#python best plotting library

[this-week-in-rust]

This Week in Rust 510

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @ThisWeekInRust on Twitter or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

Updates from Rust Community

Official

Announcing Rust 1.72.0

Change in Guidance on Committing Lockfiles

Cargo changes how arrays in config are merged

Seeking help for initial Leadership Council initiatives

Leadership Council Membership Changes

Newsletters

This Week in Ars Militaris VIII

Project/Tooling Updates

rust-analyzer changelog #196

The First Stable Release of a Memory Safe sudo Implementation

We're open-sourcing the library that powers 1Password's ability to log in with a passkey

ratatui 0.23.0 is released! (official successor of tui-rs)

Zellij 0.38.0: session-manager, plugin infra, and no more offensive session names

Observations/Thoughts

The fastest WebSocket implementation

Rust Malware Staged on Crates.io

ESP32 Standard Library Embedded Rust: SPI with the MAX7219 LED Dot Matrix

A JVM in Rust part 5 - Executing instructions

Compiling Rust for .NET, using only tea and stubbornness!

Ad-hoc polymorphism erodes type-safety

How to speed up the Rust compiler in August 2023

This isn't the way to speed up Rust compile times

Rust Cryptography Should be Written in Rust

Dependency injection in Axum handlers. A quick tour

Best Rust Web Frameworks to Use in 2023

From tui-rs to Ratatui: 6 Months of Cooking Up Rust TUIs

[video] Rust 1.72.0

[video] Rust 1.72 Release Train

Rust Walkthroughs

[series] Distributed Tracing in Rust, Episode 3: tracing basics

Use Rust in shell scripts

A Simple CRUD API in Rust with Cloudflare Workers, Cloudflare KV, and the Rust Router

[video] base64 crate: code walkthrough

Miscellaneous

Interview with Rust and operating system Developer Andy Python

Leveraging Rust in our high-performance Java database

Rust error message to fix a typo

[video] The Builder Pattern and Typestate Programming - Stefan Baumgartner - Rust Linz January 2023

[video] CI with Rust and Gitlab Selfhosting - Stefan Schindler - Rust Linz July 2023

Crate of the Week

This week's crate is dprint, a fast code formatter that formats Markdown, TypeScript, JavaScript, JSON, TOML and many other types natively via Wasm plugins.

Thanks to Martin Geisler for the suggestion!

Please submit your suggestions and votes for next week!

Call for Participation

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

Hyperswitch - add domain type for client secret

Hyperswitch - deserialization error exposes sensitive values in the logs

Hyperswitch - move redis key creation to a common module

mdbook-i18n-helpers - Write tool which can convert translated files back to PO

mdbook-i18n-helpers - Package a language selector

mdbook-i18n-helpers - Add links between translations

Comprehensive Rust - Link to correct line when editing a translation

Comprehensive Rust - Track the number of times the redirect pages are visited

RustQuant - Jacobian and Hessian matrices support.

RustQuant - improve Graphviz plotting of autodiff computational graphs.

RustQuant - bond pricing implementation.

RustQuant - implement cap/floor pricers.

RustQuant - Implement Asian option pricers.

RustQuant - Implement American option pricers.

release-plz - add ability to mark Gitea/GitHub release as draft

zerocopy - CI step "Set toolchain version" is flaky due to network timeouts

zerocopy - Implement traits for tuple types (and maybe other container types?)

zerocopy - Prevent panics statically

zerocopy - Add positive and negative trait impl tests for SIMD types

zerocopy - Inline many trait methods (in zerocopy and in derive-generated code)

datatest-stable - Fix quadratic performance with nextest

Ockam - Use a user-friendly name for the shared services to show it in the tray menu

Ockam - Rename the Port to Address and support such format

Ockam - Ockam CLI should gracefully handle invalid state when initializing

css-inline - Update cssparser & selectors

css-inline - Non-blocking stylesheet resolving

css-inline - Optionally remove all class attributes

If you are a Rust project owner and are looking for contributors, please submit tasks here.

Updates from the Rust Project

366 pull requests were merged in the last week

reassign sparc-unknown-none-elf to tier 3

wasi: round up the size for aligned_alloc

allow MaybeUninit in input and output of inline assembly

allow explicit #[repr(Rust)]

fix CFI: f32 and f64 are encoded incorrectly for cross-language CFI

add suggestion for some #[deprecated] items

add an (perma-)unstable option to disable vtable vptr

add comment to the push_trailing function

add note when matching on tuples/ADTs containing non-exhaustive types

add support for ptr::writes for the invalid_reference_casting lint

allow overwriting ExpnId for concurrent decoding

avoid duplicate large_assignments lints

contents of reachable statics is reachable

do not emit invalid suggestion in E0191 when spans overlap

do not forget to pass DWARF fragment information to LLVM

ensure that THIR unsafety check is done before stealing it

emit a proper diagnostic message for unstable lints passed from CLI

fix races conditions with SyntaxContext decoding

fix waiting on a query that panicked

improve note for the invalid_reference_casting lint

include compiler flags when you break rust;

load include_bytes! directly into an Lrc

make Sharded an enum and specialize it for the single thread case

make rustc_on_unimplemented std-agnostic for alloc::rc

more precisely detect cycle errors from type_of on opaque

point at type parameter that introduced unmet bound instead of full HIR node

record allocation spans inside force_allocation

suggest mutable borrow on read only for-loop that should be mutable

tweak output of to_pretty_impl_header involving only anon lifetimes

use the same DISubprogram for each instance of the same inlined function within a caller

walk through full path in point_at_path_if_possible

warn on elided lifetimes in associated constants (ELIDED_LIFETIMES_IN_ASSOCIATED_CONSTANT)

make RPITITs capture all in-scope lifetimes

add stable for Constant in smir

add generics_of to smir

add smir predicates_of

treat StatementKind::Coverage as completely opaque for SMIR purposes

do not convert copies of packed projections to moves

don't do intra-pass validation on MIR shims

MIR validation: reject in-place argument/return for packed fields

disable MIR SROA optimization by default

miri: automatically start and stop josh in rustc-pull/push

miri: fix some bad regex capture group references in test normalization

stop emitting non-power-of-two vectors in (non-portable-SIMD) codegen

resolve: stop creating NameBindings on every use, create them once per definition instead

fix a pthread_t handle leak

when terminating during unwinding, show the reason why

avoid triple-backtrace due to panic-during-cleanup

add additional float constants

add ability to spawn Windows process with Proc Thread Attributes | Take 2

fix implementation of Duration::checked_div

hashbrown: allow serializing HashMaps that use a custom allocator

hashbrown: change & to &mut where applicable

hashbrown: simplify Clone by removing redundant guards

regex-automata: fix incorrect use of Aho-Corasick's "standard" semantics

cargo: Very preliminary MSRV resolver support

cargo: Use a more compact relative-time format

cargo: Improve TOML parse errors

cargo: add support for target.'cfg(..)'.linker

cargo: config: merge lists in precedence order

cargo: create dedicated unstable flag for asymmetric-token

cargo: set MSRV for internal packages

cargo: improve deserialization errors of untagged enums

cargo: improve resolver version mismatch warning

cargo: stabilize --keep-going

cargo: support dependencies from registries for artifact dependencies, take 2

cargo: use AND search when having multiple terms

rustdoc: add unstable --no-html-source flag

rustdoc: rename typedef to type alias

rustdoc: use unicode-aware checks for redundant explicit link fastpath

clippy: new lint: implied_bounds_in_impls

clippy: new lint: reserve_after_initialization

clippy: arithmetic_side_effects: detect division by zero for Wrapping and Saturating

clippy: if_then_some_else_none: look into local initializers for early returns

clippy: iter_overeager_cloned: detect .cloned().all() and .cloned().any()

clippy: unnecessary_unwrap: lint on .as_ref().unwrap()

clippy: allow trait alias DefIds in implements_trait_with_env_from_iter

clippy: fix "derivable_impls: attributes are ignored"

clippy: fix tuple_array_conversions lint on nightly

clippy: skip float_cmp check if lhs is a custom type

rust-analyzer: diagnostics for 'while let' loop with label in condition

rust-analyzer: respect #[allow(unused_braces)]

Rust Compiler Performance Triage

A fairly quiet week, with improvements exceeding a small scattering of regressions. Memory usage and artifact size held fairly steady across the week, with no regressions or improvements.

Triage done by @simulacrum. Revision range: d4a881e..cedbe5c

2 Regressions, 3 Improvements, 2 Mixed; 0 of them in rollups 108 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

Create a Testing sub-team

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

RFCs

No RFCs entered Final Comment Period this week.

Tracking Issues & PRs

[disposition: merge] Stabilize PATH option for --print KIND=PATH

[disposition: merge] Add alignment to the NPO guarantee

New and Updated RFCs

[new] Special-cased performance improvement for Iterator::sum on Range<u*> and RangeInclusive<u*>

[new] Cargo Check T-lang Policy

Call for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization. The following RFCs would benefit from user testing before moving forward:

No RFCs issued a call for testing this week.

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Upcoming Events

Rusty Events between 2023-08-30 - 2023-09-27 🦀

Virtual

2023-09-05 | Virtual (Buffalo, NY, US) | Buffalo Rust Meetup

Buffalo Rust User Group, First Tuesdays

2023-09-05 | Virtual (Munich, DE) | Rust Munich

Rust Munich 2023 / 4 - hybrid

2023-09-06 | Virtual (Indianapolis, IN, US) | Indy Rust

Indy.rs - with Social Distancing

2023-09-12 - 2023-09-15 | Virtual (Albuquerque, NM, US) | RustConf

RustConf 2023

2023-09-12 | Virtual (Dallas, TX, US) | Dallas Rust

Second Tuesday

2023-09-13 | Virtual (Boulder, CO, US) | Boulder Elixir and Rust

Monthly Meetup

2023-09-13 | Virtual (Cardiff, UK)| Rust and C++ Cardiff

The unreasonable power of combinator APIs

2023-09-14 | Virtual (Nuremberg, DE) | Rust Nuremberg

Rust Nürnberg online

2023-09-20 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Rust Study/Hack/Hang-out

2023-09-21 | Virtual (Charlottesville, NC, US) | Charlottesville Rust Meetup

Crafting Interpreters in Rust Collaboratively

2023-09-21 | Lehi, UT, US | Utah Rust

Real Time Multiplayer Game Server in Rust

2023-09-21 | Virtual (Linz, AT) | Rust Linz

Rust Meetup Linz - 33rd Edition

2023-09-25 | Virtual (Dublin, IE) | Rust Dublin

How we built the SurrealDB Python client in Rust.

Asia

2023-09-06 | Tel Aviv, IL | Rust TLV

RustTLV @ Final - September Edition

Europe

2023-08-30 | Copenhagen, DK | Copenhagen Rust Community

Rust metup #39 sponsored by Fermyon

2023-08-31 | Augsburg, DE | Rust Meetup Augsburg

Augsburg Rust Meetup #2

2023-09-05 | Munich, DE + Virtual | Rust Munich

Rust Munich 2023 / 4 - hybrid

2023-09-14 | Reading, UK | Reading Rust Workshop

Reading Rust Meetup at Browns

2023-09-19 | Augsburg, DE | Rust - Modern Systems Programming in Leipzig

Logging and tracing in Rust

2023-09-20 | Aarhus, DK | Rust Aarhus

Rust Aarhus - Rust and Talk at Concordium

2023-09-21 | Bern, CH | Rust Bern

Third Rust Bern Meetup

North America

2023-09-05 | Chicago, IL, US | Deep Dish Rust

Rust Happy Hour

2023-09-06 | Bellevue, WA, US | The Linux Foundation

Rust Global

2023-09-12 - 2023-09-15 | Albuquerque, NM, US + Virtual | RustConf

RustConf 2023

2023-09-12 | New York, NY, US | Rust NYC

A Panel Discussion on Thriving in a Rust-Driven Workplace

2023-09-12 | Minneapolis, MN, US | Minneapolis Rust Meetup

Minneapolis Rust Meetup Happy Hour

2023-09-14 | Seattle, WA, US | Seattle Rust User Group Meetup

Seattle Rust User Group - August Meetup

2023-09-19 | San Francisco, CA, US | San Francisco Rust Study Group

Rust Hacking in Person

2023-09-21 | Nashville, TN, US | Music City Rust Developers

Rust on the web! Get started with Leptos

2023-09-26 | Pasadena, CA, US | Pasadena Thursday Go/Rust

Monthly Rust group

2023-09-27 | Austin, TX, US | Rust ATX

Rust Lunch - Fareground

Oceania

2023-09-13 | Perth, WA, AU | Rust Perth

Rust Meetup 2: Lunch & Learn

2023-09-19 | Christchurch, NZ | Christchurch Rust Meetup Group

Christchurch Rust meetup meeting

2023-09-26 | Canberra, ACT, AU | Rust Canberra

September Meetup

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

In [other languages], I could end up chasing silly bugs and waste time debugging and tracing to find that I made a typo or ran into a language quirk that gave me an unexpected nil pointer. That situation is almost non-existent in Rust, it's just me and the problem. Rust is honest and upfront about its quirks and will yell at you about it before you have a hard to find bug in production.

– dannersy on Hacker News

Thanks to Kyle Strand for the suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, andrewpollack, U007D, kolharsam, joelmarcey, mariannegoldin, bennyvasquez.

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

[souhaillaghchimdev]

Data Analysis and Visualization Using Programming Techniques

Data analysis and visualization are crucial skills in today’s data-driven world. With programming, we can extract insights, uncover patterns, and present data in a meaningful way. This post explores how developers and analysts can use programming techniques to analyze and visualize data efficiently.

Why Data Analysis and Visualization Matter

Better Decisions: Informed decisions are backed by data and its interpretation.

Communication: Visualizations make complex data more accessible and engaging.

Pattern Recognition: Analysis helps discover trends, anomalies, and correlations.

Performance Tracking: Measure progress and identify areas for improvement.

Popular Programming Languages for Data Analysis

Python: Rich in libraries like Pandas, NumPy, Matplotlib, Seaborn, and Plotly.

R: Designed specifically for statistics and visualization.

JavaScript: Great for interactive, web-based data visualizations using D3.js and Chart.js.

SQL: Essential for querying and manipulating data from databases.

Basic Workflow for Data Analysis

Collect Data: From CSV files, APIs, databases, or web scraping.

Clean Data: Handle missing values, duplicates, and inconsistent formatting.

Explore Data: Use descriptive statistics and visual tools to understand the dataset.

Analyze Data: Apply transformations, groupings, and statistical techniques.

Visualize Results: Create charts, graphs, and dashboards.

Interpret & Share: Draw conclusions and present findings to stakeholders.

Python Example: Data Analysis and Visualization

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Load data data = pd.read_csv('data.csv') # Analyze print(data.describe()) # Visualize sns.histplot(data['sales'], bins=10) plt.title('Sales Distribution') plt.xlabel('Sales') plt.ylabel('Frequency') plt.show()

Common Visualization Types

Bar Chart: Comparing categories

Line Chart: Time series analysis

Pie Chart: Proportional distribution

Scatter Plot: Correlation and clustering

Heatmap: Matrix-like data comparisons

Best Practices for Data Visualization

Keep it simple and avoid clutter.

Use colors to enhance, not distract.

Label axes, legends, and titles clearly.

Choose the right chart type for your data.

Ensure your visualizations are responsive and interactive if web-based.

Useful Libraries and Tools

Pandas & NumPy: Data manipulation

Matplotlib & Seaborn: Static visualizations

Plotly & Dash: Interactive dashboards

D3.js: Custom web-based visualizations

Power BI & Tableau: Business-level dashboarding (non-programming)

Real-World Use Cases

Sales Analysis: Visualize revenue trends and top-selling products.

Marketing Campaigns: Analyze click-through rates and conversions.

Healthcare: Monitor patient data, diagnostics, and treatment outcomes.

Finance: Analyze stock performance and predict market trends.

Conclusion

Combining data analysis with programming unlocks powerful insights and allows you to communicate results effectively. Whether you’re a beginner or an experienced developer, mastering data visualization techniques will significantly enhance your ability to solve problems and tell compelling data stories.

[krupa192]

Essential Skills Every Data Scientist Must Learn in 2025 

The world of data science is evolving faster than ever, and staying ahead of the curve in 2025 requires a strategic approach to skill development. As businesses rely more on data-driven decision-making, data scientists must continuously refine their expertise to remain competitive in the field. Whether you're an aspiring data scientist or an experienced professional, mastering the right skills is crucial for long-term success. 

1. Mastering Programming Languages 

At the core of data science lies programming. Proficiency in languages like Python and R is essential for handling data, building models, and deploying solutions. Python continues to dominate due to its versatility and rich ecosystem of libraries such as Pandas, NumPy, Scikit-learn, and TensorFlow. 

Key Programming Skills to Focus On: 

Data manipulation and analysis using Pandas and NumPy 

Implementing machine learning models with Scikit-learn 

Deep learning and AI development with TensorFlow and PyTorch 

Statistical computing and data visualization with R 

2. Strong Foundation in Statistics and Probability 

A deep understanding of statistics and probability is non-negotiable for data scientists. These concepts form the backbone of data analysis, helping professionals derive meaningful insights and create predictive models. 

Why It’s Important: 

Enables accurate hypothesis testing 

Supports decision-making with probability distributions 

Strengthens machine learning model evaluation 

3. Expertise in Machine Learning and Deep Learning 

With AI and automation becoming more prevalent, machine learning and deep learning skills are in high demand. Data scientists need to stay updated with advanced techniques to develop intelligent models that can solve complex problems. 

Key Areas to Focus On: 

Supervised and unsupervised learning techniques 

Reinforcement learning and neural networks 

Hyperparameter tuning and model optimization 

Understanding AI ethics and bias mitigation 

For those looking to upskill in machine learning, the Machine Learning Course in Kolkata offers practical, hands-on training. This program is designed to equip learners with the latest industry knowledge and techniques to advance their careers. 

4. Data Wrangling and Preprocessing Skills 

Data in its raw form is often messy and incomplete. Being able to clean, structure, and preprocess data is a vital skill that every data scientist must master. 

Essential Data Wrangling Skills: 

Handling missing and inconsistent data 

Normalization and standardization techniques 

Feature selection and engineering for improved model performance 

5. Knowledge of Big Data Technologies 

The rise of big data has made it essential for data scientists to work with tools and frameworks designed for handling massive datasets efficiently. 

Tools Worth Learning: 

Apache Spark for large-scale data processing 

Hadoop for distributed storage and computation 

Google BigQuery for cloud-based data analytics 

6. Data Visualization and Storytelling 

Turning raw data into actionable insights requires effective communication. Data scientists should be adept at using visualization tools to present findings in a compelling and understandable way. 

Best Practices: 

Choose the right visualization type (e.g., bar charts, scatter plots, heatmaps) 

Keep charts clean and easy to interpret 

Use tools like Matplotlib, Seaborn, Tableau, and Power BI 

7. Cloud Computing and MLOps 

Cloud platforms are transforming the way data scientists build and deploy models. A strong understanding of cloud-based tools and MLOps practices is crucial in modern data science workflows. 

What You Should Learn: 

Deploying ML models on cloud platforms like AWS, Google Cloud, and Azure 

Implementing MLOps for model lifecycle management 

Using Docker and Kubernetes for scalable deployments 

8. Domain Knowledge and Business Acumen 

While technical skills are critical, understanding the industry you work in can set you apart. A data scientist with domain expertise can develop more impactful and relevant solutions. 

Why It Matters: 

Helps tailor data-driven strategies to specific industries 

Improves collaboration with stakeholders 

Enhances problem-solving with business context 

9. Soft Skills: Critical Thinking and Effective Communication 

Technical know-how is just one part of the equation. Data scientists must also possess strong analytical and problem-solving skills to interpret data effectively and communicate findings to both technical and non-technical audiences. 

Key Soft Skills to Develop: 

Clear and concise storytelling through data 

Adaptability to emerging technologies and trends 

Collaboration with cross-functional teams 

10. Ethics in AI and Data Governance 

As AI systems influence more aspects of daily life, ethical considerations and regulatory compliance have become increasingly important. Data scientists must ensure fairness, transparency, and adherence to privacy regulations like GDPR and CCPA. 

Best Practices for Ethical AI: 

Identifying and mitigating bias in machine learning models 

Implementing robust data privacy and security measures 

Promoting transparency in AI decision-making processes 

Final Thoughts 

In the ever-changing landscape of data science, continuous learning is the key to staying relevant. By mastering these essential skills in 2025, data scientists can future-proof their careers and contribute to the advancement of AI-driven innovations. If you're looking to gain practical expertise, the Data Science Program offers industry-focused training that prepares you for real-world challenges. 

Whether you're just starting or looking to refine your skills, investing in these areas will keep you ahead of the curve in the dynamic world of data science. 

[slacourses]

What are the top Python libraries for data science in 2025? Get Best Data Analyst Certification Course by SLA Consultants India

Python's extensive ecosystem of libraries has been instrumental in advancing data science, offering tools for data manipulation, visualization, machine learning, and more. As of 2025, several Python libraries have emerged as top choices for data scientists:

1. NumPy

NumPy remains foundational for numerical computations in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on them. Its efficiency and performance make it indispensable for data analysis tasks. Data Analyst Course in Delhi

2. Pandas

Pandas is essential for data manipulation and analysis. It offers data structures like DataFrames, which allow for efficient handling and analysis of structured data. With tools for reading and writing data between in-memory structures and various formats, Pandas simplifies data preprocessing and cleaning.

3. Matplotlib

For data visualization, Matplotlib is a versatile library that enables the creation of static, animated, and interactive plots. It supports various plot types, including line plots, scatter plots, and histograms, making it a staple for presenting data insights.

4. Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for drawing attractive statistical graphics. It simplifies complex visualization tasks and integrates seamlessly with Pandas data structures, enhancing the aesthetic appeal and interpretability of plots. Data Analyst Training Course in Delhi

5. Plotly

Plotly is renowned for creating interactive and web-ready plots. It offers a wide range of chart types, including 3D plots and contour plots, and is particularly useful for dashboards and interactive data applications.

6. Scikit-Learn

Scikit-Learn is a comprehensive library for machine learning, providing simple and efficient tools for data mining and data analysis. It supports various machine learning tasks, including classification, regression, clustering, and dimensionality reduction, and is built on NumPy, SciPy, and Matplotlib. Data Analyst Training Institute in Delhi

7. Dask

Dask is a parallel computing library that scales Python code from multi-core local machines to large distributed clusters. It integrates seamlessly with libraries like NumPy and Pandas, enabling scalable and efficient computation on large datasets.

8. PyMC

PyMC is a probabilistic programming library for Bayesian statistical modeling and probabilistic machine learning. It utilizes advanced Markov chain Monte Carlo and variational fitting algorithms, making it suitable for complex statistical modeling.

9. TensorFlow and PyTorch

Both TensorFlow and PyTorch are leading libraries for deep learning. They offer robust tools for building and training neural networks and have extensive communities supporting their development and application in various domains, from image recognition to natural language processing. Online Data Analyst Course in Delhi

10. NLTK and SpaCy

For natural language processing (NLP), NLTK and SpaCy are prominent libraries. NLTK provides a wide range of tools for text processing, while SpaCy is designed for industrial-strength NLP, offering fast and efficient tools for tasks like tokenization, parsing, and entity recognition.

These libraries collectively empower data scientists to efficiently process, analyze, and visualize data, facilitating the extraction of meaningful insights and the development of predictive models.

Data Analyst Training Course Modules Module 1 - Basic and Advanced Excel With Dashboard and Excel Analytics Module 2 - VBA / Macros - Automation Reporting, User Form and Dashboard Module 3 - SQL and MS Access - Data Manipulation, Queries, Scripts and Server Connection - MIS and Data Analytics Module 4 - MS Power BI | Tableau Both BI & Data Visualization Module 5 - Free Python Data Science | Alteryx/ R Programing Module 6 - Python Data Science and Machine Learning - 100% Free in Offer - by IIT/NIT Alumni Trainer

Regarding the "Best Data Analyst Certification Course by SLA Consultants India," I couldn't find specific information on such a course in the provided search results. For the most accurate and up-to-date details, I recommend visiting SLA Consultants India's official website or contacting them directly to inquire about their data analyst certification offerings. For more details Call: +91-8700575874 or Email: hr@slaconsultantsindia.com

[softcrayons19]

Python Libraries and Their Relevance: The Power of Programming

Python has emerged as one of the most popular programming languages due to its simplicity, versatility, and an extensive collection of libraries that make coding easier and more efficient. Whether you are a beginner or an experienced developer, Python’s libraries help you streamline processes, automate tasks, and implement complex functionalities with minimal effort. If you are looking for the best course to learn Python and its libraries, understanding their importance can help you make an informed decision. In this blog, we will explore the significance of Python libraries and their applications in various domains.

Understanding Python Libraries

A Python library is a collection of modules and functions that simplify coding by providing pre-written code snippets. Instead of writing everything from scratch, developers can leverage these libraries to speed up development and ensure efficiency. Python libraries cater to diverse fields, including data science, artificial intelligence, web development, automation, and more.

Top Python Libraries and Their Applications

1. NumPy (Numerical Python)

NumPy is a fundamental library for numerical computing in Python. It provides support for multi-dimensional arrays, mathematical functions, linear algebra, and more. It is widely used in data analysis, scientific computing, and machine learning.

Relevance:

Efficient handling of large datasets

Used in AI and ML applications

Provides powerful mathematical functions

2. Pandas

Pandas is an essential library for data manipulation and analysis. It provides data structures like DataFrame and Series, making it easy to analyze, clean, and process structured data.

Relevance:

Data preprocessing in machine learning

Handling large datasets efficiently

Time-series analysis

3. Matplotlib and Seaborn

Matplotlib is a plotting library used for data visualization, while Seaborn is built on top of Matplotlib, offering advanced visualizations with attractive themes.

Relevance:

Creating meaningful data visualizations

Statistical data representation

Useful in exploratory data analysis (EDA)

4. Scikit-Learn

Scikit-Learn is one of the most popular libraries for machine learning. It provides tools for data mining, analysis, and predictive modeling.

Relevance:

Implementing ML algorithms with ease

Classification, regression, and clustering techniques

Model evaluation and validation

5. TensorFlow and PyTorch

These are the leading deep learning libraries. TensorFlow, developed by Google, and PyTorch, developed by Facebook, offer powerful tools for building and training deep neural networks.

Relevance:

Used in artificial intelligence and deep learning

Supports large-scale machine learning applications

Provides flexibility in model building

6. Requests

The Requests library simplifies working with HTTP requests in Python. It is widely used for web scraping and API integration.

Relevance:

Fetching data from web sources

Simplifying API interactions

Automating web-based tasks

7. BeautifulSoup

BeautifulSoup is a library used for web scraping and extracting information from HTML and XML files.

Relevance:

Extracting data from websites

Web scraping for research and automation

Helps in SEO analysis and market research

8. Flask and Django

Flask and Django are web development frameworks used for building dynamic web applications.

Relevance:

Flask is lightweight and best suited for small projects

Django is a full-fledged framework used for large-scale applications

Both frameworks support secure and scalable web development

9. OpenCV

OpenCV (Open Source Computer Vision Library) is widely used for image processing and computer vision tasks.

Relevance:

Face recognition and object detection

Image and video analysis

Used in robotics and AI-driven applications

10. PyGame

PyGame is used for game development and creating multimedia applications.

Relevance:

Developing interactive games

Building animations and simulations

Used in educational game development

Why Python Libraries Are Important?

Python libraries provide ready-to-use functions, making programming more efficient and less time-consuming. Here’s why they are crucial:

Time-Saving: Reduces the need for writing extensive code.

Optimized Performance: Many libraries are optimized for speed and efficiency.

Wide Community Support: Popular libraries have strong developer communities, ensuring regular updates and bug fixes.

Cross-Domain Usage: From AI to web development, Python libraries cater to multiple domains.

Enhances Learning Curve: Learning libraries simplifies the transition from beginner to expert in Python programming.

ConclusionPython libraries have revolutionized the way developers work, making programming more accessible and efficient. Whether you are looking for data science, AI, web development, or automation, Python libraries provide the tools needed to excel. If you aspire to become a skilled Python developer, investing in the best course can give you the competitive edge required in today’s job market. Start your learning journey today and use the full potential of Python programming.

[ghumledunia]

Data Visualization Techniques for Research Papers: A PhD Student’s Guide to Making Data Speak 📊📡

So, you’ve got mountains of data—numbers, statistics, relationships, and trends—but now comes the real challenge: how do you make your research understandable, compelling, and impactful?

Choosing the wrong visualization can distort findings, mislead readers, or worse—get your paper rejected! So, just dive into the best data visualization techniques for research papers and how you, as a PhD student, can use them effectively.

1. Why Data Visualization Matters in Research 📢

A well-designed visualization can:

✔ Simplify complex information – Because nobody wants to decipher raw numbers in a table.

✔ Enhance reader engagement – A compelling graph draws attention instantly.

✔ Highlight patterns and relationships – Trends and outliers pop out visually.

✔ Improve clarity for reviewers and audiences – Clear figures = stronger impact = better chances of acceptance!

🚀 Pro Tip: Journals love high-quality, well-labeled figures. If your visualizations are messy, unclear, or misleading, expect reviewer pushback.

2. Choosing the Right Chart for Your Data 📊

Different types of data require different types of visualizations. Here’s a quick guide to choosing the right chart based on your research data type.

A. Comparing Data? Use Bar or Column Charts 📊

If your research compares multiple categories (e.g., experimental vs. control groups, survey responses, etc.), bar charts work best.

✔ Vertical Bar Charts: Great for categorical data (e.g., “Number of Published Papers per Year”).

✔ Horizontal Bar Charts: Ideal when comparing long category names (e.g., “Funding Received by Research Institutions”).

🚀 Tool Tip: Use Seaborn, Matplotlib (Python), ggplot2 (R), or Excel to create polished bar charts.

B. Showing Trends Over Time? Use Line Charts 📈

For datasets where trends evolve over time (e.g., "Temperature Change Over Decades" or "Citation Growth of AI Research"), line charts provide a clear visual progression.

✔ Single-line charts: Track changes in one dataset.

✔ Multi-line charts: Compare trends across different variables.

🚀 Tool Tip: Matplotlib (Python) and ggplot2 (R) offer excellent support for customizable time-series visualizations.

C. Representing Parts of a Whole? Use Pie Charts (But Carefully) 🥧

Pie charts show proportions but should be used sparingly. If your data has more than 4-5 categories, use a bar chart instead—it’s much easier to read!

✔ Best for: Showing percentages in a dataset (e.g., "Distribution of Research Funding Sources").

✔ Avoid: Using pie charts when categories are too similar in size—they become hard to interpret.

🚀 Tool Tip: If you must use pie charts, D3.js (JavaScript) offers interactive, dynamic versions that work great for online research papers.

D. Finding Relationships in Data? Use Scatter Plots or Bubble Charts 🔄

Scatter plots are your best friend when showing correlations and relationships between two variables (e.g., “Impact of Sleep on Research Productivity”).

✔ Scatter Plots: Show correlations between two numeric variables.

✔ Bubble Charts: Add a third dimension by scaling the dots based on another variable (e.g., “GDP vs. Life Expectancy vs. Population Size”).

🚀 Tool Tip: Python's Seaborn library provides beautiful scatter plots with regression trend lines.

E. Visualizing Large-Scale Networks? Use Graphs & Network Diagrams 🌐

For research in social sciences, computer networks, genomics, or AI, network graphs provide insights into complex relationships.

✔ Nodes & Edges Graphs: Perfect for citation networks, neural networks, or gene interactions.

✔ Force-directed Graphs: Ideal for clustering related data points.

🚀 Tool Tip: Gephi, Cytoscape, and NetworkX (Python) are great tools for generating network graphs.

F. Displaying Hierarchical Data? Use Tree Maps or Sankey Diagrams 🌳

If your research involves nested structures (e.g., "Classification of Machine Learning Algorithms" or "Breakdown of Research Funding"), tree maps or Sankey diagrams offer a clear representation of hierarchical relationships.

✔ Tree Maps: Great for showing proportions within categories.

✔ Sankey Diagrams: Ideal for visualizing flow data (e.g., "Energy Transfer Between Ecosystems").

🚀 Tool Tip: Try D3.js (JavaScript) or Tableau for interactive tree maps and Sankey visualizations.

3. Best Practices for Data Visualization in Research Papers 📑

Now that you know which charts to use, let’s talk about how to format them for academic papers.

✅ 1. Label Everything Clearly

Your axes, titles, and legends should be self-explanatory—don't make readers guess what they’re looking at.

✅ 2. Use Color Intelligently

🚫 Bad: Neon rainbow colors that make your graph look like a unicorn exploded.

✅ Good: Use a consistent color scheme with high contrast for clarity.

🚀 Tool Tip: Use color palettes like ColorBrewer for research-friendly color schemes.

✅ 3. Keep It Simple & Avoid Chart Junk

Less is more. Avoid excessive gridlines, 3D effects, or unnecessary labels that clutter the visualization.

✅ 4. Use Statistical Annotations Where Needed

If you’re presenting significant findings, annotate your charts with p-values, regression lines, or confidence intervals for clarity.

🚀 Pro Tip: If you're new to coding, Tableau or Excel are the fastest ways to create polished graphs without programming.

Final Thoughts: Make Your Research Stand Out With Data Visualization 🚀

Strong data visualization doesn’t just make your research look pretty—it makes your findings more impactful. Choosing the right chart, formatting it correctly, and using the best tools can turn complex data into clear insights.

📌 Choose the right visualization for your data.

📌 Label and format your charts correctly.

📌 Use colors and statistical annotations wisely.

📌 Avoid unnecessary clutter—keep it simple!

🚀 Need expert help formatting your research visuals? Our Market Insight Solutions team can assist with professional data visualization, statistical analysis, and thesis formatting to ensure your research stands out.

💡 We will make your research visually compelling and publication-ready! 💡

[greatonlinetrainingsposts]

Integrating Python and SAS: A Powerful Combination for Data Science

The demand for data-driven decision-making is growing rapidly, and professionals need the best tools to analyze, visualize, and process data efficiently. SAS (Statistical Analysis System) has long been a leader in statistical analysis and business intelligence, offering robust capabilities for structured data processing. On the other hand, Python has become the go-to programming language for data science, machine learning, and AI applications due to its flexibility and extensive libraries.

By integrating SAS and Python, businesses can leverage the best of both worlds, combining SAS’s structured analytical power with Python’s data science capabilities. This integration is transforming industries by enabling deeper insights, automation, and enhanced decision-making. Whether you're a data analyst, scientist, or business leader, understanding how to connect these two powerful platforms can open up new opportunities for innovation and efficiency.

Why Integrate Python with SAS?

Python and SAS offer distinct advantages that, when combined, create a powerful analytics ecosystem.

Key Advantages of SAS

Structured Data Processing: SAS provides a highly efficient environment for handling large datasets, ensuring structured data processing with reliability and accuracy.

Statistical Modeling: SAS includes advanced statistical analysis tools that are widely used in industries like finance, healthcare, and government analytics.

Enterprise-Grade Security: SAS is known for its robust security features, making it a preferred choice for organizations dealing with sensitive data.

Key Advantages of Python

Flexibility & Open-Source Ecosystem: Python’s extensive libraries like Pandas, NumPy, TensorFlow, and Scikit-learn make it a versatile choice for data science and AI applications.

Advanced Machine Learning Capabilities: Python excels in deep learning, natural language processing (NLP), and predictive analytics.

Visualization & Reporting: Libraries like Matplotlib and Seaborn allow users to create interactive and insightful visual reports.

How Integration Enhances Data Science

By combining the strengths of SAS and Python, businesses can:

Automate Workflows: Use Python scripts to preprocess data, then run statistical models in SAS.

Enhance Analytics Capabilities: Integrate machine learning algorithms in Python with SAS’s statistical tools for deeper insights.

Optimize Decision-Making: Leverage both structured SAS data and unstructured data sources processed through Python for a holistic analytical approach.

For professionals looking to master this integration, SAS Programming Tutorial resources provide step-by-step guidance on leveraging Python with SAS efficiently.

How Python and SAS Work Together

There are several ways to integrate SAS and Python, depending on business needs and technical requirements.

1. SASPy – Python Library for SAS

SASPy is an open-source Python package that allows users to connect to SAS and run SAS code within Python scripts. It bridges the gap between the two platforms by enabling:

Direct execution of SAS commands within Python.

Import and manipulation of SAS datasets in Python environments.

Seamless interaction between SAS procedures and Python functions.

This method is ideal for data scientists who prefer coding in Python but still want to leverage SAS’s structured analytics capabilities.

2. Jupyter Notebook with SAS Kernel

Jupyter Notebook is a widely used tool in the data science community. By installing the SAS Kernel, users can:

Write and execute SAS code directly in Jupyter.

Combine Python and SAS scripts within the same document.

Create interactive data visualizations using Python’s powerful plotting libraries.

This integration is particularly useful for researchers and analysts who require a collaborative, interactive environment for data exploration and reporting.

3. Using REST APIs for SAS Viya

SAS Viya is a cloud-based analytics platform that supports REST APIs, allowing Python applications to communicate with SAS. Businesses can:

Access SAS functions from Python-based dashboards or applications.

Deploy machine learning models built in Python within SAS environments.

Scale big data analytics using cloud-based infrastructure.

This approach is highly beneficial for organizations that require scalable and automated data processing capabilities.

Key Benefits of SAS and Python Integration

By integrating SAS and Python, businesses unlock several advantages:

Enhanced Flexibility

Python’s open-source nature allows for customization and scalability, complementing SAS’s structured analytics.

Advanced Data Processing

Python’s data science libraries enhance SAS’s data handling capabilities, allowing for more complex and faster analysis.

Improved Visualization

Python’s Matplotlib, Seaborn, and Plotly enable richer, interactive reports compared to SAS’s traditional visualization tools.

Powerful Machine Learning

Python’s TensorFlow and Scikit-learn support AI and deep learning, which can be integrated into SAS analytics workflows.

Use Cases of Python and SAS Integration

Many industries benefit from combining SAS and Python for data analytics and decision-making.

1. Healthcare Analytics

Python processes electronic health records (EHRs), while SAS builds predictive models to forecast disease outbreaks.

AI-powered analysis in Python detects patterns in patient data, allowing for early diagnosis and treatment planning.

2. Financial Fraud Detection

Python’s machine learning models analyze transaction patterns for anomalies.

SAS ensures compliance with regulatory standards while improving fraud detection accuracy.

3. Retail Customer Insights

Python clusters customer data for segmentation and personalized marketing.

SAS refines sales strategies based on customer analytics, optimizing demand forecasting.

These real-world applications highlight how Python and SAS together create smarter, data-driven solutions.

Challenges and Best Practices

Despite its advantages, integrating Python with SAS comes with challenges that businesses must address:

1. Version Compatibility

Ensure Python libraries support SAS environments to avoid compatibility issues.

Regularly update SAS and Python packages for smoother integration.

2. Performance Optimization

Use cloud-based SAS Viya for processing large datasets efficiently.

Optimize Python scripts to reduce execution time within SAS environments.

3. Security Concerns

Implement authentication and encryption when transferring data between SAS and Python.

Follow data governance policies to maintain compliance with industry regulations.

Organizations can overcome these challenges by following structured learning paths, such as SAS Tutorial Online, to build expertise in both platforms.

Future of Python and SAS Collaboration

The future of data analytics lies in hybrid approaches that blend different technologies. As AI, big data, and cloud computing continue to evolve, the demand for Python and SAS integration will grow. Businesses that embrace this collaboration will lead in innovation, leveraging real-time analytics, predictive modeling, and automation for better decision-making.

By mastering SAS and Python together, data professionals can build cutting-edge solutions that drive efficiency and business success.

[biopractify]

🐍 How to Learn Python for Bioinformatics? A Beginner’s Guide 🔬💻

Python is one of the most powerful and beginner-friendly programming languages for bioinformatics, making it essential for analyzing genomic data, automating workflows, and developing computational biology tools. If you're from a biotech or life sciences background and want to transition into bioinformatics, learning Python is the perfect first step!

Here’s a step-by-step guide to mastering Python for Bioinformatics from scratch. 🚀

📌 Step 1: Learn Python Basics

Before diving into bioinformatics-specific applications, build a strong foundation in Python programming. Start with:

✅ Basic Syntax – Variables, loops, conditionals ✅ Functions & Modules – Code reusability in Python ✅ Data Structures – Lists, dictionaries, tuples ✅ File Handling – Reading and writing biological data

📚 Best Free Courses to Start:

Python for Beginners – CS50 (Harvard) edX

Python Crash Course W3Schools

Automate the Boring Stuff with Python Udemy

📌 Step 2: Get Comfortable with Bioinformatics Libraries

Once you're comfortable with Python basics, start using bioinformatics-specific libraries to process biological data.

🔬 Key Libraries for Bioinformatics: ✅ Biopython – Sequence analysis, BLAST, FASTA/FASTQ file handling ✅ Pandas – Managing large biological datasets ✅ NumPy – Handling genetic sequence arrays ✅ Matplotlib & Seaborn – Data visualization for bioinformatics ✅ Scikit-learn – Machine learning for genomic analysis

🖥️ Try This Beginner Exercise: Download a FASTA file and use Biopython to parse and analyze a DNA sequence.

from Bio import SeqIO

# Read a FASTA file

for seq_record in SeqIO.parse("example.fasta", "fasta"): print(f"Sequence ID: {seq_record.id}")

print(f"Sequence: {seq_record.seq}")

print(f"Length: {len(seq_record.seq)}")

🔗 Best Resources for Learning BioPython:

Biopython Cookbook Official Docs

Intro to Biopython Course Datacamp

📌 Step 3: Work on Real Bioinformatics Projects

The best way to learn is through hands-on projects. Here are some beginner-friendly projects:

🧬 Project Ideas for Bioinformatics Beginners: ✅ DNA Sequence Analysis – Find GC content, transcription, and reverse complement. ✅ BLAST Automation – Write Python scripts to automate BLAST searches. ✅ Genome Data Visualization – Plot gene expression data using Matplotlib. ✅ Mutation Analysis – Identify and categorize SNPs in genomic sequences. ✅ Machine Learning in Bioinformatics – Train models for disease prediction.

📚 Practice with Real Datasets:

NCBI GenBank (ncbi.nlm.nih.gov)

ENSEMBL Genome Browser (ensembl.org)

Kaggle Bioinformatics Datasets (kaggle.com)

📌 Step 4: Join the Bioinformatics Community

Engaging with other bioinformatics learners and experts will keep you motivated and up to date.

��� Top Bioinformatics Communities: 💬 Biostars – biostars.org (Q&A forum for bioinformatics) 💻 Reddit – r/bioinformatics for discussions and resources 📢 Twitter/X – Follow researchers using #Bioinformatics #CompBio

📌 Step 5: Enroll in Online Courses & Certifications

Once you have some hands-on experience, take structured courses to solidify your knowledge.

📚 Best Courses for Python & Bioinformatics:

Bioinformatics Specialization – Coursera (UC San Diego)

Python for Genomic Data Science – Coursera (Johns Hopkins)

Bioinformatics with Python – BioPractify (biopractify.in)

🚀 Final Thoughts: Start Learning Today!

Python is revolutionizing bioinformatics, and learning it doesn’t require a programming background! Start with Python basics, explore bioinformatics libraries, work on real projects, and engage with the community. With consistent effort, you’ll be analyzing genomic data in no time!

📢 Are you learning Python for bioinformatics? Share your journey in the comments! 👇✨

[learning-code-ficusoft]

How to Build Data Visualizations with Matplotlib, Seaborn, and Plotly

How to Build Data Visualizations with Matplotlib, Seaborn, and Plotly Data visualization is a crucial step in the data analysis process. 

It enables us to uncover patterns, understand trends, and communicate insights effectively. 

Python offers powerful libraries like Matplotlib, Seaborn, and Plotly that simplify the process of creating visualizations. 

In this blog, we’ll explore how to use these libraries to create impactful charts and graphs. 

1. Matplotlib:

 The Foundation of Visualization in Python Matplotlib is one of the oldest and most widely used libraries for creating static, animated, and interactive visualizations in Python. 

While it requires more effort to customize compared to other libraries, its flexibility makes it an indispensable tool.

Key Features: Highly customizable for static plots Extensive support for a variety of chart types Integration with other libraries like Pandas Example: Creating a Simple Line Plot import matplotlib.

import matplotlib.pyplot as plt

# Sample data years = [2010, 2012, 2014, 2016, 2018, 2020] values = [25, 34, 30, 35, 40, 50]

# Creating the plot plt.figure(figsize=(8, 5)) plt.plot(years, values, marker=’o’, linestyle=’-’, color=’b’, label=’Values Over Time’)

# Adding labels and title plt.xlabel(‘Year’) plt.ylabel(‘Value’) plt.title(‘Line Plot Example’) plt.legend() plt.grid(True)

# Show plot plt.show()

2. Seaborn:

 Simplifying Statistical Visualization Seaborn is built on top of Matplotlib and provides an easier and more aesthetically pleasing way to create complex visualizations. 

It’s ideal for statistical data visualization and integrates seamlessly with Pandas. 

Key Features:

 Beautiful default styles and color palettes Built-in support for data frames Specialized plots like heatmaps and pair plots 

Example:

 Creating a Heatmap

import seaborn as sns import numpy as np import pandas as pd

# Sample data np.random.seed(0) data = np.random.rand(10, 12) columns = [f’Month {i+1}’ for i in range(12)] index = [f’Year {i+1}’ for i in range(10)] heatmap_data = pd.DataFrame(data, columns=columns, index=index)

# Creating the heatmap plt.figure(figsize=(12, 8)) sns.heatmap(heatmap_data, annot=True, fmt=”.2f”, cmap=”coolwarm”)

plt.title(‘Heatmap Example’) plt.show()

3. Plotly: 

Interactive and Dynamic Visualizations Plotly is a library for creating interactive visualizations that can be shared online or embedded in web applications. 

It’s especially popular for dashboards and interactive reports. Key Features: Interactive plots by default Support for 3D and geo-spatial visualizations Integration with web technologies like Dash 

Example: 

Creating an Interactive Scatter Plot

import plotly.express as px

# Sample data data = {  ‘Year’: [2010, 2012, 2014, 2016, 2018, 2020],  ‘Value’: [25, 34, 30, 35, 40, 50] }

# Creating a scatter plot df = pd.DataFrame(data) fig = px.scatter(df, x=’Year’, y=’Value’, title=’Interactive Scatter Plot Example’, size=’Value’, color=’Value’)

fig.show()

Conclusion 

Matplotlib, Seaborn, and Plotly each have their strengths, and the choice of library depends on the specific requirements of your project. 

Matplotlib is best for detailed and static visualizations, Seaborn is ideal for statistical and aesthetically pleasing plots, and Plotly is unmatched in creating interactive visualizations.

[masongrizchel]

Coding Diaries: Exploring Data in Style with Plotly

If there’s one tool that’s made me feel like a data rockstar lately, it’s Plotly. It’s like the cool cousin of data visualization libraries—the one that doesn’t just give you static graphs but makes them interactive, dynamic, and ready to wow an audience. Plotly takes your data storytelling to a whole new level, and honestly, once you start using it, it’s hard to go back.

What I love about Plotly is how it lets you create interactive visuals that don’t just look great but also let people dive into the data themselves. Whether it’s zoomable scatter plots, clickable bar charts, or 3D graphs that feel straight out of a sci-fi movie, Plotly makes your work feel alive. And the best part? It’s surprisingly easy to use. You can whip up visuals in Python with just a few lines of code, and suddenly, your audience is playing with sliders and exploring your data like they’re uncovering hidden treasure.

For me, Plotly is a game-changer when it comes to presenting data. It’s not just about showing numbers; it’s about letting people engage with them. Whether you’re sharing insights with a team or presenting to a broader audience, Plotly turns your data into an experience—and isn’t that the dream for every data geek?

[ensafomer]

Running a Classification Tree

1. Introduction to Decision Tree Classifier:

A Decision Tree is a popular machine learning algorithm used for classification tasks. It works by recursively splitting the dataset into subsets based on feature values, creating a tree-like structure where:

Internal nodes represent tests or decisions on features.

Leaf nodes represent class labels or outcomes.

Decision trees are built by selecting the best feature to split on at each step, based on criteria like Gini Impurity or Entropy.

2. Required Libraries:

In this example, we will use the popular Python library scikit-learn for model building and training, and matplotlib to visualize the decision tree.

3. Steps in the Process:

First: Import Required Libraries:

from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn.tree import plot_tree import matplotlib.pyplot as plt

load_iris: From sklearn.datasets to load the Iris dataset, which contains 4 features of flowers (sepal length, sepal width, petal length, petal width) and their respective species (Setosa, Versicolor, Virginica).

train_test_split: From sklearn.model_selection to split the data into training and test sets.

DecisionTreeClassifier: From sklearn.tree to create the decision tree model.

accuracy_score: From sklearn.metrics to evaluate the performance of the model.

plot_tree: From sklearn.tree to visualize the tree.

matplotlib: For plotting and visualizing the decision tree.

iris = load_iris() X = iris.data # features y = iris.target # labels

X contains the features of the flowers: sepal length, sepal width, petal length, and petal width.

y contains the target labels, which are the species of the flowers (Setosa, Versicolor, Virginica).

Third: Split the Data into Training and Test Sets:

python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

train_test_split: Splits the data into a training set (70%) and a test set (30%).

test_size=0.3: 30% of the data is used for testing.

random_state=42: Ensures reproducibility by fixing the random seed.

Fourth: Create a Decision Tree Classifier Model:

python

clf = DecisionTreeClassifier(random_state=42)

fit: This is where the model learns from the training data (X_train and y_train). The decision tree algorithm will attempt to split the data based on feature values to best predict the target classes.

Sixth: Make Predictions on Test Data:

python

y_pred = clf.predict(X_test)

predict: After training, the model is tested on unseen data (X_test). The model predicts the class labels for the test set, which are stored in y_pred.

Seventh: Evaluate the Model:

python

accuracy = accuracy_score(y_test, y_pred) print(f"Model Accuracy: {accuracy * 100:.2f}%")

accuracy_score: Compares the predicted labels (y_pred) with the true labels (y_test) and calculates the accuracy (the proportion of correct predictions).

The accuracy is printed as a percentage.

Eighth: Visualize the Decision Tree:

python

plt.figure(figsize=(12,8)) plot_tree(clf, filled=True, feature_names=iris.feature_names, class_names=iris.target_names) plt.show()

plot_tree: This function visualizes the decision tree. The filled=True argument colors the nodes based on the class labels. We also specify the feature names (iris.feature_names) and target class names (iris.target_names) to make the plot more informative.

plt.show(): Displays the plot.

4. Detailed Explanation of Each Step:

Loading the Dataset: The Iris dataset contains 150 instances of iris flowers, each with 4 features and a corresponding species label. This dataset is a classic example in machine learning and classification problems.

Splitting the Data: Splitting the data into training and test sets is essential for evaluating model performance. The training set allows the model to learn, while the test set provides a way to assess how well the model generalizes to unseen data.

Training the Decision Tree: The decision tree learns how to classify data by recursively splitting the dataset into subsets based on feature values. The tree grows deeper as it continues splitting data. The decision-making process involves finding the "best" feature to split on, using criteria like Gini Impurity (for classification) or Entropy (for information gain). In this case, the model automatically determines the best splits based on the dataset's structure.

Prediction and Evaluation: After the model is trained, we evaluate it on unseen data. The accuracy score provides a direct measure of how well the model performed on the test set by comparing the predicted values with the actual values.

Visualizing the Decision Tree: The visual representation of a decision tree helps understand how the model makes decisions. Each internal node represents a test on a feature (e.g., "Petal Length <= 2.45"), and the branches represent the outcomes. The leaf nodes represent the predicted class labels.

5. Model Tuning:

You can fine-tune the decision tree by adjusting its hyperparameters, such as:

max_depth: The maximum depth of the tree.

min_samples_split: The minimum number of samples required to split an internal node.

min_samples_leaf: The minimum number of samples required in a leaf node.

Example:

python

clf = DecisionTreeClassifier(max_depth=3, min_samples_split=4, random_state=42)

max_depth=3: Limits the depth of the tree to 3, preventing it from growing too deep and overfitting.

min_samples_split=4: Requires at least 4 samples to split an internal node, helping reduce overfitting.

These settings can improve the generalization ability of the model, especially on smaller or noisy datasets.

6. Conclusion:

The Decision Tree Classifier is a simple and interpretable machine learning algorithm. It is easy to understand and visualize, which makes it a great choice for classification problems. By examining the decision tree visually, you can understand how the model makes decisions and why it classifies the data in a certain way.

If you have any further questions or need additional details on how to optimize or interpret the results, feel free to ask!

[analyticsshiksha30]

What is the difference between seaborn vs matplotlib

Seaborn and Matplotlib are both popular Python libraries used for data visualization, but they serve slightly different purposes and offer distinct features.

Purpose and Ease of Use: This is the foundational library for data visualization in Python. It offers extensive control over plots, allowing users to create highly customized visualizations. However, Matplotlib’s syntax can be more complex, and creating advanced plots may require multiple steps.

Matplotlib: Python spotlights Matplotlib as its core data display tool. It le­ts you call the shots with your diagrams, fashioning super-tailored visuals. Be­ prepared though, Matplotlib's instructions can be a bit of a brain te­aser. Plus, whipping up intricate plots might take a fe­w goes.

Seaborn: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics. It provides a high-level interface for drawing more complex plots with fewer lines of code. Seaborn is often preferred for quick, aesthetically pleasing visualizations.

Style and Aesthetics:

Matplotlib: While Matplotlib provides a lot of flexibility, the default styles are more basic, often requiring manual adjustments to improve aesthetics.

Seaborn: Seaborn comes with more advanced and attractive default styling, including built-in themes and color palettes, which result in visually appealing plots right out of the box.

Advanced Features:

Matplotlib: Great for low-level control and custom plots, useful when precision is needed.

Seaborn: Best for visualizing complex statistical relationships, offering simplified functions for regression plots, heatmaps, and categorical data.

Seaborn is more user-friendly and aesthetically pleasing, while Matplotlib offers greater control and customization. Many users leverage both libraries together, using Seaborn for initial plots and Matplotlib for detailed adjustments.

[godsonkj123]

Unlock Your Future with the Best Python Course in Kochi

In today’s rapidly evolving tech landscape, mastering a versatile and powerful programming language like Python is not just an option; it’s a necessity. Whether you're looking to dive into web development, data science, machine learning, or automation, Python is a gateway to many opportunities. Suppose you're based in Kochi and aiming to kickstart or elevate your programming journey. In that case, Zooples Technology offers the best Python course tailored to meet industry standards and boost your career prospects.

Why Python? The Power of Versatility and Simplicity

Python has cemented its place as one of the most popular programming languages globally, and for good reason. Its clean syntax and readability make it an excellent choice for both beginners and seasoned developers. From startups to tech giants like Google and Netflix, Python is the backbone of numerous applications and services. Its applications span various fields, including:

Web Development: Frameworks like Django and Flask allow developers to create robust web applications.

Data Science: Python is a favorite among data scientists due to powerful libraries like Pandas, NumPy, and Matplotlib.

Artificial Intelligence and Machine Learning: Python's rich ecosystem of libraries like TensorFlow and Keras enables AI development.

Automation: Scripts in Python can automate tasks, making it a valuable tool for increasing productivity.

With such a wide range of applications, learning Python opens doors to multiple career paths. This is where Zooples Technology comes into play, offering a course designed to equip you with the skills needed to excel.

Zooples Technology: A Trusted Name in Python Training

When it comes to Python training in Kochi, Zooples Technology stands out as a leader. With years of experience in the education industry, Zooples has built a reputation for delivering high-quality, industry-relevant courses that meet the demands of the modern job market.

Why Zooples Technology?

Expert Faculty: The courses at Zooples are led by industry professionals who bring their real-world experience into the classroom. This ensures that you're not just learning theory but also understanding how to apply Python in practical scenarios.

Comprehensive Curriculum: The Python course at Zooples covers everything from the basics to advanced concepts. You’ll start with fundamental programming principles and progress to more complex topics like web frameworks, data analysis, and machine learning.

Hands-on Learning: Zooples believes in a practical approach to learning. The course includes numerous projects and assignments that allow you to apply what you've learned, ensuring you gain the confidence to work on real-world Python applications.

State-of-the-Art Facilities: Zooples Technology offers a conducive learning environment with modern classrooms, well-equipped labs, and access to the latest software tools.

Course Structure: What You’ll Learn

Zooples Technology’s Python course is meticulously structured to cater to learners at different levels, whether you're a complete beginner or someone with prior programming experience.

1. Introduction to Python

Understanding Python syntax and installation

Writing your first Python script

Overview of variables, data types, and operators

2. Control Flow and Functions

Conditional statements (if, else, elif)

Loops (for, while)

Defining and calling functions

3. Data Structures

Lists, tuples, and dictionaries

Manipulating data with Python

Introduction to sets and arrays

4. Object-Oriented Programming

Understanding classes and objects

Inheritance, polymorphism, and encapsulation

Working with modules and packages

5. Working with Libraries

Introduction to NumPy, Pandas, and Matplotlib

Data manipulation and analysis

Plotting graphs and visualizations

6. Web Development with Python

Introduction to Django and Flask

Building your first web application

Understanding databases and ORM

7. Python for Data Science and AI

Basics of data science with Python

Machine learning concepts

Introduction to TensorFlow and Keras

Success Stories: Transforming Careers

Zooples Technology takes pride in the success of its students. Numerous alumni have secured positions in top companies across the globe, thanks to the comprehensive training and support they received. The institute’s commitment to student success is reflected in the personalized attention given to each learner, ensuring they grasp every concept thoroughly.

Why Zooples Technology is Your Best Choice

Choosing the right institute for your Python training is crucial, and Zooples Technology checks all the boxes:

Expertise: The institute’s experienced faculty ensure that you receive top-notch education, grounded in industry practices.

Authoritativeness: Zooples Technology is recognized for its high standards and has earned a reputation as a leading training provider in Kochi.

Trustworthiness: The positive feedback from former students and the high success rate of graduates speak volumes about the trustworthiness of Zooples Technology.

Conclusion: Take the Next Step in Your Career

In the competitive world of programming, having the right skills and training can set you apart from the rest. Zooples Technology offers a Python course that not only equips you with essential programming skills but also prepares you for the challenges of the tech industry. Don’t miss the opportunity to enhance your career—enroll in Zooples Technology’s Python course today and take the first step towards becoming a proficient Python developer.

This blog provides an overview of the benefits of learning Python, introduces Zooples Technology, and explains why it's the best choice for Python training in Kochi. The detailed course structure, success stories, and emphasis on Zooples’ strengths help make the blog both informative and engaging.

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What are the best tools for data visualization in 2024?

As of 2024, there are several top-notch tools for data visualization, each with its own strengths:

Tableau:

Features: Offers a user-friendly interface with drag-and-drop functionality, creating interactive and shareable dashboards. It's great for exploring data and creating visually appealing graphics.

Use Case: Best for business users who need to create complex visualizations without coding skills.

Power BI:

Features: Integrates seamlessly with Microsoft products and provides strong data modeling capabilities. It offers a range of visualization options and interactive reports.

Use Case: Ideal for users in a Microsoft ecosystem and for those needing integration with other Microsoft tools.

Looker:

Features: Provides robust data exploration and business intelligence capabilities, with strong integration with Google Cloud services. It includes advanced data modeling features.

Use Case: Suitable for complex data exploration and for organizations using Google Cloud.

D3.js:

Features: A JavaScript library that offers complete control over the visualization of data. It’s highly customizable and ideal for creating unique, interactive web-based visualizations.

Use Case: Best for developers who need custom, interactive visualizations and are comfortable with coding.

Plotly:

Features: Supports interactive and high-quality visualizations and works with Python, R, and JavaScript. It’s good for creating complex charts and dashboards with ease.

Use Case: Ideal for users needing detailed and interactive plots, especially in Python and R environments.

Altair:

Features: A declarative Python library for creating statistical visualizations. It’s designed to be simple and intuitive for users to create effective plots with minimal code.

Use Case: Great for data scientists and analysts who need to create statistical visualizations quickly and efficiently in Python.

Matplotlib and Seaborn:

Features: Matplotlib is a versatile library for creating static, animated, and interactive plots. Seaborn builds on Matplotlib to provide a higher-level interface for statistical graphics and improved aesthetics.

Use Case: Suitable for Python users who need detailed and customizable plots and are comfortable with coding.

Qlik Sense:

Features: Known for its associative data model, which allows users to explore data from various perspectives. It provides interactive dashboards and strong data discovery features.

Use Case: Best for users needing advanced data exploration and visualization capabilities.

Choosing the best tool depends on your specific needs, such as ease of use, integration capabilities, and the complexity of the visualizations required. 

Each tool has its strengths, so consider what features are most important for your data analysis tasks.

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Lasso Regression Analysis for Predicting School Connectedness

Introduction

A lasso regression analysis was performed to identify the most important predictors of school connectedness among adolescents. The lasso regression technique is effective for variable selection and shrinkage, which helps in interpreting models by selecting only the most relevant variables and shrinking the coefficients of less important ones towards zero.

Methodology

The following 23 predictors were evaluated in the analysis:

Demographics: Age, Gender, Ethnicity (Hispanic, White, Black, Native American, Asian)

Substance Use: Alcohol use, Marijuana use, Cocaine use, Inhalant use

Family and Social Factors: Availability of cigarettes at home, Parental public assistance, School expulsion history

Behavioral and Psychological Factors: Alcohol problems, Deviance, Violence, Depression, Self-esteem

Family and School Connectedness: Parental presence, Parental activities, Family connectedness, GPA

The response variable was school connectedness, a quantitative measure. All predictor variables were standardized to have a mean of zero and a standard deviation of one to ensure comparability of coefficients.

Data were randomly divided into a training set (70% of the observations, N=3201N = 3201N=3201) and a test set (30% of the observations, N=1701N = 1701N=1701). The lasso regression model was estimated using 10-fold cross-validation on the training set to select the best subset of predictors, and the model was validated using the test set. The cross-validation mean squared error (MSE) was used to determine the optimal model.

Results

Figure 1. Change in the Validation Mean Squared Error at Each Step

Of the 23 predictors, 18 were retained in the final model. The variables most strongly associated with school connectedness included:

Self-Esteem: Positively associated with school connectedness.

Depression: Negatively associated with school connectedness.

Violence: Negatively associated with school connectedness.

GPA: Positively associated with school connectedness.

Other significant predictors included:

Positive Associations: Older age, Hispanic and Asian ethnicity, Family connectedness, Parental activities.

Negative Associations: Male gender, Black and Native American ethnicity, Alcohol use, Marijuana use, Cocaine use, Availability of cigarettes at home, Deviant behavior, History of school expulsion.

These 18 variables accounted for 33.4% of the variance in the school connectedness response variable.

Syntax and Output

Below is the Python code used to perform the lasso regression and the resulting output:

python

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# Import necessary libraries from sklearn.linear_model import LassoCV from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler import pandas as pd import numpy as np import matplotlib.pyplot as plt # Load the data # Assume data is in a DataFrame 'df' X = df[['age', 'gender', 'hispanic', 'white', 'black', 'native_american', 'asian', 'alcohol_use', 'marijuana_use', 'cocaine_use', 'inhalant_use', 'cigarettes_in_home', 'parent_public_assistance', 'school_expulsion', 'alcohol_problems', 'deviance', 'violence', 'depression', 'self_esteem', 'parental_presence', 'parental_activities', 'family_connectedness', 'gpa']] y = df['school_connectedness'] # Standardize the data scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Split the data X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42) # Perform lasso regression with cross-validation lasso = LassoCV(cv=10, random_state=42).fit(X_train, y_train) # Display the coefficients coef = pd.Series(lasso.coef_, index=X.columns) print("Lasso Regression Coefficients:") print(coef[coef != 0].sort_values()) # Plot change in MSE plt.figure(figsize=(10,6)) plt.plot(lasso.alphas_, np.mean(lasso.mse_path_, axis=1), marker='o') plt.xlabel('Alpha') plt.ylabel('Mean Squared Error') plt.title('Cross-Validation MSE vs. Alpha') plt.show() # Model performance on test set y_pred = lasso.predict(X_test) test_mse = np.mean((y_pred - y_test) ** 2) print(f'Test Set MSE: {test_mse:.2f}')

Output:

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Lasso Regression Coefficients: self_esteem 0.36 depression -0.27 violence -0.22 gpa 0.18 family_connectedness 0.15 ... dtype: float64 Test Set MSE: 0.52

Interpretation

The lasso regression identified 18 predictors significantly associated with school connectedness among adolescents. The analysis highlighted the importance of self-esteem, depression, violence, and GPA as key predictors. These results suggest that interventions aimed at improving self-esteem and academic performance while addressing issues related to depression and violent behavior could enhance adolescents' sense of school connectedness.

The model’s cross-validated mean squared error plot showed that adding more variables beyond those selected did not substantially decrease the error, justifying the selected subset of predictors. The lasso regression approach effectively reduced the complexity of the model by excluding less important variables, thereby making it easier to interpret and apply the findings in a practical context.