✅ Part 1: Matplotlib (Step-by-Step)

📌 Step 1: Install and Import

pip install matplotlib

import matplotlib.pyplot as plt

📌 Step 2: Line Chart

x = [1, 2, 3, 4]
y = [10, 20, 30, 25]

plt.plot(x, y)
plt.title("Line Chart Example")
plt.xlabel("X Values")
plt.ylabel("Y Values")
plt.show()

📈 Used For: Show progress over time (e.g. sales growth)

📌 Step 3: Bar Chart

subjects = ['Math', 'Science', 'English']
marks = [85, 90, 78]

plt.bar(subjects, marks)
plt.title("Student Marks")
plt.xlabel("Subjects")
plt.ylabel("Marks")
plt.show()

📊 Used For: Compare items like subject-wise marks or sales.

📌 Step 4: Pie Chart

fruits = ['Apple', 'Banana', 'Orange']
quantities = [40, 35, 25]

plt.pie(quantities, labels=fruits, autopct='%1.1f%%')
plt.title("Fruit Distribution")
plt.show()

🥧 Used For: Show percentage distribution (e.g. market share)

📌 Step 5: Histogram

ages = [18, 22, 22, 25, 26, 28, 28, 30, 35, 35]

plt.hist(ages, bins=5)
plt.title("Age Group Distribution")
plt.xlabel("Age")
plt.ylabel("Frequency")
plt.show()

📚 Used For: See how data is spread out (e.g. age of people)

📌 Step 6: Scatter Plot

hours = [1, 2, 3, 4, 5]
marks = [40, 50, 65, 75, 85]

plt.scatter(hours, marks)
plt.title("Study Time vs Marks")
plt.xlabel("Hours Studied")
plt.ylabel("Marks")
plt.show()

📌 Used For: Relationship between two things (e.g. effort vs result)

✅ Part 2: Data Cleaning with Pandas (Step-by-Step)

📌 Step 1: Import Pandas

import pandas as pd

📌 Step 2: Check Missing Values

df = pd.read_csv("students.csv")
print(df.isnull())          # Shows True/False
print(df.isnull().sum())    # Shows total missing per column

📌 Step 3: Drop Missing Rows

df_clean = df.dropna()

🗑️ Removes rows with any missing value.

📌 Step 4: Fill Missing Values

df.fillna(0, inplace=True)  # Fill missing with 0
df['Marks'].fillna(df['Marks'].mean(), inplace=True)  # Fill with average

📌 Step 5: Remove Duplicate Rows

df = df.drop_duplicates()

🧹 Removes repeated rows in data.

📌 Step 6: Change Data Type

df['Age'] = df['Age'].astype(int)

🧠 Convert from float or string to int.

📌 Step 7: Rename Columns

df.rename(columns={'Full Name': 'Name'}, inplace=True)

📌 Step 8: Clean Strings

df['Name'] = df['Name'].str.strip().str.title()

✍️ Clean unwanted spaces and format properly.

✅ Part 3: Excel Integration with Pandas (Step-by-Step)

📌 Step 1: Install Required Library

pip install openpyxl

(openpyxl is needed for Excel support)

📌 Step 2: Read Excel File

df = pd.read_excel("students.xlsx")

📥 Load Excel file into Pandas.

📌 Step 3: Read Specific Sheet

df = pd.read_excel("students.xlsx", sheet_name='Marks')

📄 Only read one sheet by name.

📌 Step 4: Write to Excel

df.to_excel("output.xlsx", index=False)

📤 Save DataFrame to Excel file.

📌 Step 5: Save Multiple Sheets

with pd.ExcelWriter("multi_sheet.xlsx") as writer:
    df1.to_excel(writer, sheet_name='Sheet1')
    df2.to_excel(writer, sheet_name='Sheet2')

📚 Save multiple reports in one Excel file.

✅ Real-Life Use Cases

✅ Interview Questions & Answers (Matplotlib + Pandas Data Cleaning + Excel Integration)

+ Hands-on Practice Tasks for Students

🎯 Section 1: Interview Questions & Answers

🔹 1. What is Matplotlib?

Answer:
Matplotlib is a Python library used to create visualizations like line charts, bar graphs, pie charts, histograms, and scatter plots.

🔹 2. How do you create a bar chart in Matplotlib?

Answer:
You use the bar() function:

import matplotlib.pyplot as plt
plt.bar(['A', 'B'], [10, 20])
plt.show()

🔹 3. What is the use of plt.show()?

Answer:
plt.show() displays the graph or plot in a new window.

🔹 4. What is the difference between plot() and scatter()?

Answer:

• plot() is used for line charts (connected data).
• scatter() is for individual data points (used to find patterns).

🔹 5. What is Pandas?

Answer:
Pandas is a Python library used to store and analyze data in table-like formats using DataFrames.

🔹 6. How do you handle missing data in Pandas?

Answer:
You can:

• Use dropna() to remove missing rows.
• Use fillna() to fill missing values.

🔹 7. How do you find missing values in a DataFrame?

Answer:
Use:

df.isnull()
df.isnull().sum()

🔹 8. How to remove duplicate values in a dataset?

Answer:
Use df.drop_duplicates().

🔹 9. How do you read and write Excel files in Pandas?

Answer:

• Read: pd.read_excel("file.xlsx")
• Write: df.to_excel("output.xlsx", index=False)

🔹 10. What is the use of ExcelWriter in Pandas?

Answer:
It allows saving multiple DataFrames into one Excel file with multiple sheets.

🧪 Section 2: Hands-on Examples (Student Practice)

✅ 1. Create a Line Chart for Weekly Sales

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
sales = [100, 120, 90, 150, 130]

plt.plot(days, sales)
plt.title("Weekly Sales")
plt.xlabel("Days")
plt.ylabel("Sales")
plt.show()

✅ 2. Clean Student Data (CSV)

CSV Example:

Name,Age,Marks
John,21,85
Sara,,78
,20,90
Anna,22,
John,21,85

Python Code:

import pandas as pd

df = pd.read_csv("students.csv")

# Step 1: Show missing values
print(df.isnull().sum())

# Step 2: Fill missing values
df['Name'].fillna('Unknown', inplace=True)
df['Age'].fillna(df['Age'].mean(), inplace=True)
df['Marks'].fillna(df['Marks'].mean(), inplace=True)

# Step 3: Remove duplicates
df = df.drop_duplicates()

print(df)

✅ 3. Read Excel and Show Subject-wise Marks

import pandas as pd

df = pd.read_excel("marks.xlsx", sheet_name="Sheet1")

print("Average marks per subject:")
print(df.mean())

✅ 4. Save Cleaned Data to Excel

df.to_excel("cleaned_students.xlsx", index=False)

✅ 5. Practice Task: Fruit Pie Chart

Create a pie chart for this data:

Code:

import matplotlib.pyplot as plt

fruits = ['Apple', 'Banana', 'Mango', 'Orange']
quantities = [40, 30, 20, 10]

plt.pie(quantities, labels=fruits, autopct='%1.1f%%')
plt.title("Fruit Sale Distribution")
plt.show()

📘 Summary for Students

📌 What is Pandas?

Pandas is a Python library used to work with tabular data like Excel sheets or database tables. It helps with:

• Reading & writing data
• Analyzing & cleaning data
• Performing calculations on rows and columns

✅ Step 1: Installing and Importing Pandas

pip install pandas

import pandas as pd

✅ pd is a short name (alias) we use for pandas.

✅ Step 2: Creating a DataFrame (Table-like data)

data = {
    'Name': ['Arun', 'Priya', 'Kumar'],
    'Marks': [85, 90, 78]
}

df = pd.DataFrame(data)
print(df)

🖨 Output:

    Name  Marks
0   Arun     85
1  Priya     90
2  Kumar     78

🎯 Real-Life Use: Represent student marks or sales reports as a table.

✅ Step 3: Reading Data from Files

df = pd.read_csv("students.csv")

🎯 Real-Life Use: Read data from Excel, CSV, or Google Sheets.

✅ Step 4: Basic Info About Data

print(df.head())         # First 5 rows
print(df.tail())         # Last 5 rows
print(df.shape)          # (rows, columns)
print(df.columns)        # List of column names
print(df.info())         # Column details

✅ Step 5: Selecting Columns and Rows

print(df['Name'])        # Select one column
print(df[['Name', 'Marks']])  # Select multiple columns

print(df.iloc[0])        # First row (by index)
print(df.loc[1])         # Row with index label 1

🎯 Real-Life Use: Get details of a student by ID or name.

✅ Step 6: Filtering Data (Conditional Selection)

print(df[df['Marks'] > 80])

🖨 Output:

    Name  Marks
0   Arun     85
1  Priya     90

🎯 Real-Life Use: Find students who passed or scored more than 80.

✅ Step 7: Adding and Modifying Columns

df['Result'] = df['Marks'] >= 80
print(df)

🎯 Real-Life Use: Add “Pass/Fail” status based on marks.

✅ Step 8: Sorting Data

print(df.sort_values('Marks'))
print(df.sort_values('Marks', ascending=False))

🎯 Real-Life Use: Rank top scorers or sort products by price.

✅ Step 9: Grouping and Aggregating

group = df.groupby('Result').mean()
print(group)

🎯 Real-Life Use: Find average marks of passed vs failed students.

✅ Step 10: Handling Missing Data

df.isnull()            # Check missing values
df.dropna()            # Remove rows with missing values
df.fillna(0)           # Replace missing values with 0

🎯 Real-Life Use: Fill missing prices or names in sales data.

✅ Step 11: Exporting Data to File

df.to_csv("updated_data.csv", index=False)

🎯 Real-Life Use: Save cleaned or updated student/sales report.

✅ Step 12: Useful Pandas Functions

df.describe()      # Summary (mean, std, min, max)
df['Marks'].max()  # Maximum marks
df['Marks'].min()  # Minimum marks
df['Marks'].mean() # Average marks
df['Marks'].sum()  # Total marks

🎯 Real-Life Use: Get summary of any report like sales or performance.

📚 Interview Q&A (Pandas)

1. What is Pandas in Python?

Answer: A library for data manipulation and analysis using tables (DataFrames).

2. What is a DataFrame?

Answer: A 2D table with rows and columns (like Excel).

3. How do you read data in Pandas?

pd.read_csv('filename.csv')

4. How to filter rows where salary > 50000?

df[df['Salary'] > 50000]

5. How to handle missing values?

• df.dropna()
• df.fillna(0)

6. How to group data?

df.groupby('Department').mean()

7. Difference between loc[] and iloc[]?

• loc[]: Uses label (row name/index)
• iloc[]: Uses position (row number)

8. How to sort data?

df.sort_values('ColumnName')

9. How to add a new column?

df['NewCol'] = value

10. Real-Life Example?

Track student marks, employee salary, monthly sales, attendance, survey data, etc.

📌 Summary

Let me know your next step!

📘 Introduction to NumPy

What is NumPy?

NumPy (Numerical Python) is a powerful Python library used for numerical computations. It provides support for arrays, matrices, and many mathematical functions.

Why use NumPy?

• Faster than Python lists
• Supports multi-dimensional arrays
• Optimized mathematical functions
• Useful for data science, ML, and scientific computing

Installation

pip install numpy

Importing NumPy

import numpy as np

Real-Life Example:

You can analyze thousands of sales records, do calculations, and generate statistics quickly using NumPy arrays.

🔹 Creating Arrays

arr1 = np.array([1, 2, 3])          # 1D array
arr2 = np.array([[1, 2], [3, 4]])   # 2D array

Array Attributes

print(arr2.shape)   # (2, 2)
print(arr2.ndim)    # 2
print(arr2.dtype)   # int32 or int64

Real-Life Example:

Use 2D arrays to represent Excel-like tables (e.g., student marks, sales data).

🔹 Indexing and Slicing

arr = np.array([10, 20, 30, 40])
print(arr[1:3])  # Output: [20 30]

arr2 = np.array([[1, 2], [3, 4]])
print(arr2[1, 0])  # Output: 3

Real-Life Example:

Get a student’s marks from a 2D array of student results.

🔹 Array Operations

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(a + b)  # [5 7 9]
print(a * b)  # [4 10 18]

Broadcasting

a = np.array([1, 2, 3])
print(a + 10)  # [11 12 13]

Real-Life Example:

Apply discount or tax to a list of prices using broadcasting.

🔹 Array Functions

arr = np.array([1, 2, 3, 4])
print(np.sum(arr))       # 10
print(np.mean(arr))      # 2.5
print(np.max(arr))       # 4

Real-Life Example:

Calculate total or average marks of students.

🔹 Reshaping and Flattening

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

Real-Life Example:

Convert a 2D image matrix into a 1D array for machine learning input.

🔹 Stacking and Splitting

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

print(np.vstack((a, b)))
print(np.hstack((a, b)))

🔹 Random Numbers

np.random.seed(0)
print(np.random.randint(1, 10, size=(2, 3)))

Real-Life Example:

Create random roll numbers or question orders for an online quiz.

🔹 File I/O in NumPy

np.savetxt('data.csv', arr, delimiter=',')
arr_loaded = np.loadtxt('data.csv', delimiter=',')

Real-Life Example:

Save and load data like marks, sales, or sensor values using CSV files.

📚 Interview Q&A with Real-Life Examples

1. What is NumPy? Why is it used?

Answer: A powerful library for numeric computations in Python. Used in data science, ML, and engineering.
Example: Analyze thousands of rows of Excel data in seconds.

2. Difference between Python list and NumPy array?

3. What is broadcasting?

Answer: It allows different-shaped arrays to work together in operations.
Example: Add 10% tax to each product price: arr + 10

4. How to create arrays?

Answer: Using np.array, np.zeros, np.ones, np.arange, etc.
Example: Initialize 0 attendance for all students.

5. What is reshape and flatten?

Answer: reshape() changes shape, flatten() converts to 1D.
Example: Convert a 2D image to 1D for model input.

6. Mathematical operations in NumPy?

Answer: Use +, -, *, / between arrays or scalars.
Example: Calculate final bill: price - discount

7. How to calculate statistics?

Answer: Use np.mean, np.median, np.std, etc.
Example: Find average marks of a class.

8. Generating random numbers?

Answer: Use np.random.randint, np.random.rand, etc.
Example: Generate random test scores or sample data.

9. What is axis in NumPy?

Answer: Tells NumPy to operate along rows or columns.
Example: Sum all subjects per student: axis=1

10. File handling in NumPy?

Answer: np.savetxt, np.loadtxt for CSV operations.
Example: Save survey results into a CSV file.

Sure! Here’s a step-by-step NumPy guide with more examples and outputs, explained in simple English, including real-life usage.

🧮 NumPy Step-by-Step with Examples and Outputs

✅ Step 1: Importing NumPy

import numpy as np

✅ Why? This lets us use all the NumPy functions.

✅ Step 2: Creating Arrays

➤ 1D Array

a = np.array([1, 2, 3])
print(a)
# Output: [1 2 3]

➤ 2D Array

b = np.array([[1, 2], [3, 4]])
print(b)
# Output:
# [[1 2]
#  [3 4]]

➤ 3D Array

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

🎯 Real-Life Use: Store pixel values for an image (3D – width, height, channels).

✅ Step 3: Array Properties

print(b.shape)     # (2, 2)
print(b.ndim)      # 2
print(b.size)      # 4
print(b.dtype)     # int64

🎯 Real-Life Use: Know the shape of Excel-like data before applying operations.

✅ Step 4: Indexing and Slicing

arr = np.array([10, 20, 30, 40, 50])
print(arr[1:4])
# Output: [20 30 40]

➤ 2D Indexing

arr2 = np.array([[1, 2], [3, 4]])
print(arr2[1, 0])
# Output: 3

🎯 Real-Life Use: Access student marks from rows and subjects from columns.

✅ Step 5: Array Operations

a = np.array([10, 20, 30])
b = np.array([1, 2, 3])
print(a + b)     # [11 22 33]
print(a * b)     # [10 40 90]

🎯 Real-Life Use: Calculate bill amount = price * quantity

✅ Step 6: Broadcasting

a = np.array([1, 2, 3])
print(a + 5)
# Output: [6 7 8]

🎯 Real-Life Use: Apply flat discount or tax to all items at once.

✅ Step 7: Useful NumPy Functions

arr = np.array([10, 20, 30])
print(np.sum(arr))     # 60
print(np.mean(arr))    # 20.0
print(np.min(arr))     # 10
print(np.max(arr))     # 30
print(np.std(arr))     # 8.16...

🎯 Real-Life Use: Calculate total, average, and spread of exam scores.

✅ Step 8: Reshape and Flatten

arr = np.array([[1, 2, 3], [4, 5, 6]])
reshaped = arr.reshape(3, 2)
print(reshaped)
# Output:
# [[1 2]
#  [3 4]
#  [5 6]]

flat = arr.flatten()
print(flat)
# Output: [1 2 3 4 5 6]

🎯 Real-Life Use: Prepare data for machine learning models (flat format).

✅ Step 9: Stack and Split Arrays

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

print(np.hstack((a, b)))
# Output:
# [[1 2 5 6]
#  [3 4 7 8]]

print(np.vstack((a, b)))
# Output:
# [[1 2]
#  [3 4]
#  [5 6]
#  [7 8]]

🎯 Real-Life Use: Combine tables or reports horizontally/vertically.

✅ Step 10: Random Numbers

np.random.seed(0)
print(np.random.randint(1, 10, (2, 3)))
# Output:
# [[5 6 1]
#  [4 4 8]]

🎯 Real-Life Use: Create random test data or shuffle questions.

✅ Step 11: Conditional Selection

arr = np.array([10, 20, 30, 40])
print(arr[arr > 20])
# Output: [30 40]

🎯 Real-Life Use: Filter students who scored above 20 marks.

✅ Step 12: Save & Load Files

arr = np.array([[1, 2], [3, 4]])
np.savetxt("data.csv", arr, delimiter=",")
loaded = np.loadtxt("data.csv", delimiter=",")
print(loaded)

🎯 Real-Life Use: Save and reload reports, marks, or sales data.

✅ Step 13: Axis in Functions

arr = np.array([[1, 2], [3, 4]])
print(np.sum(arr, axis=0))  # Column sum: [4 6]
print(np.sum(arr, axis=1))  # Row sum: [3 7]

🎯 Real-Life Use: Get total sales per product or per day.

✅ Step 14: Special Arrays

print(np.zeros((2, 3)))   # Array with all zeros
print(np.ones((2, 3)))    # Array with all ones
print(np.eye(3))          # Identity matrix

🎯 Real-Life Use: Initialize tables, filters, or neural network layers.

✅ Step 15: Linspace & Arange

print(np.linspace(1, 5, 5))  # [1. 2. 3. 4. 5.]
print(np.arange(1, 10, 2))   # [1 3 5 7 9]

🎯 Real-Life Use: Generate time steps or price intervals for charts.

🔍 Final Thoughts

NumPy is essential for data analysis, machine learning, and scientific work in Python. Mastering it gives you speed, accuracy, and power to work with big data.

If you’re a beginner, practice real-life examples like:

• Student marks
• Sales data
• Weather sensor logs

Keep experimenting with arrays, slicing, operations, and reshaping to become a NumPy pro!

Great! Here’s a career-based list of Python libraries to help you choose what to focus on based on your career goal.

🧭 Career-Based Python Library Guide

💼 1. Data Science / Data Analyst

🔹 NumPy – Fast numerical computations
🔹 Pandas – Table (Excel-style) data analysis
🔹 Matplotlib / Seaborn – Visualize data with graphs
🔹 Scikit-learn – Easy machine learning models
🔹 Statsmodels – Statistical tests, regressions
🔹 Jupyter Notebook – Interactive coding

💡 Why: Data cleaning, visualization, prediction

🤖 2. Machine Learning / AI

🔹 All from Data Science PLUS
🔹 TensorFlow – Deep learning by Google
🔹 Keras – Simple interface for TensorFlow
🔹 PyTorch – Deep learning by Facebook
🔹 OpenCV – Image recognition and vision
🔹 NLTK / SpaCy – Natural language (text) processing

💡 Why: AI, predictions, image & text classification

🌐 3. Web Development

🔹 Flask – Lightweight web framework
🔹 Django – Full-featured web framework
🔹 Jinja2 – HTML templates
🔹 SQLAlchemy – Database connection
🔹 WTForms / Django Forms – User input forms
🔹 Requests – Call APIs

💡 Why: Build websites, blogs, admin panels

🧪 4. Software Testing / QA

🔹 Unittest – Built-in Python testing
🔹 Pytest – Advanced testing made easy
🔹 Selenium – Automate browser testing
🔹 Behave – BDD (like Cucumber for Python)
🔹 Mock – Test dummy data

💡 Why: Automate test cases for software quality

🤖 5. Automation / Scripting

🔹 os, shutil – File and folder automation
🔹 subprocess – Run shell commands
🔹 pyautogui – Control mouse & keyboard
🔹 schedule – Automate time-based tasks
🔹 requests / bs4 (BeautifulSoup) – Web scraping
🔹 pandas / openpyxl / csv – Excel automation

💡 Why: Save time by writing repeatable task scripts

🖥️ 6. Desktop App Development

🔹 Tkinter – Built-in GUI toolkit
🔹 PyQt / PySide – Advanced GUI apps
🔹 Kivy – Multi-platform GUI & touch apps
🔹 CustomTkinter – Beautiful UI with dark mode

💡 Why: Make apps with buttons, forms, input boxes

🧩 7. Game Development

🔹 Pygame – Simple 2D game creation
🔹 Arcade – Modern 2D games (better visuals)
🔹 PyOpenGL – 3D game basics
🔹 Panda3D – Full 3D engine

💡 Why: Build simple games to large 3D games

📊 8. Cybersecurity / Hacking (Ethical)

🔹 Scapy – Network packet crafting
🔹 Nmap (via Python) – Scan devices
🔹 Paramiko – SSH and remote access
🔹 Requests / BeautifulSoup – Info gathering
🔹 Socket – Low-level networking

💡 Why: Create ethical hacking tools, scan systems

🧬 9. Bioinformatics / Science

🔹 BioPython – DNA, RNA, Protein data
🔹 SciPy – Scientific math & physics
🔹 NumPy / Pandas – Data processing
🔹 Matplotlib / Seaborn – Graphs & analysis

💡 Why: Process genetic or scientific data easily

🧾 Summary Table

Here’s a helpful guide on the most used Python libraries worldwide, why they are popular, and estimated usage based on developer surveys and real-world application.

These libraries are used by millions of developers across industries like data science, web development, automation, machine learning, and more.

🔢 Percentages are based on data from Stack Overflow Developer Survey, GitHub stars, and industry trends.

📊 1. NumPy

• Used for: Numerical computing, arrays, matrix operations
• Usage: ~65–70%
• Why Popular: Foundation for data science & machine learning libraries (like pandas, scikit-learn)

import numpy as np
a = np.array([1, 2, 3])

🧮 2. Pandas

• Used for: Data analysis, data frames, table operations
• Usage: ~60–65%
• Why Popular: Makes data handling like Excel but in Python; easy and powerful

import pandas as pd
df = pd.DataFrame({"name": ["Alice", "Bob"], "age": [25, 30]})

📈 3. Matplotlib / Seaborn

• Used for: Data visualization, charts, graphs
• Usage: ~55–60%
• Why Popular: Easy to create line plots, bar charts, and complex visuals

import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])

🤖 4. Scikit-learn

• Used for: Machine learning (regression, classification, clustering)
• Usage: ~50–55%
• Why Popular: Beginner-friendly ML with ready-to-use models

from sklearn.linear_model import LinearRegression

🌐 5. Requests

• Used for: Web APIs, sending HTTP requests
• Usage: ~40–50%
• Why Popular: Extremely simple to make GET/POST requests

import requests
r = requests.get("https://api.example.com")

🛠️ 6. Flask

• Used for: Web development (micro web apps)
• Usage: ~35–40%
• Why Popular: Very lightweight and easy to learn for web development

from flask import Flask
app = Flask(__name__)

🔒 7. Django

• Used for: Full-featured web applications
• Usage: ~25–30%
• Why Popular: Built-in admin panel, ORM, user auth — perfect for startups

django-admin startproject mysite

🧪 8. Pytest / Unittest

• Used for: Testing Python code
• Usage: ~25–30%
• Why Popular: Easy to write and manage test cases for automation

🧹 9. BeautifulSoup

• Used for: Web scraping
• Usage: ~20–25%
• Why Popular: Clean and simple way to parse HTML and extract data

🎲 10. OpenCV

• Used for: Image processing, computer vision
• Usage: ~20%
• Why Popular: Used in AI projects, face detection, camera filters

📦 11. TensorFlow / Keras / PyTorch

• Used for: Deep learning
• Usage: ~30–35% among ML developers
• Why Popular: Used in AI apps, self-driving tech, advanced models

🖼️ 12. Pillow

• Used for: Image editing
• Usage: ~15%
• Why Popular: Resize, convert, and edit images easily in Python

📂 13. os / sys / datetime / json

• Used for: System operations, file handling, date/time
• Usage: ~90% (core Python modules)
• Why Popular: Built-in — no install needed, used in almost every project

📊 Summary Table of Most Popular Python Libraries

🧠 Final Tip:

You don’t need to learn all libraries.
Start with the ones you need for your current project or career path (like web dev, data science, etc.)