GBSSS - Govt. Boys Sr. Sec. School Khajoori Khas Delhi -90

Monday, September 27, 2021

CLASS 11 IP Notes

1. Unit - 1 (Introduction to Computer)

1 .1. G eneral Questions of Computer - 1

1.2. General Question of Computer - 2

1.8. Storage Devices, Memory, Memory Unit and its Memory Types

1.12. System Software

1.13. Application Software

1.14. Generic and Specific Types Software

1.15. Data Deletion and Recovery

1.16. Security Concern

1.17. General Computer Assignment - 1

1.18. General Computer Assignment - 2

1.19. General Computer Assignment - 3

1.20. General Computer Assignment - 4

2. Unit - 2 (Introduction to Python)

2.2. Python IDE, Distributions and Frameworks

DataFrame in Python Class - 2

DataFrame Attributes / Properties:

7. ndim:

Return the dimension of the DataFrame.

import pandas as pd

I=['IP','Bio','Chemistry','Physics','English']

D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,55,32], \

"2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Number of Dimension in Data Frame")

print(df.ndim)

Output:

2018 2019 2020

Subject

IP 50.0 40 65

Bio 60.0 35 75

Chemistry 70.0 45 85

Physics 80.5 55 45

English 90.0 32 52

Number of Dimension in Data Frame

8. empty:

Check whether a DataFrame is Empty or Not.

import pandas as pd

import numpy as np

I=['IP','Bio','Chemistry','Physics','English']

D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,np.NaN,32], \

"2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Check whether a Data Frame is Empty or not")

print(df.empty)

print(df.isna()) # It is a function. Check NaN values in DataFrame

df1=pd.DataFrame()

print(df1)

print(df1.empty)

Output:

2018 2019 2020

Subject

IP 50.0 40.0 65

Bio 60.0 35.0 75

Chemistry 70.0 45.0 85

Physics 80.5 NaN 45

English 90.0 32.0 52

Check whether a Data Frame is Empty or not

False

2018 2019 2020

Subject

IP False False False

Bio False False False

Chemistry False False False

Physics False True False

English False False False

Empty DataFrame

Columns: []

Index: []

True

9. count()

It is used to count the values in Rows and Columns of DataFrame.

df.count() : count the no. of values rowwise

df.count(0):count the no. of values rowwise

df.count(axis="rows"): count the no. of values rowwise

df.count(1): count the no. of values columnwise

df.count(axis='columns'): count the no. of values columnwise

import pandas as pd

import numpy as np

I=['IP','Bio','Chemistry','Physics','English']

D={"2018":[50,60,70,80.5,90],"2019":[40,35,45,np.NaN,32], \

"2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Count the no. of values in rows")

print(df.count())

print("Count the no. of values in columns")

print(df.count(1))

print("Count the no. of values in rows")

print(df.count(0))

print("Count the no. of values in rows")

print(df.count(axis='rows'))

print("Count the no. of values in columns")

print(df.count(axis='columns'))

2018 2019 2020

Subject

IP 50.0 40.0 65

Bio 60.0 35.0 75

Chemistry 70.0 45.0 85

Physics 80.5 NaN 45

English 90.0 32.0 52

Count the no. of values in rows

2018 5

2019 4

2020 5

dtype: int64

Count the no. of values in columns

Subject

IP 3

Bio 3

Chemistry 3

Physics 2

English 3

dtype: int64

Count the no. of values in rows

2018 5

2019 4

2020 5

dtype: int64

Count the no. of values in rows

2018 5

2019 4

2020 5

dtype: int64

Count the no. of values in columns

Subject

IP 3

Bio 3

Chemistry 3

Physics 2

English 3

dtype: int64

10. Transpose of Data Frame

import pandas as pd

import numpy as np

I=['IP','Bio','Chemistry','Physics','English']

D={"2018":[50,60,70,80.5,90],"2019":[40,35,np.NaN,55,32], \

"2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Transpose of Data Frame")

print(df.T)

Output:

2018 2019 2020

Subject

IP 50.0 40.0 65

Bio 60.0 35.0 75

Chemistry 70.0 NaN 85

Physics 80.5 55.0 45

English 90.0 32.0 52

Transpose of Data Frame

Subject IP Bio Chemistry Physics English

2018 50.0 60.0 70.0 80.5 90.0

2019 40.0 35.0 NaN 55.0 32.0

2020 65.0 75.0 85.0 45.0 52.0

Saturday, September 25, 2021

Python Version List

A list of Python versions with its released date is given below.

Python Version	Released Date
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
Python 3.7	June 27, 2018
Python 3.8	October 14, 2019

Python 3.9	October 05, 2020
Python 3.10	October 04, 2021

What is an IDE?

An IDE (Integrated Development Environment) is a software application used by developers for creating programs.

List of Python IDE

1. PyCharm

2. Visual Studio Code

3. Sublime Text

4. Vim

5. Atom

6. Jupyter Notebook

7. Eclipse + PyDev + LiClipse

8. GNU Emacs

9. Spyder

10. Thonny

11. IDLE

Python Distributions

Anaconda Python
ActivePython
CPython
Enthought Canopy
WinPython

Important Python Libraries

1. Matplotlib

2. Pandas

3. Requests

4. NumPy

5. SQLAlchemy

6. BeautifulSoup

7. Pyglet

8. SciPy

9. Scrapy

10. PyGame

11. Python Twisted

12. Pillow

13. pywin32

14. wxPython

15. iPython

16. Nose

17. Flask

18. SymPy

19. Fabric

20. PyGTK

Python Lect - 1

What is Python?

Python is a high level programming language.
Python is a Dynamically Type programming language.
Python is a Strongly Type programming language.
Python is a Scripting Language
Python was created by Guido van Rossum, and released in 1991.
Python is a simple, general purpose, high level, and object-oriented programming language.
Python is an interpreted scripting language also.

Python is used for:

Web development (server-side),
Software development,
Mathematics,
System scripting.
Data Science
Date Mining
Desktop Applications
Console-based Applications
Mobile Applications
Software Development
Artificial Intelligence
Web Applications
Enterprise Applications
3D CAD Applications
Machine Learning
Computer Vision or Image Processing Applications.
Speech Recognitions

Python Popular Frameworks and Libraries

Python has wide range of libraries and frameworks widely used in various fields such as machine learning, artificial intelligence, web applications, etc. We define some popular frameworks and libraries of Python as follows.

Web development (Server-side) - Django Flask, Pyramid, CherryPy
GUIs based applications - Tk, PyGTK, PyQt, PyJs, etc.
Machine Learning - TensorFlow, PyTorch, Scikit-learn, Matplotlib, Scipy, etc.
Mathematics - Numpy, Pandas, etc.

Pages

Monday, September 27, 2021

CLASS 11 IP Notes

1. Unit - 1 (Introduction to Computer)

1.12. System Software

1.13. Application Software

1.14. Generic and Specific Types Software

1.15. Data Deletion and Recovery

1.16. Security Concern

1.17. General Computer Assignment - 1

1.18. General Computer Assignment - 2

1.19. General Computer Assignment - 3

1.20. General Computer Assignment - 4

2. Unit - 2 (Introduction to Python)

Saturday, September 25, 2021

Python Version List

What is an IDE?

Python Distributions

Important Python Libraries

Python Popular Frameworks and Libraries