Python computer programming software

In Python, external libraries can be used using the keyword import[library]. For individual functions, you can use from [funcname] or [libname] import. Take a look at the following sample syntax:. The Python programing language comes with a lot of libraries, to begin with. The letter r before the filename string is used to prevent backslash escaping. This condition checks that a is greater than one and also less than three. Check for how many items a condition is true.

Global variables are called so because they are declared outside functions and are readable without special declarations. Otherwise, Python will bind the object to a new local variable. Take a look at the sample syntax below:.

Python knows that it an object will be bound to it later and creates a new, local object instead of accessing the global one. There is a lot to python than what is mentioned above. As always, the key to learning programming, especially Python, is to keep practicing and experimenting. Python has a huge array of libraries and vast functionality that you can discover and tap into. You can also find some other great books and resources to get more in-depth about Python.

From classes and error handling to subsets and more, your journey to Python has just started. There will be syntax errors galore, but keep going at it and utilize the excellent Python community and resources available, and you will be fluent in it in no time.

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Email ID. The course to be developed at Chicago will address the aspects of computing that we feel everyone must know in order to be a knowledgeable computer user: Basic computer organization.

Students will learn the basics of how computers work and how they are put together. Topics would include Boolean algebra, logic, and simple computer architecture e. Simply stated, this is the stuff going on "under the hood"--stated in easily understandable terms. Ultimately, we would hope to demystify computers as much as possible. An introduction to programming. This will introduce different ways people have programmed computers.

Students will learn about procedural, functional, and object oriented programming, but in an informal manner. Rather than trying to turn students into professional programmers, the goal will be to introduce students to some of the ways people have tried to simplify the use of computers.

Software architecture. In the future, it is increasingly likely that computers will be programmed largely by assembling existing software components and writing a small amount of glue code. In order to make this possible, an understanding of how software is put together will be essential. Student will learn about software design and software organization just what in the heck are all those DLL's anyway? Debugging and problem solving. How to survive when all else fails--without having to call customer support.

At all levels of education, it is vitally important that usability studies for both the software tools and the textbook material are conducted and evaluated. This is the only way that such materials and tools will be improved and tailored for the specific age and experience groups. Usability studies of the type conducted by Prof. Pausch of CMU will be developed and conducted at all three levels of teaching.

Of course, we will also develop traditional tests to allow teachers to measure individual students' performance. If our experience with Python's popularity is any indication, we won't have to: others are eager to participate in this experiment. The courses will use the new development environment described in the next section. As an incentive to make programming more "fun", we intend to connect the development environment to an existing programmable 3-D game-playing engine as used in popular computer games.

Several such engines are or will likely become available for use with Python; we will select one and create an interface library for it suitable to our audience.

Why use a 3-D game-playing engine? The experiences with Logo show that graphics are a good way to catch a younger audience's attention, but its 2-D graphics look somewhat boring compared to the video games teenagers are familiar with these days.

Alice is a good example of an engaging 3-D graphics environment. Knowbot programs are independent mobile programs capable of migrating between Knowbot "service stations" specially equipped hosts throughout the Internet. Service stations provide services to the Knowbot programs such as search services, digital object repositories, auction services, etc.

Another way of looking at a Knowbot program is as a small component working within the larger framework of the service station. A Knowbot program is a modular, independent program that can be easily written to move around the Internet, but which has powerful functionality due to its integration into the framework, and its use of the environments it encounters.

We will be exploring several ideas on how to use Knowbot programs in the teaching curriculum. We imagine cooperative gaming scenarios, where students can create Knowbot programs that exhibit certain behaviors and must work together to solve a common problem. This would be a great way to motivate students from all over the Internet to collaborate. We might envision treasure hunts, where students have to apply the programming skills they just learned in order to discover and migrate to a service station, solve a puzzle at the site, and receive the treasure.

We might design distributed virtual simulations, similar to MIT's Virtual Fishtank [Fishtank], where students can create their own discrete elements of a complex system e. Because the Knowbot technology allows for highly distributed, very complex interactions across the entire Internet, it gives us a unique platform for experimenting with rich cooperative learning opportunities. Programming Tools We will design and build a programming environment specifically intended to support the teaching of programming to users with no previous programming experience.

Our aim is provide tools to support users when they are learning programming and when they are employing those skills in their homes and offices. We believe that most ordinary users will employ their programming skills to customized and extend their computing environment.

Rather than writing new programs from scratch, most people will add new code to existing programs. There are three significant challenges that must be addressed by programming tools aimed at this audience.

First, the environment must significantly ease the burden of writing, installing, and debugging new programs. The current generation of development tools can be cumbersome for expert users, let alone novices. We must focus careful design and usability studies on the development of new programming environments. The second challenge is to provide for the continual evolution and modification of software artifacts by consumers as well as producers.

We will develop tools to help users understand the structure of large programs so that they can identify where to make changes and what impact those changes will have. Our tools will also help users manage and configure software, so that individual components can be replaced or upgraded over time.

These tools will help users share new and modified programs by automatically tracking versions and dependencies. The final challenge is to build tools that will be useful in a ubiquitous computing environment. The desktop computing environment will be rapidly overtaken by networks of computer-controlled devices and physical systems.

This new environment exacerbates problems of installing, debugging, and managing software. It also poses new challenges for system designers to build software that allows end-user customization. This section is organized around these three challenges. The first section discusses the proposed programming environment.

The second section discusses program analysis and configuration management tools. The third section discusses application frameworks to support end-user programmability of ubiquitous computing environments. Our approach to this problem will be to look at how traditional programming tools, such as editors, debuggers, and class browsers, can be augmented and enhanced by more advanced ways of analyzing, inspecting, and understanding programs.

Programming Environments The most basic activities of programmers are editing source code, running the program to test it, and debugging the program. Python doesn't have a separate compilation phase.

A programming environment must of course support these activities. We have been developing a portable programming environment for Python named IDLE, which allows the user to execute individual statements interactively. It is mostly targeted at experienced programmers, but will server as a starting point for an environment for absolute beginners. IDLE only scratches the surface of the kind of programming environment needed to help novice programmers. For example, its source code colorization and indentation features could be replaced by a much more powerful program checker which would point out all syntax errors, undefined identifiers, type mismatches, and so on, while the user is typing like a spelling checker.

The debugger could support retracing execution steps, editing the source code of the running program, etc. The program editor could support a flexible form of template-based editing the Alice group has very good experiences with this in their limited domain. The undo feature, which currently allows undoing source code changes only, could be extended to undo changes to the run-time state of the program or even side-effects to the environment within reason--we can't expect to undo printing or the sending of an email message.

As an example, the Alice software provides full undo for all actions involving changes to the 3D world it manages. Two specific areas of work are undo and an extended type checker.

Along with version control, auto-save, and other features, the ability to rollback an unlimited number of near-term changes means that the programmer has more leeway to experiment and learn. However, most undo implementations are quite limited in their scope. Our approach will investigate such concepts as selective and global undo. One of the problems with traditional undo is where some undesirable changes overlap with some desirable changes; the programmer often has to lose the desirable ones to eliminate the undesirable ones.

With selective undo, changes can be localized to a finer granularity. For example, suppose a programmer made three changes to function A at the top of the file intermingled with four changes to function Z at the bottom of the file. Now the programmer discovers that function A should never have changed; selective undo allows changes to function A to be rolled back without affecting the changes to function Z. Global undo is similar to what version control provides, where system level changes can be tagged and rolled back when they adversely affect the system.

Where global undo differs however is that no a-priori decision has to be made about tagging. We also plan to enhance the development environment with type-checking tools that help programmers find mistakes in their code and improve the performance of compiled code.

Python is a dynamically typed language, like Smalltalk or Scheme, that relies on extensive runtime checking to ensure the correct use of built-in operations. Soft typing [Cartwright] is a mechanism to statically check programs in dynamically typed languages to detect errors and eliminate unnecessary runtime checks; the analysis is integrated with the programming environment rather than with the language runtime. This mechanism has been applied to Scheme [Wright][Flanagan]. We will develop a similar type checking mechanism for Python.

The key challenges for developing a soft type system for Python are extending the analysis to objects and modules and accommodating Python's extremely dynamic execution environment, which allows modification of classes and instances at runtime. Hugunin [Hugunin2] demonstrated performance improvements of up to three orders of magnitude for JPython. Program Analysis and Configuration Management We will augment the basic programming environment with a collection of tools that aid users in understanding large programs, so that they can customize and modify them, and in managing the installation and configuration of software, so that they can upgrade the software without destroying their modifications.

We will also develop and extend tools that build interfaces between low-level software components and scripting languages, which will enable greater user control over the low-level components. It is important that these program analysis tools, aimed at beginners, are simple to use, and that they allow grasping the results of the analysis without requiring detailed understanding of how the analysis is done. At the same time, we strive for our tools to be powerful enough for experts too--they should accommodate increasing expert levels as a user becomes more familiar with the tool, and they should be able to handle large programs.

An analysis tool that can effectively be applied to, say, the source code of the Netscape browser, is much more useful than one that only works well for small example programs. Our focus on relatively inexperienced programmers requires that our tools contain excellent visualization modules, which can present the discovered design to the user without causing information overload. For example, current visualization tools often lack "common sense," and will mindlessly produce large tree or graph diagrams spanning many pages that consist of endlessly repeated similar substructures; this effect causes the user to lose sight of the forest through the trees.

We plan to focus initially on tools that work with Python programs. However, most of the techniques for program analysis that we expect to develop are essentially independent of the language being used. We will also investigate the use of tools that cross language boundaries, so that users can consider the effects of changes at the scripting level on low-level components written in C or Java.

The program analysis tools will help users understand the gross structure of programs by identifying the relationships between static program components.

One example of this kind of analysis tool is Womble, which extracts object models from Java bytecodes [Jackson]. Womble extracts object model directly from source or object code, rather than from a formal specification. Because this method does not rely on the existence of a formal specification such as a UML model , we believe it is more accessible to ordinary users.

A similar approach can be used for analyzing Python programs, although Python's dynamic typing and "first class" functions and classes pose significant challenges. We will also investigate program slicing [Tip] and program paths [Ball] as techniques to help users understand where to make changes and what impact those changes will have.

Slicing is a well-known technique for identifying subsets of programs that affect a particular variable. Analysis via program paths shows the various possible execution paths through a body of code.

Each technique has value for testing and debugging programs. Two challenges are to apply these techniques across language boundaries and to identify abstractions boundaries that are implicit in the code but expressed inexactly by the type system. Womble, for example, recognizes that Java container classes are not an interesting part of an application's object model; it merely represents a relationship between the object that uses the container and the contained objects.

Similarly, a program slice could be performed with respect to certain abstraction boundaries and aspects of the code. A program slice that presents the functional aspect of a program without including concurrency-specific code may be useful for understanding the program structure. Of course, the concurrency-specific code is important to understand, but may be a separate concern. We will work to improve and extend SWIG to improve the amount of automatic processing that can be done and to allow greater customization of bindings by users.

We will also enhance SWIG to produce more natural language bindings; for example, when strings are passed as function parameters in C programs they are typically represented by two variables, a pointer and a length. If users are empowered to modify and customize code, they will be challenged to maintain those changes when the underlying software is upgraded or when system components are replaced. Version control is already a vexing problem for software developers, who must ensure that their products are compatible with many operating systems and shared libraries.

Users will also want to share their customizations with others - co-workers or friends and relatives. We will provide tools to help users maintain and share programs and modifications to programs. One of the most important issues that our programming tools research will address is the burden that user customizability will place on system configuration management. When a user makes a change to an application, what assurances are there that a future update to the application by the vendor will be compatible with these changes?

How does the user himself keep track of just what changes they've made to an application? What happens when a future version of the product adds a feature, previously missing, that the user has added in a different form?

Our tools will help users keep track of changes they've made, through successive revisions, and help users merge their changes back when the primary application itself has been modified or updated by the vendor.

A key to this approach is identifying each version of software and a simple language for describing its properties and dependencies. For example, we intend to improve version control systems so that they track changes at different abstraction levels and granularities than current systems, e. The tool will automatically identify dependencies on other libraries and component.

We will investigate ways to integrate testing frameworks into the configuration management systems so that when the primary application is upgraded, each feature change a user has installed, will be merged and tested. We will also create community-enhancing tools that make it easier to share, integrate, and use peer-developed software.

Issues to be addressed include dependency management between packages, ease of installation and de-installation! Software must be described and classified, and forums for discussion, feedback, bug reports and patches both to the maintainer and from the maintainer must be established.

Of greatest importance is that these community-building tools must be primarily managed by the community itself; they must be highly distributed, replicated, and secure.

For example, users could share information about compatibility problems between various libraries; when coupled with automatic distribution tools, this information could prevent software upgrades that prevent other applications from working. There are several existing tools that attempt to address some or all of these issues.

It is distributed and replicated, and it makes the job of installing Perl modules easier though not always easy enough. It is not, however, very adaptable to other types of archival material. Within the Python community, the distribution-utilities special interest group is attempting to make the distribution and installation of third party software easier, but it does not address the wider issues involved, and again, it is narrowly focused on Python software [Distutils].

The most ambitious related work we are familiar with is the self-updating software [Liskov] project. Application Frameworks We will examine the impact of future changes in computing and communications on the way users control computers, and the implications of such developments as near infinite bandwidth, greater accessibility to much more powerful computers, the ubiquity of computing resources, and a much greater level of internetworking, even at the micro-device level.

These changes affect both what kinds of programs users will write and what kinds of computers those programs will run on. Our approach in this area will be to work with experimental and prototype systems in order to understand how end user programmability should be exposed. As these technologies mature, we may incorporate our experiences into teaching materials. Many non-programmers begin writing small programs that are used within specific domains and within the contexts of application frameworks.

The spreadsheet is perhaps the best example of programming in a limited context. It provides a limited language targeted specifically at manipulating tables of numerical data. A macro facility, which is one part of a spreadsheet's programming suite, aids the use of many applications, allowing users to automate repetitive tasks. A second example can be found in MS Word macros, which are also a common form of user customization.

In the future, non-programmers will be using a plethora of information appliances. One example area where programmability of these devices will significantly improve their usefulness is in the management of information flow--and perhaps more crucially, the limiting of information flow.

A person may have messages arriving over many media--text, voice, video--and access them via many devices such as PDA, mobile phone, and computer screen. We expect seamless interoperability, so that for example a mobile phone can be used to follow up to an email with a voice response without having to look up the number.

Small programs could be used to customize the interfaces on these devices and to filter and limit the flow of messages through them. Some examples include: Trying several different phone numbers to reach a person, perhaps depending on time of day or day of week. Diverting incoming phone calls to voice mail or email when you do not want to be disturbed-but still letting important calls through.

Lowering the volume on TV or radio when you answer the phone. Recording copies of certain phone calls, perhaps by invoking a speech-to-text converter. Limiting the amount of personal information transferred to stores or companies as part of an e-commerce transaction. Each of these interface features might be accommodated by the designers of a particular device.

However, feature-rich interfaces are harder to use, so designers may intentionally limit the interface. It is also likely that inventive users will always think of features the designer has overlooked. Allowing for end user programmability and customization lets the user adapt the device to her particular needs.

These examples indicate an important pattern in programming scope: users will be customizing applications and appliances in the context of the system's computational model and software framework. Thus our expedition will explore ways to modularize applications, and organize these modules so that users can add the small bits of functionality they want without having to concern themselves with the operation of the system as a whole.

One of the key goals of empowering non-programmers to modify and customize software in the context of application frameworks or embedded devices is to reduce the cognitive load required to understand how the modification fits into the larger program. This is true even for experienced programmers new to an application's code base. Applications must be modular and provide sufficient high level abstractions so that their constituent parts can be understood quickly and independently.

This lets people concentrate primarily on the parts that need to be changed. In our approach we will explore several ideas intended to improve the modularity of software. We intend to look at existing techniques, such as object-oriented programming and component composition as ways to organize the software.

In reality, it is currently very difficult to write classes and components that are independent of the rest of the system.

We will also be exploring new concepts such as Aspect Oriented Programming [AOP], a new way of modularizing software based on cross-cutting concerns. Perhaps some combination of these, or new modularizing techniques will prove effective, for example, by organizing each function as an aspect of a component.

Community Building Besides working with selected partners, we will seek the involvement of the community at large. We will do this by sharing prototypes of the developed courses and software through a website, and by soliciting feedback on those materials through a variety of channels such as newsgroups and mailing lists. CNRI has considerable experience with community involvement through the web and via other means. For example, the key focus for the Python community is the Python website [Python], which also hosts many Python-related mailing lists; an important focus for the digital library community is the D-Lib website [D-Lib].

Download Python source code and installers are available for download for all versions! Latest: Python 3. Docs Documentation for Python's standard library, along with tutorials and guides, are available online.

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