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.

DataFrame in Python Class - 1

 DataFrame:

1. DataFrame is a 2 D Data Structure.

2. DataFrame is a 2 D Structure of Python Pandas Library.

3. DataFrame is a Heterogeneous Data Structure

4. It just like a table or spreadsheet.

5. It can contains 2 or more rows and columns.

6. Types of Columns can be different.

7. Size of DataFrame is Mutable.

8. Value of DataFrame also Mutable.

9. Arithmetic Operators can be performed on rows and columns.

10. It can store different types of values.

Example:

import pandas as pd

I=['A','B','C','D','E']

D={"2019":[50,60,70,80,90],"2020":[40,35,45,55,32]}

DF=pd.DataFrame(D,I)

print(DF)

Output:

   2019  2020

A    50    40

B    60    35

C    70    45

D    80    55

E    90    32

 DataFrame Attributes / Properties:

DataFrame has the following attributes:

1. index

2. columns

3. axes

4. dtypes

5. size

6. shape

7. ndim

8. empty

9. count

10. T

1. index

It display the index of the DataFrame.

import pandas as pd

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

D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \

   "2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Index of Data Frame")

print(df.index)

Output:

           2018  2019  2020

Subject                    

IP           50    40    65

Bio          60    35    75

Chemistry    70    45    85

Physics      80    55    45

English      90    32    52

Index of Data Frame

Index(['IP', 'Bio', 'Chemistry', 'Physics', 'English'], dtype='object', name='Subject')

2. columns

It display the name of columns of DataFrame

import pandas as pd

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

D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \

   "2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Columns of Data Frame")

print(df.columns)

Output:

           2018  2019  2020

Subject                    

IP           50    40    65

Bio          60    35    75

Chemistry    70    45    85

Physics      80    55    45

English      90    32    52

Columns of Data Frame

Index(['2018', '2019', '2020'], dtype='object')

3. Axes

It display both Index name and column name of DataFrame

import pandas as pd

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

D={"2018":[50,60,70,80,90],"2019":[40,35,45,55,32], \

   "2020":[65,75,85,45,52],}

df=pd.DataFrame(D,I)

df.index.name="Subject"

print(df)

print("Axes of Data Frame")

print(df.axes)

Output:

          2018  2019  2020

Subject                    

IP           50    40    65

Bio          60    35    75

Chemistry    70    45    85

Physics      80    55    45

English      90    32    52

Axes of Data Frame

[Index(['IP', 'Bio', 'Chemistry', 'Physics', 'English'], dtype='object', name='Subject'), Index(['2018', '2019', '2020'], dtype='object')]

4. dtype:

Display the data type of columns/Values

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("Data Type of Data Frame")

print(df.dtypes)

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

Data Type of Data Frame

2018    float64

2019      int64

2020      int64

dtype: object

5.size

Display the size of DataFrame i.e. total number of elements.

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("size of Data Frame")

print(df.size)

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

size of Data Frame

15

6.shape

Display number  of rows and columns

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("Shape of Data Frame")

print(df.shape)

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

Shape of Data Frame

(5, 3)

iloc[ ] and loc[ ] in Series

 iloc[ ] and loc[ ] in Series

Indexing and accessing can also be done using iloc[ ]  and loc [ ] methods.

iloc[ ]  - iloc is used for indexing or selecting based on position.

Series Creation in Pandas - 4

 Q:1 

Create a Series and Explain the use of head() and tail()

import pandas as pd

D=[10,20,30,40,50,60,70,80,90]

I=['a','b','c','d','e','f','g','h','i']

S=pd.Series(D,I)

print(S)

print(S.head())  #print first 5 elements

print(S.tail())     #print last 5 elements

print(S.head(3))    #print first 3 elements

print(S.tail(3))    #print last elements

print(S.head(-2))   #print first n-2 elements (not print last 2 elements)

print(S.tail(-2))   #print last n-2 elements (not print first 2 elements)

Output:

a    10

b    20

c    30

d    40

e    50

f    60

g    70

h    80

i    90

dtype: int64

a    10

b    20

c    30

d    40

e    50

dtype: int64

e    50

f    60

g    70

h    80

i    90

dtype: int64

a    10

b    20

c    30

dtype: int64

g    70

h    80

i    90

dtype: int64

a    10

b    20

c    30

d    40

e    50

f    60

g    70

dtype: int64

c    30

d    40

e    50

f    60

g    70

h    80

i    90

dtype: int64

Python Video Classes

 

Python Video Class - 1

Python Video Class - 2

Python Video Class - 3

Python Video Class - 4

Python Video Class - 5

Python Video Class - 6

Python Video Class - 7

Python Video Class - 8

Python Video Class - 9

Python Video Class - 10

Series Creation in Pandas - 3

 Q - 1

Create an Empty Series using Pandas.

import pandas as pd

S=pd.Series()

print(S)

Output:

Series([], dtype: float64)

 Q - 2

Create a series using List.

import pandas as pd

L=[5,8,10,15,20]

S=pd.Series(L)

print(S)

Output:

0     5

1     8

2    10

3    15

4    20

dtype: int64

 Q - 3

Create a series using two different List.

import pandas as pd

Months=['Jan','Feb','Mar','Apr','May']

Days=[31,28,31,30,31]

S=pd.Series(Days,Months)

print(S)

Output:

Jan    31

Feb    28

Mar    31

Apr    30

May    31

dtype: int64

Q - 4

Create a series using range() method.

import pandas as pd

I=list(range(1,6))

D=list(range(2,11,2))

S=pd.Series(D,I)

print(S)

Output:

1     2

2     4

3     6

4     8

5    10

dtype: int64

Q - 5

Create a Series using range() and for loop.

import pandas as pd

I=[x for x in range(1,6)]

D=[x for x in range(3,16,3)]

S=pd.Series(D,I)

print(S)

Output:

1     3

2     6

3     9

4    12

5    15

dtype: int64

Q - 6

Handling floating point values for generating a Series.

import pandas as pd

I=[x for x in range(1,6)]

D=[2,5,8,4,9.6]

S=pd.Series(D,I)

print(S)

Output:

1    2.0

2    5.0

3    8.0

4    4.0

5    9.6

dtype: float64

Q - 7

Creating a Series using missing values (NaN)

import pandas as pd

import numpy as np

L=[2,3,5,7,8]

S=pd.Series([8,7.5,4.9,np.NaN,9])

print(S)

Output:

0    8.0

1    7.5

2    4.9

3    NaN

4    9.0

dtype: float64

Q - 8

Creating a Series and modify the index values.

import pandas as pd

S=pd.Series([31,28,31,30,31])

print(S)

S.index=['Jan','Feb','Mar','Apr','May']

print(S)

Output:

0    31

1    28

2    31

3    30

4    31

dtype: int64

Jan    31

Feb    28

Mar    31

Apr    30

May    31

dtype: int64

Q - 9

Creating a Series and access first and third element

import pandas as pd

D=[10,30,50,80,90]

S=pd.Series(D)

print(S)

print("First Element\n",S[0])

print("Third Element\n",S[2])

Output:

0    10

1    30

2    50

3    80

4    90

dtype: int64

First Element

 10

Third Element

 50

Q - 10

Creating a Series of 5 Elements and print complete Series, 2nd Element, and First 3 Element

import pandas as pd

D=[10,30,50,80,90]

S=pd.Series(D)

print(S)

print("Second Element\n",S[1])

print("First Three Element\n",S[0:3])

print("First Three Element\n",S[:3])

Output:

0    10

1    30

2    50

3    80

4    90

dtype: int64

Second Element

 30

First Three Element

 0    10

1    30

2    50

dtype: int64

First Three Element

 0    10

1    30

2    50

3    80

4    90

dtype: int64

Q - 11

Creating a Series of 5 Elements and perform slicing on Series

import pandas as pd

D=[10,20,30,40,50]

I=['a','b','c','d','e']

S=pd.Series(D,I)

print(S)

print(S['a'])

print(S[0:3])

print(S[-3:])

Output:

a    10

b    20

c    30

d    40

e    50

dtype: int64

10

a    10

b    20

c    30

dtype: int64

c    30

d    40

e    50

dtype: int64

06 Sept 2021 DOE Worksheet

 

CLASS - 6 Hindi Medium

CLASS - 6 English Medium

CLASS - 7 Hindi Medium

CLASS - 7 English Medium

CLASS - 8 Hindi Medium

CLASS - 8 English Medium

CLASS - 9 Hindi Medium

CLASS - 9 English Medium

CLASS - 10 Hindi Medium

CLASS - 10 English Medium