Data Science & Machine Learning

TOP ML Interview Problems

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4.34K views09:27

🚀 Complete Roadmap to Become a Data Scientist in 5 Months

📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.

🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.

🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.

🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.

🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.

🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.

📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).

🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).

🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).

🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.

🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.

💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.

🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.

👨‍💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.

🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.

🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.

🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.

🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!

🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥

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5.38K views15:46

Data Science & Machine Learning

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4.66K views08:28

Data Science & Machine Learning

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4.17K views13:55

Data Science & Machine Learning

Where Each Programming Language Shines 🚀👨🏻‍💻

❯ C ➟ OS Development, Embedded Systems, Game Engines
❯ C++ ➟ Game Development, High-Performance Applications, Financial Systems
❯ Java ➟ Enterprise Software, Android Development, Backend Systems
❯ C# ➟ Game Development (Unity), Windows Applications, Enterprise Software
❯ Python ➟ AI/ML, Data Science, Web Development, Automation
❯ JavaScript ➟ Frontend Web Development, Full-Stack Apps, Game Development
❯ Golang ➟ Cloud Services, Networking, High-Performance APIs
❯ Swift ➟ iOS/macOS App Development
❯ Kotlin ➟ Android Development, Backend Services
❯ PHP ➟ Web Development (WordPress, Laravel)
❯ Ruby ➟ Web Development (Ruby on Rails), Prototyping
❯ Rust ➟ Systems Programming, High-Performance Computing, Blockchain
❯ Lua ➟ Game Scripting (Roblox, WoW), Embedded Systems
❯ R ➟ Data Science, Statistics, Bioinformatics
❯ SQL ➟ Database Management, Data Analytics
❯ TypeScript ➟ Scalable Web Applications, Large JavaScript Projects
❯ Node.js ➟ Backend Development, Real-Time Applications
❯ React ➟ Modern Web Applications, Interactive UIs
❯ Vue ➟ Lightweight Frontend Development, SPAs
❯ Django ➟ Scalable Web Applications, AI/ML Backend
❯ Laravel ➟ Full-Stack PHP Development
❯ Blazor ➟ Web Apps with .NET
❯ Spring Boot ➟ Enterprise Java Applications, Microservices
❯ Ruby on Rails ➟ Startup Web Apps, MVP Development
❯ HTML/CSS ➟ Web Design, UI Development
❯ GIT ➟ Version Control, Collaboration
❯ Linux ➟ Server Management, Security, DevOps
❯ DevOps ➟ Infrastructure Automation, CI/CD
❯ CI/CD ➟ Continuous Deployment & Testing
❯ Docker ➟ Containerization, Cloud Deployments
❯ Kubernetes ➟ Scalable Cloud Orchestration
❯ Microservices ➟ Distributed Systems, Scalable Backends
❯ Selenium ➟ Web Automation Testing
❯ Playwright ➟ Modern Browser Automation

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4.53K views12:04

Data Science & Machine Learning

Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science game! 📊

ENJOY LEARNING 👍👍

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5.89K views16:46

Data Science & Machine Learning

🚀🔥 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗕𝘂𝗶𝗹𝗱𝗲𝗿 — 𝗙𝗿𝗲𝗲 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺
Master the most in-demand AI skill in today’s job market: building autonomous AI systems.

In Ready Tensor’s free, project-first program, you’ll create three portfolio-ready projects using 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻, 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵, and vector databases — and deploy production-ready agents that employers will notice.

Includes guided lectures, videos, and code.
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❤2👏1

5.49K views06:43

Data Science & Machine Learning

ML interview Question 📚

What is Quantization in machine learning?

Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.

Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.

Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.

There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING 👍👍

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5.61K views14:09

Data Science & Machine Learning

Data Scientist Roadmap 📈

📂 Python Basics
∟📂 Numpy & Pandas
∟📂 Data Cleaning
∟📂 Data Visualization (Seaborn, Plotly)
∟📂 Statistics & Probability
∟📂 Machine Learning (Sklearn)
∟📂 Deep Learning (TensorFlow / PyTorch)
∟📂 Model Deployment
∟📂 Real-World Projects
∟✅ Apply for Data Science Roles

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Data Science & Machine Learning

The Data Science Sandwich

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5.27K views04:15

Data Science & Machine Learning

✅ 8-Week Beginner Roadmap to Learn Data Science 📊🚀

🗓️ Week 1: Python Basics
Goal: Understand basic Python syntax & data types
Topics: Variables, lists, dictionaries, loops, functions
Tools: Jupyter Notebook / Google Colab
Mini Project: Calculator or number guessing game

🗓️ Week 2: Python for Data
Goal: Learn data manipulation with NumPy & Pandas
Topics: Arrays, DataFrames, filtering, groupby, joins
Tools: Pandas, NumPy
Mini Project: Analyze a CSV (e.g., sales or weather data)

🗓️ Week 3: Data Visualization
Goal: Visualize data trends & patterns
Topics: Line, bar, scatter, histograms, heatmaps
Tools: Matplotlib, Seaborn
Mini Project: Visualize COVID or stock market data

🗓️ Week 4: Statistics & Probability Basics
Goal: Understand core statistical concepts
Topics: Mean, median, mode, std dev, probability, distributions
Tools: Python, SciPy
Mini Project: Analyze survey data & generate insights

🗓️ Week 5: Exploratory Data Analysis (EDA)
Goal: Draw insights from real datasets
Topics: Data cleaning, outliers, correlation
Tools: Pandas, Seaborn
Mini Project: EDA on Titanic or Iris dataset

🗓️ Week 6: Intro to Machine Learning
Goal: Learn ML workflow & basic algorithms
Topics: Supervised vs unsupervised, train/test split
Tools: Scikit-learn
Mini Project: Predict house prices (Linear Regression)

🗓️ Week 7: Classification Models
Goal: Understand and apply classification
Topics: Logistic Regression, KNN, Decision Trees
Tools: Scikit-learn
Mini Project: Titanic survival prediction

🗓️ Week 8: Capstone Project + Deployment
Goal: Apply all concepts in one end-to-end project
Ideas: Sales prediction, Movie rating analysis, Customer churn detection
Tools: Streamlit (for simple web app)
Bonus: Upload your project on GitHub

💡 Tips:
⦁ Practice daily on platforms like Kaggle or Google Colab
⦁ Join beginner projects on GitHub
⦁ Share progress on LinkedIn or X (Twitter)

💬 Tap ❤️ for the detailed explanation of each topic!

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5.58K views13:10

Data Science & Machine Learning

🗓️ Python Basics You Should Know 🐍

✅ 1. Variables & Data Types
Variables store data. Data types show what kind of data it is.

# String (text)
name = "Alice"

# Integer (whole number)
age = 25

# Float (decimal)
height = 5.6

# Boolean (True/False)
is_student = True

🔹 Use type() to check data type:

print(type(name))  # <class 'str'>

✅ 2. Lists and Tuples
⦁ List = changeable collection

fruits = ["apple", "banana", "cherry"]
print(fruits)  # banana
fruits.append("orange")  # add item

⦁ Tuple = fixed collection (cannot change items)

colors = ("red", "green", "blue")
print(colors)  # red

✅ 3. Dictionaries
Store data as key-value pairs.

person = {
  "name": "John",
  "age": 22,
  "city": "Seoul"
}
print(person["name"])  # John

✅ 4. Conditional Statements (if-else)
Make decisions.

age = 20
if age >= 18:
    print("Adult")
else:
    print("Minor")

🔹 Use elif for multiple conditions:

if age < 13:
    print("Child")
elif age < 18:
    print("Teenager")
else:
    print("Adult")

✅ 5. Loops
Repeat code.

⦁ For Loop – fixed repeats

for i in range(3):
    print("Hello", i)

⦁ While Loop – repeats while true

count = 1
while count <= 3:
    print("Count is", count)
    count += 1

✅ 6. Functions
Reusable code blocks.

def greet(name):
    print("Hello", name)

greet("Alice")  # Hello Alice

🔹 Return result:

def add(a, b):
    return a + b

print(add(3, 5))  # 8

✅ 7. Input / Output
Get user input and show messages.

name = input("Enter your name: ")
print("Hi", name)

🧪 Mini Projects

1. Number Guessing Game

import random
num = random.randint(1, 10)
guess = int(input("Guess a number (1-10): "))
if guess == num:
    print("Correct!")
else:
    print("Wrong, number was", num)

2. To-Do List

todo = []
todo.append("Buy milk")
todo.append("Study Python")
print(todo)

🛠️ Recommended Tools
⦁ Google Colab (online)
⦁ Jupyter Notebook
⦁ Python IDLE or VS Code

💡 Practice a bit daily, start simple, and focus on basics — they matter most!

Data Science Roadmap: https://t.me/datasciencefun/3730

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4.81K viewsedited 13:24

Data Science & Machine Learning

Python for Data Science: NumPy & Pandas 📊🐍

🧮 Step 1: Learn NumPy (for numbers and arrays)

What is NumPy?
A fast Python library for working with numbers and arrays.

➤ 1. What is an array?
Like a list of numbers: [1, 2, 3, 4]

import numpy as np
a = np.array([1, 2, 3, 4])

➤ 2. Why NumPy over normal lists?
Faster for math operations:

a * 2  # array([2, 4, 6, 8])

➤ 3. Cool NumPy tricks:

a.mean()        # average  
np.max(a)       # max number  
np.min(a)       # min number  
a[0:2]          # slicing → [1, 2]

Key Topics:
⦁ Arrays are like faster, memory-efficient lists
⦁ Element-wise operations: a + b, a * 2
⦁ Slicing and indexing: a[0:2], a[:,1]
⦁ Broadcasting: operations on arrays with different shapes
⦁ Useful functions: np.mean(), np.std(), np.linspace(), np.random.randn()

————————

📊 Step 2: Learn Pandas (for tables like Excel)

What is Pandas?
Python tool to read, clean & analyze data — like Excel but supercharged.

➤ 1. What’s a DataFrame?
Like an Excel sheet, rows & columns.

import pandas as pd
df = pd.read_csv("sales.csv")
df.head()  # first 5 rows

➤ 2. Check data info:

df.info()       # rows, columns, missing data  
df.describe()   # stats like mean, min, max

➤ 3. Get a column:

df['product']

➤ 4. Filter rows:

df[df['price'] > 100]

➤ 5. Group data:
Average price by category:

df.groupby('category')['price'].mean()

➤ 6. Merge datasets:

merged = pd.merge(df1, df2, on='customer_id')

➤ 7. Handle missing data:

df.isnull()      # where missing  
df.dropna()      # drop missing rows  
df.fillna(0)     # fill missing with 0

————————

💡 Beginner Tips:
⦁ Use Google Colab (free, no setup)
⦁ Try small tasks like:
⦁ Show top products
⦁ Filter sales > $500
⦁ Find missing data
⦁ Practice daily, don’t just memorize

————————

🛠️ Mini Project: Analyze Sales Data
1. Load a CSV
2. Check number of rows
3. Find best-selling product
4. Calculate total revenue
5. Get average sales per region

Data Science Roadmap:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/1210

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3.89K viewsedited 20:11

Data Science & Machine Learning

Commonly used Power BI DAX functions:

DATE AND TIME FUNCTIONS:
- CALENDAR
- DATEDIFF
- TODAY, DAY, MONTH, QUARTER, YEAR

AGGREGATE FUNCTIONS:
- SUM, SUMX, PRODUCT
- AVERAGE
- MIN, MAX
- COUNT
- COUNTROWS
- COUNTBLANK
- DISTINCTCOUNT

FILTER FUNCTIONS:
- CALCULATE
- FILTER
- ALL, ALLEXCEPT, ALLSELECTED, REMOVEFILTERS
- SELECTEDVALUE

TIME INTELLIGENCE FUNCTIONS:
- DATESBETWEEN
- DATESMTD, DATESQTD, DATESYTD
- SAMEPERIODLASTYEAR
- PARALLELPERIOD
- TOTALMTD, TOTALQTD, TOTALYTD

TEXT FUNCTIONS:
- CONCATENATE
- FORMAT
- LEN, LEFT, RIGHT

INFORMATION FUNCTIONS:
- HASONEVALUE, HASONEFILTER
- ISBLANK, ISERROR, ISEMPTY
- CONTAINS

LOGICAL FUNCTIONS:
- AND, OR, IF, NOT
- TRUE, FALSE
- SWITCH

RELATIONSHIP FUNCTIONS:
- RELATED
- USERRELATIONSHIP
- RELATEDTABLE

Remember, DAX is more about logic than the formulas.

3.22K views19:27

Data Science & Machine Learning

✅ Data Visualization with Matplotlib 📊

🛠 Tools:
⦁ matplotlib.pyplot – Basic plots
⦁ seaborn – Cleaner, statistical plots

1️⃣ Line Chart – to show trends over time

import matplotlib.pyplot as plt

days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri']
sales = [200, 450, 300, 500, 650]

plt.plot(days, sales, marker='o')
plt.title('Daily Sales')
plt.xlabel('Day')
plt.ylabel('Sales')
plt.grid(True)
plt.show()

2️⃣ Bar Chart – compare categories

products = ['A', 'B', 'C', 'D']
revenue = [1000, 1500, 700, 1200]

plt.bar(products, revenue, color='skyblue')
plt.title('Revenue by Product')
plt.xlabel('Product')
plt.ylabel('Revenue')
plt.show()

3️⃣ Pie Chart – show proportions

labels = ['iOS', 'Android', 'Others']
market_share = [40, 55, 5]

plt.pie(market_share, labels=labels, autopct='%1.1f%%', startangle=140)
plt.title('Mobile OS Market Share')
plt.axis('equal')  # perfect circle
plt.show()

4️⃣ Histogram – frequency distribution

ages = [22, 25, 27, 30, 32, 35, 35, 40, 45, 50, 52, 60]

plt.hist(ages, bins=5, color='green', edgecolor='black')
plt.title('Age Distribution')
plt.xlabel('Age Groups')
plt.ylabel('Frequency')
plt.show()

5️⃣ Scatter Plot – relationship between variables

income = [30, 35, 40, 45, 50, 55, 60]
spending = [20, 25, 30, 32, 35, 40, 42]

plt.scatter(income, spending, color='red')
plt.title('Income vs Spending')
plt.xlabel('Income (k)')
plt.ylabel('Spending (k)')
plt.show()

6️⃣ Heatmap – correlation matrix (with Seaborn)

import seaborn as sns
import pandas as pd

data = {'Math': [90, 80, 85, 95],
        'Science': [85, 89, 92, 88],
        'English': [78, 75, 80, 85]}

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

sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Subject Score Correlation')
plt.show()

💡 Pro Tip: Customize titles, labels & colors for clarity and audience style!

Data Science Roadmap:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D/1210

💬 Tap ❤️ for more!

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3.4K viewsedited 19:29

Data Science & Machine Learning

✅ 10 Python Code Snippets for Interviews & Practice 🐍🧠

1️⃣ Find factorial (recursion):

def factorial(n):
    return 1 if n == 0 else n * factorial(n - 1)

2️⃣ Find second largest number:

nums = [10, 20, 30]
second = sorted(set(nums))[-2]

3️⃣ Remove punctuation from string:

import string
s = "Hello, world!"
s_clean = s.translate(str.maketrans('', '', string.punctuation))

4️⃣ Find common elements in two lists:

a = [1, 2, 3]
b = [2, 3, 4]
common = list(set(a) & set(b))

5️⃣ Convert list to string:

words = ['Python', 'is', 'fun']
sentence = ' '.join(words)

6️⃣ Reverse words in sentence:

s = "Hello World"
reversed_s = ' '.join(s.split()[::-1])

7️⃣ Check anagram:

def is_anagram(a, b):
    return sorted(a) == sorted(b)

8️⃣ Get unique values from list of dicts:

data = [{'a':1}, {'a':2}, {'a':1}]
unique = set(d['a'] for d in data)

9️⃣ Create dict from range:

squares = {x: x*x for x in range(5)}

🔟 Sort list of tuples by second item:

pairs = [(1, 3), (2, 1)]
sorted_pairs = sorted(pairs, key=lambda x: x)

Learn Python: https://whatsapp.com/channel/0029VbBDoisBvvscrno41d1l

💬 Tap ❤️ for more!

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3.58K viewsedited 14:23

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