What is NumPy?

NumPy stands for Numerical Python. It is an open-source Python library used for numerical computing and handling arrays efficiently.

Why NumPy?

Faster than Python lists
Memory efficient
Supports mathematical operations directly
Widely used in:
- Data Science
- Machine Learning
- AI
- Scientific Computing

Difference Between Python Lists and NumPy Arrays

Feature	Python List	NumPy Array
Speed	Slower	Faster
Memory Usage	More	Less
Data Types	Mixed allowed	Same datatype
Mathematical Operations	Requires loops	Vectorized operations
Performance	Python-based	Built on C

Installing and Importing NumPy

Installation

pip install numpy

Importing

import numpy as np

np is an alias for NumPy.

Dimensions and Shapes in NumPy

Dimensions

0D → Scalar
1D → Single row/line
2D → Rows and columns

Example

import numpy as np

arr = np.array([1,2,4])
print(arr)

Checking Dimensions

arr.ndim

Scalar Array

arr2 = np.array(34)
arr2.ndim

2D Array

arr3 = np.array([[1,3,4],[2,34,5]])

print(arr3)
arr3.ndim

Shape

arr3.shape

Output:

(2, 3)

Meaning:

2 rows
3 columns

Creating NumPy Arrays

Using `arange()`

range = np.arange(1,10,2)
print(range)

Syntax

np.arange(start, stop, step)

Start included
Stop excluded

Using `linspace()`

arr4 = np.linspace(0,1,5)
print(arr4)

Creates evenly spaced numbers.

Using `logspace()`

arr5 = np.logspace(1,3,3)
print(arr5)

Output:

[10. 100. 1000.]

Arrays Filled with Values

Zeros

arr6 = np.zeros(4)
print(arr6)

2D Zeros

arr6 = np.zeros([3,2])
print(arr6)

Ones

arr7 = np.ones([3,2])
print(arr7)

Full

arr8 = np.full(10,2)
arr8

2D Full Array

arr9 = np.full([2,4],5)
arr9

Empty Array

arr11 = np.empty([2,3])
arr11

Faster than zeros
Contains garbage values
Use only if values will be overwritten

Random Arrays in NumPy

Random Uniform Distribution

np.random.rand(10)

2D Random Array

arr12 = np.random.rand(2,3)
arr12

Values between:

0 and 1

Random Normal Distribution

arr13 = np.random.randn(2,3)
arr13

Mean = 0
Standard deviation = 1

Random Integers

arr14 = np.random.randint(10,100, size=10)
print(arr14)

2D Random Integers

arr14 = np.random.randint(10,100, size=[2,3])
print(arr14)

NumPy Data Types and Type Casting

Integer Array

arr = np.array([1,3,45,3,4])

print(arr)
type(arr)

Automatic Type Conversion

arr = np.array([1.1,3,45,3,4])

print(arr)
type(arr)

If one value is float, entire array becomes float.

Defining Datatype Manually

arr = np.array([1.3,3,45,3,4], dtype=int)

arr.dtype

Checking Datatypes

arr.dtype

Type Casting Using `astype()`

arr = np.array([1,23,4])

new_arr = arr.astype(np.float64)

new_arr
new_arr.dtype

Converts datatype without modifying original array.

Type Casting Errors

arr=["1",'2',"hello"]

arr2 = arr.astype(np.int64)
arr2

This causes error because:

"hello"

cannot be converted to integer.

Important Array Attributes

arr  = np.array([[1,2,4],
                [4,5,6]])

Datatype

print(arr.dtype)

Dimensions

print(arr.ndim)

Shape

print(arr.shape)

Size

print(arr.size)

Total elements:

rows × columns

Item Size

print(arr.itemsize)

Returns memory size of one element in bytes.

Array Reshaping

Using `reshape()`

arr = np.array([1,2,3,4,5,6])

reshaped = arr.reshape(2,3)

print(reshaped)

Reshape Again

reshape2 = reshaped.reshape(3,2)

print(reshape2)

Changes layout without changing data.

Ravel vs Flatten

Ravel

Converts array into 1D.

ravel = reshape2.ravel()

ravel

Important

ravel() returns a view. Changes affect original array.

ravel[0] = 100

print(ravel)
print(reshape2)

Flatten

flat = reshape2.flatten()

flat

Important

flatten() returns a copy. Original array remains unchanged.

flat[0] = 200

print(flat)
print(reshape2)

Difference Between Ravel and Flatten

Feature	Ravel	Flatten
Returns	View	Copy
Memory Efficient	Yes	No
Changes Affect Original	Yes	No
Speed	Faster	Slightly slower

1. Programming Foundation

Documentation Index

​What is NumPy?

​Why NumPy?

​Difference Between Python Lists and NumPy Arrays

​Installing and Importing NumPy

​Installation

​Importing

​Dimensions and Shapes in NumPy

​Dimensions

​Example

​Checking Dimensions

​Scalar Array

​2D Array

​Shape

​Creating NumPy Arrays

​Using arange()

​Syntax

​Using linspace()

​Using logspace()

​Arrays Filled with Values

​Zeros

​2D Zeros

​Ones

​Full

​2D Full Array

​Empty Array

​Random Arrays in NumPy

​Random Uniform Distribution

​2D Random Array

​Random Normal Distribution

​Random Integers

​2D Random Integers

​NumPy Data Types and Type Casting

​Integer Array

​Automatic Type Conversion

​Defining Datatype Manually

​Checking Datatypes

​Type Casting Using astype()

​Type Casting Errors

​Important Array Attributes

​Datatype

​Dimensions

​Shape

​Size

​Item Size

​Array Reshaping

​Using reshape()

​Reshape Again

​Ravel vs Flatten

​Ravel

​Important

​Flatten

​Important

​Difference Between Ravel and Flatten

What is NumPy?

Why NumPy?

Difference Between Python Lists and NumPy Arrays

Installing and Importing NumPy

Installation

Importing

Dimensions and Shapes in NumPy

Dimensions

Example

Checking Dimensions

Scalar Array

2D Array

Shape

Creating NumPy Arrays

Using `arange()`

Syntax

Using `linspace()`

Using `logspace()`

Arrays Filled with Values

Zeros

2D Zeros

Ones

Full

2D Full Array

Empty Array

Random Arrays in NumPy

Random Uniform Distribution

2D Random Array

Random Normal Distribution

Random Integers

2D Random Integers

NumPy Data Types and Type Casting

Integer Array

Automatic Type Conversion

Defining Datatype Manually

Checking Datatypes

Type Casting Using `astype()`

Type Casting Errors

Important Array Attributes

Datatype

Dimensions

Shape

Size

Item Size

Array Reshaping

Using `reshape()`

Reshape Again

Ravel vs Flatten

Ravel

Important

Flatten

Important

Difference Between Ravel and Flatten