Introduction to Data Analysis & Python

INTRODUCTION TO DATA ANALYSIS AND PYTHON

 What is Data Analysis?

Data Analysis:

• Data analysis is inspecting and modeling data to derive meaningful conclusions.  

• Data analysis is the process of collecting, cleaning, transforming, and modeling data to extract useful insights, draw conclusions, and support decision-making.

• It typically involves statistical and logical techniques for exploring and interpreting data to summarize and visualize raw data. 

• The goal is to uncover trends, patterns, and insights that help solve problems or guide future actions.

• It plays a crucial role in a wide range of fields like business, science, healthcare, finance, and government.

Data Analysis Process:

The data analysis process typically involves several steps:

1. Data Collection: Data collection is the first step in the data analysis process. In this phase, relevant and accurate data is gathered from various sources such as databases, surveys, APIs, web scraping, or sensors. It is important to ensure that the data is complete, trustworthy, and suitable for the analysis objectives. Proper documentation of data sources is also essential for reproducibility and transparency.
2. Data Cleaning: Once the data is collected, the next step is data cleaning. This involves correcting or removing inaccurate, incomplete, or irrelevant parts of the dataset. Common cleaning tasks include removing duplicate entries, handling missing values, standardizing formats (such as dates and text), and identifying outliers or inconsistent values. Clean data is crucial for producing reliable and accurate analysis results.
3. Data Exploration: Data exploration, or EDA(Exploratory Data Analysis), is used to understand the structure and characteristics of the dataset. During this phase, analysts use summary statistics and visualizations to identify patterns, spot anomalies, and explore relationships between variables. This step often includes calculating measures such as mean, median, and standard deviation, and creating visualizations like histograms, box plots, and scatter plots. EDA helps form hypotheses and guides further analysis.
4. Data Analysis: In the data analysis phase, various techniques are applied to extract meaningful insights and answer specific questions. This can involve statistical methods such as hypothesis testing or more advanced techniques like machine learning models (e.g., regression, classification, or clustering). The goal is to uncover trends, test assumptions, and make data-driven decisions based on the findings.
5. Data Interpretation: After conducting the analysis, the next step is to interpret the results. This means understanding what the analysis reveals about the original question or problem. It involves explaining the significance of the findings, identifying implications, and recognizing any limitations or assumptions made during the analysis. Interpretation bridges the gap between raw data output and actionable insight.
6. Data Visualization: The final step is data visualization, which focuses on presenting the results in a clear and compelling way. Charts, graphs, dashboards, and other visual tools are used to communicate the findings to stakeholders, whether technical or non-technical. Effective data visualization makes it easier to understand complex data and supports informed decision-making. Good visualization practices include clarity, simplicity, and relevance to the target audience.

Tools and Techniques:

At the beginner level, spreadsheet applications like Microsoft Excel are commonly used because they provide a user-friendly interface for performing basic tasks such as data manipulation, calculation, and visualization. These tools are helpful for simple analyses and are widely accessible.

However, as data becomes more complex and larger in scale, more advanced data analytics platforms become essential. Programming languages such as Python and R are highly valuable in this context. These languages are equipped with powerful libraries like Pandas, NumPy, Matplotlib, and ggplot2, which offer advanced capabilities for statistical analysis and data visualization. These platforms create a flexible and customizable environment that enables analysts to carry out sophisticated analyses and produce meaningful visual insights.

In addition to the availability of various tools, data analysts use a wide range of techniques to discover patterns, relationships, and insights from the data. Statistical techniques such as t-tests and chi-square tests help evaluate the significance of observations and differences. Regression analysis is another key method that enables analysts to understand relationships between variables and to make predictions based on historical data.

Cluster analysis is used to group similar data points together, which helps in segmentation and pattern recognition. Furthermore, machine learning methods, such as classification and predictive modeling algorithms, provide advanced capabilities for forecasting future trends and making informed decisions.

The selection of a suitable technique for data analysis depends on several factors including the research question, the nature of the data, and the expected outcomes. For instance, statistical tests are appropriate for comparing proportions in categorical data, while regression is used to study relationships between variables. Machine learning plays a significant role in predictive tasks, particularly when large and complex datasets are involved. Therefore, choosing the right technique requires careful consideration of the specific goals and limitations of the analysis.

Effective data analysis often involves the integration of multiple tools and techniques to solve complex problems and extract actionable insights. Analysts may begin with spreadsheet applications for basic preprocessing and exploration and then move on to advanced tools for in-depth analysis and visualization. By combining various tools and methods, analysts can enhance the utility of their data and uncover valuable insights that support better decision-making.

As data analysis tools and technologies continue to develop, it is important for practitioners to keep learning and stay updated with the latest advancements. This includes being aware of emerging tools, libraries, and methods. Staying curious, proactive, and adaptable allows analysts to leverage the full power of these tools and drive innovation in their work.

Types of Data Analysis:

Data analysis can be approached in different ways depending on the purpose and nature of the investigation. Each type of analysis serves a unique role and offers specific benefits. The major types of data analysis are described below:

1. Descriptive Analysis

Descriptive analysis is the initial stage of data analysis where the goal is to summarize and describe the features of a dataset. This type of analysis uses statistics and visual tools such as charts and graphs to provide a general overview of the data. It helps to identify patterns like central tendencies (mean, median, mode), distributions, and relationships between variables. Essentially, it paints a broad picture of the data and answers the question, "What has happened?"

2. Diagnostic Analysis

Diagnostic analysis goes a step further by exploring the reasons behind the patterns observed in the data. It investigates the "why" by using techniques such as hypothesis testing and statistical modeling to determine the causes of trends or anomalies. This analysis helps uncover the root causes of certain outcomes or phenomena.

3. Predictive Analysis

Predictive analysis uses historical data and statistical models to forecast what is likely to happen in the future. It applies machine learning techniques and regression models to make predictions based on past patterns. This type of analysis is helpful for anticipating future events and supporting proactive decision-making.

4. Prescriptive Analysis

Prescriptive analysis focuses on recommending specific actions to achieve desired outcomes. It uses the results from predictive analysis and evaluates different strategies to determine the best course of action. This analysis helps in decision-making by optimizing solutions and maximizing efficiency or success.

5. Exploratory Data Analysis (EDA)

Exploratory Data Analysis is an open-ended process of exploring the data without having any specific questions in mind. The purpose of EDA is to uncover hidden patterns, detect anomalies, and discover relationships that may not have been considered earlier. It is particularly useful for forming new hypotheses and understanding the structure of the data.

6. Causal Analysis

Causal analysis aims to determine cause-and-effect relationships. It uses advanced and often experimental techniques to identify the true reasons behind certain outcomes. This type of analysis is essential when the goal is to understand how one variable directly impacts another, and it is used to validate or establish causality.

Data Analysis Applications:

Data analysis is applied across nearly every industry to support decision-making, improve efficiency, and gain insights. Below are key areas where data analysis plays a vital role:

1. Business and Marketing: Data analysis helps businesses understand customer behavior, optimize marketing strategies, and improve overall operations. For example, companies use customer data to segment markets, personalize advertisements, and predict future buying trends. Sales data is analyzed to identify best-selling products, seasonal demand, and customer lifetime value.

2. Finance and Banking:In finance, data analysis is used for risk assessment, fraud detection, and investment forecasting. Banks analyze transaction patterns to detect suspicious activities and prevent fraud. Portfolio managers use data analysis to evaluate stock performance, forecast market trends, and make investment decisions based on historical and real-time financial data.

3. Healthcare: In the healthcare sector, data analysis is used to improve patient care, reduce costs, and conduct medical research. Electronic health records (EHRs) are analyzed to identify disease patterns, predict outbreaks, and personalize treatments. Hospitals use data to track patient outcomes, optimize staff scheduling, and manage resources efficiently.

4. Education: Educational institutions apply data analysis to improve student outcomes, track performance, and personalize learning. By analyzing test scores, attendance records, and engagement levels, schools can identify students who need support and adapt teaching methods accordingly. Universities also use data to manage enrollment and assess academic programs.

5. Government and Public Policy: Governments use data analysis to make evidence-based decisions and improve public services. For instance, transportation data can be analyzed to optimize traffic flow, while census data helps allocate resources and plan infrastructure. Crime data analysis is used to identify hotspots and deploy police resources effectively.

6. E-Commerce: Online retailers use data analysis to enhance user experience, manage inventory, and boost sales. Website analytics help track customer journeys, understand purchasing behavior, and improve UI/UX. Recommendation systems, powered by machine learning, use past browsing and purchase data to suggest relevant products.

Advantages of Data Analysis:

1. Enhanced Decision-Making
   Data analysis enables decisions based on factual insights rather than intuition, improving accuracy and reducing uncertainty.

2. Improved Operational Efficiency
   It identifies inefficiencies and streamlines workflows, leading to increased productivity and cost savings.

3. Better Customer Understanding
   Data analysis provides you with more insights into your customers, allowing you to tailor customer service to their needs, provide more personalization, and build stronger relationships with them.

4. Competitive Advantage
   Utilizing data analysis allows businesses to stay ahead of competitors by identifying emerging trends, anticipating market shifts, and adapting strategies accordingly.

5. Risk Mitigation
   As businesses are vulnerable to risk and fraud, Early detection of potential issues allows proactive measures, reducing exposure to financial or operational risks.

6. Cost Reduction
   With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help save money and resources on implementing the wrong strategies.

7. Innovation and Product Development
   Data analysis can inspire innovation by revealing unmet customer needs and identifying areas for product or service improvement. By understanding customer preferences and market trends, businesses can develop new products and services that better meet customer demands. 

8. Performance Monitoring
   Data analysis enables businesses to track key performance indicators (KPIs) and monitor overall performance. This allows for timely identification of areas where performance is lagging and enables businesses to make necessary adjustments to improve results. 

Disadvantages of Data Analysis:

While data analysis provides powerful insights and decision-making capabilities, it also comes with several limitations and challenges, particularly in performance, resource management, and learning complexity. Below are some key disadvantages:

1. Performance Limitations

Data analysis, especially on large datasets, can suffer from performance issues. Analytical operations such as complex joins, aggregations, or machine learning algorithms can be computationally intensive and slow. If not optimized properly, data processing can become inefficient, resulting in long runtimes and poor user experience. Performance bottlenecks are common when using non-distributed tools on big data.

2. Memory Management Overhead

Many data analysis tools (like Python's Pandas or R) load entire datasets into memory, which can lead to excessive memory usage or crashes when handling large data volumes. Improper memory management may result in slow execution, system freezing, or errors, especially when working on machines with limited RAM. This challenge often requires additional configuration or chunking of data to avoid out-of-memory issues.

3. Limited Multithreading Support

Some popular data analysis libraries are not optimized for multithreading or parallel processing. For example, Python's Global Interpreter Lock (GIL) restricts the execution of multiple threads, which limits the performance gains that could be achieved through concurrent processing. This can be a significant disadvantage for time-sensitive applications or large-scale batch processing that could otherwise benefit from parallel computation.

4. Dependency Management and Versioning Issues

Data analysis workflows often rely on multiple software packages and libraries, each with its own versions and dependencies. Conflicts between library versions can lead to compatibility issues, errors, or broken environments. Dependency management becomes more complex as projects grow in size and complexity, requiring the use of virtual environments, package managers, or containers (e.g., Docker) to maintain stability.

5. Steep Learning Curve for Advanced Topics

While basic data analysis is accessible, mastering advanced topics—such as machine learning, time series modeling, or big data frameworks—requires a deep understanding of mathematics, statistics, programming, and domain knowledge. This steep learning curve can be discouraging for beginners or non-technical users. Moreover, improper application of complex techniques can lead to misleading or incorrect results.

---

Differences between Data Analysis and Analytics

1. Focus:

• Data Analysis primarily focuses on inspecting, cleaning, transforming, and modeling data to extract useful information. It helps draw conclusions and supports decision-making based on historical data. It focuses on examining historical and current data to understand what has already happened.
• Analytics, on the other hand, goes beyond just examining historical data. It uses that information to predict future trends, prescribe actions, and optimize processes for better decision-making. Data analysis is like looking at a snapshot of the past, seeing what happened, and finding patterns. On the other hand, analytics is more like using that snapshot to predict what might happen in the future.  

2. Scope:

• Data Analysis typically involves examining historical data to identify patterns, trends, and relationships within a specific dataset.
• Analytics has a broader scope. It includes predictive (forecasting future events) and prescriptive (recommending actions) capabilities to support strategic planning.

3. Techniques:

• Data Analysis employs traditional statistical methods, visualization tools, and exploratory techniques to understand the characteristics of data.
• Analytics utilizes advanced statistical models, machine learning algorithms, and data mining techniques to uncover patterns and make predictions about future events.

4. Outcome:

• The main outcome of Data Analysis is to generate actionable insights and information that help in making informed decisions.
• The primary outcome of Analytics is to provide deeper, actionable insights that not only support decisions but also aim to improve future outcomes through proactive strategies.

5. Example:

Data Analysis Example: 

• Analyzing sales performance from the previous quarter.

• Identifying patterns in customer complaints to find recurring issues.

• Visualizing demographic trends in customer data.

Analytics Example:

• Forecasting future sales based on past trends.

• Recommending personalized products using customer behavior data.

• Optimizing supply chains through predictive maintenance models.

---

What is Python?

Python is a powerful open source, high – level, object – oriented programming language created by “Guido Van Rossum” and first released in 1991. It is further developed by the Python Software Foundation. It is one of the widely used popular and powerful programming languages. It has simple easy – to – use syntax, making it the perfect language for someone trying to learn computer programming for the first time, and it is also a good language to have in any programmer’s stack as it can be used for everything from web development to software development and scientific applications.

Features of Python: 

1) Easy to Learn and Use

• Python is easy to learn as compared to other programming languages.
• Its syntax is straightforward and much the same as the English language.
• There is no use of the semicolon or curly-bracket, the indentation defines the code block. 

2) Expressive Language 

• Python can perform complex tasks using a few lines of code.
• A simple example, the hello world program you simply type print("Hello World").
• It will take only one line to execute, while Java or C takes multiple lines. 

3) Interpreted Language

• Python is an interpreted language; it means the Python program is executed one line at a time.
• The advantage of being interpreted language, it makes debugging easy and portable. 

4) Cross-platform Language

• Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh, etc.
• So, we can say that Python is a portable language.
• It enables programmers to develop the software for several competing platforms by writing a program only once. 

5) Free and Open Source

• Python is freely available for everyone.
• It is freely available on its official website www.python.org. 
• It has a large community across the world that is dedicatedly working towards make new python modules and functions. 
• The open-source means, "Anyone can download its source code without paying any penny."

6) Object-Oriented Language

• Python supports object-oriented language and concepts of classes and objects come into existence.
• It supports inheritance, polymorphism, and encapsulation, etc. 
• The object-oriented procedure helps to programmer to write reusable code and develop applications in less code. 

7) Extensible

• It implies that other languages such as C/C++ can be used to compile the code and thus it can be used further in our Python code.
• It converts the program into byte code, and any platform can use that byte code.  

8) Large Standard Library

• It provides a vast range of libraries for the various fields such as machine learning, web developer, and also for the scripting.
• There are various machine learning libraries, such as Tensor flow, Pandas, Numpy, Keras, and Pytorch, etc.
• Django, flask, pyramids are the popular framework for Python web development.

9) GUI Programming Support

• Graphical User Interface is used for the developing Desktop application.
• PyQT5, Tkinter, Kivy are the libraries which are used for developing the web application.

10) Integrated

• It can be easily integrated with languages like C, C++, and JAVA, etc. 
• Python runs code line by line like C,C++ Java. It makes easy to debug the code.

11) Embeddable

•  The code of the other programming language can use in the Python source code.
• We can use Python source code in another programming language as well.

12) Dynamic Memory Allocation

• In Python, we don't need to specify the data-type of the variable.
• When we assign some value to the variable, it automatically allocates the memory to the variable at run time. 
• Suppose we are assigned the integer value 15 to x, then we don't need to write int x = 15. Just write x = 15. 

Advantages of Python:

• Ease of learning and use: Python's code looks like English, so it is simple to read and write. This makes it easy for beginners to start learning programming.

• Versatile: Python can be used for many different types of work, like creating websites, analyzing data, making games, automating tasks, and more.

• Large Number of Libraries and Frameworks: Python has many libraries and frameworks that help you do complex tasks easily—like data handling, machine learning, and data visualization.

• Strong Community Support: A large group of Python users and developers around the world who share tutorials, documentation, and third-party tools.

Disadvantages of python:

• Performance: Python runs slower than some other programming languages like C or C++ because it is an interpreted language, not a compiled one.

• Mobile App Development: Python is not used much for creating mobile apps. Other languages like Java or Kotlin are more common for mobile development.

• Runtime Errors: Python is dynamically typed, which means it checks variable types while running the program. This can lead to errors that only appear during execution (not before), which may affect stability.

Applications of python:

1. Web Development

Python is widely used in web development. Frameworks such as Django and Flask help developers build robust and scalable web applications. These frameworks make it easier to manage things like databases, user login systems, and web page templates.

2. Data Science and Machine Learning

Python plays a major role in data science and machine learning. It provides powerful libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch, which help in data analysis, statistical modeling, and building machine learning models.

3. Artificial Intelligence (AI)

Python is commonly used to develop applications in the field of artificial intelligence. It is used for tasks such as natural language processing, computer vision, and speech recognition. Libraries like NLTK and SpaCy support natural language processing, while OpenCV is used for image and video processing.

4. Scientific Computing

Python is heavily used in scientific computing and research work. Libraries like SciPy provide tools for performing numerical analysis, optimization, and other scientific calculations, making Python suitable for academic and research institutions.

5. Automation and Scripting

Python is an excellent choice for automation and scripting. Many people use Python to write scripts that automate repetitive tasks such as file handling, sending emails, system administration, and data processing.

6. Game Development

Python is also used in developing games. Game developers use Python for scripting and building the game logic. Popular game development libraries such as Pygame and Panda3D help in creating 2D and 3D games.

7. Network Programming

Python is useful for network programming. It includes a socket library and other frameworks like Twisted, which allow developers to build networking tools, chat applications, and programs that involve data communication.

8. Desktop GUI Applications

Python can be used to build desktop applications with graphical user interfaces (GUIs). Libraries such as Tkinter, PyQt, and Kivy are used to create user-friendly desktop applications like calculators, media players, and note-taking apps.

9. Backend Development

Python is widely used for backend development. Developers use Python to build server-side systems and APIs that connect the front-end of a website to the database. Django and Flask are two popular frameworks used for building efficient and scalable backend systems.

10. Financial and Trading Applications

Python is commonly used in the financial sector for performing tasks like algorithmic trading, risk management, and data analysis. Libraries such as Pandas and NumPy are especially useful in handling large financial datasets.

11. Internet of Things (IoT)

Python is used in IoT (Internet of Things) applications because of its simplicity and ease of integration with hardware. MicroPython, a version of Python, is commonly used in IoT devices and microcontrollers for controlling sensors and collecting data.

12. Educational Purposes

Python is a popular choice in the education sector. It is often used as a first programming language in schools and colleges due to its readability and simple syntax. Platforms like the Raspberry Pi also use Python for teaching programming concepts through hands-on projects.

---

Why Python for Data Analysis?

Python has emerged as one of the most popular and powerful programming languages for data analysis. Its rapid rise in the data science community is attributed to its simplicity, rich ecosystem, and ability to handle complex data tasks with ease.

Below are the key reasons why Python is widely used for data analysis:

1.  Ease of Learning and Use

• Python’s syntax is simple, clean, and readable, resembling plain English.

• It’s accessible to beginners and professionals alike, even those without a strong programming background.

• The learning curve is gentle, allowing analysts to focus more on data insights rather than complex programming logic.

2. Rich Ecosystem of Data Analysis Libraries

Python offers a wide range of specialized libraries that streamline data analysis:

Library	Purpose
Pandas	Data manipulation and analysis (e.g., data frames, filtering, aggregation)
NumPy	Efficient numerical computing and array operations
SciPy	Scientific and technical computing (e.g., linear algebra, optimization)
Matplotlib / Seaborn	Data visualization (charts, graphs, heatmaps)
Scikit-learn	Predictive modeling and analysis

3. Flexibility and Interoperability

• Python can handle various data formats, including CSV, Excel, JSON, SQL databases, and APIs.

• It integrates well with other tools and languages such as SQL, R, Java, Hadoop, and Spark.

• Python can be used for both small-scale analysis (e.g., local datasets) and large-scale data processing.

4. Strong Community and Support

• Python has a large, global community of developers, data analysts, and data scientists.

• Thousands of forums, tutorials, blogs, GitHub repositories, and Stack Overflow discussions exist for problem-solving and learning.

• The community actively contributes to open-source libraries, ensuring continuous improvement and innovation.

5. Seamless Integration with Machine Learning

• Python is a leading language for machine learning (ML) and artificial intelligence (AI).

• Popular ML libraries include:

  • Scikit-learn – For traditional ML algorithms like regression, classification, clustering.

  • TensorFlow / PyTorch – For deep learning, neural networks, and complex models.

• Analysts can easily scale from basic descriptive analysis to predictive analytics and advanced ML models within the same ecosystem.

6.  Open Source and Free to Use

• Python is completely open-source and free to download, use, and modify.

• This reduces the cost of entry, making it accessible to individuals, students, startups, and organizations.

• No expensive licenses or subscriptions are needed for core functionality.

7.  Cross-Platform Compatibility

• Python is platform-independent — it works on Windows, macOS, Linux, and other operating systems without major changes in code.

• This makes it easier to develop, test, and deploy data analysis projects across different environments.

8. Industry Adoption

• Python has gained widespread adoption across industries, including technology, finance, healthcare, marketing, and research. 

• Its versatility and ecosystem make it the go-to tool for data tasks in almost every industry.

---

What is a Library?

A library in programming is a collection of pre-written code modules. These modules contain a set of functions and routines that help in performing specific tasks or solving common problems.

Libraries make software development easier by providing reusable code components. This helps developers avoid writing code from scratch and saves both time and effort. Developers can integrate these libraries into different programs or projects easily.

Libraries can perform many tasks, such as:

• Mathematical operations
• File handling
• Network communication
• Data manipulation
• User interface design
• And many other programming tasks

By using libraries, developers can avoid repetitive coding and focus more on building the main features of their applications.

Libraries can be available in different forms:

• Source code form, which developers can compile and link
• Pre-compiled binary files, which can be directly used in a program

Python uses tools like pip to install libraries. These libraries are usually distributed as packages.

Definition of a Software Library:

A software library is a collection of non-volatile resources used by computer programs to help in software development. 

These resources can include:

• Configuration data
• Documentation
• Help data
• Message templates
• Pre-written code and subroutines
• Classes
• Values
• Type specifications

Purpose of Software Libraries:

The main purpose of a software library is to provide a standard way to perform common programming tasks. These tasks may include:

• Input/output operations
• String manipulation
• Data storage
• Using complex algorithms

By reusing code from libraries, developers can reduce development time, improve the quality of their code, and add more features easily.

Advantages:

• Code Reusability: This allows developers to reuse code, reducing duplicates and fostering efficient development.
• Reliability: Libraries are usually tested and maintained by experienced developers or communities. This makes them more reliable and less prone to errors than custom-written code.
• Time-Saving: Libraries help speed up the development process by offering ready-made functions and classes. This saves developers from writing everything from scratch.

Disadvantages:

• Reliance on External Libraries: If a program depends too much on external libraries, it may face problems when a library becomes outdated or is no longer supported.
• Overhead: Including large libraries for small tasks can unnecessarily increase the size of an application and slow down its loading time.
• Learning Curve: Understanding and using certain libraries correctly can take extra effort and learning time, especially for beginners.

Applications:

Software libraries are widely used in different areas of software development. Some common applications are:

• Web Development: Libraries like jQuery make it easier to work with HTML documents, handle events, and perform tasks like AJAX interactions and DOM manipulation.
• Data Analysis: Libraries such as Pandas and NumPy offer powerful tools to handle, process, and analyze large amounts of data.
• Machine Learning: Libraries like TensorFlow and Scikit-learn provide tools for data mining, building machine learning models, and performing data analysis.
  

Essential Python Libraries

1. Numpy (Numerical Python)
• NumPy is a foundational library for numerical computing in Python.
• It provides powerful N-dimensional(1-D, 2-D, 3-D) array objects for efficient data storage and manipulation.
• It offers a wide range of mathematical functions (square, sqrt), linear algebra operations(dot,transpose), and random number generation.
Advantages:
• NumPy allows efficient numerical operations on large datasets.
• It supports N-dimensional array operations for scientific computing.
• It integrates well with other libraries for data analysis and machine learning.
Applications:
• It is used in scientific and mathematical computing.
• It is applied in signal processing and image analysis.
• Data manipulation using NumPy is helpful in machine learning workflows.







2. Pandas
• Built on top of NumPy, specifically designed for data analysis and manipulation.
• Introduces DataFrames, which are tabular data structures with labeled rows and columns, similar to spreadsheets.
• Enables easy data loading, cleaning, exploring, transforming, and merging.
Advantages:
• Tabular data manipulation with DataFrames.
• Easy handling of missing data and data alignment.
• Integration with databases and time series data.
Applications:
• Data cleaning, exploration, and analysis.
• Time series analysis and financial data modelling.
• Data preparation for machine learning.






3. Matplotlib
• Cornerstone library for data visualization in Python.
• Capable of creating static, animated, and interactive visualizations (line plots, scatter plots, histograms, bar charts, heatmaps, etc.).
• Offers extensive customization options for fine-tuning all visualization elements.
Advantages:
• Creation of a wide variety of static and interactive plots.
• Fine-grained control over plot aesthetics.
• Seamless integration with Jupyter notebooks.
Applications:
• Data visualization for exploratory data analysis.
• Presentation-quality plots for reports and publications.
• Educational purposes in teaching and tutorials.


4. Seaborn
• Built on top of Matplotlib; provides a high-level interface for creating aesthetically pleasing statistical graphics.
• Simplifies the creation of common plots like heatmaps, violin plots, joint plots, and more.
Advantages:
• Simplifies the creation of complex statistical plots.
• Aesthetic enhancements for Matplotlib plots.
• Integration with Pandas for easy data manipulation.
Applications:
• Statistical data visualization.
• Exploration of complex datasets.
• Presenting the results of statistical analyses.


5. Scikit-learn
• Versatile machine learning library.
• Supports classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
• User-friendly API with consistent syntax.
Advantages:
• Consistent and simple API for various machine learning algorithms.
• Integration with other libraries like NumPy and Pandas.
• Robust tools for model selection and evaluation.
Applications:
• Classification, regression, and clustering tasks.
• Dimensionality reduction and feature extraction.
• Model selection and evaluation in machine learning pipelines.


6. Statsmodels
• Focused on statistical modeling and analysis.
• Provides tools for model fitting, hypothesis testing, and time series analysis.
• Works well with NumPy and Pandas.
Advantages:
• Emphasis on statistical models and hypothesis testing.
• Integration with Pandas for data handling.
• Comprehensive tools for linear and non-linear models.
Applications:
• Statistical analysis and hypothesis testing.
• Econometrics and financial modelling.
• Time series analysis and forecasting.


7. Requests
• Simplifies making HTTP requests and interacting with web APIs.
Advantages:
• Simplifies HTTP requests in Python.
• Versatile for various HTTP methods.
• Support for handling authentication and cookies.
Applications:
• Web scraping and data extraction.
• Interaction with web APIs to fetch data.
• Automated testing of web services.


8. Beautiful Soup
• Parses HTML and XML documents.
• Ideal for web scraping.
Advantages:
• HTML and XML parsing for web scraping.
• Navigating and searching parsed tree structures.
• Integration with other libraries for data extraction.
Applications:
• Web scraping and data mining.
• Extracting structured information from websites.
• Automating the parsing of HTML and XML documents.


9. Plotly
• Creates interactive, web-based visualizations.
• Supports zooming, panning, and hovering.
Advantages:
• Creation of interactive, web-based visualizations.
• Support for a wide range of chart types.
• Integration with Jupyter notebooks and online hosting.
Applications:
• Building interactive dashboards.
• Exploratory data analysis with interactive plots.
• Web-based data visualization applications.


10. Tensor Flow and PyTorch
• Powerful libraries for deep learning and AI.
• Used to build neural networks and solve complex tasks like image recognition and NLP.
Advantages:
• Powerful frameworks for deep learning and neural networks.
• Support for GPU acceleration.
• Extensive community support and pre-trained models.
Applications:
• Image and speech recognition.
• Natural language processing.
• Training and deploying deep learning models for various tasks.

---

Python Language Basics

Keywords:

• Keywords are the reserved words in the Python programming language. 
•  All keywords are designated with a special meaning.
•  The meaning of all keywords is fixed, and it cannot be modified or removed.
•  All the keywords need to be used as they have been defined (Lower case or Upper case).
•  In Python, there are 35 keywords.
•  All the keywords in Python are listed in the following table with their meaning: 

S.No	Keyword	Description
1	False	Boolean false value
2	None	Represents absence of value
3	True	Boolean true value
4	and	Logical AND operator
5	as	Alias (e.g. in import, with statements)
6	assert	Assert condition for debugging
7	async	Defines an async function
8	await	Awaits result of an async operation
9	break	Exit loop early
10	class	Defines a class structure
11	continue	Skip to next loop iteration
12	def	Define a function
13	del	Delete a variable or element
14	elif	Else-if in conditional chains
15	else	Fallback in conditionals or try blocks
16	except	Catch exceptions
17	finally	Always-run clause in try-except blocks
18	for	For-loop construct
19	from	Import specific items from modules
20	global	Declare a global variable
21	if	Conditional branching
22	import	Import a module
23	in	Membership test
24	is	Identity comparison
25	lambda	Define anonymous (single-expression) function
26	nonlocal	Declare non-local (enclosing scope) variable
27	not	Logical NOT operator
28	or	Logical OR operator
29	pass	No-op placeholder
30	raise	Raise an exception
31	return	Return from a function
32	try	Start a try-except block
33	while	While-loop construct
34	with	Context manager usage
35	yield	Generate values from a generator

You can use the built-in keyword module in Python to display all the current keywords.

import keyword

for kw in keyword.kwlist:

    print(kw)

---

Identifiers

• Identifiers in Python are names used to identify a variable, function, class, module, or other objects.
• An identifier can only contain letters, digits, and underscores, and cannot start with a digit. 
• In Python, identifiers are case-sensitive, meaning that swathi and SWATHI are considered to be two different identifiers. 

Rules for Identifiers in Python:

These are the rules for identifiers in Python:

• Keywords cannot be used as identifiers in Python (because they are reserved words).
• The names of identifiers in Python cannot begin with a number.
• All the identifiers in Python should have a unique name in the same scope.
• The first character of identifiers in Python should always start with an alphabet or underscore, and then it can be followed by any of the digits, characters, or underscores.
• Identifier name length is unrestricted.
• Names of identifiers in Python are case sensitive, meaning ‘car’ and ‘Car’.
• Special characters such as ‘%’, ‘#’,’@’, and ‘$’ are not allowed as identifiers in python.

Valid Identifiers: 

These are examples of valid identifiers in Python.

• yourname It contains only lowercase letters. 
• Name_school It contains only ‘_’ as a special character.
• Id1 Here, the numeric digit comes at the end.
•  roll_2 It starts with a lowercase letter and ends with a digit. 
•  _classname contains lowercase alphabets and an underscore, and it starts with an underscore ‘_’.

Invalid Identifiers:

These are examples of valid invalid identifiers in Python. 

• (for, while, in) - These are the keywords in Python that cannot be used as identifiers in Python.
• 1myname - Invalid identifier because it begins with a digit.
• \$myname - Invalid identifier because it starts with a special character 
• a b - Invalid identifier because it contains a blank space.
• (a/b and a+b) - Invalid identifiers because they contain special characters.

---

Variable:

• In Python, a variable is a named memory where a programmer can store data and retrieve it for future use using the same name.
• In Python, variables are created without specifying any data type. 
• There is no specific keyword used to create a variable.
• Variables are created directly by specifying the variable name with a value. 

We use the following syntax to create a variable:

Syntax:

 variable_name = value

 

When a variable is defined, we must create it with a value. 

roll_number = 101

print(f'Student roll number is {roll_number}') 

Output:

Student roll number is 101

Declaring multiple variables in a single statement

• In Python, it is possible to define more than one variable using a single statement.
• When multiple variables are created using a single statement, the variables and their corresponding value must be separated with a comma.  

Python code to illustrate variable declaration :

name, roll_number = ('Saranya', 101) 

print(f'{name}'s roll number is {roll_number}') 

Output: Saranya's roll number is 101 

Assigning a single value to multiple variables:

x=y=z=50 

print(x)

print(y) 

print(z) 

Output: 50 50 50 

Displaying the data type of a variable 

• In Python, the data type of a variable never fixed to a particular data type and it keeps changing according to the value assigned to it. 
• A variable in Python stores value of any data type. 
• It can change its data type dynamically. 
• The Python programming language provides a built-in function type( ) to display the data type of a variable.

Let's consider the following Python code:

a = 105

print(type(a)) 

a = 10.66 

print(type(a)) 

a = 'Ashaz' 

print(type(a))

Output: 

<class 'int'>

<class 'float'>

<class 'str'>

---

Comments

• Comments are essential for defining the code and help us and other to understand the code.
• By looking the comment, we can easily understand the intention of every line that we have written in code.
• We can also find the error very easily, fix them, and use in other applications.
• In Python, we can apply comments using the # hash character.
• The Python interpreter entirely ignores the lines followed by a hash character.
• A good programmer always uses the comments to make code under stable.

Types of Comments in Python

1. Single-Line Comments

• Begin with the # symbol.
• Used for brief explanations or notes.
• Applies only to one line.

Syntax:

# This is a single-line comment

print("Sri")

Output:

Sri

2. Multi-Line (Block) Comments

• Multiple single-line comments used together to create block comments.
• Python doesn't support block comments like /* */, so # is used at the beginning of each line.

Syntax:

# This type of comment can serve

# both as a single line as well

# as a multi-line (block) comment

Example:

# Read name from keyboard

# variable name is myName

myName = input("Enter your Name: ")

# Display data on the output screen

print("Hello,", myName)

Output:

Enter your Name: Swathi

Hello, Swathi

3. Inline Style Comments

• Placed on the same line as a statement.
• Used to explain the statement.

Example:

# Find the product of two numbers

x = 5   # value 3 stored in x

y = 7   # value 7 stored in y

z = x * y  # product stored in z

print("Product is:", z)

Output:

Product is: 35

4. Docstring Comments (Documentation Strings)

• Written using triple quotes ''' or """.
• Used to describe the purpose of a function, class, or module.
• Not exactly comments, but act like them when not assigned to a variable.

Example:

"""

Read two values through command line arguments

Then find the sum with data type conversion

"""

import sys

x = int(sys.argv[1])  # Read and convert x

y = int(sys.argv[2])  # Read and convert y

sum = x + y

print("Sum of two numbers is:", sum)

Command Line Output:

> python docstring.py 15 6

Sum of two numbers is: 21

---

Datatypes

In Python, everything is an object, and every object has a data type.

A data type defines the type of value a variable can hold and the operations that can be performed on it.

Classification of Python Built-in Data Types:

➤ 1. Numeric Data Types

These represent numbers and are of three types:

a) int

• Used to store whole numbers.
• Can be positive or negative.
• There is no limit to the size of the integer.

Example:

a = 291

print(type(a))  # <class 'int'>

b) float

• Used to store decimal (floating point) numbers.
• It can also store values in scientific notation using e or E.

Example:

b = 267.0

print(type(b))  # <class 'float'>

c) complex

• Used to represent complex numbers.
• Syntax: real + imaginary j

Example:

c = 231 + 27j

print(type(c))  # <class 'complex'>

➤ 2. Sequence Data Types

Sequences store a collection of items in a specific order.

a) str (String)

• A string is a sequence of Unicode characters.
• Defined using single, double, or triple quotes.
• No separate character data type in Python. A character is a string of length 1.

Example:

s = "Swathi"

print(type(s))  # <class 'str'>

b) list

• Ordered, mutable (can change after creation).
• Allows duplicate values.
• Can hold elements of different types.

Creating a list:

lst = ["Hello", "Swathi", 71, 2025, 6.0, 'K']

Accessing list elements:

print(lst[0])     # 'Hello'

print(lst[-1])    # 'K' (last item)

print(type(lst[2]))  # <class 'int'>

c) tuple

• Ordered, immutable (cannot change once created).
• Can contain elements of different data types.

Creating a tuple:

tpl = ('Hello', 'Swathi')

tpl2 = tuple([60.5, 27, 2025, 11, "Earth", "K"])

Nested tuple:

tpl3 = (tpl2, tpl)

Accessing elements:

print(tpl3[0][2])  # 2025

➤ 3. Boolean Type

• Has only two values: True and False
• Used in logical operations, conditions, and comparisons.
• Internally, True is treated as 1 and False as 0.

Example:

print(type(True))   # <class 'bool'>

print(type(False))  # <class 'bool'>

➤ 4. Set

• Unordered collection of unique items.
• Cannot have duplicates.
• Mutable – we can add or remove elements.
• Does not support indexing.

Creating a set:

set1 = set()

set2 = {68.0, 27, 2025, 'K', 'Swathi', 'S'}

Accessing elements:

for i in set2:

    print(i)  # Iterates through set elements

➤ 5. Dictionary

• Stores key-value pairs.
• Unordered, mutable, and does not allow duplicate keys.
• Keys must be immutable (like numbers, strings, or tuples).

Creating a dictionary:

dic = {"name": "Chinnu", "age": 2, 5: "Swathi"}

Accessing values:

print(dic['name'])     # Chinnu

print(dic[5])          # Swathi

print(dic.get('age'))  # 2

---

Operators

• In Python, an operator is a symbol used to perform arithmetical and logical operations. 
• In other words, an operator can be defined as a symbol used to manipulate the value of an operand. 
• Here, an operand is a value or variable on which the operator performs its task. 
• For example, '+' is a symbol used to perform the mathematical addition operation. Consider the expression a = 10 + 30. 
• Here, variable 'a', values '10' and '30' are known as Operands, and the symbols '=' and '+' are known as Operators.

Types of Operators in Python

In Python, there is a rich set of operators, and they are classified as follows.

• Arithmetic Operators ( +, -, *, /, %, **, // ) 
• Assignment Operators ( =, +=, -=, *=, /=, %=, **=, //= ) 
• Comparison Operators ( <, <=, >, >=, ==, != ) 
• Logical Operators ( and, or, not ) 
• Identity Operators ( is, is not ) 
• Membership Operators ( in, not in )

Arithmetic Operators 

• In Python, the arithmetic operators are the operators used to perform a basic arithmetic operation between two variables or two values. 
• The following table presents the list of arithmetic operations in Python along with their description. 
• To understand the example, let's consider two variables, a with value 10 and b with value 3.

Operator	Meaning	Description	Example
+	Addition	Adds the values on both sides of the operator	a + b = 13
-	Subtraction	Subtracts the right-hand operand from the left-hand operand	a - b = 7
*	Multiplication	Multiply values on both sides of the operator	a * b = 30
/	Division	Divides the left-hand operand by the right-hand operand	a / b = 3.33
%	Modulus	Returns the remainder of the division of the left operand by the right operand	a % b = 1
**	Exponentiation	Raises the left operand to the power of the right operand	a ** b = 100000
//	Floor Division	Divides and returns the largest whole number less than or equal to the result	a // b = 3

Example - Arithmetic Operators in Python

a = 10

b = 3

print(f"a + b = {a + b}")    # Addition

print(f"a - b = {a - b}")    # Subtraction

print(f"a * b = {a * b}")    # Multiplication

print(f"a / b = {a / b}")    # Division

print(f"a % b = {a % b}")    # Modulus

print(f"a ** b = {a ** b}")  # Exponentiation

print(f"a // b = {a // b}")  # Floor Division

Output:

a + b = 13

a - b = 7

a * b = 30

a / b = 3.3333333333333335

a % b = 1

a ** b = 1000

a // b = 3

Assignment Operators

• In Python, the assignment operators are the operators used to assign the right-hand side value to the left-hand side variable. 
• The following table presents the list of assignment operations in Python along with their description.

Operator	Meaning	Description	Example
=	Assignment	Assigns the value on the right to the variable on the left	x = 5
+=	Add and Assign	Adds the right operand to the left operand and assigns the result to the left operand	x += 3 → x = x + 3
-=	Subtract and Assign	Subtracts the right operand from the left operand and assigns the result to the left operand	x -= 2 → x = x - 2
*=	Multiply and Assign	Multiplies the left operand by the right and assigns the result to the left operand	x *= 4 → x = x * 4
/=	Divide and Assign	Divides the left operand by the right and assigns the result to the left operand	x /= 2 → x = x / 2
%=	Modulus and Assign	Takes modulus using left and right operands, assigns the result to the left operand	x %= 3 → x = x % 3
**=	Exponent and Assign	Raises the left operand to the power of the right operand and assigns it to the left operand	x **= 2 → x = x ** 2
//=	Floor Divide and Assign	Performs floor division and assigns the result to the left operand	x //= 3 → x = x // 3

Example:

a = 10

b = 3

a += b

print(f"a += b => {a}")  # a = 13

a -= b

print(f"a -= b => {a}")  # a = 10

a **= b

print(f"a **= b => {a}")  # a = 1000 

a //= b

print(f"a //= b => {a}")  # a = 333

a *= b

print(f"a *= b => {a}")  # a = 999

a /= b

print(f"a /= b => {a}")  # a = 333.0

a %= b

print(f"a %= b => {a}")  # a = 0.0

a **= b

print(f"a **= b => {a}")  # a = 0.0

Output:

a += b => 13

a -= b => 10

a **= b => 1000

a //= b => 333

a *= b => 999

a /= b => 333.0

a %= b => 0.0

a **= b => 0.0

Comparison Operators

• In Python, the comparison operators are used to compare two values. 
• In other words, comparison operators are used to check the relationship between two variables or values. 
• The comparison operators are also known as Relational Operators. 

To understand the example let's consider two variables a with value 10 and b with value 3.

Operator	Meaning	Description	Example	Result
<	Less than	Returns True if the left value is smaller than the right value, else False	a < b	False
<=	Less than or Equal to	Returns True if the left value is smaller than or equal to the right value, else False	a <= b	False
>	Greater than	Returns True if the left value is larger than the right value, else False	a > b	True
>=	Greater than or Equal to	Returns True if the left value is larger than or equal to the right value, else False	a >= b	True
==	Equal to	Returns True if the left value is equal to the right value, else False	a == b	False
!=	Not equal to	Returns True if the left value is not equal to the right value, else False	a != b	True

Example:

a = 10

b = 3

print(a < b)   # False

print(a <= b)  # False

print(a > b)   # True

print(a >= b)  # True

print(a == b)  # False

print(a != b)  # True

Output:

False

False

True

True

False

True

Logical Operators 

• In Python, the logical operators are used to merge multiple conditions into a single condition.
• In Python, the logical operators are used to merge multiple conditions into a single condition. 

Operator	Meaning	Description
and	Logical AND	Returns True if both conditions are True. Otherwise, False.
or	Logical OR	Returns True if at least one condition is True.
not	Logical NOT	Reverses the logical state of the condition.

 

Example:

a = 10

b = 3 

print(a < b and a > c)   # False and False → False

print(a < b or a > c)    # False or False → False

print(not a > b)         # not True → False

Output:

False

False

False

Identity Operators 

• In Python, identity operators are used to comparing the memory locations of two objects or variables.
• The following table presents the list of identity operations in Python along with their description. 

Operator	Meaning	Description
is	Is identical	Returns True if both variables point to the same object in memory.
is not	Is not identical	Returns True if both variables do not point to the same object in memory.

Example: 

a = 10

b = 3

print(a is b)    10 is 3 → False

print(a is not b)    10 is not 3 →True

Output:

False

True

Membership Operators

• In Python, the membership operators are used to test whether a value is present in a sequence.Here the sequence may be String, List, or Tuple.
• The following table presents the list of membership operations in Python along with their description.

Operator	Meaning	Description
in	In	Returns True if the value is found in the given sequence.
not in	Not in	Returns True if the value is not found in the sequence.

Example: 

a = 10

list1 = [1, 5, 10, 15]

print(a in list1)       # True → 10 is in list1

print(a not in list1)   # False → 10 is in list1, so not in is False

Output:

True

False

---

Input and Output Functions

Python uses two main functions to interact with the user:

Input Functions:

The input() function in Python is used to take input from the user during program execution. It always returns the input as a string.

Syntax:

variable = input("Enter your message here: ")

Example:

name = input("Enter your name: ")

print("Hello", name)

Output:

Enter your name: Swathi

Hello Swathi

Output Functions:

The print() function in Python is used to display output to the console. It can print strings, numbers, variables, and even formatted text.

Syntax:

print(object1, object2, ..., sep=' ', end='\n')

sep: defines the separator between printed items (default is space ' ').

end: defines what to print at the end (default is a newline \n).

Example:

print("Hello", "World")              # Hello World

print("A", "B", "C", sep="-")        # A-B-C

print("Hello", end=" ")              

print("Python")                      # Hello Python (on same line)

An f-string is a string literal that is prefixed with f or F. It allows you to embed expressions or variables directly inside the string using curly braces {}.

Syntax:

f"Your text {variable_or_expression}"

Example:

name = "Swathi"

age = 24

print(f"My name is {name} and I am {age} years old.")

Output:

My name is Swathi and I am 24 years old.

The format() function is a string method that inserts values into placeholders {} within a string.

Syntax:

"Your text {} and {}".format(value1, value2)

Example:

name = "Swathi"

age = 24

print("My name is {} and I am {} years old.".format(name, age))

Output:

My name is Swathi and I am 24 years old.

---

Type Conversion

Type conversion means changing the data type of a value from one type to another, like from int to float, or from str to int.

We have two types of type conversion in Python:

1. Implicit Type Conversion (Automatic)

• The Python interpreter automatically converts one data type to another without any user involvement. 

• It happens when you mix different types in an expression.

• Python converts smaller types to larger types to avoid data loss.

Example

x = 5

y = 2.0

z = x + y     # int + float → float

print(z)      # Output: 7.0

print(type(z))  # Output: <class 'float'>

2. Explicit Type Conversion (Type Casting)

• It is done manually by the programmer as per requirement.

• You use built-in functions to convert between types.

Function	Converts to	Example
int(x)	Integer	int("5") → 5
float(x)	Floating-point	float("5") → 5.0
str(x)	String	str(5) → "5"
bool(x)	Boolean	bool(0) → False
list(x)	List	list("abc") → ['a','b','c']
tuple(x)	Tuple	tuple([1,2]) → (1, 2)
set(x)	Set	set([1,2,2]) → {1,2}

Example

a = "100"

b = int(a)        # string to integer

c = float(b)      # integer to float

d = str(c)        # float to string

e = bool(b)       # int to boolean

print("a =", a, type(a))

print("b =", b, type(b))

print("c =", c, type(c))

print("d =", d, type(d))

print("e =", e, type(e))

Output

a = 100 <class 'str'>

b = 100 <class 'int'>

c = 100.0 <class 'float'>

d = 100.0 <class 'str'>

e = True <class 'bool'>

---

Flow of Control

Flow of Control refers to the order in which statements are executed in a Python program.

By default, Python executes code line by line from top to bottom. However, we can change this flow using:

1. Conditional Statements

2. Looping Statements

3. Loop Control Statements

Conditional Statements

Conditional statements allow a program to make decisions and execute specific blocks of code based on whether a condition is True or False.

Statement	Description
if	Executes a block if the condition is true
elif	Else if — checks another condition
else	Executes if all above are false

if statements

The if statement executes a block of code only if the condition is true.

Flowchart:

Syntax:

if condition:

    # Code block (runs if condition is true)

Example:

age = 20

if age >= 18:

    print("You are an adult.")

Output:

You are an adult.

elif statements

elif stands for "else if". It checks another condition if the previous if was False. 

Flowchart:

Syntax:

if condition1:

    # Code block 1

elif condition2:

    # Code block 2

Example:

marks = 75

if marks >= 90:

    print("Grade A")

elif marks >= 70:

    print("Grade B")

Output:

Grade B

else statements

The else block executes if none of the previous conditions are true.

Flowchart:

Syntax:

if condition1:

    # Code block 1

elif condition2:

    # Code block 2

else:

    # Code block 3

Example:

temperature = 15

if temperature > 30:

    print("Hot day")

else:

    print("Cold day")

Output:

Cold day

Looping Statements

Loops are used to execute a block of code repeatedly as long as a specified condition is true. Instead of writing the same code multiple times, you use loops to automate repetition.

for loop

A for loop in Python is used to iterate over a sequence such as a list, tuple, string, or a range of numbers.
It repeats a block of code for each item in the sequence.

Flowchart:

Syntax:

for variable in sequence:
     # Code block (executed for each item)

variable: A temporary name that holds the current item from the sequence.

sequence: A collection (like a list or range()) that the loop will go through.

Example:

Looping through a list

fruits = ["apple", "banana", "cherry"]

for fruit in fruits:

    print(fruit)

Output:

apple

banana

cherry

Using range()

for i in range(5):

    print("Value of i:", i)

Output:

Value of i: 0

Value of i: 1

Value of i: 2

Value of i: 3

Value of i: 4

Looping through String

for letter in "Python":

    print(letter)

Output:

P

y

t

h

o

n

while loop

A while loop repeats a block of code as long as a given condition is true.

Unlike a for loop (which iterates over a sequence), the while loop is used when the number of iterations is not known in advance.

Syntax:

while condition:

    # code block to execute

• The condition is evaluated before each iteration.

• The loop runs as long as the condition is True.

• Make sure to change a variable inside the loop to avoid an infinite loop!

Flowchart:

Example:

count = 5

while count > 0:

    print("Counting down:", count)

    count -= 1

Output

Counting down: 5

Counting down: 4

Counting down: 3

Counting down: 2

Counting down: 1

password = ""

while password != "secret":

    password = input("Enter the password: ")

print("Access granted!")

Output

Enter the password: pass

Enter the password: 1234

Enter the password: secret

Access granted!

Infinite loop

x = 1

while x > 0:

    print("This will run forever!")  # unless x is changed or loop is broken

Always make sure your loop has an exit condition.

Jumping Statements

Jumping statements are used to control the flow of loops — they allow you to:

• Exit a loop early

• Skip an iteration

• Do nothing but satisfy syntax

Statement	Description
break	Exits the loop immediately
continue	Skips current iteration, moves to next
pass	Does nothing, used as a placeholder

break statement

It is used to exit a loop (both for and while) immediately when a condition is met.

Syntax:

break;

Flowchart:

Example:

for i in range(10):

    if i == 5:

        break

    print(i)

Output:

0

1

2

3

4

continue statement

It skips the current iteration and moves to the next one, without executing the rest of the loop body.

Syntax:

continue;

Flowchart:

Example:

for i in range(6):

    if i == 3:

        continue

    print(i)

Output:

0

1

2

4

5

pass statement

Used as a placeholder where a statement is syntactically required but you don’t want to execute any code.

Syntax:

pass;

Flowchart:

Example:

x = 5

if x > 0:

    pass  # Placeholder for future code

print("This runs normally.")

Output:

This runs normally.

---