How to create simple pivot table in Python? DataAnalytics

🔹 Step 1: Import pandas
🔹 Step 2: Load your DataFrame
🔹 Step 3: Pivot the DataFrame
🔹 Step 4: Display the pivoted data

How to Use Python’s range() Function

The range() function generates a sequence of numbers, commonly used for looping a specific number of times or creating numeric lists.

The first number is included, but the last number is excluded.

For example, range(5, 10) will generate numbers from 5 to 9, but not 10.

Creating Beautiful Box Plots with Seaborn in Python

A box plot is a simple way to visualise the distribution of a dataset and identify potential outliers. It displays the minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum of the data, as well as any outliers. For more details on box plots you can watch my latest video on Insta

🔹 Step 1: Import Seaborn and load your dataset

🔹 Step 2: Create a basic box plot

Starting your career with Python is an excellent choice due to its versatility and broad range of applications. As you advance, you might discover various specializations that align with your interests:

• Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models.

• Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit.

• Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions.

• Automation and Scripting: If you’re interested in automating repetitive tasks and creating scripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling.

• Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful.

• DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for scripting and integrating with tools like Docker and Kubernetes.

• Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills.

Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools.

The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications.

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Must practise the following questions for your next Python interview:

1. How would you handle missing values in a dataset?

2. Write a python code to merge datasets based on a common column.

3. How would you analyse the distribution of a continuous variable in dataset?

4. Write a python code to pivot an dataframe.

5. How would you handle categorical variables with many levels?

6. Write a python code to calculate the accuracy, precision, and recall of a classification model?

7. How would you handle errors when working with large datasets?

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⌨️ Python Tips

Complete Syllabus for Data Analytics interview:

SQL:
1. Basic   
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING   
- Basic JOINS (INNER, LEFT, RIGHT, FULL)   
- Creating and using simple databases and tables

2. Intermediate   
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)   
- Subqueries and nested queries
- Common Table Expressions (WITH clause)   
- CASE statements for conditional logic in queries
3. Advanced   
- Advanced JOIN techniques (self-join, non-equi join)   
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)   
- optimization with indexing   
- Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic   
- Syntax, variables, data types (integers, floats, strings, booleans)   
- Control structures (if-else, for and while loops)   
- Basic data structures (lists, dictionaries, sets, tuples)   
- Functions, lambda functions, error handling (try-except)   
- Modules and packages

2. Pandas & Numpy   
- Creating and manipulating DataFrames and Series   
- Indexing, selecting, and filtering data   
- Handling missing data (fillna, dropna)   
- Data aggregation with groupby, summarizing data   
- Merging, joining, and concatenating datasets

3. Basic Visualization   
- Basic plotting with Matplotlib (line plots, bar plots, histograms)   
- Visualization with Seaborn (scatter plots, box plots, pair plots)   
- Customizing plots (sizes, labels, legends, color palettes)   
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic   
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)   
- Introduction to charts and basic data visualization   
- Data sorting and filtering   
- Conditional formatting

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

3. Advanced   
- Array formulas and advanced functions   
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables   
- Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling   
- Importing data from various sources   
- Creating and managing relationships between different datasets   
- Data modeling basics (star schema, snowflake schema)

2. Data Transformation   
- Using Power Query for data cleaning and transformation   
- Advanced data shaping techniques   
- Calculated columns and measures using DAX

3. Data Visualization and Reporting   - Creating interactive reports and dashboards   
- Visualizations (bar, line, pie charts, maps)   
- Publishing and sharing reports, scheduling 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.

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Python Roadmap
|
|-- Fundamentals
| |-- Basics of Programming
| | |-- Introduction to Python
| | |-- Setting Up Development Environment (IDE: PyCharm, VSCode, etc.)
| |
| |-- Syntax and Structure
| | |-- Basic Syntax
| | |-- Variables and Data Types
| | |-- Operators and Expressions
|
|-- Control Structures
| |-- Conditional Statements
| | |-- If-Else Statements
| | |-- Elif Statements
| |
| |-- Loops
| | |-- For Loop
| | |-- While Loop
| |
| |-- Exception Handling
| | |-- Try-Except Block
| | |-- Finally Block
| | |-- Raise and Custom Exceptions
|
|-- Functions and Modules
| |-- Defining Functions
| | |-- Function Syntax
| | |-- Parameters and Arguments
| | |-- Return Statement
| |
| |-- Lambda Functions
| | |-- Syntax and Usage
| |
| |-- Modules and Packages
| | |-- Importing Modules
| | |-- Creating and Using Packages
|
|-- Object-Oriented Programming (OOP)
| |-- Basics of OOP
| | |-- Classes and Objects
| | |-- Methods and Constructors
| |
| |-- Inheritance
| | |-- Single and Multiple Inheritance
| | |-- Method Overriding
| |
| |-- Polymorphism
| | |-- Method Overloading (using default arguments)
| | |-- Operator Overloading
| |
| |-- Encapsulation
| | |-- Access Modifiers (Public, Private, Protected)
| | |-- Getters and Setters
| |
| |-- Abstraction
| | |-- Abstract Base Classes
| | |-- Interfaces (using ABC module)
|
|-- Advanced Python
| |-- File Handling
| | |-- Reading and Writing Files
| | |-- Working with CSV and JSON Files
| |
| |-- Iterators and Generators
| | |-- Creating Iterators
| | |-- Using Generators and Yield Statement
| |
| |-- Decorators
| | |-- Function Decorators
| | |-- Class Decorators
|
|-- Data Structures
| |-- Lists
| | |-- List Comprehensions
| | |-- Common List Methods
| |
| |-- Tuples
| | |-- Immutable Sequences
| |
| |-- Dictionaries
| | |-- Dictionary Comprehensions
| | |-- Common Dictionary Methods
| |
| |-- Sets
| | |-- Set Operations
| | |-- Set Comprehensions
|
|-- Libraries and Frameworks
| |-- Data Science
| | |-- NumPy
| | |-- Pandas
| | |-- Matplotlib
| | |-- Seaborn
| | |-- SciPy
| |
| |-- Web Development
| | |-- Flask
| | |-- Django
| |
| |-- Automation
| | |-- Selenium
| | |-- BeautifulSoup
| | |-- Scrapy
|
|-- Testing in Python
| |-- Unit Testing
| | |-- Unittest
| | |-- PyTest
| |
| |-- Mocking
| | |-- unittest.mock
| | |-- Using Mocks and Patches
|
|-- Deployment and DevOps
| |-- Containers and Microservices
| | |-- Docker (Dockerfile, Image Creation, Container Management)
| | |-- Kubernetes (Pods, Services, Deployments, Managing Python Applications on Kubernetes)
|
|-- Best Practices and Advanced Topics
| |-- Code Style
| | |-- PEP 8 Guidelines
| | |-- Code Linters (Pylint, Flake8)
| |
| |-- Performance Optimization
| | |-- Profiling and Benchmarking
| | |-- Using Cython and Numba
| |
| |-- Concurrency and Parallelism
| | |-- Threading
| | |-- Multiprocessing
| | |-- Asyncio
|
|-- Building and Distributing Packages
| |-- Creating Packages
| | |-- setuptools
| | |-- Creating environment setup
| |
| |-- Publishing Packages
| | |-- PyPI
| | |-- Versioning and Documentation

Best Resource to learn Python

Python Interview Questions with Answers

Freecodecamp Python ML Course with FREE Certificate

Python for Data Analysis

Python course for beginners by Microsoft

Scientific Computing with Python

Python course by Google

Python Free Resources

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𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐨𝐝𝐢𝐧𝐠 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:

𝘐 𝘨𝘦𝘵 𝘴𝘰 𝘮𝘢𝘯𝘺 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴 𝘧𝘳𝘰𝘮 𝘥𝘢𝘵𝘢 𝘢𝘯𝘢𝘭𝘺𝘵𝘪𝘤𝘴 𝘢𝘴𝘱𝘪𝘳𝘢𝘯𝘵𝘴 𝘢𝘯𝘥 𝘱𝘳𝘰𝘧𝘦𝘴𝘴𝘪𝘰𝘯𝘢𝘭𝘴 𝘰𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘨𝘢𝘪𝘯 𝘤𝘰𝘮𝘮𝘢𝘯𝘥 𝘰𝘧 𝘗𝘺𝘵𝘩𝘰𝘯.

📍𝐋𝐞𝐚𝐫𝐧 𝐂𝐨𝐫𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Master Python libraries for data analytics, like
-pandas for dataframes,
-NumPy for numerical operations,
-Matplotlib/Seaborn for plotting,
-scikit-learn for machine learning.

📍𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬: Important concepts like list comprehensions, lambda functions, object-oriented programming, and error handling to write efficient code.

📍𝐔𝐬𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦-𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Apply data wrangling techniques, efficient loops, and vectorized operations in NumPy/pandas for optimized performance.

📍𝐃𝐨 𝐌𝐨𝐜𝐤 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Work on end-to-end Python analytics projects—data loading, cleaning, analysis, and visualization.

📍𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐏𝐚𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Review your previous Python projects to see where your code can be more efficient.