This is a short post giving steps on how to actually install R packages. Let’s suppose you want to install the ggplot2 package. Well nothing could be easier. We just fire up an R shell and type:

> install.packages("ggplot2")

In theory the package should just install, however:

• if you are using Linux and don’t have root access, this command won’t work.
• you will be asked to select your local mirror, i.e. which server should you use to download the package.

Installing packages without root access

First, you need to designate a directory where you will store the downloaded packages. On my machine, I use the directory /data/Rpackages/ After creating a package directory, to install a package we use the command:

> install.packages("ggplot2", lib="/data/Rpackages/")
> library(ggplot2, lib.loc="/data/Rpackages/")


It’s a bit of a pain having to type /data/Rpackages/ all the time. To avoid this burden,  we create a file .Renviron in our home area, and add the line R_LIBS=/data/Rpackages/ to it. This means that whenever you start R, the directory /data/Rpackages/ is added to the list of places to look for R packages and so:

> install.packages("ggplot2")
> library(ggplot2)

just works!

Setting the repository

Every time you install a R package, you are asked which repository R should use. To set the repository and avoid having to specify this at every package install, simply:

• create a file .Rprofile in your home area.
• Add the following piece of code to it:


cat(".Rprofile: Setting UK repositoryn")
r = getOption("repos") # hard code the UK repo for CRAN
r["CRAN"] = "http://cran.uk.r-project.org"
options(repos = r)
rm(r)


I found this tip in a stackoverflow answer .

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

• Free!
• Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
• Presentation quality graphics
  • Save as a png, pdf or svg
• History
  • What you do can be saved for the next time you use R.
  • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

• Lack of a comprehensive graphical user interface, but two do exist: However some do exist: R commander: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ and Limma-gui (microarrays) : http://bioinf.wehi.edu.au/limmaGUI/

Preparation

• (Optional) Download and save the tutorial data set from
  • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
  • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

• Project Home page
  • http://www.r-project.org/
  • Check out the ‘introduction to R’, which is a much more in depth guide .
  • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

• Mac/Linux: this is the directory where you typed in R
• PC: Change using the change working directory option

• Function(data, options)

Even quit is a function

• q()

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

• Vector: a list of numbers, equivalent to a column in a table
• Data Frame = a collection of vectors. Equivalent to a table

Hint:

• Up/Down arrow keys can be use to cycle through previous commands

It is possible to create a vector by typing data directly into R using the combine function ‘c’

x

same as

x

creates the vector x with the numbers between 1 and 5.

You can see what is in an object at any time by typing its name;

x

will produce the output ‘[1] 1 2 3 4 5′

Note that names need to be quoted

daysofweek ← c(‘Monday’, ‘Tuesday’, ‘Wednesday’, ‘Thursday’, ‘Friday’);

Usually however you want to input from a file. We have touched on the ‘read.table’ function already.

mydata

Now mydata is a data frame with multiple vectors

each vector can be identified by the default syntax

#if any of these are typed it will print to screen

mydata$V1 mydata$V2 mydata$V3

By default the function assumes certain things from the file

• The file is a plain text file (there are function to read excel files: not covered here)
• columns are separated by any number of tabs or spaces
• there is the same number of data points in each column
• there is no header row (labels for the columns)
• there is no column with names for the rows** [I’ll explain].

If any of these are false, we need to tell that to the function

If it has a header column

mydata header=T also works

Note that there is a comma between different parts of the functions arguments

If there is one less column in the header row, then R assumes that the 1^st column of data after the header are the row names

Now the vectors (columns) are identified by their name

#if any of these are typed it will print to screen

mydata$A mydata$B mydata$C

# Summary about the whole data frame

summary(mydata)

# Summary information of column A

summary(mydata$A)

We can shortcut having to type the data frame each time by attaching it

attach(mydata)

# summary of column B as ‘mydata’ is attached

summary(B)

Two other important options for read.table

If is is separated only by tabs and has a header

mydata

Really useful if you have spaces in the contents of some columns, so R does not mess up reading the columns . However if the columns or of an uneven length it will tell you.

If you know that the file has uneven columns

mydata

This causes R to fill empty spaces in a columns with ‘NA’ .

The last two examples will still work with our file and give the same result as with only headers=T

Graphs

to get an idea of what R is capable of type

demo(graphics)

steps through the examples, and the code is printed to the screen

We will work with simpler examples that have immediate use to biologists.

Remember to get more information about the options to a function type ‘?function’

Histogram of A

hist(mydata$A)

If there was more data we could increase the number of vertical columns with the option, breaks=50 (or another relevant number).

boxplot(mydata)

We can get rid of the need to type the data frame each time by using the attach function

# if not already done so

attach(mydata)

boxplot(mydata$A, mydata$B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

same as

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Scatter plot

# if not already done so

attach(mydata)

plot(A,B) # or plot(mydata$A, mydata$B)

SAVING an image

Windows users (Rgui) RIGHT click on image and select which you want.

These instructions work for everyone.

You need to create a new device of the type of file you need, then send the data to that device

to save as a png file (easy to load into the likes of powerpoint, also great for web applications.

png(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

or to save as a pdf

pdf(‘filename’)

boxplot(A, B, name=c(“Value A”, “Value B”) , ylab=“Count of Something”)

Note

• Nothing will appear on screen, the output is going to the file
• Also it may not be saved immediately but will once the device (or R) is turned quit.

To quit R type

q() # If you save your session, next time you start R, you will have your data preloaded.

Or if you want to remain in R

dev.off() #turns of the png (or pdf etc) device, thus forces the data to save

Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

NEW FEATURES

• anyNA() gains a recursive argument.
• When x is missing and names is not false (including the default value), Sys.getenv(x, names) returns an object of class "Dlist" and hence prints tidily.
• (Windows.) shell() no longer consults the environment variable SHELL: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). R_SHELL, the preferred way to select a non-default shell, can be used instead.
• Some unusual arguments to embedFonts() can now be specified as character vectors, and the defaults have been changed accordingly.
• Functions in the Summary group duplicate less. (PR#15798)
• (Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)
• read.fwf() and read.DIF() gain a fileEncoding argument, for convenience.
• Graphics devices can add attributes to their description in .Device and .Devices. Several of those included with R use a "filepath" attribute.
• pmatch() uses hashing in more cases and so is faster at the expense of using more memory. (PR#15697)
• pairs() gains new arguments to select sets of variables to be plotted against each other.
• file.info(, extra_cols = FALSE) allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.
• New function dir.exists() in package base to test efficiently whether one or more paths exist and are directories.
• dput() and friends gain new controls hexNumeric and digits17 which output double and complex quantities as, respectively, binary fractions (exactly, see sprintf("%a")) and as decimals with up to 17 significant digits.
• save(), saveRDS() and serialize() now support ascii = NA which writes ASCII files using sprintf("%a") for double/complex quantities. This is read-compatible with ascii = TRUE but avoids binary->decimal->binary conversions with potential loss of precision. Unfortunately the Windows C runtime’s lack of C99 compliance means that the format cannot be read correctly there in R before 3.1.2.
• The default for formatC(decimal.mark =) has been changed to be getOption("OutDec"); this makes it more consistent with format() and suitable for use in print methods, e.g. those for classes "density", "ecdf", "stepfun" and "summary.lm".

  getOption("OutDec") is now consulted by the print method for class "kmeans", by cut(), dendrogram(), plot.ts() and quantile() when constructing labels and for the report fromlegend(trace = TRUE).

  (In part, wish of PR#15819.)

• printNum() and hence format() and formatC() give a warning if big.mark and decimal.mark are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).
• merge() can create a result which uses long vectors on 64-bit platforms.
• dget() gains a new argument keep.source which defaults to FALSE for speed (dput() and dget() are most often used for data objects where this can make dget() many times faster).
• Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.
• A number of macros have been added in the new ‘share/Rd’ directory for use in package overview help pages, and promptPackage() now makes use of them.
• tools::parse_Rd() gains a new permissive argument which converts unrecognized macros into text. This is used by utils:::format.bibentry to allow LaTeX markup to be ignored.
• options(OutDec =) can now specify a multi-byte character, e.g., options(OutDec = "u00b7") in a UTF-8 locale.
• is.recursive(x) is no longer true when x is an external pointer, a weak reference or byte code; the first enables all.equal(x, x) when x .
• ls() (aka objects()) and as.list.environment() gain a new argument sorted.
• The "source" attribute (which has not been added to functions by R since before R version 2.14.0) is no longer treated as special.
• Function returnValue() has been added to give on.exit() code access to a function’s return value for debugging purposes.
• crossprod(x, y) allows more matrix coercions when x or y are vectors, now equalling t(x) %*% y in these cases (also reported by Radford Neal). Similarly, tcrossprod(x,y) and %*% work in more cases with vector arguments.
• Utility function dynGet() useful for detecting cycles, aka infinite recursions.
• The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
• apply(m, 2, identity) is now the same as the matrix m when it has named row names.
• A new function debuggingState() has been added, allowing to temporarily turn off debugging.
• example() gets a new optional argument run.donttest and tools::Rd2ex() a corresponding commentDonttest, with a default such that example(..) in help examples will run donttest code only if used interactively (a change in behaviour).
• rbind.data.frame() gains an optional argument make.row.names, for potential speedup.
• New function extSoftVersion() to report on the versions of third-party software in use in this session. Currently reports versions of zlib, bzlib, the liblzma from xz, PCRE, ICU, TRE and the iconv implementation.

  A similar function grSoftVersion() in package grDevices reports on third-party graphics software.

  Function tcltk::tclVersion() reports the Tcl/Tk version.

• Calling callGeneric() without arguments now works with primitive generics to some extent.
• vapply(x, FUN, FUN.VALUE) is more efficient notably for large length(FUN.VALUE); as extension of PR#16061.
• as.table() now allows tables with one or more dimensions of length 0 (such as as.table(integer())).
• names(x) now clears the names of call and ... objects.
• library() will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.
• A new plot() method for class "raster" has been added.
• New check_packages_in_dir_changes() function in package tools for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.
• Speed-up from Peter Haverty for ls() and methods:::.requirePackage() speeding up package loading. (PR#16133)
• New get0() function, combining exists() and get() in one call, for efficiency.
• match.call() gains an envir argument for specifying the environment from which to retrieve the ... in the call, if any; this environment was wrong (or at least undesirable) when thedefinition argument was a function.
• topenv() has been made .Internal() for speedup, based on Peter Haverty’s proposal in PR#16140.
• getOption() no longer calls options() in the main case.
• Optional use of libcurl (version 7.28.0 from Oct 2012 or later) for Internet access:
  • capabilities("libcurl") reports if this is available.
  • libcurlVersion() reports the version in use, and other details of the "libcurl" build including which URL schemes it supports.
  • curlGetHeaders() retrieves the headers for http://, https://, ftp:// and ftps:// URLs: analysis of these headers can provide insights into the ‘existence’ of a URL (it might for example be permanently redirected) and is so used in R CMD check --as-cran.
  • download.file() has a new optional method "libcurl" which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.
  • url() has a new method "libcurl" which handles more URL schemes and follows redirections. The default method is controlled by a new option url.method, which applies also to the opening of URLs via file() (which happens implicitly in functions such as read.table.)
  • When file() or url() is invoked with a https:// or ftps:// URL which the current method cannot handle, it switches to a suitable method if one is available.
• (Windows.) The DLLs ‘internet.dll’ and ‘internet2.dll’ have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an Rsession.

  The Windows internet functions are still selected by flag –internet2 or setInternet2(). This can be overridden for an url() connection via its new method argument.

  download.file() has new method "wininet", selected as the default by –internet2 or setInternet2().

• parent.env<- can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.
• New function isLoadedNamespace() for readability and speed.
• names(env) now returns all the object names of an environment env, equivalently to ls(env, all.names = TRUE, sorted = FALSE) and also to the names of the corresponding list,names(as.list(env, all.names = TRUE)). Note that although names() returns a character vector, the names have no particular ordering.
• The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.
• New function trimws() for removing leading/trailing whitespace.
• cbind() and rbind() now consider S4 inheritance during S3 dispatch and also obey deparse.level.
• cbind() and rbind() will delegate recursively to methods::cbind2 (methods::rbind2) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).
• (Windows.) download.file(quiet = FALSE) now uses text rather than Windows progress bars in non-interactive use.
• New function hsearch_db() in package utils for building and retrieving the help search database used by help.search(), along with functions for inspecting the concepts and keywords in the help search database.
• New function .getNamespaceInfo(), a no-check version of getNamespaceInfo() mostly for internal speedups.
• The help search system now takes keyword entries in Rd files which are not standard keywords (as given in ‘KEYWORDS’ in the R documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.
• New lengths() function for getting the lengths of all elements in a list.
• New function toTitleCase() in package tools, tailored to package titles.
• The matrix methods of cbind() and rbind() allow matrices as inputs which have 2^31 or more elements. (For cbind(), wish of PR#16198.)
• The default method of image() has an explicit check for a numeric or logical matrix (which was always required).
• URLencode() will not by default encode further URLs which appear to be already encoded.
• BIC(mod) and BIC(mod, mod2) now give non-NA numbers for arima() fitted models, as nobs(mod) now gives the number of “used” observations for such models. This fixes PR#16198, quite differently than proposed there.
• The print() methods for "htest", "pairwise.htest" and "power.htest" objects now have a digits argument defaulting to (a function of) getOption("digits"), and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.
• Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.
• The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.
• unzip() will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to PR#16243).
• methods() reports S4 in addition to S3 methods; output is simplified when the class argument is used. .S3methods() and methods::.S4methods() report S3 and S4 methods separately.
• Higher order functions such as the apply functions and Reduce() now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves PR#16093.

More at http://cran.rstudio.com/

Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

More tutorial at 

http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm

http://www.raetschlab.org/lectures/MLBioinformatics

http://www.raetschlab.org/lectures/bertinoro08

Book at 

https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf

Address of the bookmark: http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

http://www.broadinstitute.org/igv/

Address of the bookmark: https://wikis.utexas.edu/display/bioiteam/Integrative+Genomics+Viewer+%28IGV%29+tutorial

Professionally, Python is great for backend web development, data analysis, artificial intelligence, and scientific computing. Many developers have also used Python to build productivity tools, games, and desktop apps, so there are plenty of resources to help you learn how to do those as well.

For pros and cons visit http://www.bestprogramminglanguagefor.me/why-learn-python and http://bioinformaticsonline.com/discussion/view/459/python-vs-perl

More resources at https://github.com/CodementorIO/Python-Learning-Resources

Following are the list of useful python programming resources:

• 20 Python Libraries You Aren't Using (But Should) (Just fill the fields with any values)
• A Beginner's Python Tutorial
• A Byte of Python (3.x) (HTML, PDF, EPUB, Mobi)
• A Guide to Python's Magic Methods - Rafe Kettler
• A Whirlwind Tour of Python - Jake VanderPlas (PDF) (EPUB, MOBI)
• Automate the Boring Stuff - Al Sweigart
• Biopython (PDF)
• Build applications in Python the antitextbook (3.x) (HTML, PDF, EPUB, Mobi)
• Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho, Packt. (Just fill the fields with any values)
• Building Skills in Object-Oriented Design (Python) (PDF) (2.1.1)
• Building Skills in Python (PDF) (2.6)
• Code Like a Pythonista: Idiomatic Python
• CodeCademy Python
• Composing Programs (3.x)
• Data Structures and Algorithms in Python - B. R. Preiss (PDF)
• Dive into Python 3 - Mark Pilgrim (3.0)
  • Dive into Python - Mark Pilgrim (2.3)
• From Python to NumPy
• Full Stack Python
• Functional Programming in Python (Just fill the fields with any values)
• Fundamentals of Python Programming - Richard L. Halterman (PDF) (3.2)
• Google's Python Class (2.4 - 2.x)
• Google's Python Style Guide
• Hacking Secret Cyphers with Python - Al Sweigart (3.3)
• Hadoop with Python (Just fill the fields with any values)
• High Performance Python (PDF)
• Hitchhiker's Guide to Python! (2.6)
• How to Make Mistakes in Python - Mike Pirnat (PDF) (1st edition)
• How to Think Like a Computer Scientist: Learning with Python, Interactive Edition (3.2)
  • How to Think Like a Computer Scientist: Learning with Python - Allen B. Downey, Jeff Elkner and Chris Meyers (2.4)
  • Think Python - Allen B. Downey (2.x & 3.0)
• Intermediate Python - Muhammad Yasoob Ullah Khalid (1st edition)
• Introduction to Programming with Python (3.3)
  • Introduction to Programming Using Python - Cody Jackson (1st edition) (2.3)
• Introduction to Python - Kracekumar (2.7.3)
• Invent Your Own Computer Games With Python - Al Sweigart (3.1)
• Learn Python, Break Python
• Learn Python in Y minutes
• Learn Python The Hard Way (2.5 - 2.6)
• Learn to Program Using Python - Cody Jackson (PDF)
• Learning Python - Fabrizio Romano, Packt. (Just fill the fields with any values)
• Learning to Program
• Lectures on scientific computing with python - J.R. Johansson (2.7)
• Making Games with Python & Pygame - Al Sweigart (2.7)
• Modeling Creativity: Case Studies in Python - Tom D. De Smedt (PDF)
• Natural Language Processing with Python (3.x)
• Non-Programmer's Tutorial for Python 3 (3.3)
  • Non-Programmer's Tutorial for Python 2.6 (2.6)
• Picking a Python Version: A Manifesto (Just fill the fields with any values)
• Porting to Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
• Practical Programming in Python - Jeffrey Elkner (PDF)
• Problem Solving with Algorithms and Data Structures using Python - Bradley N. Miller and David L. Ranum
• Program Arcade Games With Python And Pygame (3.3)
• Programming Computer Vision with Python (PDF)
• Python 2 Official Documentation (PDF, HTML, TEXT) (2.x)
• Python 2.7 quick reference - New Mexico Tech (2.7)
• Python 3 Official Documentation (PDF, EPUB, HTML, TEXT) (3.x)
• Python Cookbook - David Beazley
• Python Data Science Handbook - Jake VanderPlas (HTML, Jupyter Notebooks)
• Python for Econometrics - Kevin Sheppard (PDF) (2.7.5)
• Python for Everybody Exploring Data Using Python 3 - Charles Severance (PDF, EPUB, HTML)
  • Python for Informatics: Exploring Information (2.7.5)
• Python for you and me (2.7.3)
• Python for you and me (3.x)
• Python Idioms (PDF)
• Python in Education (Just fill the fields with any values)
• Python in Hydrology - Sat Kumar Tomer
• Python Koans (2.7 or 3.x)
• Python Module of the Week (3.x)
  • Python Module of the Week (2.x)
• Python Notes for Professionals - Compiled from StackOverflow documentation (3.x)
• Python Practice Book (2.7.1)
• Python Practice Projects
• Python Programming (PDF) (2.6)
• Scipy Lecture Notes
• SICP in Python (3.2)
• Snake Wrangling For Kids (3.x)
• Suporting Python 3: An In-Depth Guide (2.6 - 2.x & 3.1 - 3.x)
• Test-Driven Web Development with Python (3.3 - 3.x)
• Text Processing in Python - David Mertz (2.3 - 2.x)
• The Coder's Apprentice: Learning Programming with Python 3 - Pieter Spronck (PDF) (3.x)
• The Definitive Guide to Jython, Python for the Java Platform - Josh Juneau, Jim Baker, Victor Ng, Leo Soto, Frank Wierzbicki (2.5)
• The Little Book of Python Anti-Patterns (Source)
• The Programming Historian - William J. Turkel, Adam Crymble and Alan MacEachern
• The Python Ecosystem: An Introduction
• The Python GTK+ 3 Tutorial
• The Standard Python Library - Fredrik Lundh
• Think Complexity - Allen B. Downey (2nd Edition) (PDF, HTML)
• Web2py: Complete Reference Manual, 6th Edition (pre-release) (2.5 - 2.x)
• Wikibooks: Python Programming (2.7)

In this tutorial series article, I will explore features and packages of python which are widely used in the big data, NGS, and bioinformatics. I will also walk through a real biological example which shows NGS data processing with the help of python packages and programming.

Python has a couple of points to recommend it to biologists and scientists specifically:

• It's widely used in the scientific community
• It has a couple of very well designed libraries for doing complex scientific computing (although we won't encounter them in this book)
• It lend itself well to being integrated with other, existing tools
• It has features which make it easy to manipulate strings of characters (for example, strings of DNA bases and protein amino acid residues, which we as biologists are particularly fond of)

In general, following are some of the important features of python which makes it a perfect fit for rapid application development.

• Python is interpreted language so the program does not need to be compiled. Interpreter parses the program code and generates the output.
• Python is dynamically typed, so the variables types are defined automatically.
• Python is strongly typed. So the developers need to cast the type manually.
• Less code and more use makes it more acceptable.
• Python is portable, extendable and scalable.

There are two major Python versions, Python 2 and Python 3. Python 2 and 3 are quite different. This tutorial uses Python 3, because it more semantically correct and supports newer features.

I will post tutorial on daily basis on this page. Check the sub-pages on right side.