Amazing NumPy Cheat Sheet.pdf
259.7 KB
Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews
❤8👍4
Top Data Science Tools — By Function 📊
A quick view of the tools commonly used across the data science workflow:
🔹 Data Collection
• Scrapy, BeautifulSoup – Web scraping
• APIs – External data access
• Selenium – Dynamic scraping
• Google BigQuery – Large-scale data ingestion
🔹 Data Cleaning & Processing
• Pandas – Data manipulation
• NumPy – Numerical computing
• OpenRefine – Data cleanup
• Excel – Basic cleaning & formatting
🔹 Modeling & Machine Learning
• Scikit-learn – Classical ML
• TensorFlow – Deep learning
• PyTorch – Research-friendly DL
• XGBoost – Gradient boosting
• Keras – Neural network APIs
🔹 Deployment
• Docker – Containerization
• Kubernetes – Model scalability
• FastAPI – ML APIs
• AWS SageMaker – End-to-end ML deployment
• MLflow – Experiment tracking
🔹 Visualization & BI
• Matplotlib, Seaborn – Statistical plots
• Plotly – Interactive charts
• Tableau, Power BI – Business dashboards
👉 Tools change, but knowing when and why to use them matters more than how many you know.
A quick view of the tools commonly used across the data science workflow:
🔹 Data Collection
• Scrapy, BeautifulSoup – Web scraping
• APIs – External data access
• Selenium – Dynamic scraping
• Google BigQuery – Large-scale data ingestion
🔹 Data Cleaning & Processing
• Pandas – Data manipulation
• NumPy – Numerical computing
• OpenRefine – Data cleanup
• Excel – Basic cleaning & formatting
🔹 Modeling & Machine Learning
• Scikit-learn – Classical ML
• TensorFlow – Deep learning
• PyTorch – Research-friendly DL
• XGBoost – Gradient boosting
• Keras – Neural network APIs
🔹 Deployment
• Docker – Containerization
• Kubernetes – Model scalability
• FastAPI – ML APIs
• AWS SageMaker – End-to-end ML deployment
• MLflow – Experiment tracking
🔹 Visualization & BI
• Matplotlib, Seaborn – Statistical plots
• Plotly – Interactive charts
• Tableau, Power BI – Business dashboards
👉 Tools change, but knowing when and why to use them matters more than how many you know.
👍6❤4
✅ Python for Machine Learning – Beginner to Job-Ready Roadmap 🤖🐍
📍 1️⃣ Python Basics
– Variables, Data Types, Operators
– if-else, loops, functions
✅ Practice: Write a BMI calculator, number guessing game
📍 2️⃣ Data Structures & Libraries
– Lists, Dicts, Tuples, Sets
– NumPy: arrays, slicing, broadcasting
– Pandas: DataFrames, filtering, merging
✅ Practice: Analyze a CSV with Pandas
📍 3️⃣ Data Visualization
– Matplotlib, Seaborn basics
– Plotting histograms, boxplots, heatmaps
✅ Project: Visualize Titanic dataset insights
📍 4️⃣ Data Preprocessing
– Handling nulls, encoding, scaling
– Feature engineering & selection
✅ Practice: Clean a housing prices dataset
📍 5️⃣ Machine Learning with Scikit-learn
– Regression, Classification, Clustering
– Model training, prediction, evaluation
✅ Project: Predict student scores using Linear Regression
📍 6️⃣ Model Evaluation
– Accuracy, Precision, Recall, F1-Score
– Confusion Matrix, ROC-AUC
✅ Practice: Evaluate a classification model
📍 7️⃣ Model Tuning & Pipelines
– GridSearchCV, cross-validation
– Build ML pipelines for clean code
✅ Project: Optimize a Random Forest model
📍 8️⃣ Real-World ML Projects
– House price prediction
– Customer churn analysis
– Image classification
✅ Tip: Use datasets from Kaggle, UCI, or open APIs
💬 Tap ❤️ for more!
📍 1️⃣ Python Basics
– Variables, Data Types, Operators
– if-else, loops, functions
✅ Practice: Write a BMI calculator, number guessing game
📍 2️⃣ Data Structures & Libraries
– Lists, Dicts, Tuples, Sets
– NumPy: arrays, slicing, broadcasting
– Pandas: DataFrames, filtering, merging
✅ Practice: Analyze a CSV with Pandas
📍 3️⃣ Data Visualization
– Matplotlib, Seaborn basics
– Plotting histograms, boxplots, heatmaps
✅ Project: Visualize Titanic dataset insights
📍 4️⃣ Data Preprocessing
– Handling nulls, encoding, scaling
– Feature engineering & selection
✅ Practice: Clean a housing prices dataset
📍 5️⃣ Machine Learning with Scikit-learn
– Regression, Classification, Clustering
– Model training, prediction, evaluation
✅ Project: Predict student scores using Linear Regression
📍 6️⃣ Model Evaluation
– Accuracy, Precision, Recall, F1-Score
– Confusion Matrix, ROC-AUC
✅ Practice: Evaluate a classification model
📍 7️⃣ Model Tuning & Pipelines
– GridSearchCV, cross-validation
– Build ML pipelines for clean code
✅ Project: Optimize a Random Forest model
📍 8️⃣ Real-World ML Projects
– House price prediction
– Customer churn analysis
– Image classification
✅ Tip: Use datasets from Kaggle, UCI, or open APIs
💬 Tap ❤️ for more!
❤9👍5
SQL interview questions with answers 😄👇
1. Question: What is SQL?
Answer: SQL (Structured Query Language) is a programming language designed for managing and manipulating relational databases. It is used to query, insert, update, and delete data in databases.
2. Question: Differentiate between SQL and MySQL.
Answer: SQL is a language for managing relational databases, while MySQL is an open-source relational database management system (RDBMS) that uses SQL as its language.
3. Question: Explain the difference between INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns rows when there is a match in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in with NULLs for non-matching rows.
4. Question: How do you remove duplicate records from a table?
Answer: Use the
5. Question: What is a subquery in SQL?
Answer: A subquery is a query nested inside another query. It can be used to retrieve data that will be used in the main query as a condition to further restrict the data to be retrieved.
6. Question: Explain the purpose of the GROUP BY clause.
Answer: The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows, like when using aggregate functions such as COUNT, SUM, AVG, etc.
7. Question: How can you add a new record to a table?
Answer: Use the
8. Question: What is the purpose of the HAVING clause?
Answer: The HAVING clause is used in combination with the GROUP BY clause to filter the results of aggregate functions based on a specified condition.
9. Question: Explain the concept of normalization in databases.
Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down tables into smaller, related tables.
10. Question: How do you update data in a table in SQL?
Answer: Use the
Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
1. Question: What is SQL?
Answer: SQL (Structured Query Language) is a programming language designed for managing and manipulating relational databases. It is used to query, insert, update, and delete data in databases.
2. Question: Differentiate between SQL and MySQL.
Answer: SQL is a language for managing relational databases, while MySQL is an open-source relational database management system (RDBMS) that uses SQL as its language.
3. Question: Explain the difference between INNER JOIN and LEFT JOIN.
Answer: INNER JOIN returns rows when there is a match in both tables, while LEFT JOIN returns all rows from the left table and the matched rows from the right table, filling in with NULLs for non-matching rows.
4. Question: How do you remove duplicate records from a table?
Answer: Use the
DISTINCT keyword in a SELECT statement to retrieve unique records. For example: SELECT DISTINCT column1, column2 FROM table;5. Question: What is a subquery in SQL?
Answer: A subquery is a query nested inside another query. It can be used to retrieve data that will be used in the main query as a condition to further restrict the data to be retrieved.
6. Question: Explain the purpose of the GROUP BY clause.
Answer: The GROUP BY clause is used to group rows that have the same values in specified columns into summary rows, like when using aggregate functions such as COUNT, SUM, AVG, etc.
7. Question: How can you add a new record to a table?
Answer: Use the
INSERT INTO statement. For example: INSERT INTO table_name (column1, column2) VALUES (value1, value2);8. Question: What is the purpose of the HAVING clause?
Answer: The HAVING clause is used in combination with the GROUP BY clause to filter the results of aggregate functions based on a specified condition.
9. Question: Explain the concept of normalization in databases.
Answer: Normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It involves breaking down tables into smaller, related tables.
10. Question: How do you update data in a table in SQL?
Answer: Use the
UPDATE statement to modify existing records in a table. For example: UPDATE table_name SET column1 = value1 WHERE condition;Here is an amazing resources to learn & practice SQL: https://bit.ly/3FxxKPz
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
❤7
Top 50 Python Interview Questions for Data Analysts (2025) ✅
1. What is Python and why is it popular for data analysis?
2. Differentiate between lists, tuples, and sets in Python.
3. How do you handle missing data in a dataset?
4. What are list comprehensions and how are they useful?
5. Explain Pandas DataFrame and Series.
6. How do you read data from different file formats (CSV, Excel, JSON) in Python?
7. What is the difference between Python’s
8. How do you filter rows in a Pandas DataFrame?
9. Explain the use of
10. What are lambda functions and how are they used?
11. How do you merge or join two DataFrames?
12. What is the difference between
13. How do you handle duplicates in a DataFrame?
14. Explain how to deal with outliers in data.
15. What is data normalization and how can it be done in Python?
16. Describe different data types in Python.
17. How do you convert data types in Pandas?
18. What are Python dictionaries and how are they useful?
19. How do you write efficient loops in Python?
20. Explain error handling in Python with
21. How do you perform basic statistical operations in Python?
22. What libraries do you use for data visualization?
23. How do you create plots using Matplotlib or Seaborn?
24. What is the difference between
25. How do you export Pandas DataFrames to CSV or Excel files?
26. What is the difference between Python’s
27. How can you profile and optimize Python code?
28. What are Python decorators and give a simple example?
29. How do you handle dates and times in Python?
30. Explain list slicing in Python.
31. What are the differences between Python 2 and Python 3?
32. How do you use regular expressions in Python?
33. What is the purpose of the
34. Explain how to use virtual environments.
35. How do you connect Python with SQL databases?
36. What is the role of the
37. How do you handle JSON data in Python?
38. What are generator functions and why use them?
39. How do you perform feature engineering with Python?
40. What is the purpose of the Pandas
41. How do you handle categorical data?
42. Explain the difference between deep copy and shallow copy.
43. What is the use of the
44. How do you detect and handle multicollinearity?
45. How can you improve Python noscript performance?
46. What are Python’s built-in data structures?
47. How do you automate repetitive data tasks with Python?
48. Explain the use of Assertions in Python.
49. How do you write unit tests in Python?
50. How do you handle large datasets in Python?
Double tap ❤️ for detailed answers!
1. What is Python and why is it popular for data analysis?
2. Differentiate between lists, tuples, and sets in Python.
3. How do you handle missing data in a dataset?
4. What are list comprehensions and how are they useful?
5. Explain Pandas DataFrame and Series.
6. How do you read data from different file formats (CSV, Excel, JSON) in Python?
7. What is the difference between Python’s
append() and extend() methods?8. How do you filter rows in a Pandas DataFrame?
9. Explain the use of
groupby() in Pandas with an example.10. What are lambda functions and how are they used?
11. How do you merge or join two DataFrames?
12. What is the difference between
.loc[] and .iloc[] in Pandas?13. How do you handle duplicates in a DataFrame?
14. Explain how to deal with outliers in data.
15. What is data normalization and how can it be done in Python?
16. Describe different data types in Python.
17. How do you convert data types in Pandas?
18. What are Python dictionaries and how are they useful?
19. How do you write efficient loops in Python?
20. Explain error handling in Python with
try-except.21. How do you perform basic statistical operations in Python?
22. What libraries do you use for data visualization?
23. How do you create plots using Matplotlib or Seaborn?
24. What is the difference between
.apply() and .map() in Pandas?25. How do you export Pandas DataFrames to CSV or Excel files?
26. What is the difference between Python’s
range() and xrange()?27. How can you profile and optimize Python code?
28. What are Python decorators and give a simple example?
29. How do you handle dates and times in Python?
30. Explain list slicing in Python.
31. What are the differences between Python 2 and Python 3?
32. How do you use regular expressions in Python?
33. What is the purpose of the
with statement?34. Explain how to use virtual environments.
35. How do you connect Python with SQL databases?
36. What is the role of the
__init__.py file?37. How do you handle JSON data in Python?
38. What are generator functions and why use them?
39. How do you perform feature engineering with Python?
40. What is the purpose of the Pandas
.pivot_table() method?41. How do you handle categorical data?
42. Explain the difference between deep copy and shallow copy.
43. What is the use of the
enumerate() function?44. How do you detect and handle multicollinearity?
45. How can you improve Python noscript performance?
46. What are Python’s built-in data structures?
47. How do you automate repetitive data tasks with Python?
48. Explain the use of Assertions in Python.
49. How do you write unit tests in Python?
50. How do you handle large datasets in Python?
Double tap ❤️ for detailed answers!
❤27
Mastering pandas%22.pdf
1.6 MB
🌟 A new and comprehensive book "Mastering pandas"
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
👨🏻💻 If I've worked with messy and error-prone data this time, I don't know how much time and energy I've wasted. Incomplete tables, repetitive records, and unorganized data. Exactly the kind of things that make analysis difficult and frustrate you.
⬅️ And the only way to save yourself is to use pandas! A tool that makes processes 10 times faster.
🏷 This book is a comprehensive and organized guide to pandas, so you can start from scratch and gradually master this library and gain the ability to implement real projects. In this file, you'll learn:
🔹 How to clean and prepare large amounts of data for analysis,
🔹 How to analyze real business data and draw conclusions,
🔹 How to automate repetitive tasks with a few lines of code,
🔹 And improve the speed and accuracy of your analyses significantly.
🌐 #DataScience #DataScience #Pandas #Python
❤10
Python Libraries You Should Know ✅
⦁ NumPy: Numerical Computing ⚙️
NumPy is the foundation for numerical operations in Python. It provides fast arrays and math functions.
Example:
Challenge: Create a 3x3 matrix of random integers from 1–10.
⦁ Pandas: Data Analysis 🐼
Pandas makes it easy to work with tabular data using DataFrames.
Example:
Challenge: Load a CSV file and show the top 5 rows.
⦁ Matplotlib: Data Visualization 📊
Matplotlib helps you create charts and plots.
Example:
Challenge: Plot a bar chart of fruit sales.
⦁ Seaborn: Statistical Plots 🎨
Seaborn builds on Matplotlib with beautiful, high-level charts.
Example:
Challenge: Create a heatmap of correlation.
⦁ Requests: HTTP for Humans 🌐
Requests makes it easy to send HTTP requests.
Example:
Challenge: Fetch and print your IP address.
⦁ Beautiful Soup: Web Scraping 🍜
Beautiful Soup helps you extract data from HTML pages.
Example:
Challenge: Extract all links from a webpage.
Next Steps:
⦁ Combine these libraries for real-world projects
⦁ Try scraping data and analyzing it with Pandas
⦁ Visualize insights with Seaborn and Matplotlib
Double Tap ♥️ For More
⦁ NumPy: Numerical Computing ⚙️
NumPy is the foundation for numerical operations in Python. It provides fast arrays and math functions.
Example:
import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2) # [2 4 6]
Challenge: Create a 3x3 matrix of random integers from 1–10.
matrix = np.random.randint(1, 11, size=(3, 3))
print(matrix)
⦁ Pandas: Data Analysis 🐼
Pandas makes it easy to work with tabular data using DataFrames.
Example:
import pandas as pd
data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
df = pd.DataFrame(data)
print(df)
Challenge: Load a CSV file and show the top 5 rows.
df = pd.read_csv("data.csv")
print(df.head())
⦁ Matplotlib: Data Visualization 📊
Matplotlib helps you create charts and plots.
Example:
import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [2, 4, 1]
plt.plot(x, y)
plt.noscript("Simple Line Plot")
plt.show()
Challenge: Plot a bar chart of fruit sales.
fruits = ["Apples", "Bananas", "Cherries"]
sales = [30, 45, 25]
plt.bar(fruits, sales)
plt.noscript("Fruit Sales")
plt.show()
⦁ Seaborn: Statistical Plots 🎨
Seaborn builds on Matplotlib with beautiful, high-level charts.
Example:
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
sns.boxplot(x="day", y="total_bill", data=tips)
plt.show()
Challenge: Create a heatmap of correlation.
corr = tips.corr()
sns.heatmap(corr, annot=True, cmap="coolwarm")
plt.show()
⦁ Requests: HTTP for Humans 🌐
Requests makes it easy to send HTTP requests.
Example:
import requests
response = requests.get("https://api.github.com")
print(response.status_code)
print(response.json())
Challenge: Fetch and print your IP address.
res = requests.get("https://api.ipify.org?format=json")
print(res.json()["ip"])
⦁ Beautiful Soup: Web Scraping 🍜
Beautiful Soup helps you extract data from HTML pages.
Example:
from bs4 import BeautifulSoup
import requests
url = "https://example.com"
html = requests.get(url).text
soup = BeautifulSoup(html, "html.parser")
print(soup.noscript.text)
Challenge: Extract all links from a webpage.
links = soup.find_all("a")
for link in links:
print(link.get("href"))
Next Steps:
⦁ Combine these libraries for real-world projects
⦁ Try scraping data and analyzing it with Pandas
⦁ Visualize insights with Seaborn and Matplotlib
Double Tap ♥️ For More
❤15
✅ Top 5 Mistakes to Avoid When Learning Python ❌🐍
1️⃣ Skipping the Basics
Many learners rush to libraries like Pandas or Django. First, master Python syntax, data types, loops, functions, and OOP. It builds the foundation.
2️⃣ Ignoring Indentation Rules
Python uses indentation to define code blocks. One wrong space can break your code — always stay consistent (usually 4 spaces).
3️⃣ Not Practicing Enough
Watching tutorials alone won’t help. Code daily. Start with small noscripts like a calculator, quiz app, or text-based game.
4️⃣ Avoiding Errors Instead of Learning from Them
Tracebacks look scary but are helpful. Read and understand error messages. They teach you more than error-free code.
5️⃣ Relying Too Much on Copy-Paste
Copying code without understanding kills learning. Try writing code from scratch and explain it to yourself line-by-line.
💬 Tap ❤️ for more!
1️⃣ Skipping the Basics
Many learners rush to libraries like Pandas or Django. First, master Python syntax, data types, loops, functions, and OOP. It builds the foundation.
2️⃣ Ignoring Indentation Rules
Python uses indentation to define code blocks. One wrong space can break your code — always stay consistent (usually 4 spaces).
3️⃣ Not Practicing Enough
Watching tutorials alone won’t help. Code daily. Start with small noscripts like a calculator, quiz app, or text-based game.
4️⃣ Avoiding Errors Instead of Learning from Them
Tracebacks look scary but are helpful. Read and understand error messages. They teach you more than error-free code.
5️⃣ Relying Too Much on Copy-Paste
Copying code without understanding kills learning. Try writing code from scratch and explain it to yourself line-by-line.
💬 Tap ❤️ for more!
❤7👍2👏2
This media is not supported in your browser
VIEW IN TELEGRAM
The #Python library #PandasAI has been released for simplified data analysis using AI.
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link 😍
You can ask questions about the dataset in plain language directly in the #AI dialogue, compare different datasets, and create graphs. It saves a lot of time, especially in the initial stage of getting acquainted with the data. It supports #CSV, #SQL, and Parquet.
And here's the link 😍
❤5
🚀 Roadmap to Master Tableau in 30 Days! 📊📈
📅 Week 1: Tableau Basics
🔹 Day 1–2: Introduction to Tableau, Interface, Installing Tableau Public
🔹 Day 3–4: Connecting to data (Excel, CSV, SQL)
🔹 Day 5–7: Dimensions vs Measures, Data types, Data pane
📅 Week 2: Building Visuals
🔹 Day 8–10: Bar, Line, Pie Charts, Tables, TreeMaps
🔹 Day 11–12: Filters, Sorting, Grouping, Sets
🔹 Day 13–14: Maps, Dual-axis charts, Combined visuals
📅 Week 3: Dashboarding Calculations
🔹 Day 15–16: Creating Dashboards, Actions, Interactivity
🔹 Day 17–18: Calculated Fields, Table Calculations
🔹 Day 19–21: Parameters, Date Calculations, LOD expressions
📅 Week 4: Advanced Features Projects
🔹 Day 22–24: Storytelling with Data, Formatting, Tooltips
🔹 Day 25–27: Real-time data, Extracts vs Live connections
🔹 Day 28–30: Build a complete project (Sales, HR, Finance) + publish to Tableau Public
💡 Tips:
• Practice with Superstore dataset
• Recreate popular dashboards from Tableau Public
• Keep dashboards simple, clean, and insightful
💬 Tap ❤️ for more!
📅 Week 1: Tableau Basics
🔹 Day 1–2: Introduction to Tableau, Interface, Installing Tableau Public
🔹 Day 3–4: Connecting to data (Excel, CSV, SQL)
🔹 Day 5–7: Dimensions vs Measures, Data types, Data pane
📅 Week 2: Building Visuals
🔹 Day 8–10: Bar, Line, Pie Charts, Tables, TreeMaps
🔹 Day 11–12: Filters, Sorting, Grouping, Sets
🔹 Day 13–14: Maps, Dual-axis charts, Combined visuals
📅 Week 3: Dashboarding Calculations
🔹 Day 15–16: Creating Dashboards, Actions, Interactivity
🔹 Day 17–18: Calculated Fields, Table Calculations
🔹 Day 19–21: Parameters, Date Calculations, LOD expressions
📅 Week 4: Advanced Features Projects
🔹 Day 22–24: Storytelling with Data, Formatting, Tooltips
🔹 Day 25–27: Real-time data, Extracts vs Live connections
🔹 Day 28–30: Build a complete project (Sales, HR, Finance) + publish to Tableau Public
💡 Tips:
• Practice with Superstore dataset
• Recreate popular dashboards from Tableau Public
• Keep dashboards simple, clean, and insightful
💬 Tap ❤️ for more!
❤22
✅How much 𝗣𝘆𝘁𝗵𝗼𝗻 is enough to crack a 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄?
📌 𝗕𝗮𝘀𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀
- Data types: Lists, Dicts, Tuples, Sets
- Loops & conditionals (for, while, if-else)
- Functions & lambda expressions
- File handling (open, read, write)
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀
-
- Filtering, sorting, and grouping data
- Handling missing values
- Merging & joining DataFrames
📈 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
- Matplotlib:
- Seaborn:
- Plot styling, noscripts, and legends
🧮 𝗡𝘂𝗺𝗣𝘆 & 𝗠𝗮𝘁𝗵 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻
- Arrays and broadcasting
- Vectorized operations
- Basic statistics: mean, median, std
🧩 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽
- Remove duplicates, rename columns
- Apply functions row-wise or column-wise
- Convert data types, parse dates
⚙️ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝗶𝗽𝘀
- List comprehensions
- Exception handling (try-except)
- Working with APIs (requests, json)
- Automating tasks with noscripts
💼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀
- Sales forecasting
- Web scraping for data
- Survey result analysis
- Excel automation with
✅ Must-Have Strengths:
- Data wrangling & preprocessing
- EDA (Exploratory Data Analysis)
- Writing clean, reusable code
- Extracting insights & telling stories with data
Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
💬 Tap ❤️ for more!
📌 𝗕𝗮𝘀𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀
- Data types: Lists, Dicts, Tuples, Sets
- Loops & conditionals (for, while, if-else)
- Functions & lambda expressions
- File handling (open, read, write)
📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀
-
read_csv, head(), info() - Filtering, sorting, and grouping data
- Handling missing values
- Merging & joining DataFrames
📈 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻
- Matplotlib:
plot(), bar(), hist() - Seaborn:
heatmap(), pairplot(), boxplot() - Plot styling, noscripts, and legends
🧮 𝗡𝘂𝗺𝗣𝘆 & 𝗠𝗮𝘁𝗵 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻
- Arrays and broadcasting
- Vectorized operations
- Basic statistics: mean, median, std
🧩 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽
- Remove duplicates, rename columns
- Apply functions row-wise or column-wise
- Convert data types, parse dates
⚙️ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝗶𝗽𝘀
- List comprehensions
- Exception handling (try-except)
- Working with APIs (requests, json)
- Automating tasks with noscripts
💼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀
- Sales forecasting
- Web scraping for data
- Survey result analysis
- Excel automation with
openpyxl or xlsxwriter ✅ Must-Have Strengths:
- Data wrangling & preprocessing
- EDA (Exploratory Data Analysis)
- Writing clean, reusable code
- Extracting insights & telling stories with data
Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
💬 Tap ❤️ for more!
❤8
🐍 How to Master Python for Data Analytics (Without Getting Overwhelmed!) 🧠
Python is powerful—but libraries, syntax, and endless tutorials can feel like too much.
Here’s a 5-step roadmap to go from beginner to confident data analyst 👇
🔹 Step 1: Get Comfortable with Python Basics (The Foundation)
Start small and build your logic.
✅ Variables, Data Types, Operators
✅ if-else, loops, functions
✅ Lists, Tuples, Sets, Dictionaries
Use tools like: Jupyter Notebook, Google Colab, Replit
Practice basic problems on: HackerRank, Edabit
🔹 Step 2: Learn NumPy & Pandas (Your Analysis Engine)
These are non-negotiable for analysts.
✅ NumPy → Arrays, broadcasting, math functions
✅ Pandas → Series, DataFrames, filtering, sorting
✅ Data cleaning, merging, handling nulls
Work with real CSV files and explore them hands-on!
🔹 Step 3: Master Data Visualization (Make Data Talk)
Good plots = Clear insights
✅ Matplotlib → Line, Bar, Pie
✅ Seaborn → Heatmaps, Countplots, Histograms
✅ Customize colors, labels, noscripts
Build charts from Pandas data.
🔹 Step 4: Learn to Work with Real Data (APIs, Files, Web)
✅ Read/write Excel, CSV, JSON
✅ Connect to APIs with
✅ Use modules like
Optional: Web scraping with BeautifulSoup or Selenium
🔹 Step 5: Get Fluent in Data Analysis Projects
✅ Exploratory Data Analysis (EDA)
✅ Summary stats, correlation
✅ (Optional) Basic machine learning with
✅ Build real mini-projects: Sales report, COVID trends, Movie ratings
You don’t need 10 certifications—just 3 solid projects that prove your skills.
Keep it simple. Keep it real.
💬 Tap ❤️ for more!
Python is powerful—but libraries, syntax, and endless tutorials can feel like too much.
Here’s a 5-step roadmap to go from beginner to confident data analyst 👇
🔹 Step 1: Get Comfortable with Python Basics (The Foundation)
Start small and build your logic.
✅ Variables, Data Types, Operators
✅ if-else, loops, functions
✅ Lists, Tuples, Sets, Dictionaries
Use tools like: Jupyter Notebook, Google Colab, Replit
Practice basic problems on: HackerRank, Edabit
🔹 Step 2: Learn NumPy & Pandas (Your Analysis Engine)
These are non-negotiable for analysts.
✅ NumPy → Arrays, broadcasting, math functions
✅ Pandas → Series, DataFrames, filtering, sorting
✅ Data cleaning, merging, handling nulls
Work with real CSV files and explore them hands-on!
🔹 Step 3: Master Data Visualization (Make Data Talk)
Good plots = Clear insights
✅ Matplotlib → Line, Bar, Pie
✅ Seaborn → Heatmaps, Countplots, Histograms
✅ Customize colors, labels, noscripts
Build charts from Pandas data.
🔹 Step 4: Learn to Work with Real Data (APIs, Files, Web)
✅ Read/write Excel, CSV, JSON
✅ Connect to APIs with
requests ✅ Use modules like
openpyxl, json, os, datetimeOptional: Web scraping with BeautifulSoup or Selenium
🔹 Step 5: Get Fluent in Data Analysis Projects
✅ Exploratory Data Analysis (EDA)
✅ Summary stats, correlation
✅ (Optional) Basic machine learning with
scikit-learn ✅ Build real mini-projects: Sales report, COVID trends, Movie ratings
You don’t need 10 certifications—just 3 solid projects that prove your skills.
Keep it simple. Keep it real.
💬 Tap ❤️ for more!
❤12
List of Top 12 Coding Channels on WhatsApp:
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
1. Python Programming:
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
2. Coding Resources:
https://whatsapp.com/channel/0029VahiFZQ4o7qN54LTzB17
3. Coding Projects:
https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502
4. Coding Interviews:
https://whatsapp.com/channel/0029VammZijATRSlLxywEC3X
5. Java Programming:
https://whatsapp.com/channel/0029VamdH5mHAdNMHMSBwg1s
6. Javanoscript:
https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32
7. Web Development:
https://whatsapp.com/channel/0029VaiSdWu4NVis9yNEE72z
8. Artificial Intelligence:
https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
9. Data Science:
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
10. Machine Learning:
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
11. SQL:
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
12. GitHub:
https://whatsapp.com/channel/0029Vawixh9IXnlk7VfY6w43
ENJOY LEARNING 👍👍
❤8
🐍 Python Interview Question (Data Analyst)
Question : What is the difference between apply() and map() in Pandas?
Answer:
map() works on Series only and is used for element-wise transformations.
apply() works on Series as well as DataFrames and can apply a function row-wise or column-wise.
Example :
df['salary_lakhs'] = df['salary'].map(lambda x: x / 100000)
df['total'] = df.apply(lambda row: row['sales'] - row['cost'], axis=1)
👉 Interview Tip:
Use map() for simple value replacement or transformation.
Use apply() when logic depends on multiple columns.
👉 Follow the channel and react ❤️ to this post for more Python & Data Analyst interview questions, tips, and cheat sheets shared regularly 🚀
Question : What is the difference between apply() and map() in Pandas?
Answer:
map() works on Series only and is used for element-wise transformations.
apply() works on Series as well as DataFrames and can apply a function row-wise or column-wise.
Example :
df['salary_lakhs'] = df['salary'].map(lambda x: x / 100000)
df['total'] = df.apply(lambda row: row['sales'] - row['cost'], axis=1)
👉 Interview Tip:
Use map() for simple value replacement or transformation.
Use apply() when logic depends on multiple columns.
👉 Follow the channel and react ❤️ to this post for more Python & Data Analyst interview questions, tips, and cheat sheets shared regularly 🚀
❤5