✅ INTERMEDIATE LEVEL NumPy Projects

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ADVANCED LEVEL NumPy Projects

BEGINNER LEVEL NumPy Projects

Pandas DataFrame for Beginners

1.Weather Data Simulation

Description:
Simulate 365 days of temperature data using normal distribution and analyze it.

Concepts: Random, mean, std

Code:

import numpy as np

temps = np.random.normal(30, 5, 365)

print("Average Temp:", temps.mean())
print("Max Temp:", temps.max())
print("Min Temp:", temps.min())

Output::--
Average Temp: 30.00229934869199
Max Temp: 44.1532483791829
Min Temp: 12.372748506925145

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2. Image Processing (Convert Image to Grayscale)

Description:
Use NumPy to convert an image to grayscale (conceptual level).

Concepts: Array manipulation

Code:

import numpy as np
from PIL import Image

img = Image.open("sample.jpg")
img_arr = np.array(img)

gray = np.dot(img_arr[...,:3], [0.2989, 0.5870, 0.1140])
print(gray)

Output::--
[[242.763  242.763  242.763  ... 254.9745 254.9745 254.9745]
 [242.763  242.763  242.763  ... 254.9745 254.9745 254.9745]
 [242.763  242.763  242.763  ... 254.9745 254.9745 254.9745]
 ...
 [  0.      61.5638 204.7217 ... 254.9745 254.9745 254.9745]
 [  0.      61.5638 204.7217 ... 254.9745 254.9745 254.9745]
 [  0.      61.5638 204.7217 ... 254.9745 254.9745 254.9745]]

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3. Matrix Calculator (Add, Multiply, Inverse)

Description:
Perform matrix operations like addition, multiplication, determinant, and inverse.

Concepts: linalg (linear algebra)

Code:

import numpy as np

A = np.array([[2, 3], [1, 4]])
B = np.array([[1, 0], [2, 5]])

print("Addition:\n", A + B)
print("Multiplication:\n", A.dot(B))
print("Determinant of A:", np.linalg.det(A))
print("Inverse of A:\n", np.linalg.inv(A))

Output::-
Addition:
 [[3 3]
 [3 9]]
Multiplication:
 [[ 8 15]
 [ 9 20]]
Determinant of A: 5.000000000000001
Inverse of A:
 [[ 0.8 -0.6]
 [-0.2  0.4]]