<jupyter_start><jupyter_text>Python Overview & Data Manipulation with NumPy<jupyter_code>print("Hello World")<jupyter_output>Hello World<jupyter_text>[Literate Programming](http://www.literateprogramming.com/knuthweb.pdf) 1. **Programming Environment**    - Jupyter Notebook Environment    - Markdown format for documentation        - Headings        - Lists- **Python Overview**    - input/output    - Conditional Statements in python        - if        - if else        - if-elif-else    - List    - Tuple    - Dictionary    - Files    - Modules & Packages- **Data Manipulation with NumPy**    - Introduction    - NumPy Arrays    - NumPy Basics    - Math    - Random    - Indexing  Markdown Syntax  Shortcuts in Jupyter Notebook- **ShiftEnter** - Execute the cell- *EscM* - code cell to matkdown cell- ***EscY*** - markdown cell to code cell- **EscA** - insert the cell above the current cell- **EscB** - Insert the cell below the current cell- **EscDD** - Deleting- **EscL** - Inserting Line Numbers for the Currret Cell  Python Overview Heading2 Heading3 Heading6Heading1 - Line1- line2    - Sub Point2        - Sub Sub Point    - Sub point2        1. Line12. Line2    1. Line 2-1    2. Line 2-2<jupyter_code>name = input("Enter Your Name")
print(name)
print(type(name))
name = int(input("Enter Your Name"))
print(type(name))<jupyter_output><class 'int'><jupyter_text>Conditional Statements - if Syntax```pythonif condition:    print("Hello World")```- if-else Syntax```pythonif condition:    print("Hello World")    print("Hello World Again")else:    print("Failed Condition")```- if-elif-else Syntax```pythonif condition:    print("Hello World")    print("Hello World Again")elif condition:    print("Hello World Agani and Again")    print("Final Hello World Again")else:    print("Failed Condition")```<jupyter_code>if True:
    print("Hello World")<jupyter_output>Hello World<jupyter_text>- int, float, complex, boolean, string- List, tuple, Disctionary, Set<jupyter_code>s1 = 's1'
s2 = "S2"
s3 = '''s3
s4
s5'''
s4 = """s6
s7
s8"""
bol1, bol2, bol3 = True, False, None

com = 3+5j
a = 5
b = 5.5<jupyter_output><empty_output><jupyter_text>ListWhich is stored Group of dissimilar data type of elements- We can modify, update, remove existing elements in the list<jupyter_code>li = [113, 5+6j, 52.5, 'APSSDC', True, None, ["Hey I'm a List"]]

print(li)
print(type(li))
li[0] = 115
print(li)<jupyter_output>[115, (5+6j), 52.5, 'APSSDC', True, None, ["Hey I'm a List"]]<jupyter_text>Tuple - ()Which is stored Group of dissimilar data type of elements- We can't modify, update, remove existing elements in the tuple<jupyter_code>tup1 = (113, 5+6j, 52.5, 'APSSDC', True, None, ["Hey I'm a List"], ("Haha I'm a Tuple"))

print(tup1)
tup1[0] = 115<jupyter_output><empty_output><jupyter_text>DictionaryThey are used to store data in `key:value` pairs```Python{"Key":"Value"}```<jupyter_code>dic1 = {'APSSDC':"Online Training"}

print(dic1)<jupyter_output>{'APSSDC': 'Online Training'}<jupyter_text>Files<jupyter_code>f = open('textFile.txt', mode = 'r')
line = f.readline()
print(line)
print('***********')
data = f.read()
print(data)

print(type(data), type(line))
f.close()
f = open('textFile.txt', mode = 'r')
lines = f.readlines()
print(lines)


print(type(lines))
f.close()<jupyter_output>['APSSDC\n', 'Online\n', 'Data\n', 'Analysis\n', 'Using\n', 'Python\n', 'Traning\n', 'Program']
<class 'list'><jupyter_text>Modules & Packages- Module is a single python file created for specific task- Packages is a bunch of module avalaible on a single folder with specific task<jupyter_code>import arthematic<jupyter_output><empty_output><jupyter_text>moduleName.FuntionName<jupyter_code>arthematic.addition(5, 10)
arthematic.pi
import arthematic as ar

ar.addition(50, 1100)
from arthematic import addition

addition(50, 500)
from arthematic import addition as add

add(50000, 500)<jupyter_output><empty_output><jupyter_text>```pythonimport packageName.module as alaisfrom package import modulefrom package.module import method/class/attributefrom package.module import method/class/attribute as alais```  **Data Manipulation with NumPy**    - Introduction    - NumPy Arrays    - NumPy Basics    - Math    - Random    - Indexing  [Numpy Official Website](https://numpy.org/)<jupyter_code>import numpy as np<jupyter_output><empty_output><jupyter_text>```pythonerror:moduleNotFoundError    pip install PackageName```  Numpy ArrayIt is used to store similar group of DataType of elements Creating of Arrays<jupyter_code>arr1 = np.array([1, 2, 3, 4, 5, 6])
arr2 = np.array((1, 2, 3, 4, 5, 6))

arr1
type(arr1), type(arr2)
arr1.dtype, arr2.dtype
arr3 = np.array([1, 2, 3, 4, 5, 6], dtype = 'int8')
type(arr3), arr3.dtype
arr4 = np.array([1, 2, 3, 5.5, 6.6, 9.9, 8.8])

arr4, type(arr4), arr4.dtype<jupyter_output><empty_output><jupyter_text>- strings , complex, float, int --> Strings- complex, float, int --> Complex- float, int --> float- int -> int<jupyter_code>arr5 = np.array([1, 2, 3, 5.5, 6.6, 9.9, 8.8, 4 + 5j])

arr5, type(arr5), arr5.dtype
arr6 = np.array([1, 2, 3, 5.5, 6.6, 9.9, 8.8, 4 + 5j, 'APSSDC'])

arr6, type(arr6), arr6.dtype<jupyter_output><empty_output><jupyter_text>- a = 0- b = 0.0- c = ' '- li =  []- t1 = ()<jupyter_code>zeros = np.zeros(50)

zeros
zeros.shape
d2 = np.array([[1, 5], [5, 6]])

d2.shape
zeros = np.zeros((5, 2))

zeros
one = np.ones((5, 5), dtype = int)
one
one_complex = np.ones((500, 500), dtype = 'complex64')
one_complex
one_complex.shape
one = np.ones((5, 15), dtype = int)
one
one.shape
d3 = one.reshape(5, 5, 3)
d3.shape
d3
one.size
one.nbytes
one.dtype
(32 * 75) // 8<jupyter_output><empty_output>
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