way while calling the function, the user will be forced to use
correct keyword for each parameter.
>>> def hello(*, name='User'):
... print("Hello %s" % name)
...
>>> hello('Kushal')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: hello() takes 0 positional arguments but 1 was given
>>> hello(name='Kushal')
Hello Kushal
Note
To learn more please read PEP-3102.
Docstrings
In Python we use docstrings to explain how to use the code, it will be useful in interactive mode and to create auto-documentation. Below we see an example of the docstring for a function called longest_side.
#!/usr/bin/env python3
import math
def longest_side(a, b):
"""
Function to find the length of the longest side of a right triangle.
:arg a: Side a of the triangle
:arg b: Side b of the triangle
:return: Length of the longest side c as float
"""
return math.sqrt(a*a + b*b)
if __name__ == '__main__':
print(longest_side(4, 5))
We will learn more on docstrings in reStructuredText chapter.
Higher-order function
Higher-order function or a functor is a function which does at least one of the
following step inside:
- Takes one or more functions as argument.
- Returns another function as output.
In Python any function can act as higher order function.
>>> def high(func, value):
... return func(value)
...
>>> lst = high(dir, int)
>>> print(lst[-3:])
['imag', 'numerator', 'real']
>>> print(lst)
map function
map is a very useful higher order function in Python. It takes one function and an iterator
as input and then applies the function on each value of the iterator and returns a list of results.
Example:
>>> lst = [1, 2, 3, 4, 5]
>>> def square(num):
... "Returns the square of a given number."
... return num * num
...
>>> print(map(square, lst))
[1, 4, 9, 16, 25]
File handling
A file is some information or data which stays in the computer storage devices. You already know about different kinds of file , like your music files, video files, text files. Python gives you easy ways to manipulate these files. Generally we divide files in two categories, text file and binary file. Text files are simple text where as the binary files contain binary data which is only readable by computer.
File opening
To open a file we use open() function. It requires two arguments, first the file path or file name, second which mode it should open. Modes are like
- “r” -> open read only, you can read the file but can not edit / delete anything inside
- “w” -> open with write power, means if the file exists then delete all content and open it to write
- “a” -> open in append mode
The default mode is read only, ie if you do not provide any mode it will open the file as read only. Let us open a file
>>> fobj = open("love.txt")
>>> fobj
<open file 'love.txt', mode 'r' at 0xb7f2d968>
Closing a file
After opening a file one should always close the opened file. We use method close() for this.
>>> fobj = open("love.txt")
>>> fobj
<open file 'love.txt', mode 'r' at 0xb7f2d968>
>>> fobj.close()
Important
Important
Always make sure you explicitly close each open file, once its job is done and you have no reason to keep it open.
Because
- There is an upper limit to the number of files a program can open. If you exceed that limit, there is no reliable way of recovery, so the program could crash.
- Each open file consumes some main-memory for the data-structures associated with it, like file descriptor/handle or file locks etc. So you could essentially end-up wasting lots of memory if you have more files open that are not useful or usable.
- Open files always stand a chance of corruption and data loss.
Reading a file
To read the whole file at once use the read() method.
>>> fobj = open("sample.txt")
>>> fobj.read()
'I love Python\nPradeepto loves KDE\nSankarshan loves Openoffice\n'
If you call read() again it will return empty string as it already read the whole file. readline() can help you to read one line each time from the file.
>>> fobj = open("sample.txt")
>>> fobj.readline()
'I love Python\n'
>>> fobj.readline()
'Pradeepto loves KDE\n'
To read all the lines in a list we use readlines() method.
>>> fobj = open("sample.txt")
>>> fobj.readlines()
['I love Python\n', 'Pradeepto loves KDE\n', 'Sankarshan loves Openoffice\n']
You can even loop through the lines in a file object.
>>> fobj = open("sample.txt")
>>> for x in f:
... print(x, end=' ')
...
I love Python
Pradeepto loves KDE
Sankarshan loves Openoffice
Let us write a program which will take the file name as the input from the user and show the content of the file in the console.
#!/usr/bin/env python3
name = input("Enter the file name: ")
fobj = open(name)
print(fobj.read())
fobj.close()
In the last line you can see that we closed the file object with the help of close() method.
The output
$ ./showfile.py
Enter the filename: sample.txt
I love Python
Pradeepto loves KDE
Sankarshan loves Openoffice
Writing in a file
Let us open a file then we will write some random text into it by using the write() method.
>>> fobj = open("ircnicks.txt", 'w')
>>> fobj.write('powerpork\n')
>>> fobj.write('indrag\n')
>>> fobj.write('mishti\n')
>>> fobj.write('sankarshan')
>>> fobj.close()
Now read the file we just created
>>> fobj = open('ircnicks.txt')
>>> s = fobj.read()
>>> print s
powerpork
indrag
mishti
sankarshan
copyfile.py
In this example we will copy a given text file to another file.
#!/usr/bin/env python3
import sys
if len(sys.argv) < 3:
print("Wrong parameter")
print("./copyfile.py file1 file2")
sys.exit(1)
f1 = open(sys.argv[1])
s = f1.read()
f1.close()
f2 = open(sys.argv[2], 'w')
f2.write(s)
f2.close()
Note
This way of reading file is not always a good idea, a file can be very large to read and fit in the memory. It is always better to read a known size of the file and write that to the new file.
You can see we used a new module here sys. sys.argv contains all command line parameters. Remember cp command in shell, after cp we type first the file to be copied and then the new file name.
The first value in sys.argv is the name of the command itself.
#!/usr/bin/env python3
import sys
print("First value", sys.argv[0])
print("All values")
for i, x in enumerate(sys.argv):
print(i, x)
The output
$ ./argvtest.py Hi there
First value ./argvtest.py
All values
0 ./argvtest.py
1 Hi
2 there
Here we used a new function enumerate(iterableobject), which returns the index number and the value from the iterable object.
Random seeking in a file
You can also randomly move around inside a file using seek() method. It takes two arguments , offset and whence. To know more about it let us read what Python help tells us
seek(...)
seek(offset[, whence]) -> None. Move to new file position.
Argument offset is a byte count. Optional argument whence defaults to
0 (offset from start of file, offset should be >= 0); other values are 1
(move relative to current position, positive or negative), and 2 (move
relative to end of file, usually negative, although many platforms allow
seeking beyond the end of a file). If the file is opened in text mode,
only offsets returned by tell() are legal. Use of other offsets causes
undefined behavior.
Note that not all file objects are speakable.
Let us see one example
>>> fobj = open('/tmp/tempfile', 'w')
>>> fobj.write('0123456789abcdef')
>>> fobj.close()
>>> fobj = open('/tmp/tempfile')
>>> fobj.tell() #tell us the offset position
0L
>>> fobj.seek(5) # Goto 5th byte
>>> fobj.tell()
5L
>>> fobj.read(1) #Read 1 byte
'5'
>>> fobj.seek(-3, 2) # goto 3rd byte from the end
>>> fobj.read() #Read till the end of the file
'def'
Count spaces, tabs and new lines in a file
Let us try to write an application which will count the spaces, tabs, and lines in any given file.
#!/usr/bin/env python3
import os
import sys
def parse_file(path):
"""
Parses the text file in the given path and returns space, tab & new line
details.
:arg path: Path of the text file to parse
:return: A tuple with count of spacaes, tabs and lines.
"""
fd = open(path)
i = 0
spaces = 0
tabs = 0
for i,line in enumerate(fd):
spaces += line.count(' ')
tabs += line.count('\t')
#Now close the open file
fd.close()
#Return the result as a tuple
return spaces, tabs, i + 1
def main(path):
"""
Function which prints counts of spaces, tabs and lines in a file.
:arg path: Path of the text file to parse
:return: True if the file exits or False.
"""
if os.path.exists(path):
spaces, tabs, lines = parse_file(path)
print("Spaces %d. tabs %d. lines %d" % (spaces, tabs, lines))
return True
else:
return False
if __name__ == '__main__':
if len(sys.argv) > 1:
main(sys.argv[1])
else:
sys.exit(-1)
sys.exit(0)
You can see that we have two functions in the program , main and parse_file where the second one actually parses the file and returns the result and we print the result in main function. By splitting up the code in smaller units (functions) helps us to organize the codebase and also it will be easier to write test cases for the functions.
Using the with statement
In real life scenarios we should try to use with statement. It will take care of closing the file for you.
>>> with open('setup.py') as fobj:
... for line in fobj:
... print line,
...
#!/usr/bin/env python
"""Factorial project"""
from setuptools import find_packages, setup
setup(name = 'factorial',
version = '0.1',
description = "Factorial module.",
long_description = "A test module for our book.",
platforms = ["Linux"],
author="Kushal Das",
author_email="kushaldas@gmail.com",
url="http://pymbook.readthedocs.org/en/latest/",
license = "http://www.gnu.org/copyleft/gpl.html",
packages=find_packages()
)
Let us write some real code
Do you know how many CPU(s) are there in your processor? or what is the model name?
Let us write some code which can help us to know these things.
If you are in Linux, then you can actually view the output of the lscpu command first.
You can actually find the information in a file located at /proc/cpuinfo.
Now try to write code which will open the file in read only mode and then read the file
line by line and find out the number of CPU(s).
Note
Always remember to read files line by line than reading them as a whole. Sometimes you may have to read files which are way bigger than your available RAM.
After you do this, try to write your own lscpu command in Python :)
Exceptions
In this chapter we will learn about exceptions in Python and how to
handle them in your code.
Any error which happens during the execution of the code is an exception. Each
exception generally shows some error message.
NameError
When one starts writing code, this will be one of the most command exception
he/she will find. This happens when someone tries to access a variable which is
not defined.
>>> print(kushal)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'kushal' is not defined
The last line contains the details of the error message, the rest of the lines
shows the details of how it happened (or what caused that exception).
TypeError
TypeError is also one of the most found exception. This happens when someone tries
to do an operation with different kinds of incompatible data types. A common example
is to do addition of Integers and a string.
>>> print(1 + "kushal")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for +: 'int' and 'str'
How to handle exceptions?
We use try...except blocks to handle any exception. The basic syntax looks like
try:
statements to be inside try clause
statement2
statement3
...
except ExceptionName:
statements to evaluated in case of ExceptionName happens
It works in the following way:
- First all lines between try and except statements.
- If ExceptionName happens during execution of the statements then except clause statements execute
- If no exception happens then the statements inside except clause does not execute.
- If the Exception is not handled in the except block then it goes out of try block.
The following examples showcase these scenarios.
>>> def get_number():
... "Returns a float number"
... number = float(input("Enter a float number: "))
... return number
...
>>>
>>> while True:
... try:
... print(get_number())
... except ValueError:
... print("You entered a wrong value.")
...
Enter a float number: 45.0
45.0
Enter a float number: 24,0
You entered a wrong value.
Enter a float number: Traceback (most recent call last):
File "<stdin>", line 3, in <module>
File "<stdin>", line 3, in get_number
KeyboardInterrupt
As the first input I provided a proper float value and it printed it back, next
I entered a value with a comma, so the except clause executed and the print
statement executed.
In the third time I pressed Ctrl+c which caused a KeyboardInterrupt, which is
not handled in the except block so the execution stopped with that exception.
An empty except statement can catch any exception. Read the following example:
>>> try:
... input() # Press Ctrl+c for a KeyboardInterrupt
... except:
... print "Unknown Exception"
...
Unknown Exception
Raising exceptions
One can raise an exception using raise statement.
>>> raise ValueError("A value error happened.")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: A value error happened.
We can catch these exceptions like any other normal exceptions.
>>> try:
... raise ValueError("A value error happened.")
... except ValueError:
... print("ValueError in our code.")
...
ValueError in our code.
Using finally for cleanup
If we want to have some statements which must be executed under all circumstances,
we can use finally clause, it will be always executed before finishing try
statements.
>>> try:
... fobj = open("hello.txt", "w")
... res = 12 / 0
... except ZeroDivisionError:
... print("We have an error in division")
... finally:
... fobj.close()
... print("Closing the file object.")
...
We have an error in division
Closing the file object.
In this example we are making sure that the file object we open, must get closed
in the finally clause.
Class
Your first class
Before writing your first class, you should know the syntax. We define a class in the following way.
class nameoftheclass(parent_class):
statement1
statement2
statement3
In the statements you can write any Python statement, you can define functions (which we call methods of a class).
>>> class MyClass(object):
... a = 90
... b = 88
...
>>> p = MyClass()
>>> p
<__main__.MyClass instance at 0xb7c8aa6c>
In the above example you can see first we are declaring a class called MyClass, writing some random statements inside that class. After the class definition, we are creating an object p of the class MyClass. If you do a dir on that
>>> dir(p)
['__doc__', '__module__', 'a', 'b']
you can see the variables a and b inside it.
__init__ method
__init__ is a special method in Python classes, it is the constructor method for a class. In the following example you can see how to use it.
class Student(object):
"""
Returns a ```Student``` object with the given name, branch and year.
"""
def __init__(self, name, branch, year):
self.name = name
self.branch = branch
self.year = year
print("A student object is created.")
def print_details(self):
"""
Prints the details of the student.
"""
print("Name:", self.name)
print("Branch:", self.branch)
print("Year:", self.year)
__init__ is called when ever an object of the class is constructed. That means when ever we will create a student object we will see the message “A student object is created” in the prompt. You can see the first argument to the method is self. It is a special variable which points to the current object (like this in C++). The object is passed implicitly to every method available in it, but we have to get it explicitly in every method while writing the methods. Example shown below.
>>> std1 = Student()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: __init__() takes exactly 4 arguments (1 given)
>>> std1 = Student('Kushal','CSE','2005')
A student object is created
In this example at first we tried to create a Student object without passing any argument and Python interpreter complained that it takes exactly 4 arguments but received only one (self). Then we created an object with proper argument values and from the message printed, one can easily understand that __init__ method was called as the constructor method.
Now we are going to call print_details() method.
>>> std1.print_details()
Name: Kushal
Branch: CSE
Year: 2005
Inheritance
In general we human beings always know about inheritance. In programming it is almost the same. When a class inherits another class it inherits all features (like variables and methods) of the parent class. This helps in reusing codes.
In the next example we first create a class called Person and create two sub-classes Student and Teacher. As both of the classes are inherited from Person class they will have all methods of Person and will have new methods and variables for their own purpose.
student_teacher.py
#!/usr/bin/env python
class Person(object):
"""
Returns a ```Person``` object with given name.
"""
def __init__(self, name):
self.name = name
def get_details(self):
"Returns a string containing name of the person"
return self.name
class Student(Person):
"""
Returns a ```Student``` object, takes 3 arguments, name, branch, year.
"""
def __init__(self, name, branch, year):
Person.__init__(self, name)
self.branch = branch
self.year = year
def get_details(self):
"Returns a string containing student's details."
return "%s studies %s and is in %s year." % (self.name, self.branch, self.year)
class Teacher(Person):
"""
Returns a ```Teacher``` object, takes a list of strings (list of papers) as
argument.
"""
def __init__(self, name, papers):
Person.__init__(self, name)
self.papers = papers
def get_details(self):
return "%s teaches %s" % (self.name, ','.join(self.papers))
person1 = Person('Sachin')
student1 = Student('Kushal', 'CSE', 2005)
teacher1 = Teacher('Prashad', ['C', 'C++'])
print(person1.get_details())
print(student1.get_details())
print(teacher1.get_details())
The output:
$ ./student_teacher.py
Sachin
Kushal studies CSE and is in 2005 year.
Prashad teaches C,C++
In this example you can see how we called the __init__ method of the class Person in both Student and Teacher classes’ __init__ method. We also reimplemented get_details() method of Person class in both Student and Teacher class. So, when we are calling get_details() method on the teacher1 object it returns based on the object itself (which is of teacher class) and when we call get_details() on the student1 or person1 object it returns based on get_details() method implemented in it’s own class.
Multiple Inheritance
One class can inherit more than one classes. It gets access to all methods and variables of the parent classes. The general syntax is:
class MyClass(Parentclass1, Parentclass2,...):
def __init__(self):
Parentclass1.__init__(self)
Parentclass2.__init__(self)
...
...
Deleting an object
As we already know how to create an object, now we are going to see how to delete an Python object. We use del for this.
>>> s = "I love you"
>>> del s
>>> s
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 's' is not defined
del actually decreases reference count by one. When the reference count of an object becomes zero the garbage collector will delete that object.
Getters and setters in Python
One simple answer, don’t. If you are coming from other languages (read Java), you will be tempted
to use getters or setters in all your classes. Please don’t. Just use the attributes directly.
The following shows a direct example.
>>> class Student(object):
... def __init__(self, name):
... self.name = name
...
>>> std = Student("Kushal Das")
>>> print(std.name)
Kushal Das
>>> std.name = "Python"
>>> print std.name
Python
Properties
If you want more fine tuned control over data attribute access, then you can use properties.
In the following example of a bank account, we will make sure that no one can set the
money value to negative and also a property called inr will give us the INR values of
the dollars in the account.
#!/usr/bin/env python
class Account(object):
"""The Account class,
The amount is in dollars.
"""
def __init__(self, rate):
self.__amt = 0
self.rate = rate
@property
def amount(self):
"The amount of money in the account"
return self.__amt
@property
def inr(self):
"Gives the money in INR value."
return self.__amt * self.rate
@amount.setter
def amount(self, value):
if value < 0:
print "Sorry, no negative amount in the account."
return
self.__amt = value
if __name__ == '__main__':
acc = Account(rate=61) # Based on today's value of INR :(
acc.amount = 20
print("Dollar amount:", acc.amount)
print("In INR:", acc.inr)
acc.amount = -100
print("Dollar amount:", acc.amount)
Output:
$ python property.py
Dollar amount: 20
In INR: 1220
Sorry, no negative amount in the account.
Dollar amount: 20
Modules
In this chapter we are going to learn about Python modules.
Introduction
Up until now, all the code we wrote in the Python interpreter was lost when we exited the interpreter. But when people write large programs they tend to break their code into multiple different files for ease of use, debugging and readability. In Python we use modules to achieve such goals. Modules are nothing but files with Python definitions and statements. The module name, to import, has the same name of the Python file without the .py extension.
You can find the name of the module by accessing the __name__ variable. It is a global variable.
Now we are going to see how modules work. Create a file called bars.py. Content of the file is given bellow.
"""
Bars Module
============
This is an example module with provide different ways to print bars.
"""
def starbar(num):
"""Prints a bar with *
:arg num: Length of the bar
"""
print('*' * num)
def hashbar(num):
"""Prints a bar with #
:arg num: Length of the bar
"""
print('#' * num)
def simplebar(num):
"""Prints a bar with -
:arg num: Length of the bar
"""
print('-' * num)
Now we are going to start the Python interpreter and import our module.
This will import the module bars. We have to use the module name to access functions inside the module.
>>> bars.hashbar(10)
##########
>>> bars.simplebar(10)
----------
>>> bars.starbar(10)
**********
Importing modules
There are different ways to import modules. We already saw one way to do this. You can even import selected functions from modules. To do so:
>>> from bars import simplebar, starbar
>>> simplebar(20)
--------------------
Warning
Never do from module import * Read this link for more information.
Submodules
We can have many submodules inside a module. A directory with a __init__.py can also be used as a module and all .py files inside it become submodules.
$ tree mymodule
mymodule
|-- bars.py
|-- __init__.py
`-- utils.py
In this example mymodule is the module name and bars and utils are two submodules in it. You can create an empty __init__.py using touch command.
$ touch mymodule/__init__.py
__all__ in __init__.py
If __init__.py file contains a list called __all__, then only the names listed there will
be public. So if the mymodule’s __init__.py
file contains the following
from mymodule.bars import simplebar
__all__ = [simplebar, ]
Then from mymodule only simplebar will be available.
Note
from mymodule import * will only work for module level objects, trying to use it to import functions or classes
will cause syntax error.
Default modules
Now your Python installation comes with different modules installed, you can use them as required and install new modules for any other special purposes. In the following few examples we are going to see many examples on the same.
>>> help()
Welcome to Python 3.5's help utility!
If this is your first time using Python, you should definitely check out
the tutorial on the Internet at http://docs.python.org/3.5/tutorial/.
Enter the name of any module, keyword, or topic to get help on writing
Python programs and using Python modules. To quit this help utility and
return to the interpreter, just type "quit".
To get a list of available modules, keywords, symbols, or topics, type
"modules", "keywords", "symbols", or "topics". Each module also comes
with a one-line summary of what it does; to list the modules whose name
or summary contain a given string such as "spam", type "modules spam".
help> modules
The above example shows how to get the list of all installed modules in your system. I am not pasting them here as it is a big list in my system :)
You can also use help() function in the interpeter to find documentation about any module/classes. Say you want to know all available methods in strings, you can use the following method
Module os
os module provides operating system dependent functionality. You can import it using the following import statement.
getuid() function returns the current process’s effective user’s id.
getpid() returns the current process’s id. getppid() returns the parent process’s id.
>>> os.getpid()
16150
>>> os.getppid()
14847
uname() returns different information identifying the operating system, in Linux it returns details you can get from the uname command. The returned object is a tuple, (sysname, nodename, release, version, machine)
>>> os.uname()
('Linux', 'd80', '2.6.34.7-56.fc13.i686.PAE', '#1 SMP Wed Sep 15 03:27:15 UTC 2010', 'i686')
getcwd()*returns the current working directory. *chdir(path) changes the current working directory to path. In the example we first see the current directory which is my home directory and change the current directory to /tmp and then again checking the current directory.
>>> os.getcwd()
'/home/kushal'
>>> os.chdir('/tmp')
>>> os.getcwd()
'/tmp'
So let us use another function provided by the os module and create our own function to list all files and directories in any given directory.
def view_dir(path='.'):
"""
This function prints all files and directories in the given directory.
:args path: Path to the directory, default is current directory
"""
names = os.listdir(path)
names.sort()
for name in names:
print(name, end =' ')
Using the view_dir example.
>>> view_dir('/')
.readahead bin boot dev etc home junk lib lib64 lost+found media mnt opt
proc root run sbin srv sys tmp usr var
There are many other very useful functions available in the OS module, you can read about them here
Requests Module
requests is a Python module which changed the way people used to write code for many many projects. It helps
you to do HTTP GET or POST calls in a very simple but elegant way. This is a third party module, that means
you have to install it from your OS distribution packages, it does not come default.
# yum install python3-requests
The above command will install Python3 version of the requests module in your system.
Getting a simple web pages
You can use the get method to fetch any website.
>>> import requests
>>> req = requests.get('http://google.com')
>>> req.status_code
200
The text attribute holds the HTML returned by the server.
Using this knowledge, let us write a command which can download a given file (URL) from Internet.
#!/usr/bin/env python3
import os
import os.path
import requests
def download(url):
'''Download the given url and saves it to the current directory.
:arg url: URL of the file to be downloaded.
'''
req = requests.get(url)
# First let us check non existing files.
if req.status_code == 404:
print('No such file found at %s' % url)
return
filename = url.split('/')[-1]
with open(filename, 'wb') as fobj:
fobj.write(req.content)
print("Download over.")
if __name__ == '__main__':
url = input('Enter a URL:')
download(url)
Here we used something new, when the module name is __main__, then only
ask for a user input and then download the given URL. This also prevents
the same when some other Python code imports this file as a Python module.
To learn more about requests module, go to their wonderful documentation.
You can actually modify the above program to become more user friendly. For example, you can check if that given
filename already exists in the current directory or not. Use os.path module for the name.
Command line arguments
Do you remember your ls command, you can pass different kind of options as command line arguments. You can do that too .. important:: your application. Read this how-to guide to learn about it.
TAB completion in your Python interpreter
First create a file as ~/.pythonrc and include the following in that file
import rlcompleter, readline
readline.parse_and_bind('tab: complete')
history_file = os.path.expanduser('~/.python_history')
readline.read_history_file(history_file)
import atexit
atexit.register(readline.write_history_file, history_file)
Next, just export PYTHONSTARTUP variable pointing to this file from your ~/.bashrc file.
export PYTHONSTARTUP=~/.pythonrc
Now from future whenever you open a bash shell, you will have TAB completion and history of code entered in your
Python interpreter.
To use it in the current shell, source the bashrc file.
Collections module
In this chapter we will learn about a module called Collections. This module implements some nice data structures which will help you to solve various real life problems.
This is how you can import the module, now we will see the available classes which you can use.
Counter
Counter is a dict subclass which helps to count hashable objects. Inside it elements are stored as dictionary keys and counts are stored as values which can be zero or negative.
Below we will see one example where we will find occurrences of words in the Python LICENSE file.
Counter example
>>> from collections import Counter
>>> import re
>>> path = '/usr/share/doc/python-2.7.3/LICENSE'
>>> words = re.findall('\w+', open(path).read().lower())
>>> Counter(words).most_common(10)
[('2', 97), ('the', 80), ('or', 78), ('1', 76), ('of', 61), ('to', 50), ('and', 47), ('python', 46), ('psf', 44), ('in', 38)]
Counter objects has a method called elements which returns an iterator over elements repeating each as many times as its count. Elements are returned in arbitrary order.
>>> c = Counter(a=4, b=2, c=0, d=-2)
>>> list(c.elements())
['a', 'a', 'a', 'a', 'b', 'b']
most_common is a method which returns most common elements and their counts from the most common to the least.
>>> Counter('abracadabra').most_common(3)
[('a', 5), ('r', 2), ('b', 2)]
defaultdict
defaultdict is a dictionary like object which provides all methods provided by dictionary but takes first argument (default_factory) as default data type for the dictionary. Using defaultdict is faster than doing the same using dict.set_default method.
defaultdict example
>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
>>> d = defaultdict(list)
>>> for k, v in s:
... d[k].append(v)
...
>>> d.items()
[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
In the example you can see even if the key is not there in the defaultdict object, it automatically creates an empty list. list.append then helps to append the value to the list.
namedtuple
Named tuples helps to have meaning of each position in a tuple and allow us to code with better readability and self-documenting code. You can use them in any place where you are using tuples. In the example we will create a namedtuple to show hold information for points.
Named tuple
>>> from collections import namedtuple
>>> Point = namedtuple('Point', ['x', 'y']) # Defining the namedtuple
>>> p = Point(10, y=20) # Creating an object
>>> p
Point(x=10, y=20)
>>> p.x + p.y
30
>>> p[0] + p[1] # Accessing the values in normal way
30
>>> x, y = p # Unpacking the tuple
>>> x
10
>>> y
20
PEP8 Guidelines
Introduction
This document gives coding conventions for the Python code comprising
the standard library in the main Python distribution. Please see the
companion informational PEP describing style guidelines for the C code
in the C implementation of Python .
This document and PEP 257 (Docstring Conventions) were adapted from
Guido’s original Python Style Guide essay, with some additions from
Barry’s style guide .
A Foolish Consistency is the Hobgoblin of Little Minds
One of Guido’s key insights is that code is read much more often than
it is written. The guidelines provided here are intended to improve
the readability of code and make it consistent across the wide
spectrum of Python code. As PEP 20 says, “Readability counts”.
A style guide is about consistency. Consistency with this style guide
is important. Consistency within a project is more important.
Consistency within one module or function is the most important.
But most importantly: know when to be inconsistent – sometimes the
style guide just doesn’t apply. When in doubt, use your best
judgment. Look at other examples and decide what looks best. And
don’t hesitate to ask!
Two good reasons to break a particular rule:
- When applying the rule would make the code less readable, even for
someone who is used to reading code that follows the rules.
- To be consistent with surrounding code that also breaks it (maybe
for historic reasons) – although this is also an opportunity to
clean up someone else’s mess (in true XP style).
Code lay-out
Indentation
Use 4 spaces per indentation level.
For really old code that you don’t want to mess up, you can continue
to use 8-space tabs.
Continuation lines should align wrapped elements either vertically
using Python’s implicit line joining inside parentheses, brackets and
braces, or using a hanging indent. When using a hanging indent the
following considerations should be applied; there should be no
arguments on the first line and further indentation should be used to
clearly distinguish itself as a continuation line.
Yes:
# Aligned with opening delimiter
foo = long_function_name(var_one, var_two,
var_three, var_four)
# More indentation included to distinguish this from the rest.
def long_function_name(
var_one, var_two, var_three,
var_four):
print(var_one)
No:
# Arguments on first line forbidden when not using vertical alignment
foo = long_function_name(var_one, var_two,
var_three, var_four)
# Further indentation required as indentation is not distinguishable
def long_function_name(
var_one, var_two, var_three,
var_four):
print(var_one)
Optional:
# Extra indentation is not necessary.
foo = long_function_name(
var_one, var_two,
var_three, var_four)
The closing brace/bracket/parenthesis on multi-line constructs may
either line up under the first non-whitespace character of the last
line of list, as in:
my_list = [
1, 2, 3,
4, 5, 6,
]
result = some_function_that_takes_arguments(
'a', 'b', 'c',
'd', 'e', 'f',
)
or it may be lined up under the first character of the line that
starts the multi-line construct, as in:
my_list = [
1, 2, 3,
4, 5, 6,
]
result = some_function_that_takes_arguments(
'a', 'b', 'c',
'd', 'e', 'f',
)
Tabs or Spaces?
Never mix tabs and spaces.
The most popular way of indenting Python is with spaces only. The
second-most popular way is with tabs only. Code indented with a
mixture of tabs and spaces should be converted to using spaces
exclusively. When invoking the Python command line interpreter with
the -t option, it issues warnings about code that illegally mixes
tabs and spaces. When using -tt these warnings become errors.
These options are highly recommended!
For new projects, spaces-only are strongly recommended over tabs.
Most editors have features that make this easy to do.
Maximum Line Length
Limit all lines to a maximum of 79 characters.
There are still many devices around that are limited to 80 character
lines; plus, limiting windows to 80 characters makes it possible to
have several windows side-by-side. The default wrapping on such
devices disrupts the visual structure of the code, making it more
difficult to understand. Therefore, please limit all lines to a
maximum of 79 characters. For flowing long blocks of text (docstrings
or comments), limiting the length to 72 characters is recommended.
The preferred way of wrapping long lines is by using Python’s implied
line continuation inside parentheses, brackets and braces. Long lines
can be broken over multiple lines by wrapping expressions in
parentheses. These should be used in preference to using a backslash
for line continuation.
Backslashes may still be appropriate at times. For example, long,
multiple with-statements cannot use implicit continuation, so
backslashes are acceptable:
with open('/path/to/some/file/you/want/to/read') as file_1, \
open('/path/to/some/file/being/written', 'w') as file_2:
file_2.write(file_1.read())
Another such case is with assert statements.
Make sure to indent the continued line appropriately. The preferred
place to break around a binary operator is after the operator, not
before it. Some examples:
class Rectangle(Blob):
def __init__(self, width, height,
color='black', emphasis=None, highlight=0):
if (width == 0 and height == 0 and
color == 'red' and emphasis == 'strong' or
highlight > 100):
raise ValueError("sorry, you lose")
if width == 0 and height == 0 and (color == 'red' or
emphasis is None):
raise ValueError("I don't think so -- values are %s, %s" %
(width, height))
Blob.__init__(self, width, height,
color, emphasis, highlight)
Blank Lines
Separate top-level function and class definitions with two blank
lines.
Method definitions inside a class are separated by a single blank
line.
Extra blank lines may be used (sparingly) to separate groups of
related functions. Blank lines may be omitted between a bunch of
related one-liners (e.g. a set of dummy implementations).
Use blank lines in functions, sparingly, to indicate logical sections.
Python accepts the control-L (i.e. ^L) form feed character as
whitespace; Many tools treat these characters as page separators, so
you may use them to separate pages of related sections of your file.
Note, some editors and web-based code viewers may not recognize
control-L as a form feed and will show another glyph in its place.
Encodings (PEP 263)
Code in the core Python distribution should always use the ASCII or
Latin-1 encoding (a.k.a. ISO-8859-1). For Python 3.0 and beyond,
UTF-8 is preferred over Latin-1, see PEP 3120.
Files using ASCII should not have a coding cookie. Latin-1 (or UTF-8)
should only be used when a comment or docstring needs to mention an
author name that requires Latin-1; otherwise, using \x, \u or
\U escapes is the preferred way to include non-ASCII data in
string literals.
For Python 3.0 and beyond, the following policy is prescribed for the
standard library (see PEP 3131): All identifiers in the Python
standard library MUST use ASCII-only identifiers, and SHOULD use
English words wherever feasible (in many cases, abbreviations and
technical terms are used which aren’t English). In addition, string
literals and comments must also be in ASCII. The only exceptions are
(a) test cases testing the non-ASCII features, and
(b) names of authors. Authors whose names are not based on the
latin alphabet MUST provide a latin transliteration of their
names.
Open source projects with a global audience are encouraged to adopt a
similar policy.
Imports
Imports should usually be on separate lines, e.g.:
Yes: import os
import sys
No: import sys, os
It’s okay to say this though:
from subprocess import Popen, PIPE
Imports are always put at the top of the file, just after any module
comments and docstrings, and before module globals and constants.
Imports should be grouped in the following order:
- standard library imports
- related third party imports
- local application/library specific imports
You should put a blank line between each group of imports.
Put any relevant __all__ specification after the imports.
Relative imports for intra-package imports are highly discouraged.
Always use the absolute package path for all imports. Even now that
PEP 328 is fully implemented in Python 2.5, its style of explicit
relative imports is actively discouraged; absolute imports are more
portable and usually more readable.
When importing a class from a class-containing module, it’s usually
okay to spell this:
from myclass import MyClass
from foo.bar.yourclass import YourClass
If this spelling causes local name clashes, then spell them
import myclass
import foo.bar.yourclass
and use “myclass.MyClass” and “foo.bar.yourclass.YourClass”.
Whitespace in Expressions and Statements
Pet Peeves
Avoid extraneous whitespace in the following situations:
Immediately inside parentheses, brackets or braces.
Yes: spam(ham[1], {eggs: 2})
No: spam( ham[ 1 ], { eggs: 2 } )
Immediately before a comma, semicolon, or colon:
Yes: if x == 4: print x, y; x, y = y, x
No: if x == 4 : print x , y ; x , y = y , x
Immediately before the open parenthesis that starts the argument
list of a function call:
Yes: spam(1)
No: spam (1)
Immediately before the open parenthesis that starts an indexing or
slicing:
Yes: dict['key'] = list[index]
No: dict ['key'] = list [index]
More than one space around an assignment (or other) operator to
align it with another.
Yes:
x = 1
y = 2
long_variable = 3
No:
x = 1
y = 2
long_variable = 3
Other Recommendations
Always surround these binary operators with a single space on either
side: assignment (=), augmented assignment (+=, -=
etc.), comparisons (==, <, >, !=, <>, <=,
>=, in, not in, is, is not), Booleans (and,
or, not).
If operators with different priorities are used, consider adding
whitespace around the operators with the lowest priority(ies). Use
your own judgement; however, never use more than one space, and
always have the same amount of whitespace on both sides of a binary
operator.
Yes:
i = i + 1
submitted += 1
x = x*2 - 1
hypot2 = x*x + y*y
c = (a+b) * (a-b)
No:
i=i+1
submitted +=1
x = x * 2 - 1
hypot2 = x * x + y * y
c = (a + b) * (a - b)
Don’t use spaces around the = sign when used to indicate a
keyword argument or a default parameter value.
Yes:
def complex(real, imag=0.0):
return magic(r=real, i=imag)
No:
def complex(real, imag = 0.0):
return magic(r = real, i = imag)
Compound statements (multiple statements on the same line) are
generally discouraged.
Yes:
if foo == 'blah':
do_blah_thing()
do_one()
do_two()
do_three()
Rather not:
if foo == 'blah': do_blah_thing()
do_one(); do_two(); do_three()
While sometimes it’s okay to put an if/for/while with a small body
on the same line, never do this for multi-clause statements. Also
avoid folding such long lines!
Rather not:
if foo == 'blah': do_blah_thing()
for x in lst: total += x
while t < 10: t = delay()
Definitely not:
if foo == 'blah': do_blah_thing()
else: do_non_blah_thing()
try: something()
finally: cleanup()
do_one(); do_two(); do_three(long, argument,
list, like, this)
if foo == 'blah': one(); two(); three()
Version Bookkeeping
If you have to have Subversion, CVS, or RCS crud in your source file,
do it as follows.
__version__ = "$Revision$"
# $Source$
These lines should be included after the module’s docstring, before
any other code, separated by a blank line above and below.
Naming Conventions
The naming conventions of Python’s library are a bit of a mess, so
we’ll never get this completely consistent – nevertheless, here are
the currently recommended naming standards. New modules and packages
(including third party frameworks) should be written to these
standards, but where an existing library has a different style,
internal consistency is preferred.
Descriptive: Naming Styles
There are a lot of different naming styles. It helps to be able to
recognize what naming style is being used, independently from what
they are used for.
The following naming styles are commonly distinguished:
b (single lowercase letter)
B (single uppercase letter)
lowercase
lower_case_with_underscores
UPPERCASE
UPPER_CASE_WITH_UNDERSCORES
CapitalizedWords (or CapWords, or CamelCase – so named because
of the bumpy look of its letters ). This is also sometimes known
as StudlyCaps.
Note: When using abbreviations in CapWords, capitalize all the
letters of the abbreviation. Thus HTTPServerError is better than
HttpServerError.
mixedCase (differs from CapitalizedWords by initial lowercase
character!)
Capitalized_Words_With_Underscores (ugly!)
There’s also the style of using a short unique prefix to group related
names together. This is not used much in Python, but it is mentioned
for completeness. For example, the os.stat() function returns a
tuple whose items traditionally have names like st_mode,
st_size, st_mtime and so on. (This is done to emphasize the
correspondence with the fields of the POSIX system call struct, which
helps programmers familiar with that.)
The X11 library uses a leading X for all its public functions. In
Python, this style is generally deemed unnecessary because attribute
and method names are prefixed with an object, and function names are
prefixed with a module name.
In addition, the following special forms using leading or trailing
underscores are recognized (these can generally be combined with any
case convention):
_single_leading_underscore: weak “internal use” indicator.
E.g. from M import * does not import objects whose name starts
with an underscore.
single_trailing_underscore_: used by convention to avoid
conflicts with Python keyword, e.g.
Tkinter.Toplevel(master, class_='ClassName')
__double_leading_underscore: when naming a class attribute,
invokes name mangling (inside class FooBar, __boo becomes
_FooBar__boo; see below).
__double_leading_and_trailing_underscore__: “magic” objects or
attributes that live in user-controlled namespaces.
E.g. __init__, __import__ or __file__. Never invent
such names; only use them as documented.
Prescriptive: Naming Conventions
Names to Avoid
Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase
letter oh), or ‘I’ (uppercase letter eye) as single character variable
names.
In some fonts, these characters are indistinguishable from the
numerals one and zero. When tempted to use ‘l’, use ‘L’ instead.
Package and Module Names
Modules should have short, all-lowercase names. Underscores can be
used in the module name if it improves readability. Python packages
should also have short, all-lowercase names, although the use of
underscores is discouraged.
Since module names are mapped to file names, and some file systems are
case insensitive and truncate long names, it is important that module
names be chosen to be fairly short – this won’t be a problem on Unix,
but it may be a problem when the code is transported to older Mac or
Windows versions, or DOS.
When an extension module written in C or C++ has an accompanying
Python module that provides a higher level (e.g. more object oriented)
interface, the C/C++ module has a leading underscore
(e.g. _socket).
Class Names
Almost without exception, class names use the CapWords convention.
Classes for internal use have a leading underscore in addition.
Exception Names
Because exceptions should be classes, the class naming convention
applies here. However, you should use the suffix “Error” on your
exception names (if the exception actually is an error).
Global Variable Names
(Let’s hope that these variables are meant for use inside one module
only.) The conventions are about the same as those for functions.
Modules that are designed for use via from M import * should use
the __all__ mechanism to prevent exporting globals, or use the
older convention of prefixing such globals with an underscore (which
you might want to do to indicate these globals are “module
non-public”).
Function Names
Function names should be lowercase, with words separated by
underscores as necessary to improve readability.
mixedCase is allowed only in contexts where that’s already the
prevailing style (e.g. threading.py), to retain backwards
compatibility.
Function and method arguments
Always use self for the first argument to instance methods.
Always use cls for the first argument to class methods.
If a function argument’s name clashes with a reserved keyword, it is
generally better to append a single trailing underscore rather than
use an abbreviation or spelling corruption. Thus class_ is better
than clss. (Perhaps better is to avoid such clashes by using a
synonym.)
Method Names and Instance Variables
Use the function naming rules: lowercase with words separated by
underscores as necessary to improve readability.
Use one leading underscore only for non-public methods and instance
variables.
To avoid name clashes with subclasses, use two leading underscores to
invoke Python’s name mangling rules.
Python mangles these names with the class name: if class Foo has an
attribute named __a, it cannot be accessed by Foo.__a. (An
insistent user could still gain access by calling Foo._Foo__a.)
Generally, double leading underscores should be used only to avoid
name conflicts with attributes in classes designed to be subclassed.
Note: there is some controversy about the use of __names (see below).
Constants
Constants are usually defined on a module level and written in all
capital letters with underscores separating words. Examples include
MAX_OVERFLOW and TOTAL.
Designing for inheritance
Always decide whether a class’s methods and instance variables
(collectively: “attributes”) should be public or non-public. If in
doubt, choose non-public; it’s easier to make it public later than to
make a public attribute non-public.
Public attributes are those that you expect unrelated clients of your
class to use, with your commitment to avoid backward incompatible
changes. Non-public attributes are those that are not intended to be
used by third parties; you make no guarantees that non-public
attributes won’t change or even be removed.
We don’t use the term “private” here, since no attribute is really
private in Python (without a generally unnecessary amount of work).
Another category of attributes are those that are part of the
“subclass API” (often called “protected” in other languages). Some
classes are designed to be inherited from, either to extend or modify
aspects of the class’s behavior. When designing such a class, take
care to make explicit decisions about which attributes are public,
which are part of the subclass API, and which are truly only to be
used by your base class.
With this in mind, here are the Pythonic guidelines:
Public attributes should have no leading underscores.
If your public attribute name collides with a reserved keyword,
append a single trailing underscore to your attribute name. This is
preferable to an abbreviation or corrupted spelling. (However,
not withstanding this rule, ‘cls’ is the preferred spelling for any
variable or argument which is known to be a class, especially the
first argument to a class method.)
Note 1: See the argument name recommendation above for class methods.
For simple public data attributes, it is best to expose just the
attribute name, without complicated accessor/mutator methods. Keep
in mind that Python provides an easy path to future enhancement,
should you find that a simple data attribute needs to grow
functional behavior. In that case, use properties to hide
functional implementation behind simple data attribute access
syntax.
Note 1: Properties only work on new-style classes.
Note 2: Try to keep the functional behavior side-effect free,
although side-effects such as caching are generally fine.
Note 3: Avoid using properties for computationally expensive
operations; the attribute notation makes the caller believe that
access is (relatively) cheap.
If your class is intended to be subclassed, and you have attributes
that you do not want subclasses to use, consider naming them with
double leading underscores and no trailing underscores. This
invokes Python’s name mangling algorithm, where the name of the
class is mangled into the attribute name. This helps avoid
attribute name collisions should subclasses inadvertently contain
attributes with the same name.
Note 1: Note that only the simple class name is used in the mangled
name, so if a subclass chooses both the same class name and attribute
name, you can still get name collisions.
Note 2: Name mangling can make certain uses, such as debugging and
__getattr__(), less convenient. However the name mangling
algorithm is well documented and easy to perform manually.
Note 3: Not everyone likes name mangling. Try to balance the
need to avoid accidental name clashes with potential use by
advanced callers.
References
Iterators, generators and decorators
In this chapter we will learn about iterators, generators and decorators.
Iterators
Python iterator objects required to support two methods while following the iterator
protocol.
__iter__ returns the iterator object itself. This is used in for
and in statements.
__next__ method returns the next value from the iterator. If there is no more items
to return then it should raise StopIteration exception.
class Counter(object):
def __init__(self, low, high):
self.current = low
self.high = high
def __iter__(self):
'Returns itself as an iterator object'
return self
def __next__(self):
'Returns the next value till current is lower than high'
if self.current > self.high:
raise StopIteration
else:
self.current += 1
return self.current - 1
Now we can use this iterator in our code.
>>> c = Counter(5,10)
>>> for i in c:
... print(i, end=' ')
...
5 6 7 8 9 10
Remember that an iterator object can be used only once. It means after it raises StopIteration
once, it will keep raising the same exception.
>>> c = Counter(5,6)
>>> next(c)
5
>>> next(c)
6
>>> next(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 11, in next
StopIteration
>>> next(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 11, in next
StopIteration
Using the iterator in for loop example we saw, the following example tries to show the code
behind the scenes.
>>> iterator = iter(c)
>>> while True:
... try:
... x = iterator.__next__()
... print(x, end=' ')
... except StopIteration as e:
... break
...
5 6 7 8 9 10
Generators
In this section we learn about Python generators. They were introduced in Python 2.3. It
is an easier way to create iterators using a keyword yield from a function.
>>> def my_generator():
... print("Inside my generator")
... yield 'a'
... yield 'b'
... yield 'c'
...
>>> my_generator()
<generator object my_generator at 0x7fbcfa0a6aa0>
In the above example we create a simple generator using the yield statements. We can use it
in a for loop just like we use any other iterators.
>>> for char in my_generator():
... print(char)
...
Inside my generator
a
b
c
In the next example we will create the same Counter class using a generator function and use it
in a for loop.
def counter_generator(low, high):
while low <= high:
yield low
low += 1
>>> for i in counter_generator(5,10):
... print(i, end=' ')
...
5 6 7 8 9 10
Inside the while loop when it reaches to the yield statement, the value of low is returned
and the generator state is suspended. During the second next call the generator resumed where
it freeze-ed before and then the value of low is increased by one. It continues with the
while loop and comes to the yield statement again.
When you call an generator function it returns a *generator* object. If you call *dir*
on this object you will find that it contains __iter__ and *__next__* methods among the
other methods.
>>> c = counter_generator(5,10)
>>> dir(c)
['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__',
'__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__iter__',
'__le__', '__lt__', '__name__', '__ne__', '__new__', '__next__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__',
'close', 'gi_code', 'gi_frame', 'gi_running', 'send', 'throw']
We mostly use generators for laze evaluations. This way generators become a good approach
to work with lots of data. If you don’t want to load all the data in the memory, you can use
a generator which will pass you each piece of data at a time.
One of the biggest example of such example is os.path.walk() function which uses a callback
function and current os.walk generator. Using the generator implementation saves memory.
We can have generators which produces infinite values. The following is a one such example.
>>> def infinite_generator(start=0):
... while True:
... yield start
... start += 1
...
>>> for num in infinite_generator(4):
... print(num, end=' ')
... if num > 20:
... break
...
4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
If we go back to the example of my_generator we will find one feature of generators.
They are not re-usable.
>>> g = my_generator()
>>> for c in g:
... print(c)
...
Inside my generator
a
b
c
>>> for c in g:
... print(c)
...
One way to create a reusable generator is Object based generators which does not hold any state. Any class with a __iter__ method which yields data can be used as a object generator.
In the following example we will recreate out counter generator.
>>> class Counter(object):
... def __init__(self, low, high):
... self.low = low
... self.high = high
... def __iter__(self):
... counter = self.low
... while self.high >= counter:
... yield counter
... counter += 1
...
>>> gobj = Counter(5, 10)
>>> for num in gobj:
... print(num, end=' ')
...
5 6 7 8 9 10
>>> for num in gobj:
... print(num, end=' ')
...
5 6 7 8 9 10
Generator expressions
Generator expressionsGenerator expressions
In this section we will learn about generator expressions which is a high
performance, memory efficient generalization of list comprehensions and generators.
For example we will try to sum the squares of all numbers from 1 to 99.
>>> sum([x*x for x in range(1,10)])
The example actually first creates a list of the square values in memory and then it
iterates over it and finally after sum it frees the memory. You can understand the memory
usage in case of a big list.
We can save memory usage by using a generator expression.
sum(x*x for x in range(1,10))
The syntax of generator expression says that always needs to be directly inside a set of parentheses and cannot have a comma on either side. Which basically means both the examples below are valid generator expression usage example.
>>> sum(x*x for x in range(1,10))
285
>>> g = (x*x for x in range(1,10))
>>> g
<generator object <genexpr> at 0x7fc559516b90>
We can have chaining of generators or generator expressions. In the following
example we will read the file */var/log/cron* and will find if any particular
job (in the example we are searching for anacron) is running successfully or not.
We can do the same using a shell command tail -f /var/log/cron |grep anacron
>>> jobtext = 'anacron'
>>> all = (line for line in open('/var/log/cron', 'r') )
>>> job = ( line for line in all if line.find(jobtext) != -1)
>>> text = next(job)
>>> text
"May 6 12:17:15 dhcp193-104 anacron[23052]: Job `cron.daily' terminated\n"
>>> text = next(job)
>>> text
'May 6 12:17:15 dhcp193-104 anacron[23052]: Normal exit (1 job run)\n'
>>> text = next(job)
>>> text
'May 6 13:01:01 dhcp193-104 run-parts(/etc/cron.hourly)[25907]: starting 0anacron\n'
You can write a for loop to the lines.
Closures
Closures are nothing but functions that are returned by another function. We use
closures to remove code duplication. In the following example we create
a simple closure for adding numbers.
>>> def add_number(num):
... def adder(number):
... 'adder is a closure'
... return num + number
... return adder
...
>>> a_10 = add_number(10)
>>> a_10(21)
31
>>> a_10(34)
44
>>> a_5 = add_number(5)
>>> a_5(3)
8
adder is a closure which adds a given number to a pre-defined one.
Decorators
Decorator is way to dynamically add some new behavior to some objects. We achieve
the same in Python by using closures.
In the example we will create a simple example which will print some statement before
and after the execution of a function.
>>> def my_decorator(func):
... def wrapper(*args, **kwargs):
... print("Before call")
... result = func(*args, **kwargs)
... print("After call")
... return result
... return wrapper
...
>>> @my_decorator
... def add(a, b):
... "Our add function"
... return a + b
...
>>> add(1, 3)
Before call
After call
4
Virtualenv
Virtual Python Environment builder or virtualenv is a tool which will help you to install different versions of Python modules in a local directory using which you can develop and test your code without requiring to install everything systemwide.
Installation
You can install virtualenv either from distro provided package or through pip.
$ sudo yum install python-virtualenv
Or
$ sudo pip install virtualenv
Usage
We will create a directory call virtual inside which we will have two different virtual environment.
The following commands will create an env called virt1.
$ cd virtual
$ virtualenv virt1
New python executable in virt1/bin/python
Installing setuptools............done.
Installing pip...............done.
Now we can activate the virt1 environment.
$ source virt1/bin/activate
(virt1)[user@host]$
The first part of the prompt is now the name of the virtual environment, it will help you identify which environment you are in when you have multiple environments.
To deactivate the environment use deactivate command.
So, now we will install a Python module called redis.
(virt1)$ pip install redis
Downloading/unpacking redis
Downloading redis-2.6.2.tar.gz
Running setup.py egg_info for package redis
Installing collected packages: redis
Running setup.py install for redis
Successfully installed redis
Cleaning up...
With the same way we can install a project called yolk, which tells us which of the modules are installed.
(virt1)$ pip install yolk
(virt1)$ yolk -l
Python - 2.7.3 - active development (/usr/lib64/python2.7/lib-dynload)
pip - 1.1 - active
redis - 2.6.2 - active
setuptools - 0.6c11 - active
wsgiref - 0.1.2 - active development (/usr/lib64/python2.7)
yolk - 0.4.3 - active
Now we will create another virtual environment virt2 where we will install the same redis module but an old 2.4 version of it.
$ virtualenv virt2
New python executable in virt1/bin/python
Installing setuptools............done.
Installing pip...............done.
$ source virt2/bin/activate
(virt2)$
(virt2)$ pip install redis==2.4
Downloading/unpacking redis
Downloading redis-2.4.0.tar.gz
Running setup.py egg_info for package redis
Installing collected packages: redis
Running setup.py install for redis
Successfully installed redis
Cleaning up...
(virt2)$ pip install yolk
(virt2)$ yolk -l
Python - 2.7.3 - active development (/usr/lib64/python2.7/lib-dynload)
pip - 1.1 - active
redis - 2.4.0 - active
setuptools - 0.6c11 - active
wsgiref - 0.1.2 - active development (/usr/lib64/python2.7)
yolk - 0.4.3 - active
As you can see, yolk says that in this envrionment we have redis 2.4 installed. This way you can have many different environments for all of your development needs.
Note
Always remember to create virtualenvs while developing new applications. This will help you keep the system modules clean.
Simple testing in Python
What we should test ?
If possible everything in our codebase, each and every function. But it depends as a choice
of the developers. You can skip it if it is not practical to write a robust test. As Nick Coghlan
said in a guest session – ... with a solid test suite, you can make big changes, confident that the externally visible behavior will remain the same
Unit testing
A method by which individual units of source code. Wikipedia says
In computer programming, unit testing is a method by which individual units of source code,
sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine if they are fit for use.
unittest module
In Python we have unittest module to help us.
Factorial code
In this example we will write a file factorial.py.
import sys
def fact(n):
"""
Factorial function
:arg n: Number
:returns: factorial of n
"""
if n == 0:
return 1
return n * fact(n -1)
def div(n):
"""
Just divide
"""
res = 10 / n
return res
def main(n):
res = fact(n)
print(res)
if __name__ == '__main__':
if len(sys.argv) > 1:
main(int(sys.argv[1]))
Output
Which function to test ?
As you can see fact(n) is function which is doing all calculations, so
we should test that at least.
Our first test case
Now we will write our first test case.
import unittest
from factorial import fact
class TestFactorial(unittest.TestCase):
"""
Our basic test class
"""
def test_fact(self):
"""
The actual test.
Any method which starts with ``test_`` will considered as a test case.
"""
res = fact(5)
self.assertEqual(res, 120)
if __name__ == '__main__':
unittest.main()
Running the test:
$ python factorial_test.py
.
----------------------------------------------------------------------
Ran 1 test in 0.000s
OK
Description
We are importing unittest module first and then the required functions
which we want to test.
A testcase is created by subclassing unittest.TestCase.
Now open the test file and change 120 to 121 and see what happens :)
Different assert statements
| Method |
Checks that |
New in |
|---|
| assertEqual(a, b) |
a == b |
|
| assertNotEqual(a, b) |
a != b |
|
| assertTrue(x) |
bool(x) is True |
|
| assertFalse(x) |
bool(x) is False |
|
| assertIs(a, b) |
a is b |
2.7 |
| assertIsNot(a, b) |
a is not b |
2.7 |
| assertIsNone(x) |
x is None |
2.7 |
| assertIsNotNone(x) |
x is not None |
2.7 |
| assertIn(a, b) |
a in b |
2.7 |
| assertNotIn(a, b) |
a not in b |
2.7 |
| assertIsInstance(a, b) |
isinstance(a, b) |
2.7 |
| assertNotIsInstance(a, b) |
not isinstance(a, b) |
2.7 |
Testing exceptions
If we call div(0) in factorial.py , we can see if raises an exception.
We can also test these exceptions, like:
self.assertRaises(ZeroDivisionError, div, 0)
Full code
import unittest
from factorial import fact, div
class TestFactorial(unittest.TestCase):
"""
Our basic test class
"""
def test_fact(self):
"""
The actual test.
Any method which starts with ``test_`` will considered as a test case.
"""
res = fact(5)
self.assertEqual(res, 120)
def test_error(self):
"""
To test exception raise due to run time error
"""
self.assertRaises(ZeroDivisionError, div, 0)
if __name__ == '__main__':
unittest.main()
mounttab.py
Here we have only one function mount_details() doing the parsing and printing mount details.
import os
def mount_details():
"""
Prints the mount details
"""
if os.path.exists('/proc/mounts'):
fd = open('/proc/mounts')
for line in fd:
line = line.strip()
words = line.split()
print()'%s on %s type %s' % (words[0],words[1],words[2]), end=' ')
if len(words) > 5:
print('(%s)' % ' '.join(words[3:-2]))
else:
print('')
if __name__ == '__main__':
mount_details()
After refactoring
Now we refactored the code and have one new function parse_mounts which we can test easily.
import os
def parse_mounts():
"""
It parses /proc/mounts and returns a list of tuples
"""
result = []
if os.path.exists('/proc/mounts'):
fd = open('/proc/mounts')
for line in fd:
line = line.strip()
words = line.split()
if len(words) > 5:
res = (words[0],words[1],words[2], '(%s)' % ' '.join(words[3:-2]))
else:
res = (words[0],words[1],words[2])
result.append(res)
return result
def mount_details():
"""
Prints the mount details
"""
result = parse_mounts()
for line in result:
if len(line) == 4:
print('%s on %s type %s %s' % line)
else:
print('%s on %s type %s' % line)
if __name__ == '__main__':
mount_details()
and the test code for the same.
#!/usr/bin/env python
import unittest
from mounttab2 import parse_mounts
class TestMount(unittest.TestCase):
"""
Our basic test class
"""
def test_parsemount(self):
"""
The actual test.
Any method which starts with ``test_`` will considered as a test case.
"""
result = parse_mounts()
self.assertIsInstance(result, list)
self.assertIsInstance(result[0], tuple)
def test_rootext4(self):
"""
Test to find root filesystem
"""
result = parse_mounts()
for line in result:
if line[1] == '/' and line[2] != 'rootfs':
self.assertEqual(line[2], 'ext4')
if __name__ == '__main__':
unittest.main()
$ python mounttest.py
..
----------------------------------------------------------------------
Ran 2 tests in 0.001s
OK
Test coverage
Test coverage is a simple way to find untested parts of a codebase. It does not
tell you how good your tests are.
In Python we already have a nice coverage tool to help us. You can install it in Fedora
# yum install python-coverage
Or using pip.
Coverage Example
$ coverage -x mounttest.py
<OUTPUT snipped>
$ coverage -rm
Name Stmts Miss Cover Missing
-----------------------------------------
mounttab2 21 7 67% 16, 24-29, 33
mounttest 14 0 100%
-----------------------------------------
TOTAL 35 7 80%
A project structure
This chapter explains a full Python project structure. What kind of directory layout
you can use and how make release a software to the world.
We will call our example project factorial.
$ mkdir factorial
$ cd factorial/
Primary code
The name of the Python module will be myfact, so we will create the directory next.
The primary code will be in a file called fact.py
"myfact module"
def factorial(num):
"""
Returns the factorial value of the given number.
:arg num: Interger value of whose factorial we will calculate.
:return: The value of the the factorial or -1 in case negative value passed.
"""
if num >= 0:
if num == 0:
return 1
return num * factorial(num -1)
else:
return -1
We also have a __init__.py file for the module.
from fact import factorial
__all__ = [factorial, ]
We also added a README.rst file. So, the directory structure looks like
$ ls
myfact README.rst
$ ls myfact/
fact.py __init__.py
MANIFEST.in
Now we have to write a MANIFEST.in file which will be used to find out which all
files will be part of the source tar ball of the project at the time of using sdist command.
include *.py
include README.rst
If you want to exclude some file, you can use exclude statements in this file.
setup.py
Finally we have to write a setup.py which then can be used to create a source tarball
or installing the software.
#!/usr/bin/env python
"""Factorial project"""
from setuptools import find_packages, setup
setup(name = 'factorial',
version = '0.1',
description = "Factorial module.",
long_description = "A test module for our book.",
platforms = ["Linux"],
author="Kushal Das",
author_email="kushaldas@gmail.com",
url="http://pymbook.readthedocs.org/en/latest/",
license = "MIT",
packages=find_packages()
)
name is the name of the project, version is the release version. You can easily
understand description and long_description. platforms is a list of the platforms
this module can work on. find_packages is a special function which can find
all modules under your source directory.
Usage of setup.py
To create a source release one execute the following command.
$ python setup.py sdist
running sdist
running egg_info
creating factorial.egg-info
writing factorial.egg-info/PKG-INFO
writing top-level names to factorial.egg-info/top_level.txt
writing dependency_links to factorial.egg-info/dependency_links.txt
writing manifest file 'factorial.egg-info/SOURCES.txt'
reading manifest file 'factorial.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'factorial.egg-info/SOURCES.txt'
running check
creating factorial-0.1
creating factorial-0.1/factorial.egg-info
creating factorial-0.1/myfact
making hard links in factorial-0.1...
hard linking MANIFEST.in -> factorial-0.1
hard linking README.rst -> factorial-0.1
hard linking setup.py -> factorial-0.1
hard linking factorial.egg-info/PKG-INFO -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/SOURCES.txt -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/dependency_links.txt -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/top_level.txt -> factorial-0.1/factorial.egg-info
hard linking myfact/__init__.py -> factorial-0.1/myfact
hard linking myfact/fact.py -> factorial-0.1/myfact
Writing factorial-0.1/setup.cfg
creating dist
Creating tar archive
removing 'factorial-0.1' (and everything under it)
One can see the tarball under dist directory.
$ ls dist/
factorial-0.1.tar.gz
Note
Remember to use a virtualenv while trying to install the code :)
To install from the source use the following command.
$ python setup.py install
Python Package Index (PyPI)
Do you remember the pip command we are using still now? Ever thought from where those packages
are coming from? The answer is PyPI. It is a
repository of software for the Python programming language.
For our example, we will use the test server of PyPI which is https://testpypi.python.org/pypi
Creating account
First register yourself in this link. You will
receive an email with a link, go to that link and confirm your registration.
Your account details genrally stay inside a file called .pypirc under your home directory.
The content of the file will look like
[distutils]
index-servers =
pypi
[pypi]
repository: https://testpypi.python.org/pypi
username: <username>
password: <password>
Replace <username> and <password> with your newly created account details.
Note
Remember to change the name of the project to something else in the `setup.py`to test following instructions.
Registering your project
Next we will register our project to the PyPI service. This is done using the register command. We will
also use -r to point it to the test server.
$ python setup.py register -r https://testpypi.python.org/pypi
running register
running egg_info
writing factorial.egg-info/PKG-INFO
writing top-level names to factorial.egg-info/top_level.txt
writing dependency_links to factorial.egg-info/dependency_links.txt
reading manifest file 'factorial.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'factorial.egg-info/SOURCES.txt'
running check
Registering factorial to https://testpypi.python.org/pypi
Server response (200): OK
Uploading your project
Now finally we can upload our project to the PyPI server using upload command.
Remember that this command needs to be invoked immediately after you build the source/binary
distribution files.
$ python setup.py sdist upload -r https://testpypi.python.org/pypi
running sdist
running egg_info
writing factorial.egg-info/PKG-INFO
writing top-level names to factorial.egg-info/top_level.txt
writing dependency_links to factorial.egg-info/dependency_links.txt
reading manifest file 'factorial.egg-info/SOURCES.txt'
reading manifest template 'MANIFEST.in'
writing manifest file 'factorial.egg-info/SOURCES.txt'
running check
creating factorial-0.1
creating factorial-0.1/factorial.egg-info
creating factorial-0.1/myfact
making hard links in factorial-0.1...
hard linking MANIFEST.in -> factorial-0.1
hard linking README.rst -> factorial-0.1
hard linking setup.py -> factorial-0.1
hard linking factorial.egg-info/PKG-INFO -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/SOURCES.txt -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/dependency_links.txt -> factorial-0.1/factorial.egg-info
hard linking factorial.egg-info/top_level.txt -> factorial-0.1/factorial.egg-info
hard linking myfact/__init__.py -> factorial-0.1/myfact
hard linking myfact/fact.py -> factorial-0.1/myfact
Writing factorial-0.1/setup.cfg
Creating tar archive
removing 'factorial-0.1' (and everything under it)
running upload
Submitting dist/factorial-0.1.tar.gz to https://testpypi.python.org/pypi
Server response (200): OK
Now if you visit the site, you will find
your project is ready to be used by others.
Introduction to Flask
What is flask?
Flask is a web framework. This means flask provides
you with tools, libraries and technologies that allow you to build a web
application. This web application can be some web pages, a blog, a wiki or go as
big as a web-based calendar application or a commercial website.
Flask is part of the categories of the micro-framework. Micro-framework are
normally framework with little to no dependencies to external libraries. This
has pros and cons. Pros would be that the framework is light, there are little
dependency to update and watch for security bugs, cons is that some time you
will have to do more work by yourself or increase yourself the list of
dependencies by adding plugins.
In the case of Flask, its dependencies are:
Note
WSGI is basically a protocol defined so that Python application can
communicate with a web-server and thus be used as web-application outside of
CGI.
What are template engines?
Have you ever built a website? Did you face the problem that to keep the style
of the website consistent, you have had to write multiple times the same text?
Did you ever tried to change the style of such website?
If your website contains only few pages, changing its style will take you some
time but is doable. However, if you have a lot of pages (for example the list of
items you sell in your store), this task become overwhelming.
Using templates you are able to set a basic layout for your pages and mention
which element will change.
This way to can define your header and keept it consistent over all the pages of
your website, and if you need to change your header, you will only have to
update it in one place.
Using a template engine will save you a lot of time when creating your
application but also when updating and maintaining it.
An “Hello world” application in flask
We are going to perform a very basic application with flask.
- Create the structure of the project
mkdir -p hello_flask/{templates,static}
This is the basic structure of your web application:
$ tree hello_flask/
hello_flask/
|-- static
`-- templates
The templates folder is the place where the templates will be put.
The static folder is the place where any files (images, css, javascript)
needed by the web application will be put.
- Create the application file
cd hello_flask
vim hello_flask.py
Put the following code in this file:
#!/usr/bin/env python
import flask
# Create the application.
APP = flask.Flask(__name__)
@APP.route('/')
def index():
""" Displays the index page accessible at '/'
"""
return flask.render_template('index.html')
if __name__ == '__main__':
APP.debug=True
APP.run()
- Create the template
index.html
Put the following code in this file
<!DOCTYPE html>
<html lang='en'>
<head>
<meta charset="utf-8" />
<title>Hello world!</title>
<link type="text/css" rel="stylesheet"
href="{{ url_for('static',
filename='hello.css')}}" />
</head>
<body>
It works!
</body>
</html>
- Run the flask application
Access http://127.0.0.1:5000/ this should simply
show you in black on white the text “It works!” (see Figure below).
Using arguments in Flask
In this section we are going to see how to use a page according to the URL used
by the user.
For this we will update hello_flask.py.
- Add the following entry in
hello_flask.py
@APP.route('/hello/<name>/')
def hello(name):
""" Displays the page greats who ever comes to visit it.
"""
return flask.render_template('hello.html', name=name)
- Create the following template
hello.html
<!DOCTYPE html>
<html lang='en'>
<head>
<meta charset="utf-8" />
<title>Hello</title>
<link type="text/css" rel="stylesheet"
href="{{ url_for('static',
filename='hello.css')}}" />
</head>
<body>
Hello {{name}}
</body>
</html>
- Run the flask application
Access http://127.0.0.1:5000/ this should simply
show you in black on white the text “It works!”.
Access http://127.0.0.1:5000/hello/you
this should return you the text “Hello you” (see Figure below).
Whatever you put behind /hello/ in the URL will be returned to you in the
page.
This is your first use of the template, we set up a variable name in
hello_flask.py (see the return line of the function hello). This
variable is then displayed in the page itself using the syntax {{name}}.
Additional work
Make use of the templates
At the moment for each page we have created a template, this is actually bad
practice, what we should do is create a master template and have each page
use it.
- Create the template
master.html
<!DOCTYPE html>
<html lang='en'>
<head>
<meta charset="utf-8" />
<title>{% block title %}{% endblock %} - Hello Flask!</title>
<link type="text/css" rel="stylesheet"
href="{{ url_for('static',
filename='hello.css')}}" />
</head>
<body>
{% block body %}{% endblock %}
</body>
</html>
- Adjust the template
index.html
{% extends "master.html" %}
{% block title %}Home{% endblock %}
{% block body %}
It works!
{% endblock %}
As you can see, in the master.html template we have defined two sections,
blocks which are named title and body.
In the template index.html we say that this template relies on the template
master.html, then we define the content to put in these two sections
(blocks). In the first block title we say to put the word Home, In the
second block we define what we want to have in the body of the page.
- As an exercise, transform the other template
hello.html to use the
master.html template as well.
- Add link to the front page from the hello page
Flask uses a specific syntax to create links from a page to another. This is
fact generates the link dynamically according to the decorator set to the
function linked to. In addition it takes care of where the application is
deployed.
For example, if you website is deployed at: /myapp/ flask will automatically
happend /myapp/ to all links without the need for you to specify it.
To create a link in a template, flask relies on the function url_for(). This
function takes as first argument the function you want to call (link to). The
following arguments are the arguments of function itself (for example the
argument name of the function hello.
Adjust the template hello.html to add a link to the front page
<a href="{{ url_for('index') }}"><button>Home</button></a>
- As an assignment add a link in the front page to the hello page for you.
