NumPy - Numerical Computing in Python

Python Made Easy - (Article 16) Telugu Explanation

4 min readNov 3, 2025

1. What is NumPy?

NumPy ante “Numerical Python”.
Idi Python lo mathematical calculations fast ga cheyyadaniki use chestham.

Normally, lists tho calculations cheyyadam slow untundhi — because lists store mixed data (int, float, string etc.).
But NumPy lo, arrays use chestham → same data type, fast speed, less memory.

2. Why NumPy?

Normal ListNumPy ArrayStores mixed data typesOnly same type (ex: all int or all float)Slow calculationsVery fastNo advanced math functionsHas many built-in math functionsMemory usage moreMemory efficient

So, NumPy = Speed + Power + Simplicity

3. Installing NumPy

Before using, install it (once only):

pip install numpy

Then import it in Python:

import numpy as np

np ane short name (alias) use chestham — easy typing kosam.

4. Creating NumPy Arrays

(a) From a Python List:

import numpy as np

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

Output:

[1 2 3 4 5]

Notice: NumPy arrays lo commas levu — that’s normal.

(b) 2D Array (Matrix like structure)

arr2 = np.array([[1, 2, 3],
                 [4, 5, 6]])
print(arr2)

Output:

[[1 2 3]
 [4 5 6]]

(c) Using Built-in Functions:

FunctionDescriptionnp.zeros(n)Create array with all zerosnp.ones(n)Create array with all onesnp.arange(start, stop, step)Like range() but gives NumPy arraynp.linspace(start, stop, num)Divides range into equal partsnp.eye(n)Creates identity matrix

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Examples:

print(np.zeros(5))
print(np.ones(3))
print(np.arange(1, 10, 2))
print(np.linspace(0, 1, 5))
print(np.eye(3))

5. Array Properties

a = np.array([[1,2,3],[4,5,6]])
print(a.ndim)    # Dimension count (2D)
print(a.shape)   # Rows, columns (2, 3)
print(a.size)    # Total elements (6)
print(a.dtype)   # Data type (int32 or float64)

6. Indexing & Slicing

Just like lists, but more powerful.

arr = np.array([10,20,30,40,50])
print(arr[0])      # First element
print(arr[-1])     # Last element
print(arr[1:4])    # Slice

2D Example:

arr2 = np.array([[10,20,30],
                 [40,50,60],
                 [70,80,90]])

print(arr2[0, 1])      # 20
print(arr2[:, 1])      # all rows, 2nd column
print(arr2[1:, :2])    # 2nd & 3rd row, first 2 columns

7. Mathematical Operations

NumPy arrays support element-wise operations (super fast).

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

print(a + b)     # [5 7 9]
print(a - b)     # [-3 -3 -3]
print(a * b)     # [ 4 10 18]
print(a / b)     # [0.25 0.4 0.5]

8. Aggregate Functions

Example:

arr = np.array([10,20,30,40,50])
print(np.sum(arr))   # 150
print(np.mean(arr))  # 30.0
print(np.std(arr))   # 14.142

9. Reshaping & Flattening

arr = np.arange(1, 10)
new_arr = arr.reshape(3, 3)
print(new_arr)

flat = new_arr.flatten()
print(flat)

10. Useful Functions

Example:

a = np.array([1,2,3])
b = np.array([4,5,6])
print(np.concatenate((a,b)))

11. Random Numbers in NumPy

from numpy import random

print(random.randint(10))        # single random int
print(random.randint(1,100,5))   # array of 5 random ints
print(random.rand(3))            # 3 random floats between 0–1

12. Matrix Operations

a = np.array([[1,2],
              [3,4]])
b = np.array([[5,6],
              [7,8]])

print(np.dot(a,b))   # Matrix multiplication
print(a.T)           # Transpose

13. Mini Example Program:

Calculate Average Marks using NumPy

import numpy as np

marks = np.array([78, 85, 69, 92, 88])
average = np.mean(marks)
highest = np.max(marks)
lowest = np.min(marks)print("Average Marks:", average)
print("Highest Marks:", highest)
print("Lowest Marks:", lowest)