1. An Introduction By Karthik Prakash

2. Day 1 What is Python | Your First Python Program Data Types | Operators and Flow Control | Functions Pythonian Philosophy 3. What is Python Dynamically typed object-oriented programming language Supports multiple paradigms : procedural, object- oriented A robust portable application development platform Minimal Coding Allows rapid prototyping and development Easy to learn, readable and maintainable 4. About the founder Guido Van Rossum - BDFL A Dutch Programmer Employed by Google in 2005 Presently working with Dropbox Guido Van Rossum created Python at CWI Labs - 1989 Birth of Python = Christmas 1989 + About two weeks of time off with no plans + Mac with Lightspeed C on 20 MB hard drive * Named after BBC series Monty Pythons Flying Circus 5. Your First Program Installation Download a copy of (Official) Python http://www.python.org/download Python 2.x https://www.python.org/ftp/python/2.7.6/python-2.7.6.msi Python 3.x https://www.python.org/ftp/python/3.4.1/python-3.4.1.msi Double click to install Add the installed path to the environment PATH variable Also create environment variables specific to different versions 6. Saying Hello World! Using IDLE (Integrated Development Environment) Using the Python command line prompt Run a python program 7. Data Types Everything is an Object Instance of type int Variable number Instance of type type Subclass of object class >>> isinstance(number, int) >>> isinstance(int, type) or int.__class__ >>> int.__bases__>>> type.__bases__ 8. Data Types and Containers Integer Float Boolean Long String Unicode Strings List Tuple Dictionary Sets 9. Operators Operation Syntax Function Addition a + b add(a,b) Subtraction a - b sub(a, b) Multiplication a * b mul(a, b) Division a / b div(a, b) Concatenation seq1 + seq2 concat(seq1, seq2) Containment Test obj in seq contains(seq, obj) Bitwise And a & b and_(a, b) Bitwise or | Exclusive or a|b | a ^ b or_(a, b) | xor(a, b) Bitwise Inversion ~ a invert(a) Exponentiation a ** b pow (a, b) Identity a is b | a is not b is_(a, b) | is_not(a, b) Indexed Assignment obj[i] = value setitem(obj, i, value) Indexed Deletion del obj[i] delitem(obj, i) Negation (Arithmetic) - a neg(a) 10. Operators contd. Operation Syntax Function Negation (Logical) not value not_(value) Sequence Repetition seq * i repeat(seq, i) Slicing seq[i : j] getslice(seq, i, j) Slice Assignment seq[i : j] = values setslice(seq, i, j, values) Slice Deletion del seq[i : j] delslice(seq, i, j) Less Than a < b lt(a, b) Less Than or Equal to a b ge(a, b) Greater Than or Equal to a >= b gt(a, b) 11. Flow Control Indentations Indentation is a critical part of Python Indentation are used for grouping of the statements i.e. Statement of the same block should have the same indentation Python functions have no explicit begin or end, and no curly braces to mark where the function code starts and stops. The only delimiter is a colon (:) and the indentation of the code itself. Use [4 Spaces] as a general role for indentation. Single [TAB] can also be used. 12. Conditional Statements if , elif , else if condition: statements [elif condition: statements] ... else: statements 13. while loop while condition: statements [else: statements] [break] for loop for var in sequence: statements [else: statements] [break] Loops 14. pass statement The pass statement does nothing. It can be used when a statement is required syntactically but the program required no action Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code break statement The break statement is used to break out of a loop i.e. stop the execution of a looping statement, even if the loop condition has not become False or the sequence of items has been completely iterated over. An important note is that if you break out of a for or while loop, any corresponding loop else block is not executed 15. continue statement The continue statement is used to skip the rest of the statements in the current loop block and to continue to the next iteration of the loop. 16. List Comprehension Mapping Lists One of the most powerful features of Python is the list comprehension, which provides a compact way of mapping a list into another list by applying a function to each of the elements of the list. Syntax [mapping-expression for element in source-list] Filtering Lists Mapping Lists can be combined with a filtering mechanism, where some elements in the list are mapped while others are skipped entirely. Syntax [mapping-expression for element in source-list if filter-expression] 17. Functions Defining Functions Function Arguments (Default, keyword, Variable) Documenting Functions 18. Defining Functions Functions are defined using the def keyword. This is followed by an identifier name for the function followed by a pair of parentheses which may enclose some names of variables and the line ends with a colon. Next follows the block of statements that are part of this function Syntax def (argument list): Block of Code.. .. 19. Function Arguments Formal Arguments : Values supplied in function calls. They are specified within the pair of parentheses in the function definition separated by a comma. Function Definition def func (param1, param2) block of code return value Function Call return value = func (arg1 , arg2) 20. default Argument values For some functions, you may want to make some of its parameters as optional and use default values if the user does not want to provide values for such parameters. This is done with the help of default argument values. You can specify default argument values for parameters by following the parameter name in the function definition with the assignment operator (=) followed by the default value. Syntax def func1 ( param1, param2 = value2) Block of code.. >>> func1( 10 ) >>> func1 (10, 20) 21. Keyword Argument Values If you have some functions with many parameters and you want to specify only some of them, then you can give values for such parameters by naming them -this is called keyword arguments. We use the name (keyword) instead of the position (which we have been using all along) to specify the arguments to the function. Syntax Def func1(param1, param2, . param(n-1), param(n)) block of code >>> func1(param(n) = 100, param(n-1) = 90, param1 = 10, param2=20) 22. variable Arguments - *args and **kwargs Variable here means, that you do not know before hand that how many arguments can be passed to your function by the user so in this case you use these two keywords. *args is used to send a non-keyworded variable length argument list to the function **kwargs allows you to pass keyworded variable length of arguments to a function. Order of using *args **kwargs and formal args Syntax func1(fargs, *args, **kwargs) 23. Documenting Functions Python has a nifty feature called documentation strings which is usually referred to by its shorter name docstrings. Docstrings are an important tool that you should make use of since it helps to document the program better and makes it more easy to understand. Syntax def func (param1, paramn) This is a sample function >>> func.__doc__ 24. Pythonian Philosophy The BDFL's (Benevolent Dictator For Life) guiding principles of Python designs are drafted into what is known as the Zen of Python by long time Pythoneer Tim Peters. Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those! 25. Day 1 Ends 26. Day 2 Python I/O|CSV , Configuration File Handling| Excel Connectivity| Modules 27. Python I/O Reading Keyboard Input The raw_input Function: name = raw_input(Enter your Name : ) The input Function: name = input(Enter your Name : ) Reading Command line Arguments >>> import sys >>> print sys.argv 28. Python I/O Contd File I/O The open Method objfile = open(file, [access mode]) The file object attributes Attribute Description objfile.closed True File is closed False Otherwise objfile.mode Returns access mode with which file was opened objfile.name Returns the name of the file The close Method objfile.close() 29. Access Modes Mode Description r Opens file for reading only. This is the Default Mode rb Opens file for reading only in Binary format. This is the Default Mode r+ Opens a file for both reading and writing. rb+ Opens a file for both reading and writing in Binary format w Opens a file for writing only. Overwrites the file if already exists, else creates a new file wb Opens a file for writing only in Binary format. w+ Opens a file for both reading and writing wb+ Opens a file for both reading and writing in Binary format a Opens a file for appending. The file pointer is at the end of the file if the file exists. If the file does not exist, it creates a new file for writing. ab Opens a file for appending in Binary format. a+ Opens a file for both appending and reading ab+ Opens a file for both appending and reading in Binary format 30. Exercises Reading and Writing CSV files Reading and Writing Configuration files Reading and Writing Excel Files 31. Modules A module allows you to logically organize your Python code. Grouping related code into a module makes the code easier to understand and use. A module can define functions, classes, and variables. A module can also include runnable code The import Statement: You can use any Python source file as a module by executing an import statement in some other Python source file Syntax >>> import >>> from import * >>> from import 32. Locating Modules When you import a module, the Python interpreter searches for the module in the following sequences: The current directory. If the module isn't found, Python then searches each directory in the shell variable PYTHONPATH. If all else fails, Python checks the default path. On UNIX, this default path is normally /usr/local/lib/python/. The module search path is stored in the system module sys as the sys.path variable. The sys.path variable contains the current directory, PYTHONPATH, and the installation-dependent default 33. Day 2 Ends 34. Day 3 Exception Handling| OO with Python 35. Exception Handling What is an Exception? An exception is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions. An exception is a Python object that represents an error. How are Exceptions Handled? Syntax try: You do your operations here; except ExceptionI: If there is ExceptionI, then execute this block. except ExceptionII: If there is ExceptionII, then execute this block. else: If there is no exception then execute this block. 36. try block This block contains statements that may throw different type of exceptions except block This block executes the statements when a particular exception occurs in the try block A single try block can have multiple except statements else block You would want the block of code, that executes if an exception did not occur, to form a part of the else block. finally block You can use a finally: block along with a try: block. The finally block is a place to put any code that must execute, whether the try-block raised an exception or not. Note You can provide except clause(s), or a finally clause, but not both. You can not use else clause as well along with a finally clause. 37. OOPs with Python Terminology Class : They are structured representation for objects. Classes implement interfaces by providing Structure and Behavior Structure consists of data and state (data members) Behavior consists of code that specifies how methods are implemented Object : A unique instance of a data structure that is defined by its own class. It comprises of both data members and methods. 38. Class Definition Syntax The class statement creates a new class definition. The name of the class immediately follows the keyword class followed by a colon as follows class : Class Documentation string Constructor :- __init__(Arguments) This is the first method of the class. Its is the initialization method which Python calls when a new instance of the class is created. Creating Class Instance , Objects To create instances of a class, you call the class using class name and pass in whatever arguments its __init__ method accepts. Syntax = (Arguments) 39. Class v.s Instance Variables Class variables are shared in the sense that they are accessed by all objects (instances) of that class. There is only one copy of the class variable and when any one object makes a change to a class variable, the change is reflected in all the other instances as well. Object variables are owned by each individual object/instance of the class. In this case, each object has its own copy of the field i.e. they are not shared and are not related in any way to the field by the same name in a different instance of the same class 40. class Employee: strOrganization = "SafeNet" intEmpCount = 0 def __init__(self, name, empid): self.name = name self.empid = empid, Employee.intEmpCount += 1 Class Variable Instance Variable 41. Data Encapsulation Public (By Default) If an identifier doesn't start with an underscore character "_" it can be accessed from outside, i.e. the value can be read and changed. Private Instance variable names starting with two underscore characters cannot be accessed from outside of the class. At least not directly, but they can be accessed through private name mangling. Protected If an identifier is only preceded by one underscore character, it is a protected member. Protected members can be accessed like public members from outside of class. 42. Inheritance Inheritance is when an object or class is based on another object or class, using the same implementation. It is a mechanism for code reuse. The relationships of objects or classes through inheritance demonstrates a has-a relationship Types A. Multiple Inheritance B. Multilevel Inheritance 43. Day 3 Ends 44. Day 4 XML Processing Regular Expression| DB Connectivity 45. XML Processing XML stands for Xtensible Markup Language XML is a portable, open source language that allows programmers to develop applications that can be read by other applications, regardless of operating system and/or developmental language. XML APIs SAX (Simple API for XML) import xml.sax Here, you register callbacks for events of interest and then let the parser proceed through the document. This is useful when your documents are large or you have memory limitations, it parses the file as it reads it from disk and the entire file is never stored in memory. DOM (Document Object Model) import xml.dom This is a World Wide Web Consortium recommendation wherein the entire file is read into memory and stored in a hierarchical (tree- based) form to represent all the features of an XML document. 46. Regular Expression re Module (Built-In) #-- Import the modules import re #-- Set the pattern pattern = #-- Compile the pattern to get the regexp object = re.compile(pattern, flag options) #-- Search/Match for the pattern result = regexp.search() 47. Database Connectivity The Python standard for database interfaces is the Python DB-API. Most Python database interfaces adhere to this standard. Python Database API provides a standard Interface to interact between Python and different Databases. DB API 2.0 is the current DB API version, and is referenced as PEP 249 The popular Python DB API 2.0 compatible modules are as below :: MySQLdb (MySQL) Available for download at - http://sourceforge.net/projects/mysql-python/ psycopg2 (PostgreSQL) Available for download at - http://www.initd.org/psycopg/ cx_Oracle (Oracle) Available for download at - http://cx-oracle.sourceforge.net/ sqlite (sqlite3) - This is a built-in module and comes with the standard python installation. 48. The DB API provides a minimal standard for working with databases, using Python structures and syntax wherever possible. This API includes the following Importing the API module. Connection objects Opening a connection to the database. Perform Transactions (Commit/Rollback) Cursor Objects SQL Statement execution (manipulate a query the database) Access to results Closing the database connection 49. Day 4 Ends