Unit 1: Introduction - Open Source Tools & Frameworks (05201333) - Parul University - PDF

Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Unit 1: Introduction What Is...

Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Unit 1: Introduction What Is Python? Python is an uncomplicated and robust programming language that delivers both the power and complexity of traditional compiled languages along with the ease-of-use (and then some) of simpler scripting and interpreted languages. You'll be amazed at how quickly you'll pick up the language as well as what kind of things you can do with Python, not to mention the things that have already been done. Your imagination will be the only limit. History of Python Work on Python began in late 1989 by Guido van Rossum, then at CWI in the Netherlands, and eventually released for public distribution in early 1991. How did it all begin? Innovative languages are usually born from one of two motivations: a large well- funded research project or general frustration due to the lack of tools that were needed at the time to accomplish mundane and/or time-consuming tasks, many of which could be automated. At the time, van Rossum was a researcher with considerable language design experience with the interpreted language ABC, also developed at CWI, but he was unsatisfied with its ability to be developed into something more. Some of the tools he envisioned were for performing general system administration tasks, so he also wanted access to the power of system calls that were available through the Amoeba distributed operating system. Although an Amoeba-specific language was given some thought, a generalized language made more sense, and late in 1989, the seeds of Python were sown. Features of Python Although practically a decade in age, Python is still somewhat relatively new to the general software development industry. We should, however, use caution with our use of the word "relatively," as a few years seem like decades when developing on "Internet time." High-level It seems that with every generation of languages, we move to a higher level. Assembly was a godsend for those who struggled with machine code, then came FORTRAN, C, and Pascal, all of which took computing to another plane and created the software development industry. These languages then evolved into the current compiled systems languages C++ and Java. And further still we climb, with powerful, system-accessible, interpreted scripting languages like Tcl, Perl, and Python. Each of these languages has higher-level data structures that reduce the "framework" development time which was  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) once required. Useful types like Python's lists (resizeable arrays) and dictionaries (hash tables) are built into the language. Providing these crucial building blocks encourages their use and minimizes development time as well as code size, resulting in more readable code. Implementing them in C is complicated and often frustrating due to the necessities of using structures and pointers, not to mention repetitious if some forms of the same data structures require implementation for every large project. This initial setup is mitigated somewhat with C++ and its use of templates, but still involves work that may not be directly related to the application that needs to be developed. Object-oriented Object-oriented programming (OOP) adds another dimension to structured and procedural languages where data and logic are discrete elements of programming. OOP allows for associating specific behaviors, characteristics, and/or capabilities with the data that they execute on or are representative of. The object-oriented nature of Python was part of its design from the very beginning. Other OO scripting languages include SmallTalk, the original Xerox PARC language that started it all, and Netscape's JavaScript. Scalable Python is often compared to batch or Unix shell scripting languages. Simple shell scripts handle simple tasks. They grow (indefinitely) in length, but not truly in depth. There is little code-reusability and you are confined to small projects with shell scripts. In fact, even small projects may lead to large and unwieldy scripts. Not so with Python, where you can grow your code from project to project, add other new or existing Python elements, and reuse code at your whim. Python encourages clean code design, high- level structure, and "packaging" of multiple components, all of which deliver the flexibility, consistency, and faster development time required as projects expand in breadth and scope. The term "scalable" is most often applied to measuring hardware throughput and usually refers to additional performance when new hardware is added to a system. We would like to differentiate this comparison with ours here, which tries to inflect the notion that Python provides basic building blocks on which you can build an application, and as those needs expand and grow, Python's pluggable and modular architecture allows your project to flourish as well as maintain manageability. Extensible As the amount of Python code increases in your project, you may still be able to organize it logically due to its dual structured and object-oriented programming environments. Or, better yet, you can separate your code into multiple files, or "modules" and be able to access one module's code and attributes from another. And what is even better is that Python's syntax for accessing modules is the same for all modules, whether you access one from the Python standard library or one you created  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) just a minute ago. Using this feature, you feel like you have just "extended" the language for your own needs, and you actually have. The most critical portions of code, perhaps those hotspots that always show up in profile analysis or areas where performance is absolutely required, are candidates for extensions as well. By "wrapping" lower-level code with Python interfaces, you can create a "compiled" module. But again, the interface is exactly the same as for pure Python modules. Access to code and objects occurs in exactly the same way without any code modification whatsoever. The only thing different about the code now is that you should notice an improvement in performance. Naturally, it all depends on your application and how resource-intensive it is. There are times where it is absolutely advantageous to convert application bottlenecks to compiled code because it will decidedly improve overall performance. This type of extensibility in a language provides engineers with the flexibility to add-on or customize their tools to be more productive, and to develop in a shorter period of time. Although this feature is self-evident in mainstream third-generation languages (3GLs) such as C, C++, and even Java, it is rare among scripting languages. Other than Python, true extensibility in a current scripting language is readily available only in the Tool Command Language (TCL). Python extensions can be written in C and C++ for CPython and in Java for JPython. Portable Python is available on a wide variety of platforms (see Section 1.4), which contributes to its surprisingly rapid growth in today's computing domain. Because Python is written in C, and because of C's portability, Python is available on practically every type of system with a C compiler and general operating system interfaces. Although there are some platform-specific modules, any general Python application written on one system will run with little or no modification on another. Portability applies across multiple architectures as well as operating systems. Easy-to-learn Python has relatively few keywords, simple structure, and a clearly defined syntax. This allows the student to pick up the language in a relatively short period of time. There is no extra effort wasted in learning completely foreign concepts or unfamiliar keywords and syntax. What may perhaps be new to beginners is the object-oriented nature of Python. Those who are not fully-versed in the ways of object-oriented programming (OOP) may be apprehensive about jumping straight into Python, but OOP is neither necessary nor mandatory. Getting started is easy, and you can pick up OOP and use when you are ready to. Easy-to-read  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Conspicuously absent from the Python syntax are the usual symbols found in other languages for accessing variables, code block definition, and pattern-matching. These include: dollar signs ( $ ), semicolons ( ; ), tildes ( ~ ), etc. Without all these distractions, Python code is much more clearly defined and visible to the eyes. In addition, much to many programmers' dismay (and relief), Python does not give as much flexibility to write obfuscated code as compared to other languages, making it easier for others to understand your code faster and vice versa. Being easy-to-read usually leads to a language's being easy-to-learn, as we described above. We would even venture to claim that Python code is fairly understandable, even to a reader who has never seen a single line of Python before. Take a look at the examples in the next chapter, Getting Started, and let us know how well you fare. Easy-to-maintain Maintaining source code is part of the software development lifecycle. Your software is permanent until it is replaced or obsoleted, and in the meantime, it is more likely that your code will outlive you in your current position. Much of Python's success is that source code is fairly easy-to-maintain, dependent, of course, on size and complexity. However, this conclusion is not difficult to draw given that Python is easy-to-learn and easy-to-read. Another motivating advantage of Python is that upon reviewing a script you wrote six months ago, you are less likely to get lost or require pulling out a reference book to get reacquainted with your software. Robust Nothing is more powerful than allowing a programmer to recognize error conditions and provide a software handler when such errors occur. Python provides "safe and sane" exits on errors, allowing the programmer to be in the driver's seat. When Python exits due to fatal errors, a complete stack trace is available, providing an indication of where and how the error occurred. Python errors generate "exceptions," and the stack trace will indicate the name and type of exception that took place. Python also provides the programmer with the ability to recognize exceptions and take appropriate action, if necessary. These "exception handlers" can be written to take specific courses of action when exceptions arise, either defusing the problem, redirecting program flow, or taking clean-up or other maintenance measures before shutting down the application gracefully. In either case, the debugging part of the development cycle is reduced considerably due to Python's ability to help pinpoint the problem faster rather than just being on the hunt alone. Python's robustness is beneficial for both the software designer as well as for the user. There is also some accountability when certain errors occur which are not handled properly. The stack trace which is generated as a result of an error reveals not only the type and location of the error, but also in which module the erroneous code resides. Effective as a Rapid Prototyping Tool  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) We've mentioned before how Python is easy-to-learn and easy-to-read. But, you say, so is a language like BASIC. What more can Python do? Unlike self-contained and less flexible languages, Python has so many different interfaces to other systems that it is powerful enough in features and robust enough that entire systems can be prototyped completely in Python. Obviously, the same systems can be completed in traditional compiled languages, but Python's simplicity of engineering allows us to do the same thing and still be home in time for supper. Also, numerous external libraries have already been developed for Python, so whatever your application is, someone may have traveled down that road before. All you need to do is plug-'n'-play (some assembly required, as usual). Some of these libraries include: networking, Internet/Web/CGI, graphics and graphical user interface (GUI) development (Tkinter), imaging (PIL), numerical computation and analysis (NumPy), database access, hypertext (HTML, XML, SGML, etc.), operating system extensions, audio/visual, programming tools, and many others. A Memory Manager The biggest pitfall with programming in C or C++ is that the responsibility of memory management is in the hands of the developer. Even if the application has very little to do with memory access, memory modification, and memory management, the programmer must still perform those duties, in addition to the original task at hand. This places an unnecessary burden and responsibility upon the developer and often provides an extended distraction. Because memory management is performed by the Python interpreter, the application developer is able to steer clear of memory issues and focus on the immediate goal of just creating the application that was planned in the first place. This lead to fewer bugs, a more robust application, and shorter overall development time. Interpreted and (Byte-) Compiled Python is classified as an interpreted language, meaning that compile-time is no longer a factor during development. Traditionally purely interpreted languages are almost always slower than compiled languages because execution does not take place in a system's native binary language. However, like Java, Python is actually byte-compiled, resulting in an intermediate form closer to machine language. This improves Python's performance, yet allows it to retain all the advantages of interpreted languages. NOTE Python source files typically end with the.py extension. The source is byte-compiled upon being loaded by the interpreter or by being byte-compiled explicitly. Depending on how you invoke the interpreter, it may leave behind byte-compiled files with a.pyc or.pyo extension.  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) The Basic Element of Python A Python program, sometimes called a script, is a sequence of definitions and commands. These definitions are evaluated and the commands are executed by the Python interpreter in something called the shell. Typically, a new shell is created whenever execution of a program begins. In most cases, a window is associated with the shell. A command, often called a statement, instructs the interpreter to do something. For example, the statement print ‘MCA rule!’ instructs the interpreter to output the string MCA rule! to the window associated with the shell. The sequence of commands print ‘MCA rule!’ print ‘In India!’ print ‘MCA rule,’, ‘In India!’ causes the interpreter to produce the output MCA rule! In India! MCA rule, In India! Notice that two values were passed to print in the third statement. The print command takes a variable number of values and prints them, separated by a space character, in the order in which they appear. Objects, expressions, and numerical types Objects are the core things that Python programs manipulate. Every object has a type that defines the kinds of things that programs can do with objects of that type. Types are either scalar or non-scalar. Scalar objects are indivisible. Think of them as the atoms of the language. Non-scalar objects, for example strings, have internal structure. Python has for types of scalar objects: int is used to represent integers. Literals of type int are written in the obvious way, eg. 3 or 10002 or -4. float is used to represent real numbers. Literals of type float are also written in the obvious way, eg. 3.0 or 3.17 or -28.72. bool is used to represent the Boolean values True and False.  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) none is a type with a single value. Objects and operators can be combined to form expressions, each of which denotes an object of some type. We will refer to this as the value of the expression. For example, the expression 3+2 denotes the object 5 of type int, and the expression 3.0 + 2.0 denotes the object 5.0 of type float. Python Basic Syntax The Python language has many similarities to Perl, C, and Java. However, there are some definite differences between the languages. First Python Program Let us execute programs in different modes of programming. Interactive Mode Programming Invoking the interpreter without passing a script file as a parameter brings up the following prompt − $ python Python 2.4.3 (#1, Nov 11 2010, 13:34:43) [GCC 4.1.2 20080704 (Red Hat 4.1.2-48)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> Type the following text at the Python prompt and press the Enter: >>> print "Hello, Python!" If you are running new version of Python, then you would need to use print statement with parenthesis as in print ("Hello, Python!");. However in Python version 2.4.3, this produces the following result: Hello, Python! Script Mode Programming Invoking the interpreter with a script parameter begins execution of the script and continues until the script is finished. When the script is finished, the interpreter is no longer active.  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Let us write a simple Python program in a script. Python files have extension.py. Type the following source code in a test.py file: print "Hello, Python!" We assume that you have Python interpreter set in PATH variable. Now, try to run this program as follows − $ python test.py This produces the following result: Hello, Python! Let us try another way to execute a Python script. Here is the modified test.py file − #!/usr/bin/python print "Hello, Python!" We assume that you have Python interpreter available in /usr/bin directory. Now, try to run this program as follows − $ chmod +x test.py # This is to make file executable $./test.py This produces the following result − Hello, Python! Python Identifiers A Python identifier is a name used to identify a variable, function, class, module or other object. An identifier starts with a letter A to Z or a to z or an underscore (_) followed by zero or more letters, underscores and digits (0 to 9). Python does not allow punctuation characters such as @, $, and % within identifiers. Python is a case sensitive programming language. Thus, Manpowerand manpower are two different identifiers in Python. Here are naming conventions for Python identifiers −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Class names start with an uppercase letter. All other identifiers start with a lowercase letter. Starting an identifier with a single leading underscore indicates that the identifier is private. Starting an identifier with two leading underscores indicates a strongly private identifier. If the identifier also ends with two trailing underscores, the identifier is a language-defined special name. Reserved Words The following list shows the Python keywords. These are reserved words and you cannot use them as constant or variable or any other identifier names. All the Python keywords contain lowercase letters only. And exec Not Assert finally or Break for pass Class from print Continue global raise def if return del import try elif in while else is with  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) except lambda yield Lines and Indentation Python provides no braces to indicate blocks of code for class and function definitions or flow control. Blocks of code are denoted by line indentation, which is rigidly enforced. The number of spaces in the indentation is variable, but all statements within the block must be indented the same amount. For example − if True: print "True" else: print "False" However, the following block generates an error − if True: print "Answer" print "True" else: print "Answer" print "False" Thus, in Python all the continuous lines indented with same number of spaces would form a block. The following example has various statement blocks − Note: Do not try to understand the logic at this point of time. Just make sure you understood various blocks even if they are without braces. #!/usr/bin/python import sys try: # open file stream  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) file = open(file_name, "w") except IOError: print "There was an error writing to", file_name sys.exit() print "Enter '", file_finish, print "' When finished" while file_text != file_finish: file_text = raw_input("Enter text: ") if file_text == file_finish: # close the file file.close break file.write(file_text) file.write("\n") file.close() file_name = raw_input("Enter filename: ") if len(file_name) == 0: print "Next time please enter something" sys.exit() try: file = open(file_name, "r") except IOError: print "There was an error reading file" sys.exit() file_text = file.read() file.close() print file_text Multi-Line Statements Statements in Python typically end with a new line. Python does, however, allow the use of the line continuation character (\) to denote that the line should continue. For example −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) total = item_one + \ item_two + \ item_three Statements contained within the [], {}, or () brackets do not need to use the line continuation character. For example − days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'] Quotation in Python Python accepts single ('), double (") and triple (''' or """) quotes to denote string literals, as long as the same type of quote starts and ends the string. The triple quotes are used to span the string across multiple lines. For example, all the following are legal − word = 'word' sentence = "This is a sentence." paragraph = """This is a paragraph. It is made up of multiple lines and sentences.""" Comments in Python A hash sign (#) that is not inside a string literal begins a comment. All characters after the # and up to the end of the physical line are part of the comment and the Python interpreter ignores them. #!/usr/bin/python # First comment print "Hello, Python!" # second comment This produces the following result − Hello, Python! You can type a comment on the same line after a statement or expression − name = "Madisetti" # This is again comment  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) You can comment multiple lines as follows − # This is a comment. # This is a comment, too. # This is a comment, too. # I said that already. Using Blank Lines A line containing only whitespace, possibly with a comment, is known as a blank line and Python totally ignores it. In an interactive interpreter session, you must enter an empty physical line to terminate a multiline statement. Waiting for the User The following line of the program displays the prompt, the statement saying “Press the enter key to exit”, and waits for the user to take action − #!/usr/bin/python raw_input("\n\nPress the enter key to exit.") Here, "\n\n" is used to create two new lines before displaying the actual line. Once the user presses the key, the program ends. This is a nice trick to keep a console window open until the user is done with an application. Multiple Statements on a Single Line The semicolon ( ; ) allows multiple statements on the single line given that neither statement starts a new code block. Here is a sample snip using the semicolon − import sys; x = 'foo'; sys.stdout.write(x + '\n') Multiple Statement Groups as Suites A group of individual statements, which make a single code block are calledsuites in Python. Compound or complex statements, such as if, while, def, and class require a header line and a suite. Header lines begin the statement (with the keyword) and terminate with a colon ( : ) and are followed by one or more lines which make up the suite. For example −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) if expression : suite elif expression : suite else : suite Command Line Arguments Many programs can be run to provide you with some basic information about how they should be run. Python enables you to do this with -h − $ python -h usage: python [option]... [-c cmd | -m mod | file | -] [arg]... Options and arguments (and corresponding environment variables): -c cmd : program passed in as string (terminates option list) -d : debug output from parser (also PYTHONDEBUG=x) -E : ignore environment variables (such as PYTHONPATH) -h : print this help message and exit [ etc. ] Python Variable Types Variables are nothing but reserved memory locations to store values. This means that when you create a variable you reserve some space in memory. Based on the data type of a variable, the interpreter allocates memory and decides what can be stored in the reserved memory. Therefore, by assigning different data types to variables, you can store integers, decimals or characters in these variables. Assigning Values to Variables Python variables do not need explicit declaration to reserve memory space. The declaration happens automatically when you assign a value to a variable. The equal sign (=) is used to assign values to variables. The operand to the left of the = operator is the name of the variable and the operand to the right of the = operator is the value stored in the variable. For example −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) #!/usr/bin/python counter = 100 # An integer assignment miles = 1000.0 # A floating point name = "John" # A string print counter print miles print name Here, 100, 1000.0 and "John" are the values assigned to counter, miles, andname variables, respectively. This produces the following result − 100 1000.0 John Multiple Assignment Python allows you to assign a single value to several variables simultaneously. For example − a=b=c=1 Here, an integer object is created with the value 1, and all three variables are assigned to the same memory location. You can also assign multiple objects to multiple variables. For example − a, b, c = 1, 2, "john" Here, two integer objects with values 1 and 2 are assigned to variables a and b respectively, and one string object with the value "john" is assigned to the variable c. Standard Data Types The data stored in memory can be of many types. For example, a person's age is stored as a numeric value and his or her address is stored as alphanumeric characters. Python has various standard data types that are used to define the operations possible on them and the storage method for each of them.  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Python has five standard data types − Numbers String List Tuple Dictionary Python Numbers Number data types store numeric values. Number objects are created when you assign a value to them. For example − var1 = 1 var2 = 10 You can also delete the reference to a number object by using the del statement. The syntax of the del statement is − del var1[,var2[,var3[....,varN]]]] You can delete a single object or multiple objects by using the del statement. For example − del var del var_a, var_b Python supports four different numerical types − int (signed integers) long (long integers, they can also be represented in octal and hexadecimal) float (floating point real values) complex (complex numbers) Examples Here are some examples of numbers −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) int long float complex 10 51924361L 0.0 3.14j 100 -0x19323L 15.20 45.j -786 0122L -21.9 9.322e-36j 080 0xDEFABCECBDAECBFBAEl 32.3+e18.876j -0490 535633629843L -90. -.6545+0J -0x260 -052318172735L -32.54e100 3e+26J 0x69 -4721885298529L 70.2-E12 4.53e-7j Python allows you to use a lowercase l with long, but it is recommended that you use only an uppercase L to avoid confusion with the number 1. Python displays long integers with an uppercase L. A complex number consists of an ordered pair of real floating-point numbers denoted by x + yj, where x and y are the real numbers and j is the imaginary unit. Python Strings Strings in Python are identified as a contiguous set of characters represented in the quotation marks. Python allows for either pairs of single or double quotes. Subsets of strings can be taken using the slice operator ([ ] and [:] ) with indexes starting at 0 in the beginning of the string and working their way from -1 at the end. The plus (+) sign is the string concatenation operator and the asterisk (*) is the repetition operator. For example − #!/usr/bin/python  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) str = 'Hello World!' print str # Prints complete string print str # Prints first character of the string print str[2:5] # Prints characters starting from 3rd to 5th print str[2:] # Prints string starting from 3rd character print str * 2 # Prints string two times print str + "TEST" # Prints concatenated string This will produce the following result − Hello World! H llo llo World! Hello World!Hello World! Hello World!TEST Python Lists Lists are the most versatile of Python's compound data types. A list contains items separated by commas and enclosed within square brackets ([]). To some extent, lists are similar to arrays in C. One difference between them is that all the items belonging to a list can be of different data type. The values stored in a list can be accessed using the slice operator ([ ] and [:]) with indexes starting at 0 in the beginning of the list and working their way to end -1. The plus (+) sign is the list concatenation operator, and the asterisk (*) is the repetition operator. For example − #!/usr/bin/python list = [ 'abcd', 786 , 2.23, 'john', 70.2 ] tinylist = [123, 'john'] print list # Prints complete list print list # Prints first element of the list  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) print list[1:3] # Prints elements starting from 2nd till 3rd print list[2:] # Prints elements starting from 3rd element print tinylist * 2 # Prints list two times print list + tinylist # Prints concatenated lists This produce the following result − ['abcd', 786, 2.23, 'john', 70.200000000000003] abcd [786, 2.23] [2.23, 'john', 70.200000000000003] [123, 'john', 123, 'john'] ['abcd', 786, 2.23, 'john', 70.200000000000003, 123, 'john'] Python Tuples A tuple is another sequence data type that is similar to the list. A tuple consists of a number of values separated by commas. Unlike lists, however, tuples are enclosed within parentheses. The main differences between lists and tuples are: Lists are enclosed in brackets ( [ ] ) and their elements and size can be changed, while tuples are enclosed in parentheses ( ( ) ) and cannot be updated. Tuples can be thought of as read-only lists. For example − #!/usr/bin/python tuple = ( 'abcd', 786 , 2.23, 'john', 70.2 ) tinytuple = (123, 'john') print tuple # Prints complete list print tuple # Prints first element of the list print tuple[1:3] # Prints elements starting from 2nd till 3rd print tuple[2:] # Prints elements starting from 3rd element print tinytuple * 2 # Prints list two times print tuple + tinytuple # Prints concatenated lists This produce the following result −  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) ('abcd', 786, 2.23, 'john', 70.200000000000003) abcd (786, 2.23) (2.23, 'john', 70.200000000000003) (123, 'john', 123, 'john') ('abcd', 786, 2.23, 'john', 70.200000000000003, 123, 'john') The following code is invalid with tuple, because we attempted to update a tuple, which is not allowed. Similar case is possible with lists − #!/usr/bin/python tuple = ( 'abcd', 786 , 2.23, 'john', 70.2 ) list = [ 'abcd', 786 , 2.23, 'john', 70.2 ] tuple = 1000 # Invalid syntax with tuple list = 1000 # Valid syntax with list Python Dictionary Python's dictionaries are kind of hash table type. They work like associative arrays or hashes found in Perl and consist of key-value pairs. A dictionary key can be almost any Python type, but are usually numbers or strings. Values, on the other hand, can be any arbitrary Python object. Dictionaries are enclosed by curly braces ({ }) and values can be assigned and accessed using square braces ([]). For example − #!/usr/bin/python dict = {} dict['one'] = "This is one" dict = "This is two" tinydict = {'name': 'john','code':6734, 'dept': 'sales'} print dict['one'] # Prints value for 'one' key print dict # Prints value for 2 key  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) print tinydict # Prints complete dictionary print tinydict.keys() # Prints all the keys print tinydict.values() # Prints all the values This produce the following result − This is one This is two {'dept': 'sales', 'code': 6734, 'name': 'john'} ['dept', 'code', 'name'] ['sales', 6734, 'john'] Dictionaries have no concept of order among elements. It is incorrect to say that the elements are "out of order"; they are simply unordered. Data Type Conversion Sometimes, you may need to perform conversions between the built-in types. To convert between types, you simply use the type name as a function. There are several built-in functions to perform conversion from one data type to another. These functions return a new object representing the converted value. Function Description int(x [,base]) Converts x to an integer. base specifies the base if x is a string. long(x [,base] ) Converts x to a long integer. base specifies the base if x is a string. float(x) Converts x to a floating-point number. complex(real Creates a complex number. [,imag]) str(x) Converts object x to a string representation.  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) repr(x) Converts object x to an expression string. eval(str) Evaluates a string and returns an object. tuple(s) Converts s to a tuple. list(s) Converts s to a list. set(s) Converts s to a set. dict(d) Creates a dictionary. d must be a sequence of (key,value) tuples. frozenset(s) Converts s to a frozen set. chr(x) Converts an integer to a character. unichr(x) Converts an integer to a Unicode character. ord(x) Converts a single character to its integer value. hex(x) Converts an integer to a hexadecimal string. oct(x) Converts an integer to an octal string. Python Basic Operators Operators are the constructs which can manipulate the value of operands. Consider the expression 4 + 5 = 9. Here, 4 and 5 are called operands and + is called operator. Types of Operator  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) Python language supports the following types of operators. Arithmetic Operators Comparison (Relational) Operators Assignment Operators Logical Operators Bitwise Operators Membership Operators Identity Operators Let us have a look on all operators one by one. Python Arithmetic Operators Assume variable a holds 10 and variable b holds 20, then − [ Show Example ] Operator Description Example + Addition Adds values on either side of the operator. a+b= 30 - Subtraction Subtracts right hand operand from left hand operand. a–b=- 10 * Multiplies values on either side of the operator a*b= Multiplication 200 / Division Divides left hand operand by right hand operand b/a=2 % Modulus Divides left hand operand by right hand operand and b%a=0 returns remainder  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) ** Exponent Performs exponential (power) calculation on operators a**b =10 to the power 20 // Floor Division - The division of operands where the result 9//2 = 4 is the quotient in which the digits after the decimal point and are removed. But if one of the operands is negative, the 9.0//2.0 result is floored, i.e., rounded away from zero (towards = 4.0, - negative infinity): 11//3 = -4, - 11.0//3 = -4.0 Python Comparison Operators These operators compare the values on either sides of them and decide the relation among them. They are also called Relational operators. Assume variable a holds 10 and variable b holds 20, then − [ Show Example ] Operator Description Example == If the values of two operands are equal, then the condition (a == b) becomes true. is not true. != If values of two operands are not equal, then condition becomes true. If values of two operands are not equal, then condition (a b) becomes true. is true. This is similar to != operator. > If the value of left operand is greater than the value of right (a > b) is  Parul University Faculty of IT & Computer Science Department of MCA Semester – 5 Open Source Tools & Frameworks (05201333) operand, then condition becomes true. not true. < If the value of left operand is less than the value of right (a < b) is operand, then condition becomes true. true. >= If the value of left operand is greater than or equal to the (a >= b) value of right operand, then condition becomes true. is not true.