python report collect information about matplotlib , opencv, numpy

1. A Summer Training Report
On
Python and it’s Libraries
Under the Guidance of
Mr. Anand Handa Sir(IITK)
Done By
SHUBHAM YADAV
(1573613037)
At
IQRA Software Technologies Private Limited
Sharda Nagar ,Kanpur Nagar,U.P.
Submitted To
Department of Information Technology
Rajkiya Engineering College , Azamgarh
Approved By AICTE, New Delhi & Govt. of U.P.,
Affiliated to AKTU, LUCKNOW
Vill- Akbalpur, Post Devgaon, Azamgarh, Uttar Pradesh 276201

2. REPORT CONTENT
Introduction of Industry
History of Python
Why Python ?
Characteristics of Python
Data Structures in Python
File Handling in Python
Use of Numpy
Use of Matplotlib
Use of Pandas
Use of OpenCV
Conclusion

3. Introduction of Industry
IQRA Software Technologies, is a premier institute which provides IT
and software skills training in Scientific & Engineering field with best
quality at lower costs. We are one of the fastest growing software
solution, technical consultancy and knowledge outsourcing company
situated in India with offices at Bangalore, Kanpur and Lucknow.
Mission
IQRA Software is committed to its role in technical training individuals
or corporate in areas of Speech Compression, Image Processing,
Control System, Wireless LAN, VHDL, Verilog, MATLAB (SciLab), DSP
TMS320C67xx, Java, Microsoft.Net, Software Quality Testing, SDLC &
Implementation, Project Management, Manual Testing, Silk Test,
Mercury Test, QTP, Test Director for Quality Center.
Vision
DSP, VLSI, Embedded and Software testing are one of the fastest
growing areas in IT across the globe. Our vision is to create a platform,
where trainees/students are able to learn different features of
technologies to secure a better position in IT industry or to improve
their careers.

4. History of Python
Python was developed in 1980 by Guido van Rossum at the National
Research Institute for Mathematics and Computer Science in the
Netherlands as a successor of ABC language capable of exception
handling and interfacing. Python features a dynamic type system and
automatic memory management. It supports multiple programming
paradigms, including object-oriented, imperative, functional and
procedural, and has a large and comprehensive standard library.
Van Rossum picked the name Python for the new language from a TV
show, Monty Python's Flying Circus.
In December 1989 the creator developed the 1st python interpreter as
a hobby and then on 16 October 2000, Python 2.0 was released with
many new features.
...In December 1989, I was looking for a "hobby" programming project
that would keep me occupied during the week around Christmas. My
office ... would be closed, but I had a home computer, and not much
else on my hands. I decided to write an interpreter for the new
scripting language I had been thinking about lately: a descendant of
ABC that would appeal to Unix/C hackers. I chose Python as a working
title for the project, being in a slightly irreverent mood (and a big fan of
Monty Python's Flying Circus)
— Guido van Rossum

5. Python Releases
Python 1.0 - January 1994
Python 1.5 - December 31, 1997
Python 1.6 - September 5, 2000
Python 2.0 - October 16, 2000
Python 2.1 - April 17, 2001
Python 2.2 - December 21, 2001
Python 2.3 - July 29, 2003
Python 2.4 - November 30, 2004
Python 2.5 - September 19, 2006
Python 2.6 - October 1, 2008
Python 2.7 - July 3, 2010
Python 3.0 - December 3, 2008
Python 3.1 - June 27, 2009
Python 3.2 - February 20, 2011
Python 3.3 - September 29, 2012
Python 3.4 - March 16, 2014
Python 3.5 - September 13, 2015
Python 3.6 - December 23, 2016

6. Why Python ?
The language's core philosophy is summarized in the document The Zen of Python
(PEP 20), which includes aphorisms such as…
 Beautiful is better than ugly
 Simple is better than complex
 Complex is better than complicated
 Readability counts
 Explicit is better than implicit

7. Characteristics of Python
Interpreted Language: Python is processed at runtime by Python Interpreter
• Easy to read: Python source-code is clearly defined and visible to the eyes.
• Portable: Python codes can be run on a wide variety of hardware platforms
having the same interface.
• Extendable: Users can add low level-modules to Python interpreter.
• Scalable: Python provides an improved structure for supporting large
programs than shell-scripts.
• Object-Oriented Language: It supports object-oriented features
and techniques of programming.
compile
execut
e
output source code
Hello.java
byte code
Hello.c
lass

8. • Interactive Programming Language: Users can interact with the python
interpreter directly for writing programs.
• Easy language: Python is easy to learn language especially for beginners.
• Straight forward Syntax: The formation of python syntax is simple and
straightforward which also makes it popular.
Data Structures in Python
LISTS-
 Ordered collection of data.
 Supports similar slicing and indexing functionalities as in the case of Strings.
 They are mutable.
 Advantage of a list over a conventional array
• Lists have no size or type constraints(no setting restrictions
beforehand).
• They can contain different object types.
• We can delete elements from a list by using Del list_name[index_val]
 Example-
• my_list = ['one', 'two','three',4,5]
• len(my_list) would output 5.
Dictionary-
 Lists are sequences but the dictionaries are mappings.
 They are mappings between a unique key and a value pair.
 These mappings may not retain order.
 Constructing a dictionary.
 Accessing object from a dictionary.
 Nesting Dictionaries.
 Basic Dictionary Methods.

9.  Basic Syntax
o d={} empty dictionary will be generated and assign keys and values to
it, like d[‘animal’] = ‘Dog’
o d = {'K1':'V1', 'K2’:’V2'}
o d['K1'] outputs 'V1‘
Tuples-
 Immutable in nature, i.e they cannot be changed.
 No type restriction
 Indexing and slicing, everything's same like that in strings and lists.
 Constructing tuples.
 Basic tuple methods.
 Immutability.
 When to use tuples?
 We can use tuples to present things that shouldn’t change, such as days of
the week, or dates on a calendar, etc.
Sets-
 A set contains unique and unordered elements and we can construct them
by using a set() function.
 Convert a list into Set-
 l=[1,2,3,4,1,1,2,3,6,7]
 k = set(l)
 k becomes {1,2,3,4,6,7}
 Basic Syntax-
 x=set()
 x.add(1)
 x = {1}
 x.add(1)
 This would make no change in x now

10. File Handling in Python-
Python too supports file handling and allows users to handle files i.e., to read and
write files, along with many other file handling options, to operate on files. The
concept of file handling has stretched over various other languages, but the
implementation is either complicated or lengthy, but alike other concepts of
Python, this concept here is also easy and short. Python treats file differently as
text or binary and this is important. Each line of code includes a sequence of
characters and they form text file. Each line of a file is terminated with a special
character, called the EOL or End of Line characters like comma {,} or newline
character. It ends the current line and tells the interpreter a new one has begun.
Let’s start with Reading and Writing files.
We use open () function in Python to open a file in read or write mode. As
explained above, open ( ) will return a file object. To return a file object we use
open () function along with two arguments, that accepts file name and the mode,
whether to read or write. So, the syntax being: open(filename, mode). There are
three kinds of mode, that Python provides and how files can be opened:
• “ r “, for reading.
• “ w “, for writing.
• “ a “, for appending.
• “ r+ “, for both reading and writing
Ex-It is a notepad file (101.txt)

11. Code in python
It read the words from 101.txt file and print the all words which are present in
the file and also tell that word occurring howmany times.

12. Use of Numpy-
NumPy is a Python package. It stands for 'Numerical Python'. It is a library
consisting of multidimensional array objects and a collection of routines for
processing of array.
Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another
package Numarray was also developed, having some additional functionalities. In
2005, Travis Oliphant created NumPy package by incorporating the features of
Numarray into Numeric package. There are many contributors to this open source
project.
OperationsusingNumPy
Using NumPy, a developer can perform the following operations −
 Mathematical and logical operations on arrays.
 Fourier transforms and routines for shape manipulation.
 Operations related to linear algebra. NumPy has in-built functions for linear
algebra and random number generation.
Simple program to create a matrix-
First of all we import numpy package then using this we take input in numpy
function as a list then we create a matrix
There is many more function can be perform by using this like that take sin value
of the given value ,print a zero matrix etc. we also take any image in the form of
array.

13. Use of Matplotlib-
Matplotlib is a library for making 2D plots of arrays in Python. Although it has its
origins in emulating the MATLAB graphics commands, it is independent of
MATLAB, and can be used in a Pythonic, object oriented way. Although Matplotlib
is written primarily in pure Python, it makes heavy use of NumPy and other
extension code to provide good performance even for large arrays.
Matplotlib is designed with the philosophy that you should be able to create
simple plots with just a few commands, or just one! If you want to see a
histogram of your data, you shouldn’t need to instantiate objects, call methods,
set properties, and so on; it should just work.
These are the some example of matplotlib..

16. Use of Pandas-
Pandas is an open-source, BSD-licensed Python library providing high-
performance, easy-to-use data structures and data analysis tools for the Python
programming language. Python with Pandas is used in a wide range of fields
including academic and commercial domains including finance, economics,
Statistics, analytics, etc.
Pandas is an open-source Python Library providing high-performance data
manipulation and analysis tool using its powerful data structures. The name
Pandas is derived from the word Panel Data – an Econometrics from
Multidimensional data.
Key Features of Pandas-
• Fast and efficient DataFrame object with default and customized indexing.
• Tools for loading data into in-memory data objects from different file
formats.
• Data alignment and integrated handling of missing data.
• Reshaping and pivoting of date sets.
• Label-based slicing, indexing and subsetting of large data sets.
• Columns from a data structure can be deleted or inserted.
• Group by data for aggregation and transformations.
Pandas deals with the following three data structures −
 Series
 DataFrame
 Panel
These data structures are built on top of Numpy array, which means they are
fast.

17. Use of OpenCV-
OpenCV was started at Intel in 1999 by Gary Bradsky and the first release came
out in 2000. Vadim Pisarevsky joined Gary Bradsky to manage Intel’s Russian
software OpenCV team. In 2005, OpenCV was used on Stanley, the vehicle who
won 2005 DARPA Grand Challenge. Later its active development continued under
the support of Willow Garage, with Gary Bradsky and Vadim Pisarevsky leading
the project. Right now, OpenCV supports a lot of algorithms related to Computer
Vision and Machine Learning and it is expanding day-by-day.
Below is the list of contributors who submitted tutorials to OpenCV-Python.
-Alexander Mordvintsev (GSoC-2013 mentor)
-Abid Rahman K. (GSoC-2013 intern)

18. Use the function-
cv2.imread() to read an image. The image should be in the working directory or a
full path of image should be given.
Second argument is a flag which specifies the way image should be read.
• cv2.IMREAD_COLOR : Loads a color image. Any transparency of image will
be neglected. It is the default flag.
• cv2.IMREAD_GRAYSCALE : Loads image in grayscale mode
• cv2.IMREAD_UNCHANGED : Loads image as such including alpha channel
Use the function cv2.imshow() to display an image in a window. The window
automatically fits to the image size.
This program change image from colour to black&white.

19. Trackbar as the Color Palette-
Code-
Result-
It works on BGR colour system.

20. Conclusion-
I believe the trial has shown conclusively that it is both possible and desirable to
use Python as the principal teaching language:
• It is Free (as in both cost and source code).
• It is trivial to install on a Windows PC allowing students to take their
interest further. For many the hurdle of installing a Pascal or C compiler on
a Windows machine is either too expensive or too complicated;
• It is a flexible tool that allows both the teaching of traditional procedural
programming and modern OOP.
• It can be used to teach a large number of transferable skills.
• It is a real-world programming language that can be and is used in
academia and the commercial world.
• It appears to be quicker to learn and, in combination with its many libraries,
this offers the possibility of more rapid student development allowing the
course to be made more challenging and varied.
• and most importantly, its clean syntax offers increased understanding and
enjoyment for students.
The training program having three destination was a lot more useful than staying
at one place throughout the whole 4 weeks. In my opinion. I have gained lots of
knowledge and experience needed to be successful in great engineering
challenge as in my opinion, Engineering is after all a Challenge ,and not a job .